Gut Microbiota and Metabolomic Changes Associated to the Beneficial Effects of Polyphenols on Obesity

Faculty of Pharmacy

GUT MICROBIOTA AND METABOLOMIC CHANGES ASSOCIATED TO THE BENEFICIAL EFFECTS OF POLYPHENOLS ON OBESITY

Usune Etxeberria Aranburu

Pamplona, 2015

Faculty of Pharmacy

GUT MICROBIOTA AND METABOLOMIC CHANGES ASSOCIATED TO THE BENEFICIAL EFFECTS OF POLYPHENOLS ON OBESITY

Memoria presentada por Dña. Usune Etxeberria Aranburu para aspirar al grado de Doctor por la Universidad de Navarra

Usune Etxeberria Aranburu

El presente trabajo ha sido realizado bajo nuestra dirección en el Departamento de Ciencias de la Alimentación y Fisiología y autorizamos su presentación ante el tribunal que lo ha de juzgar.

Pamplona, 26 de Noviembre de 2015.

Dr. Fermin I. Milagro Yoldi Prof. J. Alfredo Martínez Hernández

If you always do what you always did, you will always get what you always got (Albert Einstein)

Acknowledgements/Agradecimientos

Acknowledgements/Agradecimientos

A lo largo de estas líneas quisiera mostrar mi más sincero agradecimiento a todas aquellas personas que de alguna manera u otra, han contribuido y me han ayudado a llegar al final de esta etapa. Me siento una persona privilegiada, he contado con vuestro apoyo, cariño y me habéis transmitido mucha fuerza y ánimo. Hoy, sólo puedo y me queda daros las gracias, MUCHAS GRACIAS a todos.

En primer lugar, mis primeras palabras de agradecimiento están dedicadas a los directores de esta tesis el Prof.

Alfredo Martínez y el Dr. Fermín Milagro. Quisiera daros las gracias por haber sabido guiar y dirigir este trabajo.

Gracias porque en vosotros he encontrado el rigor y la diligencia necesarias, así como vuestro apoyo, tiempo, paciencia e implicación en todo momento. Os quiero dar las gracias por la confianza que habéis depositado en mí desde el primer momento, por haberme dado la oportunidad de llevar a cabo este trabajo. Junto a vosotros puedo decir que he crecido no sólo profesionalmente, sino también personalmente. Os agradezco todo el tiempo que habéis invertido en mí y sinceramente espero haber cumplido, aunque sea parcialmente, con vuestras expectativas. Muchísimas gracias a los dos.

En segundo lugar, quiero agradecer a la Universidad de Navarra estos años de formación. A la Asociación de

Amigos de la Universidad de Navarra, así como al Gobierno Vasco la ayuda económica que me han otorgado sin la cual todo esto no hubiese sido posible. En particular, quisiera agradecer al Departamento de Ciencias de la

Alimentación y Fisiología por haberme acogido, y haberme ofrecido la oportunidad de realizar esta etapa investigadora junto a los mejores profesionales, a todos y cada uno de vosotros gracias. Gracias, a todos los profesores, técnicos, PAS. En general, a todos los compañeros y amigos de CAF, aquellos con los que compartí mis primeros años de tesis y aquellos con los que finalizo este camino. Así mismo, me gustaría a agradecer también a los compañeros que han estado de paso por el departamento, gracias porque he tenido la oportunidad de conocer a grandes personas. Gracias a mis amigos de máster E-MENU (Lucía, Blanca, Candela, Alfredo, Angels) por todos los momentos disfrutados, nuestras conversaciones de café y comidas que echaré de menos. Gracias a Miguelin por tantos y tantos momentos divertidos que hemos pasado en el laboratorio y fuera de él, has sido un gran apoyo para mí, he ganado un gran amigo y espero poder compartir en el futuro muy buenos momentos contigo. Gracias a mis chicas del “Granada team”, os echaré enormemente de menos, espero que tengamos oportunidad de juntarnos y repetir algún que otro viaje juntas.

Este trabajo no hubiera sido posible sin la gran aportación y la colaboración del grupo Nutrición y Obesidad de la

Universidad del País Vasco. Muy especialmente, me gustaría trasladar mi más sincero agradecimiento a la Prof.

i Acknowledgements/Agradecimientos

Mª Puy Portillo, muchísimas gracias por todo, ha sido un gusto y una suerte tener la oportunidad de trabajar con todos vosotros. Así mismo, me gustaría extender este agradecimiento a la Dra. Mª Teresa Macarulla, Dr. Alfredo

Fernández, Leixuri, Ana, Saioa, Iñaki y a todo el grupo en general. Por último, me gustaría agradecer a Noemí

Arias toda su disposición, ayuda y en definitiva su gran profesionalidad, muchas gracias, ha sido un lujo conocerte y poder trabajar contigo.

I would like to express my gratitude to Prof. Nathalie Delzenne and Dr. Audrey Neyrinck for giving me the opportunity to stay and work with your team in the Metabolism and Nutrition Research Group, Louvain Drug

Research Institute (Brussels). I would like to thank you for your warm welcome, for your trust on me, for you generosity, hope we will have the chance to keep in touch in the future. It was a great experience working with you. I would also like to thank all the people from the lab. Particularly, I wish to thank Remi for his help. I would like to express also my sincere gratitude to Nuria and Marcelo.

Por la suerte que tengo de contar con vuestra amistad, quiero dar las gracias a todos mis amigos. A Anetxu, que aunque no tengamos muchas oportunidades para juntarnos, siempre que nos vemos parece como si el tiempo no hubiera pasado. A mis amigos de la Kuadrilla, quizás no sepáis qué es lo que he estado haciendo exactamente durante todo este tiempo, pero vuestras preguntas especialmente vuestro continuo interés sobre “mis ratitas”, han sido suficientes para arrancarme siempre una sonrisa. Por todos esos viernes de reencuentro, desconexión y buenos momentos que me habéis dado, mila esker Kuadrilla. Quiero agradecer especialmente a Ainara su apoyo.

Askotan denbora luzez ikusi gabe egonda ere, asteroko mezutso hori falta izan ez degulako, zure animoengatik, zure laguntasunagatik, mila esker Ainari. A mis pittinas Nai y Elitxu, qué deciros, habéis sido uno de mis mayores apoyos, gracias por animarme, por creer tanto en mí, por alegraros con mis pequeños éxitos y avances profesionales, gracias por estar a mi lado siempre, por vuestra amistad incondicional. Mila esker.

Hitz hauek ez dira nahikoak matti eskertu behar dizudan guztia eskertu eta nere pentsamendu guztiak papelean isladatzeko, baina era labur batean bada ere saiatuko naiz. Maitia, esfortzu honen fruitu hau zurea ere delako

ESKERRIK ASKO, mila esker. Mila esker bide hau nerekin batera ibiltzeagatik, momento errazenetan bai, baina momentu zailenetan ere, hor izan zaitudalako. Mila esker beti nigan sinistu izanagatik eta nik konfidantza falta izan dudan momentu gogor horietan, indarrak eman eta aurrera eginarazi izanagatik. Eskerrak eman nahi dizkizut, urte hauetako egunerokotasunean, eskeini eta eman dizkidazun maitasun, indar eta goxotasunarekin etapa hau bizitzako epe polit eta ahaztezin bihurtu dituzulako. Asko maite zaitut.

Gracias, eskerrik asko, a mis “suegros” Maite y Xabier, y a toda la family, eskerrik asko zuei guztiei.

ii Acknowledgements/Agradecimientos

Me gustaría dedicar las últimas palabras a mi familia. A mis tíos, tías, primos, primas que siempre han estado ahí, mostrando interés por todo lo que hago y arrancándome una sonrisa cada vez que nos reuníamos. Me gustaría agradecer muy especialmente a mi AMONA todo el cariño que me da, sus palabras de aliento, su fuerza y vitalidad que hace que me sienta orgullosa de mí misma, sin saber que el orgullo es mío por poder tenerle a mi lado. Eres única, una joya. Maite zaitut amona. Eskerrik asko Andertxo beti zugan aurkitu dudan babes eta laguntzagatik, edozein momentutan entzuteko prest egon zarelako, aholkorik hoberenak zugan aurkitu izan ditudalako. Izugarri haundia zarelako eta zorte handia dudalako zu anai bezela izateagatik, asko asko maite zaitut eta faltan botatzen zaitut.

Mis últimas palabras de agradecimiento están dirigidas a mis padres. Aita, ama, no puedo agradeceros lo suficiente todo lo que habéis hecho por mí. Sois lo más importante de mi vida. Sois el pilar de mi vida, sin vuestro apoyo no hubiera llegado a estar hoy donde estoy y no me sentiría capaz de luchar por metas que se me hacen inalcanzables. Gracias por todos vuestros consejos, vuestros ánimos y cariño, vuestra confianza y comprensión.

Os lo debo todo, absolutamente todo, a vosotros. Por ello, esta tesis os la quiero dedicar especialmente a vosotros con todo mi cariño, os quiero mucho.

iii

Dedicado a las dos personas que todo me lo han dado, Mis aitas, Javier y Mertxe

Con especial cariño a, Mi hermano, Ander Mi amona, Mª Carmen

Maitia, beti zuri

List of abbreviations/Listado de abreviaturas

List of abbreviations/Listado de abreviaturas

‐ WHO World Health Organization ‐ BMI Body Mass Index ‐ LEP Leptin ‐ LEPR Leptin receptor ‐ MC4R Melanocortin 4 receptor ‐ POMC Pro‐opiomelanocortin ‐ PCSK1 Prohormone convertase 1 ‐ BDNF Brain‐derived neurotrophic factor ‐ SIM1 Single‐minded 1 ‐ NTRK2 Neurotrophic tyrosine kinase, receptor, type 2 ‐ BW Body weight ‐ T2DM Type 2 Diabetes Mellitus ‐ CVD Cardiovascular disease ‐ AHA/ACC/TOS American Heart Association/American College of Cardiology/The Obesity Society ‐ GI Gastrointestinal ‐ EPIC European Prospective Investigation into Cancer and Nutrition ‐ UK United Kingdom ‐ GLUT Glucose transporter ‐ PPARγ Peroxisome proliferator‐activated receptor gamma ‐ HFD High‐fat diet ‐ VAS iNOS Visceral adipose tissue inducible nitric oxide ‐ COX2 Cyclooxygenase‐2 ‐ Nrf2 Nuclear factor‐related factor‐2 ‐ HO‐1 Heme oxygenase‐1 ‐ NF‐κβ Nuclear factor Kappa β ‐ ERK1/2 Extracellular signal‐related kinase ‐ AMPK AMP‐activated protein kinase ‐ LPH Lactase phlorizin hydrolase ‐ UGT Uridine 5’‐diphosphate glucuronosyltransferase ‐ COMP Catechol‐O‐methyltransferase ‐ PST Phenol sulfotransferase ‐ BCRP1 Breast cancer resistance protein 1 ‐ MRP2 Multidrug resistance associated‐protein 2 ‐ LDL Low density lipoprotein ‐ CHD Coronary heart disease ‐ SIRT1 Sirtuin 1 ‐ HMP Human Microbiome Project

ix List of abbreviations/Listado de abreviaturas

‐ NIH National Institute of Health ‐ CFU Colony‐forming units ‐ CNS Central nervous system ‐ IBD Inflammatory bowel disease ‐ CD Crohn’s disease ‐ UC Ulcerative colitis ‐ IBS Irritable bowel syndrome ‐ GBA Gut‐brain‐axis ‐ ASD Autism spectrum disorder ‐ GF Germ‐free ‐ SCFA Short chain fatty acids ‐ RS Resistant starch ‐ XOS Xylooligosaccharides ‐ IMOS Isomaltooligosaccharides ‐ SO Soybean oligosaccharides ‐ POS Pectooligosaccharides ‐ PDX Polydextrose ‐ EPS Exopolysaccharides ‐ VLDL Very low density lipoproteins ‐ SREBP‐1c Sterol response element binding protein 1c ‐ ChREBP Carbohydrate response element binding protein ‐ GPCR G protein‐coupled receptor ‐ FFR Free fatty acid receptor ‐ GLP‐2 Glucagon like peptide‐1 ‐ PYY Peptide tyrosine tyrosine ‐ TNF‐α Tumor necrosis alpha ‐ IL Interleukin ‐ HDAC Histone deacetylases ‐ HAT Histone acetyltransferases ‐ iNOS Inducible nitric oxide synthase ‐ ICAM‐1 Intercellular adhesion molecule‐1 ‐ VCAM‐1 Vascular adhesion molecule‐1 ‐ TCR‐α T cell receptor‐α ‐ Fiaf Fasting‐induced adipose factor ‐ Angptl‐4 Angiopoietin‐like 4 protein ‐ FFA Free fatty acid ‐ LPL Lipoprotein lipase ‐ HFS High‐fat sucrose ‐ Pgc‐1 Proliferator‐activated receptor coactivator ‐ PAMP Pathogen‐associated molecular pattern

x List of abbreviations/Listado de abreviaturas

‐ PRR Pattern recognition receptor ‐ MAMP Microbe‐associated molecular pattern ‐ TLR Toll‐like receptor ‐ NOD Nucleotide oligomerization domain ‐ NLR NOD‐like receptor ‐ RIG‐I Retinoic acid inducible gene‐I ‐ RLR RIG‐like receptor ‐ LPS Lipopolysaccharide ‐ LPA Lipoteichoic acid ‐ REG III Regenerating islet‐derived protein III ‐ DC Dendritic cell ‐ TJ Tight junction ‐ ZO‐1 Zonula ocludens‐1 ‐ DSS Dextran sodium sulphate ‐ SAT Saturated fatty acid ‐ MUFA Monounsaturated fatty acid ‐ PUFA Polyunsaturated fatty acid ‐ FMT Faecal microbiota transplantation ‐ rRNA Small‐subunit ribosomal RNA ‐ NMR Nuclear magnetic resonance ‐ MS Mass‐spectrometry ‐ BCAA Branched chain amino acids ‐ SMCSO S‐methyl‐L‐cysteine sulphoxide ‐ UHPLC Ultra‐high performance liquid chromatography ‐ ESI Electrospray ionization mode ‐ m/z Mass‐to‐charge ratio ‐ TOF Time of flight ‐ IPGTT Intraperitoneal glucose tolerance test ‐ LBP Lipopolysaccharide binding protein ‐ DHA Docosahexaenoic acid ‐ NEFA Non‐esterified fatty acid ‐ ACSL3 Acyl‐CoA synthetase long‐chain family member 3 ‐ LIPG Endothelial lipase ‐ KLF5 Krüppel‐like factor 5 ‐ AREG Amphiregulin

xi

Glossary of terms/Glosario de términos

Glossary of terms/Glosario de términos

16S rRNA/rDNA: a component of the 30S small subunit of prokaryotic ribosomes. Sequencing of the 16S rRNA/rDNA has been used to identify prokaryotic taxonomy in complete environmental samples such as the microbiome (Cho and Blaser, 2012).

Commensals: microorganisms which are neither harmful nor beneficial for the host (Eloe‐Fadrosh and Rasko, 2013).

Co‐metabolites: metabolites that are synthesized or degraded by the host or one or more bacterial species (Ley et al., 2008).

Dysbiosis: alterations in the relative abundance of microbial groups or functions that cause an imbalance compared to a healthy state, generally leading to a detrimental change in health (Eloe‐Fadrosh and Rasko, 2013).

Enterotype: to classify the microbiota of an individual into discrete configurations. But recent data have conflicted with this definition and suggest that enterotypes may be less discrete (Tremaroli and Backhed, 2012).

Eubiosis: the normal state of the human gut microbiota (Doré et al., 2010).

Gnotobiotic mice: from Greek gnosis (known/knowledge) and bios (life). Mice reared under sterile (germ‐free) conditions are classified as gnotobiotic, as are animals that are reared under germ‐free conditions for a period of their lives and then colonized with various collections of microbes (Gordon, 2012).

Humanized mice: germ‐free mice into which fresh or frozen human faecal microbial communities have been transplanted (Eloe‐Fadrosh and Rasko, 2013).

Metagenome: the total DNA that can be extracted from an environment. The human metagenome is the aggregate of the DNA of the host and the microbiota. Metagenome and microbiome are often used interchangeably (Tremaroli and Backhed, 2012).

Metagenomics: defined as culture‐independent analyses of the composition and dynamic operations of microbial communities (Ley et al., 2008)

Mutualists: symbiotically beneficial microbes (Aagaard et al., 2013).

Pathogens: microbes which are detrimental for the host (Aagaard et al., 2013).

Phylotype: taxon‐neutral description of microorganisms based on phylogenetic relationship (Eloe‐Fadrosh and Rasko, 2013).

xiii Glossary of terms/Glosario de términos

Phylogenetic analysis: an analysis based on a phylogenetic tree that treat some sequences as more or less similar to others based on the number of changes that they have accumulated over evolutionary time (Ley et al., 2008).

Prebiotics: non‐digestible food ingredients or substances that pass undigested through the upper part of the gastrointestinal tract and stimulate the growth and/or activity of health‐promoting bacteria that colonize the large bowel (Bindels et al., 2015).

Probiotics: living microorganisms that, when administered in adequate amounts confer a health benefit for the host. Many bacterial strains in the Lactobacillus and Bifidobacterium genera are considered to be probiotic (Tremaroli and Backhed, 2012).

xiv List of publications/Listado de publicaciones

List of publications/Listado de publicaciones

The seven publications listed below compile the work contained in the present Doctoral Thesis.

1. Chapter 1: Etxeberria U, de la Garza AL, Martínez JA and Milagro F.I. Diet‐induced hyperinsulinemia differentially affects glucose and protein metabolism: a high‐throughput metabolomic approach in rats. Journal of Physiology and Biochemistry, 2013; 69 (3): 613‐623. Doi: 10.1007/s13105‐013‐ 0232‐0.

2. Chapter 2: Etxeberria U, Arias N, Boqué N, Macarulla MT, Portillo MP, Milagro FI, Martínez JA. Shifts in microbiota species and fermentation products in a dietary model enriched in fat and sucrose. Beneficial Microbes, 2015; 6 (1): 97‐111. Doi: 10.3920/BM2013.0097.

3. Chapter 3: Etxeberria U, de la Garza AL, Martínez JA, Milagro FI. Biocompounds attenuating the development of obesity and insulin resistance produced by a high‐fat sucrose diet. Natural Product Communication, 2015; 10 (8): 1417‐1420.

4. Chapter 4: Etxeberria U, Fernández‐ Quintela A, Milagro FI, Aguirre L, Martínez JA, Portillo MP. Impact of polyphenols and polyphenol‐rich dietary sources on gut microbiota composition. Journal of Agricultural and Food Chemistry, 2013; 61 (40): 9517–9533. Doi: 10.1021/jf402506c.

5. Chapter 5: Etxeberria U, Arias N, Boqué N, Macarulla MT, Portillo MP, Martínez JA, Milagro FI. Reshaping faecal gut microbiota composition by the intake of trans‐resveratrol and quercetin in high‐fat sucrose diet‐fed rats. Journal of Nutritional Biochemistry, 2015; 26 (6): 651‐660. Doi: 10.1016/j.jnutbio.2015.01.002.

6. Chapter 6: Etxeberria U, Arias N, Boqué N, Romo‐Hualde A, Macarulla MT, Portillo MP, Milagro FI, Martínez JA. Metabolic faecal fingerprinting of trans‐resveratrol and quercetin following a high‐fat sucrose dietary model using liquid chromatography coupled to high‐resolution mass spectrometry. Food & Function, 2015; 6 (8): 2758‐2767. Doi: 10.1039/c5fo00473j.

7. Chapter 7: Etxeberria U, Castilla‐Madrigal R, Lostao MP, Martínez JA, Milagro FI. Trans‐ resveratrol induces a potential anti‐lipogenic effect in lipopolysaccharide‐stimulated enterocytes. Accepted in Cellular and Molecular Biology.

xv

Abstract/Resumen

Abstract/Resumen

The contribution of the gut microbiota to the development of a large number of diseases, including obesity, is being explored. Although mechanisms are not fully understood, perturbations on gut microbiota composition seem to be an important factor. Therefore, modulation of gut bacterial community with approaches that could enhance the growth of “friendly” bacteria and reduce harmful bacteria might be an effective therapeutic tool. In this context, polyphenols, widely present in fruits and vegetables, such as flavonoids (i.e. quercetin) and non‐flavonoids (i.e. trans‐resveratrol), exert health protective effects and the bi‐directional interaction between these natural compounds and the gut microbiota taking place at intestinal level, has been proposed to be partly responsible for the health beneficial outcomes. Furthermore, metabolism of polyphenols might lead to the formation of other metabolites or bacterial‐derived co‐ metabolites with higher biological activity. Consequently, knowledge about the impact of pure natural compounds on gut microbiota and in turn, host metabolism is of relevance in order to be able to further understand the health benefits derived from the intake of polyphenols.

Therefore, this thesis aimed to provide a snapshot of this complex system consisting of gut microbiota, diet and polyphenols, host metabolism and health. For this purpose, this work has taken advantage of advanced technologies namely next‐generation sequencing and untargeted metabolomics in order to address the following questions: I) How does diet‐induced obesity influence on host metabolome? (Chapter 1); II) How does diet‐induced obesity affect the gut microbiota composition? (Chapter 2); III) Do bioactive compounds exert any beneficial effect on diet‐induced obesity and what has been studied about their interplay with gut microbiota up‐to‐date? (Chapter 3 and Chapter 4); IV) How does supplementation with polyphenols modulate gut microbiota dysbiosis in diet‐induced obesity? (Chapter 5); V) Does faecal metabolome analysis conducted in diet‐induced obese animals reflect the biological impact exerted by polyphenols’ supplementation? (Chapter 6); VI) What are the effects of polyphenols on gene expression level in enterocytes? (Chapter 7).

In summary, the results presented in this thesis have reported that consumption of a high‐fat high‐sucrose diet strongly affects host metabolome altering serum levels of a different set of metabolites, which might be reflective of the physiopathology of diet‐induced obesity. Therefore, non‐targeted metabolomics performed in serum samples has been found to be an effective approach to distinguish disease from non‐disease individuals. Moreover, the intake of a high‐fat high‐sucrose diet strongly impacts gut microbiota composition perturbing the bacterial balance towards an obesity‐associated gut microbial pattern. Remarkably, the use of pure natural compounds, particularly quercetin, could counteract the disturbance of gut microbiota related to diet‐induced obesity, promoting the growth of some beneficial bacteria while reducing some pathogenic and obesity‐associated microbes. In contrast, trans‐resveratrol significantly influences gene expression level in the intestine in vivo, without notably modifying gut microbial profile. In this context, trans‐resveratrol seems to favour intestinal epithelial homeostasis promoting the repair of intestinal epithelial barrier, upon damage. Furthermore, from the in vitro studies conducted in Caco‐2 cells, the inhibitory action of the stilbene on genes implicated in lipid metabolism has been observed. Finally, biological outcomes exerted by

xvii Abstract/Resumen polyphenols on global host metabolome might be distinguished through a faecal non‐targeted metabolomic analysis, proposing the potential of this technique to elucidate interactions between gut bacteria and polyphenols, and also to discriminate individuals in different groups based on the dietary intervention they have been subjected to.

La influencia de la microbiota intestinal en el desarrollo de enfermedades tales como la obesidad está siendo ampliamente estudiada. Los mecanismos específicos que contribuyen a este proceso son aún desconocidos. No obstante, ha sido demostrado que las alteraciones producidas en el perfil bacteriano intestinal representan un factor importante. En este sentido, la manipulación de la microbiota a través de estrategias que favorecen el crecimiento de las bacterias “beneficiosas” sobre las descritas como “patógenas”, se considera una alternativa eficaz a la hora de prevenir el desarrollo de diversas patologías. Los polifenoles, compuestos naturales ampliamente distribuidos en frutas y verduras, como los flavonoides (p. ej. quercetina) y los no‐flavonoides (p. ej. el estilbeno trans‐resveratrol) ejercen efectos protectores sobre la salud. Cabe destacar la importancia del contacto bi‐direccional existente entre estos compuestos y las bacterias en el intestino a la hora de explicar los efectos beneficiosos observados. Así mismo, es bien sabido que los co‐metabolitos generados como consecuencia de esta interacción, contribuyen a los efectos biológicos finales. Por ello, para poder comprender mejor el impacto del consumo de los polifenoles sobre la salud, es necesario analizar la acción de los mismos sobre la microbiota intestinal y el metaboloma.

El propósito de esta tesis doctoral era ofrecer una visión global sobre este sistema complejo que engloba las bacterias intestinales, la dieta y más concretamente los polifenoles, el metabolismo del huésped y la salud. Con esta finalidad, el presente trabajo se ha desarrollado en el marco de las ciencias “Ómicas”, haciendo uso de técnicas avanzadas como la secuenciación de última generación y la metabolómica no dirigida. De acuerdo con lo expresado, este trabajo de investigación ha tratado de esclarecer los siguientes interrogantes: I) Cómo afecta la obesidad inducida por el consumo de una dieta alta en grasa y azúcar al metabolismo del huésped? (Capítulo 1); II) De qué manera afecta la obesidad inducida por la dieta sobre la composición de la microbiota intestinal? (Capítulo 2); III) Pueden los polifenoles ejercer un papel protector ante el desarrollo de la obesidad y qué se conoce hasta la fecha sobre la relación entre estos biocompuestos y las bacterias intestinales? (Capítulo 3 y Capítulo 4); IV) De qué manera afectaría la suplementación con polifenoles a la “disbiosis” asociada a la obesidad? (Capítulo 5); V) El análisis metabolómico no dirigido realizado en heces puede llegar a detectar el impacto biológico derivado de la suplementación de los compuestos naturales? (Capítulo 6); VI) Cuál es el efecto de los polifenoles sobre la expresión de genes a nivel intestinal? (Capítulo 7).

En resumen, los resultados recopilados en esta Tesis Doctoral concluyen que la ingesta de una dieta rica en grasa y azúcar afecta de forma significativa al perfil metabolómico en suero provocando alteraciones en los niveles de diversos metabolitos. Este perfil diferenciado puede reflejar la fisiopatología de la obesidad inducida por la dieta, por lo tanto, un análisis metabolómico no dirigido en suero puede ser considerado una herramienta efectiva a la hora de diferenciar personas sanas frente a personas con algún tipo de enfermedad. Por otra parte, el consumo de una dieta alta en grasa y azúcar influye notablemente la composición de la xviii Abstract/Resumen microbiota intestinal, produciendo modificaciones en el equilibrio bacteriano del intestino e induciendo un perfil bacteriano asociado a la obesidad. Cabe señalar que la suplementación con polifenoles, particularmente la quercetina, puede llegar a contrarrestar los cambios en la microbiota intestinal asociados a la obesidad inducida por la dieta. El trans‐resveratrol por su parte, pese a que no produce grandes modificaciones sobre el perfil bacteriano, muestra la capacidad para regular la expresión de genes a nivel intestinal y mejora la integridad de la barrera intestinal. Además, el estudio in vitro realizado en células intestinales Caco‐2, presenta la acción inhibitoria del trans‐resveratrol sobre genes implicados en el metabolismo lipídico. Finalmente, se ha comprobado que el análisis de las heces a través de la metabolómica no dirigida tiene la capacidad para detectar el impacto biológico de los compuestos naturales sobre el metabolismo de los individuos, y parece ser una herramienta útil que permite explorar las interacciones entre las bacterias intestinales y los polifenoles, así como para discriminar a las personas en diferentes grupos, en función del tratamiento al que han sido sometidos.

xix

Table of contents/Índice

Table of contents/Índice

Acknowledgements/Agradecimientos ...... i

List of abbreviations/Listado de abreviaturas ...... ix

Glossary of terms/Glosario de términos ...... xiii

List of publications/Listado de publicaciones ...... xv

Abstract/Resumen ...... xvii

I. INTRODUCTION ...... 1

1. Nutrition and health ...... 3

2. Obesity ...... 3

2.1. Definition and prevalence ...... 3

2.2. Causes and physiopathology ...... 4

2.3. Therapeutic approaches in the management of obesity: use of polyphenols ...... 5

2.3.1. Flavonoids ...... 9

2.3.1.1. Flavonols: quercetin ...... 9

2.3.2. Non‐flavonoids ...... 15

2.3.2.1. Stilbene: trans‐resveratrol ...... 15

3. Gut microbiota: a new contributory factor in obesity development ...... 19

3.1. Conceptual approach ...... 19

3.2. Establishment and evolution ...... 20

3.2.1. Age‐related gut microbiota composition ...... 21

3.2.2. Microbial community through the body: focus on the gastrointestinal tract ...... 23

3.2.3. Gut microbiota: a sign of metabolic status ...... 26

3.3. Crosstalk between gut microbiota and the host ...... 30

3.3.1. Metabolic functions ...... 30

3.3.2. Immune functions and anti‐inflammatory roles ...... 34

3.3.3. Structural and protective functions ...... 35

3.4. Modulation of gut microbiota composition by different approaches ...... 37

3.4.1. Dietary modulation ...... 37

3.4.1.1. Effect of non‐digestible dietary carbohydrates, proteins and fat intake ...... 39

3.4.1.2. Effect of polyphenols intake ...... 40

3.4.2. Use of prebiotics, probiotics and synbiotics ...... 40

Table of contents/Índice

3.4.3. Faecal microbiota transplants ...... 41

3.5. Metagenomic approaches ...... 42

4. Metabolomics: a new “Omics” science in nutrition ...... 42

4.1. Concept and strategies ...... 44

4.2. Nutrimetabolomics ...... 45

4.3. Diet and microbial‐derived metabolites ...... 47

5. Integration of metagenomics and metabolomics ...... 48

II. HYPOTHESIS AND AIMS ...... 49

1. Hypothesis ...... 51

2. General aim...... 51

3. Specific objectives ...... 51

III. MATERIAL AND METHODS ...... 53

1. Metabolomic analyses (Chapter 1, Chapter 6) ...... 56

1.1. Experimental design Chapter 1: Impact of a HFS diet on serum metabolome...... 56

1.2. Experimental design Chapter 6: Impact of trans‐resveratrol and quercetin on faecal metabolome...... 58

2. Impact of different phenolic compounds on obesity (Chapter 3) ...... 60

2.1. Experimental design Chapter 3: Impact of different set of phenolic compounds on obesity ...... 60

3. Gut microbiota composition analyses (Chapter 2 and Chapter 5)...... 61

3.1. Experimental design Chapter 2: Impact of a HFS diet on gut microbiota...... 61

3.2. Experimental design Chapter 5: Impact of trans‐resveratrol and quercetin on faecal gut microbiota composition...... 62

4. In vitro assay and microarray analysis (Chapter 7) ...... 65

4.1. Experimental design Chapter 7: Impact of trans‐resveratrol on gene expression levels in enterocytes...... 65

IV. RESULTS ...... 67

Chapter 1: Diet‐induced hyperinsulinemia differentially affects glucose and protein metabolism: a high‐throughput metabolomic approach in rats ...... 69

Chapter 2: Shifts in microbiota species and fermentation products in a dietary model enriched in fat and sucrose ...... 83

Chapter 3: Biocompounds Attenuating the Development of Obesity and Insulin Resistance Produced by a High‐fat Sucrose Diet ...... 101

Table of contents/Índice

Chapter 4: Impact of Polyphenols and Polyphenol‐Rich Dietary Sources on Gut Microbiota Composition ...... 107

Chapter 5: Reshaping faecal gut microbiota composition by the intake of trans‐resveratrol and quercetin in high‐fat sucrose diet‐fed rats ...... 127

Supplementary data CHAPTER 5 ...... 139

Chapter 6: Metabolic faecal fingerprinting of trans‐resveratrol and quercetin following a high‐ fat sucrose dietary model using liquid chromatography coupled to high‐resolution mass spectrometry ...... 147

Supplementary data CHAPTER 6 ...... 159

Chapter 7: Trans‐resveratrol induces a potential anti‐lipogenic effect in lipopolysaccharide‐ stimulated enterocytes ...... 179

Supplementary data CHAPTER 7 ...... 197

V. GENERAL DISCUSSION ...... 203

1. Understanding the physiopathology of obesity through “Omics” approaches ...... 206

2. Influence of natural bioactive compounds on obese gut microbiota and metabolome ...... 208

3. Strengths and limitations ...... 212

4. Corollary ...... 214

VI. CONCLUSIONS ...... 215

VII. REFERENCES ...... 219

VIII. APPENDICES ...... 241

Appendix 1: Role of dietary polyphenols and inflammatory processes on disease progression mediated by the gut microbiota ...... 243

Appendix 2: Antidiabetic effects of natural plant extracts via inhibition of carbohydrate hydrolysis enzymes with emphasis on pancreatic alpha amylase ...... 247

Appendix 3: Helichrysum and grapefruit extracts inhibit carbohydrate digestion and absorption, improving postprandial glucose levels and hyperinsulinemia in rats ...... 251

Appendix 4: Modulation of hyperglycemia and TNFα‐ mediated inflammation by helichrysum and grapefruit extracts in diabetic db/db mice ...... 255

Appendix 5: Helichrysum and grapefruit extracts boost weight loss in overweight rats reducing inflammation ...... 259

Appendix 6: Congress communications ...... 263

Appendix 7: Other congress communications ...... 271

I. INTRODUCTION

Introduction

1. Nutrition and health

The role of nutrition as the keystone to health is widely recognized (McNiven et al., 2011). From the origins of the first civilizations the relationship among health and dietary habits has been suspected. In fact, the Greek physician Hippocrates, nearly 2 500 years ago, stated: “let food be thy medicine and medicine be thy food”, without being conscious that his allegation would concur with nowadays concept. However, until the era of Hippocrates, statements in ancient times were not founded in scientific evidences but were result of observations of the relation among diet and health that were probably passed down over many years (Kleisiaris et al., 2014). In contrast, with time, our comprehension of diet and its influence on health has developed, and current scientists are able to go beyond associations to give conclusive information (Mitroi and Mota, 2008).

Modern nutrition has evolved from the perspective that food is mainly required for the survival of an organism, to the concept that food is of relevance to prevent illnesses and enhance individual’s life quality. Thus, proper nutrition is essential to ensure individuals’ optimal growth, development and disease prevention (Pang et al., 2012). The challenges of human nutrition have been focused on overcoming serious problems that population has suffered through the years. Nutritional deficiencies, especially those generated as a consequence of a lack of micronutrients and vitamins, have been a major problem for the population, indeed, the problem of hunger and malnutrition remains up‐to‐date. However, the focus of nutrition research has changed to the problem of over‐nutrition (Morgan, 2012). Scientific research is demonstrating the positive health outcomes derived from the adherence to specific dietary patterns such as the Mediterranean diet (Gotsis et al., 2015). Besides, evidence‐based nutritional recommendations have been published (Gargallo Fernandez et al., 2012) and although an increase in consumers’ interest on foods and food components that have the capacity to improve health (namely functional foods) has been observed (Pang et al., 2012), still, modern societies are suffering a wave of lifestyle‐related chronic diseases, where diet is a strong contributor. Therefore, current research needs to direct its efforts to the understanding of the mechanisms surrounding the interplay between diet and human body metabolism, in order to be able to prevent the development of diet‐related chronic diseases.

2. Obesity

2.1. Definition and prevalence

The concept of obesity has changed over the human history being partly considered as an evolutionary process related to food scarcity (Barja‐Fernandez et al., 2014). Prehistorically, human beings suffered cycles of pestilence and famine thus, survival of the human population depended on the ability of the individual to harvest energy from the least amount of food intake (Wells, 2012). This statement was supported by the “thrifty gene hypothesis”, which proposes that individuals underwent a selection of genes that facilitate efficient food gathering and fat deposition to survive in critical periods of famine (van der Klaauw and Farooqi, 2015).

3 Introduction

The first’s statements pointing out the dangers of obesity were given by the ancient Greeks. Hippocrates mentioned the association between poor diet and illnesses, highlighting the health benefits derived from diet modifications (Haslam, 2007). However, it was not until the seventeenth century that the term “obesity” was used in the English language referring to excessive fatness or corpulence (Eknoyan, 2006), and it was in the middle of the nineteenth century when obesity was acknowledged to impair health (Eknoyan, 2006).

Nowadays, the World Health Organization (WHO) recognizes obesity as a preventable risk factor for non‐ communicable diseases characterized by an abnormal or excessive fat accumulation (WHO, 2015). Concerning obesity detection, currently, body mass index (BMI) is commonly used to classify individual’s nutritional status in clinical practice. This standard index is calculated as a person’s weight in kilograms divided by height in square metres (BMI= kg/m2). WHO defined obesity as BMI≥ 30kg/m2 and overweight as BMI≥ 25kg/m2 (WHO, 1995).

The prevalence of obesity and overweight has increased substantially, widespread and in a short period of time (Ng et al., 2014). In 2014, over 39% of adults (≥ 18 years old) were overweight, and 13% were obese worldwide (WHO, 2015). Thus, obesity is nowadays considered a global pandemic (Swinburn et al., 2011) that requires a global action plan (WHO, 2013).

2.2. Causes and physiopathology

Although the ethiopathogenesis of obesity is still unclear, it might be classified as a multifactorial disease which results from a chronic positive imbalance between energy intake and energy expenditure giving rise to the storage of energy as adipose tissue (Hall et al., 2012). Obesity is determined by genetic and environmental factors (Speakman and O'Rahilly, 2012). In relation to genetic heritability, monogenic and polygenic forms of obesity have been described. Monogenic obesity is a rare form of the disease caused by mutations in a single gene (Levian et al., 2014). To date, eight genes encoding leptin (LEP), the leptin receptor (LEPR), melanocortin 4 receptor (MC4R), pro‐opiomelanocortin (POMC), prohormone convertase 1 (PCSK1), brain‐derived neurotrophic factor (BDNF), the single‐minded 1 (SIM1) and neurotrophic tyrosine kinase, receptor, type 2 (NTRK2) have been identified for monogenic obesity (Waalen, 2014; Apalasamy and Mohamed, 2015). In contrast, polygenic or common obesity refers to the presence of DNA variations in several genes (Hinney et al., 2010), thus, it is the consequence of the cumulative influence of many genes that confer different levels of susceptibility (Albuquerque et al., 2015). Nevertheless, the genetic susceptibility to obesity could not explain the contemporary obesity pandemic and inter‐individual differences in susceptibility are ascribed to an important role of environmental factors interacting with the genome (Marti et al., 2008). Overall, even if inherited propensity for the development of obesity has been agreed to vary between 40% and 70% (Xia and Grant, 2013), this heritability might be influenced by diverse environmental factors.

Dietary factors, especially consumption of highly processed foods rich in saturated fat and refined carbohydrates, together with reduced physical activity patterns play a crucial role in BW gain (Gortmaker et al., 2011). Nevertheless, other multiple features might also promote the development of obesity (Dhurandhar and Keith, 2014) (Figure 1).

4 Introduction

Figure 1. Factors that may promote the development of obesity.

A solid correlation between the enlargement of adiposity and the development of obesity‐associated co‐ morbidities has been ascertained (Dixon, 2010). In the lasts years, the role of adipose tissue producing pro‐ inflammatory cytokines has been well‐established (Lee et al., 2009). Importantly, obesity drives substantial direct and indirect socio‐economic costs due to the increment in healthcare utilization derived from obesity and obesity‐related health problems (Lehnert et al., 2013). All these health co‐morbidities show a common feature, the chronic low‐grade inflammatory status (Guh et al., 2009). In a comprehensive meta‐analysis conducted by Guh et al., 2009 (Guh et al., 2009), 20 co‐morbidities that determine risk factors for overweight and obesity were reviewed, providing the incidence for 18 co‐morbidities ascribed to overweight and obesity that were classified within type 2 diabetes mellitus (T2DM), cancer, cardiovascular diseases (CVDs) and others. These complications are considered the leading cause of death in obese people (Pi‐Sunyer, 2002).

2.3. Therapeutic approaches in the management of obesity: use of polyphenols

Obesity requires a long‐term intervention involving primarily non‐pharmacologic treatment (Orio et al., 2014). This approach consisting of lifestyle modifications is the keystone for obesity management and involves controlled nutrition, physical activity and behavioural therapy (Aronne, 2014). However, even if lifestyle changes are the first steps to be performed, patients who have not met the weight or weight‐related treatment goals may be eligible for pharmacotherapy (Jensen et al., 2014). Moreover, individuals with a BMI ≥30Kg/m2 or ≥27Kg/m2 that exhibit risk factors or illnesses that are considered

5 Introduction severe enough for pharmacologic treatment, such as hypertension, dyslipidaemia, CVD, T2DM, fatty liver disease and obstructive sleep apnea could make use of this adjuvant therapy (Fitch et al., 2013). In addition, based on the American Heart Association/American College of Cardiology/The Obesity Society (AHA/ACC/TOS) panel, bariatric surgery might be an alternative in patients with BMI≥ 40Kg/m2 or BMI≥ 35Kg/m2 and obesity associated comorbidities who are willing to lose weight but have not responded to behavioural treatment with or without pharmacotherapy (Fitch et al., 2013).

Although a large variety of anti‐obesity drugs have been developed acting on different mechanisms that include appetite suppression, increased energy expenditure and decreased food absorption from the gastrointestinal (GI) tract (Kushner, 2014), pharmacological therapy has been characterized by a large number of failures and reiterated side effects (Di Dalmazi et al., 2013). Past and currently approved anti‐obesity drugs are summarized in Table 1.

6 Introduction

Table 1. Anti‐obesity drugs

Mechanism of action and adverse effects of anti‐obesity drugs Drug Mechanism of action Main adverse Effects Withdrawn in the European market Thyroxin and Triiodotyronine Increase the basal metabolic rate Thyrotoxicosis 2,4‐dinitrophenol Increase the basal metabolic rate Hyperthermia Amphetamine derivatives (desoxyephedrine, Stimulate release of norepinephrine and Intolerance, psychosis, cardiovascular methamphetamine, phendietrazine, dopamine, act on satiety centers of the problems diethylpropion, benzphetamine, hypothalamic and limbic regions of the brain mazindol) Aminorex, fenfluramine, Serotonin releasers/reuptake inhibitors, suppress Pulmonary hypertension and cardiac dexfenfluramine appetite and reduce food intake valvulopathy Increased systolic and diastolic blood Sibutramine Norepinephrine and serotonin reuptake inhibitor pressure, increased pulse rate Psychiatric events (depressive mood Rimonabant Cannabinoid type 1 receptor antagonist disorders, anxiety and suicidal ideation) Heart valve disease, pulmonary Phentermine & Fenfluramine Appetite suppressant hypertension US FDA approved drugs

(in the market) Gastrointestinal (GI) side effects (steatorrhea), increased defecation, Orlistat Pancreatic and gastric lipase inhibitor soft stools, fatty or oily evacuation, abdominal pain, kidney and pancreas injuries Headaches, upper respiratory tract Lorcaserin Selective serotonin 2c (5‐HT2C) receptor agonist infection, nausea and dizziness Gastrointestinal effects (nausea and Liraglutide Glucagon‐like peptide‐1 (GLP‐1) analogue vomiting) Appetite suppressant via αMSH and pro‐ Depression, nausea, headache, Naltrexone & Bupropion opiomelanocortin neural stimulation vomiting, dizziness Suicidal thoughts, heart palpitations, Phentermine & Topiramate ER memory lapses, foetal toxicity, dry Sympathomimetic amine with anorectic effect (extended release) mouth, constipation, paresthesia and dysgeusia Other ongoing drugs Decreased hepatic synthesis of glucose and Metformin Lactic acidosis, GI disturbance decreases peripheral insulin resistance Headache, insomnia and nausea, while Zonisamide & Bupropion Appetite suppressant urticarial (hives) Gastrointestinal effects (nausea and Exenatide, albiglutide, taspoglutide Glucagon‐like peptide‐1 (GLP‐1) analogues vomiting) Serotonin/noradrenalin/dopamine reuptake Tesofensine Increase in blood pressure, heart rate inhibitor Allergic reaction, GI discomfort, Pramlintide Amylin analogue headache, nervousness, sweating GT 389‐255 Pancreatic lipase GI discomfort Cetilistat Gastrointestinal and pancreatic lipase inhibitor GI discomfort AOD9604 Synthetically modified hGH, stimulates lipolysis No major side effects Fatty acid ester of estrone, Selective B3 receptor Phase 2a studies for oral oleoyl‐ Oleoyl‐estrone agonist estrone failed Y2R agonist and Oxyntomodulin, a GLP‐1 receptor PYY 3‐36 and Oxyntomodulin agonist, are co‐secreted by intestinal L‐cells acting No major side effects as appetite suppressants TM 30338 Y2‐Y4 receptor agonist, appetite suppressant No major side effects Anti‐ghrelin vaccine Gastrointestinal peptide hormone antagonist No major side effects Tyrosine kinase receptor TrkB Appetite suppressant No major side effects agonists Melanin concentrating hormone Appetite suppressant Prolong QTc interval (MCH) receptor antagonist Melanocortin receptor agonist (MC4) Increase energy expenditure Nausea, diarrhoea and loose stools MK‐0493 FDA, Food and Drug Administration; GI, gastrointestinal; ER, extended release; GLP‐1, glucacon‐like peptide‐1; MCH, melanocortin concentrating hormone; MC4, melanocortin receptor agonist. Adapted from (Khan et al., 2012; Adan, 2013; Di Dalmazi et al., 2013; Patel, 2015)

7 Introduction

In this context, the use of natural products, as an effective and a safe treatment to fight against obesity and related disorders might be an alternative therapy which is under intensive exploration (Mohamed et al., 2014).

A large range of medicinal herbs (Vasudeva et al., 2012) and natural products (Mohamed et al., 2014) have been used to induce weight loss and protect from obesity through a variety of mechanisms. Pure natural compounds, namely dietary phytochemicals, are attracting much attention due to their effectiveness in modulating the inflammatory response in different chronic inflammatory diseases (Pan et al., 2010). Within the wide range of natural dietary phytochemicals, polyphenols are the leading ones. Some years ago these compounds were considered anti‐nutritional components (Bravo, 1998). However, currently, there is sufficient evidence derived from in vitro and in vivo studies to assume not only their antioxidant properties, but also their capacity to influence different biological pathways that are responsible for altering disease risk (Joseph et al., 2015).

Polyphenols are plant‐derived secondary metabolites composed of phenolic structures and one or more hydroxyl moieties attached. Depending on the chemical structure, polyphenols have been traditionally assigned into two main groups called flavonoids and non‐flavonoids (Andrés‐Lacueva et al., 2009) (Figure 2). From a recent cross‐sectional analysis conducted within the European Prospective Investigation into Cancer and Nutrition (EPIC) participants, it was reported that daily mean intake of total polyphenols was the highest in United Kingdom (UK) health‐conscious group (1521 mg/day), followed by non‐ Mediterranean countries (1284 mg/day) and the Mediterranean countries (1011 mg/day), which may appear surprising according to the origin of foods (Zamora‐Ros et al., 2015).

Figure 2. Classification of phytochemicals (Andrés‐Lacueva et al., 2009; Gonzalez‐Castejon and Rodriguez‐ Casado, 2011; Del Rio et al., 2013).

8 Introduction

2.3.1. Flavonoids

Flavonoids consist of a basic structure (C6‐C3‐C6) that includes two benzene rings (A and B) linked to a pyrane ring (C) as shown in Figure 3 (Tsao and McCallum, 2009). Flavonoids are considered one of the most extensive groups of phytochemicals that include more than 4000 compounds and are divided in several sub‐classes termed flavones, flavonols, flavanols, flavanones, flavanonols, anthocyanidins, isoflavones, chalchones and neoflavonoids Figure 3. Basic flavonoid backbone (Tsao and McCallum, 2009). Generally, in plants, flavonoids happen (Tsao and McCallum, 2009). as glycosylated forms linked to glucose, rhamnose or even to other sugars. Actually, in plant foods, more than 80 different sugars have been found to be linked to flavonoids (Hollman and Arts, 2000). Nevertheless, at a lower extent, they might also occur as methylated, acylated and sulfated derivatives (Andersen and Markham, 2005). In a comprehensive study, it has been estimated that total daily flavonoids intake for Mediterranean countries is 449 mg/day and 522 mg/day for non‐Mediterranean countries (Zamora‐Ros et al., 2015).

2.3.1.1. Flavonols: quercetin

Quercetin is an aglycone flavonoid with a phenyl benzo(c)pyrone‐ derived structure as shown in Figure 4 (Nabavi et al., 2012). It is usually found as the glycoside form linked to glucose or rutinose (Lommen et al., 2000), being particularly abundant in fruits, vegetables and nuts (Harnly et al., 2006). The flavonoid content may greatly vary depending on a number of factors such as diseases, insect/pest attack, climate stress, ultraviolet radiation, cultivar, Figure 4. Chemical structure of growing location, agricultural practices, harvesting and storage quercetin (Nabavi et al., 2015). conditions and processing and preparation methods (Haytowitz et al., 2013). Several efforts have been conducted on elucidating the concentrations of different flavonoids in a range of food products (Bhagwat et al., 2011). This information is relevant in order to be able to assess the precise intake of these compounds from the diet and hence, to understand the outcomes produced by polyphenols on human health. Quercetin is nearly ubiquitous in plants and its most important dietary sources are the lettuce, chili pepper, blackelderberry, onion, kale, broccoli and apples as shown in Table 2 (Neveu et al., 2010).

Quercetin is considered a natural multifunctional compound with a broad number of biological activities (Nabavi et al., 2015). In this context, the role of quercetin as anti‐inflammatory, anti‐proliferative, anti‐ atherogenic, hypolipidemic, anti‐diabetic, anti‐cancer, anti‐hypertensive, anti‐histamine and anti‐oxidant is well known (Kleemann et al., 2011; Larson et al., 2012; Siriwardhana et al., 2013; Arias et al., 2014).

9 Introduction

Table 2. Amount of quercetin in selected foods1

FOOD SOURCE QUERCETIN MEAN CONTENT (mg/100 g)

Vegetables

Leaf vegetables Red lettuce, raw 40.27

Fruit vegetables Yellow chilli pepper 32.59 Onion‐family Red onion, raw 17.22

Cabbages Kale, raw 7.71 Pod vegetables Green vean, raw 3.12

Gourds Cucumber 0.04 Fruits and fruit products

Fruits‐Berries Black elderberry 42.00

Fruits‐Drupes Sour cherry 2.92

Fruits‐Pomes Apple, whole, raw 2.47

Fruits‐Citrus Lime 0.40 Seasonings Herbs Lovage, fresh 170.0

Spices Horseradish, fresh 0.28 Seeds Nuts Pistachio 1.33 Beans White bean, whole, raw 1.25

Alcoholic beverages Wine‐grape wines Red wine 0.87 mg/100 mL Wine‐berry wines Blackurrant wine 0.84 mg/100 mL Non‐alcoholic beverages Tea infusions Green Tea, infusion 1.53 mg/100 mL

Fruit juices‐Citrus juices Lemmon or pummelo, pure juice 0.49 mg/100 mL Cocoa beverage Chocolate, milk, beverage 0.13 mg/100 mL

1Database adapted from Phenol‐explorer 3.0. Food content of quercetin analysed by chromatography after hydrolysis.

 Anti‐obesity effects of quercetin: in vitro, in vivo and human studies

As far as the amelioration of obesity is concerned, the use of quercetin has demonstrated to give rise to obesity‐related protective effects acting through different molecular pathways.

 In vitro studies

From studies in different cell lines (Motoyashiki et al., 1996; Ohkoshi et al., 2007; Park et al., 2008), it was concluded that quercetin exerts a delipidating effect reducing triacylglycerol content in pre‐adipocytes (Ahn et al., 2008; Eseberri et al., 2015), increasing lipolysis (Kuppusamy and Das, 1992; Seo et al., 2015), decreasing adipogenesis (Ahn et al., 2008) and inducing apoptosis (Hsu and Yen, 2006; Yang et al., 2008), through specific molecular pathways (Aguirre et al., 2011). Studies analysing anti‐obesity effects of quercetin in vitro and in vivo are summarized in Table 3.

10 Introduction

 Studies conducted in animal models

Quercetin has been demonstrated to exert a suppressive effect against obesity in numerous studies (Table 3). In some investigations, no effect on BW or body fat was observed (Arias et al., 2014; Le et al., 2014), while in others, a decrease in these two features was found (Dong et al., 2014; Henagan et al., 2015). In a recent review, the beneficial role of quercetin to fight against obesity has been summarized (Nabavi et al., 2015). Molecular mechanisms reported are the following: inhibition of glucose transporters (GLUTs), specifically GLUT4 and GLUT2, which mediate insulin‐stimulated glucose uptake; the suppression of the expression of important adipogenic genes like peroxisome proliferator‐activated receptor gamma (PPARγ), a transcription factor that controls high‐fat diet (HFD)‐induced obesity; the improvement of the expression of markers related to oxidative stress and inflammation, such as visceral adipose tissue inducible nitric oxide synthase (VAT iNOS), cyclooxygenase‐2 (COX‐2), nuclear factor‐related factor‐2 (Nrf2), heme oxygenase‐1 (HO‐1), nuclear factor kappa β (NF‐κβ); inhibition of extracellular signal‐related kinase (ERK1/2) phosphorylation; enhanced phosphorylation of AMP‐activated protein kinase (AMPK) catalytic subunit; inhibition of the secretion of pro‐inflammatory cytokines, among others (Nabavi et al., 2015). The authors concluded that “indirect” mechanisms namely anti‐oxidant and anti‐inflammatory actions should also need to be taken into consideration when none “direct” targets of quercetin are identified as responsible for the amelioration of obesity, associating quercetin’s anti‐inflammatory and anti‐oxidant effects with the attenuation of obesity (Nabavi et al., 2015).

11 Introduction

Table 3. Anti‐obesity effects of quercetin in vitro and in vivo.

Animal model Treatment conditions Major outcomes References In vitro anti‐obesity effects Adipocytes from epididymal adipose tissue of male 30‐50 μM Stimulation of lipolysis (Kuppusamy and Das, 1992) Wistar rats Adipocytes from epididymal Inhibition of the adipose tissue of male 100‐500 μM Vanadate‐increasing (Motoyashiki et al., 1996) Wistar rats effect on LPL activity 3T3‐L1 adipocytes 50‐250 μM Induction of apoptosis (Hsu and Yen, 2006) Adipocytes from SAT and 100 μM ‐ (Ohkoshi et al., 2007) VAT of female C5BL/6J Induction of adipogenesis 3T3‐L1 adipocytes 10‐100 μM Inhibition of de novo (Ahn et al., 2008) lipogenesis 3T3‐L1 mouse embryo 12.5‐100 μM Increase in apoptosis (Yang et al., 2008) fibroflasts 3T3‐L1 mouse embryo Inhibition of lipid 25 μM (Park et al., 2008) fibroflasts accumulation Inhibit differentiation OP9 cells 0, 5, 10, 50 and 100 μM Decrease adipogenesis (Seo et al., 2015) Enhance lipolytic activity 3T3‐L1 preadipocytes 0.1‐10 μM Inhibit adipogenesis (Eseberri et al., 2015) In vivo anti‐obesity effects No effects on BW or body Male C57BL/6J mice 8 g/kg diet for 3 and 8 weeks (Stewart et al., 2008) fat 10 mg/kg BW/day for 10 Male Zucker rats Decrease in BW (Rivera et al., 2008) weeks Male and female C57BL/6J Protection against BW 66 mg/kg BW/day for 4 weeks (Liang et al., 2009) mice gain induced by HFD Decrease in BW Male C57/BL6J mice 5 g/kg diet for 20 weeks Decrease in visceral and (Kobori et al., 2011) hepatic fat Decrease in plasmatic TG Male Wistar rats 25 mg/kg BW/day for 4 weeks (Wein et al., 2010) No effect on BW Decrease in BW and Ovariectomized rats 20‐625 mg/kg for 8 weeks (Lai et al., 2011) adiposity 100 mg/kg BW/day for 7 Male Sprague‐Dawley rats No effects on BW (Kim et al., 2011) weeks Wistar rats fed a HFS diet 8 g/kg diet for 8 weeks Decrease in abdominal fat (Panchal et al., 2012) Male wistar rats monosodium‐glutamate‐ 75 mg/kg i.p. for 42 days Decrease BW (Seiva et al., 2012) induced obesity Decrease BW gain Reduce epididymal C57BL/6J mice fed a HFD 0.025% (w/w) for 9 weeks adipose tissue and liver (Jung et al., 2013) weight and liver fat accumulation No effects on maternal 0, 50, 100 or 200 mg/kg BW Female Sprague‐Dawley rats BW during gestation (21 days) and (Wu et al., 2014) fed a HFD Decrease in BW of male lactation (21 days) and female offspring 0.05% (0.5 g quercetin/100 g Male C7BL/6 mice fed a HFD No effects on BW (Le et al., 2014) diet) for 9 weeks 30 mg/kg BW/day (0.045% No effect on BW or Wistar rats fed a HFS diet (Arias et al., 2014) diet) for 6 weeks adipose tissue size Decrease BW Male C57BL/6 mice fed a Reduce adipose tissue 0.1% (w/w) for 12 weeks (Dong et al., 2014) HFD weight and adipocyte sizes Attenuation of BW increase C57BL/6J mice fed a HFD 17 mg/kg BW for 9 weeks (Henagan et al., 2015) Decrease fat accumulation and body fat LPL, lipoprotein lipase; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; BW, body weight; HFD, high‐fat diet; TG, triglycerides; i.p., intraperitoneal. Adapted from (Aguirre et al., 2011)

12 Introduction

 Human studies

Intervention studies using quercetin as a supplement are still relatively scarce. However, Egert and collaborators (Egert et al., 2008) conducted a study in healthy human volunteers supplemented with different doses of the flavonol (50, 100 and 150 mg/day for 2 weeks), in order to assess its effect on antioxidant status, inflammation and metabolism. Plasma quercetin levels increased dose‐dependently without affecting different metabolic markers (Egert et al., 2008). Dose‐dependent but highly variable increases were also reported in plasma of participants that were not explained by differences in age, gender, BMI, fitness level or dietary intake (Jin et al., 2010). Other studies have assessed the effect of oral quercetin supplementation on the inhibition of platelet aggregation (Hubbard et al., 2004), on blood pressure in hypertensive subjects (Edwards et al., 2007), on cardiometabolic risk (Lee et al., 2011) and also on exercise performance (Nieman et al., 2010).

Nevertheless, human intervention studies devoted to elucidate the protective or beneficial effects of this flavonoid in overweight and obese patients are limited. Egert et al. (Egert et al., 2009) explored the effect of oral quercetin supplementation (150 mg/day for 6 weeks) in overweight or obese subjects with metabolic syndrome traits and concluded that quercetin protected them against CVD by reducing systolic blood pressure and plasma oxidized low‐density lipoprotein (LDL) concentrations. Interestingly, depending on the Apolipoprotein E genotype of overweight‐obese subjects, a differential response in not only blood‐pressure lowering capacity (Egert et al., 2010), but also in BMI, BW and waist circumference was observed (Pfeuffer et al., 2013). To our knowledge there is only one clinical trial ongoing (ClinicalTrials.gov, 2013), which aims to investigate whether quercetin influences the way glucose is absorbed by the body in obese subjects or type 2 diabetic obese subjects. Further intervention studies are required so that the health beneficial effects demonstrated in vivo could be extrapolated to humans.

 Intake, absorption, metabolism and excretion

Quercetin intake has been estimated to be 17.4 mg/day, and it is considered the highest contributor (70.2 %) to the mean total flavonol dietary intake (24.8 mg/day) (Zamora‐Ros et al., 2014). Quercetin shows poor low water solubility and poor bioavailability (Kaushik et al., 2012). As in the case of other polyphenols, its low bioavailability might be the result of a low absorption, an extensive metabolism, and/or rapid elimination (Manach et al., 2004).

Absorption of quercetin primarily depends on its chemical structure. While quercetin aglycone might be absorbed in stomach and the small intestine, both through a passive diffusion or through a pH‐dependent mechanism (Nait Chabane et al., 2009), glycosidic forms of quercetin (glucoside, galactoside and arabinoside) will firstly require a deglycosilation process in order to be absorbed in small intestine by passive diffusion (Day et al., 2003).

In general, polyphenols suffer a xenobiotic metabolism (Del Rio et al., 2013) consisting of 3 phases: phase I conversion, which comprises a modification of a functional group through oxidation, reduction or hydrolysis; phase II modification, that includes conjugation processes where a highly hydrophilic moiety is attached,

13 Introduction which make the compounds more water‐soluble so that they could be excreted through urine or bile; and the phase III process, related to the elimination of the compounds (Melo‐Filho et al., 2014). Overall, these processes will be devoted to limit polyphenol absorption and accumulation (Guo and Bruno, 2015). Phase I metabolism of quercetin has not been reported, but its structure is similar to the flavone apigenin that takes part in cytochrome P450 metabolism, considered a major class of enzymes involved in Phase I metabolism (Guo and Bruno, 2015). Concerning quercetin glycosides, as a first step, a deglycosylation or a hydrolysis process occur in order to remove the sugar molecule attached. The hydrolysis of some flavonoid glycosides might be given already in the oral cavity by the action of both saliva and oral microbiota β‐glucosidase activity. However, the main mechanism for flavonoid deglycosylation involves hydrolysis by the lactase phlorizin hydrolase (LPH) in the brush border of the small intestine epithelial cells (Duenas et al., 2015). The aglycone derived as a result of this process enters the enterocytes by passive diffusion. Subsequently, the aglycone molecule will be transformed through glucuronidation, methylation and sulfation processes that occur by Phase II enzymes, namely uridine 5’‐diphosphate glucuronosyltransferase (UGT), catechol‐O‐ methyltransferase (COMPT) and phenol sulfotransferase (PST) (Scalbert and Williamson, 2000). As a result, a large variety of conjugated metabolites are produced that will be secreted to portal and lymph circulation (Del Rio et al., 2013). In this context, the vast majority of quercetin in the portal vein plasma or lymph has been reported to be conjugated quercetin metabolites, while the aglycone form seems to be below detection limits. Once reaching the liver through the portal vein, quercetin Phase II metabolites will be further metabolized by Phase II conjugating enzymes, and will finally enter the circulation or will be excreted in the bile (Guo and Bruno, 2015). The Phase III efflux of quercetin metabolites have been also detected in small intestinal lumen (Williamson et al., 2007). It has been evidenced that this process is probably mediated by the Phase III transporters present in the apical side of enterocytes, such as breast cancer resistance protein 1 (BCRP1) and multidrug resistance associated‐protein 2 (MRP2). Moreover, quercetin that has not been absorbed in the small intestine, together with the conjugated metabolites that return to the intestinal lumen through enterohepatic circulation, will reach the colonic region where they will be subjected to the action of intestinal microbiota (Duenas et al., 2015).

Quercetin is removed in urine and faeces and its metabolites 3‐hydroxyphenylacetic acid, benzoic acid and hippuric acid have been detected (Mullen et al., 2008). Despite the fact that deconjugation of quercetin metabolites in vivo has not been well defined, an involvement of colonic gut microbiota is plausible prior to the degradation process of quercetin aglycone to phenolic acids (Guo and Bruno, 2015).

Consequently, knowledge about the metabolites produced from quercetin biotransformation (Figure 5) is essential in order to be able to completely understand the health benefits attributed to this molecule.

14 Introduction

Figure 5. Schematic representation of quercetin biotransformation (Guo and Bruno, 2015). COMT, catechol‐O‐ methyl transferase; LPH, lactase phlorizin hydrolase; SULT, sulfotransferase; UGT, uridine 5’‐diphospho‐ glucuronosyltransferase.

2.3.2. Non‐flavonoids

There are a different set of non‐flavonoid compounds that have also shown dietary significance such as, the stilbenes (trans‐resveratrol).

2.3.2.1. Stilbene: trans‐resveratrol

Resveratrol (3, 5, 4’ trihydroxystilbene) is a polyphenol from the stilbenoid class (Figure 6) naturally present in and produced by many plants in response to injury and fungal attack (Leiherer et al., 2013). The main source of resveratrol in nature is a plant from traditional Chinese and Japanese medicine called Polygonum cuspidatum, but it is also found in small amounts in peanuts, mulberries, grapes and end‐products of grapes, as for instance in red wine, present on average in 1.9 mg/L Figure 6. Structure of resveratrol (Stervbo et al., 2007). The content of resveratrol in different food sources (McCormack and McFadden, 2013). is shown in Table 4. Resveratrol might happen in two isoforms, cis‐resveratrol and trans‐resveratrol, the latter being quite stable and the main isoform in nature (Borriello et al., 2010). In 1990’s great attention was paid to resveratrol since it was associated with the

15 Introduction

French paradox, an epidemiological observation that French people who consume great amount of fat as saturated fats in their diet showed lower incidence of coronary heart disease (CHD) (Renaud and de Lorgeril, 1992). The extensive research conducted on resveratrol comprehends studies carried out to analyse the role of this molecule to prevent or treat aging and diseases such as cancer, CVD, aging, metabolic syndrome, bone health, eye health and Alzheimer’s disease (Britton et al., 2015; Rabassa et al., 2015). Indeed, it has been described as an anti‐oxidant, anti‐ inflammatory and estrogenic molecule (Gambini et al., 2015).

Table 4. Amount of resveratrol in selected foods1

FOOD SOURCE RESVERATROL MEAN CONTENT (mg/100g)

Fruits and fruit products Fruits‐Berries Lingonberry 3.00 Seasonings Other seasonings Vinegar 0.01 Seeds Nuts Pistachio, dehulled 0.07 Oils Oils‐Nuts Oils Peanut, butter 0.04 Alcoholic beverages Wines‐berry wines Muscadine grape, red wine 3.02 mg/100 mL Wines‐grape wines Red wine 0.27 mg/100 mL Wines‐sparkling wines Champagne 0.01 mg/100 mL Non‐alcoholic beverages Cocoa beverage Chocolate, milk, beverage 0.04 mg/100 mL Fruit juices‐Berry juices Green grape, pure juice 0.01 mg/100 mL

1 Database adapted from Phenol‐explorer 3.0. Food content of resveratrol analysed by chromatography.

 Anti‐obesity effects of resveratrol: in vitro, in vivo and human studies

 In vitro studies

As recently reviewed by Aguirre et al. (Aguirre et al., 2014), the in vitro anti‐adipogenic effects of resveratrol have been thoroughly studied using different types of adipocytes but especially, 3T3‐L1 pre‐adipocytes. In this context, main outcomes described in the literature are the role of this compound in reducing lipid accumulation, probably as a consequence of a decreased cell viability and inhibition of adipogenesis when concentrations higher than 10 μΜ (20‐100 μΜ) are used (Aguirre et al., 2014). The apoptotic potential of this molecule has been also reported at high concentrations. On the other hand, in vitro resveratrol delipidating and lipolytic effects have been also reported. In 2003, resveratrol was designated as an activator of sirtuin 1 (SIRT1) (Howitz et al., 2003) which is induced by calorie restriction and exercise (Ruderman et al., 2010), and is known to modulate aspects of glucose and lipid metabolism (Yu and Auwerx, 2009). Taking into account that resveratrol induces the activity of this molecule, its role as a calorie restriction‐like molecule has been suggested (Timmers et al., 2011). In fact, many of the mechanisms involved seem to be regulated by the resveratrol‐induced activation of SIRT1.

16 Introduction

 Studies conducted in animal models

Previously mentioned activities have been also demonstrated in vivo. Moreover, increased mitochondriogenesis that leads to an enhanced fatty acid oxidation in liver and skeletal muscle by resveratrol has been shown in several animal studies (Aguirre et al., 2014). This mechanism is of relevance, as obesity is known to present impaired fatty acid oxidation process (Isken et al., 2010). Plethora of research work has been conducted to elucidate the effects of resveratrol on body fat in animal models. In general, the potential of resveratrol to reduce body fat accumulation has been observed at doses that vary between 10 and 200 mg/kg BW/day (Arias, 2015). Health beneficial effects of resveratrol against diet‐induced obesity and associated comorbidities (i.e. insulin resistance) are based on its role in reducing white adipose tissue and liver lipogenic activity, enhancing fatty acid oxidation in the liver and improving brown adipose tissue and skeletal muscle thermogenesis (Alberdi et al., 2011; Alberdi et al., 2013a; Alberdi et al., 2013b).

 Human studies

Clinical studies devoted to shed light on the effects of resveratrol on overweight or obese patients are scarce. Up to date, five human studies have addressed the metabolic effects of resveratrol in the context of overweight or obesity. Timmers et al. (Timmers et al., 2011) conducted a randomized double‐blind cross‐ over study where 150 mg/day of resveratrol or placebo was administered to 11 obese participants for 30 days and improvements in insulin sensitivity, inflammation, hepatic lipid content and blood pressure were found. The authors concluded that resveratrol exerted calorie restriction‐like effects. Furthermore, in a more recent study from the same group, the impact of resveratrol supplementation in adipose tissue function of the previously described obese population was analysed (Konings et al., 2014). Microarray analysis and adipose tissue morphology were examined in subcutaneous adipose tissue biopsies. The authors concluded that resveratrol supplementation led to an increased amount of small adipocytes and increased immune and inflammatory response. In contrast, in another study conducted in non‐obese postmenopausal women with normal glucose tolerance, 75 mg/day of resveratrol supplementation for 12 weeks did not produce any metabolic modifications (Yoshino et al., 2012). Besides, from a study conducted by Poulsen et al. (Poulsen et al., 2013) where 24 male obese volunteers were given tablets containing 500 mg of trans‐ resveratrol per day or placebo for 4 weeks, no metabolic effects were obtained. In contrast, overweight and obese participants who presented hypertriglyceridemia and were treated with even higher doses of the molecule for a short period of time (1000 mg/day for 1 week followed by 2000 mg/day for the subsequent week), showed improvements in intestinal and liver lipoprotein formation (Dash et al., 2013). Anyway, although in vitro and animal data might be promising, still more clinical studies are required since the available results are not enough to recommend chronic administration to humans (Vang et al., 2011).

 Intake, absorption, metabolism and excretion

Daily intake of the stilbenes has been estimated to be 3.1 mg/day in Mediterranean countries, and 2.1 mg/day, in Non‐Mediterranean countries being wine the main food source (> 92%) in these countries (Zamora‐Ros et al., 2015). The estimated average of resveratrol intake in Spanish population is 933 μg/day (Zamora‐Ros et al., 2008). Red wine contains high amounts of resveratrol, with a mean quantity of

17 Introduction

1.5 to 3.0 mg per liter. Resveratrol is found in the skin of grapes, thus the amount of this polyphenol in white wine is lower than in red wine. In fact, the concentration of resveratrol in different types of wine might vary by a factor of 20 (Zamora‐Ros et al., 2008).

Large amount of data exist on the absorption, transport and metabolism of resveratrol (Kaldas et al., 2003). Resveratrol metabolism is carried out at intestinal level within enterocytes, but also it is subjected to hepatocyte metabolism. It has been concluded that trans‐resveratrol is metabolized in enterocytes up to a limit. Accumulation of trans‐resveratrol in Caco‐2 cells has been also reported, suggesting the relevance of enterocytes as a target effect site (Kaldas et al., 2003). Besides, other target tissues have been postulated such as kidneys, liver and lungs (Bertelli, 1998). No Phase I metabolites of resveratrol have been detected (Yu et al., 2002). In contrast, this compound suffers conjugation processes within Phase II metabolism giving rise to glucuronides and sulfates mainly, trans‐resveratrol‐3‐O‐glucuronide and the trans‐resveratrol‐3‐ sulfate (Yu et al., 2012b). In this sense, while glucuronidation has been considered as the major conjugation pathway in humans, other studies have proposed sulfation process to be more relevant (Walle, 2011). Some of the metabolites formed in the liver will be secreted to the bile, in order to be deconjugated and reabsorbed in the entero‐hepatic circulation; otherwise, these metabolites will reach the colon. In the colonic tissue, the role of gut microbiota in the metabolism of resveratrol has been demonstrated and several metabolites have been identified, namely dihydroresveratrol, 3,4’‐dihydroxy‐trans‐stilbene and 3,4’‐dihydroxybibenzyl or lunularin (Bode et al., 2013). In summary, it has been demonstrated that non‐methylated polyphenols (i.e. resveratrol) are quickly metabolized and discarded. In contrast, methylated stilbenes such as pterostilbene have been demonstrated to be protected from rapid hepatic metabolism and excretion, conferring them an increased intestinal absorption and higher metabolic stability (Wen and Walle, 2006).

The output of stilbenes will mainly happen through urine (53‐85%). However, the remaining amounts of these natural compounds, which have not been metabolized, will be also excreted in faeces (Walle, 2011). An outline of resveratrol metabolism in vivo is reported in Figure 7.

Figure 7. Outline of resveratrol metabolism in vivo. Adapted from (Scalbert et al., 2002; van Duynhoven et al., 2011).

18 Introduction

3. Gut microbiota: a new contributory factor in obesity development

3.1. Conceptual approach

Human beings have a gut reservoir of 1023‐1024 microbial cells (Ley et al., 2006). Gut microbiota is defined as the collection of microorganisms that include bacteria, archaea, viruses and some unicellular eukaryotes that represents an incredibly complex, diverse and vast microbial community residing in the intestine (Harris et al., 2012; Mar Rodriguez et al., 2015; Ogilvie and Jones, 2015). The gut houses up to 100 trillion (1014) bacterial cells (Backhed et al., 2005). Furthermore, a number of 1000 to 1500 bacterial species have been reported to be widespread in the gut, and indicated that each person has around 160 species (Qin et al., 2010). Moreover, microbial cells exceed 10 times the number of somatic cells in the human body and stand for a biomass of ~ 1.5 Kg in our organism (Backhed et al., 2005). The term microbiome was suggested by the Nobel laureate Joshua Lederberg to designate the collective genome of the microbiota (Hooper and Gordon, 2001). Accordingly, the microbiome has been found to contain ≥ 100‐fold more genes than the human genome, contributing extensively to our physiology and metabolism (Qin et al., 2010). This human microbial genome is known as the “metagenome” and is usually recognized as our second genome (Aagaard et al., 2013). In view of that, gut microbiota could be contemplated as a superorganism within our organism constituted of diverse cell lineages that have the capacity to communicate with each other and encode plenty of biochemical and physiologic functions that may benefit the host (Petrosino et al., 2009).

In the last decades scientific attention on gut microbiota has raised considerably. There is increasing interest from research community to understand the role of the microbial community in maintaining human health. Accordingly, several projects have been launched all over the world (Huse et al., 2012). One of the first studies, namely Human Microbiome Project (HMP), was conducted by the National Institute of Health (NIH) and began in 2008. The HMP lasted 5 years, was funded by $150 million and sought to characterize the human microbiome of healthy individuals with the aim of analysing modifications of this ecosystem with population, genotype, disease, age, nutrition, medication and environment (Peterson et al., 2009). Subsequently, more studies have appeared (Jeffery and O'Toole, 2013). In fact, there also exists an International Human Microbiome Consortium launched in 2008 (Table 5), that coordinates the activities and policies of the international groups studying the human microbiome (Lepage et al., 2013). Outcomes from these studies are enabling to demonstrate the plethora of biological functions in which the resident microbiota is involved (Jeffery and O'Toole, 2013).

19 Introduction

Table 5. Main microbiome consortiums and microbiome‐related projects.

Funding Program Duration Conditions of interest Organization National Institute of 2007‐ Generate 200 complete bacterial genome sequences and perform Health (NIH) Jumpstart NIH, USA 2008 compositional analysis of various body regions program NIH Human Microbiome NIH Roadmap Characterize the microbes of the human body and correlate the 2007 Project Program, USA changes in these microbial populations with human health Data Analysis and NIH Human 2008‐ Assist in standardization of data pipelines (storage, analysis and Coordination Center Microbiome Project 2013 display of data) and provide access to data. (DACC) (HMP), USA MetaHIT, Metagenomics Describe the role of the microbiota in Inflammatory Bowel Disease 2008‐ European Commission of the Human Intestinal (IBD) and obesity, and generate a reference catalogue of intestinal 2011 (FP7) tract microbial genes. Canadian Institute of Canadian Human A number of project relating to human microbial interactions and 2009 Health Research Microbiome Initiative their effect on health (CIHR) Common wealth The Australian Sequencing of specific bacterial strains and the application of Scientific and Jumpstart Human 2009 metagenomics techniques to investigate the interaction between Industrial Research Microbiome Project intestinal microbes and their host. Organisation (CSIRO) MicroObes, Human French National Identify metagenomic signatures that characterise the relationship Intestinal Microbiome 2008‐ Agency for Research between the intestinal microbiota and the nutritional and in Obesity and 2010 (ANR) metabolic status of the host. Nutritional Transition Determine the microbiomes in various epithelial sites of the human Korean Microbiome 2010‐ National Research body using Korean Twin Cohort and investigate the relationship Diversity Using Korean 2015 Foundation of Korea between human microbiomes and disease. To establish a dedicated Twin Cohort Project centre for Korean microbiome information and analysis. Human MetaGenome MEXT, Ministry of Develop and establish methods for metagenomics analysis of the Consortium Japan 2005 labour and Welfare, human gut microbiome (HMGJ) JST and JSPS, Japan National Development Characterize the faecal microbiota associated with ageing and 2007– Food Research Health ELDERMET Project correlate diversity, composition, and metabolic potential of the 2013 Initiative and Science faecal microbial metagenome with health, diet and lifestyle Foundation Ireland International Human Optimization of methods for the assessment of the effects of the gut 2011‐ European Commission Microbiome Standards microbiome on human health through standardization of 2015 (FP7) (IHMS) procedures and protocols. Demonstrate the impact of the human gut microbiota on health and French initiative 2012‐ disease, making available cutting‐edge metagenomics technology, MetaGenoPolis “Investissements 2019 quantitative and functional, to the medical, academic and industrial d’Avenir” communities. Analyse how the human gut microbiota and the microbiome (its 2013‐ European Commission genome) influence obesity, behavioural‐ and lifestyle‐related MyNewGut 2018 (FP7) disorders and vice versa. Besides, it seeks to identify specific dietary strategies to improve long‐term health of population. Understand modern diseases within the context of human’s Crowd funded and Human Food Project 2012 ancestral and microbial past, including the co‐evolution of humans partnerships and their microbes within their bodies. An open access project that allows participants to be involved in finding out the gut microbiota composition of their guts and American gut 2013 Crowd funding contribute to obtaining knowledge on how different diet and lifestyle habits affect human health through gut microbes‐ An open access project that allows participants to be involved in British gut 2013 Crowd funding finding out the gut microbiota composition of their guts in order to understand the bacterial diversity of the British Gut.

NIH, National Institute of Health; DACC, Data Analysis and Coordination Center; HMP, Human Microbiome Project; CIHR, Canadian Institute of Health Research; CSIRO, Common wealth Scientific and Industrial Research Organisation; HMGJ, Human MetaGenome

Consortium Japan; IHMS, International Human Microbiome Standards; ANR, French National Agency for Research; MEXT, the Ministry of Education, Culture, Sports, Science and Technology‐Japan; JST, the Japan Science and Technology Agency; JSPS, the Japan Society for the

Promotion of Science. Adapted from (Jeffery and O'Toole, 2013; Lepage et al., 2013; Hattori and Taylor, 2014).

3.2. Establishment and evolution

Establishment and evolution of the intestinal microbiota is a complex process determined by the interaction of many factors as for instance delivery mode, type of feeding, exposure to antibiotics and environmental factors such as diet and lifestyle factors (Figure 8). Furthermore, the plasticity of the gut microbiota through

20 Introduction lifespan is pivotal for its implications in health and disease (Douglas‐Escobar et al., 2013). In this sense, the development of gut microbiota during infancy and childhood has attracted much interest due to its relevance for the correct development of immune system (Francino, 2014). However, phylogenetic and functional changes occurring with age, and the impact of these modifications in health and longevity are also matter of study (Biagi et al., 2013).

Figure 8. Factors influencing gut microbiota development Adapted from (Matamoros et al., 2013; Arrieta et al., 2014; Munyaka et al., 2014).

3.2.1. Age‐related gut microbiota composition

 Foetal gut microbiota

Until now it was thought that the foetus grows sterile in utero and microbiota colonization was believed to begin at delivery while passing through maternal birth canal (Tissier, 1900). Conversely, if recent findings are confirmed, the development of foetus in a sterile environment does not seem to be correct (Romano‐Keeler and Weitkamp, 2015), since several studies have already corroborated the presence of bacteria in placenta of normal pregnancy (Stout et al., 2013; Aagaard et al., 2014). Interestingly, placenta microbiome profile was found to resemble the non‐pregnant human oral microbiome (Aagaard et al., 2014) hypothesizing that mother’s oral bacteria might find a way to the foetus. Despite the mechanism of internal maternal transmission of bacteria is not well understood, a possible passage of the microbes to the placenta via the bloodstream has been suggested.

 Gut microbiota in neonates

The first year after birth, the mother exerts a robust influence on the infant’s microbiota. Indeed, the child’s intestinal microbiota is both functionally and phylogenetically close to the mothers particularly within the first month of life (Matamoros et al., 2013). Nonetheless, at 1 year of age, although similarities remain at functional level, phylogenetic modifications began (Vaishampayan et al., 2010). In this way, there are several

21 Introduction intrinsic and extrinsic factors that might induce highly variable microbial patterns on the neonate which will ultimately affect infant’s health (Funkhouser and Bordenstein, 2013).

Mode of delivery has been found to be a factor strongly influencing the colonization process (Matamoros et al., 2013). The intestinal microbiota of vaginally delivered infants resembles mother’s vaginal microbiota (Dominguez‐Bello et al., 2010). In contrast, the intestinal microbiota of children born by C‐section is characterized by the presence of microbes associated to hospital environment (Biasucci et al., 2010) and also, to be distinctive of skin surfaces (Biasucci et al., 2008). Notably, these alterations due to delivery process have been reported to be permanent up for weeks (Kozyrskyj et al., 2011), months (Gronlund et al., 1999), and even years (Jakobsson et al., 2014). Although the clinical relevance of these modifications is unknown, there is a wide range of evidence supporting the significance of the initial gut microbiota colonization process on the postnatal evolving of immune system (Neu and Rushing, 2011). Another factor significantly modulating the gut microbiota composition of the infant is the type of feeding (Le Huerou‐Luron et al., 2010). Breast‐feeding is recommended over formula‐feeding as the best nutritional option associated to short‐ and long‐term health protective effects (Le Huerou‐Luron et al., 2010). It has been suggested that the chemical composition of the breast milk might directly (promoting or avoiding the growth of specific bacteria), or indirectly (inducing protective physiological effects at intestinal level) impact on gut microbiota (Le Huerou‐Luron et al., 2010). Moreover, the existence of an entero‐mammary pathway has been proposed as a possible explanation for the transfer of mothers’ gut bacteria to the child (Jost et al., 2014). Apart from the mothers’ influence, family members, as well as biodiversity in the homes and the surrounding environment, are known to directly impact the microbiota of the infants (Penders et al., 2006). In addition, recent studies have revealed pronounced geographic differences in gut microbiota profiles among children and adults of Westernized and non‐Westernized countries (Yatsunenko et al., 2012). Finally, the exposure to antibiotics during early life has been associated with health detrimental outcomes (Hussey et al., 2011; Risnes et al., 2011). Importantly, the restoration capacity of the gut microbiota following antibiotic treatment is low, thereby modifications might remain during a long‐term period of time producing negative effects on host health (Dethlefsen and Relman, 2011).

 Gut microbiota in adults

Microbiome of infants experiences a profound modification towards the acquisition of a stable adult‐like microbiota, especially between 9 and 18 months after birth, parallel to the cessation of the breast feeding and the introduction of complementary foods (Bergstrom et al., 2014). Thus, much more important than other environmental and physiological factors, dietary habits are considered a decisive factor for the long‐ term gut microbiota composition, and in turn, for the individual’s predisposition to develop diseases (Voreades et al., 2014). In any case, gut microbiome stability is not gained before 3 years of age (Bergstrom et al., 2014).

Almost 70% of the bacterial strains in an adult‐like microbiota are maintained unaltered over the time (Faith et al., 2013). In this sense, efforts have been made in order to define a “core” microbiota, which reflects a “normal” healthy state (Huse et al., 2012). This concept would be a helpful tool since variations on it might

22 Introduction be indicative of a health disturbance resulting from or contributing to disease development (Voreades et al., 2014). Indeed, classification of individuals depending on the faecal gut microbiota profile has been contemplated as a possibility to predict long‐term health risks. In this context, Arumugan and collegues (Arumugam et al., 2011) identified three distinct “enterotypes” or variants, irrespective to nationality, sex, age or BMI and each cluster was characterized by the abundance of a specific bacterial genus namely Bacteroides, Prevotella and Ruminococcus. Wu et al. (Wu et al., 2011) confirmed that faecal microbiota from 98 healthy adults that were subjected to either a high‐fat/low‐fibre or low‐fat/high‐fibre diet, was partitioned into two main enterotypes depending on the dietary pattern. Indeed, Bacteroides cluster was associated with the consumption of a Westernized diet, high in animal protein and fat, while Prevotella cluster was related to low carbohydrate intake (Wu et al., 2011). The authors also observed that gut microbiota profile was markedly modified within 24 hours. In contrast, enterotypes were found to only explain long‐term dietary patterns (Wu et al., 2011). Nevertheless, disparities have been found in different studies particularly as a result of a lack of consistency in the methodologies (Koren et al., 2013). Therefore, the existence of an “enterotype” or a “faecotype” is not so clear yet (Knights et al., 2014).

The human gut microbiota will be susceptible to variations owing to illnesses, stress, antibiotics, and also, as a consequence of diet and lifestyle (Zhang et al., 2015). Anyway, in the absence of relevant stressors the loss of balance in the gut microbiota will appear with the old age (Salazar et al., 2014).

 Gut microbiota in elderly

In contrast to childhood and infancy periods, not many studies have been published neither in relation to phylogenetic and functional changes of gut microbiota in ageing process, nor regarding the impact of these rearrangements on health and lifespan (Biagi et al., 2013). Nonetheless, it is known that elderdy people present innumerable clinical changes undergoing many comorbid diseases that lead to the continuous administration of medications and antibiotics (Zapata and Quagliarello, 2015). Alterations that occur at GI level (Biagi et al., 2012), impact on the absorption and/or metabolism of nutrients (calcium, iron and vitamin B12) and are associated with numerous dietary habit changes (Biagi et al., 2012). Together all these factors could affect gut microbiota composition. In this sense, elderdy people have been reported to present a drastic variability in gut microbiota profile between individuals compared to younger adults (Claesson et al., 2011). Moreover, it has been observed that gut microbiota remodelling does not follow a linear relation with age (Biagi et al., 2010). Interestingly, whereas 30 years old adults and 70 years old young elderly people seem to share similar gut microbiota composition and diversity, gut microbiota ecosystem of centenarians show marked differences. These alterations are mainly characterized by a proliferation of pathobionts at the expense of symbionts, as for instance Faecalibacterium prausnutzii and Clostridium cluster XIVa bacteria which have ascribed anti‐inflammatory properties (Biagi et al., 2010).

3.2.2. Microbial community through the body: focus on the gastrointestinal tract

Colonization of microbiota occurs in all surfaces of the body that are exposed to an external environment (Sekirov et al., 2010). Composition of the microbiota has been reported to be distinct depending on the anatomical area of the body. It seems that the microbiota is a large ecosystem with different habitats

23 Introduction

(Spor et al., 2011). Indeed, location through the body is described as the major factor that determines gut microbiota (Costello et al., 2009). Furthermore, it has been postulated that microbial profile is not homogenous and might vary not only longitudinally along the GI tract, but also, radially from lumen to mucosa (Frank and Pace, 2008). Variations in microbial number and composition across the length of the GI tract are shown in Figure 9.

Figure 9. Variations in microbial number and composition across the length of the GI tract. Adapted from (Sekirov et al., 2010).

The GI tract contains the largest number of microorganisms in humans. Every location has its own chemical environment and thus, the microbes inhabiting each site are extremely different as shown in Figure 10 (Harris et al., 2012). The GI tract extends up to 200 m2 and is defined as a dynamic and complex organ where interplay between several components (intestinal mucosal cells, defence molecules, immune system, nutrients and microbiota) take place (Lopetuso et al., 2014). Population of microbial ecosystem varies along the digestive tract. At the stomach 103 colony‐forming units (CFU)/mL have been estimated, in the small intestine between 102 and 109 CFU/mL and in the large intestine from 104 to 1012 CFU/mL (Lopetuso et al., 2014). Indeed, the colon is the site exhibiting the highest density found in humans (1012 microorganisms/mL). The major part of the intestinal microbiota is composed of strict anaerobes, whereas facultative anaerobes and aerobes have been concluded to correspond less in number (Sankar et al., 2015). The gut microbiota is composed of more than 30 bacterial phyla and 7 phyla have been identified as the groups containing the largest amount of detected bacterial species: Firmicutes, Bacteroidetes, Actinobacteria, Cyanobacteria, Fusobacteria, Proteobacteria and Verrucomicrobia (Sankar et al., 2015). Moreover, among them, Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria are largely present in human mucosal biopsies, luminal contents and faeces. Firmicutes and Bacteroidetes represent 98%, the dominating phyla of the gut microbiota, and are classified within three groups of strict extremophile anaerobes namely Bacteroides, Clostridium cluster XIVa (also called Clostridium coccoides group), and Clostridium cluster IV (or the Clostridium leptum group) (Hold et al., 2002; Eckburg et al., 2005; Ley et al., 2005; Ley et al., 2006). Firmicutes phylum primarily includes Gram‐positive bacteria like Lactobacillus and

24 Introduction

Clostridium species and has the greatest diversity in the GI tract with low GC content. Proteobacteria phylum is not so representative and this group includes the well‐known pathogen Escherichia coli and Actinobacteria. Within Actinobacteria phylum, the most characteristic group is Bifidobacterium (Lin et al., 2013).

Figure 10. The relative abundance of the six dominant bacterial phyla in each of the different body sites (Spor et al., 2011).

Importantly, the type of sample collected for gut microbiota composition analyses will make results to differ. Till now, particularly, mucosa adhered microbiota obtained from biopsy samples and faecal samples have been mostly used to analyse gut microbiota composition (Gerritsen et al., 2011). In this sense, there has been continuous discussion since, despite faecal samples are considered convenient as they are easy to isolate, they are not considered a very good reflect of the whole microbial diversity of the proximal large intestine. It has been described that during the movement of the bolus through the colon, there are changes in substrates, pH and water content that will make the composition of the microbiota to be modified (Mai et al., 2010). Biopsies (colon biopsies) are not either representative of the real gut microbiota composition in the intestine (Mai et al., 2010). When preparing and cleaning colons for the sampling, part of the outer colonic mucosa is removed and with this, the microbiota attached to this site. Some studies have already reported the impact of bowel preparations in biopsies on colon physiology (Croucher et al., 2008). Consequently, the discrepancies observed between bacterial community composition of different sample types and the microbiota composition from diverse body niches should be overcome. Notwithstanding as far as technologies are developed to bridge this gap, faecal samples and biopsies will still be utilized as representatives of the colonic microbiota (Rodriguez et al., 2015).

25 Introduction

3.2.3. Gut microbiota: a sign of metabolic status

In general, gut bacteria and the host live in a mutualistic relationship (Spasova and Surh, 2014). Commensal bacteria are appreciated for its contributions to host development and well‐being due to the metabolic and immunological functions that microbes fulfil ensuring homeostasis (Spasova and Surh, 2014). Culture‐independent molecular techniques have currently allowed defining the normal state of the gut microbiota, known as “eubiosis” or “normobiosis. This concept, paralelly, supports the identification of deviations of this normal state, usually detected in metabolic, immune or degenerative diseases. These gut ecosystem modifications are called “dysbiosis” (Doré and Corthier, 2010; Doré et al., 2010), disruption of the symbiosis that lead to disturbed host‐microbe homeostasis and thus, might be the cause, or at least, be a factor contributing to a wide range of diseases.

A number of diseases have been associated with gut microbiota dysbiosis, including GI tract‐related disorders, systemic diseases or central nervous system‐ (CNS)‐related disorders (Carding et al., 2015).

 GI tract‐related disorders: Inflammatory bowel disease (IBD) and its prevalent forms Crohn’s disease (CD) and ulcerative colitis (UC), are characterized by recurrent intestinal inflammation. Aberrant changes in gut microbiota composition have been identified in the pathophysiology of these disorders, with reported differences having found between CD and UC (Cammarota et al., 2015). Additionally, intestinal microbiota has been also implicated in irritable bowel syndrome (IBS), coeliac disease and colorectal cancer, but without the identification of consistent patterns of microbiota modifications (Carding et al., 2015). Furthermore, at present, it could not be concluded whether the microbial dysbiosis is a direct cause of these disorders or in contrast, it is a consequence of an altered ecosystem in the GI tract (Cammarota et al., 2015).  CNS‐related disorders: There exist a bidirectional communication between the digestive tract and the brain (Dinan et al., 2015). This crosstalk occurs due to the “gut‐brain‐axis” (GBA) (Rhee et al., 2009) and associations between gut microbiota and anxiety‐related, depressive‐like behaviours (Diaz Heijtz et al., 2011), autism spectrum disorder (ASD) (Song et al., 2004), schizophrenia and bipolar disorders have been demonstrated (Fond et al., 2015).  Systemic diseases: In addition to T2DM (Tilg and Moschen, 2014), hypertension (Yang et al., 2015) and CVD (Serino et al., 2014), one of the most important metabolic disease associated to gut microbiota dysbiosis is obesity (Escobedo et al., 2014). Pioneering data proposing the relationship between gut microbiota and obesity, specifically with energy homeostasis and fat storage, rose from studies conducted with germ‐free (GF) animals (Conterno et al., 2011), as briefly described in Table 6. Bäckhed et al. (Backhed et al., 2004) were the first authors demonstrating that GF mice that acquired gut microbiota from conventionally raised animals had increased fat deposition, hence they were the first authors connecting gut microbiota functionality with mammalian energy balance and obesity (Backhed et al., 2004). Few years later, Turnbaugh et al. (Turnbaugh et al., 2006) showed that this trait was transmissible since GF animals that received gut microbiota from obese animals exhibited greater body weight and adiposity than those animals colonized with gut microbiota from lean animals. In this context, obesity‐associated gut microbiota was featured by increased levels of Firmicutes phylum compared to that of the lean ones. Moreover, metagenomic sequencing analysis

26 Introduction confirmed that these bacterial profile modifications impacted on the metabolic potential of gut microbiota. Furthermore, subsequent studies conducted using GF animals proposed that these animals were resistant to diet‐induced obesity and that obese phenotype was rapidly transmissible (Rabot et al., 2010; Ridaura et al., 2013; Duca et al., 2014), although contradictory data have been also reported by Fleissner et al. (Fleissner et al., 2010) which might be attributed to differences in strains of mice used. Interestingly, the role of gut microbiota in reducing adiposity has been also recently concluded from experiments where faecal slurry from bariatric surgery patients was transplanted into GF mice (Tremaroli et al., 2015).

Greater evidence from imbalance in the intestinal bacterial community in obesity came from obese (ob/ob) mice, deficient in the gene encoding leptin, a hormone that promotes satiety (Ley et al., 2005). Ley et al. (Ley et al., 2005) demonstrated that the gut microbiota from ob/ob mice differed from that of their lean ob/+ and +/+ siblings, particularly in relation to Firmicutes and Bacteroidetes phyla. Genetic obesity was associated to an enlargement in Firmicutes together with a parallel decrease in Bacteroidetes. Interestingly, this was the first study showing that a single genetic mutation could affect gut microbiota composition. These results were also confirmed in a rat model of genetic obesity resistant to leptin (Zucker fa/fa), that better reflect diet‐induced obesity typical from the modern society (Waldram et al., 2009). Importantly, data obtained from studies conducted with diet‐induced models of obesity have uncovered the complex interaction between dietary habits, the gut microbiota, and host energy metabolism predisposing to the development of different metabolic diseases as, for instance obesity (Xu and Knight, 2015). In this regard, several studies have evidenced the impact of diet on gut microbiota composition, even independent of obesity (Hildebrandt et al., 2009). Turnbaugh et al. (Turnbaugh et al., 2008) observed that a diet high in sugar and fat led to an increase in Firmicutes phylum (especially Mollicutes class) and a decrease in Bacteroidetes. Growing data from human studies have also demonstrated a dysbiotic gut microbiota in obese when compared to lean individuals (Turnbaugh et al., 2009a; Turnbaugh et al., 2009b). However, not all studies have obtained the same results (Schwiertz et al., 2010). For instance, some human studies contradict the statement that obese‐type microbiota is characterized by higher Firmicutes/Bacteroidetes ratio, anyway, elevated faecal SCFA concentrations have been found in obese subjects, agreeing with data obtained from ob/ob mice (Schwiertz et al., 2010). Therefore, despite the fact that current findings are not able to determine a specific gut microbiota signature of obese subjects versus lean subjects, a distinct bacterial community between obese and lean people has been consistently demonstrated. The remaining question is whether dysbiosis might be considered a direct cause of metabolic diseases or whether this aberrant microbiota composition is an adaptation to changes in environmental conditions such as the diet. In this context, two points should be taken into account (Carding et al., 2015). On one hand, the fact that microbiota transfer from lean donors into individuals with metabolic syndrome has been shown to be effective in improving insulin sensitivity and general symptoms of the metabolic disorder (Vrieze et al., 2012), and, on the other hand, the fact that dietary interventions have been shown to induce rapidly and in a reversible way, changes in predominant bacterial groups (Walker et al., 2011). Anyway, characterization of “dysbiosis” in different diseases is an essential step in order to be able to design

27 Introduction

strategies that will unable to restore the eubiosis and the homeostatic state of the host (Doré and Corthier, 2010). Nevertheless, the functionality of the gut microbiota, which will impact on host physiology, should be born in mind. In this sense, despite microbial taxonomic composition among individuals might be highly variant at least at the deeper phylogenetic level or strain level (Greenblum et al., 2015), it has been demonstrated that functional gene composition is ubiquitous across individuals (Turnbaugh et al., 2009a; 2012), proposing that we have a core microbiome. Importantly, the identification of functional gene composition variations that may be limited to specific bacterial species or strains might help to detect clinically relevant components that could serve as therapeutic strategies (Lozupone et al., 2012).

28 Introduction

Table 6. Main studies conducted in germ‐free animal models supporting the causal role of gut microbiota on obesity and obese phenotype transmissibility.

Experimental Animal model Conclusions Reference procedure  A 60% increase in body fat and GF mice colonized with insulin resistance was found upon GF, CONV‐R mice cecal content of CONV‐R conventionalization. and CONV‐D mice‐ (Backhed et al., 2004) donors  Microbiota promoted C57BL/6J (conventionalization) monosaccharides absorption and Fiaf suppression.  An increased capacity for energy harvest is found in obese microbiome. GF C57BL/6J mice GF mice are colonized  Colonization of GF animals with and obese (ob/ob) with cecal content of (Turnbaugh et al., 2006) obese donors’ microbiota led to a and lean (+/+) mice obese and lean mice larger increase in total body fat than colonization with lean donor’s microbiota.  GF mice were resistant to diet‐ induced obesity and insulin GF and CONV‐R Animals were fed a HFD resistance. (Rabot et al., 2010) C57BL/6J mice for 11 weeks  GF mice showed lower food intake and increased faecal lipid excretion.  A reliable replication of human donor microbiota in gnotobiotic mice was found. Faecal samples from  A reproducible transmission of GF C57BL/6J mice each twin (lean and donor’s body composition into and monozygotic obese) were gnotobiotic mice was observed. and dizygotic twins transplanted into GF  Co‐housing mice that contain the Ob (Ridaura et al., 2013) discordant for mice and “humanized” or microbiota with animals containing obesity gnotobiotic animals Ln microbiota, showed that bacterial were co‐housed. members from Ln were transferred to Ob but not vice versa and prevented the development of obese metabolic traits.  OP and OR rats differed in gut microbiota composition profile and phenotypic characteristics.  Phenotype and behavioural GF mice were characteristics of OP and OR were conventionalized with transferred to GF mice. GF C57BL/6J mice (Duca et al., 2014) faecal microbiota of OP  GF mice inoculated with OP and OR rats fed a HFD microbiota showed obese phenotype of the donor and associated differences in chemosensory, metabolic and neural dysregulations.

 GF mice colonized with RYGB and VBG patients’ faecal microbiota, GF mice were colonized accumulated reduced fat mass. GF Swiss Webster with faecal slurry of  Mice receiving RYGB microbiota had (Tremaroli et al., 2015) female mice RYGM, VBG and OBS reduced RQ (reduced use of CHO, patients. increased use of fat).

GF, germ‐free; CONV‐R, conventionally raised; CONV‐D, conventionalized; Fiaf, fasting‐induced adipocyte factor; HFD, high‐fat diet; Ob, obese twin’s; Ln, lean twin’s; OP, obese‐prone; OR, obese‐resistant; RYGB, Roux‐en‐Y gastric bypass; VBG, vertical banded gastroplasty; OBS, obese; RQ, respiratory quotient; CHO, carbohydrates.

29 Introduction

3.3. Crosstalk between gut microbiota and the host

Until the last decade, intestinal bacteria have been regarded as organisms with pathogenicity thereby, as microbes that could cause diseases. Fortunately, in the last decade, an increasing number of research initiatives have surged studying the role of commensal bacteria on mammalian gut (Sekirov et al., 2010), thus, the view of gut microbiota as a threat has completely changed. Nowadays, strong evidence supports the important and specific functions (structural, protective, metabolic and immune) of gut microbiota in host physiology and health (Villanueva‐Millan et al., 2015).

3.3.1. Metabolic functions

Gut microbial community plays a pivotal role on four important host’s metabolic processes: synthesis of essential vitamins, fermentation of non‐digestible polysaccharides, production of short chain fatty acids (SCFAs), and control of host energy balance and storage.

 Vitamin synthesis

A pivotal service of the gut microbiota is the synthesis of vitamins for the host (Degnan et al., 2014). Vitamins are essential micronutrients that usually act as precursors of enzymes involved in biochemical processes. Human beings are not able to produce the majority of vitamins so they need to be taken exogenously from food (LeBlanc et al., 2013). Gut microbiota, yet, supplies a large variety of vitamins, as for instance, most of hydrosoluble B vitamins (biotin, cobalamin, folates, nicotinic acid, pantothenic acid, pyridoxine, riboflavin and thiamine), as well as vitamin K (Hill, 1997). As recently reviewed by LeBlanc et al. (LeBlanc et al., 2013), particular species of Bifidobacterium and Lactobacillus genera have been identified as suppliers of specific B‐ family vitamins. Interestingly, microbial genome sequencing is allowing gaining insights into the genetic characteristics of enteric bacteria, which will help to understand the contribution of this microbiome to the human physiology (Gill et al., 2006).

 Fermentation of non‐digestible polysaccharides

Another important function which is not evolved by human host and requires the action of gut microbiota is processing of food constituents (Tremaroli and Backhed, 2012). There is a mutual dependence between the host and symbiotic microorganisms since mammalian genome do not encode the enzymes required to degrade non‐digestible polysaccharides of plant origin (Flint et al., 2012), but gut bacteria provide the host with glycoside hydrolase and polysaccharide lyases that will allow breaking down glycosidic linkages in plant glycans (Backhed et al., 2005). It has been estimated that 20 to 60g of dietary carbohydrates (plant cell wall polysaccharides and non‐digestible polysaccharides, mainly) scape enzymatic hydrolysis and reach the colon every day (Cummings and Macfarlane, 1991; Silvester et al., 1995). Overall, major non digestible carbohydrates include:

 Resistant starch: Resistant starch (RS) has been defined as the “total amount of starch, and the products of starch degradation that resist digestion in the small intestine of healthy people” (Asp, 1997). The non‐fermentable portion of starch will vary depending on the diet composition and

30 Introduction

intake, cooking methods and individual differences. Anyway, compelling evidence is demonstrating the health beneficial effects derived from the fermentation of RS, partly mediated by its growth stimulating effects on intestinal microbiota (Keenan et al., 2015).  Plant cell wall polysaccharides: Plant cell wall polysaccharides, principally cellulose, hemicellulose and pectin, are found in cereals, vegetables and fruits and are one of the most important fractions within dietary fibres (Chassard et al., 2012). Europeans daily intake of plant cell wall polysaccharides varies from 10 to 25g or represents about 30% of the total dietary fibre consumed (Lairon et al., 2003). The microbial digestion of these compounds is known to give rise to health beneficial effects. However, there are limited studies analysing the digestive process of these compounds. Nevertheless, cellulose‐degrading microbial organisms have been identified (McDonald et al., 2012). Ruminococcus genus, belonging to Firmicutes phylum, represents 5‐15% of the total colonic bacterial population that has been identified from human faeces and has been demonstrated to contain cellulolytic strains (Chassard et al., 2012). Other bacterial species with this capacity are Enterococcus sp., Roseburia sp. and Bacteroides sp. (Chassard et al., 2010). Surprisingly, despite cellulose‐degrading bacteria might be present in all individuals, Chassard and colleagues (Chassard et al., 2010) discovered that their structure and activity was depended on the methanogenic status of individuals. These authors found out that in people who excrete methane, Ruminococcus sp. was predominant, while in those individuals who were non‐methane excreting, Bacteroides sp. was identified, being hypothesized that there might be an interaction between

methanogenic archaea (H2 users and methane producers) and Ruminococcus sp. (H2 producers).  Inulin, oligosaccharides and prebiotics: Prebiotics are currently defined as non‐digestible food ingredients or compounds (oligosaccharides) that are not digested in the upper part of the gastrointestinal tract and pass to the large intestine, where they stimulate the growth and/or activity of health‐promoting bacteria (Bindels et al., 2015). Some of them include xylo‐ oligosaccharides (XOS), galacto‐oligosaccharides (GOS) and fructans, that comprise inulin and fructo‐oligosaccharides (FOS), lactulose, lactosucrose, isomaltooligosaccharides (IMOS), glucooligosaccharides, or soybean oligosaccharides (SO) and new ones that are in phase of study, such as pectooligosaccharides (POS), polydextrose (PDX), bacterial exopolysaccharides (EPS) or polysaccharides from seaweeds (Corzo et al., 2015). Several studies suggest that prebiotics might be inducing beneficial physiological effects due to their capacity to modulate the intestinal microbiota. In this sense, the positive outcomes have been reported to be systemic and not only local (at colonic level), contributing to reduce the risk of developing intestinal and/or systemic diseases (Corzo et al., 2015).

 Production of SCFAs

The major end products of carbohydrate fermentation are SCFA, heat and gases (CO2, H2 and CH4) (Macfarlane and Macfarlane, 2003). It should be mentioned that specific amino acids (glycine, threonine, glutamate, lysine, ornithine and aspartate) obtained from anaerobic metabolism of peptides and proteins might be an additional source of SCFA. In this process, other substances are formed (ammonia, amines, phenols, thiols and indoles) which might exert additional physiological effects (Neis et al., 2015). SCFAs are

31 Introduction organic fatty acids of 1 to 6 carbon atoms length which might exist in straight‐ and brached‐chain conformations (Miller and Wolin, 1979; Cummings and Macfarlane, 1991). These compounds reduce intestinal pH and present antimicrobial properties against specific bacterial groups that might cause harmful effects (i.e. Escherichia coli), and could also affect the growth of certain commensal bacteria showing varying sensibility to acidic pH such as Bacteroides spp. (Duncan et al., 2009). SCFAs are daily produced from the utilization of dietary fibre in a range of 400‐600mmol SCFAs/day (Bergman, 1990). 95% of the SCFAs are rapidly absorbed in the colon and it has been estimated that these microbial fermentation‐derived compounds, considered together, contribute to approximately 5‐10% of the human energy requirements (McNeil, 1984). The most common SCFAs are formic acid, propionic, butyric, isobutyric, valeric, isovaleric and caproic acid (Bergman, 1990). However, from 90‐95% of the SCFAs in the colonic region are acetate (C2), propionate (C3) and butyrate (C5) (Mortensen and Clausen, 1996).

 Control of energy homeostasis through SCFAs

Butyrate is particularly important and has been extensively studied since it is the major energy fuel for cellular metabolism in the colonic epithelium. Nevertheless, the importance of butyrate is not only due to its role as energy supply. Butyrate has also been reported to prevent colonic carcinogenesis, to have protective effects against inflammation, and oxidative stress, and to contribute to improve the intestinal barrier (reducing intestinal permeability and regulating mucus barrier) (Canani et al., 2011). In contrast, propionate is taken up by the liver and is used for gluconeogenesis, while acetate enters the peripheral circulation to reach peripheral tissues and might be used for lipogenesis (Puddu et al., 2014). Interestingly, the use of propionate as a gluconeogenic substrate inhibits the utilization of acetate for lipids and cholesterol synthesis, which might be related to a number of studies that have evidenced reduced hepatic lipogenesis and lower serum cholesterol and triglyceride concentrations after the consumption of fermentable carbohydrates (Byrne et al., 2015).

SCFAs act as energy source for different organs such as the colonic mucosa, the liver, and at least in part for skeletal muscle and adipose tissue (Wolever et al., 1989). Increased levels of these products are known to induce lipogenesis in the liver and to increase the synthesis of very‐low‐density lipoproteins (VLDL), which are responsible for transporting triglycerides from the liver to other tissues (Backhed et al., 2004; Velagapudi et al., 2010). In fact, it has been demonstrated that gut microbial‐derived metabolites could regulate host gene expression, as evidenced by the observed activation of two transcription factors, sterol response element binding protein‐1c (SREBP‐1c) and carbohydrate response element binding protein (ChREBP) in germ‐free mice 14 days after receiving microbiota from conventional mice (Backhed et al., 2004). Moreover, enrichment of gene functions associated to the fermentation of dietary polysaccharides was found in ob/ob mice, which also present larger quantities of SCFAs in their caecum, but lower energy content in their faeces when compared to their lean littermates (Turnbaugh et al., 2006). Human studies, although indirectly, have also shown that obese people exhibit higher levels of ethanol in their breath, as wells as higher amounts of faecal SCFAs, indicating that fermentation might be altered in these individuals, possibly as a consequence of enlarged energy harvest by the gut microbiota (Nair et al., 2001; Schwiertz et al., 2010). However, when transplanting faecal microbiota of twins discordant for obesity to germ‐free mice, lean mice were found to present greater caecal propionate

32 Introduction and butyrate levels, suggesting that the increased weight gain in obese animals was not resultant of the increased energy harvest by the gut microbiota, but instead, SCFAs impeded fat accumulation (Ridaura et al., 2013). In addition, evidence suggests that SCFAs increase energy expenditure as, for instance, rising oxygen consumption, enhancing both the adaptive thermogenesis and fat oxidation, as well as promoting mitochondrial function in rodents (Byrne et al., 2015).

SCFAs could, besides, act as signalling molecules and regulate energy homeostasis. This effect has been found to be related to the activation of G protein‐coupled receptors (GPCRs), GPR41 (also known as free fatty acid receptor or FFR3) and GPR43 (also known as FFAR2), which are SCFAs receptors. These receptors are activated by physiological concentrations of SCFA, but affinity preferences have been reported (FFAR2 for acetate and FFAR3 for propionate and butyrate). Furthermore, they have been found to be expressed not only at intestinal epithelium, but also in adipose tissue, immune cells, skeletal muscle and within peripheral nervous system (Chambers et al., 2014).

 Regulation of gut hormones through SCFAs

SCFAs could also act by regulating energy intake (Conterno et al., 2011). Increased levels of SCFAs have been associated with enhanced satiety and reduced food intake, partly as a consequence of the role of these molecules on gut hormone secretion by the activation of FFAR2. FFAR2 and FFAR3 are expressed in enteroendocrine (L) cells (Tolhurst et al., 2012) and these cells are known to secrete glucagon‐like peptide 1 (GLP‐1) and the anorexigenic peptide tyrosine tyrosine (PYY) which are important gut hormones that reduce appetite and energy intake (Delzenne et al., 2010). The finding that FFAR2 and FFAR3 were localized at enteroendocrine cells led to the hypothesis that the activation of these receptors by SCFAs might induce the release of GLP‐1 and PYY. Several investigations have been performed to confirm this statement (Chambers et al., 2014) and, although human studies are still limited, evidence suggests that consumption of fermentable fibre and in turn, SCFAs produced might stimulate the secretion of such anorectic hormones via activation of FFAR2 (Chambers et al., 2014).

 Regulation of inflammation and immunity by SCFAs

SCFAs have been evidenced to inhibit the inflammatory process by acting on leucocyte, endothelial and intestinal cell function (Vinolo et al., 2011). Evidence has shown that administration of butyrate to HFD‐fed rats lead to significant reductions in hepatic expression of tumor necrosis factor‐α (TNF‐α), interleukin‐1β (IL‐1β) and IL‐6 improving steatosis and inflammation (Mattace Raso et al., 2013). In Staphylococcus aureus‐ stimulated human monocytes, butyrate strongly inhibited the production of IL‐12 and enhanced IL‐10 secretion (Saemann et al., 2000). Down‐regulation of pro‐inflammatory cytokines in adipose tissue have also been reported with propionate and butyrate (Roelofsen et al., 2010). The molecular mechanisms through which these molecules might exert the anti‐inflammatory outcomes have been related to the activation of FFAR2 and FFAR3, but interestingly, their function as inhibitors of histone deacetylases (HDAC) activity has been demonstrated as well (Vinolo et al., 2011). HDAC, together with histone acetyltransferases (HAT), regulate the degree of protein acetylation and modulate gene expression. Inhibition of the activity of HDAC has been shown by SCFAs, especially butyrate. This mechanism is of crucial relevance for the ability of butyrate to regulate the expression of genes involved in colonic tissue homeostasis

33 Introduction

(Daly and Shirazi‐Beechey, 2006). In fact, butyrate‐induced inhibition of NF‐κβ has been demonstrated to act through this mechanism. Importantly, NF‐κβ is known to be a regulator of immunity‐related genes (IL‐1β, TNF‐α, IL‐2, IL‐8, IL‐12, inducible nitric oxide synthase (iNOS), COX‐2, intercellular adhesion molecule‐1 (ICAM‐1), vascular adhesion molecule‐1 (VCAM‐1) and T cell receptor‐α (TCR‐α). Thus, their inhibition might be the basis for the anti‐inflammatory effects of butyrate (Canani et al., 2011). In addition, the HDAC‐dependent mechanism has been shown to be responsible for the formation of peripheral regulatory T‐cells (Furusawa et al., 2013) favouring host‐microbe immunological tolerance.

 Control of host energy balance and storage by gut microbiota

Importantly, gut microbiota has been demonstrated to control both sites of the energy equation (Backhed et al., 2007). On one hand, gut microbiota might influence energy storage through its capacity to extract energy from food. On the other hand, gut microbiota affects host genes that are involved in regulating the way energy is used and stored (Backhed et al., 2007).

In this sense, gut microbiota has been demonstrated to reduce the intestinal expression of the protein fasting‐induced adipose factor (Fiaf) or angiopoietin‐like 4 protein (Angptl‐4). Fiaf protein is critical inhibitor of the enzyme responsible for the hydrolysis of circulating triglycerides to free fatty acids (FFA), known as lipoprotein lipase (LPL) (Sukonina et al., 2006). Furthermore, Bäckhed et al. (Backhed et al., 2004) demonstrated that germ‐free mice, following conventionalization, were characterized by 122% enlargement of LPL activity and, thus, enhanced body adiposity, which was associated to a reduced Angptl‐4 expression in the ileum. Importantly, there are several interlaced pathways by which gut microbiota controls energy balance (Backhed et al., 2007). Data from GF mice and GF Fiaf‐deficient mice showed that resistance to diet‐ induced obesity in GF mice might be associated with the fact that the animals present lower LPL activity or, at least in part, with the fact that GF mice had increased AMPK activity and enlarged fatty acid oxidation in peripheral tissues. These results suggest that gut microbiota may also act via a metabolic pathway involving phosphorylation of AMPK. In contrast, the authors discovered that GF Fiaf‐deficient mice lost their resistance to high‐fat sucrose (HFS) diet‐induced obesity, and exhibited significantly reduced expression of peroxisomal proliferator‐activated receptor coactivator (Pgc‐1α) and enzymes involved in fatty acid oxidation, without presenting differences in phosphorylated AMPK levels (Backhed et al., 2007).

3.3.2. Immune functions and anti‐inflammatory roles

The gut microbiota plays a central role in both immune system development and immunological tolerance (Mason et al., 2008). A substantial part of the immune system is presented in intestinal mucosa. Indeed, it is considered that intestinal mucosa is the largest surface area comprising almost 70% of the immune cells which will be continuously subjected to the exposure of commensal microbiota and pathobionts (de Kivit et al., 2014). Thus, gut microbiota represents the majority of the antigens that are presented to the host immune cells. The importance of resident microbiota for a correct immune system development was observed in studies conducted in GF mice. These animals showed an impeded immune system (Chung et al., 2012) and reconstitution of intestinal microbiota was demonstrated to restore mucosal immune defects (Chung et al., 2012). In this sense, intestinal mucosal immune system needs to have two

34 Introduction main functions under control: the level of tolerance to the overlying microbiota with the aim of avoiding an excessive and deleterious systemic immune response, and the control of the overgrowth and translocation of bacteria to systemic sites (Sekirov et al., 2010). Commensal microbiota has the ability to stimulate innate immune system, which is responsible for detecting the presence and the nature of infection and to provide the first line of defence. The innate immune system regulates the adaptive immune system, which typically is delayed to 4‐7 days (Medzhitov, 2001). Targets of the innate immune system are pathogen‐associated molecular patterns (PAMPs) and the receptors of the innate immune system that recognize these PAMPs are known as pattern recognition receptors (PRRs) (Janeway, 1989). These conserved molecular structures also exist in non‐pathogenic microbes, thus the use of microbe‐associated molecular patterns (MAMP) is expanding when referring to host‐commensal interactions (Wells et al., 2011). PPRs are signalling receptors that induce the production of innate effector molecules (Rakoff‐Nahoum and Medzhitov, 2008) and are classified in three main families: Toll‐like receptors (TLRs), nucleotide oligomerization domain (NOD)‐like receptors (NLRs) and retinoic acid inducible gene I (RIG‐I)‐like receptor (RLR). The interaction with PRRs does not uniquely involve live bacteria since they might be stimulated by diffusible compounds such as bacterial lipopolysaccharides (LPS), lipoproteins, peptidoglycans and lipoteichoic acid (LTA) (Wells et al., 2011). However, their stimulation leads to the activation of signalling cascades that promote epithelial cells to produce a wide range of antimicrobial factors including defensins, cathelecidins, calprotectin, and antimicrobial polipeptides, such as regenerating islet‐derived protein III (REGIII) proteins (Wells et al., 2010). The main function of secreted antimicrobial peptides will be to defend host against enteric pathogens (Bevins and Salzman, 2011).

3.3.3. Structural and protective functions

An important role of the gut microbiota is to promote epithelial barrier integrity (Yu et al., 2012a). The intestinal barrier is organized as a multi‐layer system where two major parts are differentiated: an external physical barrier that aims to restrict bacterial adhesion and regulates paracellular diffusion to the underlying host tissues, and an internal functional immunological barrier, that has the ability to distinguish commensal bacteria from pathogenic microbes (Scaldaferri et al., 2012). Food ingredients through the oral route allow exogenous organisms to entry in the GI tract, thus, nutrients and microorganisms are in close contact with intestinal mucosa (Yu et al., 2012a). The luminal surface of the GI tract from the stomach to the rectum is covered by a monolayer of epithelial cells (Yu et al., 2012a). Initially, intestinal epithelium was considered only as a barrier that physically protected and separated the gut lumen from the intestinal mucosa. However, the underlying intestinal mucosa contain different cell types belonging to innate and adaptive immune system including macrophages, dendritic cells (DC), T cells, and B cells. Kagnoff and collaborators (Kagnoff, 2014) afterwards discovered that intestinal epithelial cells might modify their phenotype and produce proinflammatory mediators (Jung et al., 1995). Moreover, it was also mentioned that they express receptors for a wide range of cytokines and chemokines and produce antimicrobial peptides (Kagnoff, 2014).

Intestinal crypts are recognized as developmental structures that suffer turnover rates consisting of cell proliferation and shedding which, in turn, favour renewal of the intestinal epithelium during adulthood (Sancho et al., 2003). The intestinal epithelium is composed of four distinguished cell types that are responsible for the functions performed by the intestinal epithelium. Columnar cells, also called enterocytes

35 Introduction in the small intestine or colonocytes in the large intestine, are the most abundant. They are polarized, with a basal nucleus and a brush border. The second types of cells are mucin‐secreting cells or goblet cells, which secrete mucus that will protect and lubricate the mucosa. Endocrine cells, also known as neuroendocrine or enteroendocrine cells, are present thorough the epithelium. These cells contain neuroendocrine granulocytes and secret peptide hormones. Finally, Paneth cells, mainly located at the crypt base, contain secretory granules and express different proteins and low molecular weight peptides that have antibacterial actions. Paneth cells have also been reported to present phagocytic features (Yen and Wright, 2006). Figure 11 shows an outline of the proliferation and differentiation of stem cells into enterocytes.

Figure 11. Proliferation and differentiation of stem cells into enterocytes in the crypt regions (Yu et al., 2012a).

Importantly, intestinal epithelial cells are sealed with tight‐junctions (TJ). A healthy intestinal gut should have an intact epithelial barrier that will avoid the paracellular entry of molecules and bacteria. In this context, TJs regulate the influx between epithelial cells. Briefly, the main proteins contributing to the development of tight junctions are the following (Camilleri et al., 2012):

 Integral membrane proteins: Proteins from claudin family which are associated to other transmembrane proteins called occludins.  Junctional complex proteins: Zonula occludens (ZO)‐1 and other cytoplasmatic proteins.  Cell cytoskeleton structures: Microtubules, intermediate‐ and microfilaments.

Commensal bacteria have been demonstrated to be implicated in the control of turnover rates of enterocytes, important for the regulation of epithelial permeability, a feature that has been associated with a large number of human diseases, such as IBD, celiac disease and IBS (Camilleri et al., 2012). Evidence from the capability of microbes to modulate intestinal permeability comes from both in vitro and in vivo models.

It is important to bear in mind that a balance exists between cell proliferation and cell death. Reduced cell death and thus, hyperproliferation may lead to tumor formation, while increased apoptosis may result in

36 Introduction barrier damage (Yu et al., 2012a). In this sense, from studies conducted in GF, gnotobiotic and conventionally‐raised animals, an important role of luminal bacteria in cell proliferation, apopotosis and differentiation was elucidated (Pull et al., 2005; Shirkey et al., 2006). GF animals present abnormal intestinal morphology, with longer villi and shorter crypts, when compared to related conventionally raised counterparts. Moreover, these models lacking gut bacteria showed reduced epithelial apoptosis and crypt cell proliferation (Willing and Van Kessel, 2007). On the other hand, in conditions of intestinal inflammation (colitis) induced by the administration of dextran sodium sulphate (DSS), a toxic compound for colonocytes (Kitajima et al., 1999), conventionally‐raised animals were protected from epithelial injury. In addition, intestinal epithelial restitution and wound closure have been reported to be favoured by commensal bacteria in a redox dependent manner, suggesting that redox signalling might be a key mechanism in intestinal homeostasis and restitution processes (Alam et al., 2014). Nevertheless, contradictory results have been reported in GF mice models that did not develop colitis, proposing that commensal bacteria may, in some cases, induce chronic intestinal inflammation (Strober et al., 2002).

3.4. Modulation of gut microbiota composition by different approaches

A century ago the idea of modifying gut microbiota composition in order to improve health was already proposed (Metchnikoff, 2004). Currently, there is awareness that a dysbiotic gut microbiota composition represents an important mechanism of disease, thereby, there is a need to find therapeutic tools that restore gut microbial composition and its metabolic activity (Bindels et al., 2015). Approaches to modulate gut microbiota include dietary interventions, use of prebiotics, probiotics and synbiotic, transplantation of faecal microbiota and the use of bioactive foods (Zatorski and Fichna, 2014).

3.4.1. Dietary modulation

Gut microbiota is metabolically adaptable and diet is the primary factor that influences its composition. Based on the findings observed in identical twins, where marked variations were detected on gut microbial ecology, the relevance of diet on gut microbiota has been recognized to be considerable, even greater than genetic factors (Turnbaugh et al., 2009a). In fact, food may affect health not only directly, but also indirectly, activating cell‐surface or nuclear receptors in target tissues (Ryan and Seeley, 2013). Moreover, food is able to interact with gut microbial community to induce indirect signals (Fallucca et al., 2015). Therefore, dietary recommendations represent an attractive option in cases were a link between gut microbiota and the risk for the development of an illness is established, or even as a treatment when a disease condition has been confirmed (Dore and Blottiere, 2015).

Several studies have been conducted to examine the impact of different dietary patterns on intestinal bacterial community profiles. As reviewed by Janssen et al. (Janssen and Kersten, 2015), diet strongly influences gut microbiota composition at the phylum level, but particularly at the lowest taxonomic levels. Recent studies have demonstrated clear differences in gut microbial profiles between populations with distinct dietary patterns. For instance, African children from Burkina Faso, characterized by the intake of diets rich in plant‐derived fibre, exhibited significantly increased levels of Prevotella and Xylanobacter, higher amounts of SCFAs and reduced levels of Enterobacteriaceae, when compared to Italian children

37 Introduction

(De Filippo et al., 2010). As mentioned thorough section 2 of the present thesis, diets rich in animal proteins and fat, similar to a western dietary pattern, will favour the Bacteroides enterotype, while diets with high content of fruits and vegetables, and thus rich in fibre, would be associated with the Prevotella enterotype (Wu et al., 2011). Analogously, as a consequence of different dietary patterns, Ou et al. (Ou et al., 2013) linked the gut microbiota of native Africans to Prevotella ecologies. In contrast, African Americans were associated with the Bacteroides enterotype and higher levels of secondary bile acids, which have confirmed carcinogenic properties (Bernstein et al., 2011). Furthermore, drastic differences were identified in Hadza hunter‐gatherers from Tanzania, who follow a foraging lifestyle, with dietary habits close to our ancestors (Schnorr et al., 2014). In this community, higher microbial richness and diversity (i.e. large presence of unrecognized taxa) were detected. Besides, sex differences were found in gut microbiota composition probably attributed to sexual division of labour. Furthermore, apart from a clear influence of long‐term dietary patterns on the structure and activity of intestinal bacteria, short‐term dietary alterations, as for instance diets that contain only animal or plant products, have been demonstrated to strongly modulate gut microbiota composition, being these modifications larger than interpersonal differences (David et al., 2014).

In obesity, apart from the alterations in gut microbial profiles, a reduced bacterial gene richness has been described (Cotillard et al., 2013), and differences in species richness between overweight or obese subjects with a disparate dietary pattern have been reported (Kong et al., 2014). Interestingly, in a study conducted by Cotillard and colleagues, an association between reduced microbial richness (40%) and higher low‐grade inflammatory parameters was detected (Cotillard et al., 2013). Indeed, low gene richness and hypercholesterolemia, inflammation and insulin resistance were shown to be improved with dietary interventions. Moreover, since the response to dietary intervention in terms of weight loss and amelioration of metabolic and inflammatory markers was found to be better in obese and overweight subjects with the greatest gene richness, this feature was postulated as a possible predictor of the intervention efficacy (Cotillard et al., 2013). Likewise, Salonen and collaborators reported that microbial diversity was inversely associated with the subject’s dietary responsiveness, concluding that individual’s might be stratified into responders and non‐responders based on their basal gut microbial trait (Salonen et al., 2014). Interestingly, the potential of gut microbiota for personalized nutrition has been stated. In a recent study conducted in three different cohorts of obese individuals (Belgium, Finland and Britain) the predictability of the response of gut microbiota to dietary interventions based on the composition of initial microbiota has been stated (Korpela et al., 2014). In this context, the prognostic value of the intestinal microbiota in relation to host’s metabolic response to a dietary intervention was demonstrated for the first time. For this purpose, three types of dietary interventions were tested: addition of a prebiotic compound, modifications of the type of grains in the diet, and alteration of macronutrient composition of the diet. Noteworthy, the authors evidenced that particular species within Clostridial group might be bioindicators of the adaptability of the gut microbiota to dietary interventions and might be considered predictive organisms of the host lipid metabolism (Korpela et al., 2014). In spite of these proof‐of‐principle studies, still, there is lack of awareness regarding the ideal composition of diet and type of macronutrients to support a “healthy” microbiota and avoid dysbiosis (Nagpal et al., 2014). However, it is plausible that the knowledge on gut microbiota will revolutionize the concept of “we are what we eat” by “we are what our gut microbiome is” (Nagpal et al., 2014). Therefore, we could adventure to say that information about individual’s gut

38 Introduction microbiota for personalized nutritional therapy will have great chance concerning treatment of obesity and obesity‐related metabolic comorbidities (Korpela et al., 2014).

Since it is not known which dietary components or nutrients affect more to the gut microbiota composition, the effects of the major nutritional components will be addressed below:

3.4.1.1. Effect of non‐digestible dietary carbohydrates, proteins and fat intake

Gut bacteria are usually competing for most types of non‐digestible dietary carbohydrates that reach the large intestine. Thus, non‐fermentable (for the host) carbohydrates have been extensively reviewed as important regulators of gut microbiota composition (Lopez‐Legarrea et al., 2014). In this context, the study of commensal bacteria genome sequences has shown that bacterial species are dependent on these dietary components for their growth (Louis et al., 2007). For instance, Bacteroides thetaiotaomicron and Bifidobacterium longum species devoted at least 8% of their genomes to carbohydrate transport and metabolism (Louis et al., 2007). Therefore, it is obvious that the content of non‐digestible carbohydrates in the diet affect the count of specific bacterial groups (Maukonen and Saarela, 2015). Accordingly, in contrast to the role of cellulose and lignin which is still not well known, it has been reported that arabinoxylan stimulates Bacteroides spp. and Roseburia spp.; that resistant starch enhances the growth of bifidobacteria, Bacteroides spp., Ruminococcus bromii, Eubacterium rectale and Roseburia spp.; that β‐glucans increase bifidobacteria counts, while fructans induce the growth of bifidobacteria, Bacteroides spp, lactobacilli, and butyrate‐producer species (Maukonen and Saarela, 2015). Importantly, health beneficial properties such as anti‐obesity capacity of these dietary constituents have also been largely explored (Hobden et al., 2015).

High‐protein diets give rise to fermentation of diet‐derived proteins in the colon, resulting in a plethora of compounds, namely amino acid‐derived products, branched‐chain fatty acids, phenylacetic acid, phenols, indoles, p‐cresol, nitrogenous products (N‐nitroso compounds), and many of them have been found to be deleterious for the host, contributing to carcinogenic effects and thus, to promote cancer (Louis et al., 2014). Furthermore, changes in gut bacterial profiles have been observed after consuming a high‐beef diet as compared to a meatless diet. It has been also demonstrated that high sulphide levels originated from a high‐ beef diet promoted the increase of a specific group of bacteria known as sulphate‐reducing bacteria, which have been shown to exert harmful effects in colonic epithelium (Maukonen and Saarela, 2015). Moreover, in an intervention study conducted in obese men following a high‐protein diet low in carbohydrates, a reduction in Roseburia/Eubacterium rectale group was found, which was related to a decrease in butyrate production. Besides, these weight‐loss diets high in protein were found to reduce cancer‐protective metabolites, while increased hazardous metabolites for colonic epithelium (Russell et al., 2011).

Few intervention studies have been conducted to investigate the outcomes of consuming a HFD on gut microbiota, and still, fewer studies have been conducted to determine the effects of different types of fats on microbial population. For instance, bifidobacterial group was inhibited as concluded in a study conducted in overweight obese participants that consumed a HFD with low‐carbohydrate content (Brinkworth et al., 2009). Concerning the type of fat, distinct consequences have been reported in intestinal bacterial profiles concluding that diets rich in saturated fat (SFA), monounsaturated fat (MUFA) and

39 Introduction polyunsaturated fat (PUFA) have variant effects, but mostly influence bacterial richness, bifidobacteria and lactobacillus groups (Maukonen and Saarela, 2015). What it is obvious is that the intake of fat‐rich diets leads to increases in bile acids and fat that reach the colon, thereby they will be subject of gut microbiota metabolism and resultant compounds, such as secondary bile acids, will be absorbed in the small intestine (Fava et al., 2013). As a consequence, it might be stated that consuming diets which are balanced in their fat content will be critical for the gut microbiota and hence for the host health (Maukonen and Saarela, 2015).

3.4.1.2. Effect of polyphenols intake

The composition of bacteria inhabiting the gut is also influenced by the intake of phytochemicals and their derived metabolites. Numerous research has been focused on analysing the antimicrobial or bacteriostatic activities of dietary polyphenols or phenolic substances and their metabolites against pathogenic bacteria such as Salmonella spp., Clostridium perfringens, Clostridium difficile, Escherichia coli and Staphylococcus aureus (Maukonen and Saarela, 2015). Other polyphenols and/or polyphenol‐rich dietary sources have been also observed to exert prebiotic‐like effects (Duenas et al., 2015). As reviewed by Maukonen et al., (Maukonen and Saarela, 2015) human intervention studies have reported the capacity of red wine polyphenols consumed for 4 weeks to promote the growth of Enterococcus, Prevotella, Bacteroides, Bifidobacterium, Egerthella and Lachnospiraceae family members, whereas drinking a high‐cocoa flavanol beverage increased lactobacilli and bifidobacteria numbers and reduced clostridial counts. In addition, the intake of ellagitannins, commonly found in strawberries, raspberries and cloudberries, has been associated with changes in Lachnospiraceae and Ruminococcaceae members (Maukonen and Saarela, 2015). In contrast, as reviewed by Cardona et al. (Cardona et al., 2013), a proanthocyanidin‐rich extract from grape seeds administered to healthy adults for 2 weeks was reported to significantly enhance the growth of bifidobacteria. This group of bacteria was also increased as a consequence of 6 week wild blueberry drink consumption. Summing up, the modulatory effect of polyphenols on intestinal ecology has been ascertained. In fact, this influence has been proposed to be the result of several mechanisms. Overall, bacterial growth and metabolism will be affected by polyphenols’ structure, the dosage administered and the presence of microbial strains. Indeed, the higher resistance of Gram‐negative bacteria to polyphenols, compared to that of the Gram‐positive bacteria, probably owing to differences in their wall composition (Cardona et al., 2013), has been shown.

3.4.2. Use of prebiotics, probiotics and synbiotics

Prebiotics were firstly defined in 1990s as a pioneering concept since it brought gut microbiota as a factor influencing human and animal nutrition (Gibson and Roberfroid, 1995). Although, the concept of prebiotics has been known for the past 20 years, it is still in evolution. Current definition of prebiotics was given by Gibson et al. (Gibson et al., 2010) in 2010, defining it as “selectively fermented ingredient that results in specific changes in the composition and/or activity of the gastrointestinal microbiota, thus conferring benefit(s) upon host health”. However, this concept has been recently revisited by Bindels et al. (Bindels et al., 2015) proposing the prebiotic as “a nondigestible compound that, through its metabolization by microorganisms in the gut, modulates composition and/or activity of the gut microbiota, thus conferring a beneficial physiological effect on the host.”

40 Introduction

Definition of probiotics was approved in a recent consensus report as “live microorganisms that, when administered in adequate amounts, confer a health benefit on the host” (Hill et al., 2014). Probiotics could consist on a single or multiple live bacterial species that directly modulate the gut microbiota composition and most of them belong to two main genera, Bifidobacteria and Lactobacilli (Gerritsen et al., 2011), but the probiotic effect of other species, such as Lactococcus, Streptococcus, and Enterococcus, even of non‐ pathogenic strains of Escherichia coli (E. coli Nissle 1917) and yeast strains (Saccharomyces boulardii) has been also demonstrated (Miniello et al., 2015). Increasing evidence demonstrates the health‐benefits attributed to probiotic microorganisms (Wang et al., 2015). In this sense, probiotics mechanisms of action have been classified in three major levels. The first one would consist on the effect of probiotic microorganisms on GI tract, as for instance improving gut microbiota composition, maintaining its stability, impeding the growth of pathogens or influencing the enzymatic activity of the gut microbiota. The second one, would be their ability to interact with the intestinal epithelium and the mucus layer since the capacity of probiotics to strength mucosal barrier by increasing mucus production, repairing and maintaining tight juntions in order to reduce gut permeability has been reported. Finally, the third mechanism through which probiotics would exert a beneficial effect would be as a result of their anti‐inflammatory effect and immune system modulatory effect outside the gut (Gerritsen et al., 2011; Bermudez‐Brito et al., 2012). It should be taken into consideration that each probiotic species might exert different health benefits, thus, physiological effects attributed to one probiotic may not be extrapolated to another strain (Lopez et al., 2010). There is also lack of sufficient evidence to ascertain that probiotic mixtures are more effective than single strains (Chapman et al., 2011). In addition, it has been demonstrated that not all gut bacterial species affected by the effect of probiotics will be functionally important phylotypes in relation to positive metabolic outcomes (Wang et al., 2015). Although the study of the effectiveness of probiotics administration in different disease states have shown distinct clinical outcomes such as IBD, IBS, constipation, diarrhoea, colon cancer, CVD, allergic disease, obesity and metabolic syndrome, it is still complicated to conclude about general changes in microbiota composition derived from the intake of probiotics, principally due to the lack of standardized methods in the study of gut microbiota, and also because of its complexity (Gerritsen et al., 2011).

Synbiotics are products combining probiotics and prebiotics aiming to favour the survival and activity of probiotics in vivo and also seek to promote the growth of anaerobic bacteria (Miniello et al., 2015). It is considered that synergistic effects among both ingredients promote health benefits (Patel and DuPont, 2015). For example, the intake of a synbiotic (Lactobacillus acidophilus, Bifidobacterium bifidum and fructooligosaccharides) in elderdy people has been shown to increase HDL cholesterol and to ameliorate glycemia levels (Moroti et al., 2012). Moreover, positive effects of synbiotics have been also demonstrated when combining Bifidobacterium and FOS in the treatment of hepatic encephalopathy (Malaguarnera et al., 2010).

3.4.3. Faecal microbiota transplants

Faecal microbiota transplantation (FMT), also known as faecal bacteriotherapy, is considered another approach to restore human gut microbial diversity by transferring gut microbiota from healthy donors to the patients (Van den Abbeele et al., 2013). This technique has been already applied to treat Clostridium difficile‐ related infection reporting cure rates over the 90% (Borgia et al., 2015), and microbiota from donor faeces

41 Introduction has been regarded as a special organ to be transplanted regardless of the danger for immunological rejection (Borody and Khoruts, 2012). Interestingly, taking into consideration that metabolic diseases such as obesity, may partly result from microbiota‐associated disturbance, this type of therapy has intensified the interest to investigate its impact on several extra‐intestinal disorders (Xu et al., 2015). Accordingly, transfer of intestinal microbiota from lean donors to subjects with metabolic syndrome was demonstrated to improve insulin sensitivity (Vrieze et al., 2012) and a similar study is currently ongoing (ClinicalTrials.gov, 2014). Nevertheless, this is a novel intervention that requires further research (Singh et al., 2014).

3.5. Metagenomic approaches

Metagenomic refers to the analysis of a complete genome from an environmental sample (Thomas et al., 2012). The development of culture‐independent techniques has expanded the knowledge on the diversity of GI tract microbial communities. Currently, there exist two main methods that enable the characterization of the microbiome that are not based on pure culture (Hamady and Knight, 2009). Sequencing technologies that are based on the small‐subunit ribosomal RNA (rRNA) sequence analysis, where 16S rRNA gene sequences (for archaea and bacteria) or 18S rRNA (for eukaryotes) are used as stable phylogenetic markers to define which lineages exist in a sample, and metagenomics studies, which are based on the whole metagenome shotgun sequencing (Rondon et al., 2000). The latter approach is allowing not only characterizing the diverse microbial communities residing at the different body compartments, but also characterizing the complete functional genetic repertoire of the microbiota or the microbiome (Preidis and Hotez, 2015). In some cases, the former are also termed as “metagenomic” studies in that they analyse a heterogeneous sample of community DNA (Hamady and Knight, 2009).

4. Metabolomics: a new “Omics” science in nutrition

Current nutritional research is beyond the objective of providing nutrients to nourish population seeking to improve health with the diet. Health promotion, protection and disease prevention are major cornerstones in modern nutrition (Kussmann et al., 2006). Moreover, the development of “functional foods”, which are known to give health benefits over the basic nutritional value (Koletzko et al., 1998), also requires the understanding of the underlying molecular mechanisms, and the unravelling of the biologically active molecules as well as their effectiveness on well‐being (Kussmann et al., 2006). In this context, the complex physiological interactions happening between nutrients and bioactive constituents and human organism, in the past twenty years, are being comprehensively investigated at molecular level. Nutrition sciences have entered in a post‐genomic era (Zulyniak and Mutch, 2011). Since the concepts of “Nutrigenomics” (nutrients affect the genetic makeup of an individual) and “Nutrigenetics” (the genetic makeup of an individual affects nutrient metabolim) (Ordovas and Mooser, 2004; Abete et al., 2012) other “Omics” disciplines have emerged (Table 7).

42 Introduction

Table 7. “Omics” technologies applied to food and nutrition research. Omics sciences Omics sciences Target (Main categories) (Subcategories) Genomics Genomics Genes (DNA sequence) Epigenomics Modifications of DNA and DNA‐binding proteins Transcriptomics Transcriptomics mRNA ncRNAomics Non‐coding RNA (including microRNA) Proteomics Proteomics Proteins Phosphoproteomics Protein phosphorylation Localizomics Protein localization Fluxomics Protein flux Interactomics Protein‐protein interaction Structural proteomics Protein structure Metabolomics Metabolomics Metabolites Lipidomics Lipids Aminomics Amino acids Metagenomics Metagenomics Microbes species and functions Others Glycomics Sugar chains Cytomics Cells Populomics Human population Exposomics Environmental exposure

Source: (Kato et al., 2011; Ordovas Munoz, 2013)

Indeed, it has been stated that there are three major characterization levels of biological systems (Figure 12), from genomics‐transcriptomics that raised in 80s, proteomics that began in 90s, to metabolomics, which was introduced around 15 years ago (Courant et al., 2014).

Figure 12. The “Omics” cascade. Modified from (Dettmer et al., 2007; Patti et al., 2012).

43 Introduction

4.1. Concept and strategies

Metabolomics is defined as a high‐throughput approach that analyses the small‐molecules (<1500 Da) or metabolites that exist in a sample of biological origin (Brennan, 2013). In contrast to genes and proteins, the complete set of metabolites, known as the metabolome, is considered a direct signature of biochemical activity and thus, predictive of the phenotype (Patti et al., 2012). Therefore, the profiling of all the metabolites or the metabolome, offers a snapshot of the metabolism which could be considered an index of the biological state of an organism (Astarita and Langridge, 2013).

Metabolomic analysis might be directed to investigate predefined metabolites (targeted approach) or might aim to conduct a global approach (non‐targeted approach), which means that no specific type of metabolites are pursuing; instead, the largest possible amount of metabolites aim to be detected (Patti et al., 2012). Thereby, metabolomics studies are usually classified in hypothesis‐driven or exploratory studies

(Figure 13A and B).

Figure 13. Typical workflow for a metabolomics approach. A) Non‐targeted metabolomics, B) Targeted metabolomics. Adapted from (Patti et al., 2012; Astarita and Langridge, 2013).

Two main analytical techniques are currently employed for metabolomics studies; nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS)‐based techniques combined with chromatographic separation. Each of them presents some advantages and disadvantages. In addition, owing

44 Introduction to the complex nature of the matrix and the large amount of metabolites that might be present in varying concentrations (from millimolar to picomolar) within the biofluids, the use of a single analytical tool that allows identifying and measuring all metabolites at the same time is unavailable (O'Gorman and Brennan, 2015). In this context, NMR techniques are characterized by examining molecules by their similar properties, the existence of NMR active hydrogen or carbon and, despite it yields relatively low‐sensitivity, this is a quantitative, reproducible and non‐destructive technique (German et al., 2005). In contrast, MS‐based techniques analyse large set of small molecular weight metabolites based on their molecular weights. It presents high sensitivity, but produces qualitative data instead of quantitative data (German et al., 2005). Moreover, this technique allows to conduct the metabolic profiling of a number of biofluids as for instance, whole blood, serum, urine, faeces, saliva, cerebrospinal fluid, synovial fluid, semen and tissue homogenates (Zhang et al., 2012). Among them, urine samples are waste products that contain metabolic end products of biochemical processes that have occurred in the organism. Urine comprises both endogenous and exogenous compounds derived from diet and gut microbiota, hence, it is known to provide the overall picture of the metabolic phenotype. Blood samples are obtained in a more invasive way; but, plasma and serum samples show the physiological alterations occurring in the individual at the sampling time. Faecal extracts or faecal water contain metabolic end products resulting from the interactions between the gut microbiota and the organism. In fact, metabolites from faecal samples are considered to be resultant from dietary intake and biochemical processes occurring during their digestion (Claus and Swann, 2013). However, for a more complete understanding of the system, the relevance of analysing different compartments has been demonstrated (Jove et al., 2011).

Nowadays, metabolomics is applied to a wide range of fields including toxicology and drug development (Keun, 2006), medicine (McNiven et al., 2011), as well as in food science and nutrition (Ibanez et al., 2015). Through this approach, key biomarkers could be identified that make possible not only the early detection of the onset of a disease, but also the identification of a pre‐disease state (Ramautar et al., 2013).

4.2. Nutrimetabolomics

Nutrimetabolomics or nutritional metabolomics has appeared as the science that investigates humans or animals’ metabolic response to dietary interventions and enables to understand variations in response among individuals to certain nutrients or diets, as well as analysing the role of gut microbiota in mammalian metabolism (Zhang et al., 2008). Applications of metabolomics in nutrition might be classified according to two major objectives. On one hand, dietary interventions are conducted in order to understand the effects of food and food constituents on metabolic pathways. On the other hand, metabolomics is applied as a tool for biomarker discovery, not only to assess biomarkers associated to diseases, but also to search for biomarkers of dietary intake, dietary surveillance and compliance to the treatment (Brennan, 2013). Moreover, metabolomics profile has been found to be effective in classifying subjects based on the dietary pattern they follow (O'Sullivan et al., 2011), which has been designated as the “nutritype”.

Several studies have tried to identify obesity‐associated biomarkers. In a review conducted by Rauschert et al. (Rauschert et al., 2014), it was concluded that in obese humans the metabolites typically

45 Introduction indicative of obesity were branched‐chain amino acids (BCAAs), several acylcarnitines, amino acids other than BCAAs (glutamine, methionine, and phenylalanine), lysophosphatidylcholine and phosphatidylcholines, stating that, in general, alterations in fatty acid oxidation and increases in levels of BCAAs were given (Rauschert et al., 2014).

Concerning the accurate estimation of nutritional intake, the identification of objective biomarkers represents a feasible alternative to the information obtained by questionnaires. Currently, there exist some general biomarkers, such as urinary nitrogen, to estimate protein intake (Bingham, 2003). However, biomarkers that are specific for certain foods are lacking. The identification of such indicators would allow to accurately monitor dietary intake and, hence, would make easier to establish causal links between diet and health (Claus and Swann, 2013). Biomarkers are classified into short‐term markers (estimate the intake over past hours/days), medium‐term markers (estimate the intake over weeks/months) and long‐term markers (estimate the intake over months/years), depending on the type of sample used, as for instance blood, hair or adipose tissue (Potischman, 2003). Good biomarkers of habitual food consumption are considered those that are absorbed in the upper part of the GI tract from 1.0 to 1.5h after eating, are metabolically inert and are usually excreted 1.5‐2.5h later (Claus and Swann, 2013). It should be beard in mind that there are some factors that may also bias estimations of intake obtained by biomarkers. Among them, genetic variability, lifestyle/physiologic factors (i.e. smoking), dietary factors (i.e. nutrient interactions), sample type and analytical methodology will need to be considered (Hedrick et al., 2012). In conclusion, validity, reproducibility and robustness across populations is required (Hedrick et al., 2012). Apart from food macronutrient biomarkers, markers of different foods and dietary components have been identified (Hedrick et al., 2012). For instance, proline betaine has been demonstrated to be a specific and sensitive biomarker of citrus fruit consumption (Heinzmann et al., 2010), the intake of cruciferous vegetables, such as broccoli and Brussels sprout, has been associated to the appearance of S‐methyl‐L‐cysteine sulphoxide (SMCSO) and its three derivatives (Edmands et al., 2011) and also to the ascorbate excretion in urine, attributed to the high vitamin C content of broccoli (Lloyd et al., 2011). Meat consumption has been associated to several putative biomarkers (carnitine, anserine and l‐methylhistidine) (Dragsted, 2010). Biomarkers of consumption have been also described for caffeine, wine, cocoa and garlic intake among others (Hedrick et al., 2012). However, more research is required so that these indicators might be considered solid biomarkers of intake.

Metabolomics approaches give rise to some challenges. Indeed, alterations produced by the intake of relatively low doses of food compounds or bioactive molecules might be unmasked by inter‐individual metabolic variations. On the other hand, the dynamism and complexity of metabolism means that many factors might be influencing the perturbations observed and, thus, it will be complex to associate detected metabolites back to genes and proteins and it will be also complicated to differ between individual effects exerted by each compound (Zhang et al., 2008).

Importantly, the fact that plant phytochemicals, particularly polyphenols, reduce disease risk has been assumed from data obtained from epidemiological studies that associate a high fruit and vegetables consumption with health sustaining (Cencic and Chingwaru, 2010). Accordingly, the identification of

46 Introduction bioactive compounds responsible for these beneficial effects attracts large interest (Carpene et al., 2015). Consequently, metabolomics discipline is a good tool to understand the complex biological effects exerted by these specific constituents (McGhie and Rowan, 2012). In this context, polyphenols are known to suffer biotransformation by the body and the gut microbiota. Unlike most compounds, their absorption in the small intestine is small, and the majority reach the colon where they are subject of microbial activity (Moco et al., 2012).

4.3. Diet and microbial‐derived metabolites

Polyphenols’ absorption and bioactivity are facilitated by hydrolysis of polymeric, glycosylated and/or esterified original compounds by brush border and/or microbial enzymes (Bolca et al., 2013). Subsequently, aglycones and phenolic acids suffer Phase I (oxidation, reduction and hydrolysis) and Phase II (conjugation) transformation in ileal epithelium (enterocytes) and the liver (hepatocytes). Metabolites (methylated, sulphated or glucuronidated conjugates) return to the lumen via entero‐hepatic circulation (Aura, 2008) and microbial glucuronidases and sulfatases deconjugate Phase II metabolites allowing their uptake. Luminal phenolic compounds enter the colon undergoing microbial transformations of the polyphenolic core that are based on ring‐fission and several cleavages of functional groups (dihydroxylation, demethylation and decarboxylation) (Aura, 2008). Compared to the diverse group of polyphenols, metabolites produced from their digestion are limited (Rechner et al., 2004), but some of them have been demonstrated to show higher biological activity than their original compounds, as it has been demonstrated for equol generated from soy isoflavone daidzein or enterodiol and enterolactone obtained from degradation of plant lignans by intestinal bacteria (Woting et al., 2010; Matthies et al., 2012). Due to the rapid and extensive transformation suffered, the parent compound of certain polyphenols, such as resveratrol, has been estimated to be in concentrations lower than 1% in plasma, while circulating conjugates may reach concentrations up to 20‐fold higher (Walle et al., 2004). In this sense, in vitro studies conducted in maturing pre‐adipocytes and in mature adipocytes have evidenced that resveratrol metabolites (trans‐resveratrol‐3‐O‐sulfate, trans‐resveratrol‐3‐O‐glucuronide, trans‐resveratrol‐4’‐O‐glucuronide) contribute to the observed delipidating effects. Thus, it has been proposed that these conjugates might contribute to some extent to the anti‐obesity outcomes exerted by the polyphenol (Lasa et al., 2012; Eseberri et al., 2013). In this context, resveratrol metabolites have been detected in liver, adipose tissue and skeletal muscle, tissues involved in the body fat lowering effect of the stilbene, while in contrast, the parent compound was not detected (Andres‐Lacueva et al., 2012). Moreover, Bode et al. (Bode et al., 2013) identified in faecal samples two novel resveratrol co‐metabolites (lunularin and 3, 4’‐dihydroxy‐trans‐stilbene) produced by intestinal bacteria, whose physiological effects need to be elucidated. The authors concluded that there might be relevant interindividual differences in resveratrol bioconversion depending on the intestinal microbial diversity (Bode et al., 2013). In accordance, metabolism of polyphenols has been stated to be quite variable due to three main reasons. First of all, because of inter‐individual differences attributed to differential gut microbiota composition that makes individuals to be distinguished in low or high flavonoid converters. Second, due to the small structural differences in polyphenols. And third, due to diet‐dependent factors (van Duynhoven et al., 2011).

47 Introduction

5. Integration of metagenomics and metabolomics

The homeostasis and metabolism of an organism is influenced by the host and by the interaction among host and its intestinal microbial population. Therefore, knowledge on this interaction is highly relevant to understand the impact of nutrition on health (Xie et al., 2013). Gut bacterial ecosystem is considered a metabolically active organ that provides the host with metabolic traits that are not encoded in the human genome. Metabolites produced by the gut microbiota are in direct contact with host’s cells and influence its physiology, locally and systemically. Dysbiotic states regarding gut microbiota composition have been associated to numerous diseases (i.e. obesity). Interestingly, manipulation of gut microbial profile by different dietary constituents, such as polyphenols, is considered a promising therapeutic target to prevent diseases or improve health (Verbeke et al., 2015). The inter‐relationship between gut microbiota, diet, host metabolism and health implies the requirement of an integrated view of data from “Omics” technologies at different molecular levels. In this context, newest “Omics” technologies, namely metagenomics and metabolomics, are rapidly developing and together with other “Omics” approaches, will enable progress for the more in‐depth understanding of this area (Figure 14).

It is currently stated that it is not sufficient to characterize which microbes are in the intestinal ecosystem, but it is necessary to uncover what is the genetic potential or the functionality of these microbes, in order to comprehend the interaction between gut microbiota and host physiology or pathology (Lepage et al., 2013). In this context, a challenge in gut microbiota studies is to decipher the link between microbial community structures with the metabolic capacity that could impact human cells. Metabolomics allows defining this functional status of host‐microbial integration in biological fluids and tissues (i.e. urine, blood and faeces) (Marcobal et al., 2013). Therefore, despite the complexity to confirm the distinctive small molecules, when established, these data will provide insights into the chemical intermediates that intercede in microbe‐ microbe and host‐microbe interactions (Marcobal et al., 2013).

Figure 14. Schematic overview of the integration of new “Omics” sciences (metagenomics and metabolomics) to gain insight about the link between diet, gut microbiota, host metabolome and health.

48

II. HYPOTHESIS AND AIMS

Hypothesis and aims

1. Hypothesis

The intake of fruits and vegetables rich in polyphenols is being promoted owing to the protective effects that phenolic compounds exert on health. Moreover, the straight interaction that occur among food and food constituents and microbes residing in the intestine, makes the use of natural compounds a possible therapeutic approach to improve health via manipulation of gut bacterial community. Accordingly, the integration of new “Omics” approaches, namely next generation sequencing and metabolomics, are bringing promising insights on this complex system. These techniques, favour the identification of co‐metabolites formed as a consequence of the interplay between diet, gut microbiota and the host organism, and enable to find out potential markers that will help for the prevention and treatment of nutrition‐related diseases.

2. General aim

The main goal of the current research was to explore the modifications produced on the host metabolome and gut microbiota composition in a diet‐induced obesity model and to investigate the in vivo anti‐obesity effects of different bioactive compounds by analysing their ability to modulate gut bacterial composition and the impact on metabolomics profile.

3. Specific objectives

1. To explore serum metabolome changes reflective of a diet‐induced obese animal model fed a high‐fat high‐sucrose diet (Chapter 1).

2. To analyse gut microbiota composition dysbiosis occurring in animals fed a high‐fat high‐sucrose diet (Chapter 2).

3. To evaluate the possible anti‐obesity effects of a set of different phenolic compounds in a diet‐induced animal model fed a high‐fat high‐sucrose diet (Chapter 3).

4. To overview current knowledge on the impact of polyphenols or polyphenol‐rich dietary extracts on intestinal bacterial population (Chapter 4).

5. To assess the potential of two well‐known polyphenols, trans‐resveratrol and quercetin, to re‐shape diet‐induced obese animal model gut microbiota composition (Chapter 5).

6. To study the effects of trans‐resveratrol and quercetin supplementation in the faecal metabolome of a diet‐induced obese animal model and to examine possible associations with animal’s gut microbial community (Chapter 6).

7. To investigate the impact of trans‐resveratrol on gene expression profile at intestinal level, analysing the differential expression of genes related to metabolic pathways in human (Caco‐2) enterocytes (Chapter 7).

51

III. MATERIAL AND METHODS

Material and methods

Figure 15. Overview of the experimental designs conducted from Chapter 1 to Chapter 7, a brief description of the objective pursued and the related publication. 55 Material and methods

The objectives proposed for the present work have been addressed in different chapters (Chapter 1 to Chapter 7). Figure 15 shows an overview of the experimental design conducted in each chapter, the objective pursued and the related publication.

To describe material and methods section, the order of the chapters has been followed. However, in order to avoid repetitive information, an arrangement of the chapters has been done taking into consideration the two major approaches conducted: metabolomics and next‐generation sequencing. Accordingly, first of all, the experimental designs of Chapter 1 and Chapter 6 (belonging to non‐targeted metabolomic analyses) have been described. Secondly, the experimental design of Chapter 3, referring to the use of polyphenols as anti‐ obesity compounds has been reported. Thirdly, Chapter 2 and Chapter 5 (belonging to gut microbiota analyses) have been reported. Finally, the experimental design of Chapter 7, the unique in vitro study has been explained.

In this section a brief explanation of each experimental design and the most relevant laboratory techniques and procedures used will be given. Besides, a short description about the general basis of the main analyses conducted (metabolomics and next‐generation sequencing) and the respective equipment used will be reported.

1. Metabolomic analyses (Chapter 1, Chapter 6)

1.1. Experimental design Chapter 1: Impact of a HFS diet on serum metabolome.

The experiment was conducted on 16 male Wistar rats (n=16) that were assigned into two different dietary intervention groups, a control group (n=8) fed a standard diet and a HFS diet‐fed group (n=8). The techniques used in this study and the measurements that were carried out are the following:

‐ Body‐ weight and food intake record: weight scale. ‐ Plasma biochemical parameters: Glucose HK‐CP kit (ABX, Diagnostic, Montpellier, France) for glucose, Total Protein CP kit (ABX, France) for total protein, albumin was measured with Albumin CP kit (ABX, France) and urea levels, with the Urea CP kit (ABX, France) using Pentra C200 equipment (Horiba Medical, Kyoto, Japan). Leptin and insulin were measured using specific ELISA kits. ‐ Non‐targeted metabolomic analysis: 1D 1H‐NMR spectra using a 600.2‐MHz frequency Avance III 600 Brucker spectrometer (Brucker, Germany).

56 Material and methods

Figure 16. Overview of the study design of Chapter 1. Abbreviations: HFS, high‐fat sucrose diet; BW, body weight; H1‐NMR, one‐dimensional nuclear magnetic resonance.

57 Material and methods

1.2. Experimental design Chapter 6: Impact of trans‐resveratrol and quercetin on faecal metabolome.

In this study, Wistar rats (n=24) were randomly assigned into four experimental groups: the control group (HFS, n=6) fed a HFS obesogenic diet; trans‐resveratrol group (RSV, n=6), supplemented with 15 mg/kg BW/day of trans‐resveratrol; quercetin group (Q, n=6), supplemented with 30 mg/kg BW/day of quercetin; and trans‐resveratrol+quercetin group (RSV+Q, n=6), treated with a combination of trans‐resveratrol 15 mg/kg BW/day and quercetin 30 mg/kg BW/day. The following reagents and equipments were used in order to analyse body weight and food intake, serum biochemical parameters and faecal metabolomic profile.

‐ Body‐ weight and food intake record: weight scale. ‐ Serum biochemical parameters: Glucose oxidase/peroxidase kit (Ref. 110504; BioSystems, Barcelona, Spain) for glucose; Mercodia Rat Insulin ELISA kit (Ref. 10‐1250‐01; Mercodia AB, Uppsala, Sweden) for insulin. ‐ Non‐targeted metabolomic analysis: 1290 infinity ultra‐high performance liquid chromatography (UHPLC) system (Agilent Technologies) coupled to a 6550 ESI‐QTOF (Agilent Technologies) operated in positive (ESI+) or negative (ESI‐) electrospray ionization mode.

Figure 17. Overview of the study design of Chapter 6. Abbreviations: HFS, high‐fat sucrose; RSV, trans‐resveratrol; Q, quercetin; RSV+Q, trans‐resveratrol and quercetin; BW, body weight; UHPLC, ultra‐high performance liquid chromatography, ESI, electrospray ionization; HRMS, high‐resolution mass spectrometry.

58 Material and methods

 Overview of the untargeted metabolomic profiling

LC coupled to MS using electrospray ionization (LC/ESI‐MS) is an especially popular platform that brings high sensitivity, combining a separation and a detection method (Smith et al., 2006). We will briefly describe the basis of the UHPLC‐(ESI)‐HRMS technology when performing an untargeted analysis.

During LC analysis, the components of the sample are distributed into a stationary and a mobile phase. The pressurized liquid solvent containing the sample is pumped through a column filled with a solid adsorbent material. Separation of the components of the sample will depend on the small differences in interactions with the stationary phase which generates different flow rates that lead to the separation of the components as they come out of the column. Moreover, depending on the chemical composition of both phases, LC might work in normal or reversed‐phase. While in normal phase chromatography the stationary phase is hydrophilic, reversed‐phase chromatography relies on the separation of the molecules based on their hydrophobicity, mainly using gradients of increasing concentrations of organic solvents (Pitt, 2009). MS detectors convert the analytes in charged (ionised) molecules and, then, ions are analysed taking into account their mass‐to‐charge ratio (m/z). The ionisation technique used in the present study is the ESI, which has been shown to be appropriate for moderately polar molecules including many metabolites, xenobiotics and peptides. In fact, it has been mainly used for biological molecules (Pitt, 2009). For the qualitative analysis of the metabolomic profile of a sample, one of the most appropriate mass analysers is the time of flight (TOF), which allows acquiring mass spectra quickly and with high sensitivity. It works accelerating ions through a high voltage. Therefore, the time that takes the ions to travel down a flight tube and reach the detector relies on the m/z value of metabolites (Pitt, 2009). A schematic overview of UHPLC‐ (ESI)‐HRMS metabolomic analysis is found in Figure 18.

Figure 18. Schematic overview of UHPLC‐ESI‐HRMS metabolomic analysis. Modified from Agilent Technologies “Basics of LC/MS” (http://www.agilent.com/home). Abbreviations: HPLC, high‐performance liquid chromatography; ESI, electrospray ionization; TOF, time of flight.

59 Material and methods

2. Impact of different phenolic compounds on obesity (Chapter 3)

2.1. Experimental design Chapter 3: Impact of different set of phenolic compounds on obesity

In this study, male Wistar rats (n=70) were randomly distributed into seven experimental groups: a standard‐diet fed control group (control, n=10); a HFS diet‐fed group (HFS, n=10) and five groups that were fed the same obesogenic diet but supplemented with either curcumin (Curcumin, n=10), chlorogenic acid (Chl. A., n=10), p‐coumaric acid (Cou. A., n=10), naringin (Naringin, n=10) each of them at a dose of 100 mg/kg BW/day and leucine (Leucine, n=10) at a dose of 1% of diet. The following measurements and assessments were conducted.

‐ Body‐ weight and food intake record: weight scale. ‐ Glucose levels in vivo through an intraperitoneal glucose tolerance test (IPGTT): glucometer (Roche Diagnostics, Mannheim, Germany) and blood glucose test trips (Optium Plus, Abbott Diabetes Care, Witney Oxon, UK). ‐ Serum parameters: Mercodia Rat Insulin ELISA Kit (Ref. 10‐1250‐01; Mercodia AB, Uppsala, Sweden) using an automatized Triturus® equipment (Grifols International S. A., Barcelona, Spain).

Figure 19. Overview of the study design of Chapter 3. Abbreviations; HFS, high‐fat sucrose; Chl. A, chlorogenic acid; Cou. A, p‐coumaric acid; BW, body weight; IPGTT, intraperitoneal glucose tolerance test.

60 Material and methods

3. Gut microbiota composition analyses (Chapter 2 and Chapter 5)

3.1. Experimental design Chapter 2: Impact of a HFS diet on gut microbiota.

Male Wistar rats (n=5) were fed a standard diet during the adaptation period and then changed to an obesogenic HFS diet for 6 weeks. Samples were taken before and after HFS dietary intervention. The following approaches and techniques were used in order to analyse body weight, food intake, serum biochemical profile, bacterial community structure, and SCFA profile in faecal samples.

‐ Body‐weight and food intake record: weight scale. ‐ Serum parameters: Glucose oxidase/peroxidase kit (Ref. 110504; BioSystems, Barcelona, Spain) for glucose and Mercodia Rat Insulin ELISA kit (Ref. 10‐1250‐01; Mercodia AB, Uppsala, Sweden) for insulin. ‐ DNA extraction from faeces: QIAamp DNA Stool Mini kit (Qiagen, Hilden, Germany), Nanodrop ND‐1000 spectrophotometer (Thermo Scientific). ‐ Polymerase chain reaction (PCR): T100 Thermal Cycler (Bio‐rad laboratories Inc., Spain), Pfu DNA polymerase (ref. M774; Promega, Madison, WI, USA), Custom DNA oligos (Sigma‐Aldrich), QIAquick PCR purification Kit (Qiagen). ‐ Bacterial community analysis: 454 pyrosequencing on a Roche Genome Sequencer FLX system (Roche, Penzberg, Germany). ‐ Gas chromatography‐mass spectrometry (GC‐MS): 7890 A series gas chromatograph coupled to a 7000 series Triple Quad GC/MS (Agilent Technologies, Santa Clara, CA, USA).

Figure 20. Overview of the study design of Chapter 2. Abbreviations: HFS, high‐fat sucrose; BW, body weight; GC‐MS, gas chromatography‐mass spectrometry.

61 Material and methods

3.2. Experimental design Chapter 5: Impact of trans‐resveratrol and quercetin on faecal gut microbiota composition.

Dietary intervention consisted of four groups: the control group fed a HFS diet (HFS, n=5); trans‐resveratrol group (RSV, n=6) supplemented with 15 mg/kg BW/day of trans‐resveratrol; quercetin group (Q, n=6), supplemented with 30 mg/kg BW/day of quercetin; and trans‐resveratrol+quercetin group (RSV+Q, n=6), supplemented with a combination of trans‐resveratrol 15 mg/kg BW/day and quercetin 30 mg/kg BW/day. The following techniques and methods were used in order to analyse: body weight and food intake, serum biochemical parameters, gene expression analysis, gut microbiota composition and SCFAs profile.

‐ Body‐weight and food intake: weight scale. ‐ Serum biochemical parameters: Glucose oxidase/peroxidase kit (Ref. 110504; BioSystems, Barcelona, Spain) for glucose; Merkodia Rat Insulin ELISA kit (Ref. 10‐ 1250‐01; Mercodia AB, Uppsala, Sweden) for insulin; TNF‐alpha Platinum ELISA kit (EBioScience, Vienna, Austria) for TNFα levels; LPS levels, with the Limulus Amebocyte Lysate QCL‐1000 chromogenic assay (Lonza, Walkersville, MD, USA); and LPS‐binding protein (LBP) levels, with the Mouse LBP ELISA Kit (Cell Sciences, Canton, MA, USA). ‐ DNA extraction from faeces: QIAamp DNA Stool Mini kit (Qiagen, Hilden, Germany). Nanodrop ND‐1000 spectrophotometer (Thermo Scientific). ‐ RNA extraction from colonic mucosa: All Prep DNA/RNA Mini kit (Qiagen). Nanodrop ND‐1000 spectrophotometer (Thermo Scientific). ‐ RT‐PCR: ABI PRISM 7000 HT Sequence Detection System and predesigned TaqMan Assays (IL‐18, TNF‐α, TLR‐2, TLR‐4, LBP, MyD88, NFκ‐β, NKGd2 TJP‐1, TJP‐2, Ocln) from (Applied Biosystems, Texas, USA). ‐ PCR: T100 Thermal Cycler (Bio‐rad laboratories Inc., Spain), Pfu DNA polymerase (ref. M774; Promega, Madison, WI, USA), Custom DNA oligos (Sigma‐Aldrich), QIAquick PCR purification Kit (Qiagen). ‐ Bacterial community analysis: 454 pyrosequencing on a Roche Genome Sequencer FLX system (Roche, Penzberg, Germany). ‐ GC‐MS: 7890 A series gas chromatograph coupled to a 7000 series Triple Quad GC/MS (Agilent Technologies, Santa Clara, CA, USA).

62 Material and methods

Figure 21. Overview of the study design of Chapter 5. Abbreviations: HFS, high‐fat sucrose; RSV, trans‐resveratrol; Q, quercetin; RSV+Q, trans‐resveratrol and quercetin; BW, body weight; TNF‐α, tumor necrosis factor α; LPS, lipopolysaccharide; LBP, lipopolysaccharide binding protein; IL‐18, interleukin 18; TLR‐2, toll‐like receptor 2; TLR‐4, toll‐like receptor 4; MyD88, myeloid differentiation primary response 88; NFκβ, nuclear factor kappa β; NKGd2, killer cell lectin‐like receptor subfamily K, member 1; TJP‐1, tight‐junction protein 1; TJP‐2, tight junction protein 2; Ocln, occluding; GC‐MS, gas chromatography‐mass spectrometry.

63 Material and methods

 Basis of the high‐throughput DNA sequencing

The 16S rRNA gene is highly conserved among all microorganisms, its length (around 1500 bp) is adequate for bioinformatics analysis and it is considered an excellent molecule to differentiate evolutionary relationships among prokaryotic organisms (Van de Peer et al., 1996). Briefly, in order to study the microbial population of an ecosystem, in this approach we examined the sequence of a gene that all microbes have and used variations in this gene to identify differences in species. In this thesis, 16S rDNA gene has been sequenced taking the V4 and V6 hypervariable regions as a target. Figure 22 shows an overview of the process carried out to conduct gut microbiota composition analysis.

Figure 22. Schematic overview of the high‐throughput sequencing analysis performed to detect and quantify bacterial community in animal’s faecal samples in Chapter 2 and Chapter 5 of the present thesis. Abbreviations: 16S rDNA, ribosomal DNA; RT‐PCR, real‐time PCR; bp, base pair.

64 Material and methods

4. In vitro assay and microarray analysis (Chapter 7)

4.1. Experimental design Chapter 7: Impact of trans‐resveratrol on gene expression levels in enterocytes.

For the in vitro study a Caco‐2 cell line, a human intestinal epithelial cell model was used. For the experiments cells were treated with standard LPS from E. coli K12 strain‐ TLR4 ligand (InvivoGen, San Diego, CA, USA). Concentrations of LPS (1 μg/mL) and time of exposure (48h) were established based on studies published in the literature (Cianciulli et al., 2012). Before LPS stimulation some wells were pre‐treated with trans‐resveratrol, dissolved in ethanol, in a concentration of 50 μM. Upon 1h of incubation at 37°C, cell cultures were stimulated with endotoxin as previously mentioned. Untreated cells were used as controls.

‐ RNA extraction from cells: TRIzol® reagent (Invitrogen, CA, USA), RNeasy Mini‐kit (Qiagen, Hilden, Germany), nanodrop ND‐1000 spectrophotometer (Thermo Scientific), Agilent RNA Nano Labchip (Agilent Technologies). ‐ Microarray analysis: Affymetrix human Gene 2.0 ST microarray.

Figure 23. Overview of the in vitro experimental design of Chapter 7. Abbreviations: LPS, lipopolysaccharide; RSV, trans‐resveratrol.

65

IV. RESULTS

CHAPTER 1

Diet‐induced hyperinsulinemia differentially affects glucose and protein metabolism: a high‐throughput metabolomic approach in rats

Etxeberria U.1, de la Garza A.L.1, Martínez J.A.1, Milagro F.I.1

1 Dept. of Nutrition, Food Science and Physiology, University of Navarra, C/Irunlarrea, 1, 31008, Pamplona, Spain

J Physiol Biochem, 2013

doi: 10.1007/s13105‐013‐0232‐0

Impact Factor (2013): 2.496

35/81 in Physiology (Q2)

69

Etxeberria U, de la Garza AL, Martínez JA, Milagro FI. Diet‐induced hyperinsulinemia differentially affects glucose and protein metabolism: a high‐throughput metabolomic approach in rats. Journal of Physiology and Biochemistry, 2013, 69(3): 613-623. http://doi.org/10.1007/s13105-013-0232-0

CHAPTER 2

Shifts in microbiota species and fermentation products in a dietary model enriched in fat and sucrose

Etxeberria U. 1,2, Arias N. 3, Boqué N. 4, Macarulla M.T. 3,5, Portillo M.P. 3,5, Milagro F.I. 1,2,5, Martinez J.A. 1,2,4*

1Department of Nutrition, Food Science and Physiology, University of Navarra, C/Irunlarrea, s/n, 31008, Pamplona, Spain

2 Centre for Nutrition Research, University of Navarra. Irunlarread St. E‐31008 Pamplona, Spain

3Nutrition and Obesity group, Department of Nutrition and Food Sciences, Faculty of Pharmacy, University of the Basque Country (UPV/EHU), Paseo de la Universidad 7, 01006 Vitoria, Spain

4 Nutrition and Health Research Group. Technological Center of Nutrition and Health (CTNS), TECNIO, CEIC S. Avinguda Universitat 1, 43204 Reus, Spain; 5CIBERobn Fisiopatología de la Obesidad y Nutrición

5(CIBERobn), Instituto de Salud Carlos III, 28029 Madrid, Spain

Benef Microbes, 2015

doi: 10.3920/BM2013.0097

Impact Factor (2014): 2.614

35/77 in Nutrition & Dietetics (Q2)

57/119 in Microbiology (Q2)

83 Etxeberria U, Arias N, Boqué N, Macarulla MT, Portillo MP, Milagro FI, Martínez JA. Shifts in microbiota species and fermentation products in a dietary model enriched in fat and sucrose. Beneficial Microbes, 2015, 6(1): 97-111. http://doi.org/10.3920/BM2013.0097

CHAPTER 3

Biocompounds Attenuating the Development of Obesity and Insulin Resistance Produced by a High‐fat Sucrose Diet

Etxeberria U. 1,§, de la Garza A.L. 1,§, Martínez J.A. 1,2,* and Milagro F.I. 1,2

1Department of Nutrition, Food Science and Physiology, Centre for Nutrition Research, University of Navarra, C/Irunlarrea 1, 31008 Pamplona, Spain

2CIBERobn Physiopathology of Obesity and Nutrition, Carlos III Health Research Institute, Madrid, Spain

§These co‐authors contributed equally to this work

Nat Prod Commun, 2015

Published online (www.naturalproduct.us)

Impact Factor (2014): 0.906

76/123 in Food Science & Technology (Q3)

101

2015 NPC Natural Product Communications Vol. 10

No. 8 Biocompounds Attenuating the Development of Obesity and Insulin 1417 - 1420 Resistance Produced by a High-fat Sucrose Diet

Usune Etxeberriaa,§, Ana Laura de la Garzaa,§, J. Alfredo Martíneza,b,* and Fermín I. Milagroa,b aDepartment of Nutrition, Food Science and Physiology, Centre for Nutrition Research, University of Navarra, C/Irunlarrea 1,31008 Pamplona, Spain bCIBERobn Physiopathology of Obesity and Nutrition, Carlos III Health Research Institute, Madrid, Spain

§These co-authors contributed equally to this work [email protected]

Received: April 15th, 2015; Accepted: May 13th, 2015

The use of biocompounds as agents with potential anti-obesity effects might be a feasible alternative to the prescription of traditional drugs in the near future. The goal of the present study was to screen five different compounds in relation to their ability to prevent body weight gain and ameliorate obesity-associated metabolic impairments, namely insulin resistance. For this purpose, seventy Wistar rats were randomly assigned into seven experimental groups. A standard diet-fed control group (control, n=10); a high-fat, high-sucrose diet-fed group (HFS, n=10) and five experimental groups which were fed the HFS diet supplemented with one of the following biocompounds; curcumin (100 mg/kg bw, n=10), chlorogenic acid (50 mg/kg bw, n=10), coumaric acid (100 mg/kg bw, n=10), naringin (100 mg/kg bw, n=10) and leucine (1 % of diet, n=10). These results confirm the effectiveness of all the compounds to reduce significantly food efficiency, despite the significant higher food intake. Moreover, visceral fat mass percentage was significantly decreased after naringin and coumaric acid supplementation. In fact, this finding might be related to the considerable amelioration of HOMA-IR index detected in naringin-treated animals. A significant reduction in serum insulin levels and an improvement in the intraperitoneal glucose tolerance test and AUC were found in leucine- and coumaric acid-treated rats, respectively. In summary, the tested biocompounds, particularly naringin, coumaric acid and leucine, showed potential benefits in the prevention of obesity-related complications in rats, at least at the proved doses.

Keywords: Curcumin, Chlorogenic acid, Coumaric acid, Naringin, Leucine, Anti-obesity.

Obesity has been defined as the pandemic of the 21st century by the has been a scientific focus [7, 8]. In this sense, polyphenols such as World Health Organization [1]. Obesity is a health problem itself, flavanones (naringin), simple phenolic acids (coumaric acid and but it is also associated with numerous metabolic complications [2], chlorogenic acid), curcuminoids (curcumin) [9], and other resulting in a public health burden that implies high socio-economic biocompounds, as for instance leucine, a branched-chain amino acid costs [3]. Even if obesity is considered a preventable disease, [10], have been suggested to exert potential health beneficial effects greatly influenced by diet and exercise [4], still, limited effective in the context of obesity. Indeed, knowledge of the impact of these conventional medical treatments have been developed [5]. In this types of compounds on the organism is being further unravelled context, a growing interest has been focused on the development of through novel approaches such as the omics techniques [11]. The natural products with clinical effectiveness, but lower side effects aim of this study was to evaluate the anti-obesity effects exerted by than the current anti-obesity drugs [6]. five biocompounds (naringin, curcumin, chlorogenic acid, coumaric acid and leucine) in a high-fat sucrose (HFS) diet-fed animal model In the recent decades, the use of phenolic phytochemicals or natural generally used to mimic overweight and insulin resistance status of extracts for the management of obesity and other related diseases Westernized societies [12].

Figure 1: Body-weight gain development over the 37 days treatment period. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. $(Curcumin), ~(Leucine), §(Coumaric acid), #(Naringin), p< 0.05; ***(Control), p< 0.001. 1418 Natural Product Communications Vol. 10 (8) 2015 Etxeberria et al.

Table 1: Body-weight related parameters of rats at the end of the experimental period. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student T- test to analyse differences in the mean of each group with HFS group. *p< 0.05; ***p< 0.001.

HFS + biocompounds Measurements Control HFS Curcumin Chl.A. Cou.A. Naringin Leucine (n = 10) (n = 10) (n = 10) (n = 10) (n = 10) (n = 10) (n = 10) Initial body weight (g) 337 + 9 336 + 3 337 + 6 338 + 6 336 + 7 337 + 5 336 + 7 Food intake (g) 24.87 + 0.07 24.02 + 0.07 27.06 + 0.40 *** 26.72 + 0.54 *** 27.54 + 0.88 *** 27.01 + 0.32 *** 26.65 + 0.40 *** Liver weight (g/100 kg bw) 2.18 + 0.07 * 1.95 + 0.08 1.92 + 0.13 1.97 + 0.06 1.96 + 0.09 1.94 + 0.07 1.91 + 0.08 Spleen weight (g/100 kg bw) 0.17 + 0.01 * 0.15 + 0.00 0.15 + 0.01 0.14 + 0.01 0.16 + 0.01 0.16 + 0.01 0.15 + 0.01 Gastrocnemius weight (g/100 kg bw) 0.49 + 0.01 0.45 + 0.02 0.40 + 0.02 0.43 + 0.02 0.46 + 0.02 0.44 + 0.02 0.45 + 0.02 Fat mass/ gastrocnemius ratio 10.08 + 1.01*** 18.50 + 1.48 20.74 + 1.39 17.91 + 1.37 15.79 + 1.45 16.07 + 1.11 17.28 + 1.35

gastrocnemius), neither in the mentioned groups nor in the rest of the supplemented groups (Table 1). Protective effects of naringin on fat deposition might confirm the already proposed mechanism of action dependent on the activation of AMP kinase, leading to a suppression of lipid synthesis and inhibition of insulin resistance [14]. The prevention of adipose tissue accumulation exerted by the intake of coumaric acid was also reported in a model of HFD-fed Wistar rats. In this study, the anti-obesity effects of this compound were accompanied by a decrease in dyslipidaemia, hepatic steatosis, and oxidative stress [15]. Indeed, this research previously demonstrated in vitro the potential of coumaric acid to inhibit Figure 2: Food efficiency (g/ 100 Kcal) of rats at the end of the experimental period. intracellular triglycerides and glycerol- 3- phosphate dehydrogenase Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. (GPDH) in 3T3-L1 adipocytes [16]. Furthermore, the role of $(Curcumin), ~ (Leucine), §(Coumaric acid), p< 0.05; ###(Naringin), p< 0.001. coumaric acid was described to inhibit the expression of peroxisome proliferator-activated receptor gamma (PPARȖ), involved in the Rats supplemented with the biocompounds (curcumin, regulation of insulin sensitivity and glucose homeostasis [17] and coumaric acid, naringin and leucine) showed lower food CCAT enhancer binding protein alpha (C/EBPĮ) protein levels, efficiency than the non-treated HFS rats: Final body-weight gain, concluding the adipogenesis suppressive effect of these compounds. which was significantly increased (165%, p< 0.001) after the HFS diet treatment (Figure 1), was slightly reduced at the end of the Naringin and leucine showed insulin sensitizing effects, while study in the animals supplemented with naringin and leucine coumaric acid tended to improve impaired glucose tolerance: (Figure 1). Moreover, naringin and leucine significantly reduced Impaired glucose tolerance is one of the major characteristics of body-weight gain at day 28 of the intervention period when insulin resistance [18] and the homeostasis model assessment of compared with the HFS diet-fed control rats (Figure 1). Despite the insulin resistance (HOMA-IR) index is usually used to assess the fact that significantly greater food intake was observed in all insulin resistance status. In our study, a significant reduction of supplemented groups (Table 1) when compared with the HFS HOMA-IR was found following naringin supplementation (Figure control rats, food efficiency was significantly lowered in all the 4A) when compared with the HFS diet-fed obese rats, restating the groups supplemented with biocompounds, except in rats consuming beneficial effects of this flavonoid preventing the development of chlorogenic acid (Figure 2). These data suggest that the bioactive insulin resistance. Concomitantly, our data demonstrated that serum compounds impacted on the utilization of the food provided, as the insulin levels were significantly lower in rats supplemented with animals gained less body-weight per ingested calories. Our data leucine (Figure 4B). Leucine has been demonstrated to ameliorate might agree with results obtained by Zhang et al. [10], who stated glucose metabolism, in part due to a better insulin sensitivity that high-fat diet (HFD) fed obese mice were highly fuel efficient, resulting from a reduction in adiposity, while the induction of which implied a greater body-weight gain per unit of calories insulin secretion [19] has also been indicated as a possible consumed compared with the chow diet-fed mice. In contrast, HFD mechanism responsible for the glycaemic control. Interestingly, in fed animals supplemented with leucine showed higher resting our study, even if leucine supplementation did not significantly energy expenditure with lower fuel efficiency [10]. reduce fat accumulation, this branched-chain amino acid was found to be effective in decreasing insulin levels, a feature that might be In relation to the action of the biocompounds tested in the present secondary to the improved insulin sensitivity, as suggested by study, although no data regarding animals’ energy expenditure is Zhang et al. [10]. Furthermore, an intraperitoneal glucose tolerance available, it might be suggested that the treatment period with either test (IPGTT) was conducted to analyse the effects of biocompounds curcumin, chlorogenic acid, coumaric acid, naringin and leucine, that showed the most promising effects concerning glucose might have impacted on the animals’ metabolism, inducing higher metabolism. In this context, coumaric acid marginally decreased resting energy expenditure, higher capacity to burn food via diet- (p= 0.052) the area under the curve (AUC) and significantly induced thermogenesis and/or inducing a greater capacity for fat reduced glucose levels 90 minutes after glucose administration oxidation [13]. (Figures 5A and 5B).

Naringin and coumaric acid reduced the adiposity levels The present work highlights certain health beneficial effects derived induced by a HFS diet: HFS diet-fed animals evidenced a from the intake of biocompounds in relation to obesity and obesity- significant enlargement in total body fat and visceral adipose tissue associated metabolic disorders. Although individual outcomes compared with the control rats (Figure 3). In contrast, coumaric acid produced by each bioactive compound might not be particularly produced a statistically significant reduction in visceral adipose remarkable, emphasis should be made in the fact that foods such as tissue, while rats supplemented with naringin showed a statistically fruits and vegetables are complex mixtures of polyphenols, thus, significant decrease in both visceral and total adipose tissue depots consuming a dietary pattern rich in this sort of food may induce after a 37 days treatment period (Figure 3). However, no significant beneficial outcomes due to possible synergistic effects among the differences were achieved in other organ weights (liver, spleen and different compounds. Biocompounds with potential anti-obesity effects Natural Product Communications Vol. 10 (8) 2015 1419

Figure 3: Total adipose tissue and visceral adipose tissue at the end of the 37 days treatment period. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. §(Coumaric acid), #(Naringin), p< 0.05; ***(Control), p< 0.001.

Figure 4: A) HOMA-IR index and B) Serum insulin levels of rats supplemented or not with naringin, leucine and coumaric acid. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. ~ (Leucine), # (Naringin), p< 0.05.

without supplementation of different biocompounds: curcumin (100 mg/kg bw), chlorogenic acid (50 mg/kg bw), coumaric acid (100 mg/kg bw), naringin (100 mg/kg bw) and leucine (1%). In order to select the mentioned doses, concentrations used in previous studies reporting obesity-associated metabolic improvements were taken into consideration [10, 15, 20-24]. Body-weight and energy intake were recorded 3 times per week. After 37 days of supplementation, rats were sacrificed by decapitation without anaesthesia, and blood was collected from the trunk. Liver, spleen, gastrocnemius muscle and different adipose depots (subcutaneous, retroperitoneal, epididymal and mesenteric) were carefully dissected and weighed. Figure 5: A) AUC and B) Serum glucose levels during the IPGTT of rats fed a HFS Tissue and serum samples were immediately stored and frozen (- supplemented or not with naringin, leucine and coumaric acid. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse 80°C) for further analyses. differences in the mean of each group with HFS group. §(Coumaric acid), p< 0.05; t< 0.1. Intraperitoneal glucose tolerance test: HFS, naringin, leucine and coumaric acid groups were selected to conduct an IPGTT during the Experimental 26th and 27th days of the total experimental period. After a 15-h fast,

Biocompounds and diets: A standard pelleted chow diet containing rats were injected intraperitoneally with glucose (2 g/kg bw in 30%, 20% of energy as protein, 67% as carbohydrates (5% sucrose and w/v, solution). Blood glucose levels were determined from the tail 62% starch) and 13% as fat by dry weight (290 kcal/100 g diet), was vein before (0’) and after glucose injection (30’, 60’, 90’, 150’, purchased from Harlan Ibérica (Teklad Global, Barcelona, Spain; 210’) with a glucometer (Roche Diagnostics, Mannheim, Germany) ref. 2014). HFS diet contained 20% of energy as protein, 35% as and blood glucose test trips (Optium Plus, Abbott Diabetes Care, carbohydrates (18% sucrose, 10% maltodextrin and 7% starch) and Witney Oxon, UK). The glucose content was expressed as mg/dL, 45% as lipids by dry weight (473 kcal/100 g diet) as detailed by the and the AUC were determined by the trapezoidal rule approach supplier (Research Diets, New Brunswick, NJ, USA; ref. D12451). [25], as measured elsewhere [8]. Naringin, curcumin, L-leucine, chlorogenic acid and p-coumaric acid were purchased from Sigma-Aldrich Co. (St. Louis, MO 63103 Serum measurements: Serum insulin (Mercodia AB, Uppsala, USA). Sweden) was determined by enzyme-linked-immunosorbent assay (ELISA) using an automatized TRITURUS equipment (Grifols Experimental animals: Seventy male Wistar rats from the Center International S.A., Barcelona, Spain). Fasting glucose levels were for Applied Pharmacobiology Research (CIFA) of the University of measured using a glucometer (Roche Diagnostics) and blood Navarra (Pamplona, Spain), with an initial average weight of 337 + glucose test trips (Optimum Plus, Abbott Diabetes Care). Insulin 20 g, were kept in an isolated room with a constantly regulated resistance was assessed by the HOMA-IR index formula [26]: temperature between 21 and 23°C, controlled humidity (50 + 10%), [serum glucose levels (mmol/L) x insulin levels (mU/L)/ 22.5]. under a 12 h:12 h artificial light/dark cycle and with water and food ad libitum. The experimental protocol was approved by the Animal Statistical analysis: All the results are expressed as mean + Research Ethics Committee of the University of Navarra (04/2011). standard error of the mean (SEM). Statistical significance of The animals were randomly divided into 7 groups (n=10) and were differences among the groups was evaluated using Student’s T-test. fed either a standard diet (control group) or a HFS diet with or A level of probability of p< 0.05 was set as statistically significant. 1420 Natural Product Communications Vol. 10 (8) 2015 Etxeberria et al.

All analyses were performed using SPSS 15.0 packages of Nutrition for the financial support. The authors also wish to thank Windows (Chicago, IL). Asociación de Amigos from University of Navarra for the predoctoral grant given to A.L. de la Garza and the Department of Acknowledgments – The authors wish to acknowledge Línea Education, Language policy and Culture from the Government of Especial about Nutrition, Obesity and Health (University of Navarra the Basque Country for the predoctoral grant given to U. Etxeberria. LE/97, Spain) and CIBERobn Physiopathology of Obesity and

References

[1] WHO. World Health Organization. Obesity and overweight. 2015 [Available from: http://www.who.int/mediacentre/factsheets/fs311/en/]. [2] Jung UJ, Choi MS. (2014) Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. International Journal of Molecular Sciences, 15, 6184-6223. [3] Withrow D, Alter DA. (2011) The economic burden of obesity worldwide: a systematic review of the direct costs of obesity. Obesity Reviews, 12, 131-141. [4] Hoey H. (2014) Management of obesity in children differs from that of adults. The Proceedings of the Nutrition Society, 73, 519-525. [5] Manning S, Pucci A, Finer N. (2014) Pharmacotherapy for obesity: novel agents and paradigms. Therapeutic Advances in Chronic Disease, 5, 135-148. [6] Vasudeva N, Yadav N, Sharma SK. (2012) Natural products: a safest approach for obesity. Chinese Journal of Integrative Medicine, 18, 473-480. [7] Alves N, Valdes S, Silveira C, Duarte-Martino H, Milagro FI, Moreno-Aliaga MJ, Ribeiro S. (2012) Studies on mechanistic role of natural bioactive compounds in the management of obesity an overview. The Open Nutraceuticas Journal 5, 193-206. [8] de la Garza AL, Etxeberria U, Palacios-Ortega S, Haslberger AG, Aumueller E, Milagro FI, Martinez JA. (2014) Modulation of hyperglycemia and TNFalpha-mediated inflammation by helichrysum and grapefruit extracts in diabetic db/db mice. Food & Function, 5, 2120-2128. [9] Gonzalez-Castejon M, Rodriguez-Casado A. (2011) Dietary phytochemicals and their potential effects on obesity: a review. Pharmacological Research, 64, 438-455. [10] Zhang Y, Guo K, LeBlanc RE, Loh D, Schwartz GJ, Yu YH. (2007) Increasing dietary leucine intake reduces diet-induced obesity and improves glucose and cholesterol metabolism in mice via multimechanisms. Diabetes, 56, 1647-1654. [11] van Duynhoven J, Vaughan EE, Jacobs DM, Kemperman RA, van Velzen EJ, Gross G, Roger LC, Possemiers S, Smilde AK, Dore J, Westerhuis JA, Van de Wiele T. (2011) Metabolic fate of polyphenols in the human superorganism. Proceedings of the National Academy of Sciences of the United States of America, 108 Supplement 1, 4531-4538. [12] Lomba A, Milagro FI, Garcia-Diaz DF, Marti A, Campion J, Martinez JA. (2010) Obesity induced by a pair-fed high fat sucrose diet: methylation and expression pattern of genes related to energy homeostasis. Lipids in Health and Disease, 9, 60. Doi: 10.1186/1476-511X-9-60. [13] Dulloo AG. (2011) The search for compounds that stimulate thermogenesis in obesity management: from pharmaceuticals to functional food ingredients. Obesity Reviews, 12, 866-883. [14] Pu P, Gao DM, Mohamed S, Chen J, Zhang J, Zhou XY, Zhou NJ, Xie J, Jiang H. (2012) Naringin ameliorates metabolic syndrome by activating AMP-activated protein kinase in mice fed a high-fat diet. Archives of Biochemistry and Biophysics, 518, 61-70. [15] Hsu CL, Wu CH, Huang SL, Yen GC. (2009) Phenolic compounds rutin and o-coumaric acid ameliorate obesity induced by high-fat diet in rats. Journal of Agricultural and Food Chemistry, 57, 425-431. [16] Hsu CL, Yen GC. (2008) Phenolic compounds: evidence for inhibitory effects against obesity and their underlying molecular signaling mechanisms. Molecular Nutrition & Food Research, 52, 53-61. [17] Berger J, Moller DE. (2002) The mechanisms of action of PPARs. Annual Review of Medicine, 53, 409-435. [18] Bellinger DA, Merricks EP, Nichols TC. (2006) Swine models of type 2 diabetes mellitus: insulin resistance, glucose tolerance, and cardiovascular complications. ILAR Journal, 47, 243-258. [19] Leclercq-Meyer V, Marchand J, Woussen-Colle MC, Giroix MH, Malaisse WJ. (1985) Multiple effects of leucine on glucagon, insulin, and somatostatin secretion from the perfused rat pancreas. Endocrinology, 116, 1168-1174. [20] Alam MA, Kauter K, Brown L. (2013) Naringin improves diet-induced cardiovascular dysfunction and obesity in high carbohydrate, high fat diet- fed rats. Nutrients, 5, 637-650. [21] Oner-Iyidogan Y, Kocak H, Seyidhanoglu M, Gurdol F, Gulcubuk A, Yildirim F, Cevik A, Uysal M. (2013) Curcumin prevents liver fat accumulation and serum fetuin-A increase in rats fed a high-fat diet. Journal of Physiology and Biochemistry, 69, 677-686. [22] Karthikesan K, Pari L, Menon VP. (2010) Antihyperlipidemic effect of chlorogenic acid and tetrahydrocurcumin in rats subjected to diabetogenic agents. Chemico-Biological Interactions, 188, 643-650. [23] Huang K, Liang XC, Zhong YL, He WY, Wang Z. (2015) 5-Caffeoylquinic acid decreases diet-induced obesity in rats by modulating PPARalpha and LXRalpha transcription. Journal of the Science of Food and Agriculture, 1903-1910. [24] Torres-Leal FL, Fonseca-Alaniz MH, Teodoro GF, de Capitani MD, Vianna D, Pantaleao LC, Matos-Neto EM, Rogero MM, Donato J, Jr., Tirapegui J. (2011) Leucine supplementation improves adiponectin and total cholesterol concentrations despite the lack of changes in adiposity or glucose homeostasis in rats previously exposed to a high-fat diet. Nutrition & Metabolism, 8, 62. Doi: 10.1186/1743-7075-8-62. [25] Tai MM. (1994) A mathematical model for the determination of total area under glucose tolerance and other metabolic curves. Diabetes Care, 17, 152-154. [26] Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. (1985) Homeostasis model assessment: insulin resistance and beta- cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia, 28, 412-419.

CHAPTER 4

Impact of Polyphenols and Polyphenol‐Rich Dietary Sources on Gut Microbiota Composition

Etxeberria U. 1, Fernández‐Quintela A. 2,3, Milagro F.I. 1,3, Aguirre L. 2,3, Martínez J.A. 1,3, Portillo M.P. 2,3

1Department of Nutrition and Food Sciences, Physiology and Toxicology, University of Navarra, Pamplona, Spain

2Nutrition and Obesity Group. Dpt. Nutrition and Food Sciences. Faculty of Pharmacy. University of the Basque Country. Paseo de la Universidad, 7. 01006 Vitoria (Spain)

3CIBER de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Spain

J Agr Food Chem, 2013

doi: 10.1021/jf402506c.

Impact Factor (2013): 3.107

13/123 in Food Science & Technology (Q1)

11/71 in Chemistry, Applied (Q1)

2/56 in Agriculture, Multidisciplinary (Q1)

107 Etxeberria U, Fernández‐Quintela A, Milagro FI, Aguirre L, Martínez JA, Portillo MP. Impact of Polyphenols and Polyphenol‐Rich Dietary Sources on Gut. Journal of Agricultural and Food Chemistry, 2013, 61(40):9517-9533. http://doi.org/10.1021/jf402506c

CHAPTER 5

Reshaping faecal gut microbiota composition by the intake of trans‐ resveratrol and quercetin in high‐fat sucrose diet‐fed rats

Etxeberria U. 1,2, Arias N. 3,5 , Boqué N. 4 , Macarulla M.T. 3,5 , Portillo M.P. 3,5 , Martínez J.A. 1,2,5,* , Milagro F.I. 1,2,5

1Department of Nutrition, Food Science and Physiology, University of Navarra. C/Irunlarrea s/n, 31008 Pamplona, Spain

2Centre for Nutrition Research, University of Navarra. Irunlarrea St. E‐31008 Pamplona, Spain

3Nutrition and Obesity group, Department of Nutrition and Food Sciences, Faculty of Pharmacy, University of the Basque Country (UPV/EHU), Paseo de la Universidad 7, 01006 Vitoria, Spain

4Nutrition and Health Research Group. Technological Center of Nutrition and Health (CTNS), TECNIO, CEIC S. Avinguda Universitat 1, 43204 Reus, Spain

5Physiopathology of Obesity and Nutrition, CIBERobn, Carlos III Health Research Institute, 28029 Madrid, Spain

J Nutr Biochem, 2015

doi: 10.1016/j.jnutbio.2015.01.002

Impact Factor (2013): 3.794

14/77 in Nutrition & Dietetics (Q1)

127

Available online at www.sciencedirect.com ScienceDirect

Journal of Nutritional Biochemistry 26 (2015) 651–660

Reshaping faecal gut microbiota composition by the intake of trans-resveratrol and quercetin in high-fat sucrose diet-fed rats

U. Etxeberriaa,b,N.Ariasc,e,N.Boquéd, M.T. Macarullac,e,M.P.Portilloc,e,J.A.Martíneza,b,e,⁎,F.I.Milagroa,b,e

aDepartment of Nutrition, Food Science and Physiology, University of Navarra, C/Irunlarrea s/n, 31008 Pamplona, Spain bCentre for Nutrition Research, University of Navarra, Irunlarrea St. E-31008 Pamplona, Spain cNutrition and Obesity group, Department of Nutrition and Food Sciences, Faculty of Pharmacy, University of the Basque Country (UPV/EHU), Paseo de la Universidad 7, 01006 Vitoria, Spain dNutrition and Health Research Group, Technological Center of Nutrition and Health (CTNS), TECNIO, CEIC S. Avinguda Universitat 1, 43204 Reus, Spain ePhysiopathology of Obesity and Nutrition, CIBERobn, Carlos III Health Research Institute, 28029 Madrid, Spain

Received 24 June 2014; received in revised form 25 November 2014; accepted 7 January 2015

Abstract

Diet-induced obesity is associated to an imbalance in the normal gut microbiota composition. Resveratrol and quercetin, widely known for their health beneficial properties, have low bioavailability, and when they reach the colon, they are targets of the gut microbial ecosystem. Hence, the use of these molecules in obesity might be considered as a potential strategy to modulate intestinal bacterial composition. The purpose of this study was to determine whether trans-resveratrol and quercetin administration could counteract gut microbiota dysbiosis produced by high-fat sucrose diet (HFS) and, in turn, improve gut health. Wistar rats were randomised into four groups fed an HFS diet supplemented or not with trans-resveratrol [15 mg/kg body weight (BW)/day], quercetin (30 mg/kg BW/day) or a combination of both polyphenols at those doses. Administration of both polyphenols together prevented body weight gain and reduced serum insulin levels. Moreover, individual supplementation of trans-resveratrol and quercetin effectively reduced serum insulin levels and insulin resistance. Quercetin supplementation generated a great impact on gut microbiota composition at different taxonomic levels, attenuating Firmicutes/Bacteroidetes ratio and inhibiting the growth of bacterial species previously associated to diet-induced obesity (Erysipelotrichaceae, Bacillus, Eubacterium cylindroides). Overall, the administration of quercetin was found to be effective in lessening HFS-diet-induced gut microbiota dysbiosis. In contrast, trans-resveratrol supplementation alone or in combination with quercetin scarcely modified the profile of gut bacteria but acted at the intestinal level, altering the mRNA expression of tight-junction proteins and inflammation-associated genes. © 2015 Elsevier Inc. All rights reserved.

Keywords: Flavonol; Stilbene; Gut bacteria; Inflammation; Obesity; Intestinal permeability

1. Introduction converge on making clear the intimate relationships among the gut microbiota, diet, host metabolism and the immune system [9]. In this Obesity is regarded as a major public health issue with an context, the current use of genomic-based molecular techniques such increasing worldwide prevalence [1]. Strong evidence supports a as transcriptomics, metabolomics and metagenomics, together with direct link between intestinal dysbiosis, namely, alterations of the gut the involvement of in vivo host models (i.e., germ-free animals), has microbial composition, and metabolic diseases encompassing obesity allowed to boost the studies aiming to identify the most representative [2,3], diabetes [4], liver disease [5,6], cardiometabolic complications and significant gut bacterial groups or species in different disease [7] and even cancer [8]. These findings have driven research interest to conditions [10–12].Thisspecific characterisation is helping to elucidate the role of gut microorganisms in the development of metabolic diseases [13]. Obesity has been characterised by a specific Abbreviations: HFS, high-fat sucrose; TJPs, tight-junction proteins; HOMA- “bacterial trademark” represented by enlarged Firmicutes and reduced IR, homeostasis model assessment of insulin resistance; LPS, lipopolysaccha- Bacteroidetes phyla [14], even if the association with the latter still κβ ride; LBP, LPS-binding protein; IL-18, interleukin- 18; NF , nuclear factor remains a matter of debate [15,16]. Furthermore, dysbiosis produced β α α kappa ; TNF- , tumour necrosis factor ; TLR-2, toll-like receptor 2; TLR-4, by high-caloric diets, i.e., high-fat diets [17,18],hasbeenalso toll-like receptor 4; TJP-1, tight-junction protein 1; TJP-2, tight-junction demonstrated to be susceptible to modulation by a variety of protein 2; Ocln, occludin; SCFA, short-chain fatty acid; GC/MS, gas- chromatography-mass spectrometry; SEM, standard error of the mean. components such as probiotics and prebiotics [19]. Apparently, ⁎ Corresponding author. Department of Nutrition, Food Science and nondigestible polysaccharides could come to the aid of proliferating Physiology, University of Navarra, C/Irunlarrea s/n, 31 008 Pamplona, Spain. “friendly bacteria” and enhance microbial diversity in the gut [20], Tel.: +34 948425600 (80 6424). which might contribute to the amelioration of obesity and obesity- E-mail address: [email protected] (J.A. Martínez). associated metabolic disorders [21]. http://dx.doi.org/10.1016/j.jnutbio.2015.01.002 0955-2863/© 2015 Elsevier Inc. All rights reserved. 652 U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660

Natural bioactive compounds are widely recognised to have manufacturer (ref. 10-1250-01, Mercodia AB, Spain). Insulin resistance was assessed potential biological properties [22,23], such as antiadiposity [24] and by the homeostasis model assessment of insulin resistance (HOMA-IR) formula [42]: [serum glucose levels (mmol/L)×insulin levels (mU/L)]/22.5. Inflammatory-related antidiabetic effects [25,26]. Within the existing plant secondary markers were determined in serum with commercial ELISA kits. Tumour necrosis factor metabolites, trans-resveratrol (stilbene) and quercetin (flavonoid) (TNF)-α levels were determined with the TNF-alpha Platinum ELISA Kit (EBioScience, have been described to exert anti-inflammatory and antiobesity Vienna, Austria); lipopolysaccharide (LPS) levels, with the Limulus Amebocyte Lysate effects [27–29] and might be potential candidates for the amelioration QCL-1000 chromogenic assay (Lonza, Walkersville, MD, USA); and LPS-binding protein (LBP) levels, with the Mouse LBP ELISA Kit (Cell Sciences, Canton, MA, USA). of metabolic impairments (i.e., insulin resistance) associated to comorbidities of overweight or obesity [30,31]. Nevertheless, the 2.2. Faeces collection health outcomes are intimately dependent on the dose and the form they are provided and also on their bioavailability within the organism Fresh faecal samples were collected at the end of the intervention period early in the [32]. Overall, it is estimated that around 5%–10% of the total dietary morning, prior to the overnight fasting. Each animal was taken one by one and located in polyphenols are absorbed in the small intestine, while 90%–95% of the a clean, single cage with the aim of obtaining faeces directly after defecation. A soft total polyphenol intake reaches the colonic region unabsorbed, being abdominal massage was also exerted to the animals in order to facilitate bowel movement and, in turn, fresh faeces collection. Samples were gathered in 15-ml Falcon the subject of enzymatic activities of gut microbial ecosystem tubes and immediately frozen at −80°C for future analyses. producing diverse metabolites with a variety of physiological roles [33]. Although the metabolism of these bioactive compounds has been 2.3. Tissue collection well reported [34–36], few studies have described its bioconversion by gut bacteria [37]. Nevertheless, in view of the direct contact and the At the end of the experimental period (6 weeks), animals were fasted overnight, on fi “ ” dual interaction existing between these natural bioactive compounds one hand, to ensure that all the animals were sacri ced in similar feeding conditions without the bias of the dietary treatment and, on the other hand, to guarantee the and the colonic gut microbial ecosystem [38], possible composition suitability of biochemical values. Sacrifice was conducted under anaesthesia (chloral modifications in intestinal bacteria might be expected as a result of the hydrate) by cardiac exsanguination between 9:00 a.m. to 12:00 p.m. Liver was collected, consumption of these compounds, which might ultimately contribute weighted and then stored frozen at −80°C. In all animals, colonic mucosa was excised to functional modifications in the host. carefully by scratching and frozen immediately in liquid nitrogen for future RNA isolation. Therefore, the present study was conducted to assess the potential of trans-resveratrol, quercetin and a mixture of trans-resveratrol and 2.4. DNA extraction, bar-coded polymerase chain reaction (PCR) and bacterial 16S rDNA pyrosequencing quercetin supplementation to reverse HFS-diet-induced gut microbi- ota dysbiosis in rats. Furthermore, this research work sought to Faecal DNA was extracted using QIAamp DNA Stool Mini Kit (Qiagen, Hilden, analyse whether intestinal barrier integrity and inflammation might Germany), following supplier’s instructions. DNA was measured by NanoDrop ND-1000 be affected as a consequence of polyphenol intake. spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Samples for 454 pyrose- quencing were amplified for the 16S rDNA hypervariable regions (V4 to V6) using 16S- 2. Material and methods 0515F (5'-TGYCAGCMGCCGCGGTA-3') and 16S-1061R (5'-TCACGRCACGAGCTGACG-3') universal primers. With the Roche 454 FLX chemistry, read lengths up to 550 nt are – 2.1. Animals, diets and serum biochemical parameters achieved on PCR products which are in the range of 600 800 nt when using the off- instrument in amplicon mode. This mode favourises the sequence quality of sequencing The experiment was performed with a substudy of 23 Wistar rats obtained from wells producing high signals. Since the 16S molecules are very homogeneous and balanced Harlan Ibérica (Barcelona, Spain). Animals were single housed in polypropylene cages in nucleotide composition, this mode produces better results than the shotgun mode, and kept in an isolated room with a constantly regulated temperature (22°C±2°C) which is more tolerant to low signal beads, typically carrying GC- or extreme AT-rich – under a 12-h:12-h artificial light/dark cycle. Concisely, the artificial light/dark cycle in contents. With the V4 V6 amplicon being 560 nt in length for most bacteria, the full this experiment was inverted, so that light was switched off at 9:00 a.m. and switched amplicon sequence can be recovered using a library construction protocol involving blunt on at 9:00 p.m. so that animals were expected to start eating as soon as the dark cycle ligation of sequencing adapters, the so-called Y-adapters, carrying DNA barcodes referred fi began. Rats were fed a standard-chow diet (C; 2.9 kcal/g) from Harlan Iberica (ref. 2014, to as "multiplex identi ers" by the manufacturer. Sequencing on the 454 instrument is Barcelona, Spain) during an adaptation period that lasted 6 days. Afterwards, animals single-ended by nature, yet through the blunt ligation approach, approximately as many were randomly distributed into four groups and changed to an HFS commercial molecules are sequenced from the PCR 5' end as there are molecules sequenced from the obesogenic diet (ref. D12451M, OpenSource) (HFS; 4.7 kcal/g) for 6 weeks. The HFS diet PCR 3' end. contained 20% of energy as proteins, 35% of energy as carbohydrates (17% sucrose, 10% Given the unit read length, the overlap between forward and reverse sequences is in maltodextrin and 7% of corn starch) and 45% of energy as fat (31.4% as saturated fats, the order of 90% and spanning the three variable regions. Thus, a sequence clustering 35.5% as monounsaturated fats, 33.1% as polyunsaturated fats). All animals had free approach using a de novo EST assembly process becomes possible without running the access to food and water. The experimental groups were distributed as follows: the risk of generating chimeric sequence consensus. The Mira assembler [43] in EST mode control group (HFS; n=5), rats were fed the HFS diet; trans-resveratrol group (RSV; n= can handle the kind of copy number variation corresponding with relative abundance 6), rats were supplemented with trans-resveratrol 15 mg/kg body weight (BW)/day; differences observed among bacterial species in a metagenome sample. At the same quercetin group (Q; n=6), rats were supplemented with quercetin 30 mg/kg BW/day; time, this assembler can be made tolerant to single base changes often occurring and trans-resveratrol+quercetin group (RSV+Q; n= 6), rats were treated with a between individual copies of the 16S gene on a bacterial chromosome, which can be in mixture of trans-resveratrol 15 mg/kg BW/day and quercetin 30 mg/kg BW/day. the order of zero to two nucleotide changes over 100 nucleotides. The Mira assembler fi Polyphenols were daily incorporated to the powdered diet in quantities that ensured has a 454 speci c error correction model and thus is capable of making the distinction that each animal consumed the prescribed levels. Briefly, in a previous study, it was between instrument basecall errors and intrinsic base-pair changes. Furthermore, reported that resveratrol was degraded over the feeding period when it was mixed in combining forward and reverse sequences into a single consensus sequence not only the diet. Moreover, it was observed that rats started eating immediately upon daily diet achieves full-length coverage but also increases sequence reliability since the reverse replacement at the beginning of the dark period [39]. Thus, based on these assumptions sequences have high quality where the forward sequences exhibit quality drop-off and and taking into account that resveratrol and quercetin have limited solubility in water, vice versa. Samples with high species diversity will generate a lower proportion of reads the compounds were added to the surface of the diet dissolved in ethanol. Variations in clustering than samples with more limited diversity. the amount of ethanol received by each animal were avoided as ethanol was added to the diet to reach 1 ml/kg BW/day. Although the volume of ethanol provided was 2.4.1. Sequence postprocessing negligible, the control group was also given the same amount without natural The off-instrument 454 downstream processing software was used in amplicon compounds. Body weight and food intake were daily recorded. The doses of polyphenols mode to optimise read length as outlined above and to demultiplex the libraries and were chosen based on previous studies [39–41]. Trans-resveratrol (N98% purity) was trim off 3' end adapter sequences, if any. The sequencing of the 23 amplicon libraries supplied by Monteloeder (Elche, Spain); and quercetin (≥98% purity), by Sigma-Aldrich produced 16S rDNA reads ranging from 9780 to 71690, generating read lengths from (St. Louis, MO, USA). All the experiments were performed in agreement with the Ethical 479 to 520 nt, with a modal of 510 nt. Committee of the University of the Basque Country (document reference CUEID CEBA/ The Mira assembler was used at 98% similarity threshold, meaning sequences 30/2010), following the European regulations (European Convention, Strasburg 1986, carrying up to 2 high-quality differences out of 100 in any interval were allowed to Directive 2003/65/EC and Recommendation 2007/526/EC). Fasting serum glucose was assemble alongside with similar sequences. Singletons were carried forward into measured using a glucose oxidase/peroxidase kit (ref. 110504; BioSystems, Barcelona, identification provided their sequence length equalled at least 400 nt. The same Spain), and insulin levels were determined by using a specificenzyme-linked criterion was applied to contig consensus sequences produced. The proportion of reads immunosorbent assay (ELISA) kit according to the protocol described by the producing clusters ranged from 36.1% to 58.5% on a per-sample basis. U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660 653

The singleton and consensus sequences were blasted using NCBI blastall version Table 2 2.2.22 in blastN mode, using an expectation value cutoff of 10^-5 and requesting the 25 Relative abundance (% of total 16S rDNA) of detected bacterial phyla in faeces of HFS- best hits produced against the RDP database release 10 update 29, which was curated diet-fed rats supplemented or not with trans-resveratrol and quercetin for 6 weeks. for redundant entries representing identical sequence and entries with taxonomic Phyla HFS (n=5) RSV (n=6) Q (n=6) RSV+Q (n=6) P value classification of less than four levels down, counting from the "bacteria" taxon. These hits were duplicated according to the per contig sequence count and kept as is for Firmicutes 85.0±4.5 87.6±2.7 56.0±8.1 72.2±7.8 .053 singletons before being provided to the MEGAN classifier version 4 [44]. MEGAN Bacteroidetes 10.8±3.8 9.3±2.3 27.3±9.3 14.3±3.8 NS incorporates alpha rarefaction and assigns operational taxonomic units by comparing Proteobacteria 2.7±0.5 2.7±0.5 1.9±0.5 2.8±0.7 NS taxonomic information associated with equivalent best hits for each input sequence. Verrucomicrobia 1.0±0.7 0.3±0.1 14.6±7.7 7.5±4.5 NS Construction of libraries and sequencing were all provided as a custom service of Tenericutes 0.4±0.2 0.4±0.1 0.2±0.1 2.8±2.1 NS Beckman Coulter Genomics. Actinobacteria 0.1±0.0 0.2±0.0 0.3±0.1 0.2±0.0 NS Cyanobacteria 0.5±0.0 0.5±0.0 0.4±0.1 0.4±0.1 NS 2.5. Gene expression in colonic mucosa Deferribacteres 0.5±0.0 0.4±0.1 0.4±0.1 0.5±0.0 NS Unclassified bacteria 0.3±0.1 0.4±0.1 0.3±0.1 0.2±0.1 NS

Colonic mucosa RNA was extracted using All-Prep DNA/RNA Mini kit as described in All results are expressed as the mean±S.E.M. Statistical analyses were performed using ’ the manufacturer s instructions (Qiagen, Germantown, MD, USA). RNA concentration Kruskal–Wallis followed by Mann–Whitney U test, and P values were corrected with and quality were measured with a Nanodrop ND-1000 spectrophotometer (Thermo the Bonferroni test. tb0.1 after correction with the Bonferroni multiple comparison test fi μ Scienti c). Subsequently RNA (2 g) was reverse-transcribed to cDNA using MMLV was set out as marginal trend. NS, nonsignificant values after Kruskal–Wallis Moloney murine leukemia virus reverse transcriptase (Invitrogen) as described by comparison test. the suppliers. Reverse transcriptase PCR assays were carried out following the manufacturer’s instructions using an ABI PRISM 7000 HT Sequence Detection System, – – b Taqman Universal Master Mix and predesigned TaqMan Assays-on-Demand (Applied nonparametric Kruskal Wallis test followed by Mann Whitney U test when P .05. Biosystems, Austin, TX, USA). Interleukin 18 (IL-18), Rn01422083_m1; nuclear factor Bonferroni correction test was applied as a correction for multiple comparisons. SPSS kappa β (NFκβ), Rn00595794_m1; TNF-α, Rn01525859_g1; Toll-like receptor 2 (TLR2), 15.0 software (Chicago, IL, USA) was used to perform statistical analysis. Rn02133647_s1; Toll-like receptor 4 (TLR4), Rn00569848_m1; lipopolysaccharide binding protein (LBP), Rn00567985_m1; myeloid differentiation primary response 88, 3. Results Rn01640049_m1; killer cell lectin-like receptor subfamily K, member 1, Rn00591670_m1; tight-junction protein 1 (TJP-1), Rn 02116071_s1; tight-junction protein 2 (TJP-2), Rn01501483_m1; occludin (Ocln), Rn 00580064_m1. mRNA levels 3.1. Animal characteristics were normalised with glyceraldehyde-3-phosphate dehydrogenase, Rn 99999916_s1, as a housekeeping gene (Applied Biosystems). All samples were analysed in triplicate. Body-weight-related measurements and serum biochemical vari- −ΔΔCt The relative expression level of each gene was calculated by the 2 method. ables in the four experimental groups are reported in Table 1. Animals supplemented with a combination of trans-resveratrol and quercetin 2.6. Short-chain fatty acid (SCFA) analysis presented a trend towards prevention of body weight gain induced by Short-chain fatty acids were analysed in faeces. Briefly, 500 μl of acid water, containing an HFS diet intake and showed a slight reduction in liver proportions internal standard (propionic acid-d6, 98 atom % D), was added to 25 mg of each (% body weight) (Table 1). Moreover, supplementation with trans- lyophilised sample. Overnight lyophilisation was carried out protecting samples from resveratrol and quercetin alone or in combination significantly reduced light. Mixture was shaken for 1 min. Afterwards, extraction was carried out with 500 μlof serum insulin levels. HOMA-IR was also decreased in the groups diethyl ether followed by 1 min of shaking and centrifugation at 4°C at 14,000 rpm for 10 min. Supernatants were analysed by gas chromatography–mass spectrometry (GC/MS) supplemented with trans-resveratrol or quercetin (Table 1). In addition, in a 7890A Series gas chromatograph coupled to a 7000 GC/MS Triple Quad (Agilent serum inflammation- and endotoxin-related markers (LBP, LPS and Technologies, Santa Clara, CA, USA). The chromatographic column was a J&W Scientific TNF-α levels) were determined. Although slightly higher amounts were μ fi μ HP-FFAP (30 m×0.25 mm i.d., 0.25- m lm) (Agilent Technologies). A volume of 1 lof observed for LPS and TNF-α in trans-resveratrol supplemented rats, sample was automatically injected into a split/splitless inlet (in splitless mode), which was fi kept at 175°C. Helium (99.999% purity) was used as a carrier gas at a flow rate of 1 ml/min these results were not statistically signi cant (data not shown). in constant flow mode. The oven program was set at an initial temperature of 90°C, increased to 150°C at a rate of 12°C/min, then increased to 240°C at a rate of 20°C/min and 3.2. Gut microbiota analysis held at 240°C for 5 min. Ionisation was done by positive chemical ionisation with methane gas. SIM mode and GC-QqQ MassHunter software (Agilent Technologies) were used for data acquisition. At the phylum level, quercetin supplementation was found to considerably modify relative percentages of detected phyla when 2.7. Statistical analysis compared to the HFS-diet-fed control rats (Table 2). Quercetin supplementation was able to marginally (P=.053) reduce Firmicutes Results are expressed as the mean±standard error of the mean (S.E.M.). Normality (−34.2%). This value reflects the percentage of change measured was tested by Shapiro–Wilk test. For parametric variables, one-way analysis of variance (ANOVA) test followed by Dunnett post hoc test was carried out. For nonparametric taking into account the mean relative abundance of Firmicutes variables, statistical significance of differences among the groups was tested by

Table 1 Body-weight-related measurements and serum biochemical parameters of animals fed with an HFS diet supplemented or not with trans-resveratrol and quercetin for 6 weeks. HFS RSV Q RSV+Q ANOVA (n=5) (n=6) (n=6) (n=6)

Body weight measurements Initial body weight (g) 194±2 193±1 191±2 201±5 NS Body weight gain (g) 176±7 169±6 162±7 144±11 P=.070 Food intake (g/day) 17.0±0.2 16.6±0.4 17.0±0.8 15.8±0.4 NS Food efficiency (g/kcal) 5.3±0.2 5.2±0.1 5.1±0.1 4.7±0.2 P=.076 Liver weight (% BW) 2.7±0.1 2.7±0.0 2.6±0.1 2.4±0.1 P=.056 Serum biochemical variables Glucose (mg/dl) 119±4 105±4 107±3 110±6 NS Insulin (mU/L) 51.5±8.7a 24.3±4.8b 24.6±7.0b 27.4±2.6b Pb.05 HOMA-IR 15.2±2.6a 6.0±1.6b 6.5±1.9b 8.3±0.8ab Pb.05

All results are expressed as the mean±S.E.M. Statistical analyses were performed using Fig. 1. Firmicutes/Bacteroidetes ratio of HFS-diet-fed rats supplemented or not with one-way ANOVA followed by Dunnett post hoc test. Data with different superscript trans-resveratrol and quercetin for 6 weeks. Results are expressed as mean±S.E.M. of letters are significantly different, Pb.05. the relative abundance (% of total 16 S rDNA) of each phylum. 654 U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660 phlylum in the quercetin-supplemented group and the mean relative Table 3 abundance of this bacterial group in the HFS-diet-fed group, which is Relative abundance (% of total 16S rDNA) of the most representative families in faeces of HFS-diet-fed rats supplemented or not with trans-resveratrol and quercetin for 6 weeks. considered the reference group in the present study. Thus, these data Family HFS (n=5) RSV (n=6) Q (n=6) RSV+Q (n=6) P value suggest that, after quercetin supplementation, this bacterial phylum, usually up-regulated in rats fed an HF diet [45], was reduced in by Erysipelotrichaceae 42.7±6.5a 51.9±5.0a 6.9±2.2b 23.2±4.3a .018 34.2% when measured against the levels found in the reference group, Ruminococcaceae 17.5±2.4 14.7±2.0 18.0±2.5 23.4±5.1 NS fi Clostridiaceae 14.2±1.7 10.7±1.3 16.1±2.3 12.8±1.5 NS although this diminution was not statistically signi cant after Bacteroidaceae 8.0±2.6 7.2±1.6 20.5±7.0 11.2±2.9 NS correction with the Bonferroni multiple comparison test. Moreover, Lachnospiraceae 3.7±0.6 4.5±0.9 3.8±1.1 5.3±1.3 NS a considerable decrease (−80.5%) was detected in Firmicutes/ Acidaminococcaceae 2.1±1.1 1.0±0.1 2.5±0.7 1.7±0.6 NS Bacteroidetes ratio of animals supplemented with quercetin (Fig. 1). Eubacteriaceae 1.9±0.1 1.8±0.2 2.5±0.6 2.1±0.3 NS Prevotellaceae 0.6±0.1 0.3±0.1 2.2±1.1 0.7±0.3 NS In contrast, bacterial composition of faeces from the groups supple- Acholeplasmataceae 0.4±0.2 0.4±0.1 0.2±0.1 2.7±2.1 NS mented with trans-resveratrol or the combination of trans-resveratrol Lactobacillaceae 0.2±0.1 0.5±0.3 1.9±1.7 0.8±0.5 NS and quercetin was not significantly affected at the phylum level. Graciibacteraceae 0.2±0.0a 0.1±0.0b 0.2±0.1a 0.4±0.1a .033 fi At the class level, Erysipelotrichi (classi ed within the Firmicutes Results are expressed as the mean±S.E.M. of the relative abundance (% of total 16S phylum) was the most affected bacterial group after quercetin rDNA) of different families detected in faecal samples by 16S rDNA pyrosequencing. supplementation, as the mean relative abundance of this bacterial Statistical analyses were performed using Kruskal–Wallis followed by Mann–Whitney group was significantly (P=.018) reduced (−83.9%) compared to the U test, and P values were corrected by Bonferroni test. Pb.05 was considered statistically significant after correction with the Bonferroni multiple comparison test. Data with HFS-diet-fed control rats (Appendix Table A.1). different superscript letters are significantly different. At the family level, groups supplemented with quercetin and the combination of trans-resveratrol and quercetin presented a bacterial profile which substantially differed from the relative abundance of Regarding the genus level, quercetin supplementation for 6 weeks bacterial families detected in the faeces of the HFS-diet-fed control and significantly reduced the levels of Bacillus genus (−74.3%) when trans-resveratrol-supplemented rats, with this variation being espe- compared to the HFS-diet-fed rats (Appendix Table B.1). In contrast, cially notable for the Erysipelotrichaceae family (Fig. 2). Nevertheless, trans-resveratrol supplementation alone significantly lessened the trans-resveratrol supplementation produced a statistically significant abundance of Parabacteroides genus (−76.3%) compared to the mean inhibition in the Graciibacteraceae family (−57.7%) compared to the relative abundance found in the reference group (Appendix Table B.2). HFS-diet-fed control rats. Relative abundance (% of total 16S rDNA) of The combination of both polyphenols did not produce any changes at the most representative families is also reported (Table 3). this taxonomic level.

HFS RSV

Q RSV+Q

Fig. 2. Relative bacterial composition at the family level (% of total 16S rDNA) in faeces of HFS-diet-fed rats supplemented or not with trans-resveratrol and quercetin for 6 weeks. All data are displayed on a relative scale based on the 16S rDNA frequency taxa. U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660 655

Fig. 3. Relative abundance (% of total 16S rDNA) of bacterial species significantly different from the HFS-diet-fed control rats. Results are expressed as the mean±S.E.M. Statistical analyses were performed using Kruskal–Wallis followed by Mann–Whitney U test, and P values were corrected with the Bonferroni test. *Pb.05 after correction with the Bonferroni multiple comparison test. NS, nonsignificant values after Kruskal–Wallis comparison test.*Pb.05, HFS vs. RSV; $Pb.05 HFS vs. Q; #Pb.05, HFS vs. RSV+Q.

At the species level, the group supplemented with quercetin consumption of both polyphenols is concerned, the relative abun- showed a statistically significant inhibition in four bacterial species dance of two Clostridia species (Clostridium clariflavum, 327.0% and after multiple adjustments with Bonferroni test (Fig. 3). In this regard, Clostridium methylpentosum, 245.6%), as well as the relative abun- the statistically significant down-regulation detected in the mean dance of a member of Lachnospiraceae family (Blautia stercoris, relative abundance of Eubacterium cylindroides (−80.4%) compared to 321.7%), was significantly enhanced compared to the percentage the relative percentage found in the control group is noteworthy determined in the reference group (Fig. 3). Moreover, although (Fig. 4). Moreover, supplementation with trans-resveratrol signifi- nonstatistically significant, the relative percentage of diverse bacterial cantly reduced the mean relative abundance of different Clostridia species was also found to be differentially affected by the intake of species such as Clostridium aldenense (−93.1%), Clostridium hathewayi polyphenols (Appendix Tables C.1, C.2 and C.3). (−73.2%), Clostridium sp. C9 (−76.3%) and Clostridium sp. MLG661 In addition, Akkermansia muciniphila ATCC BAA-835, the unique (−53.7%) in opposition to the mean relative abundance of Clostridium bacteria belonging to Verrucomicrobia phylum, showed a notable sp. XB90 (266.6%) that was notably enhanced when compared to the increase (1384.0%) after the 6-week period of quercetin intake when HFS-diet-fed control rats (Fig. 3). Furthermore, the percentage of compared to the relative abundance detected in diet-induced change in the mean relative abundance of Gracilibacter thermotolerans overweight control rats (Fig. 5). However, these differences were (−57.7%) and Parabacteroides distasonis (−77.4%) was negatively not statistically significant. Furthermore, the relative abundance (% of affected by trans-resveratrol in comparison to that detected in the total 16S rDNA) of bacterial species represented in Fig. 6, especially HFS-diet-fed control group (Fig. 3). As far as the effect of the combined belonging to Bacteroidia class, such as Bacteroides sp. S-18 (105.2%), Bacteroides sp. dnLKV (87.8%), Barnesiella intestinihominis (127.6%),

Fig. 4. Relative abundance (% of total 16S rDNA) of Eubacterium cylindroides in faeces of HFS-diet-fed rats supplemented or not with trans-resveratrol and quercetin for Fig. 5. Relative abundance (% of total 16S rDNA) of Akkermansia muciniphila ATCC BAA- 6 weeks. Results are expressed as the mean±S.E.M. Statistical analyses were performed 835 in faeces of HFS-diet-fed control rats supplemented or not with trans-resveratrol using Kruskal–Wallis followed by Mann–Whitney U test, and P values were corrected and quercetin for 6 weeks. Results are expressed as the mean±S.E.M. Statistical with the Bonferroni test. $Pb.05, HFS vs. Q after correction with the Bonferroni multiple analyses were performed using Kruskal–Wallis followed by Mann–Whitney U test, and comparison test. P values were corrected with the Bonferroni test. 656 U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660

Fig. 6. Relative abundance (% of total 16S rDNA) of bacterial species in faeces of HFS-diet-fed control rats supplemented or not with trans-resveratrol and quercetin for 6 weeks. Results are expressed as the mean±S.E.M.

Bacteroides dorei (295.0%), Bacteroides chinchillae (334.8%) and cance with TLR4, LBP and IL-18 gene expression (Fig. 7). No statistical Candidatus Prevotella conceptionensis (200.5%), was boosted by differences were found in colonic inflammation-related gene expression quercetin supplementation, whereas one species belonging to in the rats supplemented with the combination of both polyphenols. Deltaproteobacteria class (Bilophila wadsworthia, −36.9%) was down- Regarding variables related to intestinal permeability, those exper- regulated in comparison to the HFS-diet-fed control group. These species, imental groups supplemented with trans-resveratrol alone or with the even if the differences were not statistically significant, were found to be mixture of trans-resveratrol and quercetin showed a statistically the most affected after a 6-week dietary treatment with quercetin. significant up-regulation in the expression levels of TJP-2 and Ocln gene (Pb.05). In contrast, quercetin supplementation did not seem to modify the intestinal barrier permeability associated markers (Fig. 8). 3.3. Gene expression in colonic mucosa

After 6 weeks of supplementation, the rats treated with quercetin 3.4. SCFA analysis showed a significantly higher colonic expression of IL-18. In contrast, the intake of trans-resveratrol produced a considerable increase in most of the SCFA profile detected by GC/MS analysis in faecal contents of the measured inflammation-related parameters, reaching statistical signifi- four experimental groups revealed no statistically significant

Fig. 7. Relative mRNA expression of inflammation-related genes in colonic mucosa. Results are expressed as the mean±S.E.M. Statistical analyses were performed using Kruskal–Wallis followed by Mann–Whitney U test.*Pb.05, HFS vs. RSV; $Pb.05, HFS vs. Q. MyD88, myeloid differentiation primary response 88; NKGd2, killer cell lectin-like receptor subfamily K, member 1. U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660 657

Fig. 8. Relative mRNA expression of TJPs and Ocln in colon. Results are expressed as the mean±S.E.M. Statistical analyses were performed using Kruskal–Wallis followed by Mann– Whitney U test. P values were corrected by Bonferroni test.*Pb.05, HFS vs. RSV; #Pb.05, HFS vs. RSV+Q. differences. Supplementation with quercetin, although not significant, treatment of metabolic diseases such as insulin resistance [30,31].To was found to slightly increase concentrations of acetate (2.00± our knowledge, few studies have explored the effects of pure 0.24 mg/g dry weight), propionate (0.51±0.07 mg/g dry weight) and resveratrol and quercetin consumption alone or combined on resident butyrate (0.58±0.11 mg/g dry weight) when compared to the HFS- microbiota composition in order to find a possible association between diet-fed control rats (1.68±0.39, 0.41±0.11 and 0.38±0.11 mg/g dry their beneficial properties in the host and specific gut microbial weight, respectively). In contrast, dietary supplementation with trans- modifications [55]. In contrast, there is strong evidence supporting the resveratrol alone and with the combination of trans-resveratrol and role of gut microbiota in several metabolic functions [56]. Further- quercetin produced no changes in SCFA levels (Appendix Table D.1). more, modifications carried out in microbial ecology have been demonstrated to derive in host metabolic phenotype modulation, 4. Discussion influencing host biochemistry and increasing host susceptibility to diseases [57,58]. The obese metabolic phenotype transmissibility in Therapeutic effects have been attributed to resveratrol in meta- germ-free mice that were transplanted the gut microbiota from obese bolic derangements [46], and also to quercetin, mainly due to allegedly (ob/ob) mice has been also proven [3]. Nevertheless, gut microbiota antioxidant and anti-inflammatory properties [47,48]. Mechanisms modifications have been attributed both to genetic obesity [2] and to surrounding their beneficial effects have attracted research interest diet-induced alterations independent of obesity [45]. [49]. Nevertheless, studies investigating the interaction within In general, obese gut microbiota has been repeatedly associated bioactive compounds and gut microbes are rather limited [50–52]. with a reduced Bacteroidetes/Firmicutes ratio [2,59]. Our findings agree The goal of the present research was to determine whether with this feature, as HFS-diet-fed control rats exhibited the highest supplementation with trans-resveratrol and/or quercetin was able to Firmicutes/Bacteroidetes ratio. Interestingly, quercetin administration modify gut microbiota composition, differing from microbial profile to the HFS diet prominently decreased this ratio. Likewise, animals observed in HFS-diet-fed control rats. Moreover, we sought to analyse supplemented with this polyphenol showed a considerable reduction whether these modifications in microbial ecology concur not only in Firmicutes phylum commonly dominating in obesity and over- with body weight and biochemical changes but also with alterations at weight status [14,60]. The enrichment of Erysipelotrichaceae family in the intestinal epithelial level. diet-induced obesity has been similarly reported in several studies In the current research, dietary treatment with pure polyphenolic [61,62]. Indeed, in a metagenomic study carried out by Turnbaugh compounds, especially when jointly administered, decreased body et al.[61] in mice that developed obesity by the intake of high-fat, high- weight gain. The lack of antiobesity effect of quercetin is in agreement sucrose diet, it was concluded that the microbiome of HFS-diet-fed with data in the literature, which suggest the need of treatment mice was rich in genes encoding components of a major system, which periods longer than 9 weeks for this polyphenol to achieve an effective is essential for the sugar uptake for most intestinal bacteria [61]. Thus, body-fat-lowering effect [53]. As far as resveratrol is concerned, the it was suggested that Erysipelotrichaceae contributed to the function of dose used in the present experiment was effective in reducing body fat these genes, leading to a more efficient energy extraction from the diet after 6 weeks of treatment, but in a model of genetic obesity (Zucker [61,63]. In the current investigation, the potential of quercetin to fa/fa rats) orally administered resveratrol for 6 weeks reduced liver deeply modify general bacterial profile at the family level is under- weight, visceral adiposity and plasma cytokine levels [40,54].Ina lined, indicating an important reduction in the relative abundance of model of obesity induced by using the same high-fat, high-sucrose diet Erysipelotrichaceae family, in contrast to what it was found with used in the present study, our group previously showed that a dose of trans-resveratrol administration alone or in combination. Likewise, it 6 mg/kg BW/day of resveratrol did not induce changes in adipose was previously demonstrated by our group [17] that Erysipelotrichi tissue weight [39]. By contrast, a dose of 30 mg/kg BW/day was very taxon was profoundly enlarged (1025.4%) after an HFS diet treatment effective [41]. It seems that, in this obesity model, a dose higher than for 6 weeks [17]. Interestingly, concerning the enrichment found in the 15 mg/kg BW/day is needed to effectively reduce body fat. The dosage Bacteroidetes phylum, quercetin supplementation also deeply in- used in the current experiment for resveratrol and quercetin was creased the abundance of Bacteroidaceae and Prevotellaceae families, effective to decrease insulin levels and also to improve insulin which were stated to be negatively influenced in high-fat-fed mice sensitivity. These findings support results from previous studies [45]. Besides, quercetin supplementation resulted in the reduction of describing resveratrol and quercetin as potential candidates for the the genus Bacillus, another predominant bacterium in Western-diet-fed 658 U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660 obese mice [64]. Surprisingly, trans-resveratrol solely did not produce sensors of microbial infection, initiating inflammatory and immune significant modifications at the highest division levels but was detected defence responses [79]. However, the bacterial ligands are shared by all to follow the opposite pattern of the previously mentioned results. classes of bacteria, being also produced by commensal microorganisms Furthermore, when combining both polyphenols together, gut micro- [80]. In line with this statement, Rakoff-Nahoum et al. demonstrated a biota modifications emulated quercetin’sindividualconsequences, nonimmune function of TLRs in the maintenance of epithelial although these alterations were found to be weaker. Notwithstanding, homeostasis [80]. Based on the recognition of commensal bacteria as at the species level, trans-resveratrol supplementation significantly health- promoting “symbionts”, emerging evidence has confirmed the inhibited several bacterial species, such as Gracilibacter thermotolerans, role of luminal microbes to signal the interfacing epithelial layer, to Parabacteroides distasonisandspeciesfromClostridia class (Clostridium control the turnover rate of enterocytes and to fortify the epithelial aldenense, Clostridium hathewayi, Clostridium sp. MLG661), whereas it turnover regenerative and barrier functions [81]. Rakoff-Nahoum et al. significantly enhanced Clostridium sp. XB90. Of especial mention, suggested a beneficial role generated from the recognition of commen- Parabacteroides distasonis has been found to be reduced in obese sal bacteria by TLRs [80]. The recognition of commensal bacteria and individuals [65], and suppressions in Clostridium aldenense and posterior activation of TLRs was hypothesised to induce generation of Clostridium hathewayi species, members of Clostridium cluster XIVa, protective factors (i.e., cytokines) upon epithelial damage that have have been reported to be major butyrate producers [66]. On the other been previously reported in vivo and in vitro to play a beneficial role by hand, the differential sensitivity to polyphenols has been postulated, initiating repair responses (i.e., process known as restitution) [82] and indicating a higher sensitivity to phenolic compounds of gram-positive have been defined as markers of protective responses in colonic tissue bacteria rather than the gram-negative bacteria, possibly attributable to [80]. In fact, a growing body of evidence indicates the relevance of changes in their wall composition [67]. The intake of quercetin certain mediators (IL-α, IL-1β, IL-18) to preserve mucosal homeostasis, positively affected the growth of three species (Bacteroides vulgatus, as well as to protect and restore epithelial barrier [83]. In particular, the Clostridium clariflavum and Clostridium sp. MLG661) and strongly up-regulation of IL-18 (a family member of IL-1) found in our study inhibited the abundance of Eubacterium cylindroides. Bacteroides might be related to its dichotomous role in the gut, as IL-18 derived from vulgatus and Clostridium clariflavum were previously found to be intestinal epithelial cells has been described to facilitate tissue repair diminished in obese subjects [65,68] and HFS-diet-fed overweight rats and promote mechanisms to induce homeostasis in the early phase of [17].Indeed,Eubacterium cylindroides has been detected in faecal inflammation [84]. Experiments conducted by other researchers have microbiota of obese humans [14]. It was up-regulated in high-fat-diet- concluded the essential role of IL-18 to maintain epithelial integrity and induced obesity [14,61] and reported to be enlarged (4072.1%) in rats prevent the translocation of bacteria, associating this feature to the fed an HFS diet for 6 weeks [17]. Blautia stercoris, Clostridium clariflavum activation of TLRs in inducing the signal to repair [85,86]. From our data, and Clostridium methylpentosum were also up-regulated by the a presumable promotion of intestinal epithelial repair by protective combination of polyphenols. In agreement with these results, factors (i.e., IL-18) induced by commensal-bacteria-derived activation Clostridium and Eubacterium have been considered as the main of TLR signalling might be hypothesised, particularly after trans- genera involved in the metabolism of a large variety of phenolic resveratrol administration, as a possible mechanism leading to compounds, including isoflavones (daidzein), flavones (naringenin intestinal epithelial homeostasis. It might be speculated that, although and ixoxanthumol), flavan-3-ols (catechin and epicatechin) and nonstatistically significant, inflammation- and endotoxin-related flavonols (quercetin and kaempferol) [69]. According to our data, markers (LBP, TLR4 and TLR2 expression levels; LPS serum levels) given the poor absorption of quercetin into the bloodstream from the showed a trend to a subtle increase after trans-resveratrol supplemen- gut, the mechanisms contributing to the amelioration of insulin tation. This outcome might underlie a slight inflammation and immune resistance might imply a direct gut microbiota modulatory effect. In response taking place in the intestine [87]. TLRs could be acting upon contrast, resveratrol might act as an antimicrobial compound that damage as a defence mechanism to prevent bacteria and bacterial counteracts the individual effects of quercetin, suggesting that the products translocation through rearrangement of TJPs to enhance improvements in insulin resistance could be due to indirect mecha- intestinal epithelial integrity [88–90]. Nevertheless, these results nisms which do not involve big changes in gut microbiota composition. deserve further research in order to confirm the mechanisms through Given the connection between gut microbiota, metabolism and which polyphenols interact with gut microbiota to cause host gene intestinal epithelium [70], the expression of genes implicated in the expression modifications and also to be able to conclude the overall permeability of the gut epithelium was analysed. It is widely known that health consequences of this interactions in health. intact mucosal barrier is essential to avoid the development of inflammation [71]. Indeed, impaired intestinal barrier function is 5. Conclusions regarded as a relevant contributory factor to obesity and associated metabolic disturbances [4,72]. In the present study, although being From this experiment, it was concluded that the intake of trans- aware that the fasting period to which animals were subjected before resveratrol and quercetin at the same time acted synergistically to sacrifice might have an effect on intestinal integrity [73],anup- reduce body weight gain. Moreover, administration of both polyphe- regulation in the expression of TJPs and Ocln was found to be induced by nols alone or in combination improved basal insulin levels and HOMA- the administration of trans-resveratrol alone and when combined with IR index. On the other hand, one of the major findings was related to quercetin, which was not perceived with the intake of quercetin alone. the impact of quercetin supplementation on gut microbiota compo- Furthermore, the expression levels of inflammation-associated genes sition. In fact, treatment with quercetin attenuated the increase in were induced by trans-resveratrol supplementation, such as the Firmicutes/Bacteroidetes ratio in HFS-diet-fed rats and notably mod- expression of TLR2 and, specially, TLR4, LBP and IL-18. TLRs, more ified gut microbial pattern at the family level, significantly reducing specifically TLR2 and TLR4, have been largely implicated in inflamma- the abundance of Erysipelotrichaceae and the genus Bacillus, closely tory states in the gastrointestinal tract [74–76]. TLRs comprise a family related to the consumption of Western diets and body weight gain. of pattern-recognition receptors that identify conserved molecular Furthermore, supplementation with quercetin produced a significant products of microorganisms, such as lipopolysaccharides and lipotei- alteration in specific bacterial species, increasing some that have been choic acid [77]. Thus, TLR2 recognises gram-positive-bacteria-derived inversely related to obesity (Bacteroides vulgatus, Akkermansia products, whereas TLR4 is stimulated by LPS derived from gram- muciniphila) and reducing the relative abundance of others associated negative bacteria [77]. Their expression is dependent on dysbiosis to diet-induced obesity (Eubacterium cylindroides, Bilophila wadsworthia). associated to altered host–bacterial interactions [78]. Indeed, TLRs act as In contrast, after trans-resveratrol supplementation, no profound effects U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660 659 on gut microbiota composition were identified, especially at the highest [3] Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity- associated gut microbiome with increased capacity for energy harvest. Nature division levels. Indeed, from results combining trans-resveratrol with 2006;444:1027–31. quercetin, it can be speculated that individual effects attributed to [4] CaniPD,BibiloniR,KnaufC,WagetA,NeyrinckAM,DelzenneNM,etal. quercetin were lessened. On the other hand, trans-resveratrol supple- Changes in gut microbiota control metabolic endotoxemia-induced inflam- mation in high-fat diet-induced obesity and diabetes in mice. Diabetes 2008; mentation altered the mRNA expression levels of TJPs as well as some 57:1470–81. inflammation-associated markers. [5] Yin YN, Yu QF, Fu N, Liu XW, Lu FG. Effects of four Bifidobacteria on obesity in In summary, this study demonstrates the gut microbiota modula- high-fat diet induced rats. World J Gastroenterol 2010;16:3394–401. tory role of quercetin when administered to HFS-diet-fed overweight [6] Brun P, Castagliuolo I, Di Leo V, Buda A, Pinzani M, Palu G, et al. Increased intestinal permeability in obese mice: new evidence in the pathogenesis of animals, and left some doubts for future research about the nonalcoholic steatohepatitis. Am J Physiol Gastrointest Liver Physiol 2007;292: mechanisms of trans-resveratrol to exert health benefits due to its G518–25. uncertain potential to modulate gut microbial population and its [7] Shen J, Obin MS, Zhao L. The gut microbiota, obesity and insulin resistance. Mol Aspects Med 2013;34:39–58. impact on host intestinal gene expression. Limitations of this study are [8] Tlaskalova-Hogenova H, Stepankova R, Kozakova H, Hudcovic T, Vannucci L, that there is no a group fed with a standard diet to identify the Tuckova L, et al. The role of gut microbiota (commensal bacteria) and the mucosal modifications induced by the type of diet to conclude whether barrier in the pathogenesis of inflammatory and autoimmune diseases and cancer: contribution of germ-free and gnotobiotic animal models of human polyphenols, especially quercetin, reversed diet-induced gut micro- diseases. Cell Mol Immunol 2011;8:110–20. biota dysbiosis. On the other hand, this study could not differentiate [9] Power SE, O'Toole PW, Stanton C, Ross RP, Fitzgerald GF. Intestinal microbiota, diet whether the outcomes discovered were derived from the original and health. Br J Nutr 2014;111:387–402. [10] Serino M, Chabo C, Burcelin R. Intestinal MicrobiOMICS to define health and compounds provided or from the metabolites produced in the gut. disease in human and mice. Curr Pharm Biotechnol 2012;13:746–58. Another issue to be considered is the fact that SCFA measurements [11] Kellermayer R, Dowd SE, Harris RA, Balasa A, Schaible TD, Wolcott RD, et al. were conducted ex vivo in faeces, which could not be an estimation of Colonic mucosal DNA methylation, immune response, and microbiome patterns in Toll-like receptor 2-knockout mice. FASEB J 2011;25:1449–60. the real production of SCFA, as rats are caecum fermenters and a great [12] Vital M, Howe AC, Tiedje JM. Revealing the bacterial butyrate synthesis pathways amount of these compounds (95%–99%) is absorbed in the large by analyzing (meta)genomic data. MBio 2014;5:e00889. intestine [91]. In addition, the measurement of the mRNA levels of TJPs [13] Khanna S, Tosh PK. A clinician's primer on the role of the microbiome in human – is an indirect method to assess the impact of these polyphenols on gut health and disease. Mayo Clin Proc 2014;89:107 14. [14] Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes integrity; gene expression levels are not always a reflection of their associated with obesity. Nature 2006;444:1022–3. functional active proteins. As a result, we should be cautious because [15] Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P, et al. Human further analyses are required to be able to draw conclusions about the colonic microbiota associated with diet, obesity and weight loss. Int J Obes (Lond) 2008;32:1720–4. protective functions of these polyphenols on intestinal epithelial [16] Zhang H, DiBaise JK, Zuccolo A, Kudrna D, Braidotti M, Yu Y, et al. Human gut barrier. Furthermore, an important question addressed in this study is microbiota in obesity and after gastric bypass. Proc Natl Acad Sci U S A 2009;106: that the positive effects derived from the combination of trans- 2365–70. [17] Etxeberria U, Arias N, Boque N, Macarulla MT, Portillo MP, Milagro FI, et al. Shifts resveratrol and quercetin in body weight were not explained by gut in microbiota species and fermentation products in a dietary model enriched in fat microbiota modifications. Quercetin produced the most profound and sucrose. Benefic Microbes 2014;11:1–15. effect on gut microbial ecology, and this outcome was associated with [18] Karlsson F, Tremaroli V, Nielsen J, Backhed F. Assessing the human gut microbiota – fi in metabolic diseases. Diabetes 2013;62:3341 9. signi cant metabolic improvements. In contrast, the administration of [19] Delzenne NM, Neyrinck AM, Backhed F, Cani PD. Targeting gut microbiota in trans-resveratrol also improved some metabolic derangements, but obesity: effects of prebiotics and probiotics. Nat Rev Endocrinol 2011;7:639–46. this effect was not mediated by a modulatory effect on gut microbiota. [20] Xu X, Xu P, Ma C, Tang J, Zhang X. Gut microbiota, host health, and polysaccharides. Biotechnol Adv 2013;31:318–37. From the present data, it could be stated that quercetin supplemen- [21] Delzenne NM, Neyrinck AM, Cani PD. Modulation of the gut microbiota by tation robustly reshapes gut microbiota dysbiosis associated to a nutrients with prebiotic properties: consequences for host health in the Westernised dietary intake, which presumably might contribute to context of obesity and metabolic syndrome. Microb Cell Fact 2011;10(Suppl. beneficial metabolic effects. Besides, from the interaction between 1):S10. [22] Ali F, Ismail A, Kersten S. Molecular mechanisms underlying the potential polyphenols and bacteria, changes at the intestinal level might be antiobesity-related diseases effect of cocoa polyphenols. Mol Nutr Food Res 2014; postulated in the host (local inflammatory tone and intestinal mucosal 58:33–48. barrier integrity), principally with trans-resveratrol administration. [23] de la Iglesia R, Milagro FI, Campion J, Boque N, Martinez JA. Healthy properties of proanthocyanidins. Biofactors 2010;36:159–68. Supplementary data to this article can be found online at http://dx. [24] de la Garza AL, Milagro FI, Boque N, Campion J, Martinez JA. Natural inhibitors of doi.org/10.1016/j.jnutbio.2015.01.002. pancreatic lipase as new players in obesity treatment. Planta Med 2011;77: 773–85. [25] Babu PV, Liu D, Gilbert ER. Recent advances in understanding the anti-diabetic Acknowledgments actions of dietary flavonoids. J Nutr Biochem 2013;24:1777–89. [26] Etxeberria U, de la Garza AL, Campion J, Martinez JA, Milagro FI. Antidiabetic effects of natural plant extracts via inhibition of carbohydrate hydrolysis enzymes This study was supported by grants from the Ministerio de with emphasis on pancreatic alpha amylase. Expert Opin Ther Targets 2012;16: Economía y Competitividad (AGL2011-27406-ALI), Instituto de 269–97. Salud Carlos III (CIBERobn), Government of the Basque Country (IT- [27] Zhang M, Swarts SG, Yin L, Liu C, Tian Y, Cao Y, et al. Antioxidant properties of – 572-13) and University of the Basque Country (UPV/EHU) (ELDUNA- quercetin. Adv Exp Med Biol 2011;701:283 9. [28] Rivera L, Moron R, Sanchez M, Zarzuelo A, Galisteo M. Quercetin ameliorates NOTEK UFI11/32). The authors wish to acknowledge Línea Especial metabolic syndrome and improves the inflammatory status in obese Zucker rats. about Nutrition, Obesity and Health (University of Navarra LE/97, Obesity (Silver Spring) 2008;16:2081–7. Spain) for the financial support and the Department of Education, [29] Poulsen MM, Jorgensen JO, Jessen N, Richelsen B, Pedersen SB. Resveratrol in metabolic health: an overview of the current evidence and perspectives. Ann N Y Language Policy and Culture from the Government of the Basque Acad Sci 2013;1290:74–82. Country for the predoctoral grant given to Usune Etxeberria. [30] Henagan TM, Lenard NR, Gettys TW, Stewart LK. Dietary quercetin supplemen- tation in mice increases skeletal muscle PGC1alpha expression, improves mitochondrial function and attenuates insulin resistance in a time-specific References manner. PLoS One 2014;9:e89365. [31] Liu K, Zhou R, Wang B, Mi MT. Effect of resveratrol on glucose control and insulin [1] Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al. National, sensitivity: a meta-analysis of 11 randomized controlled trials. Am J Clin Nutr regional, and global trends in body-mass index since 1980: systematic analysis of 2014;99:1510–9. health examination surveys and epidemiological studies with 960 country-years [32] Del Rio D, Rodriguez-Mateos A, Spencer JP, Tognolini M, Borges G, Crozier A. and 9.1 million participants. Lancet 2011;377:557–67. Dietary (poly)phenolics in human health: structures, bioavailability, and evidence [2] Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters of protective effects against chronic diseases. Antioxid Redox Signal 2013;18: gut microbial ecology. Proc Natl Acad Sci U S A 2005;102:11070–5. 1818–92. 660 U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660

[33] Cardona F, Andres-Lacueva C, Tulipani S, Tinahones FJ, Queipo-Ortuno MI. Benefits [61] Turnbaugh PJ, Backhed F, Fulton L, Gordon JI. Diet-induced obesity is linked to of polyphenols on gut microbiota and implications in human health. J Nutr marked but reversible alterations in the mouse distal gut microbiome. Cell Host Biochem 2013;24:1415–22. Microbe 2008;3:213–23. [34] Walle T, Hsieh F, DeLegge MH, Oatis Jr JE, Walle UK. High absorption but very low [62] Fleissner CK, Huebel N, Abd El-Bary MM, Loh G, Klaus S, Blaut M. Absence of bioavailability of oral resveratrol in humans. Drug Metab Dispos 2004;32: intestinal microbiota does not protect mice from diet-induced obesity. Br J Nutr 1377–82. 2010;104:919–29. [35] Wang D, Hang T, Wu C, Liu W. Identification of the major metabolites of [63] Blaut M, Klaus S. Intestinal microbiota and obesity. In: Joost H-G, editor. Appetite resveratrol in rat urine by HPLC–MS/MS. J Chromatogr B Analyt Technol Biomed control. Springer Berlin Heidelberg; 2012. p. 251–73. Life Sci 2005;829:97–106. [64] Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The effect of diet on [36] Burkon A, Somoza V. Quantification of free and protein-bound trans-resveratrol the human gut microbiome: a metagenomic analysis in humanized gnotobiotic metabolites and identification of trans-resveratrol-C/O-conjugated diglucuro- mice. Sci Transl Med 2009;1:6ra14. nides — two novel resveratrol metabolites in human plasma. Mol Nutr Food Res [65] Verdam FJ, Fuentes S, de Jonge C, Zoetendal EG, Erbil R, Greve JW, et al. Human 2008;52:549–57. intestinal microbiota composition is associated with local and systemic [37] Bode LM, Bunzel D, Huch M, Cho GS, Ruhland D, Bunzel M, et al. In vivo and in vitro inflammation in obesity. Obesity (Silver Spring) 2013;21:E607–15. metabolism of trans-resveratrol by human gut microbiota. Am J Clin Nutr 2013; [66] Neyrinck AM, Possemiers S, Verstraete W, De Backer F, Cani PD, Delzenne NM. 97:295–309. Dietary modulation of clostridial cluster XIVa gut bacteria (Roseburia spp.) by [38] Etxeberria U, Fernandez-Quintela A, Milagro FI, Aguirre L, Martinez JA, Portillo MP. chitin-glucan fiber improves host metabolic alterations induced by high-fat diet in Impact of polyphenols and polyphenol-rich dietary sources on gut microbiota mice. J Nutr Biochem 2012;23:51–9. composition. J Agric Food Chem 2013;61:9517–33. [67] Puupponen-Pimia R, Nohynek L, Hartmann-Schmidlin S, Kahkonen M, Heinonen [39] Macarulla MT, Alberdi G, Gomez S, Tueros I, Bald C, Rodriguez VM, et al. Effects of M, Maatta-Riihinen K, et al. Berry phenolics selectively inhibit the growth of different doses of resveratrol on body fat and serum parameters in rats fed a intestinal pathogens. J Appl Microbiol 2005;98:991–1000. hypercaloric diet. J Physiol Biochem 2009;65:369–76. [68] Bervoets L, Van Hoorenbeeck K, Kortleven I, Van Noten C, Hens N, Vael C, et al. [40] Gomez-Zorita S, Fernandez-Quintela A, Macarulla MT, Aguirre L, Hijona E, Bujanda Differences in gut microbiota composition between obese and lean children: a L, et al. Resveratrol attenuates steatosis in obese Zucker rats by decreasing fatty cross-sectional study. Gut Pathog 2013;5:10. http://dx.doi.org/10.1186/1757- acid availability and reducing oxidative stress. Br J Nutr 2012;107:202–10. 4749-5-10. [41] Alberdi G, Rodriguez VM, Miranda J, Macarulla MT, Arias N, Andres-Lacueva C, [69] Selma MV, Espin JC, Tomas-Barberan FA. Interaction between phenolics and gut et al. Changes in white adipose tissue metabolism induced by resveratrol in rats. microbiota: role in human health. J Agric Food Chem 2009;57:6485–501. Nutr Metab (Lond) 2011;8:29. [70] Tremaroli V, Backhed F. Functional interactions between the gut microbiota and [42] Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. host metabolism. Nature 2012;489:242–9. Homeostasis model assessment: insulin resistance and beta-cell function from [71] Hooper LV, Wong MH, Thelin A, Hansson L, Falk PG, Gordon JI. Molecular analysis of fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28: commensal host-microbial relationships in the intestine. Science 2001;291:881–4. 412–9. [72] Teixeira TF, Collado MC, Ferreira CL, Bressan J, Peluzio Mdo C. Potential [43] Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Muller WE, Wetter T, et al. Using mechanisms for the emerging link between obesity and increased intestinal the miraEST assembler for reliable and automated mRNA transcript assembly and permeability. Nutr Res 2012;32:637–47. SNP detection in sequenced ESTs. Genome Res 2004;14:1147–59. [73] Yamauchi K, Kamisoyama H, Isshiki Y. Effects of fasting and refeeding on [44] Huson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC. Integrative analysis of structures of the intestinal villi and epithelial cells in White Leghorn hens. Br Poult environmental sequences using MEGAN4. Genome Res 2011;21:1552–60. Sci 1996;37:909–21. [45] Hildebrandt MA, Hoffmann C, Sherrill-Mix SA, Keilbaugh SA, Hamady M, Chen YY, [74] Erridge C, Duncan SH, Bereswill S, Heimesaat MM. The induction of colitis and et al. High-fat diet determines the composition of the murine gut microbiome ileitis in mice is associated with marked increases in intestinal concentrations of independently of obesity. Gastroenterology 2009;137:1716–24. stimulants of TLRs 2, 4, and 5. PLoS One 2010;5:e9125. [46] Jeon BT, Jeong EA, Shin HJ, Lee Y, Lee DH, Kim HJ, et al. Resveratrol attenuates [75] Buchholz BM, Bauer AJ. Membrane TLR signaling mechanisms in the gastrointes- obesity-associated peripheral and central inflammation and improves memory tinal tract during sepsis. Neurogastroenterol Motil 2010;22:232–45. deficit in mice fed a high-fat diet. Diabetes 2012;61:1444–54. [76] Teran-Ventura E, Aguilera M, Vergara P, Martinez V. Specific changes of gut [47] Timmers S, Hesselink MK, Schrauwen P. Therapeutic potential of resveratrol in commensal microbiota and TLRs during indomethacin-induced acute intestinal obesity and type 2 diabetes: new avenues for health benefits? Ann N Y Acad Sci inflammation in rats. J Crohns Colitis 2014;8:1043–54. 2013;1290:83–9. [77] Takeda K, Kaisho T, Akira S. Toll-like receptors. Annu Rev Immunol 2003;21: [48] Boots AW, Haenen GR, Bast A. Health effects of quercetin: from antioxidant to 335–76. nutraceutical. Eur J Pharmacol 2008;585:325–37. [78] Tanaka K. Expression of Toll-like receptors in the intestinal mucosa of patients with [49] Farghali H, Kutinova Canova N, Lekic N. Resveratrol and related compounds as inflammatory bowel disease. Expert Rev Gastroenterol Hepatol 2008;2:193–6. antioxidants with an allosteric mechanism of action in epigenetic drug targets. [79] Ewaschuk JB, Backer JL, Churchill TA, Obermeier F, Krause DO, Madsen KL. Surface Physiol Res 2013;62:1–13. expression of Toll-like receptor 9 is upregulated on intestinal epithelial cells in [50] van Duynhoven J, Vaughan EE, van Dorsten F, Gomez-Roldan V, de Vos R, Vervoort response to pathogenic bacterial DNA. Infect Immun 2007;75:2572–9. J, et al. Interactions of black tea polyphenols with human gut microbiota: [80] Rakoff-Nahoum S, Paglino J, Eslami-Varzaneh F, Edberg S, Medzhitov R. implications for gut and cardiovascular health. Am J Clin Nutr 2013;98: Recognition of commensal microflora by toll-like receptors is required for 1631S–41S. intestinal homeostasis. Cell 2004;118:229–41. [51] Queipo-Ortuno MI, Boto-Ordonez M, Murri M, Gomez-Zumaquero JM, Clemente- [81] Yu LC, Wang JT, Wei SC, Ni YH. Host-microbial interactions and regulation of Postigo M, Estruch R, et al. Influence of red wine polyphenols and ethanol on the intestinal epithelial barrier function: from physiology to pathology. World J gut microbiota ecology and biochemical biomarkers. Am J Clin Nutr 2012;95: Gastroenterol Pathophysiol 2012;3:27–43. 1323–34. [82] Yoo JLML, Matthews JB. Cytokines in restitution. Baltimore, Md: Karger; 2002. [52] Sanchez-Patan F, Cueva C, Monagas M, Walton GE, Gibson GR, Quintanilla-Lopez [83] Lopetuso LR, Chowdhry S, Pizarro TT. Opposing functions of classic and novel IL-1 JE, et al. In vitro fermentation of a red wine extract by human gut microbiota: family members in gut health and disease. Front Immunol 2013;4:181. changes in microbial groups and formation of phenolic metabolites. J Agric Food [84] Reuter BK, Pizarro TT. Commentary: the role of the IL-18 system and other Chem 2012;60:2136–47. members of the IL-1R/TLR superfamily in innate mucosal immunity and the [53] Arias N, Macarulla MT, Aguirre L, Martinez-Castano MG, Portillo MP. Quercetin pathogenesis of inflammatory bowel disease: friend or foe? Eur J Immunol 2004; can reduce insulin resistance without decreasing adipose tissue and skeletal 34:2347–55. muscle fat accumulation. Genes Nutr 2014;9:361. http://dx.doi.org/10.1007/ [85] Dupaul-Chicoine J, Yeretssian G, Doiron K, Bergstrom KS, McIntire CR, LeBlanc PM, s12263-013-0361-7. et al. Control of intestinal homeostasis, colitis, and colitis-associated colorectal [54] Gomez-Zorita S, Fernandez-Quintela A, Lasa A, Hijona E, Bujanda L, Portillo MP. cancer by the inflammatory caspases. Immunity 2010;32:367–78. Effects of resveratrol on obesity-related inflammation markers in adipose tissue of [86] Siegmund B. Interleukin-18 in intestinal inflammation: friend and foe? Immunity genetically obese rats. Nutrition 2013;29:1374–80. 2010;32:300–2. [55] Qiao Y, Sun J, Xia S, Tang X, Shi Y, Le G. Effects of resveratrol on gut microbiota and [87] Fasano A, Shea-Donohue T. Mechanisms of disease: the role of intestinal barrier fat storage in a mouse model with high-fat-induced obesity. Food Funct 2014;5: function in the pathogenesis of gastrointestinal autoimmune diseases. Nat Clin 1241–9. Pract Gastroenterol Hepatol 2005;2:416–22. [56] Blaut M, Clavel T. Metabolic diversity of the intestinal microbiota: implications for [88] Hsu D, Fukata M, Hernandez YG, Sotolongo JP, Goo T, Maki J, et al. Toll-like health and disease. J Nutr 2007;137:751S–5S. receptor 4 differentially regulates epidermal growth factor-related growth factors [57] Nicholson JK, Wilson ID. Opinion: understanding 'global' systems biology: in response to intestinal mucosal injury. Lab Invest 2010;90:1295–305. metabonomics and the continuum of metabolism. Nat Rev Drug Discov 2003;2: [89] Cario E, Gerken G, Podolsky DK. Toll-like receptor 2 enhances ZO-1-associated 668–76. intestinal epithelial barrier integrity via protein kinase C. Gastroenterology 2004; [58] Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, et al. Host–gut 127:224–38. microbiota metabolic interactions. Science 2012;336:1262–7. [90] Cario E, Gerken G, Podolsky DK. Toll-like receptor 2 controls mucosal [59] Duca FA, Sakar Y, Lepage P, Devime F, Langelier B, Dore J, et al. Replication of inflammation by regulating epithelial barrier function. Gastroenterology 2007; obesity and associated signaling pathways through transfer of microbiota from 132:1359–74. obese-prone rats. Diabetes 2014;63:1624–36. [91] Campbell JM, Fahey Jr GC, Wolf BW. Selected indigestible oligosaccharides affect [60] Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A large bowel mass, cecal and fecal short-chain fatty acids, pH and microflora in rats. core gut microbiome in obese and lean twins. Nature 2009;457:480–4. J Nutr 1997;127:130–6.

Supplementary data CHAPTER 5

139

Supplementary data Chapter 5

Table A.1. Frequencies and percentage of change (%) of the relative abundance of bacterial groups at class level in HFS diet-fed control rats and rats supplemented with quercetin for 6 weeks.

Unadjust Ribosomal Database HFS Q30 Adjusted p Change Bacterial group ed p Project classification (n=5) (n=6) value (%) value Firmicutes; Erysipelotrichi 42.69 ± 6.45 6.87 ± 2.20 0.006 0.018 -83.9* Erysipelotrichi

Taxonomic breakdown of intestinal bacterial V4- V6 regions obtained by pyrosequencing indicated as percentage of the identified reads. All results are expressed as the mean ± SEM. Statistical analyses were performed using Kruskal- Wallis followed by Mann Whitney U test and p values were corrected with the Bonferroni test. *p< 0.05 after correcting for Bonferroni multiple comparison test.

Table B.1. Frequencies and percentage of change (%) of the relative abundance of bacterial groups at genus level in HFS diet-fed control rats and rats supplemented with quercetin for 6 weeks Ribosomal Database Project Bacterial HFS Q30 Unadjusted Adjusted p Change classification group (n=5) (n=6) p value value (%) Firmicutes; Bacilli; Bacillales; Bacillus 0.50 ± 0.00 0.13 ± 0.08 0.013 0.038 -74.3* Bacillaceae; Bacillus

Taxonomic breakdown of intestinal bacterial V4- V6 regions obtained by pyrosequencing indicated as percentage of the identified reads. All results are expressed as the mean ± SEM. Statistical analyses were performed using Kruskal- Wallis followed by Mann Whitney U test and p values were corrected with the Bonferroni test. *p< 0.05 after correcting for Bonferroni multiple comparison test.

Table B.2. Frequencies and percentage of change (%) of the relative abundance of bacterial groups at genus level in HFS-diet fed control rats and rats supplemented with trans-resveratrol for 6 weeks.

Ribosomal Database Project HFS RSV15 Unadjuste Adjusted Change Bacterial group classification (n=5) (n=6) d p value p value (%) Bacteroidetes; Bacteroidia; Parabacteroides 0.26 ± 0.06 0.06 ± 0.01 0.006 0.018 -76.3* Bacteroidales; Porphyromonadaceae; Parabacteroides

Taxonomic breakdown of intestinal bacterial V4- V6 regions obtained by pyrosequencing indicated as percentage of the identified reads. All results are expressed as the mean ± SEM. Statistical analyses were performed using Kruskal- Wallis followed by Mann Whitney U test and p values were corrected by Bonferroni test. *p< 0.05 after correcting for Bonferroni multiple comparison test.

141 Supplementary data Chapter 5

Table C.1. Frequencies and percentage of change (%) of the relative abundance of bacterial species in HFS diet-fed control rats and rats supplemented with quercetin for 6 weeks.

HFS Q30 Unadjusted p Adjusted Change Phyla Family Genus Bacterial group (n=5) (n=6) value p value (%) Firmicutes Oscillospiraceae Oscillibacter Oscillibacter valericigenes 0.20 ± 0.05 0.43 ± 0.07 0.045 0.128 99.2 Sjm18-20 Firmicutes Ruminococcaceae Ruminococcus Ruminococcus sp. YE281 0.19 ± 0.05 0.52 ± 0.09 0.018 0.052 177.4 Firmicutes Clostridiaceae Clostridium Clostridium 0.05 ± 0.01 0.18 ± 0.07 0.018 0.052 250.3

methylpentosum Firmicutes Peptococcaceae Dehalobacter Dehalobacter sp. MS 0.25 ± 0.11 0.50 ± 0.00 0.037 0.106 104.5

Firmicutes Clostridiaceae Clostridium Clostridium sp. FG4 0.24 ± 0.11 0.50 ± 0.00 0.037 0.106 106.4

Tenericutes Acholeplasmataceae Acholeplasma Acholeplasma modicum 0.22 ± 0.12 0.50 ± 0.00 0.037 0.106 128.6

Bacteroidetes Bacteroidaceae Bacteroides Bacteroides dorei 0.26 ± 0.08 1.02 ± 0.27 0.018 0.052 295.0

Bacteroidetes Bacteroidaceae Bacteroides Bacteroides sartorii 0.50 ± 0.00 0.19 ± 0.10 0.034 0.100 -62.6

Bacteroidetes Bacteroidaceae Bacteroides Bacteroides sp. dnLKV3 0.50 ± 0.00 0.19 ± 0.10 0.034 0.100 -62.4

Taxonomic breakdown of intestinal bacterial V4- V6 regions obtained by pyrosequencing indicated as percentage of the identified reads. All results are expressed as the mean ± SEM. Statistical analyses were performed using Kruskal-Wallis followed by Mann Whitney U test and p values were corrected by Bonferroni test.

142 Supplementary data Chapter 5

Table C.2. Taxa frequencies and percentage of change (%) of the relative abundance of bacterial species in HFS diet-fed control rats and rats supplemented with trans-resveratrol for 6 weeks

HFS RSV15 Adjusted Change Phyla Family Genus Bacterial group Unadjusted p value (n=5) (n=6) p value (%) Firmicutes Erysipelotrichaceae Eubacterium 19.20 ± 4.10 32.45 ± 4.20 0.045 0.128 69.0 cylindroides Firmicutes Ruminococcaceae Ruminococcus Ruminococcus sp. 0.51 ± 0.18 0.13 ± 0.05 0.028 0.083 -74.6 MLG080-3 Firmicutes Ruminococcaceae Ruminococcus Ruminococcus sp. 0.19 ± 0.05 0.43 ± 0.09 0.018 0.052 130.8 YE281 Firmicutes Clostridiaceae Clostridium Clostridium 0.41 ± 0.10 0.28 ± 0.10 0.040 0.115 -89.8 polysaccharolyticum

Firmicutes Clostridiaceae Clostridium Clostridium sp. FG4 0.24 ± 0.11 0.50 ± 0.00 0.037 0.106 106.0

Tenericutes Acholeplasmataceae Acholeplasma Acholeplasma 0.22 ± 0.12 0.50 ± 0.00 0.037 0.106 128.2 modicum Firmicutes Clostridiaceae Clostridium Clostridium sp. ID5 0.50 ± 0.00 0.20 ± 0.10 0.034 0.100 -60.0

Bacteroidetes Bacteroidaceae Bacteroides Bacteroides vulgatus 0.29 ± 0.09 1.16 ± 0.39 0.018 0.052 296.7

Taxonomic breakdown of intestinal bacterial V4- V6 regions obtained by pyrosequencing indicated as percentage of the identified reads. All results are expressed as the mean ± SEM. Statistical analyses were performed using Kruskal-Wallis followed by Mann Whitney U test and p values were corrected by Bonferroni test.

143 Supplementary data Chapter 5

Table C.3. Taxa frequencies and percentage of change (%) of the relative abundance of bacterial species in HFS diet-fed control rats and rats supplemented with a combination of trans-resveratrol and quercetin for 6 weeks. HFS RSV15+ Q30 Phyla Family Genus Bacterial group Unadjusted p value Adjusted p value Change (%) (n=5) (n=6) Firmicutes Ruminococcaceae Ruminococcus Ruminococcus sp. 0.19 ± 0.05 0.55 ± 0.18 0.045 0.128 195.5 YE281 Firmicutes Lachnospiraceae Blautia Blautia sp. canine 0.23 ± 0.05 0.50 ± 0.08 0.018 0.052 118.1 oral taxon 143 Firmicutes Clostridiaceae Clostridium Clostridium sp. 0.50 ± 0.00 0.22 ± 0.09 0.034 0.100 -56.9 ID4 Taxonomic breakdown of intestinal bacterial V4- V6 regions obtained by pyrosequencing indicated as percentage of the identified reads. All results are expressed as the mean ± SEM. Statistical analyses were performed using Kruskal-Wallis followed by Mann Whitney U test and p values were corrected by Bonferroni test.

144 Supplementary data Chapter 5

Table D.1. Taxa frequencies and percentage of change (%) of the relative abundance of bacterial species in HFS diet-fed control rats and rats supplemented with a combination of trans-resveratrol and quercetin for 6 weeks.

HFS RSV Q RSV+Q SCFA levels of faeces (mg/g) (n=5) (n=6) (n=6) (n=6) Acetate 1.68 ± 0.39 1.60 ± 0.44 2.00 ± 0.24 1.13 ± 0.12 Propionate 0.41 ± 0.11 0.28 ± 0.09 0.51 ± 0.07 0.21 ± 0.04 Butyrate 0.38 ± 0.11 0.28 ± 0.10 0.58 ± 0.11 0.33 ± 0.11 All results are expressed as the mean ± SEM. Statistical analyses were performed using One- Way ANOVA followed by Dunnett post-hoc test. HFS, high-fat sucrose diet fed control rats; RSV, supplemented with 15 mg/kg BW/day of trans-resveratrol; Q, supplemented with 30 mg/kg BW/day of quercetin; RSV + Q, supplemented with a combination of trans-resveratrol and quercetin at the same doses.

145

CHAPTER 6

Metabolic faecal fingerprinting of trans‐resveratrol and quercetin following a high‐fat sucrose dietary model using liquid chromatography coupled to high‐resolution mass spectrometry

Etxeberria U. 1,2 , Arias N. 3,4 , Boqué N. 5 , Romo‐Hualde A. 2,4 , Macarulla M.T. 3,4 , Portillo M.P. 3,4 , Milagro F.I. 1,2,4 and Martínez J.A. *,1,2,4

1Department of Nutrition, Food Science and Physiology, University of Navarra. C/Irunlarrea 1, 31008 Pamplona, Spain

2Centre for Nutrition Research, University of Navarra. C/Irunlarrea 1, 31008 Pamplona, Spain

3Nutrition and Obesity group, Department of Nutrition and Food Sciences, Faculty of Pharmacy, University of the Basque Country (UPV/EHU), Paseo de la Universidad 7, 01006 Vitoria, Spain

4CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain

eNutrition and Health Research Group. Technological Center of Nutrition and Health (CTNS), TECNIO, CEIC S. Avinguda Universitat 1, 43204 Reus, Spain

Food Funct, 2015

doi: 10.1039/c5fo00473j

Impact Factor (2014): 2.791

17/123 in Food Science & Technology (Q1)

147

Etxeberria U, Arias N, Boqué N, Romo‐Hualde A, Macarulla MT, Portillo MP, Milagro FI, Martínez JA. Metabolic faecal fingerprinting of trans‐resveratrol and quercetin following a high‐fat sucrose dietary model using liquid chromatography coupled to high‐ resolution mass spectrometry. Food & Function, 2015, (8):2409-2864. http://doi.org/10.1039/c5fo00473j

Supplementary data CHAPTER 6

159

CHAPTER 7

Trans‐resveratrol induces a potential anti‐lipogenic effect in lipopolysaccharide‐stimulated enterocytes

Etxeberria U. 1, Castilla‐Madrigal R. 1 , Lostao P. 1,3 , Martínez J.A. 1,2,3,* , Milagro F.I. 1,2

1Department of Nutrition, Food Science and Physiology, Centre for Nutrition Research, University of Navarra. C/Irunlarrea 1, 31008 Pamplona, Spain

2CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain

3IdiSNA, Navarra’s Health Research Institute. Pamplona, Spain

Accepted in Cell Mol Biol

179

Accepted in Cell Mol Biol

Trans-resveratrol induces a potential anti-lipogenic effect in lipopolysaccharide-stimulated enterocytes

Etxeberria U1, Castilla-Madrigal R1, Lostao MP1,3, Martínez JA1,2,3,*, Milagro FI1,2

1Department of Nutrition, Food Science and Physiology, Centre for Nutrition Research, University of Navarra.

C/Irunlarrea 1, 31008 Pamplona, Spain

2CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain

3IdiSNA, Navarra’s Health Research Institute. Pamplona, Spain

*Corresponding author: Professor J. Alfredo Martínez

Department of Nutrition, Food Science and Physiology, University of Navarra. C/Irunlarrea 1, 31 008

Pamplona, Spain. Tel.: +34 948425600 (Ext. 80 6424); Fax: 00 34 948 425 649

Email address: [email protected]

181 Accepted in Cell Mol Biol

Abstract

A DNA microarray analysis was conducted in Caco-2 cells to analyse the protective effects of trans- resveratrol on enterocyte physiology and metabolism in pro-inflammatory conditions. Cells were pre-treated with 50 μΜ of trans-resveratrol and, subsequently, lipopolysaccharide (LPS) was added for 48 h. The microarray analysis revealed 121 genes differentially expressed between resveratrol-treated and non- treated cells (B> 0, is the odd that the gene is differentially expressed). Inhibitor of DNA binding 1 (ID1), histidine-rich glycoprotein (HRG), NADPH oxidase (NOX1) and sprouty homolog 1 (SPRY), were upregulated by LPS treatment, but significantly blocked by trans-resveratrol pre-treatment (padj< 0.05, after adjusting for Benjamini-Hocheberg procedure). Moreover, genes implicated in synthesis of lipids (z-score= -1.195) and concentration of cholesterol (z-score= -0.109), were markedly downregulated by trans-resveratrol. Other genes involved in fat turnover, but also in cell death and survival function, such as transcription factors Krüppel-like factor 5 (KLF5) and amphiregulin (AREG), were also significantly inhibited by trans-resveratrol pre-treatment. RT-qPCR-data confirmed the microarray results. Special mention deserves acyl-CoA synthetase long-chain family member 3 (ACSL3) and endothelial lipase (LIPG), which were downregulated by this stilbene and have been previously associated with fatty acid synthesis and obesity in other tissues. This study envisages that trans-resveratrol might exert an important anti-lipogenic effect at intestinal level under pro-inflammatory conditions, which has not been previously described.

Keywords: gut, resveratrol, in vitro, lipid metabolism, inflammation, enterocytes,

Running title: Gene expression modulatory capacity of trans-resveratrol in enterocytes

182 Accepted in Cell Mol Biol

Background

Our intestine is the first defence barrier located between the host and the luminal environment (Peterson and Artis, 2014). The intestinal epithelium controls the passage of nutrients and fluids, but also protects the organism from the permeation of external antigens into the intestinal mucosa and circulatory system (Romier et al., 2009). Scientific evidence has demonstrated the implication of intestinal barrier integrity impairment in the development of abnormal inflammatory response (Azuma et al., 2013). Intestinal inflammation is a continuous and protective process that aims to maintain gut integrity and normal functionality (Martin and Wallace, 2006). For this purpose, a crosstalk between different cell types from the gut is required, which results in the regulation of the secretion of a range of cytokines and growth factors. However, when a dysregulation of one of these components happens, an inappropriate inflammatory stimulus could lead to several diseases such as inflammatory bowel disease, including ulcerative colitis and Crohn’s disease (Van De Walle et al., 2010), celiac disease, food allergies, inflammatory bowel syndrome and metabolic diseases (Brown et al., 2012). Moreover, although controversial results have been reported about the occurrence of alterations in gut barrier integrity in obese animals (Kless et al., 2015), emerging data corroborate the presence of an intestinal inflammatory condition (de La Serre et al., 2010). Importantly, the presence of a chronic low-grade inflammatory response that leads to metabolic dysfunctions is well established in obesity (Bondia-Pons et al., 2012) and it has been reported that obesity-related comorbidities, such as type 2 diabetes and atherosclerosis, are usually accompanied by higher circulating levels of pro- inflammatory cytokines (Laakso, 2010).

The administration of natural compounds (i.e. polyphenols) to fight against inflammation-related metabolic diseases is under research (Carpene et al., 2015). In this context, trans-resveratrol (trans-3, 5, 4’-trihydroxystilbene), is a stilbene that has been extensively studied for its antioxidant, anti- adipogenic and anti-lipogenic properties (Aguirre et al., 2014). In addition, beneficial properties of resveratrol on cardiovascular system have been widely studied (Riccioni et al., 2015). Accordingly, within the mechanisms involved in the anti-atherogenic effects of resveratrol, modulation of lipid metabolism has been reported (Ramprasath and Jones, 2010). Besides, the anti-inflammatory role of the stilbene seems to be implicated in the protection against the development of cardiovascular risk factors (Ramprasath and Jones, 2010) and the action of this molecule against acute inflammation at intestinal level has been demonstrated (Bereswill et al., 2010).

Caco-2 cells are a human intestinal epithelial cell model that has been previously used to investigate the intestinal absorption and metabolism of trans-resveratrol (Kaldas et al., 2003) and the impact of the stilbene on intestinal barrier function (Carrasco-Pozo et al., 2013). Lipopolysaccharide (LPS), a Gram-negative bacterial outer membrane constituent, is known to interact with intestinal epithelial cells and to induce stimulation of transcription and translation of pro-inflammatory mediators (Lu et al., 2008). In this sense, high LPS levels are considered to be an important factor in the pathophysiology of intestinal inflammatory diseases, has been postulated as one of the leading causes of obesity (Boutagy et al., 2015) and has been associated with increased risk of atherosclerosis in humans (Pussinen et al., 2007; Martinez et al., 2013).

183 Accepted in Cell Mol Biol

Thus, induction of inflammation in enterocytes by LPS administration is considered an appropriate in vitro model to mimic intestinal inflammation (Wang et al., 2015).

Accordingly, this investigation sought to analyse the molecular functions and pathways that might be affected by trans-resveratrol in LPS-treated enterocytes, a model that mimics the low-grade inflammatory condition usually present in metabolic diseases, such as obesity and atherosclerosis (Boutagy et al., 2015).

Material and methods

Cell culture and treatment

Caco-2 cells were maintained in an incubator set at 37°C and 5% carbon dioxide and 90% of relative humidity. The cells were cultured in Dulbecco’s modified Eagle’s medium with GlutaMax (DMEM, Gibco, Rockville, MD, USA) containing 10% fetal bovine serum (FBS, Gibco), 1% of non-essential amino acids (NEAA, Lonza, Basel, Switzerland), 1% penicillin (10000 U/mL)-streptomicyn (10000 μg/mL) (Gibco) and 1% amphoterizin B (250 μg/mL, Gibco). The culture medium was changed every 2 days. Once cells reached 80% confluence, confirmed by microscopic observance, they were dissociated with 0.05% trypsin-EDTA solution and subcultured on a 75 cm2 flasks at a density of 250000 cells per cm2. The cells were seeded in 6-well cell culture plate at 30,000 cells per cm2. Culture medium was replaced every 2 days until the day of the experiment. Experiments were conducted 15-19 days post-seeding.

For the experiments, cells were treated with standard LPS from E. coli K12 strain- TLR4 ligand (InvivoGen, San Diego, CA, USA). Concentrations of LPS (1 μg/mL) and time of exposure (48h) were established based on previous studies (Cianciulli et al., 2012). Before LPS stimulation, some samples were pre-treated with 50 μM of trans-resveratrol, dissolved in ethanol, kindly provided by Prof. María Puy Portillo (Nutrition and Obesity group, University of the Basque Country, Vitoria, Spain). This dose was reported to be a realistic polyphenol concentration commonly found in the gut following the intake of 500 mg of polyphenols (Scalbert and Williamson, 2000). Upon 1h of incubation at 37°C, cell cultures were stimulated with endotoxin as previously mentioned. Untreated cells were used as controls.

RNA isolation and microarray analysis

Total RNA was extracted from Caco-2 cells using TRIzol® reagent according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA, USA). As a last step of the extraction procedure, the RNA was purified with the RNeasy Mini-kit (Qiagen, Hilden, Germany). Before cDNA synthesis, RNA integrity from each sample was confirmed by using Agilent RNA Nano LabChips (Agilent Technologies, Santa Clara, CA, USA).

The sense cDNA was prepared from 300 ng of total RNA using the Ambion® WT Expression Kit (Thermo Fisher Scientific, Waltham, MA, USA). The sense strand cDNA was then fragmented and biotinylated with the Affymetrix GeneChip® WT Terminal Labeling Kit (PN 900671). Labeled sense cDNA was hybridized to the Affymetrix Human Gene 2.0 ST microarray according to the manufacturer protocols and using

184 Accepted in Cell Mol Biol

GeneChip® Hybridization, Wash and Stain Kit. Genechips were scanned with the Affymetrix GeneChip® Scanner 3000.

Microarray analysis

Both background correction and normalization were done using RMA (Robust Multichip Average) algorithm (Irizarry et al., 2003). After quality assessment, a filtering process was performed to eliminate low expression probe sets. Applying the criterion of an expression value greater than 16 in 3 samples for each experimental condition (CONTROL, LPS, LPS+RSV), 38959 probe sets were selected for statistical analysis. R and Bioconductor (Gentleman et al., 2006) were used for preprocessing and statistical analysis. LIMMA (Linear Models for Microarray Data) was used to find out the probe sets that showed significant differential expression between experimental conditions (Smyth, 2004). Adjusted p value was calculated with Benjamini-Hochberg procedure. Genes were selected as significant using criteria of B> 0. The Log Odds or B value is the odds or probability that the gene is differentially expressed, meaning that a gene with B=0 has a 50% chance to be differentially expressed.

Functional enrichment analysis of Gene Ontology (GO) categories was carried out using standard hypergeometric test (Draghici, 2003). The biological knowledge extraction was complemented through the use of Ingenuity Pathway Analysis (Ingenuity Systems, www.ingenuity.com), whose database includes manually curated and fully traceable data derived from literature sources.

Microarray data are accessible at the NCBI Gene Expression Omnibus website (http://www.ncbi.nlm.nih.gov/geo/) with the accession number of GSE73650.

Confirmatory real-time quantitative PCR

Some genes whose expression was affected by trans-resveratrol in the microarray analysis were validated using quantitative real-time polymerase chain reaction (RT-qPCR). For this purpose, RNA concentrations and quality were assessed by Nanodrop Spectrophotometer 1000 (Thermo Scientific, Wilminton, DE, USA). RNA (2 μg) were reverse-transcribed to cDNA using Moloney murine leukemia virus reverse transcriptase (MMLV, Invitrogen). Taqman® Universal Master Mix and the following pre-designed Taqman® assays-on- demand were used: KLF5 (Krüppel-like factor 5 (intestinal), Hs00156145_m1; ACSL3 (acyl-CoA synthetase long-chain family member -3), Hs00244853_m1; LIPG (lipase, endothelial), Hs00195812_m1; AREG (amphiregulin), Hs00950669_m1; NOX1 (NADPH oxidase 1), Hs01071088_m1; GAPDH (glyceraldehyde-3- phosphate dehydrogenase), Hs02758991_g1 (Applied Biosystems, Foster City, CA, USA). Amplification and detection of specific products were conducted using ABI PRISM 7000 HT Sequence Detection System (Applied Biosystems). All samples were analysed in duplicate. The relative expression of each gene was calculated by the 2-∆∆Ct method (Livak and Schmittgen, 2001).

Statistical analyses

Results are expressed as the average mean ± standard error of the mean (SEM). Statistical significance between experimental groups was assessed by Student’s t test when results were validated. A probability of

185 Accepted in Cell Mol Biol p< 0.05 was set up for determining statistically significant differences. SPSS 15.0 for Windows (SPSS, Chicago, IL, USA) was used for statistical analyses.

Results

Differential gene expression profile in cells treated with trans-resveratrol

When comparing the gene expression profile of Caco-2 cells that were treated with LPS and those that were previously exposed to trans-resveratrol (50 μΜ), a total of 121 genes showed a B> 0 in the microarray analysis: 63 genes were downregulated and 58 genes upregulated (Supplementary table 1). From these, there were four that were upregulated by LPS, but were significantly reversed (padj< 0.05) by trans- resveratrol pre-treatment (Figure 1): the inhibitor DNA binding 1, dominant helix-loop-helix protein

(ID1, log FC= -0.88 and padj< 0.01), histidine-rich glycoprotein (HRG, log FC= -1.24 and padj=0.01), NADPH oxidase 1 (NOX1, log FC= -1.94 and padj=0.01) and sprouty homolog 1, antagonist of FGF signalling (SPRY1, log FC= -0.57 and p=0.03).

Biologically relevant networks and pathways

Functional enrichment analysis with Ingenuity Pathway Analysis (IPA) software, detected altered (B> 0) molecular and cellular functions in the imported data set associated to cell death and survival; infectious diseases; lipid metabolism, small molecule biochemistry; lymphoid tissue structure and development, tissue morphology; cellular development, cellular growth and proliferation; DNA replication, recombination and repair. The genes involved in these pathways are listed in Table 1.

Networks analysed by the Ingenuity software describe the functional relationship between gene products based on known interactions reported in the literature. The most significant network that was affected by trans-resveratrol was cell death and survival, cellular assembly and organization, cellular function and maintenance (44 score). A second network was related to hereditary disorder, neurological disease and organ morphology (38 score). The third network was related to lipid metabolism, molecular transport, small molecule biochemistry (31 score) and, finally, the forth and the fifth networks (28 score each) were associated with cell cycle. Figure 2 represents the integrated network analysis and shows the relationship between genes involved in the first network related to cell death and survival, cellular assembly and organization, cellular function and maintenance and those related to the third network, lipid metabolism, molecular transport, small molecule biochemistry -related network.

Validation of the expression of genes implicated in lipid metabolism

Genes that were selected for validation by RT-qPCR were implicated in both, lipid metabolism but also in cell death and survival categories (Table 1). Genes of interest were ACLS3, LIPG, NOX1, KLF5 and AREG. All the genes selected for validation showed a B> 1. Due to the action of resveratrol as an anti-oxidant compound, NOX1 was selected based on its relevance in oxidative stress processes. ACSL3 and LIPG were chosen since they were genes belonging to the lipid metabolism pathway and were not implicated in other pathways.

186 Accepted in Cell Mol Biol

Finally, KLF5 and AREG were selected since they were implicated in both lipid metabolism and in the first important pathway altered by resveratrol (cell death and survival). The RT-qPCR findings were consistent with data of the microarray analysis (Figure 3).

In summary, the expression of key genes involved in synthesis of lipids (ACSL3, AREG and KLF5), cholesterol metabolism (LIPG) and control of reactive oxygen species (NOX1) was downregulated by trans-resveratrol in LPS-stimulated Caco-2 cells.

Discussion

The burden of chronic diseases related to inflammation and characterized by metabolic dysregulations is increasing (Rodriguez-Hernandez et al., 2013). In order to be able to prevent such disturbances or to find a treatment for these problems, it is essential to understand the pivotal cellular components and the specific tissues that participate in such metabolic impairments (Strable and Ntambi, 2010). In the present research, Caco-2 cells were selected since enterocytes have been described to be a target site for trans-resveratrol action (Kaldas et al., 2003). Gene expression analysis conducted in this in vitro study revealed some of the previously reported data on the anti-proliferative role and apoptosis-promoting effect of resveratrol (Vanamala et al., 2010). In this context, in the current study, molecular pathways involved in cell death and survival (apoptosis) were enhanced (z-score= 1.174), while pathways related to cellular development, cellular growth and proliferation (proliferation of tumor cell lines) were inhibited (z-score= -1.625). Moreover, resveratrol has been described to affect all aspects of DNA metabolism (Gatz and Wiesmuller, 2008). Accordingly, in our study, a suppressive action of trans-resveratrol in DNA replication, recombination and repair (metabolism of DNA) was detected (z-score= -1.996). This outcome might be produced through direct mechanisms, as for instance inhibition of genes related to tumorigenesis, but also through indirect mechanisms (Gatz and Wiesmuller, 2008). Trans-resveratrol significantly repressed the expression levels of LPS-induced ID1 and HRG genes, that particularly in the case of ID1, have been associated with tumorigenesis (Ma et al., 2013; Johnson et al., 2014). However, indirect mechanisms that involve the abrogation of endogenous reactive oxygen species (ROS) formation might also influence this molecular pathway, since the redox state of a cell has been reported to control the stability of genomic DNA (Gatz and Wiesmuller, 2008). In this context, ROS formation might be avoided when the amide adenine dinucleotide phosphate (NADPH) oxidase or NOX1 activity (Gatz and Wiesmuller, 2008) is suppressed since it is considered one of the most relevant enzymes of intracellular ROS generation (Fu et al., 2014). Accordingly, in the current study, NOX1 expression, significantly upregulated by LPS treatment was inhibited by trans-resveratrol in enterocytes. In this context, it has been previously reported that resveratrol treatment inhibited LPS-induced NOX1 expression and ROS generation in macrophages, which has been related to a suppression of LPS-induced foam cell formation (Park et al., 2009). Nevertheless, another mechanism previously reported for other antioxidant molecules (i.e. ascorbic acid) might be plausible (Carcamo et al., 2004). This mechanism is related to the capacity of resveratrol to inhibit the activation of nuclear factor kappa β (NF-κβ) (Cianciulli et al., 2012). NF-κβ is a transcription factor important in the regulation of immunity, inflammation, cell proliferation, cell transformation and tumour development. It has been demonstrated that ROS stress is a relevant stimuli that activates NF-κβ through the activation of inhibitors of kappa β kinase (IKK)

187 Accepted in Cell Mol Biol

(Trachootham et al., 2008). Since resveratrol has been reported to suppress nuclear translocation of p65 through the inhibition of IKK in LPS-stimulated Caco-2 cells, downregulation of NOX1 by the stilbene observed in our study might be avoiding the NF-κβ signalling pathway, favouring an anti-inflammatory and apoptosis promoting effect of the stilbene in addition to its antioxidant function.

The main finding of this research work is the potential action of trans-resveratrol on the lipid synthesis process occurring at intestinal level. Indeed, the general molecular pathway associated with lipid synthesis was found to be inhibited (z-score= - 1.195). As far as we know, this is the first study conducted in intestinal cells showing an anti-lipogenic effect of resveratrol.

The small intestine synthesizes triglycerides (TG) through two main processes, the monoacylglycerol (MAG) pathway, which occurs in enterocytes after feeding, and the glycerol-3-phosphate (G-3-P) pathway, which is the de novo pathway for triglyceride synthesis (Shi and Cheng, 2009). Under normal conditions, these processes have been reported to represent 20 to 80% of total TG levels in chylomicrons (Ho et al., 2002). In addition, in the absence of dietary fat, the contribution of the intestine to the total TG levels in plasma has been reported to be around 20% or more (Cenedella and Crouthamel, 1974), while, in vivo, it might supply up to 40% in fasting conditions (Ockner et al., 1969). Long-chain acyl-CoA synthetases (ACSL) are enzymes responsible for the activation process of fatty acids that precede their entrance to MAG or G-3-P pathway for TG synthesis.

Five members of ACSL family have been described but, in small intestine, the expression of two isoforms (ACSL3 and ACSL5), seems to predominate (Mansbach and Gorelick, 2007). ACSL3 is located in lipid droplets or endoplasmic reticulum, and it is supposed to play a key role in fatty acid uptake and lipid synthesis (Bu et al., 2009; Poppelreuther et al., 2012). In this trial, an inhibitory action of trans-resveratrol on the expression of ACSL3 gene was detected. In a study conducted by Bu et al. (2009), it was observed that knockdown of ACSL3 significantly suppressed the gene activity of certain lipogenic transcription factors such as peroxisome proliferator activation receptor-γ (PPAR-γ), carbohydrate responsive element-binding protein (ChRBP), sterol regulatory element-binding protein-1c (SREBP-1c), and liver X receptor-α (LXR), as well as the expression of their target genes (Bu et al., 2009). In accordance, data from the current investigation showed that treatment of Caco-2 cells with 50 μM of trans-resveratrol, repressed the expression of ACSL3, but also inhibited the expression levels of certain transcription factors such as KLF5 and AREG. Likewise, in relation to cholesterol metabolism, trans-resveratrol inhibited the expression of endothelial lipase (LIPG). This lipase belongs to the TG lipase gene family (Lamarche and Paradis, 2007) and the members of this group show different substrate specificity. It has been stated that high density lipoprotein (HDL) is the main substrate for LIPG (McCoy et al., 2002). Interestingly, LIPG was implicated in the pathway related to concentration of cholesterol showing a z-score of - 0.109. It should be taken into consideration that the small intestine plays a role in cholesterol homeostasis (Lutton and Champarnaud, 1994). Remarkably, from human studies, a positive correlation between plasma levels of endothelial lipase and obesity-associated parameters (i.e. body mass index and waist circumference) have been demonstrated (Badellino et al., 2006). From our data, it might be proposed that, apart from the already described inhibitory

188 Accepted in Cell Mol Biol effect of trans-resveratrol on hepatic lipogenesis (Jin et al., 2013) and adipogenesis in 3T3-L1 adipocytes (Baile et al., 2011), the stilbene may affect TG synthesis and cholesterol metabolism in enterocytes.

In our study, the expression of fatty acid synthase (FASN), which is considered one of the rate limiting enzymes in de novo lipogenesis, was not differentially expressed between the experimental groups (log FC= - 0.34 when comparing the trans-resveratrol pre-treated Caco-2 cells and those exposed to LPS). However, FASN has been shown to be target of resveratrol and either the expression of the gene (Gracia et al., 2014) or the activity of the enzyme (Alberdi et al., 2011) have been found to be affected by the stilbene. Importantly, regulation of FASN has been reported to take place mainly at transcriptional level, but also at post- transcriptional level (Katsurada et al., 1990; Kim et al., 1992). In our study, trans-resveratrol inhibited the expression of particular transcription factors that regulate the expression of FASN, such as KLF5 and AREG. KLF5, also called basic transcription element-binding (BTEB) 2, is highly expressed in the gut (Conkright et al., 1999) and has been reported to control proliferation of different cell types, including fibroblasts, smooth muscle cells, white adipose tissue and intestinal epithelial cells (Brey et al., 2009). Importantly, KLF5 plays a key role in the pathogenesis of cardiovascular diseases (Brey et al., 2009). KLF5 has been demonstrated to be a pivotal regulator in the control of FASN expression (the key lipogenic gene) through an interaction with SREBP-1 (Brey et al., 2009). AREG is a common ligand for the epidermal growth factor receptor (EGFR), which contributes to the growth of various cell types including intestinal epithelial cells (Nam et al., 2015). Ligands of EGFR, such as the EGF peptide, have been found to stimulate FASN expression mediated by SREBPs in certain cancer cell types (Swinnen et al., 2000). Besides, increases in protein expression of EGF (Carver et al., 2002) and EGFR (Scheving et al., 1989) in the liver appeared to be related to cholesterol synthesis (Edwards et al., 1972) and fatty acid synthesis (Hems et al., 1975). Accordingly, in human studies, a positive correlation between EGF ligands and serum cholesterol levels has been reported (Berrahmoune et al., 2009). Therefore, our data suggest that downregulation of AREG and KLF5 expression by trans- resveratrol might be linked somehow to alterations in lipid metabolism, including cholesterol levels, and a reduction of lipogenesis.

Noteworthy, resveratrol is believed to modulate lipoprotein metabolism, decreasing circulating levels of LDL cholesterol and reducing cardiovascular disease risk (Riccioni et al., 2015). Supplementation with high doses of resveratrol to healthy humans with mild hypertriglyceridemia has been demonstrated to reduce intestinal and hepatic triglyceride-rich lipoproteins (TRL) production, independent of its action on plasma TG concentrations (Dash et al., 2013). In this context, it might be hypothesized that the potential action of resveratrol at either intestinal level and also in the liver might be a mechanism to be taken into consideration when exploring the protective role of this stilbene against metabolic diseases characterized by a low-grade inflammatory status such as atherosclerosis. To the best of our knowledge, the inhibitory action of trans- resveratrol on lipid metabolism has not been previously described in enterocytes. Hence, results from this microarray and further validation analysis might be taken into consideration when evaluating the mechanisms implicated in the hypolipidemic effects of trans-resveratrol, particularly based on the interest of this compound owing to the potential health-promoting effects on cardioprotection.

189 Accepted in Cell Mol Biol

Conclusions

In summary, the microarray analysis performed in the current study evidenced that trans-resveratrol acts upon LPS-stimulated enterocytes activating genes related to cell death and repressing genes associated with cellular development, growth and proliferation, as well as DNA replication, recombination and repair. Indeed, the main finding of this experimental trial is that pre-treatment of Caco-2 cells with trans-resveratrol before exposure to LPS significantly affects the expression of enzymes directly associated with lipid metabolism, particularly those involved in fatty acid synthesis and cholesterol turnover. Moreover, a significant influence of the stilbene was also demonstrated on the expression of several transcription factors, such as KLF5 and AREG that, through their target genes, might impact on intestinal lipid metabolism and blood cholesterol and TG levels, contributing to further explain the beneficial effects of resveratrol.

List of abbreviations

LPS, lipopolysaccharide; DMEM, Dulbecco’s modified Eagle’s medium; FBS, fetal bovine serum; NEAA, non- essential amino acids; RT-Qpcr, quantitative real-time polymerase chain reaction; KLF5, krüppel-like factor 5; LIPG, endothelial lipase; AREG, amphiregulin; NOX1, NADPH oxidase-1; GAPDH, glyceraldehyde-3- phosphate deshydrogenase; SEM, standard error of the mean; ID1, inhibitor DNA binding 1, dominant helix- loop-helix protein; HRG, histidine-rich glycoprotein; SPRY1, sprouty homolog 1, antagonist of FGF signalling; IPA, Ingenuity Pathway Analysis; ROS, reactive oxygen species; NADPH, amide adenine dinucleotide phosphate; MAG, monoacylglycerol; G-3-P, glycerol -3-phosphate; ACSL, long-chain acyl-CoA synthases; PPAR-γ, peroxisome proliferator activation receptor-γ; ChRBP, carbohydrate responsive element-binding protein; SREBP1c, sterol regulatory element-binding protein-1c; LXR, liver X receptor-α; HDL, high-density lipoprotein; FASN, fatty acid synthase; EGFR, epidermal growth factor receptor; TRL, triglyceride-rich lipoprotein.

Acknowledgements

This study was supported by Instituto de Salud Carlos III (CIBERobn) and IdiSNA. The authors wish to acknowledge Línea Especial about Nutrition, Obesity and Health (University of Navarra, LE/97, Spain) and MINECO (Nutrigenio Project, AGL2013-45554R) for the financial support and the Department of Education, Language Policy and Culture from the Government of the Basque Country for the predoctoral grant given to Usune Etxeberria. The authors acknowledge Victor Segura for the help provided with the IPA.

190 Accepted in Cell Mol Biol

References

Aguirre L., Fernandez-Quintela A., Arias N., Portillo M.P. 2014. Resveratrol: anti-obesity mechanisms of action. Molecules 19: 18632-18655. Alberdi G., Rodriguez V.M., Miranda J., Macarulla M.T. et al. 2011. Changes in white adipose tissue metabolism induced by resveratrol in rats. Nutr Metab (Lond) 8: 29. Azuma T., Shigeshiro M., Kodama M., Tanabe S., Suzuki T. 2013. Supplemental naringenin prevents intestinal barrier defects and inflammation in colitic mice. J Nutr 143: 827-834. Badellino K.O., Wolfe M.L., Reilly M.P., Rader D.J. 2006. Endothelial lipase concentrations are increased in metabolic syndrome and associated with coronary atherosclerosis. PLoS Med 3: e22. Baile C.A., Yang J.Y., Rayalam S., Hartzell D.L. et al. 2011. Effect of resveratrol on fat mobilization. Ann N Y Acad Sci 1215: 40-47. Bereswill S., Munoz M., Fischer A., Plickert R. et al. 2010. Anti-inflammatory effects of resveratrol, curcumin and simvastatin in acute small intestinal inflammation. PLoS One 5: e15099. Berrahmoune H., Lamont J.V., Herbeth B., Lambert D. et al. 2009. Association between EGF and lipid concentrations: a benefit role in the atherosclerotic process? Clin Chim Acta 402: 196-198. Bondia-Pons I., Ryan L., Martinez J.A. 2012. Oxidative stress and inflammation interactions in human obesity. J Physiol Biochem 68: 701-711. Boutagy N.E., McMillan R.P., Frisard M.I., Hulver M.W. 2015. Metabolic endotoxemia with obesity: Is it real and is it relevant? Biochimie. Doi:10.1016/j.biochi.2015.06.020. Brey C.W., Nelder M.P., Hailemariam T., Gaugler R., Hashmi S. 2009. Kruppel-like family of transcription factors: an emerging new frontier in fat biology. Int J Biol Sci 5: 622-636. Brown K., DeCoffe D., Molcan E., Gibson D.L. 2012. Diet-induced dysbiosis of the intestinal microbiota and the effects on immunity and disease. Nutrients 4: 1095-1119. Bu S.Y., Mashek M.T., Mashek D.G. 2009. Suppression of long chain acyl-CoA synthetase 3 decreases hepatic de novo fatty acid synthesis through decreased transcriptional activity. J Biol Chem 284: 30474-30483. Carcamo J.M., Pedraza A., Borquez-Ojeda O., Zhang B. et al. 2004. Vitamin C is a kinase inhibitor: dehydroascorbic acid inhibits IkappaBalpha kinase beta. Mol Cell Biol 24: 6645-6652. Carpene C., Gomez-Zorita S., Deleruyelle S., Carpene M.A. 2015. Novel strategies for preventing diabetes and obesity complications with natural polyphenols. Curr Med Chem 22: 150-164. Carrasco-Pozo C., Morales P., Gotteland M. 2013. Polyphenols protect the epithelial barrier function of Caco-2 cells exposed to indomethacin through the modulation of occludin and zonula occludens-1 expression. J Agric Food Chem 61: 5291-5297. Carver R.S., Stevenson M.C., Scheving L.A., Russell W.E. 2002. Diverse expression of ErbB receptor proteins during rat liver development and regeneration. Gastroenterology 123: 2017-2027. Cenedella R.J., Crouthamel W.G. 1974. Intestinal versus hepatic contribution to circulating triglyceride levels. Lipids 9: 35-42. Cianciulli A., Calvello R., Cavallo P., Dragone T. et al. 2012. Modulation of NF-kappaB activation by resveratrol in LPS treated human intestinal cells results in downregulation of PGE2 production and COX-2 expression. Toxicol In Vitro 26: 1122-1128. Conkright M.D., Wani M.A., Anderson K.P., Lingrel J.B. 1999. A gene encoding an intestinal-enriched member of the Kruppel-like factor family expressed in intestinal epithelial cells. Nucleic Acids Res 27: 1263-1270. Dash S., Xiao C., Morgantini C., Szeto L., Lewis G.F. 2013. High-dose resveratrol treatment for 2 weeks inhibits intestinal and hepatic lipoprotein production in overweight/obese men. Arterioscler Thromb Vasc Biol 33: 2895-2901. de La Serre C.B., Ellis C.L., Lee J., Hartman A.L. et al. 2010. Propensity to high-fat diet-induced obesity in rats is associated with changes in the gut microbiota and gut inflammation. Am J Physiol Gastrointest Liver Physiol 299: G440-448. Draghici S. 2003. Data analysis tools for DNA microarrays. Chapman & Hall/CRC mathematical biology and medicine series Show all parts in this series. Edwards P.A., Muroya H., Gould R.G. 1972. In vivo demonstration of the circadian thythm of cholesterol biosynthesis in the liver and intestine of the rat. J Lipid Res 13: 396-401. Fu X.J., Peng Y.B., Hu Y.P., Shi Y.Z. et al. 2014. NADPH oxidase 1 and its derived reactive oxygen species mediated tissue injury and repair. Oxid Med Cell Longev: 282854. Doi: 10.1155/2014/282854. Gatz S.A., Wiesmuller L. 2008. Take a break--resveratrol in action on DNA. Carcinogenesis 29: 321-332.

191 Accepted in Cell Mol Biol

Gentleman R., Carey V., Huber W., Irizarry R., Dudoit S. 2006. Bioinformatics and computational biology solutions using R and Bioconductor. Springer Science & Business Media. Gracia A., Elcoroaristizabal X., Fernandez-Quintela A., Miranda J. et al. 2014. Fatty acid synthase methylation levels in adipose tissue: effects of an obesogenic diet and phenol compounds. Genes Nutr 9: 411. Hems D.A., Rath E.A., Verrinder T.R. 1975. Fatty acid synthesis in liver and adipose tissue of normal and genetically obese (ob/ob) mice during the 24-hour cycle. Biochem J 150: 167-173. Ho S.Y., Delgado L., Storch J. 2002. Monoacylglycerol metabolism in human intestinal Caco-2 cells: evidence for metabolic compartmentation and hydrolysis. J Biol Chem 277: 1816-1823. Irizarry R.A., Bolstad B.M., Collin F., Cope L.M. et al. 2003. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31: e15. Jin S.H., Yang J.H., Shin B.Y., Seo K. et al. 2013. Resveratrol inhibits LXRalpha-dependent hepatic lipogenesis through novel antioxidant Sestrin2 gene induction. Toxicol Appl Pharmacol 271: 95-105. Johnson L.D., Goubran H.A., Kotb R.R. 2014. Histidine rich glycoprotein and cancer: a multi-faceted relationship. Anticancer Res 34: 593-603. Kaldas M.I., Walle U.K., Walle T. 2003. Resveratrol transport and metabolism by human intestinal Caco-2 cells. J Pharm Pharmacol 55: 307-312. Katsurada A., Iritani N., Fukuda H., Matsumura Y. et al. 1990. Effects of nutrients and hormones on transcriptional and post-transcriptional regulation of fatty acid synthase in rat liver. Eur J Biochem 190: 427-433. Kim K.S., Park S.W., Kim Y.S. 1992. Regulation of fatty acid synthase at transcriptional and post-transcriptional levels in rat liver. Yonsei Med J 33: 199-208. Kless C., Muller V.M., Schuppel V.L., Lichtenegger M. et al. 2015. Diet-induced obesity causes metabolic impairment independent of alterations in gut barrier integrity. Mol Nutr Food Res 59: 968-978. Laakso M. 2010. Cardiovascular Disease in Type 2 Diabetes From Population to Man to Mechanisms: The Kelly West Award Lecture 2008. Diabetes Care 33: 442-449. Lamarche B., Paradis M.E. 2007. Endothelial lipase and the metabolic syndrome. Curr Opin Lipidol 18: 298-303. Livak K.J., Schmittgen T.D. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25: 402-408. Lu Y.C., Yeh W.C., Ohashi P.S. 2008. LPS/TLR4 signal transduction pathway. Cytokine 42: 145-151. Lutton C., Champarnaud G. 1994. Cholesterol synthesis and high density lipoprotein uptake are regulated independently in rat small intestinal epithelium. Gut 35: 343-346. Ma J., Shi M., Li G., Wang N. et al. 2013. Regulation of Id1 expression by epigallocatechin3gallate and its effect on the proliferation and apoptosis of poorly differentiated AGS gastric cancer cells. Int J Oncol 43: 1052-1058. Mansbach C.M., 2nd, Gorelick F. 2007. Development and physiological regulation of intestinal lipid absorption. II. Dietary lipid absorption, complex lipid synthesis, and the intracellular packaging and secretion of chylomicrons. Am J Physiol Gastrointest Liver Physiol 293: G645-650. Martin G.R., Wallace J.L. 2006. Gastrointestinal inflammation: a central component of mucosal defense and repair. Exp Biol Med (Maywood) 231: 130-137. Martinez J.A., Etxeberria U., Galar A., Milagro F.I. 2013. Role of dietary polyphenols and inflammatory processes on disease progression mediated by the gut microbiota. Rejuvenation Res 16: 435-437. McCoy M.G., Sun G.S., Marchadier D., Maugeais C. et al. 2002. Characterization of the lipolytic activity of endothelial lipase. J Lipid Res 43: 921-929. Nam S.O., Yotsumoto F., Miyata K., Fukagawa S. et al. 2015. Warburg effect regulated by amphiregulin in the development of colorectal cancer. Cancer Med 4: 575-587. Ockner R.K., Hughes F.B., Isselbacher K.J. 1969. Very low density lipoproteins in intestinal lymph: origin, composition, and role in lipid transport in the fasting state. J Clin Invest 48: 2079-2088. Park D.W., Baek K., Kim J.R., Lee J.J. et al. 2009. Resveratrol inhibits foam cell formation via NADPH oxidase 1- mediated reactive oxygen species and monocyte chemotactic protein-1. Exp Mol Med 41: 171-179. Peterson L.W., Artis D. 2014. Intestinal epithelial cells: regulators of barrier function and immune homeostasis. Nat Rev Immunol 14: 141-153. Poppelreuther M., Rudolph B., Du C., Grossmann R. et al. 2012. The N-terminal region of acyl-CoA synthetase 3 is essential for both the localization on lipid droplets and the function in fatty acid uptake. J Lipid Res 53: 888-900.

192 Accepted in Cell Mol Biol

Pussinen P.J., Tuomisto K., Jousilahti P., Havulinna A.S. et al. 2007. Endotoxemia, immune response to periodontal pathogens, and systemic inflammation associate with incident cardiovascular disease events. Arterioscler Thromb Vasc Biol 27: 1433-1439. Ramprasath V.R., Jones P.J. 2010. Anti-atherogenic effects of resveratrol. Eur J Clin Nutr 64: 660-668. Riccioni G., Gammone M.A., Tettamanti G., Bergante S. et al. 2015. Resveratrol and anti-atherogenic effects. Int J Food Sci Nutr 66: 603-610. Rodriguez-Hernandez H., Simental-Mendia L.E., Rodriguez-Ramirez G., Reyes-Romero M.A. 2013. Obesity and inflammation: epidemiology, risk factors, and markers of inflammation. Int J Endocrinol 2013: 678159. Romier B., Schneider Y.J., Larondelle Y., During A. 2009. Dietary polyphenols can modulate the intestinal inflammatory response. Nutr Rev 67: 363-378. Scalbert A., Williamson G. 2000. Dietary intake and bioavailability of polyphenols. J Nutr 130: 2073S-2085S. Scheving L.A., Tsai T.H., Cornett L.E., Feuers R.J., Scheving L.E. 1989. Circadian variation of epidermal growth factor receptor in mouse liver. Anat Rec 224: 459-465. Shi Y., Cheng D. 2009. Beyond triglyceride synthesis: the dynamic functional roles of MGAT and DGAT enzymes in energy metabolism. Am J Physiol Endocrinol Metab 297: E10-18. Smyth G.K. 2004. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3: Article3. Strable M.S., Ntambi J.M. 2010. Genetic control of de novo lipogenesis: role in diet-induced obesity. Crit Rev Biochem Mol Biol 45: 199-214. Swinnen J.V., Heemers H., Deboel L., Foufelle F. et al. 2000. Stimulation of tumor-associated fatty acid synthase expression by growth factor activation of the sterol regulatory element-binding protein pathway. Oncogene 19: 5173-5181. Trachootham D., Lu W., Ogasawara M.A., Valle N.R.-D., Huang P. 2008. Redox Regulation of Cell Survival. Antioxidants & Redox Signaling 10: 1343-1374. Van De Walle J., Hendrickx A., Romier B., Larondelle Y., Schneider Y.J. 2010. Inflammatory parameters in Caco-2 cells: effect of stimuli nature, concentration, combination and cell differentiation. Toxicol In Vitro 24: 1441-1449. Vanamala J., Reddivari L., Radhakrishnan S., Tarver C. 2010. Resveratrol suppresses IGF-1 induced human colon cancer cell proliferation and elevates apoptosis via suppression of IGF-1R/Wnt and activation of p53 signaling pathways. BMC Cancer 10: 238. Wang W., Xia T., Yu X. 2015. Wogonin suppresses inflammatory response and maintains intestinal barrier function via TLR4-MyD88-TAK1-mediated NF-kappaB pathway in vitro. Inflamm Res 64: 423-431.

193 Accepted in Cell Mol Biol

10 9 **

8 *** * ## 7 ## #

6 * CONTROL 5 ## LPS

Averageexpression 4 LPS+RSV 3

2

1

0 ID1 NOX1 HRG SPRY1 Figure 1. Average expression of genes that were downregulated by trans-resveratrol pre-treatment, according to Affymetrix Human Gene 2.0 ST DNA microarray analysis. Results are presented as mean ± SEM. Linear Models for Microarray Data was used to show probe sets with significant differential expression. # ## *padj< 0.05, **padj< 0.01, ***padj< 0.001 vs CONTROL group; padj< 0.05, padj< 0.01 vs LPS group. Adjusted p value was calculated with Benjamini-Hochberg procedure. SEM, standard error of the mean; LPS, lipopolysaccharide; RSV, trans-resveratrol; ID1, inhibitor of DNA binding 1, dominant negative helix-loop- helix protein; NOX1, NADPH oxidase 1; HRG, histidine-rich glycoprotein; SPRY1, sprouty homolog 1, antagonist of FGF signalling.

Figure 2. Integrated network analysis of upregulated and downregulated genes in Caco-2 cells pre-treated with trans-resveratrol and exposed to lipopolysaccharide. The node colour indicates the gene expression level.

194 Accepted in Cell Mol Biol

A 11

p< 0.001 p< 0.001 10

p< 0.001 9 p< 0.001 LPS

8 p< 0.001 LPS+RSV

Averageexpression 7

6 B ACSL3 LIPG NOX1 KLF5 AREG

1.2

1

* 0.8 * * 0.6 LPS ** LPS+RSV 0.4

*** 0.2

expression) (Relative mRNA

0 ACSL3 LIPG NOX1 KLF5 AREG Figure 3. Average and relative expression of genes chosen for validation. A) Average expression of genes from microarray analysis in LSP-stimulated Caco-2 cells pre-treated with trans-resveratrol. B) Relative expression of genes following validation by RT-qPCR in LPS-stimulated Caco-2 cells pre-treated with trans- resveratrol. Statistical analyses were conducted with Student T-test, ***p< 0.001, **p< 0.01, *p< 0.05. LPS, lipopolysaccharide; RT-qPCR, quantitative real-time polymerase chain reaction.

195 Accepted in Cell Mol Biol

Table 1. Classification of metabolic pathways and genes targeted by trans-resveratrol in LPS-treated Caco-2 cells. Diseases or Category functions p value z-score Genes annotation Cell Death and Survival ABCB1,AHR,ANKRD1,AREG,BLNK,CLYBL,ENC1,EPHX2,HOXB9,HSPA8,ID1,IER3,IFI Necrosis 2,97E-03 1,804 6,KLF5,KRT18,LUM,MT1X,MT2A,NEO1,NFE2L2,NOX1,PHB2,PKP2,RPS3,SLC20A1, SPTBN1,SSTR5-AS1,TOP1 ABCB1,AHR,ANKRD1,ANXA4,AREG,BBS2,BLNK,ENC1,GLS2,HOXB9,HRG,HSPA8,ID Apoptosis 8,98E-03 1,174 1,IER3,IFI6,KLF5,KRT18,LUM,MT2A,NFE2L2,NOX1,PHB2,PKP2,RPS3,SLC20A1,SP TBN1,TOP1 Infectious diseases AREG,BMP2K,CRIM1,ENC1,KRT18,MT1X,MT2A,NOP56,OSBPL3,RPL5,SLC20A1,SP Infection of cells 2,06E-03 -1,140 TBN1,ZBTB2 ABCB1,AHR,AREG,BMP2K,CRIM1,ENC1,FAM135A,IER3,IFI6,KRT18,MT1X,MT2A,N Viral Infection 3,35E-02 -0,595 OP56,OSBPL3,RPL5,SPTBN1,ZBTB2 Lipid metabolism, Small molecule Biochemistry Synthesis of lipids 1,48E-02 -1,195 ABCB1,ACSL3,ACSM3,AHR,AREG,EPHX2,HSPA8,KLF5,ME1,NOX1 Fatty acid metabolism 6,43E-03 -1,060 ABCB1,ACSL3,ACSM3,AREG,EPHX2,HSPA8,KLF5,ME1,SC5D Concentration of cholesterol 2,62E-02 0,109 AHR,EPHX2,LIPG,LPGAT1,SC5D Lymphoid tissue structure and development, tissue morphology Quantity of lymphatic system cells 1,99E-02 -1,408 ABCB1,AHR,BLNK,ID1,SLC20A1 Cellular Development, Cellular Growth and Proliferation ABCB1,AHR,AREG,CLYBL,DDX21,ENC1,HOXB9,ID1,IER3,KLF5,MT2A,MTUS1,NEO Proliferation of tumour cell lines 3,29E-02 -1,625 1,NFE2L2,NFS1,TOP1 DNA replication, Recombination and Repair Metabolism of DNA 3,50E-02 1,996 ABCB1,AHR,AREG,FAM135A,ID1,TOP1

Functional enrichment analysis conducted by the Ingenuity Pathway Analysis.

196

Supplementary data CHAPTER 7

197

Supplementary data Chapter 7

Supplementary table 1. List of genes whose expression was significantly affected by trans-resveratrol in LPS-induced Caco-2 cells .

Gene Name Gene Description Transcript ID Log FC B Downregulated genes ID1 inhibitor of DNA binding 1, dominant negative helix-loop-helix protein NM_002165 -0.88 7.39 HRG histidine-rich glycoprotein ENST00000232003 -1.24 5.78 NOX1 NADPH oxidase 1 NM_001271815 -1.94 5.50 SPRY1 sprouty homolog 1, antagonist of FGF signaling (Drosophila) NM_001258038 -0.57 4.26 ETV4 ets variant 4 NM_001079675 -0.89 3.29 TOP1 topoisomerase (DNA) I NM_003286 -0.54 3.14 ABCB1 ATP-binding cassette, sub-family B (MDR/TAP), member 1 NM_000927 -1.42 2.71 SLC7A6 solute carrier family 7 (amino acid transporter light chain, y+L system), member 6 NM_001076785 -0.62 2.68 KRT18P15 keratin 18 pseudogene 15 ENST00000458108 -0.66 2.66 AREG amphiregulin NM_001657 -1.11 2.14 ANKRD1 ankyrin repeat domain 1 (cardiac muscle) NM_014391 -0.62 2.14 DDX21 DEAD (Asp-Glu-Ala-Asp) box helicase 21 NM_001256910 -0.67 2.11 MTUS1 microtubule associated tumor suppressor 1 NM_001001924 -0.70 1.83 LPGAT1 lysophosphatidylglycerol acyltransferase 1 NM_014873 -0.75 1.81 KRT18P10 keratin 18 pseudogene 10 ENST00000388836 -0.66 1.80 LIN54 lin-54 homolog (C. elegans) NM_001115007 -0.65 1.77 IER3 immediate early response 3 NM_003897 -0.46 1.66 C3orf52 chromosome 3 open reading frame 52 NM_024616 -0.63 1.64 RAPH1 Ras association (RalGDS/AF-6) and pleckstrin homology domains 1 NM_203365 -0.58 1.55 LUM lumican NM_002345 -0.98 1.50 MGAT4A mannosyl (alpha-1,3-)-glycoprotein beta-1,4-N-acetylglucosaminyltransferase, isozyme A ENST00000264968 -0.56 1.43 SPTBN1 spectrin, beta, non-erythrocytic 1 NM_003128 -0.63 1.34 LIPG lipase, endothelial NM_006033 -0.51 1.29 ACSL3 acyl-CoA synthetase long-chain family member 3 ENST00000357430 -0.41 1.22 KLF5 Kruppel-like factor 5 (intestinal) NM_001286818 -0.44 1.18 ZBTB2 zinc finger and BTB domain containing 2 NM_020861 -0.37 1.10 TNPO1 transportin 1 NM_002270 -0.47 1.01 MIR3916 microRNA 3916 NR_037480 -0.51 0.95 NFE2L2 nuclear factor, erythroid 2-like 2 NM_001145412 -0.58 0.94 ANXA4 annexin A4 NM_001153 -0.46 0.89 MIR622 microRNA 622 NR_030754 -0.68 0.86

199 Supplementary data Chapter 7

Suplementary table 1. Continued

Gene Name Gene Description Transcript ID Log FC B SC5D sterol-C5-desaturase NM_001024956 -0.47 0.80 MIR548V microRNA 548v NR_036103 -0.80 0.79 SAMD5 sterile alpha motif domain containing 5 NM_001030060 -0.71 0.73 MIR3911 microRNA 3911 NR_037473 -0.34 0.73 SLC20A1 solute carrier family 20 (phosphate transporter), member 1 NM_005415 -1.00 0.73 AHR aryl hydrocarbon receptor NM_001621 -0.49 0.70 OSBPL3 oxysterol binding protein-like 3 NM_015550 -0.42 0.68 SPAG1 sperm associated antigen 1 NM_003114 -0.45 0.63 IFI6 interferon, alpha-inducible protein 6 NM_002038 -0.62 0.62 KRT18P49 keratin 18 pseudogene 49 ENST00000427083 -0.56 0.62 LACTB2 lactamase, beta 2 NM_016027 -0.49 0.56 PGP phosphoglycolate phosphatase NM_001042371 -0.48 0.52 ENC1 ectodermal-neural cortex 1 (with BTB domain) NM_001256574 -0.64 0.51 PKP2 plakophilin 2 NM_001005242 -0.44 0.39 BZW1 basic leucine zipper and W2 domains 1 NM_001207067 -0.39 0.38 BMP2K BMP2 inducible kinase NM_198892 -0.79 0.31 GPR111 G protein-coupled receptor 111 NM_153839 -0.77 0.30 COL12A1 collagen, type XII, alpha 1 NM_004370 -0.64 0.29 FNDC3B fibronectin type III domain containing 3B NM_001135095 -0.62 0.25 BLNK B-cell linker NM_001114094 -0.51 0.22 MAK16 MAK16 homolog (S. cerevisiae) NM_032509 -0.37 0.16 DYNLT3 dynein, light chain, Tctex-type 3 NM_006520 -0.60 0.16 ME1 malic enzyme 1, NADP(+)-dependent, cytosolic NM_002395 -0.64 0.15 PSME4 proteasome (prosome, macropain) activator subunit 4 NM_014614 -0.39 0.15 CRIM1 cysteine rich transmembrane BMP regulator 1 (chordin-like) NM_016441 -0.46 0.15 ATP1B1 ATPase, Na+/K+ transporting, beta 1 polypeptide ENST00000494797 -0.46 0.15 MPZL2 myelin protein zero-like 2 NM_144765 -0.38 0.15 ZNRF2 zinc and ring finger 2 NM_147128 -0.53 0.14 HOXB9 homeobox B9 NM_024017 -0.67 0.13 KRT18P54 keratin 18 pseudogene 54 ENST00000513396 -0.62 0.11 LCOR ligand dependent nuclear receptor corepressor NM_001170765 -0.54 0.10 FAM135A family with sequence similarity 135, member A NM_001105531 -0.86 0.10

200 Supplementary data Chapter 7

Supplmentary table 1. Continued Genes identified by Affymetrix Human Gene 2.0 ST DNA microarray. Downregulated and upregulated genes are ranked by B criteria.

Gene Name Gene Description Transcript ID Log FC B Upregulated genes CLYBL citrate lyase beta like NM_206808 0.58 3.17 SNORD100 small nucleolar RNA, C/D box 100 NR_002435 0.52 2.25 ZBED5-AS1 ZBED5 antisense RNA 1 ENST00000529014 0.47 1.95 SNORD82 small nucleolar RNA, C/D box 82 ENST00000365530 0.54 1.93 SNORA76A small nucleolar RNA, H/ACA box 76A NR_002980 0.47 1.82 IRF2BPL interferon regulatory factor 2 binding protein-like NM_024496 0.40 1.72 SNORD55 small nucleolar RNA, C/D box 55 NR_000015 0.60 1.60 SSTR5-AS1 SSTR5 antisense RNA 1 NR_027242 0.49 1.52 TSTD1 thiosulfate sulfurtransferase (rhodanese)-like domain containing 1 NM_001113206 0.42 1.48 MT1X metallothionein 1X NM_005952 0.84 1.38 SNORD36A small nucleolar RNA, C/D box 36A NR_002448 0.62 1.37 SNORD104 small nucleolar RNA, C/D box 104 NR_004380 0.67 1.28 SNORD92 small nucleolar RNA, C/D box 92 NR_003074 0.53 1.24 SNORA80E small nucleolar RNA, H/ACA box 80E NR_002974 0.50 1.16 MT2A metallothionein 2A ENST00000245185 0.89 1.07 SNORD21 small nucleolar RNA, C/D box 21 NR_000006 0.41 1.02 SNORA65 small nucleolar RNA, H/ACA box 65 NR_002449 0.78 0.98 SNORA72 small nucleolar RNA, H/ACA box 72 NR_002581 0.76 0.94 MFSD4 major facilitator superfamily domain containing 4 NM_181644 0.38 0.90 MGC16275 uncharacterized protein MGC16275 NR_026914 0.56 0.90 NEO1 neogenin 1 NM_001172623 0.36 0.90 RNU6ATAC RNA, U6atac small nuclear (U12-dependent splicing) NR_023344 0.49 0.73 RNU6ATAC2P RNA, U6atac small nuclear 2, pseudogene ENST00000387943 0.48 0.72 SNORA20 small nucleolar RNA, H/ACA box 20 NR_002960 0.77 0.70 EPHX2 epoxide hydrolase 2, cytoplasmic NM_001256484 0.60 0.61 RNA5SP242 RNA, 5S ribosomal pseudogene 242 ENST00000364171 0.54 0.57 SNORD56 small nucleolar RNA, C/D box 56 NR_002739 0.41 0.57 ALDH9A1 aldehyde dehydrogenase 9 family, member A1 NM_000696 0.38 0.56 SNORA64 small nucleolar RNA, H/ACA box 64 NR_002326 0.43 0.54 ACP6 acid phosphatase 6, lysophosphatidic NM_016361 0.41 0.53

201 Supplementary data Chapter 7

Supplementary table 1. Continued

Gene Name Gene Description Transcript ID Log FC B PARM1 prostate androgen-regulated mucin-like protein 1 NM_015393 0.34 0.51 SNORD46 small nucleolar RNA, C/D box 46 NR_000024 0.43 0.50 SNHG8 small nucleolar RNA host gene 8 (non-protein coding) NR_003584 0.45 0.50 HYKK hydroxylysine kinase NM_001013619 0.35 0.44 SNORA54 small nucleolar RNA, H/ACA box 54 NR_002982 0.53 0.41 OGDHL oxoglutarate dehydrogenase-like NM_001143996 0.42 0.39 SNORD15A small nucleolar RNA, C/D box 15A NR_000005 0.43 0.35 NYNRIN NYN domain and retroviral integrase containing NM_025081 0.35 0.34 GLS2 glutaminase 2 (liver, mitochondrial) NM_001280796 0.44 0.29 LINC00349 long intergenic non-protein coding RNA 349 ENST00000448748 0.44 0.27 ACSM3 acyl-CoA synthetase medium-chain family member 3 NM_005622 0.53 0.26 SCARNA12 small Cajal body-specific RNA 12 NR_003010 0.36 0.22 OSER1-AS1 OSER1 antisense RNA 1 (head to head) NR_038337 0.30 0.20 CES3 carboxylesterase 3 NM_001185177 0.53 0.19 SNORD87 small nucleolar RNA, C/D box 87 NR_002598 0.37 0.18 SNORD85 small nucleolar RNA, C/D box 85 NR_003066 0.89 0.12 SNORD14C small nucleolar RNA, C/D box 14C NR_001453 0.39 0.12 LOC154761 family with sequence similarity 115, member C pseudogene NR_015421 0.37 0.12 RNA5SP422 RNA, 5S ribosomal pseudogene 422 ENST00000516778 0.34 0.11 IFT46 intraflagellar transport 46 homolog (Chlamydomonas) ENST00000525060 0.41 0.09 UQCR10 ubiquinol-cytochrome c reductase, complex III subunit X NM_001003684 0.34 0.08 TRAPPC6A trafficking protein particle complex 6A NM_001270891 0.42 0.08 SNORD22 small nucleolar RNA, C/D box 22 NR_000008 0.52 0.08 CMBL carboxymethylenebutenolidase homolog (Pseudomonas) NM_138809 0.33 0.06 BBS2 Bardet-Biedl syndrome 2 NM_031885 0.29 0.05 NFS1 NFS1 cysteine desulfurase ENST00000374085 0.35 0.04 SNORD59A small nucleolar RNA, C/D box 59A NR_002737 0.70 0.03 Genes identified by Affymetrix Human Gene 2.0 ST DNA microarray. Downregulated and upregulated genes are ranked by B criteria.

202

V. GENERAL DISCUSSION

General discussion

Obesity has become a worldwide health burden that affects not only developed countries but also, low‐ and middle‐income countries (WHO, 2015). Increasing BMI has been related to a set of comorbidities including hypertension, glucose intolerance, T2DM, dyslipidaemia, kidney failure, osteoarthritis, as well as asthma, heart failure and mental disorders (Martin‐Rodriguez et al., 2015). Thereby, this metabolic health problem leads to high morbidity and mortality rates (Martin‐Rodriguez et al., 2015). In addition, obesity is a multifactorial disease, where many factors are involved (Harris et al., 2012). A major contributory factor is the consumption of high‐caloric diets rich in fat and sucrose, accompanied by inactive lifestyle habits (Brantley et al., 2005). This outcome has made essential the implementation of actions directed to promote healthy dietary patterns and increased physical activity (WHO, 2015). Moreover, since lifestyle interventions might not be effective enough in some cases, pharmacological treatment is considered an important strategy (Hussain et al., 2015). Unfortunately, the main reason why only few anti‐obesity agents are found in the market, is attributed to the large amount of adverse events associated to the drugs (Hussain et al., 2015).

Taking this in mind, efforts in scientific research have two major challenges. On one hand, it is essential to understand the surrounding mechanisms in the physiopathology of obesity. This awareness is of relevance to find new therapeutic targets that will allow searching for effective treatments, helping to manage the disease and associated complications and to improve individual’s life quality. On the other hand, this information will guide professionals to make nutritional recommendations and medical treatments from a personalized point of view (McNiven et al., 2011).

Accordingly, in nutritional sciences, the concept of “molecular nutrition research” has risen (Norheim et al., 2012). This term implies the use of molecular techniques in nutrition to further understand the impact of nutrients and food or food components on whole body physiology and health status (Norheim et al., 2012). This way, scientific world is gaining knowledge on the molecular mechanisms underlying obesity, which has favoured the identification of new contributory factors in obesity and related comorbidities, such as the gut microbiota. Besides, this notion is enabling researchers to explore new therapeutic targets with potential beneficial biological activities, but without or minor side effects (i.e. plant polyphenols), that may contribute to prevent the development of metabolic diseases and even help to treat them (Wang et al., 2014).

The present investigation, through the application of the newest “Omics” technologies, was conducted to shed some light on the complex interaction existing among host metabolism, gut microbiota, food or food‐derived constituents and health. More specifically, this research work sought, firstly, to understand metabolomic alterations and gut microbiota composition modifications related to the intake of a high‐fat high‐sucrose diet (resembling dietary patterns from westernized societies); and secondly, to investigate the effectiveness of the use of certain polyphenols (i.e. trans‐resveratrol, quercetin) to counteract or at least to mitigate some of the diet‐induced obesity‐associated perturbations, to provide new data and views on the biological effects of such compounds and, thus, the possibility to use them as a promising therapeutic approach against obesity.

205 General discussion

1. Understanding the physiopathology of obesity through “Omics” approaches

Metabolic alterations are a key feature in disorders such as obesity. Therefore, processes directed to measure global metabolic changes will provide an overall picture that might be considered a description of the phenotype of the disease (Du et al., 2013) and could help to find disease biomarkers that will allow early diagnosis and the possibility to stratify healthy and diseased patients. Besides, metabolomics approaches identify disturbed biological pathways and the detection of these small molecules or metabolites favours monitoring of the disease progression or evaluating the effectiveness of the treatment (Zhang et al., 2013).

In our study (Chapter 1), animals that were subjected to two different dietary interventions (standard diet vs HFS diet) for the same period of time showed a differential metabolomic pattern. Biologically significant metabolites identified in our research work provided information on the biochemical pathways that might have been affected as a consequence of the treatment that led the animals fed the HFS diet gain more BW than the control rats, and besides, acquire obesity associated complications, such as hyperglycaemia, hyperleptinemia, hyperinsulinemia and insulin resistance. In this context, serum non‐ targeted metabolomics approach detected altered levels of amino acids, particularly those of BCAAs (namely isoleucine, leucine and valine), but also others, such as phenylalanine and tyrosine. Impaired levels of nitrogenous (urea, creatine and creatinine) and lipid compounds, namely total PUFA, docosahexaenoic fatty acid (DHA), and phospholipids (i.e. phosphatidylcholines and sphingomyelin), were likewise detected. Moreover, differences in the levels of certain aqueous compounds (choline, acetyl carnitine, formate and allantoin) were also identified. Taking into consideration these data, it was proposed that apart from the well‐known carbohydrate and lipid metabolic disturbances found in obesity (Singla et al., 2010), HFS diet‐fed obese animals might present impaired protein metabolism. The data suggested that diet‐induced obese animals might be more effective preserving body protein during an overnight fasting. Thus, it was hypothesized that a protein‐sparing effect of fat might be given. Furthermore, mitochondrial function processes related to purine biosynthesis and remethylation processes were proposed to be disturbed. Importantly, this exploratory metabolomics approach conducted in serum samples was able to discriminate metabolites between disease and non‐disease conditions. These data are partly in accordance with scientific evidence reporting the metabolic signature of human obesity to be characterized by altered levels of BCAAs, nonesterified fatty acids (NEFAs), organic acids, acylcarnitines and phospholipids (Rauschert et al., 2014). Nevertheless, results from our study show controversial levels in some of the mentioned molecules, as for instance the extensively studied BCAAs which have been repeatedly reported to be significantly increased in blood samples of obese people (Xie et al., 2014). This finding requires the interpretation of the data to be made with caution since there are confounding factors that might affect the observed results. In our study, an experimental animal model of obesity was used as reported elsewhere (Lomba et al., 2010). This model allows a strict control of agents namely diet and other environmental factors (Xie et al., 2012). Nonetheless, components such as the nutritional status of the animals at the time of sampling might influence the results (Mellert et al., 2011). Indeed, the overnight fasting period of animals might be a key issue to take into consideration. In fact, our results agreed with other studies conducted in animals that were fasted prior to serum sample collection (Shearer et al., 2008; Duggan et al., 2011). However, we have not been able to compare results since, as far as we are concerned, there are no metabolomics studies comparing control

206 General discussion diet‐fed lean rats and HFS diet‐fed obese rats, where the effects of fasting period on metabolomics profile has been determined.

From the evidences emerging in the last decade, it has been confirmed that another factor that deeply influences host metabolism is the gut microbiota (Marcobal et al., 2013). Indeed, recently, a urinary metabolic signature of adiposity has been described in a metabolomic study conducted in two different populations (Elliott et al., 2015). Interestingly, in this study, gut microbial‐derived co‐metabolites that showed associations with BMI were identified reporting several host‐gut transformation microbial pathways (Elliott et al., 2015). The resident microbiota and the host interact in a mutualistic manner. Thus, metabolites that are produced by microbiota influence the host wellness (Nicholson et al., 2012). In this sense, it is of relevance to acquire knowledge on the bacterial species and their functionalities, in order to be able to establish the bacterial species responsible for the production of specific metabolites (Aw and Fukuda, 2015). In summary, when trying to understand metabolic alterations in disease conditions, a relevant aspect that might not be disregarded is the gut microbiota or the gut microbiome (Drosos et al., 2015).

Consequently, a purpose of the present research was devoted to assess the bacterial trademark associated to a HFS diet‐induced overweight condition (Chapter 2). In this sense, faecal samples from animals fed a control diet that subsequently were turned to a HFS diet were analysed by next generation sequencing approach (Claesson et al., 2012). Furthermore, based on the fact that SCFAs are considered the primary end‐ products of gut microbial metabolism in the large intestine (Macfarlane and Macfarlane, 2003), the impact of the diet on the excreted SCFA levels was also evaluated. The most relevant finding from our work was that diet‐induced overweight led to an imbalance, also known as dysbiosis, in gut microbiota composition. A robust effect of diet on intestinal bacterial community was revealed, evidencing not only alterations in the two dominant phyla, the Gram‐positive Firmicutes and the Gram‐negative Bacteroidetes, which are consistent with previous reports (Fleissner et al., 2010; Parks et al., 2013), but also, perturbations in the levels of other minor bacterial groups (Erysipelotri, Deltaproteobacteria, Desulfovibrionaceae, Bacteroidia, Bacilli, Prevotellaceae, Clostridia) and bacterial species (Mitsuokella jalaludinii, Eubacterium ventriosum, Eubacterium cylindroides, Bilophila wadsworthia, Lactobacillus reuteri, Prevotella spp., Bacteroides spp. and Clostridium spp.) were identified. Therefore, one of the conclusions from our study is that an obesogenic diet rich in fat and sucrose has the potential to influence the composition of the gut microbiota negatively. The bacterial fingerprint identified in animals at the end of the intervention was characterized by enhancement of certain pathogens at expensed of symbiotic commensal microbes. Nonetheless, if gut microbiota profile described in our study is compared with results published in another study using the mentioned diets (Fleissner et al., 2010), some of the shifts found in bacterial groups in our experiment are consistent, while in contrast, there are some modifications that do not agree with results reported from studies where only the impact of a HFD has been evaluated (Hildebrandt et al., 2009; de La Serre et al., 2010) bringing to light our inability to differentiate between modifications attributed to the fat content of the diet or otherwise, to the carbohydrate content of diet. Importantly, it was found that some bacterial species showed an opposite pattern to the family or genus they belong to, for instance family Prevotellaceae and Eubacteriaceae were found to be decreased but Prevotella nanceiensis and Eubacterium ventriosum within these groups were increased, genus Mitsuokella was reduced while Mitsuokella jalaludini was enlarged. Moreover, an

207 General discussion unexpected result was to find out that Clostridia group was reduced after the HFS diet, but showed a positive correlation with visceral adipose tissue. This outcome was attributed to the variation in the type and content of carbohydrates, suggesting the relevance of specific nutrients of the diet in certain bacteria. Furthermore, a Lactobacillus strain, commonly characterized as a probiotic species (Ting et al., 2015), was enhanced through the HFS dietary treatment. Discrepancies exist in relation to the levels of this Lactobacillus strain in obesity. Some studies have found higher levels of this bacterium in obesity, showing positive correlations with BMI (Million et al., 2012; Million et al., 2013). Importantly, another study demonstrated the strain‐specific effects of Lactobacillus reuteri on BW and adiposity (Fak and Backhed, 2012). Therefore, these facts re‐assure a relevant conclusion from our study corroborating the relevance of analysing gut microbiota modifications at deep taxonomic ranks, since effects might be species‐ or even, strain‐dependent.

In addition, results obtained regarding SCFAs levels in faeces led us confirm the strong impact of the diet as reported by previous research works (Topping and Clifton, 2001; Jakobsdottir et al., 2013). In this sense, the relevance of the type of substrate in the rate and ratio of SCFAs has been widely explored (den Besten et al., 2013). In our experiment, at the end of the treatment period, levels of major SCFAs (acetate and propionate), and almost those of butyrate, were significantly lower compared to the concentrations detected at the beginning of the study. These results were expected as the quantity of dietary fibre in the HFS diet was considerably lower compared to that of the standard diet. Nevertheless, in genetically obese phenotypes a greater production of these molecules has been stated (Ley et al., 2005; Turnbaugh et al., 2006), which corroborates the hypothesis that the experimental model used (diet‐induced overweight animals) may be the cause for the divergences found among the studies. Importantly, in our trial we could not discriminate whether certain variations in microbiota composition might result from the BW gain or other features associated to adiposity. Anyway, this work is, as far as we know, the first study devoted to investigate the effect of a high‐fat high‐sucrose diet on gut microbiota composition of Wistar rats.

Microbes exert a metabolic activity which might be playing an important role on the development of diseases (Guinane and Cotter, 2013). Hence, understanding whether the involvement of these bacteria is direct or indirect (through metabolites they produce), is a subject of much debate (Drosos et al., 2015). Moreover, knowledge about the degree of bacteria’s susceptibility to dietary constituents is of relevance, since plasticity of the gut microbiota to dietary modifications makes it manageable with several bioactive compounds such as polyphenols.

2. Influence of natural bioactive compounds on obese gut microbiota and metabolome

There is much work being conducted in relation to modulation of the gut microbiota composition through the use of probiotics (Bubnov et al., 2015), prebiotics (Druart et al., 2014) and in the last years faecal transplants are also capturing research interest (Vrieze et al., 2012). Likewise, the links between polyphenolic compounds and the gut microbiota are being explored to gain insights on the health benefits derived from polyphenols (Koutsos et al., 2015). In a study were flavonoid targets were analysed, it was demonstrated that around 1000 targets present homologues with human gut metagenome and

208 General discussion demonstrated that the majority were related to bacterial metabolism. These results confirm that health benefits of flavonoids, at least to some extent, come from the gut microbiota modulatory capacity that affects metabolism of bacteria and, in turn, host metabolism (Lu et al., 2013).

Accordingly, part of our work aimed to evaluate current proof of principal on the bi‐directional interaction between polyphenols and the gut microbiota. In this sense, there are two main concepts that need to be considered when determining health beneficial effects of polyphenols and/or their metabolites. On one hand, we should be aware that each individual contains a unique bacterial profile, which might be referred as our fingerprint or signature, and this fact makes the capacity of individuals to transform polyphenols variable, affecting the formation of final metabolites (Nakatsu et al., 2014). Briefly, the host functionality to metabolize polyphenols will be dependent on the inter‐individual variability related to gut microbiota. On the other hand, pure polyphenols or foods rich in natural compounds, as well as their metabolites, might affect gut microbiota composition, not only acting as antimicrobial compounds with bacteriostatic or bactericidal properties against diverse bacteria, but also promoting the growth of commensal beneficial bacteria, leading to the emergence of the idea that polyphenols might exert prebiotic‐like effects (Cardona et al., 2013). In any case, while most of the efforts have been directed to study the metabolism of polyphenols by the gut microbiota, it is in the last years that research work has started to address this relationship in the opposite direction. However, in most cases, studies have focused on the use of polyphenol‐rich supplements or dietary extracts (Valdes et al., 2015). Accordingly, and based on the fact that polyphenols have been proposed as the most important candidates to explain health protective effects of plant‐derived foods, it is of interest to explore whether potential anti‐obesity effects and improvements on obesity‐related comorbidities produced by certain pure natural compounds might be mediated through changes on gut microbiota composition.

In this sense, our next objective was focused on screening a set of bioactive compounds to evaluate their anti‐obesity potential (Chapter 3). From the tested compounds, mainly naringin, followed by coumaric acid and leucine showed some mild beneficial effects, namely a slight reduction in final body weight, significant decrease in adiposity and/or improved insulin resistance in HFS diet‐fed obese animals. Nevertheless, the individual effects were not very encouraging; hence, further experiments were conducted with natural compounds that in scientific literature have shown greater promise as anti‐obesity molecules, such as trans‐ resveratrol and quercetin (Aguirre et al., 2014) (Chapter 5). In this context, in our study, the combined administration of trans‐resveratrol and quercetin, considerably decreased BW gain. However, neither trans‐resveratrol nor quercetin alone exerted an anti‐obesity effect. To achieve this goal, for quercetin, a supplementation period of 9 weeks has been demonstrated to be needed (Arias et al., 2014). As far as trans‐resveratrol is concerned, when orally administered for 6 weeks, the dose used in our study was found to be effective in reducing liver weight, visceral adiposity and plasma cytokine levels in a model of genetic obesity (Zucker fa/fa rats) (Gomez‐Zorita et al., 2012; Gomez‐Zorita et al., 2013). However, based on previous studies (Macarulla et al., 2009; Alberdi et al., 2011), in a diet‐induced obesity model, higher doses to the ones administered in our study might be required to observe a BW gain preventive effect of trans‐resveratrol. In any case both, trans‐resveratrol and quercetin, were found to significantly reduce serum insulin levels and to improve HOMA index demonstrating their effectiveness as natural compounds to treat obesity‐associated metabolic complications such as insulin resistance. Active research is available around

209 General discussion the complex interaction among red wine consumption, gut microbiota, its metabolites and final health effects (Queipo‐Ortuno et al., 2012; Cueva et al., 2015). In contrast, few studies (Qiao et al., 2014) have been devoted to analyse the influence of one of the most famous components of the red wine (trans‐resveratrol) and another polyphenol (quercetin) on gut microbiota composition. In this sense, according to our work performed in HFS‐diet fed rats, it could be concluded that at least at the proved doses, gut microbiota was especially susceptible to the action of quercetin (Chapter 5). Likewise, it might be proposed that direct gut microbiota modulatory capacity of quercetin might be involved in the mechanisms leading to improved insulin sensitivity. Interestingly, treatment with quercetin considerably reduced Firmicutes/Bacteroidetes ratio and particularly decreased the levels of some bacterial groups (i.e. Firmicutes phyla and Erysipelotrichaceae group) commonly associated to diet‐induced obesity (Fleissner et al., 2010). In fact, in our previous work (Chapter 2), these bacteria were found to be more abundant in the rats that followed HFS dietary intervention. Moreover, this compound suppressed the growth of previously defined pathogenic bacterial species such as Eubacterium cylindroides, while it markedly promoted the growth of a specific strain of Akkermansia genus, known as Akkermansia muciniphila, which has been associated with improved metabolic health at the beginning and at the end of a weight loss dietary intervention (Dao et al., 2015). Indeed, the abundance of this mucin‐degrading bacterium has been inversely associated to BW and diabetes (Everard et al., 2013). Importantly, oral administration of Akkermansia as a probiotic has been reported to reverse diet‐induced obesity associated metabolic disorders (Everard et al., 2013). In addition, in a study conducted with a polyphenol‐rich extract such as the cranberry extract, although the authors were not able to establish a causal relationship, the positive metabolic effects observed in HFD‐induced obese mice that were administered the cranberry extract, were associated with a significant increase in Akkermansia population (Anhe et al., 2015). Therefore, in our study, it might be theorised that the modulatory role of quercetin on general gut microbiota composition and maybe, the specific modifications the flavonol produced in certain bacterial groups, might be implicated in the amelioration of insulin sensitivity found in our study. As far as the influence of trans‐resveratrol is concerned, no significant modifications were identified at phylum level. Unexpectedly, gut microbiota modification patterns with the intake of this compound were opposed to that of quercetin. However, trans‐resveratrol significantly acted on intestinal gene expression, upregulating inflammation‐associated genes TLR4, LBP and IL‐18, and also inducing the expression of variables related to intestinal barrier integrity, particularly TJP‐1 and Ocln. This outcome might be explained by the fact that upon damage, commensal bacteria‐derived activation of TLR signalling might be given which acts as a defence mechanism, promoting intestinal repair processes (Rakoff‐Nahoum et al., 2004). Nevertheless, the possibility of a slight inflammatory state in the intestine could not be disregarded. In any case, from our data it could be theorised that the insulin sensitizing effect produced by trans‐resveratrol might be attributed to indirect mechanisms that do not involve changes on gut microbiota composition. Moreover, the combination of the stilbene with quercetin was found to lessen the impact of the flavonol on gut microbiota and no synergistic effect was found as expected initially based on the reported data (Zhou et al., 2012). The inhibitory effects of quercetin on the sulfation process of trans‐ resveratrol has been previously described in vitro and this action has been considered to increase the bioavailability of trans‐resveratrol (De Santi et al., 2000). In contrast, this feature has not been found in humans (la Porte et al., 2010). Anyway, in our study, this explanation would be acceptable to argue if a synergistic impact of polyphenols on gut microbiota composition was observed in the combined group.

210 General discussion

Accordingly, we could not speculate and propose a hypothesis in this regard and further studies are required to find out if these effects are reproduced and to elucidate the pathway through which the combination of both polyphenols does not show synergistic effects on gut microbial ecology.

Notably, faeces contain a great amount of information that might be a valuable source of data on the health and nutrition status of the tested individual (Deda et al., 2015). Moreover, the straight interplay between polyphenols and gut microbial species may be reflected in metabolites that are produced and detected in faecal samples (Duda‐Chodak et al., 2015). Therefore, through a non‐targeted metabolomics approach, faecal metabolites were analysed to identify candidate compounds that might be generated as a consequence of intestinal microbial metabolic action on polyphenols, and vice versa (Chapter 6). In this sense, exploratory metabolomics allowed the detection of specific microbial co‐metabolites of trans‐resveratrol, namely dihydroresveratrol and lunularin. Interestingly, non‐targeted metabolomics offers the opportunity to identify indicators of the effect of polyphenols on metabolism providing information about the contribution of the tested natural compounds to health beneficial or detrimental outcomes. In this context, our interest was focused on metabolites that were differentially identified in faeces of each experimental group. In this sense, similarly to the impact of trans‐resveratrol and quercetin supplementation on gut microbiota composition followed an opposite pattern, a different set of putative metabolites was also identified in faeces. Trans‐resveratrol group was characterized by compounds related to nucleotide metabolism, while tentatively identified compounds in quercetin treated rats, were related to carbohydrate derivatives or conjugates. Moreover, putative metabolites proposed in each intervention group showed a significant correlation with certain bacterial species suggesting a possible relationship between gut microbial ecology of the intestine, metabolites produced and host physiology. Despite the fact that detected compounds have not been previously reported and no commercial standards have been used to confirm them, these results revealed that each polyphenol exerted a marked metabolome alteration that was profound enough to discriminate experimental groups one from each other. Therefore, faecal untargeted metabolomics was effective in discriminating a distinct metabolic fingerprint based on the dietary intervention animals were subjected to and this metabolic signature was considered to be reflective of the overall impact of trans‐resveratrol and quercetin on host metabolism. These kind of studies are of interest, particularly in the case of trans‐resveratrol, since its low bioavailability leads to obtain negligible presence of the original compound in the bloodstream, thus, it is considered that these concentrations are not sufficient to justify the beneficial effects of the compound (Tome‐Carneiro et al., 2013). Therefore, it is of particular relevance to detect the metabolite or metabolites that might be responsible for the effects observed, if any.

Polyphenols reach the intestine and may produce alterations at this local site with potential systemic consequences. In this sense, the influence of polyphenols supplementation at colonic level was studied in vivo and the expression of specific genes‐related to inflammation and intestinal barrier integrity were found to be altered (Chapter 5). The human equivalent doses for trans‐resveratrol and quercetin in a person of 70 kg in our study would be 170 mg/day and 340 mg/day, respectively (Reagan‐Shaw et al., 2008). Concerning trans‐resveratrol, the dose provided is similar to the one that has been reported in a human study carried out with obese healthy people (150 mg/day (99%); resVida™ for 30 days) where beneficial effects on metabolic profile have been demonstrated attributed to the calorie‐restriction‐like effects of the compound

211 General discussion

(Timmers et al., 2011). In relation to quercetin, the dose used in our study doubled the ones provided in human intervention studies conducted with overweight or obese people with metabolic syndrome traits (150 mg/day for 6 weeks), however in this study although improvements in systolic blood pressure and plasma concentrations of LDL were shown, no effects on body composition were observed (Egert et al., 2009). Since trans‐resveratrol was found to produce modifications at intestinal level in vivo, to further analyse the role of the stilbene in gene expression profile in an inflamed intestinal condition, a study was conducted in Caco‐2 cells, a human intestinal epithelial cell model, stimulated with LPS and pretreated with 50 μΜ of trans‐resveratrol (Chapter 7). This dose was based on previously published studies that were able to produce effective anti‐inflammatory effects (Cianciulli et al., 2012). Nevertheless, while this dose seems to be proximate to the levels of total polyphenols present in plasma following ~ 500 mg of wine consumption, in the intestine higher proportions are estimated to reach (Scalbert et al., 2002). From the microarray analysis it was concluded that trans‐resveratrol inhibited the expression levels of genes involved in cellular development, cellular growth and proliferation, as well as the expression of genes involved in DNA replication, recombination and repair. In contrast, trans‐resveratrol was found to promote cell apoptosis in LPS‐stimulated Caco‐2 cells. Nevertheless, the major finding from this study was the action of the natural compound on the expression of genes related to lipid metabolism. Indeed, trans‐resveratrol was shown to suppress lipid metabolism, repressing the expression of genes implicated in lipid synthesis i.e. acyl‐CoA synthetase long‐chain family member 3 (ACSL3) and cholesterol metabolism, such as endothelial lipase (LIPG), as well as downregulating the expression levels of transcription factors known as Krüppel‐like factor 5 (KLF5) and amphiregulin (AREG), which have been theorised to be implicated in this metabolic pathway. The action of trans‐resveratrol in enterocytes and more specifically, its role in lipid metabolism at intestinal level might be taken into consideration when exploring the mechanisms of the stilbene explaining its protective outcomes against metabolic diseases such as atherosclerosis.

3. Strengths and limitations

This research work provides a descriptive picture of the global impact that polyphenols exert on gut microbiota composition and metabolome of diet‐induced obese animals. The use of innovative techniques, based on the 16S rDNA sequencing and exploratory metabolomic analyses, are a key component to be highlighted (Odriozola and Corrales, 2015). Indeed, through the experiments carried out, it has been demonstrated that the “Omics” approaches are useful to identify modifications at deep taxonomic levels, to identify biomarkers of the effect produced by polyphenols on host metabolome and to discriminate individuals into different clusters depending on the dietary treatment they have been subjected to. Nevertheless, no mechanistic studies have been conducted that may help to explain some data. Accordingly, the major limitation of this study is that no clear association could be given among health beneficial effects found with the administration of trans‐resveratrol and quercetin with changes observed on gut microbiota or host metabolome. In conclusion, this work opens new questions that need to be addressed in the future and also presents several limitations that should be declared.

Firstly, a major concern is related to the experimental design. In order to be able to state that perturbations observed on gut microbial ecology are diet‐driven alterations, in all cases, a control group of animals fed a

212 General discussion standard diet for the same period of time as the duration of the HFS dietary intervention would be required. Thus, at least, the impact of diet on microbes would be differentiated from the impact of adiposity, weight gain and other obesity‐associated parameters. Still, other confounding factors would be present, as for instance the influence of aging. When studying metabolomic changes at the end of a HFS dietary treatment, although results were compared to data obtained from a control diet‐fed group, a similar limitation arose since there could not be certainty that changes in metabolite levels were derived as a result of the diets consumed or to the developed obesity. In this case, another group fed the same obesogenic diet, but which did not develop obesity, would overcome the problem. Importantly, alterations produced in metabolome and intestinal microbes could not be attributed to a specific component of the diet. Thus, it would have been interesting to be able to differentiate between a high‐fat diet and a high‐sucrose diet to elucidate which macronutrient is mostly affecting. Ideally, if metabolomics and gut microbiota modifications would have been conducted in the same experiment, a better overview and understanding of the influence of a HFS diet on gut microbiota composition and host metabolome would be achieved.

Secondly, sample size particularly in the first two studies (Chapter 1 and Chapter 2) was small (n=8 and n=5), which could be stated as a weakness. Moreover, in most of the studies, inter‐individual variability between animals is a matter that should be taken into consideration, since variability among individuals might be unmasking results that in fact, are of biological relevance. Indeed, concerning intestinal bacterial population, the relevance of inter‐individual variability compared to that of intra‐subject has been reported to be strong (Eckburg et al., 2005). Importantly, inter‐individual variability on gut microbes has been demonstrated to lead to a different response to the diet (Conlon and Bird, 2015), which supports the need for a personalized recommendation considering gut microbiota profile.

Thirdly, another important shortcoming of our research is that metabolomic analyses are not hypothesis‐ driven, but exploratory analysis. As far as the last study is concerned, no confirmation of the metabolites identified has been conducted by the use of commercial standards and the use of a targeted approach. Therefore, we could only propose different hypothesis based on candidate compounds that could not be verified. Likewise, our study has been focused on gut microbiota composition “dysbiosis” and the potential of polyphenols to restore it. However, nowadays, it is widely accepted that the collective genome of the gut microbiota, namely the gut microbiome, should be analysed owing to the relevance of these bacterial functionalities. It seems that there exists a functional core which does not fully correspond to the phylogenetic core (Gerritsen et al., 2011). Therefore, knowledge about gut microbiota compositional diversity (“who is present”) does not necessarily lead to the understanding of its function (“what are they doing”) (Lozupone et al., 2012).

Fourthly, a major limitation in all gut microbiota experiments is the lack of standardization. The proportion of bacteria detected will be dependent on different factors including sampling and storage, DNA extraction method, PCR errors, hypervariable regions analysed, primers employed, but also on the sequencing method used. 16S sequencing is the most largely applied technique and the Illumina sequencing (Illumina Inc., San Diego, CA) or 454 pyrosequencing (Roche, Brandfort, CT) the most popular sequencers, but they may provide different information (van Best et al., 2015).

213 General discussion

Fifthly, gut microbiota composition analyses were performed in faecal samples since they are easily obtained in a non‐invasive manner. This situation represents another potential shortcoming as these samples may not be a good predictor of the whole bacterial community in the GI tract (Durban et al., 2011), which in contrast, has been found to be similar in mucosal biopsies along the colon (Lepage et al., 2005).

Lastly, in this experimental investigation, a dietary murine model has been chosen to conduct the experiments. Caution should be paid when trying to make parallels between rats and human gut microbiota, since despite similar physiology and anatomical structures, there are several macroscopic (i.e. intestinal villi longitude, cecum size, presence of appendix) and microscopic (i.e. mucosal wall, distribution of mucin‐ producing goblet cells and Paneth cells) differences that might affect gut microbiota composition, as a consequence of the influences in host‐microbiota interactions (Nguyen et al., 2015).

4. Corollary

The work carried out in the present thesis shows that the intake of a high‐fat high‐sucrose diet and the developed overweight/obesity exerts a strong impact on host serum metabolome and gut microbial profile. Moreover, our research adds novel findings to the existing literature on the plasticity of gut microbiota to be manipulated by dietary factors such as polyphenols, emphasizing the relevance of intestinal bacterial ecology as a therapeutic target to be considered in metabolic diseases, as for instance obesity.

Our work proposes that trans‐resveratrol and quercetin, at least at the doses administered, improve insulin sensitivity and protect from body weight gain in diet‐induced obese animals when combined. Furthermore, this work shows that particularly the aglycone quercetin, is able to modulate gut microbiota composition dysbiosis, favouring the growth of “friendly” bacteria at the expense of some pathogenic microbes which were enhanced as a result of a dietary treatment with an obesogenic diet. In contrast, trans‐resveratrol induced fewer changes in microbiota composition, suggesting that the beneficial effects of the stilbene are not probably related to its effects on microbiota. Moreover, it could be emphasized that biological effects exerted by tested polyphenols (trans‐resveratrol and quercetin), although not perfectly elucidated, caused a significant impact on host faecal metabolome. Importantly, faecal non‐targeted metabolomics approach, conducted by the combination of LC‐MS, has been shown to be a robust technique that could be useful in nutrition science evidencing its potential to cluster individuals based on the dietary intervention they have been subjected to. This technique has allowed us to detect and discriminate the metabolic fingerprint of the impact of quercetin and trans‐resveratrol on metabolism. Besides, the capacity of this kind of approaches to detect gut microbial co‐metabolites has been proven. Furthermore, based on the fact that beneficial outcomes of polyphenols such as trans‐resveratrol might come from its straight contact with the intestine, in our study the strong influence produced by such polyphenol at the intestinal level has been revealed, demonstrating that trans‐resveratrol could affect the expression patterns of different genes in enterocytes, especially those associated to lipid metabolism.

214

VI. CONCLUSIONS

Conclusions

Conclusions

1. A non‐targeted metabolomics (H1‐NMR) approach identified a differential serum metabolic fingerprint between control diet‐fed lean and HFS diet‐fed obese rats, reflecting alterations in biochemical pathways, specifically influencing amino acid and protein metabolism.

2. High‐throughput next generation sequencing evidenced that the intake of an obesogenic diet produces structural modifications in gut microbiota composition leading to an obesity‐associated microbial profile. Intestinal bacterial dysbiosis was characterized by modifications at different taxonomic levels, not only affecting the abundance of major phyla, but also the levels of several pathogenic species.

3. From all the tested compounds, naringin was the most promising one, producing a slight amelioration on diet‐induced obesity‐associated features such as adiposity and insulin sensitivity.

4. Insulin sensitizing effects of quercetin might be mediated by the re‐shaping of diet‐induced gut microbiota, being particularly remarkable the reversion of Firmicutes‐to‐Bacteroidetes ratio, the inhibition of Erysipelotrichaceae and Eubacterium cylindroides, and the enhancement of Akkermansia muciniphila. In contrast, trans‐resveratrol administration scarcely modified the gut microbial community profile.

5. Trans‐resveratrol modulated the expression of inflammation‐related genes (TLR‐4, IL‐18, LPB) in colonic mucosa inferring that upon damage, this stilbene induces a defence mechanism through the activation of pattern recognition receptors that may promote intestinal repair mechanisms, such as the increase in tight junction proteins and occludin.

6. Faecal non‐targeted metabolomics identified gut microbial trans‐resveratrol co‐metabolites (i.e. dihydroresveratrol and lunularin) and compounds related to nucleotide metabolism. However, candidate compounds in the quercetin‐treated group were related to carbohydrate derivatives or conjugates. A distinctive faecal metabolic fingerprint was detected, which might be reflective of the overall impact of polyphenols on host metabolome.

7. Pre‐treatment with trans‐resveratrol exerted an inhibitory action on the expression of lipid metabolism‐related genes in human intestinal cells. Enzymes directly involved in lipid synthesis and cholesterol metabolism (ACSL3 and LIPG), but also transcription factors (KLF5 and AREG) were downregulated, suggesting that trans‐resveratrol might exert anti‐lipogenic effects at intestinal level under pro‐inflammatory conditions.

217

VII. REFERENCES

References

References

‐A‐

Aagaard K., Ma J., Antony K.M., Ganu R. et al. 2014. The placenta harbors a unique microbiome. Sci Transl Med 6: 237ra265. Aagaard K., Petrosino J., Keitel W., Watson M. et al. 2013. The Human Microbiome Project strategy for comprehensive sampling of the human microbiome and why it matters. FASEB J 27: 1012‐1022. Abete I., Navas‐Carretero S., Marti A., Martinez J.A. 2012. Nutrigenetics and nutrigenomics of caloric restriction. Prog Mol Biol Transl Sci 108: 323‐346. Adan R.A. 2013. Mechanisms underlying current and future anti‐obesity drugs. Trends Neurosci 36: 133‐140. Aguirre L., Arias N., Macarulla M.T., Gracia A., Portillo M.P. 2011. Beneficial effects of quercetin on obesity and diabetes. Open Nutraceuticals J 4: 189‐198. Aguirre L., Fernandez‐Quintela A., Arias N., Portillo M.P. 2014. Resveratrol: anti‐obesity mechanisms of action. Molecules 19: 18632‐18655. Ahn J., Lee H., Kim S., Park J., Ha T. 2008. The anti‐obesity effect of quercetin is mediated by the AMPK and MAPK signaling pathways. Biochem Biophys Res Commun 373: 545‐549. Alam A., Leoni G., Wentworth C.C., Kwal J.M. et al. 2014. Redox signaling regulates commensal‐mediated mucosal homeostasis and restitution and requires formyl peptide receptor 1. Mucosal Immunol 7: 645‐655. Alberdi G., Rodriguez V.M., Macarulla M.T., Miranda J. et al. 2013a. Hepatic lipid metabolic pathways modified by resveratrol in rats fed an obesogenic diet. Nutrition 29: 562‐567. Alberdi G., Rodriguez V.M., Miranda J., Macarulla M.T. et al. 2011. Changes in white adipose tissue metabolism induced by resveratrol in rats. Nutr Metab (Lond) 8: 29. Alberdi G., Rodriguez V.M., Miranda J., Macarulla M.T. et al. 2013b. Thermogenesis is involved in the body‐fat lowering effects of resveratrol in rats. Food Chem 141: 1530‐1535. Albuquerque D., Stice E., Rodriguez‐Lopez R., Manco L., Nobrega C. 2015. Current review of genetics of human obesity: from molecular mechanisms to an evolutionary perspective. Mol Genet Genomics. Doi:10.1007/s00438‐015‐1015‐9. Andersen O.M., Markham K.R. 2005. Flavonoids: Chemistry, Biochemistry and Applications. CRC Press. Andres‐Lacueva C., Macarulla M.T., Rotches‐Ribalta M., Boto‐Ordonez M. et al. 2012. Distribution of resveratrol metabolites in liver, adipose tissue, and skeletal muscle in rats fed different doses of this polyphenol. J Agric Food Chem 60: 4833‐4840. Andrés‐Lacueva C., Medina‐Remon A., Llorach R., Urpi‐Sarda M. et al. 2009. Phenolic Compounds: Chemistry and Occurrence in Fruits and Vegetables. in Fruit and Vegetable Phytochemicals, pp. 53‐88. Wiley‐Blackwell. Anhe F.F., Roy D., Pilon G., Dudonne S. et al. 2015. A polyphenol‐rich cranberry extract protects from diet‐induced obesity, insulin resistance and intestinal inflammation in association with increased Akkermansia spp. population in the gut microbiota of mice. Gut 64: 872‐883. Apalasamy Y.D., Mohamed Z. 2015. Obesity and genomics: role of technology in unraveling the complex genetic architecture of obesity. Hum Genet 134: 361‐374. Arias N. 2015. Efectos de la combinación de biomoléculas presentes en los alimentos, ácido linoléico conjugado, resveratrol y quercetina, sobre la acumulación de grasa corporal. Doctoral Thesis performed in the Department of Nutrition and Food Science. University of the Basque Country, Vitoria‐Gasteiz. Arias N., Macarulla M.T., Aguirre L., Martinez‐Castano M.G., Portillo M.P. 2014. Quercetin can reduce insulin resistance without decreasing adipose tissue and skeletal muscle fat accumulation. Genes Nutr 9: 361. Aronne L.J. 2014. Evolving directions in obesity management. J Fam Pract 63: S27‐33. Arrieta M.C., Stiemsma L.T., Amenyogbe N., Brown E.M., Finlay B. 2014. The intestinal microbiome in early life: health and disease. Front Immunol 5: 427. Arumugam M., Raes J., Pelletier E., Le Paslier D. et al. 2011. Enterotypes of the human gut microbiome. Nature 473: 174‐ 180. Asp N.G. 1997. Resistant starch‐‐an update on its physiological effects. Adv Exp Med Biol 427: 201‐210. Astarita G., Langridge J. 2013. An emerging role for metabolomics in nutrition science. J Nutrigenet Nutrigenomics 6: 181‐ 200. Aura A.‐M. 2008. Microbial metabolism of dietary phenolic compounds in the colon. Phytochem Rev 7: 407‐429.

221 References

Aw W., Fukuda S. 2015. Toward the comprehensive understanding of the gut ecosystem via metabolomics‐based integrated omics approach. Semin Immunopathol 37: 5‐16. ‐B‐

Backhed F., Ding H., Wang T., Hooper L.V. et al. 2004. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A 101: 15718‐15723. Backhed F., Ley R.E., Sonnenburg J.L., Peterson D.A., Gordon J.I. 2005. Host‐bacterial mutualism in the human intestine. Science 307: 1915‐1920. Backhed F., Manchester J.K., Semenkovich C.F., Gordon J.I. 2007. Mechanisms underlying the resistance to diet‐induced obesity in germ‐free mice. Proc Natl Acad Sci U S A 104: 979‐984. Barja‐Fernandez S., Leis R., Casanueva F.F., Seoane L.M. 2014. Drug development strategies for the treatment of obesity: how to ensure efficacy, safety, and sustainable weight loss. Drug Des Devel Ther 8: 2391‐2400. Bergman E.N. 1990. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol Rev 70: 567‐590. Bergstrom A., Skov T.H., Bahl M.I., Roager H.M. et al. 2014. Establishment of intestinal microbiota during early life: a longitudinal, explorative study of a large cohort of Danish infants. Appl Environ Microbiol 80: 2889‐2900. Bermudez‐Brito M., Plaza‐Diaz J., Munoz‐Quezada S., Gomez‐Llorente C., Gil A. 2012. Probiotic mechanisms of action. Anna Nutr Metab 61: 160‐174. Bernstein C., Holubec H., Bhattacharyya A.K., Nguyen H. et al. 2011. Carcinogenicity of deoxycholate, a secondary bile acid. Arch Toxicol 85: 863‐871. Bevins C.L., Salzman N.H. 2011. Paneth cells, antimicrobial peptides and maintenance of intestinal homeostasis. Nat Rev Microbiol 9: 356‐368. Bhagwat S., Haytowitz D.B., Holden J.M. 2011. USDA database for the flavonoid content of selected foods, Release 3.1. Beltsville: US Department of Agriculture: 03‐01. Biagi E., Candela M., Fairweather‐Tait S., Franceschi C., Brigidi P. 2012. Aging of the human metaorganism: the microbial counterpart. Age (Dordr) 34: 247‐267. Biagi E., Candela M., Turroni S., Garagnani P. et al. 2013. Ageing and gut microbes: perspectives for health maintenance and longevity. Pharmacol Res 69: 11‐20. Biagi E., Nylund L., Candela M., Ostan R. et al. 2010. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS One 5: e10667. Biasucci G., Benenati B., Morelli L., Bessi E., Boehm G. 2008. Cesarean delivery may affect the early biodiversity of intestinal bacteria. J Nutr 138: 1796S‐1800S. Biasucci G., Rubini M., Riboni S., Morelli L. et al. 2010. Mode of delivery affects the bacterial community in the newborn gut. Early Hum Dev 86 Suppl 1: 13‐15. Bindels L.B., Delzenne N.M., Cani P.D., Walter J. 2015. Towards a more comprehensive concept for prebiotics. Nat Rev Gastroenterol Hepatol. Doi:10.1038/nrgastro.2015.47. Bingham S.A. 2003. Urine nitrogen as a biomarker for the validation of dietary protein intake. J Nutr 133 Suppl 3: 921S‐ 924S. Bode L.M., Bunzel D., Huch M., Cho G.S. et al. 2013. In vivo and in vitro metabolism of trans‐resveratrol by human gut microbiota. Am J Clin Nutr 97: 295‐309. Bolca S., Van de Wiele T., Possemiers S. 2013. Gut metabotypes govern health effects of dietary polyphenols. Curr Opin Biotechnol 24: 220‐225. Borgia G., Maraolo A.E., Foggia M., Buonomo A.R., Gentile I. 2015. Fecal microbiota transplantation for Clostridium difficile infection: back to the future. Expert Opin Biol Ther 15: 1001‐1014. Borody T.J., Khoruts A. 2012. Fecal microbiota transplantation and emerging applications. Nat Rev Gastroenterol Hepatol 9: 88‐96. Borriello A., Cucciolla V., Della Ragione F., Galletti P. 2010. Dietary polyphenols: focus on resveratrol, a promising agent in the prevention of cardiovascular diseases and control of glucose homeostasis. Nutr Metab Cardiovasc Dis 20: 618‐ 625. Brantley P.J., Myers V.H., Roy H.J. 2005. Environmental and lifestyle influences on obesity. J La State Med Soc 157 Spec No 1: S19‐27. Bravo L. 1998. Polyphenols: chemistry, dietary sources, metabolism, and nutritional significance. Nutr Rev 56: 317‐333. Brennan L. 2013. Metabolomics in nutrition research: current status and perspectives. Biochem Soc Trans 41: 670‐673.

222 References

Brinkworth G.D., Noakes M., Clifton P.M., Bird A.R. 2009. Comparative effects of very low‐carbohydrate, high‐fat and high‐carbohydrate, low‐fat weight‐loss diets on bowel habit and faecal short‐chain fatty acids and bacterial populations. Br J Nutr 101: 1493‐1502. Britton R.G., Kovoor C., Brown K. 2015. Direct molecular targets of resveratrol: identifying key interactions to unlock complex mechanisms. Ann N Y Acad Sci. Doi:10.1111/nyas.12796. Bubnov R.V., Spivak M.Y., Lazarenko L.M., Bomba A., Boyko N.V. 2015. Probiotics and immunity: provisional role for personalized diets and disease prevention. EPMA J 6: 14. Byrne C.S., Chambers E.S., Morrison D.J., Frost G. 2015. The role of short chain fatty acids in appetite regulation and energy homeostasis. Int J Obes (Lond) 39: 1331‐1338. ‐C‐

Camilleri M., Madsen K., Spiller R., Greenwood‐Van Meerveld B., Verne G.N. 2012. Intestinal barrier function in health and gastrointestinal disease. Neurogastroenterol Motil 24: 503‐512. Cammarota G., Ianiro G., Cianci R., Bibbo S. et al. 2015. The involvement of gut microbiota in inflammatory bowel disease pathogenesis: potential for therapy. Pharmacol Ther 149: 191‐212. Canani R.B., Costanzo M.D., Leone L., Pedata M. et al. 2011. Potential beneficial effects of butyrate in intestinal and extraintestinal diseases. World J Gastroenterol 17: 1519‐1528. Carding S., Verbeke K., Vipond D.T., Corfe B.M., Owen L. 2015. Dysbiosis of the gut microbiota in disease. Microb Ecol Health Dis 26: 26191‐http://dx.doi.org/10.3402/mehd.v26.26191 Cardona F., Andres‐Lacueva C., Tulipani S., Tinahones F.J., Queipo‐Ortuno M.I. 2013. Benefits of polyphenols on gut microbiota and implications in human health. J Nutr Biochem 24: 1415‐1422. Carpene C., Gomez‐Zorita S., Deleruyelle S., Carpene M.A. 2015. Novel strategies for preventing diabetes and obesity complications with natural polyphenols. Curr Med Chem 22: 150‐164. Cencic A., Chingwaru W. 2010. The role of functional foods, nutraceuticals, and food supplements in intestinal health. Nutrients 2: 611‐625. Chambers E.S., Morrison D.J., Frost G. 2014. Control of appetite and energy intake by SCFA: what are the potential underlying mechanisms? Proc Nutr Soc. Doi:10.1017/s0029665114001657: 1‐9. Chapman C.M., Gibson G.R., Rowland I. 2011. Health benefits of probiotics: are mixtures more effective than single strains? Eur J Nutr 50: 1‐17. Chassard C., Delmas E., Robert C., Bernalier‐Donadille A. 2010. The cellulose‐degrading microbial community of the human gut varies according to the presence or absence of methanogens. FEMS Microbiol Ecol 74: 205‐213. Chassard C., Delmas E., Robert C., Lawson P.A., Bernalier‐Donadille A. 2012. Ruminococcus champanellensis sp. nov., a cellulose‐degrading bacterium from human gut microbiota. Int J Syst Evol Microbiol 62: 138‐143. Cho I., Blaser M.J. 2012. The human microbiome: at the interface of health and disease. Nat Rev Genet 13: 260‐270. Chung H., Pamp S.J., Hill J.A., Surana N.K. et al. 2012. Gut immune maturation depends on colonization with a host‐ specific microbiota. Cell 149: 1578‐1593. Cianciulli A., Calvello R., Cavallo P., Dragone T. et al. 2012. Modulation of NF‐kappaB activation by resveratrol in LPS treated human intestinal cells results in downregulation of PGE2 production and COX‐2 expression. Toxicol In Vitro 26: 1122‐1128. Claesson M.J., Cusack S., O'Sullivan O., Greene‐Diniz R. et al. 2011. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc Natl Acad Sci U S A 108 Suppl 1: 4586‐4591. Claesson M.J., Jeffery I.B., Conde S., Power S.E. et al. 2012. Gut microbiota composition correlates with diet and health in the elderly. Nature 488: 178‐184. Claus S.P., Swann J.R. 2013. Nutrimetabonomics:applications for nutritional sciences, with specific reference to gut microbial interactions. Annu Rev Food Sci Technol 4: 381‐399. ClinicalTrials.gov. 2013. Investigating the Use of Quercetin on Glucose Absorption in Obesity, and Obesity With Type 2 Diabetes. Available at: https://clinicaltrials.gov/ct2/show/NCT00065676 [2 October 2015]. ClinicalTrials.gov. 2014. Fecal microbiota transplantation for the treatment of metabolic syndrome. Available at: https://clinicaltrials.gov/ct2/show/NCT02050607 [2 October 2015]. Conlon M.A., Bird A.R. 2015. The impact of diet and lifestyle on gut microbiota and human health. Nutrients 7: 17‐44. Consortium T. 2012. Structure, function and diversity of the healthy human microbiome. Nature 486: 207‐214. Conterno L., Fava F., Viola R., Tuohy K.M. 2011. Obesity and the gut microbiota: does up‐regulating colonic fermentation protect against obesity and metabolic disease? Genes Nutr 6: 241‐260.

223 References

Corzo N., Alonso J.L., Azpiroz F., Calvo M.A. et al. 2015. [Prebiotics: concept, properties and beneficial effects]. Nutr Hosp 31 Suppl 1: 99‐118. Costello E.K., Lauber C.L., Hamady M., Fierer N. et al. 2009. Bacterial community variation in human body habitats across space and time. Science 326: 1694‐1697. Cotillard A., Kennedy S.P., Kong L.C., Prifti E. et al. 2013. Dietary intervention impact on gut microbial gene richness. Nature 500: 585‐588. Courant F., Antignac J.P., Dervilly‐Pinel G., Le Bizec B. 2014. Basics of mass spectrometry based metabolomics. Proteomics 14: 2369‐2388. Croucher L.J., Bury J.P., Williams E.A., Riley S.A., Corfe B.M. 2008. Commonly used bowel preparations have significant and different effects upon cell proliferation in the colon: a pilot study. BMC Gastroenterol 8: 54. Cueva C., Bartolome B., Moreno‐Arribas M.V., Bustos I. et al. 2015. Susceptibility and tolerance of human gut culturable aerobic microbiota to wine polyphenols. Microb Drug Resist 21: 17‐24. Cummings J.H., Macfarlane G.T. 1991. The control and consequences of bacterial fermentation in the human colon. J Appl Bacteriol 70: 443‐459. ‐D‐

Daly K., Shirazi‐Beechey S.P. 2006. Microarray analysis of butyrate regulated genes in colonic epithelial cells. DNA Cell Biol 25: 49‐62. Dao M.C., Everard A., Aron‐Wisnewsky J., Sokolovska N. et al. 2015. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut. Doi:10.1136/gutjnl‐2014‐308778. Dash S., Xiao C., Morgantini C., Szeto L., Lewis G.F. 2013. High‐dose resveratrol treatment for 2 weeks inhibits intestinal and hepatic lipoprotein production in overweight/obese men. Arterioscler Thromb Vasc Biol 33: 2895‐2901. David L.A., Maurice C.F., Carmody R.N., Gootenberg D.B. et al. 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505: 559‐563. Day A.J., Gee J.M., DuPont M.S., Johnson I.T., Williamson G. 2003. Absorption of quercetin‐3‐glucoside and quercetin‐4'‐ glucoside in the rat small intestine: the role of lactase phlorizin hydrolase and the sodium‐dependent glucose transporter. Biochem Pharmacol 65: 1199‐1206. De Filippo C., Cavalieri D., Di Paola M., Ramazzotti M. et al. 2010. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A 107: 14691‐14696. de Kivit S., Tobin M.C., Forsyth C.B., Keshavarzian A., Landay A.L. 2014. Regulation of Intestinal Immune Responses through TLR Activation: Implications for Pro‐ and Prebiotics. Front Immunol 5: 60. de La Serre C.B., Ellis C.L., Lee J., Hartman A.L. et al. 2010. Propensity to high‐fat diet‐induced obesity in rats is associated with changes in the gut microbiota and gut inflammation. Am J Physiol Gastrointest Liver Physiol 299: G440‐448. De Santi C., Pietrabissa A., Spisni R., Mosca F., Pacifici G.M. 2000. Sulphation of resveratrol, a natural compound present in wine, and its inhibition by natural flavonoids. Xenobiotica 30: 857‐866. Deda O., Gika H.G., Wilson I.D., Theodoridis G.A. 2015. An overview of fecal sample preparation for global metabolic profiling. J Pharm Biomed Anal 113: 137‐150. Degnan P.H., Taga M.E., Goodman A.L. 2014. Vitamin B12 as a modulator of gut microbial ecology. Cell Metab 20: 769‐ 778. Del Rio D., Rodriguez‐Mateos A., Spencer J.P., Tognolini M. et al. 2013. Dietary (poly)phenolics in human health: structures, bioavailability, and evidence of protective effects against chronic diseases. Antioxid Redox Signal 18: 1818‐1892. Delzenne N., Blundell J., Brouns F., Cunningham K. et al. 2010. Gastrointestinal targets of appetite regulation in humans. Obes Rev 11: 234‐250. den Besten G., van Eunen K., Groen A.K., Venema K. et al. 2013. The role of short‐chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res 54: 2325‐2340. Dethlefsen L., Relman D.A. 2011. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U S A 108 Suppl 1: 4554‐4561. Dettmer K., Aronov P.A., Hammock B.D. 2007. Mass spectrometry‐based metabolomics. Mass Spectrom Rev 26: 51‐78. Dhurandhar E.J., Keith S.W. 2014. The aetiology of obesity beyond eating more and exercising less. Best Pract Res Clin Gastroenterol 28: 533‐544. Di Dalmazi G., Vicennati V., Pasquali R., Pagotto U. 2013. The unrelenting fall of the pharmacological treatment of obesity. Endocrine 44: 598‐609.

224 References

Diaz Heijtz R., Wang S., Anuar F., Qian Y. et al. 2011. Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci U S A 108: 3047‐3052. Dinan T.G., Stilling R.M., Stanton C., Cryan J.F. 2015. Collective unconscious: How gut microbes shape human behavior. J Psychiatr Res 63: 1‐9. Dixon J.B. 2010. The effect of obesity on health outcomes. Mol Cel Endocrinol 316: 104‐108. Dominguez‐Bello M.G., Costello E.K., Contreras M., Magris M. et al. 2010. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci U S A 107: 11971‐ 11975. Dong J., Zhang X., Zhang L., Bian H.X. et al. 2014. Quercetin reduces obesity‐associated ATM infiltration and inflammation in mice: a mechanism including AMPKalpha1/SIRT1. J Lipid Res 55: 363‐374. Dore J., Blottiere H. 2015. The influence of diet on the gut microbiota and its consequences for health. Curr Opin Biotechnol 32: 195‐199. Doré J., Corthier G. 2010. The human intestinal microbiota. Gastroentérologie Clinique et Biologique 34, Supplement 1: S7‐ S15. Doré J., Leclerc M., Juste C., Lepage P. et al. 2010. The human intestinal microbiota: from phylogenetics to functional metagenomics. Intestinal Microbiomics: Novel Indicators of Health and Disease Herborn: Old Herborn University Foundation: 15‐22. Douglas‐Escobar M., Elliott E., Neu J. 2013. Effect of intestinal microbial ecology on the developing brain. JAMA Pediatr 167: 374‐379. Dragsted L.O. 2010. Biomarkers of meat intake and the application of nutrigenomics. Meat Sci 84: 301‐307. Drosos I., Tavridou A., Kolios G. 2015. New Aspects on the Metabolic role of Intestinal Microbiota in the Development of Atherosclerosis. Metabolism 64: 476‐481. Druart C., Alligier M., Salazar N., Neyrinck A.M., Delzenne N.M. 2014. Modulation of the gut microbiota by nutrients with prebiotic and probiotic properties. Adv Nutr 5: 624S‐633S. Du F., Virtue A., Wang H., Yang X.F. 2013. Metabolomic analyses for atherosclerosis, diabetes, and obesity. Biomark Res 1: 17. Duca F.A., Sakar Y., Lepage P., Devime F. et al. 2014. Replication of obesity and associated signaling pathways through transfer of microbiota from obese‐prone rats. Diabetes 63: 1624‐1636. Duda‐Chodak A., Tarko T., Satora P., Sroka P. 2015. Interaction of dietary compounds, especially polyphenols, with the intestinal microbiota: a review. Eur J Nutr 54: 325‐341. Duenas M., Munoz‐Gonzalez I., Cueva C., Jimenez‐Giron A. et al. 2015. A survey of modulation of gut microbiota by dietary polyphenols. Biomed Res Int 2015: 850902. Duggan G.E., Hittel D.S., Sensen C.W., Weljie A.M. et al. 2011. Metabolomic response to exercise training in lean and diet‐ induced obese mice. J Appl Physiol (1985) 110: 1311‐1318. Duncan S.H., Louis P., Thomson J.M., Flint H.J. 2009. The role of pH in determining the species composition of the human colonic microbiota. Environ Microbiol 11: 2112‐2122. Durban A., Abellan J.J., Jimenez‐Hernandez N., Ponce M. et al. 2011. Assessing gut microbial diversity from feces and rectal mucosa. Microb Ecol 61: 123‐133. ‐E‐

Eckburg P.B., Bik E.M., Bernstein C.N., Purdom E. et al. 2005. Diversity of the human intestinal microbial flora. Science 308: 1635‐1638. Edmands W.M., Beckonert O.P., Stella C., Campbell A. et al. 2011. Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling. J Proteome Res 10: 4513‐4521. Edwards R.L., Lyon T., Litwin S.E., Rabovsky A. et al. 2007. Quercetin reduces blood pressure in hypertensive subjects. J Nutr 137: 2405‐2411. Egert S., Boesch‐Saadatmandi C., Wolffram S., Rimbach G., Muller M.J. 2010. Serum lipid and blood pressure responses to quercetin vary in overweight patients by apolipoprotein E genotype. J Nutr 140: 278‐284. Egert S., Bosy‐Westphal A., Seiberl J., Kurbitz C. et al. 2009. Quercetin reduces systolic blood pressure and plasma oxidised low‐density lipoprotein concentrations in overweight subjects with a high‐cardiovascular disease risk phenotype: a double‐blinded, placebo‐controlled cross‐over study. Br J Nutr 102: 1065‐1074. Egert S., Wolffram S., Bosy‐Westphal A., Boesch‐Saadatmandi C. et al. 2008. Daily quercetin supplementation dose‐ dependently increases plasma quercetin concentrations in healthy humans. J Nutr 138: 1615‐1621.

225 References

Eknoyan G. 2006. A history of obesity, or how what was good became ugly and then bad. Adv Chronic Kidney Dis 13: 421‐ 427. Elliott P., Posma J.M., Chan Q., Garcia‐Perez I. et al. 2015. Urinary metabolic signatures of human adiposity. Sci Transl Med 7: 285ra262. Eloe‐Fadrosh E.A., Rasko D.A. 2013. The human microbiome: from symbiosis to pathogenesis. Annu Rev Med 64: 145‐ 163. Escobedo G., Lopez‐Ortiz E., Torres‐Castro I. 2014. Gut microbiota as a key player in triggering obesity, systemic inflammation and insulin resistance. Rev Invest Clin 66: 450‐459. Eseberri I., Lasa A., Churruca I., Portillo M.P. 2013. Resveratrol metabolites modify adipokine expression and secretion in 3T3‐L1 pre‐adipocytes and mature adipocytes. PLoS One 8: e63918. Eseberri I., Miranda J., Lasa A., Churruca I. et al. 2015. Doses of Quercetin in the Range of Serum Concentrations Exert Delipidating Effects in 3T3‐L1 Preadipocytes by Acting on Different Stages of Adipogenesis, but Not in Mature Adipocytes. Oxid M Cel Longev 2015: 11. Everard A., Belzer C., Geurts L., Ouwerkerk J.P. et al. 2013. Cross‐talk between Akkermansia muciniphila and intestinal epithelium controls diet‐induced obesity. Proc Natl Acad Sci U S A 110: 9066‐9071. ‐F‐

Faith J.J., Guruge J.L., Charbonneau M., Subramanian S. et al. 2013. The long‐term stability of the human gut microbiota. Science 341: 1237439. Fak F., Backhed F. 2012. Lactobacillus reuteri prevents diet‐induced obesity, but not atherosclerosis, in a strain dependent fashion in Apoe‐/‐ mice. PLoS One 7: e46837. Fallucca F., Fontana L., Fallucca S., Pianesi M. 2015. Gut microbiota and Ma‐Pi 2 macrobiotic diet in the treatment of type 2 diabetes. World J Diabetes 6: 403‐411. Fava F., Gitau R., Griffin B.A., Gibson G.R. et al. 2013. The type and quantity of dietary fat and carbohydrate alter faecal microbiome and short‐chain fatty acid excretion in a metabolic syndrome 'at‐risk' population. Int J Obes (Lond) 37: 216‐223. Fitch A., Everling L., Fox C., Goldberg J. et al. 2013. Prevention and management of obesity for adults. Available at: https://www.icsi.org/guidelines__more/catalog_guidelines_and_more/catalog_guidelines/catalog_endocrine_guide lines/obesity__adults/ [08 October]. Fleissner C.K., Huebel N., Abd El‐Bary M.M., Loh G. et al. 2010. Absence of intestinal microbiota does not protect mice from diet‐induced obesity. Br J Nutr 104: 919‐929. Flint H.J., Scott K.P., Duncan S.H., Louis P., Forano E. 2012. Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3: 289‐306. Fond G., Boukouaci W., Chevalier G., Regnault A. et al. 2015. The "psychomicrobiotic": Targeting microbiota in major psychiatric disorders: A systematic review. Pathol Biol (Paris) 63: 35‐42. Francino M.P. 2014. Early development of the gut microbiota and immune health. Pathogens 3: 769‐790. Frank D.N., Pace N.R. 2008. Gastrointestinal microbiology enters the metagenomics era. Curr Opin Gastroenterol 24: 4‐10. Funkhouser L.J., Bordenstein S.R. 2013. Mom knows best: the universality of maternal microbial transmission. PLoS Biol 11: e1001631. Furusawa Y., Obata Y., Fukuda S., Endo T.A. et al. 2013. Commensal microbe‐derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504: 446‐450. ‐G‐

Gambini J., Ingles M., Olaso G., Lopez‐Grueso R. et al. 2015. Properties of Resveratrol: In Vitro and In Vivo Studies about Metabolism, Bioavailability, and Biological Effects in Animal Models and Humans. Oxid Med Cell Longev: 837042. Doi: 10.1155/2015/837042. Gargallo Fernandez M., Quiles Izquierdo J., Basulto Marset J., Breton Lesmes I. et al. 2012. Evidence‐based nutritional recommendations for the prevention and treatment of overweight and obesity in adults (FESNAD‐SEEDO consensus document). The role of diet in obesity prevention (II/III). Nutr Hosp 27: 800‐832. German J.B., Hammock B.D., Watkins S.M. 2005. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics 1: 3‐9. Gerritsen J., Smidt H., Rijkers G.T., de Vos W.M. 2011. Intestinal microbiota in human health and disease: the impact of probiotics. Genes Nutr 6: 209‐240.

226 References

Gibson G.R., Roberfroid M.B. 1995. Dietary modulation of the human colonic microbiota: introducing the concept of prebiotics. J Nutr 125: 1401‐1412. Gibson G.R., Scott K.P., Rastall R.A., Tuohy K.M. et al. 2010. Dietary prebiotics: current status and new definition. Food Sci Technol Bull Funct Foods: 1‐19. Gill S.R., Pop M., Deboy R.T., Eckburg P.B. et al. 2006. Metagenomic analysis of the human distal gut microbiome. Science 312: 1355‐1359. Gomez‐Zorita S., Fernandez‐Quintela A., Lasa A., Hijona E. et al. 2013. Effects of resveratrol on obesity‐related inflammation markers in adipose tissue of genetically obese rats. Nutrition 29: 1374‐1380. Gomez‐Zorita S., Fernandez‐Quintela A., Macarulla M.T., Aguirre L. et al. 2012. Resveratrol attenuates steatosis in obese Zucker rats by decreasing fatty acid availability and reducing oxidative stress. Br J Nutr 107: 202‐210. Gonzalez‐Castejon M., Rodriguez‐Casado A. 2011. Dietary phytochemicals and their potential effects on obesity: a review. Pharmacol Res 64: 438‐455. Gordon J.I. 2012. Honor thy gut symbionts redux. Science 336: 1251‐1253. Gortmaker S.L., Swinburn B.A., Levy D., Carter R. et al. 2011. Changing the future of obesity: science, policy, and action. Lancet 378: 838‐847. Gotsis E., Anagnostis P., Mariolis A., Vlachou A. et al. 2015. Health benefits of the Mediterranean Diet: an update of research over the last 5 years. Angiology 66: 304‐318. Greenblum S., Carr R., Borenstein E. 2015. Extensive strain‐level copy‐number variation across human gut microbiome species. Cell 160: 583‐594. Gronlund M.M., Lehtonen O.P., Eerola E., Kero P. 1999. Fecal microflora in healthy infants born by different methods of delivery: permanent changes in intestinal flora after cesarean delivery. J Pediatr Gastroenterol Nutr 28: 19‐25. Guh D.P., Zhang W., Bansback N., Amarsi Z. et al. 2009. The incidence of co‐morbidities related to obesity and overweight: a systematic review and meta‐analysis. BMC Public Health 9: 88. Guinane C.M., Cotter P.D. 2013. Role of the gut microbiota in health and chronic gastrointestinal disease: understanding a hidden metabolic organ. Therap Adv Gastroenterol 6: 295‐308. Guo Y., Bruno R.S. 2015. Endogenous and exogenous mediators of quercetin bioavailability. J Nutr Biochem 26: 201‐210. ‐H‐

Hall K.D., Heymsfield S.B., Kemnitz J.W., Klein S. et al. 2012. Energy balance and its components: implications for body weight regulation. Am J Clin Nutr 95: 989‐994. Hamady M., Knight R. 2009. Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome Res 19: 1141‐1152. Harnly J.M., Doherty R.F., Beecher G.R., Holden J.M. et al. 2006. Flavonoid content of U.S. fruits, vegetables, and nuts. J Agric Food Chem 54: 9966‐9977. Harris K., Kassis A., Major G., Chou C.J. 2012. Is the gut microbiota a new factor contributing to obesity and its metabolic disorders? J Obes 2012: 879151. Haslam D. 2007. Obesity: a medical history. Obes Rev 8 Suppl 1: 31‐36. Hattori M., Taylor T. 2014. HMGJ (Japan). in Encyclopedia of Metagenomics (ed. KE Nelson), pp. 1‐5. Springer New York. Haytowitz D., Bhagwat S., Holden J. 2013. Sources of variability in the flavonoid content of foods. Procedia Food Science 2: 46‐51. Hedrick V.E., Dietrich A.M., Estabrooks P.A., Savla J. et al. 2012. Dietary biomarkers: advances, limitations and future directions. Nutr J 11: 109. Heinzmann S.S., Brown I.J., Chan Q., Bictash M. et al. 2010. Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am J Clin Nutr 92: 436‐443. Henagan T.M., Cefalu W.T., Ribnicky D.M., Noland R.C. et al. 2015. In vivo effects of dietary quercetin and quercetin‐rich red onion extract on skeletal muscle mitochondria, metabolism, and insulin sensitivity. Genes Nutr 10: 451. Hildebrandt M.A., Hoffmann C., Sherrill–Mix S.A., Keilbaugh S.A. et al. 2009. High‐Fat Diet Determines the Composition of the Murine Gut Microbiome Independently of Obesity. Gastroenterology 137: 1716‐1724.e1712. Hill C., Guarner F., Reid G., Gibson G.R. et al. 2014. Expert consensus document. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat Rev Gastroenterol Hepatol 11: 506‐514. Hill M.J. 1997. Intestinal flora and endogenous vitamin synthesis. Eur J Cancer Prev 6 Suppl 1: S43‐45.

227 References

Hinney A., Vogel C.I., Hebebrand J. 2010. From monogenic to polygenic obesity: recent advances. Eur Child Adolesc Psychiatry 19: 297‐310. Hobden M.R., Guerin‐Deremaux L., Rowland I., Gibson G.R., Kennedy O.B. 2015. Potential anti‐obesogenic properties of non‐digestible carbohydrates: specific focus on resistant dextrin. Proc Nutr Soc 74: 258‐267. Hold G.L., Pryde S.E., Russell V.J., Furrie E., Flint H.J. 2002. Assessment of microbial diversity in human colonic samples by 16S rDNA sequence analysis. FEMS Microbiol Ecol 39: 33‐39. Hollman P.C.H., Arts I.C.W. 2000. Flavonols, flavones and flavanols–nature, occurrence and dietary burden. J Sci Food Agric 80: 1081‐1093. Hooper L.V., Gordon J.I. 2001. Commensal host‐bacterial relationships in the gut. Science 292: 1115‐1118. Howitz K.T., Bitterman K.J., Cohen H.Y., Lamming D.W. et al. 2003. Small molecule activators of sirtuins extend Saccharomyces cerevisiae lifespan. Nature 425: 191‐196. Hsu C.L., Yen G.C. 2006. Induction of cell apoptosis in 3T3‐L1 pre‐adipocytes by flavonoids is associated with their antioxidant activity. Mol Nutr Food Res 50: 1072‐1079. Hubbard G.P., Wolffram S., Lovegrove J.A., Gibbins J.M. 2004. Ingestion of quercetin inhibits platelet aggregation and essential components of the collagen‐stimulated platelet activation pathway in humans. J Thromb Haemost 2: 2138‐ 2145. Huse S.M., Ye Y., Zhou Y., Fodor A.A. 2012. A core human microbiome as viewed through 16S rRNA sequence clusters. PLoS One 7: e34242. Hussain H.T., Parker J.L., Sharma A.M. 2015. Clinical trial success rates of anti‐obesity agents: the importance of combination therapies. Obes Rev 16: 707‐714. Hussey S., Wall R., Gruffman E., O'Sullivan L. et al. 2011. Parenteral antibiotics reduce bifidobacteria colonization and diversity in neonates. Int J Microbiol 2011. ‐I‐

Ibanez C., Simo C., Garcia‐Canas V., Acunha T., Cifuentes A. 2015. The role of direct high‐resolution mass spectrometry in foodomics. Anal Bioanal Chem 407: 6275‐6287. Isken F., Klaus S., Petzke K.J., Loddenkemper C. et al. 2010. Impairment of fat oxidation under high‐ vs. low‐glycemic index diet occurs before the development of an obese phenotype. Am J Physiol Endocrinol Metab 298: E287‐295. ‐J‐

Jakobsdottir G., Xu J., Molin G., Ahrne S., Nyman M. 2013. High‐fat diet reduces the formation of butyrate, but increases succinate, inflammation, liver fat and cholesterol in rats, while dietary fibre counteracts these effects. PLoS One 8: e80476. Jakobsson H.E., Abrahamsson T.R., Jenmalm M.C., Harris K. et al. 2014. Decreased gut microbiota diversity, delayed Bacteroidetes colonisation and reduced Th1 responses in infants delivered by caesarean section. Gut 63: 559‐566. Janeway C.A., Jr. 1989. Approaching the asymptote? Evolution and revolution in immunology. Cold Spring Harb Symp Quant Biol 54 Pt 1: 1‐13. Janssen A.W., Kersten S. 2015. The role of the gut microbiota in metabolic health. FASEB J. Doi:10.1096/fj.14‐269514. Jeffery I.B., O'Toole P.W. 2013. Diet‐microbiota interactions and their implications for healthy living. Nutrients 5: 234‐ 252. Jensen M.D., Ryan D.H., Apovian C.M., Ard J.D. et al. 2014. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol 63: 2985‐3023. Jin F., Nieman D.C., Shanely R.A., Knab A.M. et al. 2010. The variable plasma quercetin response to 12‐week quercetin supplementation in humans. Eur J Clin Nutr 64: 692‐697. Joseph S.V., Edirisinghe I., Burton‐Freeman B.M. 2015. Fruit polyphenols: A review of anti‐inflammatory effects in humans. Crit Rev Food Sci Nutr. Doi:10.1080/10408398.2013.767221: 0. Jost T., Lacroix C., Braegger C.P., Rochat F., Chassard C. 2014. Vertical mother‐neonate transfer of maternal gut bacteria via breastfeeding. Environ Microbiol 16: 2891‐2904. Jove M., Serrano J.C., Ortega N., Ayala V. et al. 2011. Multicompartmental LC‐Q‐TOF‐based metabonomics as an exploratory tool to identify novel pathways affected by polyphenol‐rich diets in mice. J Proteome Res 10: 3501‐ 3512. Jung C.H., Cho I., Ahn J., Jeon T.I., Ha T.Y. 2013. Quercetin reduces high‐fat diet‐induced fat accumulation in the liver by regulating lipid metabolism genes. Phytother Res 27: 139‐143.

228 References

Jung H.C., Eckmann L., Yang S.K., Panja A. et al. 1995. A distinct array of proinflammatory cytokines is expressed in human colon epithelial cells in response to bacterial invasion. J Clin Invest 95: 55‐65. ‐K‐

Kagnoff M.F. 2014. The intestinal epithelium is an integral component of a communications network. J Clin Invest 124: 2841‐2843. Kaldas M.I., Walle U.K., Walle T. 2003. Resveratrol transport and metabolism by human intestinal Caco‐2 cells. J Pharm Pharmacol 55: 307‐312. Kato H., Takahashi S., Saito K. 2011. Omics and integrated omics for the promotion of food and nutrition science. J Tradit Complement Med 1: 25‐30. Kaushik D., O'Fallon K., Clarkson P.M., Dunne C.P. et al. 2012. Comparison of quercetin pharmacokinetics following oral supplementation in humans. J Food Sci 77: H231‐238. Keenan M.J., Zhou J., Hegsted M., Pelkman C. et al. 2015. Role of resistant starch in improving gut health, adiposity, and insulin resistance. Adv Nutr 6: 198‐205. Keun H.C. 2006. Metabonomic modeling of drug toxicity. Pharmacol Ther 109: 92‐106. Khan A., Raza S., Khan Y., Aksoy T. et al. 2012. Current updates in the medical management of obesity. Recent Pat Endocr Metab Immune Drug Discov 6: 117‐128. Kim J.H., Kang M.J., Choi H.N., Jeong S.M. et al. 2011. Quercetin attenuates fasting and postprandial hyperglycemia in animal models of diabetes mellitus. Nutr Res Pract 5: 107‐111. Kitajima S., Takuma S., Morimoto M. 1999. Changes in colonic mucosal permeability in mouse colitis induced with dextran sulfate sodium. Exp Anim 48: 137‐143. Kleemann R., Verschuren L., Morrison M., Zadelaar S. et al. 2011. Anti‐inflammatory, anti‐proliferative and anti‐ atherosclerotic effects of quercetin in human in vitro and in vivo models. Atherosclerosis 218: 44‐52. Kleisiaris C.F., Sfakianakis C., Papathanasiou I.V. 2014. Health care practices in ancient Greece: The Hippocratic ideal. J Med Ethics Hist Med 7: 6. Knights D., Ward T.L., McKinlay C.E., Miller H. et al. 2014. Rethinking "enterotypes". Cell Host Microbe 16: 433‐437. Kobori M., Masumoto S., Akimoto Y., Oike H. 2011. Chronic dietary intake of quercetin alleviates hepatic fat accumulation associated with consumption of a Western‐style diet in C57/BL6J mice. Mol Nutr Food Res 55: 530‐540. Koletzko B., Aggett P.J., Bindels J.G., Bung P. et al. 1998. Growth, development and differentiation: a functional food science approach. Br J Nutr 80 Suppl 1: S5‐45. Kong L.C., Holmes B.A., Cotillard A., Habi‐Rachedi F. et al. 2014. Dietary patterns differently associate with inflammation and gut microbiota in overweight and obese subjects. PLoS One 9: e109434. Konings E., Timmers S., Boekschoten M.V., Goossens G.H. et al. 2014. The effects of 30 days resveratrol supplementation on adipose tissue morphology and gene expression patterns in obese men. Int J Obes (Lond) 38: 470‐473. Koren O., Knights D., Gonzalez A., Waldron L. et al. 2013. A guide to enterotypes across the human body: meta‐analysis of microbial community structures in human microbiome datasets. PLoS Comput Biol 9: e1002863. Korpela K., Flint H.J., Johnstone A.M., Lappi J. et al. 2014. Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals. PLoS One 9: e90702. Koutsos A., Tuohy K.M., Lovegrove J.A. 2015. Apples and cardiovascular health‐is the gut microbiota a core consideration? Nutrients 7: 3959‐3998. Kozyrskyj A.L., Bahreinian S., Azad M.B. 2011. Early life exposures: impact on asthma and allergic disease. Curr Opin Allergy Clin Immunol 11: 400‐406. Kuppusamy U.R., Das N.P. 1992. Effects of flavonoids on cyclic AMP phosphodiesterase and lipid mobilization in rat adipocytes. Biochem Pharmacol 44: 1307‐1315. Kushner R.F. 2014. Weight loss strategies for treatment of obesity. Prog Cardiovasc Dis 56: 465‐472. Kussmann M., Raymond F., Affolter M. 2006. OMICS‐driven biomarker discovery in nutrition and health. J Biotechnol 124: 758‐787. ‐L‐ la Porte C., Voduc N., Zhang G., Seguin I. et al. 2010. Steady‐State pharmacokinetics and tolerability of trans‐resveratrol 2000 mg twice daily with food, quercetin and alcohol (ethanol) in healthy human subjects. Clin Pharmacokinet 49: 449‐454.

229 References

Lai C.Y., Yang J.Y., Rayalam S., Della‐Fera M.A. et al. 2011. Preventing bone loss and weight gain with combinations of vitamin D and phytochemicals. J Med Food 14: 1352‐1362. Lairon D., Bertrais S., Vincent S., Arnault N. et al. 2003. Dietary fibre intake and clinical indices in the French Supplementation en Vitamines et Mineraux AntioXydants (SU.VI.MAX) adult cohort. Proc Nutr Soc 62: 11‐15. Larson A.J., Symons J.D., Jalili T. 2012. Therapeutic potential of quercetin to decrease blood pressure: review of efficacy and mechanisms. Adv Nutr 3: 39‐46. Lasa A., Churruca I., Eseberri I., Andres‐Lacueva C., Portillo M.P. 2012. Delipidating effect of resveratrol metabolites in 3T3‐L1 adipocytes. Mol Nutr Food Res 56: 1559‐1568. Le Huerou‐Luron I., Blat S., Boudry G. 2010. Breast‐ v. formula‐feeding: impacts on the digestive tract and immediate and long‐term health effects. Nutr Res Rev 23: 23‐36. Le N.H., Kim C.S., Park T., Park J.H. et al. 2014. Quercetin protects against obesity‐induced skeletal muscle inflammation and atrophy. Mediators Inflamm: 834294. Doi: 10.1155/2014/834294. LeBlanc J.G., Milani C., de Giori G.S., Sesma F. et al. 2013. Bacteria as vitamin suppliers to their host: a gut microbiota perspective. Curr Opin Biotechnol 24: 160‐168. Lee D.E., Kehlenbrink S., Lee H., Hawkins M., Yudkin J.S. 2009. Getting the message across: mechanisms of physiological cross talk by adipose tissue. Am J Physiol Endocrinol Metab 296: E1210‐1229. Lee K.H., Park E., Lee H.J., Kim M.O. et al. 2011. Effects of daily quercetin‐rich supplementation on cardiometabolic risks in male smokers. Nutr Res Pract 5: 28‐33. Lehnert T., Sonntag D., Konnopka A., Riedel‐Heller S., König H.‐H. 2013. Economic costs of overweight and obesity. Best Pract Res Clin EndocrinoL Metab 27: 105‐115. Leiherer A., Mundlein A., Drexel H. 2013. Phytochemicals and their impact on adipose tissue inflammation and diabetes. Vascul Pharmacol 58: 3‐20. Lepage P., Leclerc M.C., Joossens M., Mondot S. et al. 2013. A metagenomic insight into our gut's microbiome. Gut 62: 146‐ 158. Lepage P., Seksik P., Sutren M., de la Cochetiere M.F. et al. 2005. Biodiversity of the mucosa‐associated microbiota is stable along the distal digestive tract in healthy individuals and patients with IBD. Inflamm Bowel Dis 11: 473‐480. Levian C., Ruiz E., Yang X. 2014. The pathogenesis of obesity from a genomic and systems biology perspective. Yale J Biol Med 87: 113‐126. Ley R.E., Backhed F., Turnbaugh P., Lozupone C.A. et al. 2005. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A 102: 11070‐11075. Ley R.E., Lozupone C.A., Hamady M., Knight R., Gordon J.I. 2008. Worlds within worlds: evolution of the vertebrate gut microbiota. Nat Rev Microbiol 6: 776‐788. Ley R.E., Peterson D.A., Gordon J.I. 2006. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell 124: 837‐848. Liang C., Oest M.E., Prater M.R. 2009. Intrauterine exposure to high saturated fat diet elevates risk of adult‐onset chronic diseases in C57BL/6 mice. Birth Defects Res B Dev Reprod Toxicol 86: 377‐384. Lin C.‐Y., Lin Y.‐J., Chen H.‐A. 2013. Gut Microbiota: Physiology and Relationship with Inflammatory Bowel Disease. OJEMD. Doi: 10.4236/ojemd.2013.37039. Lloyd A.J., Fave G., Beckmann M., Lin W. et al. 2011. Use of mass spectrometry fingerprinting to identify urinary metabolites after consumption of specific foods. Am J Clin Nutr 94: 981‐991. Lomba A., Milagro F.I., Garcia‐Diaz D.F., Marti A. et al. 2010. Obesity induced by a pair‐fed high fat sucrose diet: methylation and expression pattern of genes related to energy homeostasis. Lipids Health Dis 9: 60. Lommen A., Godejohann M., Venema D.P., Hollman P.C., Spraul M. 2000. Application of directly coupled HPLC‐NMR‐MS to the identification and confirmation of quercetin glycosides and phloretin glycosides in apple peel. Anal Chem 72: 1793‐1797. Lopetuso L.R., Scaldaferri F., Franceschi F., Gasbarrini A. 2014. The gastrointestinal microbiome ‐ functional interference between stomach and intestine. Best Pract Res Clin Gastroenterol 28: 995‐1002. Lopez‐Legarrea P., Fuller N.R., Zulet M.A., Martinez J.A., Caterson I.D. 2014. The influence of Mediterranean, carbohydrate and high protein diets on gut microbiota composition in the treatment of obesity and associated inflammatory state. Asia Pac J Clin Nutr 23: 360‐368. Lopez P., Gueimonde M., Margolles A., Suarez A. 2010. Distinct Bifidobacterium strains drive different immune responses in vitro. Int J Food Microbiol 138: 157‐165.

230 References

Louis P., Hold G.L., Flint H.J. 2014. The gut microbiota, bacterial metabolites and colorectal cancer. Nat Rev Microbiol 12: 661‐672. Louis P., Scott K.P., Duncan S.H., Flint H.J. 2007. Understanding the effects of diet on bacterial metabolism in the large intestine. J Appl Microbiol 102: 1197‐1208. Lozupone C.A., Stombaugh J.I., Gordon J.I., Jansson J.K., Knight R. 2012. Diversity, stability and resilience of the human gut microbiota. Nature 489: 220‐230. Lu M.F., Xiao Z.T., Zhang H.Y. 2013. Where do health benefits of flavonoids come from? Insights from flavonoid targets and their evolutionary history. Biochem Biophys Res Commun 434: 701‐704. ‐M‐

Macarulla M.T., Alberdi G., Gomez S., Tueros I. et al. 2009. Effects of different doses of resveratrol on body fat and serum parameters in rats fed a hypercaloric diet. J Physiol Biochem 65: 369‐376. Macfarlane S., Macfarlane G.T. 2003. Regulation of short‐chain fatty acid production. Proc Nutr Soc 62: 67‐72. Mai V., Ukhanova M., Baer D.J. 2010. Understanding the extent and sources of variation in gut microbiota studies; a prerequisite for establishing associations with disease. Diversity 2: 1085‐1096. Malaguarnera M., Gargante M.P., Malaguarnera G., Salmeri M. et al. 2010. Bifidobacterium combined with fructo‐ oligosaccharide versus lactulose in the treatment of patients with hepatic encephalopathy. Eur J Gastroenterol Hepatol 22: 199‐206. Manach C., Scalbert A., Morand C., Remesy C., Jimenez L. 2004. Polyphenols: food sources and bioavailability. Am J Clin Nutr 79: 727‐747. Mar Rodriguez M., Perez D., Javier Chaves F., Esteve E. et al. 2015. Obesity changes the human gut mycobiome. Sci Rep 5: 14600. Marcobal A., Kashyap P.C., Nelson T.A., Aronov P.A. et al. 2013. A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice. ISME J 7: 1933‐1943. Marti A., Martinez‐Gonzalez M.A., Martinez J.A. 2008. Interaction between genes and lifestyle factors on obesity. Proc Nutr Soc 67: 1‐8. Martin‐Rodriguez E., Guillen‐Grima F., Marti A., Brugos‐Larumbe A. 2015. Comorbidity associated with obesity in a large population: The APNA study. Obes Res Clin Pract. Doi:10.1016/j.orcp.2015.04.003. Mason K.L., Huffnagle G.B., Noverr M.C., Kao J.Y. 2008. Overview of gut immunology. Adv Exp Med Biol 635: 1‐14. Matamoros S., Gras‐Leguen C., Le Vacon F., Potel G., de La Cochetiere M.F. 2013. Development of intestinal microbiota in infants and its impact on health. Trends Microbiol 21: 167‐173. Mattace Raso G., Simeoli R., Russo R., Iacono A. et al. 2013. Effects of sodium butyrate and its synthetic amide derivative on liver inflammation and glucose tolerance in an animal model of steatosis induced by high fat diet. PLoS One 8: e68626. Matthies A., Loh G., Blaut M., Braune A. 2012. Daidzein and genistein are converted to equol and 5‐hydroxy‐equol by human intestinal Slackia isoflavoniconvertens in gnotobiotic rats. J Nutr 142: 40‐46. Maukonen J., Saarela M. 2015. Human gut microbiota: does diet matter? Proc Nutr Soc 74: 23‐36. McCormack D., McFadden D. 2013. A review of pterostilbene antioxidant activity and disease modification. Oxid Med Cell Longev 2013: 575482. McDonald J.E., Rooks D.J., McCarthy A.J. 2012. Methods for the isolation of cellulose‐degrading microorganisms. Methods Enzymol 510: 349‐374. McGhie T.K., Rowan D.D. 2012. Metabolomics for measuring phytochemicals, and assessing human and animal responses to phytochemicals, in food science. Mol Nutr Food Res 56: 147‐158. McNeil N.I. 1984. The contribution of the large intestine to energy supplies in man. Am J Clin Nutr 39: 338‐342. McNiven E.M., German J.B., Slupsky C.M. 2011. Analytical metabolomics: nutritional opportunities for personalized health. J Nutr Biochem 22: 995‐1002. Medzhitov R. 2001. Toll‐like receptors and innate immunity. Nat Rev Immunol 1: 135‐145. Mellert W., Kapp M., Strauss V., Wiemer J. et al. 2011. Nutritional impact on the plasma metabolome of rats. Toxicol Lett 207: 173‐181. Melo‐Filho C.C., Braga R.C., Andrade C.H. 2014. Advances in methods for predicting phase I metabolism of polyphenols. Curr Drug Metab 15: 120‐126. Metchnikoff I.I. 2004. The prolongation of life: optimistic studies. Springer Publishing Company.

231 References

Miller T.L., Wolin M. 1979. Fermentations by saccharolytic intestinal bacteria. Am J Clin Nutr 32: 164‐172. Million M., Angelakis E., Maraninchi M., Henry M. et al. 2013. Correlation between body mass index and gut concentrations of Lactobacillus reuteri, Bifidobacterium animalis, Methanobrevibacter smithii and Escherichia coli. Int J Obes (Lond) 37: 1460‐1466. Million M., Maraninchi M., Henry M., Armougom F. et al. 2012. Obesity‐associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter smithii. Int J Obes (Lond) 36: 817‐825. Miniello V.L., Colasanto A., Cristofori F., Diaferio L. et al. 2015. Gut microbiota biomodulators, when the stork comes by the scalpel. Clin Chim Acta. Doi:10.1016/j.cca.2015.01.022. Mitroi N., Mota M. 2008. Nutrigenomics/Nutrigenetics. Rom J Intern Med 46: 295‐304. Moco S., Martin F.P., Rezzi S. 2012. Metabolomics view on gut microbiome modulation by polyphenol‐rich foods. J Proteome Res 11: 4781‐4790. Mohamed G.A., Ibrahim S.R.M., Elkhayat E.S., El Dine R.S. 2014. Natural anti‐obesity agents. B‐FOPCU, 52: 269‐284. Morgan P.J. 2012. Back to the future: the changing frontiers of nutrition research and its relationship to policy. Proc Nutr Soc 71: 190‐197. Moroti C., Souza Magri L.F., de Rezende Costa M., Cavallini D.C., Sivieri K. 2012. Effect of the consumption of a new symbiotic shake on glycemia and cholesterol levels in elderly people with type 2 diabetes mellitus. Lipids Health Dis 11: 29. Mortensen P.B., Clausen M.R. 1996. Short‐chain fatty acids in the human colon: relation to gastrointestinal health and disease. Scand J Gastroenterol Suppl 216: 132‐148. Motoyashiki T., Morita T., Ueki H. 1996. Involvement of the rapid increase in cAMP content in the vanadate‐stimulated release of lipoprotein lipase activity from rat fat pads. Biol Pharm Bull 19: 1412‐1416. Mullen W., Rouanet J.M., Auger C., Teissedre P.L. et al. 2008. Bioavailability of [2‐(14)C]quercetin‐4'‐glucoside in rats. J Agric Food Chem 56: 12127‐12137. Munyaka P.M., Khafipour E., Ghia J.E. 2014. External influence of early childhood establishment of gut microbiota and subsequent health implications. Front Pediatr 2: 109. ‐N‐

Nabavi S.F., Russo G.L., Daglia M., Nabavi S.M. 2015. Role of quercetin as an alternative for obesity treatment: you are what you eat! Food Chem 179: 305‐310. Nabavi S.M., Nabavi S.F., Eslami S., Moghaddam A.H. 2012. In vivo protective effects of quercetin against sodium fluoride‐ induced oxidative stress in the hepatic tissue. Food Chem 132: 931‐935. Nagpal R., Yadav H., Marotta F. 2014. Gut microbiota: the next‐gen frontier in preventive and therapeutic medicine? Front Med (Lausanne) 1: 15. Nair S., Cope K., Risby T.H., Diehl A.M. 2001. Obesity and female gender increase breath ethanol concentration: potential implications for the pathogenesis of nonalcoholic steatohepatitis. Am J Gastroenterol 96: 1200‐1204. Nait Chabane M., Al Ahmad A., Peluso J., Muller C.D., Ubeaud G. 2009. Quercetin and naringenin transport across human intestinal Caco‐2 cells. J Pharm Pharmacol 61: 1473‐1483. Nakatsu C.H., Armstrong A., Clavijo A.P., Martin B.R. et al. 2014. Fecal bacterial community changes associated with isoflavone metabolites in postmenopausal women after soy bar consumption. PLoS One 9: e108924. Neis E.P., Dejong C.H., Rensen S.S. 2015. The role of microbial amino acid metabolism in host metabolism. Nutrients 7: 2930‐2946. Neu J., Rushing J. 2011. Cesarean versus vaginal delivery: long‐term infant outcomes and the hygiene hypothesis. Clin Perinatol 38: 321‐331. Neveu V., Perez‐Jimenez J., Vos F., Crespy V. et al. 2010. Phenol‐Explorer: an online comprehensive database on polyphenol contents in foods. Database (Oxford) 2010: bap024. Ng M. ,Fleming T. ,Robinson M. ,Thomson B. et al. 2014. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980‐2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384: 766‐781. Nguyen T.L., Vieira‐Silva S., Liston A., Raes J. 2015. How informative is the mouse for human gut microbiota research? Dis Model Mech 8: 1‐16. Nicholson J.K., Holmes E., Kinross J., Burcelin R. et al. 2012. Host‐gut microbiota metabolic interactions. Science 336: 1262‐1267.

232 References

Nieman D.C., Williams A.S., Shanely R.A., Jin F. et al. 2010. Quercetin's influence on exercise performance and muscle mitochondrial biogenesis. Med Sci Sports Exerc 42: 338‐345. Norheim F., Gjelstad I.M., Hjorth M., Vinknes K.J. et al. 2012. Molecular nutrition research: the modern way of performing nutritional science. Nutrients 4: 1898‐1944. ‐O‐

O'Gorman A., Brennan L. 2015. Metabolomic applications in nutritional research: a perspective. J Sci Food Agric. Doi:10.1002/jsfa.7070. O'Sullivan A., Gibney M.J., Brennan L. 2011. Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am J Clin Nutr 93: 314‐321. Odriozola L., Corrales F.J. 2015. Discovery of nutritional biomarkers: future directions based on omics technologies. Int J Food Sci Nutr 66 Suppl 1: S31‐40. Ogilvie L.A., Jones B.V. 2015. The human gut virome: a multifaceted majority. Front Microbiol 6: 918. Ohkoshi E., Miyazaki H., Shindo K., Watanabe H. et al. 2007. Constituents from the leaves of Nelumbo nucifera stimulate lipolysis in the white adipose tissue of mice. Planta Med 73: 1255‐1259. Ordovas J.M., Mooser V. 2004. Nutrigenomics and nutrigenetics. Curr Opin Lipidol 15: 101‐108. Ordovas Munoz J.M. 2013. Predictors of obesity: the "power" of the omics. Nutr Hosp 28 Suppl 5: 63‐71. Orio F., Tafuri D., Ascione A., Marciano F. et al. 2014. Lifestyle changes in the management of adulthood and childhood obesity. Minerva Endocrinol. In press. Ou J., Carbonero F., Zoetendal E.G., DeLany J.P. et al. 2013. Diet, microbiota, and microbial metabolites in colon cancer risk in rural Africans and African Americans. Am J Clin Nutr 98: 111‐120. ‐P‐

Pan M.H., Lai C.S., Ho C.T. 2010. Anti‐inflammatory activity of natural dietary flavonoids. Food Funct 1: 15‐31. Panchal S.K., Poudyal H., Brown L. 2012. Quercetin ameliorates cardiovascular, hepatic, and metabolic changes in diet‐ induced metabolic syndrome in rats. J Nutr 142: 1026‐1032. Pang G., Xie J., Chen Q., Hu Z. 2012. How functional foods play critical roles in human health. Food Science and Human Wellness 1: 26‐60. Park H.J., Yang J.Y., Ambati S., Della‐Fera M.A. et al. 2008. Combined effects of genistein, quercetin, and resveratrol in human and 3T3‐L1 adipocytes. J Med Food 11: 773‐783. Parks B.W., Nam E., Org E., Kostem E. et al. 2013. Genetic control of obesity and gut microbiota composition in response to high‐fat, high‐sucrose diet in mice. Cell Metab 17: 141‐152. Patel D. 2015. Pharmacotherapy for the management of obesity. Metabolism 64: 1376‐1385. Patel R., DuPont H.L. 2015. New approaches for bacteriotherapy: prebiotics, new‐generation probiotics, and synbiotics. Clin Infect Dis 60 Suppl 2: S108‐121. Patti G.J., Yanes O., Siuzdak G. 2012. Innovation: Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13: 263‐269. Penders J., Thijs C., Vink C., Stelma F.F. et al. 2006. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics 118: 511‐521. Peterson J., Garges S., Giovanni M., McInnes P. et al. 2009. The NIH Human Microbiome Project. Genome Res 19: 2317‐ 2323. Petrosino J.F., Highlander S., Luna R.A., Gibbs R.A., Versalovic J. 2009. Metagenomic pyrosequencing and microbial identification. Clin Chem 55: 856‐866. Pfeuffer M., Auinger A., Bley U., Kraus‐Stojanowic I. et al. 2013. Effect of quercetin on traits of the metabolic syndrome, endothelial function and inflammation in men with different APOE isoforms. Nutr Metab Cardiovasc Dis 23: 403‐ 409. Pi‐Sunyer F.X. 2002. The Medical Risks of Obesity. Obesity Surgery 12: S6‐S11. Pitt J.J. 2009. Principles and applications of liquid chromatography‐mass spectrometry in clinical biochemistry. Clin Biochem Rev 30: 19‐34. Potischman N. 2003. Biologic and methodologic issues for nutritional biomarkers. J Nutr 133 Suppl 3: 875S‐880S. Poulsen M.M., Vestergaard P.F., Clasen B.F., Radko Y. et al. 2013. High‐dose resveratrol supplementation in obese men: an investigator‐initiated, randomized, placebo‐controlled clinical trial of substrate metabolism, insulin sensitivity, and body composition. Diabetes 62: 1186‐1195.

233 References

Preidis G.A., Hotez P.J. 2015. The newest "omics"‐metagenomics and metabolomics‐enter the battle against the neglected tropical diseases. PLoS Negl Trop Dis 9: e0003382. Puddu A., Sanguineti R., Montecucco F., Viviani G.L. 2014. Evidence for the gut microbiota short‐chain fatty acids as key pathophysiological molecules improving diabetes. Mediators Inflamm: 162021. Doi: 10.1155/2014/162021. Pull S.L., Doherty J.M., Mills J.C., Gordon J.I., Stappenbeck T.S. 2005. Activated macrophages are an adaptive element of the colonic epithelial progenitor niche necessary for regenerative responses to injury. Proc Natl Acad Sci U S A 102: 99‐ 104. ‐Q‐

Qiao Y., Sun J., Xia S., Tang X. et al. 2014. Effects of resveratrol on gut microbiota and fat storage in a mouse model with high‐fat‐induced obesity. Food Funct 5: 1241‐1249. Qin J., Li R., Raes J., Arumugam M. et al. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464: 59‐65. Queipo‐Ortuno M.I., Boto‐Ordonez M., Murri M., Gomez‐Zumaquero J.M. et al. 2012. Influence of red wine polyphenols and ethanol on the gut microbiota ecology and biochemical biomarkers. Am J Clin Nutr 95: 1323‐1334. ‐R‐

Rabassa M., Zamora‐Ros R., Urpi‐Sarda M., Andres‐Lacueva C. 2015. Resveratrol metabolite profiling in clinical nutrition research‐from diet to uncovering disease risk biomarkers: epidemiological evidence. Ann N Y Acad Sci. Doi:10.1111/nyas.12851. Rabot S., Membrez M., Bruneau A., Gerard P. et al. 2010. Germ‐free C57BL/6J mice are resistant to high‐fat‐diet‐induced insulin resistance and have altered cholesterol metabolism. FASEB J 24: 4948‐4959. Rakoff‐Nahoum S., Medzhitov R. 2008. Innate immune recognition of the indigenous microbial flora. Mucosal Immunol 1 Suppl 1: S10‐14. Rakoff‐Nahoum S., Paglino J., Eslami‐Varzaneh F., Edberg S., Medzhitov R. 2004. Recognition of commensal microflora by toll‐like receptors is required for intestinal homeostasis. Cell 118: 229‐241. Ramautar R., Berger R., van der Greef J., Hankemeier T. 2013. Human metabolomics: strategies to understand biology. Curr Opin Chem Biol 17: 841‐846. Rauschert S., Uhl O., Koletzko B., Hellmuth C. 2014. Metabolomic biomarkers for obesity in humans: a short review. Ann Nutr Metab 64: 314‐324. Reagan‐Shaw S., Nihal M., Ahmad N. 2008. Dose translation from animal to human studies revisited. FASEB J 22: 659‐661. Rechner A.R., Smith M.A., Kuhnle G., Gibson G.R. et al. 2004. Colonic metabolism of dietary polyphenols: influence of structure on microbial fermentation products. Free Radic Biol Med 36: 212‐225. Renaud S., de Lorgeril M. 1992. Wine, alcohol, platelets, and the French paradox for coronary heart disease. Lancet 339: 1523‐1526. Rhee S.H., Pothoulakis C., Mayer E.A. 2009. Principles and clinical implications of the brain‐gut‐enteric microbiota axis. Nat Rev Gastroenterol Hepatol 6: 306‐314. Ridaura V.K., Faith J.J., Rey F.E., Cheng J. et al. 2013. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341: 1241214. Risnes K.R., Belanger K., Murk W., Bracken M.B. 2011. Antibiotic exposure by 6 months and asthma and allergy at 6 years: Findings in a cohort of 1,401 US children. Am J Epidemiol 173: 310‐318. Rivera L., Moron R., Sanchez M., Zarzuelo A., Galisteo M. 2008. Quercetin ameliorates metabolic syndrome and improves the inflammatory status in obese Zucker rats. Obesity (Silver Spring) 16: 2081‐2087. Rodriguez J.M., Murphy K., Stanton C., Ross R.P. et al. 2015. The composition of the gut microbiota throughout life, with an emphasis on early life. Microb Ecol Health Dis 26: 26050. Roelofsen H., Priebe M.G., Vonk R.J. 2010. The interaction of short‐chain fatty acids with adipose tissue: relevance for prevention of type 2 diabetes. Benef Microbes 1: 433‐437. Romano‐Keeler J., Weitkamp J.H. 2015. Maternal influences on fetal microbial colonization and immune development. Pediatr Res 77: 189‐195. Rondon M.R., August P.R., Bettermann A.D., Brady S.F. et al. 2000. Cloning the soil metagenome: a strategy for accessing the genetic and functional diversity of uncultured microorganisms. Appl Environ Microbiol 66: 2541‐2547. Ruderman N.B., Xu X.J., Nelson L., Cacicedo J.M. et al. 2010. AMPK and SIRT1: a long‐standing partnership? Am J Physiol Endocrinol Metab 298: E751‐760.

234 References

Russell W.R., Gratz S.W., Duncan S.H., Holtrop G. et al. 2011. High‐protein, reduced‐carbohydrate weight‐loss diets promote metabolite profiles likely to be detrimental to colonic health. Am J Clin Nutr 93: 1062‐1072. Ryan K.K., Seeley R.J. 2013. Food as a Hormone. Science 339: 918‐919. ‐S‐

Saemann M.D., Bohmig G.A., Osterreicher C.H., Burtscher H. et al. 2000. Anti‐inflammatory effects of sodium butyrate on human monocytes: potent inhibition of IL‐12 and up‐regulation of IL‐10 production. FASEB J 14: 2380‐2382. Salazar N., Arboleya S., Valdes L., Stanton C. et al. 2014. The human intestinal microbiome at extreme ages of life. Dietary intervention as a way to counteract alterations. Front Genet 5: 406. Salonen A., Lahti L., Salojarvi J., Holtrop G. et al. 2014. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. ISME J 8: 2218‐2230. Sancho E., Batlle E., Clevers H. 2003. Live and let die in the intestinal epithelium. Curr Opin Cell Biol 15: 763‐770. Sankar S.A., Lagier J.‐C., Pontarotti P., Raoult D., Fournier P.‐E. 2015. The human gut microbiome, a taxonomic conundrum. Syst Appl Microbiol 38: 276‐286. Scalbert A., Morand C., Manach C., Remesy C. 2002. Absorption and metabolism of polyphenols in the gut and impact on health. Biomed Pharmacother 56: 276‐282. Scalbert A., Williamson G. 2000. Dietary intake and bioavailability of polyphenols. J Nutr 130: 2073S‐2085S. Scaldaferri F., Pizzoferrato M., Gerardi V., Lopetuso L., Gasbarrini A. 2012. The gut barrier: new acquisitions and therapeutic approaches. J Clin Gastroenterol 46 Suppl: S12‐17. Schnorr S.L., Candela M., Rampelli S., Centanni M. et al. 2014. Gut microbiome of the Hadza hunter‐gatherers. Nat Commun 5: 3654. Schwiertz A., Taras D., Schafer K., Beijer S. et al. 2010. Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring) 18: 190‐195. Seiva F.R., Chuffa L.G., Braga C.P., Amorim J.P., Fernandes A.A. 2012. Quercetin ameliorates glucose and lipid metabolism and improves antioxidant status in postnatally monosodium glutamate‐induced metabolic alterations. Food Chem Toxicol 50: 3556‐3561. Sekirov I., Russell S.L., Antunes L.C.M., Finlay B.B. 2010. Gut microbiota in health and disease. Physiol Rev 90: 859‐904. Seo Y.S., Kang O.H., Kim S.B., Mun S.H. et al. 2015. Quercetin prevents adipogenesis by regulation of transcriptional factors and lipases in OP9 cells. Int J Mol Med 35: 1779‐1785. Serino M., Blasco‐Baque V., Nicolas S., Burcelin R. 2014. Far from the eyes, close to the heart: dysbiosis of gut microbiota and cardiovascular consequences. Curr Cardiol Rep 16: 540. Shearer J., Duggan G., Weljie A., Hittel D.S. et al. 2008. Metabolomic profiling of dietary‐induced insulin resistance in the high fat‐fed C57BL/6J mouse. Diabetes Obes Metab 10: 950‐958. Shirkey T.W., Siggers R.H., Goldade B.G., Marshall J.K. et al. 2006. Effects of commensal bacteria on intestinal morphology and expression of proinflammatory cytokines in the gnotobiotic pig. Exp Biol Med (Maywood) 231: 1333‐1345. Silvester K.R., Englyst H.N., Cummings J.H. 1995. Ileal recovery of starch from whole diets containing resistant starch measured in vitro and fermentation of ileal effluent. Am J Clin Nutr 62: 403‐411. Singh R., Nieuwdorp M., ten Berge I.J., Bemelman F.J., Geerlings S.E. 2014. The potential beneficial role of faecal microbiota transplantation in diseases other than Clostridium difficile infection. Clin Microbiol Infect 20: 1119‐ 1125. Singla P., Bardoloi A., Parkash A.A. 2010. Metabolic effects of obesity: A review. World J Diabetes 1: 76‐88. Siriwardhana N., Kalupahana N.S., Cekanova M., LeMieux M. et al. 2013. Modulation of adipose tissue inflammation by bioactive food compounds. J Nutr Biochem 24: 613‐623. Smith C.A., Want E.J., O'Maille G., Abagyan R., Siuzdak G. 2006. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78: 779‐787. Song Y., Liu C., Finegold S.M. 2004. Real‐time PCR quantitation of clostridia in feces of autistic children. Appl Environ Microbiol 70: 6459‐6465. Spasova D.S., Surh C.D. 2014. Blowing on embers: commensal microbiota and our immune system. Front Immunol 5: 318. Speakman J.R., O'Rahilly S. 2012. Fat: an evolving issue. Dis Model Mech 5: 569‐573. Spor A., Koren O., Ley R. 2011. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat Rev Microbiol 9: 279‐290.

235 References

Stervbo U., Vang O., Bonnesen C. 2007. A review of the content of the putative chemopreventive phytoalexin resveratrol in red wine. Food Chem 101: 449‐457. Stewart L.K., Soileau J.L., Ribnicky D., Wang Z.Q. et al. 2008. Quercetin transiently increases energy expenditure but persistently decreases circulating markers of inflammation in C57BL/6J mice fed a high‐fat diet. Metabolism 57: S39‐46. Stout M.J., Conlon B., Landeau M., Lee I. et al. 2013. Identification of intracellular bacteria in the basal plate of the human placenta in term and preterm gestations. Am J Obstet Gynecol 208: 226 e221‐227. Strober W., Fuss I.J., Blumberg R.S. 2002. The immunology of mucosal models of inflammation. Annu Rev Immunol 20: 495‐549. Sukonina V., Lookene A., Olivecrona T., Olivecrona G. 2006. Angiopoietin‐like protein 4 converts lipoprotein lipase to inactive monomers and modulates lipase activity in adipose tissue. Proc Natl Acad Sci U S A 103: 17450‐17455. Swinburn B.A., Sacks G., Hall K.D., McPherson K. et al. 2011. The global obesity pandemic: shaped by global drivers and local environments. Lancet 378: 804‐814. ‐T‐

Thomas T., Gilbert J., Meyer F. 2012. Metagenomics ‐ a guide from sampling to data analysis. Microb Inform Exp 2: 3. Tilg H., Moschen A.R. 2014. Microbiota and diabetes: an evolving relationship. Gut 63: 1513‐1521. Timmers S., Konings E., Bilet L., Houtkooper R.H. et al. 2011. Calorie restriction‐like effects of 30 days of resveratrol supplementation on energy metabolism and metabolic profile in obese humans. Cell Metab 14: 612‐622. Ting W.J., Kuo W.W., Kuo C.H., Yeh Y.L. et al. 2015. Supplementary heat‐killed Lactobacillus reuteri GMNL‐263 ameliorates hyperlipidaemic and cardiac apoptosis in high‐fat diet‐fed hamsters to maintain cardiovascular function. Br J Nutr. Doi:10.1017/s0007114515002469: 1‐7. Tissier H. 1900. Recherches sur la flore intestinale des nourrissons : état normal et pathologique. G. Carré et C. Naud, Paris. Tolhurst G., Heffron H., Lam Y.S., Parker H.E. et al. 2012. Short‐Chain Fatty Acids Stimulate Glucagon‐Like Peptide‐1 Secretion via the G‐Protein–Coupled Receptor FFAR2. Diabetes 61: 364‐371. Tome‐Carneiro J., Larrosa M., Gonzalez‐Sarrias A., Tomas‐Barberan F.A. et al. 2013. Resveratrol and clinical trials: the crossroad from in vitro studies to human evidence. Curr Pharm Des 19: 6064‐6093. Topping D.L., Clifton P.M. 2001. Short‐chain fatty acids and human colonic function: roles of resistant starch and nonstarch polysaccharides. Physiol Rev 81: 1031‐1064. Tremaroli V., Backhed F. 2012. Functional interactions between the gut microbiota and host metabolism. Nature 489: 242‐249. Tremaroli V., Karlsson F., Werling M., Stahlman M. et al. 2015. Roux‐en‐Y Gastric Bypass and Vertical Banded Gastroplasty Induce Long‐Term Changes on the Human Gut Microbiome Contributing to Fat Mass Regulation. Cell Metab 22: 228‐238. Tsao R., McCallum J. 2009. Chemistry of Flavonoids. in Fruit and Vegetable Phytochemicals, pp. 131‐153. Wiley‐Blackwell. Turnbaugh P.J., Backhed F., Fulton L., Gordon J.I. 2008. Diet‐induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3: 213‐223. Turnbaugh P.J., Hamady M., Yatsunenko T., Cantarel B.L. et al. 2009a. A core gut microbiome in obese and lean twins. Nature 457: 480‐484. Turnbaugh P.J., Ley R.E., Mahowald M.A., Magrini V. et al. 2006. An obesity‐associated gut microbiome with increased capacity for energy harvest. Nature 444: 1027‐1031. Turnbaugh P.J., Ridaura V.K., Faith J.J., Rey F.E. et al. 2009b. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 1: 6ra14. ‐V‐

Vaishampayan P.A., Kuehl J.V., Froula J.L., Morgan J.L. et al. 2010. Comparative metagenomics and population dynamics of the gut microbiota in mother and infant. Genome Biol Evol 2: 53‐66. Valdes L., Cuervo A., Salazar N., Ruas‐Madiedo P. et al. 2015. The relationship between phenolic compounds from diet and microbiota: impact on human health. Food Funct 6: 2424‐2439. van Best N., Jansen P.L., Rensen S.S. 2015. The gut microbiota of nonalcoholic fatty liver disease: current methods and their interpretation. Hepatol Int 9: 406‐415. Van de Peer Y., Chapelle S., De Wachter R. 1996. A quantitative map of nucleotide substitution rates in bacterial rRNA. Nucleic Acids Res 24: 3381‐3391.

236 References

Van den Abbeele P., Verstraete W., El Aidy S., Geirnaert A., Van de Wiele T. 2013. Prebiotics, faecal transplants and microbial network units to stimulate biodiversity of the human gut microbiome. Microb Biotechnol 6: 335‐340. van der Klaauw A.A., Farooqi I.S. 2015. The hunger genes: pathways to obesity. Cell 161: 119‐132. van Duynhoven J., Vaughan E.E., Jacobs D.M., Kemperman R.A. et al. 2011. Metabolic fate of polyphenols in the human superorganism. Proc Natl Acad Sci U S A 108 Suppl 1: 4531‐4538. Vang O., Ahmad N., Baile C.A., Baur J.A. et al. 2011. What is new for an old molecule? Systematic review and recommendations on the use of resveratrol. PLoS One 6: e19881. Vasudeva N., Yadav N., Sharma S.K. 2012. Natural products: a safest approach for obesity. Chin J Integr Med 18: 473‐480. Velagapudi V.R., Hezaveh R., Reigstad C.S., Gopalacharyulu P. et al. 2010. The gut microbiota modulates host energy and lipid metabolism in mice. J Lipid Res 51: 1101‐1112. Verbeke K.A., Boobis A.R., Chiodini A., Edwards C.A. et al. 2015. Towards microbial fermentation metabolites as markers for health benefits of prebiotics. Nutr Res Rev 28: 42‐66. Villanueva‐Millan M.J., Perez‐Matute P., Oteo J.A. 2015. Gut microbiota: a key player in health and disease. A review focused on obesity. J Physiol Biochem. Doi:10.1007/s13105‐015‐0390‐3. Vinolo M.A., Rodrigues H.G., Nachbar R.T., Curi R. 2011. Regulation of inflammation by short chain fatty acids. Nutrients 3: 858‐876. Voreades N., Kozil A., Weir T.L. 2014. Diet and the development of the human intestinal microbiome. Front Microbiol 5: 494. Vrieze A., Van Nood E., Holleman F., Salojarvi J. et al. 2012. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 143: 913‐916 e917. ‐W‐

Waalen J. 2014. The genetics of human obesity. Transl Res 164: 293‐301. Waldram A., Holmes E., Wang Y., Rantalainen M. et al. 2009. Top‐down systems biology modeling of host metabotype‐ microbiome associations in obese rodents. J Proteome Res 8: 2361‐2375. Walker A.W., Ince J., Duncan S.H., Webster L.M. et al. 2011. Dominant and diet‐responsive groups of bacteria within the human colonic microbiota. ISME J 5: 220‐230. Walle T. 2011. Bioavailability of resveratrol. Ann N Y Acad Sci 1215: 9‐15. Walle T., Hsieh F., DeLegge M.H., Oatis J.E., Jr., Walle U.K. 2004. High absorption but very low bioavailability of oral resveratrol in humans. Drug Metab Dispos 32: 1377‐1382. Wang J., Tang H., Zhang C., Zhao Y. et al. 2015. Modulation of gut microbiota during probiotic‐mediated attenuation of metabolic syndrome in high fat diet‐fed mice. ISME J 9: 1‐15. Wang S., Moustaid‐Moussa N., Chen L., Mo H. et al. 2014. Novel insights of dietary polyphenols and obesity. J Nutr Biochem 25: 1‐18. Wein S., Behm N., Petersen R.K., Kristiansen K., Wolffram S. 2010. Quercetin enhances adiponectin secretion by a PPAR‐ gamma independent mechanism. Eur J Pharm Sci 41: 16‐22. Wells J.C. 2012. The evolution of human adiposity and obesity: where did it all go wrong? Dis Model Mech 5: 595‐607. Wells J.M., Loonen L.M., Karczewski J.M. 2010. The role of innate signaling in the homeostasis of tolerance and immunity in the intestine. Int J Med Microbiol 300: 41‐48. Wells J.M., Rossi O., Meijerink M., van Baarlen P. 2011. Epithelial crosstalk at the microbiota‐mucosal interface. Proc Natl Acad Sci U S A 108 Suppl 1: 4607‐4614. Wen X., Walle T. 2006. Methylated flavonoids have greatly improved intestinal absorption and metabolic stability. Drug Metab Dispos 34: 1786‐1792. WHO. 1995. Physical status : the use of and interpretation of anthropometry, report of a WHO expert committee, Geneva. World Health Organization technical report series 854, 1‐452. WHO. 2013. Development of a WHO global action plan for the prevention and control of NCDs 2013‐2020. Available at: http://www.who.int/nmh/events/2012/draft_action_plan/en/ [04 October 2015]. WHO W.H.O. 2015. Available at: http://www.who.int/mediacentre/factsheets/fs311/en/ [04 October 2015]. Williamson G., Aeberli I., Miguet L., Zhang Z. et al. 2007. Interaction of positional isomers of quercetin glucuronides with the transporter ABCC2 (cMOAT, MRP2). Drug Metab Dispos 35: 1262‐1268. Willing B.P., Van Kessel A.G. 2007. Enterocyte proliferation and apoptosis in the caudal small intestine is influenced by the composition of colonizing commensal bacteria in the neonatal gnotobiotic pig. J Anim Sci 85: 3256‐3266.

237 References

Wolever T.M., Brighenti F., Royall D., Jenkins A.L., Jenkins D.J. 1989. Effect of rectal infusion of short chain fatty acids in human subjects. Am J Gastroenterol 84: 1027‐1033. Woting A., Clavel T., Loh G., Blaut M. 2010. Bacterial transformation of dietary lignans in gnotobiotic rats. FEMS Microbiol Ecol 72: 507‐514. Wu G.D., Chen J., Hoffmann C., Bittinger K. et al. 2011. Linking long‐term dietary patterns with gut microbial enterotypes. Science 334: 105‐108. Wu Z., Zhao J., Xu H., Lyv Y. et al. 2014. Maternal quercetin administration during gestation and lactation decrease endoplasmic reticulum stress and related inflammation in the adult offspring of obese female rats. Eur J Nutr 53: 1669‐1683. ‐X‐

Xia Q., Grant S.F.A. 2013. The genetics of human obesity. Ann N Y Acad Sci 1281: 178‐190. Xie B., Waters M.J., Schirra H.J. 2012. Investigating potential mechanisms of obesity by metabolomics. J Biomed Biotechnol 2012: 805683. Xie G., Li X., Li H., Jia W. 2013. Toward personalized nutrition: comprehensive phytoprofiling and metabotyping. J Proteome Res 12: 1547‐1559. Xie G., Ma X., Zhao A., Wang C. et al. 2014. The metabolite profiles of the obese population are gender‐dependent. J Proteome Res 13: 4062‐4073. Xu M.Q., Cao H.L., Wang W.Q., Wang S. et al. 2015. Fecal microbiota transplantation broadening its application beyond intestinal disorders. World J Gastroenterol 21: 102‐111. Xu Z., Knight R. 2015. Dietary effects on human gut microbiome diversity. Br J Nutr 113 Suppl: S1‐5. ‐Y‐

Yang J.Y., Della‐Fera M.A., Rayalam S., Ambati S. et al. 2008. Enhanced inhibition of adipogenesis and induction of apoptosis in 3T3‐L1 adipocytes with combinations of resveratrol and quercetin. Life Sci 82: 1032‐1039. Yang T., Santisteban M.M., Rodriguez V., Li E. et al. 2015. Gut Dysbiosis Is Linked to Hypertension. Hypertension. Doi:10.1161/hypertensionaha.115.05315. Yatsunenko T., Rey F.E., Manary M.J., Trehan I. et al. 2012. Human gut microbiome viewed across age and geography. Nature 486: 222‐227. Yen T.H., Wright N.A. 2006. The gastrointestinal tract stem cell niche. Stem Cell Rev 2: 203‐212. Yoshino J., Conte C., Fontana L., Mittendorfer B. et al. 2012. Resveratrol supplementation does not improve metabolic function in nonobese women with normal glucose tolerance. Cell Metab 16: 658‐664. Yu C., Shin Y.G., Chow A., Li Y. et al. 2002. Human, rat, and mouse metabolism of resveratrol. Pharm Res 19: 1907‐1914. Yu J., Auwerx J. 2009. The role of sirtuins in the control of metabolic homeostasis. Ann N Y Acad Sci 1173 Suppl 1: E10‐19. Yu L.C., Wang J.T., Wei S.C., Ni Y.H. 2012a. Host‐microbial interactions and regulation of intestinal epithelial barrier function: From physiology to pathology. World J Gastrointest Pathophysiol 3: 27‐43. Yu W., Fu Y.C., Wang W. 2012b. Cellular and molecular effects of resveratrol in health and disease. J Cell Biochem 113: 752‐759. ‐Z‐

Zamora‐Ros R., Andres‐Lacueva C., Lamuela‐Raventos R.M., Berenguer T. et al. 2008. Concentrations of resveratrol and derivatives in foods and estimation of dietary intake in a Spanish population: European Prospective Investigation into Cancer and Nutrition (EPIC)‐Spain cohort. Br J Nutr 100: 188‐196. Zamora‐Ros R., Forouhi N.G., Sharp S.J., Gonzalez C.A. et al. 2014. Dietary intakes of individual flavanols and flavonols are inversely associated with incident type 2 diabetes in European populations. J Nutr 144: 335‐343. Zamora‐Ros R., Knaze V., Rothwell J.A., Hemon B. et al. 2015. Dietary polyphenol intake in Europe: the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Eur J Nutr. Doi:10.1007/s00394‐015‐0950‐x. Zapata H.J., Quagliarello V.J. 2015. The Microbiota and Microbiome in Aging: Potential Implications in Health and Age‐ Related Diseases. J Am Geriatr Soc. Doi:10.1111/jgs.13310. Zatorski H., Fichna J. 2014. What is the Future of the Gut Microbiota‐Related Treatment? Toward Modulation of Microbiota in Preventive and Therapeutic Medicine. Front Med (Lausanne) 1: 19. Zhang A., Sun H., Wang P., Han Y., Wang X. 2012. Recent and potential developments of biofluid analyses in metabolomics. J Proteomics 75: 1079‐1088.

238 References

Zhang A., Sun H., Wang X. 2013. Power of metabolomics in biomarker discovery and mining mechanisms of obesity. Obes Rev 14: 344‐349. Zhang X., Yap Y., Wei D., Chen G., Chen F. 2008. Novel omics technologies in nutrition research. Biotechnol Adv 26: 169‐ 176. Zhang Y.J., Li S., Gan R.Y., Zhou T. et al. 2015. Impacts of Gut Bacteria on Human Health and Diseases. Int J Mol Sci 16: 7493‐7519. Zhou M., Wang S., Zhao A., Wang K. et al. 2012. Transcriptomic and metabonomic profiling reveal synergistic effects of quercetin and resveratrol supplementation in high fat diet fed mice. J Proteome Res 11: 4961‐4971. Zulyniak M.A., Mutch D.M. 2011. Harnessing metabolomics for nutrition research. Curr Pharm Biotechnol 12: 1005‐1015.

239

VIII. APPENDICES

Appendix 1: Role of dietary polyphenols and inflammatory processes on disease progression mediated by the gut microbiota

Rejuvenation Res, 2013

doi: 10.1089/rej.2013.1481

243

Documento sujeto a copyright. Uso exclusivo para fines de investigación. Prohibida su distribución o copia. REJUVENATION RESEARCH Correspondence Volume 16, Number 5, 2013 ª Mary Ann Liebert, Inc. DOI: 10.1089/rej.2013.1481

Role of Dietary Polyphenols and Inflammatory Processes on Disease Progression Mediated by the Gut Microbiota

J. Alfredo Martı´nez,1–3 Usune Etxeberrı´a,1 Alicia Galar,4 and Fermı´n I. Milagro1,2

Abstract

Mendelsohn and Larrick have recently discussed in a recent article in Rejuvenation Research that dietary modi- fications of the gut microbiota affect the risk for cardiovascular disease. In this context, dietary patterns and single specific nutrients appear to produce singular consequences on the gut microbiota, subsequently impacting on maintenance of well-being and disease onset or evolution, whose intimate influences and mechanisms are now starting to be disentangled. Thus, the consumption of dietary fiber and particular polysaccharides affects colonic fermentation processes involving short-chain fatty acid production, accompanying changes in the en- vironmental pH, inhibiting Bacteroides spp., and rising levels of butyrate-producing Gram-positive bacteria. This scenario may contribute to the design of novel therapeutic approaches to manipulate gut microbiota to treat cardiovascular diseases and obesity. Indeed, cardiovascular risk may be indirectly dependent on pathways associated with microbe-induced obesity or diabetes through inflammation. Diverse components of the diet, including bioactive molecules with bactericidal functions, such as polyphenols, may play a role on intestinal mucosa inflammation and permeability and contribute to explaining the mutual interactions between obesity, diabetes, and adverse cardiovascular events.

Introduction polysaccharides affects colonic fermentation processes in- volving short-chain fatty acids production and accompany- endelsohn and Larrick have discussed in a recent ing changes in the environmental pH that inhibit Bacteroides Marticle in Rejuvenation Research that dietary modifica- spp. and increase butyrate-producing Gram-positive bacte- tions of the gut microbes affect the risk for cardiovascular ria.5 On the other hand, subjects on a habitual diet rich in disease.1 Indeed, the intestinal microbiota (gut microbiome) animal protein could increase Bacteroides populations by consists of a complex community of microorganisms, mainly supplying specific amino acids and saturated fat.5 bacteria, that have been considered as the forgotten organ Within this framework, the commentary from Mendel- because it may contribute to perhaps 2 kg in mass to the shon and Larrick1 emphasizes this concept because the in- body weight.2 A broad-range sequencing screening of the take of some meat components, such as carnitine and lecithin enterotypes of the human gut microbiome has provided (phosphatidylcholine), which are metabolized by intestinal evidence that despite the fact that a high variability among microbes to trimethylamine (TMA), and in turn by liver en- individuals exists, some genes significantly correlate with zymes to form trimethylamine-N-oxide (TMAO), may have age and three functional modules are associated with the deleterious effects on cardiovascular health mediated by the body mass index, hinting at a diagnoses potential of micro- gut microbiota. These findings complement another recent bial markers.3 research report from Hazen and collaborators6 that associ- In this context, dietary patterns and single specific nutri- ated the circulating levels of TMAO with an increased inci- ents appear to produce singular consequences on the gut dence of major adverse cardiovascular events. Globally, microbiota, subsequently impacting on maintenance of well- both studies suggest that the presence of specific as yet un- being and disease onset or evolution, whose intimate influ- identified gut microorganisms linked to diet are required for ences and mechanisms are now starting to be disentangled.4 inducing high TMAO levels and TMAO-mediated cardio- Thus, the consumption of dietary fiber and particularly vascular disease progression.1,6 This scenario may contribute

1Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain. 2CIBER de Fisiopatologı´a de la Obesidad y Nutricio´n (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain. 3Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts. 4Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts.

435

Appendix 2: Antidiabetic effects of natural plant extracts via inhibition of carbohydrate hydrolysis enzymes with emphasis on pancreatic alpha amylase

Expert Opin Ther Targets, 2012

doi: 10.1517/14728222.2012.664134

247

Review Antidiabetic effects of natural plant extracts via inhibition of carbohydrate hydrolysis enzymes 1. Introduction 2. Antidiabetic plants used in with emphasis on pancreatic traditional medicine alpha amylase 3. Pancreatic alpha amylase inhibitory activity in vitro Usune Etxeberria, Ana Laura de la Garza, Javier Campio´n, † 4. Phenolic phytochemicals J Alfredo Martı´nez & Fermı´n I Milagro 5. Pancreatic alpha amylase University of Navarra, Department of Nutrition, Food Science, Physiology and Toxicology, inhibitory activity of natural Pamplona, Spain plant extracts in vivo Introduction: The increasing prevalence of type 2 diabetes mellitus and the 6. Pancreatic alpha amylase negative clinical outcomes observed with the commercially available anti- inhibitory activity of natural diabetic drugs have led to the investigation of new therapeutic approaches plant extracts in humans focused on controlling postprandrial glucose levels. The use of carbohydrate 7. Side effects of natural digestive enzyme inhibitors from natural resources could be a possible strat- alpha-amylase inhibitors egy to block dietary carbohydrate absorption with less adverse effects than 8. Conclusions synthetic drugs. Areas covered: in vitro 9. Expert opinion This review covers the latest evidence regarding and in vivo studies in relation to pancreatic alpha-amylase inhibitors of plant origin, and presents bioactive compounds of phenolic nature that exhibit anti-amylase activity. Expert opinion: Pancreatic alpha-amylase inhibitors from traditional plant extracts are a promising tool for diabetes treatment. Many studies have con- firmed the alpha-amylase inhibitory activity of plants and their bioactive com- pounds in vitro, but few studies corroborate these findings in rodents and very few in humans. Thus, despite some encouraging results, more research is required for developing a valuable anti-diabetic therapy using pancreatic alpha-amylase inhibitors of plant origin.

Keywords: alpha-amylase, flavonoids, proanthocyanidins, tannins, type 2 diabetes mellitus

Expert Opin. Ther. Targets (2012) 16(3):1-29

1. Introduction

Diabetes mellitus is one of the world´s major diseases, with an estimated 347 million adults affected in 2011 [1]. Type 2 diabetes mellitus, by far the most common type, is a metabolic disorder of multiple etiology characterized by carbohydrate, lipid and protein metabolic disorders that includes defects in insulin secretion, almost always with a major contribution of insulin resistance [2]. These abnormalities could lead to lesions such as retinopathy, nephropathy, neuropathy and angiopathy [3]. In this context, the inhibition of carbohydrate digestive enzymes is considered a therapeutic tool for the treatment of type 2 diabetes [4]. The most important digestive enzyme is pancreatic alpha-amylase (EC 3.2.1.1), a calcium metalloenzyme that catalyzes the hydrolysis of the alpha-1,4 glycosidic linkages of starch, amylose, amylopectin, gly- cogen and various maltodextrins and is responsible of most of starch digestion in humans. A second enzyme named alpha-glucosidase or maltase (EC 3.2.1.20) cata- lyzes the final step of the digestive process of carbohydrates acting upon 1,4-alpha bonds and giving as a result glucose [4].

10.1517/14728222.2012.664134 © 2012 Informa UK, Ltd. ISSN 1472-8222 1 All rights reserved: reproduction in whole or in part not permitted

Appendix 3: Helichrysum and grapefruit extracts inhibit carbohydrate digestion and absorption, improving postprandial glucose levels and hyperinsulinemia in rats

J Agric Food Chem, 2013

doi: 10.1021/jf4021569

251

Article

pubs.acs.org/JAFC

Helichrysum and Grapefruit Extracts Inhibit Carbohydrate Digestion and Absorption, Improving Postprandial Glucose Levels and Hyperinsulinemia in Rats † † † § # † Ana Laura de la Garza, Usune Etxeberria, Marıá Pilar Lostao, , Beleń San Roman,́ Jaione Barrenetxe, † § † § J. Alfredo Martínez,*, , and Fermıń I. Milagro , † Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain § Physiopathology of Obesity and Nutrition, CIBERobn, Carlos III Health Research Institute, Madrid, Spain # Discovery Laboratory, Research Department, Biosearch S.A., 18004 Granada, Spain

ABSTRACT: Several plant extracts rich in flavonoids have been reported to improve hyperglycemia by inhibiting digestive enzyme activities and SGLT1-mediated glucose uptake. In this study, helichrysum (Helichrysum italicum) and grapefruit (Citrus × paradisi) extracts inhibited in vitro enzyme activities. The helichrysum extract showed higher inhibitory activity of α-glucosidase α α α (IC50 = 0.19 mg/mL) than -amylase (IC50 = 0.83 mg/mL), whereas the grapefruit extract presented similar -amylase and - glucosidase inhibitory activities (IC50 = 0.42 mg/mL and IC50 = 0.41 mg/mL, respectively). Both extracts reduced maltose digestion in noneverted intestinal sacs (57% with helichrysum and 46% with grapefruit). Likewise, both extracts inhibited SGLT1-mediated methylglucoside uptake in Caco-2 cells in the presence of Na+ (56% of inhibition with helichrysum and 54% with grapefruit). In vivo studies demonstrated that helichrysum decreased blood glucose levels after an oral maltose tolerance test (OMTT), and both extracts reduced postprandial glucose levels after the oral starch tolerance test (OSTT). Finally, both extracts improved hyperinsulinemia (31% with helichrysum and 50% with grapefruit) and HOMA index (47% with helichrysum and 54% with grapefruit) in a dietary model of insulin resistance in rats. In summary, helichrysum and grapefruit extracts improve postprandial glycemic control in rats, possibly by inhibiting α-glucosidase and α-amylase enzyme activities and decreasing SGLT1-mediated glucose uptake. KEYWORDS: α-amylase, α-glucosidase, Caco-2 cells, flavonoids, SGLT1

■ INTRODUCTION Hence, the inhibition of these enzymes is an interesting strategy 8,17,18 Type 2 diabetes mellitus (T2DM) is a metabolic disorder that for the control of postprandial hyperglycemia. Other ff ff approaches are directed to develop agents that inhibit intestinal a ects di erent organs and tissues and is characterized by 17 chronically elevated levels of blood glucose due to insulin glucose uptake. Thus, in the intestine, glucose is absorbed resistance.1 The increasing incidence of T2DM, one of the mainly by two transporters, depending on the luminal glucose world’s most common chronic diseases, is primarily associated concentration. At low concentrations, glucose is transported with lifestyle-related obesity and sedentarism and is modulated across the brush border membrane against a concentration by the genetic background.2,3 Considering that postprandial gradient by the sodium-dependent glucose transporter 1 (SGLT1). At higher concentrations, glucose is transported hyperglycemia is an early detected symptom in T2DM related to ffi overconsumption of carbohydrate-rich foods, consumption of mainly by the low-a nity facilitated transporter, glucose transporter 2 (GLUT2).19 Dietary flavonoids, given their relative phytochemicals occurring in natural products could be a possible ff 15 strategy to control T2DM.4 safety and low incidence of adverse gastrointestinal side e ects, Phytochemicals are non-nutritive plant compounds that have are candidate agents for managing postprandial hyperglycemia due to their interactions with the intestinal α-glucosidase and health-protective properties. One of the most relevant families of α 17 phytochemicals with proven health benefits is the polyphenols. pancreatic -amylase and the inhibition of glucose uptake. In this context, there are more than 500 species of the They are found in many plants including vegetables, fruits, and 20 grains,5 being flavonoids the most prevalent phenolic com- Helichrysum genus distributed around the world. Plants of this pounds. Epidemiological data strongly suggest that diets rich in genus have been found to possess antimicrobial, antiallergic, fl 20,21 flavonoids are generally associated with a preventive role against antioxidant, and anti-in ammatory properties. The bio- − the development of chronic diseases.6 8 logical activities of Helichrysum plants have been attributed to fl ff This situation has led to an increased interest in finding specific several classes of avonoids detected in di erent parts of the fl 22 natural dietary sources that could be used for treating diabetes. In plant, such as kaempferol-3-O-glucoside and other avanones. this sense, numerous studies have investigated flavonoid-rich plant extracts that could reduce or suppress intestinal glucose Received: May 17, 2013 − uptake through the inhibition of digestive enzyme activities.9 16 Revised: October 15, 2013 In mammals, dietary carbohydrates are hydrolyzed by Accepted: November 22, 2013 pancreatic α-amylase and intestinal α-glucosidase enzymes. Published: November 22, 2013

© 2013 American Chemical Society 12012 dx.doi.org/10.1021/jf4021569 | J. Agric. Food Chem. 2013, 61, 12012−12019

Appendix 4: Modulation of hyperglycemia and TNFα‐ mediated inflammation by helichrysum and grapefruit extracts in diabetic db/db mice

Food Fuct, 2014

doi: 10.1039/c4fo00154k

255

Food & Function

PAPER

Modulation of hyperglycemia and TNFa-mediated inflammation by helichrysum and grapefruit Cite this: Food Funct.,2014,5,2120 extracts in diabetic db/db mice

Ana Laura de la Garza,a Usune Etxeberria,a Sara Palacios-Ortega,b c c ad Alexander G. Haslberger, Eva Aumueller, Ferm´ın I. Milagro ad and J. Alfredo Mart´ınez*

Type-2 diabetes is associated with a chronic low-grade systemic inflammation accompanied by an increased production of adipokines/cytokines by obese adipose tissue. The search for new antidiabetic drugs with different mechanisms of action, such as insulin sensitizers, insulin secretagogues and a- glucosidase inhibitors, has directed the focus on the potential use of flavonoids in the management of type-2 diabetes. Thirty six diabetic male C57BL/6J db/db mice were fed a standard diet and randomly assigned into four experimental groups: non-treated control, (n ¼ 8); acarbose (5 mg per kg bw, n ¼ 8); helichrysum (1 g per kg bw, n ¼ 10) and grapefruit (0.5 g per kg bw, n ¼ 10) for 6 weeks. The mRNA expression in pancreas, liver and epididymal adipose tissue was determined by RT-PCR. DNA methylation was quantified in epididymal fat using pyrosequencing. Mice supplemented with helichrysum and grapefruit extracts showed a significant decrease in fasting glucose levels (p < 0.05). A possible mechanism of action could be the up-regulation of liver glucokinase (p < 0.05). The antihyperglycemic effect of both extracts was accompanied by decreased mRNA expression of some proinflammatory genes (monocyte chemotactic protein-1, tumor necrosis factor-a, cyclooxygenase-2, nuclear factor- kappaB) in the liver and epididymal adipose tissue. The CpG3 site of TNFa, located 5 bp downstream of the transcription start site, showed increased DNA methylation in the grapefruit group compared with Received 25th February 2014 the non-treated group (p < 0.01). In conclusion, helichrysum and grapefruit extracts improved Accepted 7th June 2014 hyperglycemia through the regulation of glucose metabolism in the liver and reduction of the expression DOI: 10.1039/c4fo00154k of proinflammatory genes in the liver and visceral fat. The hypermethylation of TNFa in adipose tissue www.rsc.org/foodfunction may contribute to reduce the inflammation associated with diabetes and obesity.

Introduction inammatory cytokines, many of them secreted by the hyper- trophied adipocytes, are controlled through transcription Type 2 diabetes mellitus (T2DM) is a metabolic disorder char- nuclear factor-kappaB (NF-kB), whereby the inammatory acterized by chronic hyperglycemia as a result of impairments response can be down-regulated.5 In addition, this transcription in insulin secretion and insulin action in target tissues.1 Insulin factor represents a link between inammation and IR, as it is resistance (IR) is produced as soon as the pancreatic b-cells activated by factors known to promote IR and T2DM.6 One cannot compensate a reduced insulin function, leading to important downstream target of NF-kB is cyclooxygenase 2 elevated circulating glucose levels.2 Insulin inhibits gluconeo- (COX2), which catalyzes the production of prostaglandins, the genesis in the liver and reduces lipolysis in adipose tissue.3 key molecules in inammation processes of the body.7 More- Likewise, adipose tissue in diabetes and obesity is characterized over, NF-kB is involved in the expression of many cytokines, by hypertrophy, relative hypoxia, low-grade chronic inamma- including TNFa.5 On the other hand, epigenetic changes are tion and endocrine dysfunctions.4 In this context, the pro- heritable yet reversible modications that occur without alter- ations in the primary DNA sequence. These modications may provide a link between the environment (i.e. nutrition) and aDepartment of Nutrition, Food Science and Physiology, University of Navarra, T2DM.8 Recently, epigenetic modications have also been C/Irunlarrea 1, 31008 Pamplona, Navarra, Spain. E-mail: [email protected]; Fax: implicated in disease-associated changes inuencing gene +34 948425740; Tel: +34 948425600 expression.9 b Department of Biochemistry and Genetics, University of Navarra, Pamplona, Spain  c Targeting the reduction of chronic in ammation is a bene- Department of Nutritional Sciences, University of Vienna, Vienna, Austria  dPhysiopathology of Obesity and Nutrition, CIBERobn, Carlos III Health Research cial strategy to combat several metabolic diseases, including 10 Institute, Madrid, Spain T2DM. Thus, numerous studies have underlined the interest

2120 | Food Funct.,2014,5,2120–2128 This journal is © The Royal Society of Chemistry 2014

Appendix 5: Helichrysum and grapefruit extracts boost weight loss in overweight rats reducing inflammation

J Med Food, 2015

doi: 10.1039/c4fo00154k

259

JOURNAL OF MEDICINAL FOOD J Med Food 18 (8) 2015, 890–898 # Mary Ann Liebert, Inc., and Korean Society of Food Science and Nutrition DOI: 10.1089/jmf.2014.0088

Helichrysum and Grapefruit Extracts Boost Weight Loss in Overweight Rats Reducing Inflammation

Ana Laura de la Garza,1,2 Usune Etxeberria,1,2 Alexander Haslberger,3 Eva Aumueller,3 J. Alfredo Martı´nez,1,2,4 and Fermı´n I. Milagro1,2,4

1Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain. 2Centre for Nutrition Research, Faculty of Pharmacy, University of Navarra, Pamplona, Spain. 3Department of Nutritional Sciences, University of Vienna, Vienna, Austria. 4Physiopathology of Obesity and Nutrition, CIBERobn, Carlos III Health Research Institute, Madrid, Spain.

ABSTRACT Obesity is characterized by an increased production of inflammatory markers. High levels of circulating free fatty acids and chronic inflammation lead to increased oxidative stress, contributing to the development of insulin resistance (IR). Recent studies have focused on the potential use of flavonoids for obesity management due to their antioxidant and anti- inflammatory properties. This study was designed to investigate the antioxidant and anti-inflammatory effects of helichrysum and grapefruit extracts in overweight insulin-resistant rats. Thirty-eight male Wistar rats were randomly distributed in two groups: control group (n = 8) and high-fat sucrose (HFS) group (n = 30). After 22 days of ad libitum water and food access, the rats fed HFS diet changed to standard diet and were reassigned into three groups (n = 10 each group): nonsupplemented, helichrysum extract (2 g/kg bw), and grapefruit extract (1 g/kg bw) administered for 5 weeks. Rats supplemented with both extracts gained less body weight during the 5-week period of treatment, showed lower serum insulin levels and liver TBARS levels. Leptin/adiponectin ratio, as an indicator of IR, was lower in both extract-administered groups. These results were accompanied by a reduction in TNFa gene expression in epididymal adipose tissue and intestinal mucosa, and TLR2 expression in intestinal mucosa. Helichrysum and grapefruit extracts might be used as complement hypocaloric diets in weight loss treatment. Both extracts helped to reduce weight gain, hyperinsulinemia, and IR, improved inflammation markers, and decreased the HFS diet-induced oxidative stress in insulin-resistant rats.

KEY WORDS:  flavonoids  grapefruit  helichrysum  insulin resistance  TLR2  TNFa

INTRODUCTION in obese adipose tissue.7 There is increasing evidence con- cerning the involvement of TLRs in obesity-induced in- igh-fat diets may play an important role in the in- flammation and IR.8 In this sense, TLR2 has been shown to creased prevalence of obesity.1 Obesity is a multifac- H be stimulated by free fatty acids, and the activation of these torial disease usually accompanied by insulin resistance (IR) receptors has been found to induce IR.9 Likewise, oxidative and an increase in oxidative stress and inflammatory mark- stress is a mediator mechanism implicated in these patho- ers, including tumor necrosis factor-a (TNFa) and toll-like logical conditions and contributes to the release of cytokines receptors (TLRs).2 TNFa is an important mediator of IR and inflammation.10 Oxidative stress induces tissue damage, acting as a link between obesity and inflammation.3 Adipose which is responsible for the lower insulin sensitivity in tissue not only stores fat, but also acts as a major endocrine different tissues.11 and secretor organ that produces different adipokines such Numerous strategies have been developed to treat chronic as leptin and adiponectin.4 Thus, leptin is a hormone se- diseases. Natural extracts have been studied as dietary sup- creted by adipocytes that regulates energy homeostasis,5 plements for body weight management. Helichrysum and while adiponectin is an anti-inflammatory adipokine that grapefruit extracts contain different kind of flavonoids,12,13 improves insulin sensitivity.6 TLRs play a crucial role in the and they are appreciable candidates like antioxidants and anti- innate immune response, being their expression elevated inflammatory agents because they are abundant in nature and may have fewer side effects.14,15 Likewise, other investiga- Manuscript received 26 May 2014. Revision accepted 2 December 2014. tions demonstrated antihyperglycemic and anti-inflammatory properties of helichrysum and grapefruit extracts in diabetic Address correspondence to: J. Alfredo Martı´nez, PhD, MD, RN, Department of Nutrition, 16 Food Science and Physiology, University of Navarra, C/Irunlarrea 1, Pamplona 31008, mice, or the role of naringenin (a flavanone occurring in both Navarra, Spain, E-mail: [email protected] extracts) concerning the hypocholesterolemic properties in

890

Appendix 6: Congress communications

263

Poster

20th International Congress of Nutrition (IUNS)

Annals of Nutrition and Metabolism. 2013; 63(1): 1264 (P02085); Granada (Spain), 15th‐20th Sept 2013

Effect of the intake of a high‐fat sucrose diet on gut microbiota composition in rats

Etxeberria U1, Arias N2, Macarulla MT2,3, Portillo MP2,3, Milagro FI1, 3, Martínez JA1, 3

1Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain

2Department of Nutrition and Food Science, Faculty of Pharmacy, University of País Vasco, Vitoria, Spain

3CIBER Fisiopatología, Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Spain

Background and objective: Intestinal microbiota has been recently considered as an important factor related to obesity. Gut microbiota is altered as a consequence of a long‐term dietary manipulation, which could have a metabolic impact and affect host health. In this sense, the understanding of the diet‐responsive groups of bacteria could give valuable information to treat diet‐induced obesity and accompanying complications. Therefore, the aim of this study was to investigate the effect produced by the intake of a high‐fat sucrose diet on gut microbiota composition in rats.

Methods: Male Wistar rats were fed on a high‐fat sucrose (HFS) diet during 6 weeks. Faeces were freshly collected at baseline and at the end of the treatment. DNA was extracted and analyzed by RT‐qPCR. Specific primers for the following groups of bacteria were used: Bacteroidetes, Bacteroides‐Prevotella, Clostridium coccoides‐ Eubacterium rectale, Bifidobacterium spp., Lactobacillus group, Enterobacteriaceae and Actinobacteria.

Results: High‐fat sucrose diet treatment significantly (p< 0.05) reduced the levels of Bacteroidetes and Clostridium coccoides‐ Eubacterium rectale. However, no statistical differences were found in Bifidobacterium spp., Lactobacillus spp., Bacteroides‐ Prevotella, Actinobacteria and Enterobacteriaceae levels at the end of the study when compared to the baseline.

Conclusion: This study showed that the intake of an obesogenic diet rich in fat and sucrose induces changes in the populations of gut microorganisms, confirming that diet is shaping the composition of gut microbiota. HFS diet‐induced obesity is accompanied by lower levels of one of the major bacterial phyla in mammalian gut microbiota.

Key words: Microbiota, diet‐induced obesity, gastrointestinal tract, RT‐qPCR

Acknowledgemets: The authors thank the financial support of the Department of Education, Universities and Research of Basque Government for the pre‐ doctoral grant given to Usune Etxeberria Aranburu. LE/97 of the University of Navarra is also gratefully credited.

265

Poster

21st European Congress on Obesity (ECO)

Obesity Facts. 2014; 7(1):139 (PO.094); Sofia (Bulgaria), 28th ‐31st May 2014

Evidence of gut microbial species differential colonization under a model of high‐fat sucrose dietary intake: a high‐ throughput approach by 16S rDNA pyrosequencing

Etxeberria U1, Arias N2, Macarulla MT2,3, Portillo MP2,3, Martinez JA1,3, Milagro FI1,3

1Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain

2Nutrition and Obesity group, Department of Nutrition and Food Sciences, Faculty of Pharmacy, University of the Basque Country (UPV/EHU), Vitoria, Spain

3CIBERobn Fisiopatología de Obesidad y Nutrición.Instituto de Salud Carlos III, Madrid, Spain

Introduction: Gut microbiota (GM) is recognized as the bacterial community that colonizes the gastrointestinal tract (GI). These commensal microbes perform essential metabolic functions for the host, while modifications on the composition of microbial population are known to influence the susceptibility to several diseases such as obesity. Westernized diets, rich in fat and sucrose macronutrients, could produce gut microbiota dysbiosis resulting in growth or inhibition of both pathogenic or beneficial bacterial species. The aim of the present study was to characterize gut microbial changes produced as a consequence of feeding rats with a diet rich in fat and sucrose (HFS).

Material and Methods: Comparison of the faecal microbiota composition at baseline and after a 6‐week HFS intervention was performed. DNA extracted from rat faeces was analysed by the amplification of the V4‐V6 region of the 16S rDNA (ribosomal DNA) gene tailed on each end with the Roche multiplex identifiers by 454 pyrosequencing.

Results: HFS diet feeding not only produced an increase in animal weight and adiposity‐related markers, but also displayed statistically significant differences in gut microbiota composition. At phylum level, Firmicutes, Bacteroidetes and Proteobacteria, were markedly affected and an important “bloom” in Erysipelotrichi class was found at the end of the dietary intervention. Species from Veillonellaceae, Prevotellaceae and Clostridiaceae families were the most influenced after the HFS diet consumption.

Conclusion: This study illustrates the potential of culture‐independent methods to characterize and more precisely identify members of the gut microbiota as influenced by diets enriched in fat and sucrose involving acquired overweight.

Funding: This study was supported by grants from Ministerio de Economía y Competitividad (AGL2011‐27406‐ALI), Instituto de Salud Carlos III (CIBERobn), Government of the Basque Country (IT‐572‐13), University of the Basque Country (UPV/EHU) (ELDUNANOTEK UFI11/32) and the Línea Especial about Nutrition, Obesity and Health (University of Navarra LE/97).

266

Poster

Belgian Nutrition Society (IUNS)

Archives of Public Health 2014, 72 (Suppl 1): P7, 2014; Brussels (Belgium), 25th April 2015

Supplementation with crude rhubarb extract lessens liver inflammation and hepatic lipid accumulation in a model of acute alcohol‐induced steato‐hepatitis

Neyrinck AM*, Etxeberria U*, Jouret A, Delzenne NM

Louvain Drug Research Institute, Metabolism and Nutrition Research Group, Université catholique de Louvain, Brussels, Belgium

*These authors contributed equally to this work

Background: Binge consumption of alcohol is an alarming global health problem and is implicated in the pathophysiology of alcoholic liver disease (ALD) [1]. In its early stages, ALD it is characterized by fatty liver[1]. Moreover, numerous studies are supporting the concept that beyond this hepatic steatosis physiological processes, inflammation occurred in the progression of the disease [2]. Strategies directed to reduce fat accumulation and local hepatic inflammation caused by alcohol consumption might succeed blocking the evolution of ALD. In this sense, natural plants rich in bioactive constituents are attracting a growing interest as new therapeutic agents. Several studies have reported health benefits coming from rhubarb extract rich in anthraquinones [3, 4]. The aim of the present study was to test the potential hepatoprotective effects of rhubarb extract supplementation in a model of acute alcohol‐induced steato‐ hepatitis.

Material and methods: Male C57Bl6J mice were fed with a control diet supplemented or not with 0.3% rhubarb extract (Laboratoires Ortis, Belgium). After 17 days, mice received a huge dose of ethanol (6g/kg bw) and were sacrificed 6 hours after the alcohol challenge. Liver oil red O staining and hepatic lipid contents were determined to assess hepatic steatosis whereas the expression of pro‐inflammatory markers were analysed in the liver tissue by quantitative PCR to assess hepatic inflammation.

Results: Ethanol administration caused a massive increase of hepatic triglycerides and, in a lesser extent, in total cholesterol inside the liver tissue (table 1). Rhubarb extract decreased hepatic triglyceride content. This effect was confirmed by histological analysis (figure 1). Interestingly, rhubarb extract supplementation blunted the ethanol‐induced F4/80 expression, suggesting a lower recruitment of inflammatory cells inside the liver (figure 2). Moreover, proinflammatory markers such as TNF‐α, IL‐6, MCP‐1 and COX‐2 were down‐regulated in the liver of mice fed with rhubarb extract (figure 2).

Conclusions: These results suggest that rhubarb extract rich in anthraquinones might decrease liver tissue injury, namely hepatic lipid accumulation and inflammatory disorders caused by acute alcohol consumption.

Acknowledgements: Financial support has been provided by a grant from the Walloon Region (Nutrigutior project, Convention 6918)

267

Poster

XVI Congress of the Spanish Nutrition Society (SEÑ)

Nutrición Hospitalaria. 2014; 30(1):34 (PO067); Pamplona (Spain), 3rd ‐5th Jul 2014

La suplementación con quercetina revierte los cambios en la composición de la microbiota intestinal asociadas a la ingesta de una dieta rica en grasas y azúcares

Etxeberria U1, Arias N2, Macarulla MT2,3, Portillo MP2,3, Milagro FI1,3, Martínez JA1,3

1Departamento de Ciencias de la Alimentación, Fisiología y Nutrición, Universidad de Navarra, Pamplona, España

2Área de Nutrición y Bromatología, Facultad de Farmacia, Universidad del País Vasco (UPV/EHU), Paseo de la Universidad 7, 01006, Vitoria, España

3CIBERobn Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de salud Carlos III, 28029 Madrid, España

Introducción: La microbiota intestinal, como agente que modula el riesgo de padecer obesidad, ha sido ampliamente descrita lo que ha contribuido a la realización de numerosos estudios para determinar la relevancia de los diferentes grupos bacterianos en el desarrollo de la misma. La dieta es un factor clave que modifica el ecosistema microbiano del intestino, pudiendo generar un desequilibrio entre los diferentes grupos bacterianos. Debido a la estrecha relación metabólica existente entre los nutrientes ingeridos y las bacterias, la composición de la microbiota intestinal puede ser modificada con el fin de favorecer el crecimiento de las especies “beneficiosas” frente a las “patógenas”.

Objetivo: El objetivo del presente trabajo fue esclarecer el potencial de los polifenoles resveratrol y quercetina para revertir la disbiosis provocada por el consumo de una dieta rica en grasa y en azúcares in vivo.

Material y métodos: El experimento se realizó con 23 ratas Wistar alimentadas con una dieta alta en grasas y azúcares durante 6 semanas. Grupo control (HFS, n=5), grupo resveratrol (RSV; 15 mg/ kg p.c /día, n=6), grupo quercetina (Q; 30 mg/ kg p.c/ día, n=6) y grupo suplementado con la combinación de ambos polifenoles a la misma dosis (RSV +Q, n=6). El DNA de las heces fue extraído y el análisis filogenético realizado a través de la pirosecuenciación.

Resultados: La suplementación con quercetina redujo significativamente los niveles del grupo Firmicutes (‐34.2 %) y el ratio Firmicutes/Bacteroidetes disminuyó considerablemente (‐4.9 %). Además, la suplementación con quercetina provocó una disminución significativa en la familia Erysipelotrichaceae (‐83.9 %). Al contrario, los grupos suplementados con resveratrol o la combinación de ambos compuestos no mostraron cambios significativos tras el test de comparaciones múltiples.

Conclusión: La suplementación con quercetina mitigó los cambios en la composición de la microbiota intestinal asociados a la ingesta de una dieta de tipo occidental.

268

Poster

3rd Congress of the Spanish Federation of Societies in Nutrition, Food and Dietetics (FESNAD)

Nutrición Clínica en Medicina; IX (1): 4 (P099); Sevilla (Spain), 5th‐7th March 2015.

Perfil metabolómico fecal en animales suplementados con resveratrol, quercetina y la combinación de ambos compuestos

Etxeberria U 1,2, Arias N 3,5, Boqué N 4, Macarulla MT 3,5, Portillo MP 3,5, Milagro FI 1,2,5, Martínez JA1,2,5

1 Departamento de Ciencias de la Alimentación y Fisiología, Universidad de Navarra, Pamplona

2 Centro de Investigación en Nutrición, Universidad de Navarra, Pamplona

3 Área de Nutrición y Bromatología, Facultad de Farmacia, Universidad del País Vasco (UPV/EHU), Vitoria

4 Grupo de Investigación en Nutrición y Salud (GRNS). Centro Tecnológico de Nutrición y Salud (CTNS), TECNIO, CEICS, Reus

5 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid

Introducción: La metabolómica permite detectar cambios en metabolitos asociados a enfermedades, y resulta una herramienta útil para caracterizar la contribución de la dieta y/o los componentes finales procedentes de la misma al bienestar general.

Objetivo: Identificar los cambios metabolómicos observados en las heces en relación con la suplementación dietética de dos compuestos bioactivos, trans‐resveratrol y quercetina, en animales alimentados con una dieta alta en grasa y azúcar.

Métodos: La cromatografía de líquidos (LC) acoplada a la espectrometría de masas como sistema de detección (TOF) fue utilizada para llevar a cabo un análisis no dirigido en heces. Las muestras se obtuvieron de ratas Wistar pertenecientes a los siguientes grupos experimentales: Control (HFS, n=5), Resveratrol (RSV; 15 mg/ kg pc/día, n=6), Quercetina (Q; 30 mg/ kg pc/ día, n=6) y la Mezcla de ambos (RSV + Q, n=6). El análisis se realizó mediante ANOVA de un factor seguido de la prueba HSD de Tukey y los resultados estadísticamente significativos se corrigieron por el método de Benjamini‐ Hochberg. El análisis de componentes principales (PCA) se llevó a cabo con el software Mass Profiler Professional.

Resultados: El análisis de los datos evidenció diferencias estadísticamente significativas en 59 de los metabolitos cuantificados (p< 0,05). El grupo Quercetina se caracterizó por la presencia diferencial de 15 metabolitos (p< 0,05, Log FC> 5 o Log FC< ‐5). Algunos de los metabolitos candidatos identificados se relacionaron con el metabolismo de los hidratos de carbono. A su vez, el grupo Resveratrol se distinguió por la presencia diferencial de otros 16 metabolitos (p< 0,05, Log FC> 5 o Log FC< ‐5), reflejando cambios en el metabolismo de los aminoácidos, los lípidos y los nucleótidos, preferentemente.

Conclusiones: Los metabolitos detectados reflejaron modificaciones en varias vías metabólicas y permitieron la estratificación por PCA de los grupos experimentales en función de los tratamientos.

269

Poster

37th Congress of The European Society for Clinical Nutrition and Metabolism (ESPEN)

Clinical Nutrition; 4 (1): S142 (PP040); Lisboa (Portugal), 5th‐6th Septermber 2015.

A crude rhubarb extract protects from acute ethanol‐induced liver damage in association with increased Akkermansia muciniphila population in the gut microbiota of mice

Neyrinck AM1*, Etxeberria U1*, Taminiau B2, Daube G2, Cani PD1, Bindels LB1, Delzenne NM1

1Louvain Drug Research Institute, Metabolism and Nutrition Research Group, Université catholique de Louvain, Brussels, Belgium

2Fundamental and Applied Research for Animal and Health, Université de Liège, Belgique

*These authors contributed equally to this work

Rationale: Binge consumption of alcohol is an alarming global health problem and is involved in the pathophysiology of alcoholic liver disease (ALD). Strategies directed to reduce fat accumulation and hepatic inflammation might succeed blocking the evolution of ALD in its early stage. Several studies have reported health benefits coming from bioactive constituents of rhubarb extract, such as anthraquinones. The aim of the present study was to test the potential hepatoprotective effects of rhubarb extract supplementation in a model of acute alcohol‐induced steato‐hepatitis and to determine whether these effects can be related to a modulation of the gut microbiota.

Methods: Male C57Bl6J mice were fed a control diet supplemented or not with 0.3% rhubarb extract (Laboratoires Ortis, Belgium). After 17 days, mice received an intragatric administration of ethanol (6g/kg bw) and were necropsied 6 hours after the alcohol challenge.

Results: Ethanol administration caused a massive hepatic steatosis, as shown by oil red staining. Rhubarb extract did not significantly modified hepatic lipid content. Importantly, a significant down‐regulation in key inflammatory genes (TNF‐ α, IL‐6, MCP‐1) and macrophage‐related markers (F4/80, CD68, CD11c) in the liver tissue were evidenced upon crude extract supplementation. Combination of pyrosequencing and qPCR analyses of the 16S rRNA gene revealed that rhubarb extract decreased microbial diversity and richness of caecal microbiota which was associated with a huge increase Akkermansia muciniphila proportion. In addition, the expression of two antimicrobial proteins (Reg3g and Pla2g2a) was upregulated following rhubarb treatment.

Conclusion: Rhubarb extract rich in anthraquinones decreased binge drinking‐induced liver inflammation. This effect was associated with both a higher Akkermansia muciniphila abundance and a regulation of antimicrobial protein secretion in the colon, suggesting an improved gut barrier function. Our study supports the importance of research focusing on gut microbes‐host interactions for managing systemic diseases, such as ALD

270

Appendix 7: Other congress communications

271

‐ “Metabolomic analysis of serum from obese rats fed a high‐fat sucrose diet with an emphasis in amino acids profile”. Etxeberria U, de la Garza AL, Martínez JA, Milagro FI. 9th Comunidad de Trabajo de los Pirineos Meeting. Toulouse, France, 2012 (20 July). Oral communication.

‐ “Búsqueda de extractos ricos en polifenoles con actividad inhibidora de la α‐amilasa pancreática: efectos sobre la prueba oral de tolerancia al almidón en ratas.” de la Garza AL, Etxeberria U, Milagro FI, Olivares M, Bañuelos O, Campión J, Martínez JA. XIV Congress of the Spanish Nutrition Society (SEÑ), Zaragoza, Spain, 2012 (27‐ 29 September). Nutr Hosp 2012; 27 (5): 1728 (PO76).

‐ “Polyphenolic inhibitors of pancreatic alpha‐ amylase retard progression to diabetes in db/db mice.” de la Garza AL, Etxeberria U, Campión J, Olivares M, Bañuelos O, Martínez JA, Milagro FI. EMBO│EMBL Symposium Diabetes and Obesity. Heidelberg, Germany, 2012 (13‐16 September).

‐ “Antiobesity and antihyperglicemic effect of natural inhibitors of pancreatic‐ amylase in wistar rats.” A. L. de la Garza, U. Etxeberria, J. Campión, M. Olivares, Bañuelos, A. Martínez, F. Milagro. 19th European Congress on Obesity (ECO), Lyon, France, 2012 (6‐9 May). Obes Facts 2012; 5 (Suppl 1): 143 (530).

‐ “Anti‐inflammatory and antioxidant properties of flavonoid‐rich extracts from grapefruit and helichysum in insulin resistant rats.” de la Garza AL, U. Etxeberria U, Campión J, Olivares M, Bañuelos O, Martínez JA, Milagro FI. IUNS 20th International Congress of Nutrition (ICN), Granada, Spain, 2013 (15‐20 September). Oral Communication. Ann Nutr Metab 2013; 63 (suppl. 1): 244 (PO172).

‐ “La suplementación con extractos de helicriso y pomelo ricos en flavonoides disminuye la inflamación asociada a la diabetes en ratones db/db.” de la Garza AL, U. Etxeberria U, Haslberg A, Aümuller E, Martínez JA, Milagro FI. XVI Congress of the Spanish Nutrition Society (SEÑ), Pamplona, Spain, 2014 (3‐5 July). Nutr Hosp 2014; 30: 37 (PO66).

‐ “Modulatory effect of resveratrol and quercetin on high‐fat sucrose diet‐induced gut microbiota dysbiosis”. Etxeberria U, Arias N, Macarulla MT, Portillo MP, Milagro FI, Martínez JA. Herrenhausen Conference: Beyond the Intestinal Microbiome. Hanover, Germany, 2014 (8‐10 October).

‐ “Evaluación de cinco compuestos bioactivos con potencial implicación en la prevención de la obesidad y la resistencia a la insulina inducida por una dieta alta en grasa y sacarosa”. de la Garza AL, Etxeberria U, Martínez JA, Milagro FI. XX Congress of AMMFEN (Asociación Mexicana de Miembros de Escuelas y Facultades de Nutrición), Cancún, México, 2015 (5‐8 May).

273