DOCSLIB.ORG

Gut Microbiota: Effect of Pubertal Status Xin Yuan, Ruimin Chen* , Ying Zhang, Xiangquan Lin and Xiaohong Yang

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Gut Microbiota: Effect of Pubertal Status Xin Yuan, Ruimin Chen* , Ying Zhang, Xiangquan Lin and Xiaohong Yang Yuan et al. BMC Microbiology (2020) 20:334 https://doi.org/10.1186/s12866-020-02021-0 RESEARCH ARTICLE Open Access Gut microbiota: effect of pubertal status Xin Yuan, Ruimin Chen* , Ying Zhang, Xiangquan Lin and Xiaohong Yang Abstract Background: The make-up of gut microbiota at different puberty stages has not been reported. This cross-sectional study analyzed the bio-diversity of gut microbiota at different puberty stages. Result: The subjects (aged 5–15 years) were divided into non-pubertal (n = 42, male%: 66.7%) or pubertal groups (n = 47, male%:44.68); in both groups, Firmicutes, Bacteroidetes and Proteobacteria were the dominant phylum. There was no difference of alpha- and beta-diversity among disparate puberty stages. Non-pubertal subjects had members of the order Clostridiales, family Clostridiaceae, genus Coprobacillus which were significantly more prevalent than puberty subjects. Also, the pubertal subjects had members of class Betaproteobacteria, order Burkholderiales which were significantly more prevalent than the non-pubertal subjects. Their relative abundance was independent of BMI-Z. In the pubertal subjects, the abundance of genus Adlercreutzia, Ruminococcus, Dorea, Clostridium and Parabacteroides was associated with the level of testosterone. Conclusions: This is the first report of the diversity of gut microbiota at different puberty stages. The various species of gut microbiota changed gradually associated with puberty stages. Differences in gut microflora at different pubertal status may be related to androgen levels. Keywords: Puberty, Children, Adolescent, 16 s rRNA, Gut microbiota Background colony plating to assess numbers of several bacterial Puberty constitutes an important phase of life, which is genera, found no noticeable changes in children between associated with profound physiological changes related 2 and 18 years old [5]. Recently, with the expansion and to sexual maturation during the transition toward adult- availability of bacterial DNA sequencing technology, a hood. These somatic developmental changes are pre- study compared the distal intestinal microbiota compos- dominantly driven by hormones and are accompanied ition between adolescents (11–18 years of age) and by psychological adjustment. Therefore, this dynamic adults, founding the abundance of the genera Bifidobac- period represents an unparalleled opportunity to assess terium and Clostridium was statistically significant potential hormonal impacts on gut microbiota [1]. Previ- higher in adolescent samples compared with adult ous studies pointed out that human gut microbiota was samples [6]. adult-like after the first 3 years of life and were relatively Every organ is affected by the extensive changes in cir- stable [2, 3]. However, although healthy pre-adolescent culating hormone during puberty [7, 8]. We postulated children (ages 7–12 years) and adults harbored similar that these marked changes in hormone levels would alter numbers of taxa and functional genes, their relative the intestinal flora. No previous microbiota study has ex- composition differed significantly [4]. Nonetheless, Enck plored the full span of growth from pre-puberty to late et al. conducted a large-scale study, using conventional puberty. Such information is instructive given the associ- ation between gut microbiota during growth and adult * Correspondence: [email protected] disease risk [9]. To this end, we utilized 16 s rRNA gene Department of Endocrinology, Genetics and Metabolism, Fuzhou Children’s sequencing to compare fecal microbiota profiles from Hospital of Fujian Medical University, NO.145, 817 Middle Road, Fuzhou 350005, China © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Yuan et al. BMC Microbiology (2020) 20:334 Page 2 of 9 pre-puberty to adolescence, ranging in age from 5 to 15 Microbiota profiles with different puberty status years. OTU classification Using PLS-DA, the non-pubertal and pubertal groups can be distinguished to a certain extent, suggesting that Results the two groups differed in the classification of the gut Study subjects microbiota (Fig. 3). The mean age of the 89 participates was 9.75 ± 1.92 years (ranging from 5.5 to 14.3 years) and 55.06% were boys. Alpha- and beta-diversity The majority (73.03%) were obese based on BMI, and Regarding alpha-diversity, there was no significant differ- 26.97% had normal BMI. ence of the Shannon diversity index, Observed OTUs, Based on puberty status, the subjects were divided into Faith’s phylogenetic diversity and Pielou’s evenness non-pubertal group (n = 42, 66.7% male) and pubertal based on OTU distribution between non-pubertal and group (n = 47, 44.68% male). The average age was 8.36 ± pubertal groups (all p > 0.05, Table S4). 1.64 years and 10.99 ± 1.15 years, respectively. There was Beta-diversity also did not differ significantly between no statistical difference in BMI-Z scores or dietary habits these two aforementioned groups after correction for between the two groups (p = 0.783 and 0.641, respect- multiple testing (Table S5). By Distance method Bray- ively). The non-pubertal group was further subdivided Curtis and Statistical method PERMANOVA, PCoA into a younger children group (n = 18, 66.7% male) and analysis illustrated that the gut microbiota samples from pre-pubertal group (n = 24, 66.7% male). And the puber- the non-pubertal group could not distinguish from the tal group classified as early (n = 18, 77.8% male), middle pubertal group [PERMANOVA] F-value: 0.712; R- (n = 14, 35.7% male), late (n = 15, 13.3% male). Of the 40 squared: 0.008; p-value < 0.735 (Fig. 4a). girls, 21 had E2 measured, 2 were non-pubertal with a level of E2 < 5 pg/ml, and 19 were pubertal with a level of E2 33.68 ± 35.80 pg/ml; 21 of the 49 boys had T mea- Bacterial taxa differences in subjects with different puberty sured, 6 were non-pubertal with a level of T 5.10 ± 4.16 status ng/dl, and 15 were pubertal with a level of T 83.20 ± We used Lefse analysis to identify bacteria where the 98.55 ng/dl. There was no statistical difference in BMI-Z relative abundance was significantly increased or de- scores, mode of birth, feeding patterns or dietary habits creased in each phenotypic category. Non-pubertal among the groups (p > 0.05). Tables 1, 2 and Table S1, subjects had members of the Non-pubertal subjects had Clostridiales Pasteurellales S2 describes the characteristics of the subjects. members of the order , , family Clostridiaceae, genus Coprobacillus and Haemophilus that were significantly more prevalent than puberty Core microbiota in all the subjects subjects. Also, the pubertal subjects had members of With 16S rRNA gene sequencing, 671 discrete bacterial class Betaproteobacteria, order Burkholderiales that were taxa (OTUs) were identified. Most of these species significantly more prevalent than the non-pubertal belonged to the “shared” category of those common to subjects (Fig. 5). multiple but not all samples. We also identified a “core” Spearman correlation analysis was used to detect an of 557 species shared among all fecal samples. The non- impact of BMI-Z on class Betaproteobacteria, order puberty group had 49 unique species and the puberty Clostridiales, Pasteurellales, Burkholderiales, family group had 66 unique species (Fig. 1). The core micro- Clostridiaceae, genus Coprobacillus and Haemophilus. biota was dominated by phylum Firmicutes, Bacteroi- The results showed the relative abundance of order detes and Proteobacteria in both the non-pubertal and Pasteurellales and genus Haemophilus correlate with pubertal groups (Fig. 2 and Table S3). BMI-Z (r = 0.223 and 0.222, p = 0.036 and 0.036, respect- ively), whereas other differential bacterial taxa had no Table 1 Clinical characteristics of the study population divided association with BMI-Z (p > 0.05). by puberty status Non-puberty (n = 42) Puberty (n =47 P value Microbiota profiles during puberty transition Age (years) 8.36 ± 1.64 10.99 ± 1.15 < 0.001 Alpha-, beta-diversity and bacterial taxa differences in non- Gender (male%) 66.7% 44.68% 0.054 puberty subgroups Height (cm) 132.94 ± 10.62 148.96 ± 8.03 < 0.001 As for the alpha-diversity between younger children and Weight (kg) 39.72 ± 15.53 54.09 ± 13.57 < 0.001 pre-pubertal groups, there was no statistical difference 2 of the Shannon diversity index, observed OTUs, Faith’s BMI (kg/cm ) 21.70 ± 5.56 24.07 ± 4.40 0.028 phylogenetic diversity and Pielou’s evenness based on BMI-Z 1.92 ± 1.79 2.01 ± 1.13 0.783 OTU distribution (all p > 0.05, Table S4). Yuan et al. BMC Microbiology
Load more

Recommended publications
  • Supplementary Information for Acute Infection of Mice with Clostridium
    IMM-2013-2862, Sadighi Akha et al-Supplementary Information 1 Supplementary Information for Acute infection of mice with Clostridium difficile leads to eIF2α phosphorylation and pro-survival signalling as part of the mucosal inflammatory response Amir A. Sadighi Akha1, Casey M. Theriot2, John R. Erb-Downward1, Andrew J. McDermott3, Nicole R. Falkowski1, Heather M. Tyra4, D. Thomas Rutkowski4, Vincent B. Young2,3 and Gary B. Huffnagle1,3* IMM-2013-2862, Sadighi Akha et al-Supplementary Information 2 Supplementary Text Materials and Methods for Figures S1 and S2 DNA isolation, amplicon library preparation and 454 pyrosequencing- The DNeasy blood and tissue kit (Qiagen) was used to extract genomic DNA from the caecal and colonic contents obtained from untreated and C. difficile-infected mice. The extraction was performed according to the manufacturer’s instructions except for the following modifications: adding a bead-beating step using UltraClean fecal DNA bead tubes (Mo Bio Laboratories, Carlsbad, CA); doubling the amount of ATL buffer and the proteinase K used in the protocol; and decreasing by half the amount of the AE buffer used to elute the DNA. Subsequently, the V3, V4 and V5 hyper-variable regions of the 16S ribosomal RNA gene in each of the samples were targeted for amplification with the 357F and 929R primer sets [1]. Amplicons were purified with the Agencourt AMPure XP PCR purification system (Beckman Coulter, Indianapolis, IN), and quantified with the Quant- iT PicoGreen dsDNA kit (Life Technologies) to obtain an equal pool for pyrosequencing. They were then sequenced on a Roche 454 GS Junior Titanium platform according to the manufacturer’s specifications.
    [Show full text]
  • Fatty Acid Diets: Regulation of Gut Microbiota Composition and Obesity and Its Related Metabolic Dysbiosis
    International Journal of Molecular Sciences Review Fatty Acid Diets: Regulation of Gut Microbiota Composition and Obesity and Its Related Metabolic Dysbiosis David Johane Machate 1, Priscila Silva Figueiredo 2 , Gabriela Marcelino 2 , Rita de Cássia Avellaneda Guimarães 2,*, Priscila Aiko Hiane 2 , Danielle Bogo 2, Verônica Assalin Zorgetto Pinheiro 2, Lincoln Carlos Silva de Oliveira 3 and Arnildo Pott 1 1 Graduate Program in Biotechnology and Biodiversity in the Central-West Region of Brazil, Federal University of Mato Grosso do Sul, Campo Grande 79079-900, Brazil; [email protected] (D.J.M.); [email protected] (A.P.) 2 Graduate Program in Health and Development in the Central-West Region of Brazil, Federal University of Mato Grosso do Sul, Campo Grande 79079-900, Brazil; pri.fi[email protected] (P.S.F.); [email protected] (G.M.); [email protected] (P.A.H.); [email protected] (D.B.); [email protected] (V.A.Z.P.) 3 Chemistry Institute, Federal University of Mato Grosso do Sul, Campo Grande 79079-900, Brazil; [email protected] * Correspondence: [email protected]; Tel.: +55-67-3345-7416 Received: 9 March 2020; Accepted: 27 March 2020; Published: 8 June 2020 Abstract: Long-term high-fat dietary intake plays a crucial role in the composition of gut microbiota in animal models and human subjects, which affect directly short-chain fatty acid (SCFA) production and host health. This review aims to highlight the interplay of fatty acid (FA) intake and gut microbiota composition and its interaction with hosts in health promotion and obesity prevention and its related metabolic dysbiosis.
    [Show full text]
  • Environment-Dependent Variation in Gut Microbiota of an Oviparous Lizard (Calotes Versicolor)
    animals Article Environment-Dependent Variation in Gut Microbiota of an Oviparous Lizard (Calotes versicolor) Lin Zhang 1,2,*,† , Fang Yang 3,†, Ning Li 4 and Buddhi Dayananda 5 1 School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wuhan 430065, China 2 State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China 3 School of Laboratory Medicine, Hubei University of Chinese Medicine, Wuhan 430065, China; [email protected] 4 College of Food Science, Nanjing Xiaozhuang University, Nanjing 211171, China; [email protected] 5 School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Simple Summary: The different gut sections potentially provide different habitats for gut microbiota. We found that Bacteroidetes, Firmicutes, and Proteobacteria were the three primary phyla in gut micro- biota of C. versicolor. The relative abundance of dominant phyla Bacteroidetes and Firmicutes exhibited an increasing trend from the small intestine to the large intestine, and there was a higher abun- dance of genus Bacteroides (Class: Bacteroidia), Coprobacillus and Eubacterium (Class: Erysipelotrichia), Parabacteroides (Family: Porphyromonadaceae) and Ruminococcus (Family: Lachnospiraceae), and Family Odoribacteraceae and Rikenellaceae in the hindgut, and some metabolic pathways were higher in the hindgut. Our results reveal the variations of gut microbiota composition and metabolic pathways in Citation: Zhang, L.; Yang, F.; Li, N.; different parts of the lizards’ intestine. Dayananda, B. Environment- Dependent Variation in Gut Abstract: Vertebrates maintain complex symbiotic relationships with microbiota living within their Microbiota of an Oviparous Lizard gastrointestinal tracts which reflects the ecological and evolutionary relationship between hosts and (Calotes versicolor).
    [Show full text]
  • Enrofloxacin Shifts Intestinal Microbiota and Metabolic Profiling and Hinders Recovery
    bioRxiv preprint doi: https://doi.org/10.1101/2020.07.13.201830; this version posted July 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Enrofloxacin shifts intestinal microbiota and metabolic profiling and hinders recovery 2 from Salmonella enterica subsp. enterica serovar Typhimurium infection in neonatal 3 chickens 4 5 Boheng Ma,a,b,c,& Xueran Mei,a,b,c,& Changwei Lei,a,b,c Cui Li,a,b,c Yufeng Gao,a,b,c Linghan 6 Kong,a,b,c Xiwen Zhai,a,b,c,d Hongning Wanga,b,c,# 7 8 a College of Life Sciences, Sichuan University, Chengdu, People’s Republic of China 9 b Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, Chengdu, 10 People’s Republic of China 11 c Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, Chengdu, 12 People’s Republic of China 13 d Sichuan Water Conservancy Vocational Collage, Chengdu, People’s Republic of China 14 15 Running head: Effects of enrofloxacin on chickens 16 17 # Address correspondence to Hongning Wang, [email protected] 18 & Both authors are identified as Co-First Author. 19 20 Word count: 211 words (Abstract); 4895 words (main text) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.13.201830; this version posted July 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
    [Show full text]
  • The Gut Microbiota Profile According to Glycemic Control in Type 1
    microorganisms Article The Gut Microbiota Profile According to Glycemic Control in Type 1 Diabetes Patients Treated with Personal Insulin Pumps Sandra Mrozinska 1,2 , Przemysław Kapusta 3 , Tomasz Gosiewski 4 , Agnieszka Sroka-Oleksiak 4, Agnieszka H. Ludwig-Słomczy ´nska 3 , Bartłomiej Matejko 1,2 , Beata Kiec-Wilk 1,2, Malgorzata Bulanda 4, Maciej T. Malecki 1,2, Pawel P. Wolkow 3 and Tomasz Klupa 1,2,* 1 Jagiellonian University Medical College, Faculty of Medicine, Department of Metabolic Diseases, 2 Jakubowskiego Street, 30-688 Krakow, Poland; [email protected] (S.M.); [email protected] (B.M.); [email protected] (B.K.-W.); [email protected] (M.T.M.) 2 University Hospital, Department of Metabolic Diseases, 2 Jakubowskiego Street, 30-688 Krakow, Poland 3 Jagiellonian University Medical College, Center for Medical Genomics OMICRON, 7c Kopernika Street, 31-034 Krakow, Poland; [email protected] (P.K.); [email protected] (A.H.L.-S.); [email protected] (P.P.W.) 4 Jagiellonian University Medical College, Faculty of Medicine, Department of Microbiology, 18 Czysta Street, 31-121 Krakow, Poland; [email protected] (T.G.); [email protected] (A.S.-O.); [email protected] (M.B.) * Correspondence: [email protected]; Tel.: +48-1240-029-50 Abstract: Recently, several studies explored associations between type 1 diabetes (T1DM) and micro- biota. The aim of our study was to assess the colonic microbiota structure according to the metabolic control in T1DM patients treated with insulin pumps. We studied 89 T1DM patients (50.6% women) at the median age of 25 (IQR, 22–29) years.
    [Show full text]
  • Taxonomy of Lactobacilli and Bifidobacteria
    Curr. Issues Intestinal Microbiol. 8: 44–61. Online journal at www.ciim.net Taxonomy of Lactobacilli and Bifdobacteria Giovanna E. Felis and Franco Dellaglio*† of carbohydrates. The genus Bifdobacterium, even Dipartimento Scientifco e Tecnologico, Facoltà di Scienze if traditionally listed among LAB, is only poorly MM. FF. NN., Università degli Studi di Verona, Strada le phylogenetically related to genuine LAB and its species Grazie 15, 37134 Verona, Italy use a metabolic pathway for the degradation of hexoses different from those described for ‘genuine’ LAB. Abstract The interest in what lactobacilli and bifdobacteria are Genera Lactobacillus and Bifdobacterium include a able to do must consider the investigation of who they large number of species and strains exhibiting important are. properties in an applied context, especially in the area of Before reviewing the taxonomy of those two food and probiotics. An updated list of species belonging genera, some basic terms and concepts and preliminary to those two genera, their phylogenetic relationships and considerations concerning bacterial systematics need to other relevant taxonomic information are reviewed in this be introduced: they are required for readers who are not paper. familiar with taxonomy to gain a deep understanding of The conventional nature of taxonomy is explained the diffculties in obtaining a clear taxonomic scheme for and some basic concepts and terms will be presented for the bacteria under analysis. readers not familiar with this important and fast-evolving area,
    [Show full text]
  • Downloaded from the VFDB Data- Base (Virulence Factors Database) Containing Nucleotide Sample Collection and Metagenomic Sequencing Sequences of 2585 Genes [53]
    Dubinkina et al. Microbiome (2017) 5:141 DOI 10.1186/s40168-017-0359-2 RESEARCH Open Access Links of gut microbiota composition with alcohol dependence syndrome and alcoholic liver disease Veronika B. Dubinkina1,2,3,4, Alexander V. Tyakht2,5*, Vera Y. Odintsova2, Konstantin S. Yarygin1,2, Boris A. Kovarsky2, Alexander V. Pavlenko1,2, Dmitry S. Ischenko1,2, Anna S. Popenko2, Dmitry G. Alexeev1,2, Anastasiya Y. Taraskina6, Regina F. Nasyrova6, Evgeny M. Krupitsky6, Nino V. Shalikiani7, Igor G. Bakulin7, Petr L. Shcherbakov7, Lyubov O. Skorodumova2, Andrei K. Larin2, Elena S. Kostryukova1,2, Rustam A. Abdulkhakov8, Sayar R. Abdulkhakov8,9, Sergey Y. Malanin9, Ruzilya K. Ismagilova9, Tatiana V. Grigoryeva9, Elena N. Ilina2 and Vadim M. Govorun1,2 Abstract Background: Alcohol abuse has deleterious effects on human health by disrupting the functions of many organs and systems. Gut microbiota has been implicated in the pathogenesis of alcohol-related liver diseases, with its composition manifesting expressed dysbiosis in patients suffering from alcoholic dependence. Due to its inherent plasticity, gut microbiota is an important target for prevention and treatment of these diseases. Identification of the impact of alcohol abuse with associated psychiatric symptoms on the gut community structure is confounded by the liver dysfunction. In order to differentiate the effects of these two factors, we conducted a comparative “shotgun” metagenomic survey of 99 patients with the alcohol dependence syndrome represented by two cohorts—with and without liver cirrhosis. The taxonomic and functional composition of the gut microbiota was subjected to a multifactor analysis including comparison with the external control group. Results: Alcoholic dependence and liver cirrhosis were associated with profound shifts in gut community structures and metabolic potential across the patients.
    [Show full text]
  • Draft Genome and Description of Merdibacter Massiliensis Gen.Nov., Sp
    www.nature.com/scientificreports OPEN Draft genome and description of Merdibacter massiliensis gen.nov., sp. nov., a new bacterium genus Received: 21 November 2018 Accepted: 9 May 2019 isolated from the human ileum Published: xx xx xxxx Hussein Anani1, Rita Abou Abdallah1, Nisrine Chelkha2, Anthony Fontanini2, Davide Ricaboni2, Morgane Mailhe2, Didier Raoult2,3 & Pierre-Edouard Fournier 1 We used phenotypic, genomic and phylogenetic information following the taxono-genomics approach to demonstrate that strain Marseille–P3254, isolated from an ileal sample of a 76-year old woman who underwent upper and lower digestive tract endoscopy for esophagitis and colonic polyp, is representative of a novel bacterial genus within the family Erysipelotrichaceae in the phylum Firmicutes. It is an anaerobic Gram-negative bacterium without catalase and oxidase activities. The genome of strain Marseille–P3254 is 2,468,496-bp long with a 40.1% G + C content. This new bacterium is most closely related to Eubacterium dolichum, with which it shares 90.7% 16S rRNA sequence similarity. In addition, genomic comparison using the digital DNA–DNA hybridization and OrthoANI analyses between the novel organism and the E. dolichum type strain revealed identities of 25.2 and 68.91%, respectively. The major fatty acids were C16: 0, C18: 1n9 and C18: 0. Based on these data, we propose the creation of the new genus Merdibacter gen. nov., with strain Marseille-P3254T (=CSUR P3254 = DSM 103534) being the type strain of the new species Merdibacter massiliensis gen. nov., sp. nov. A thorough knowledge of the gut microbiota composition appears essential to understand many aspects of health and diseases in humans.
    [Show full text]
  • Metagenome-Wide Association of Gut Microbiome Features for Schizophrenia
    ARTICLE https://doi.org/10.1038/s41467-020-15457-9 OPEN Metagenome-wide association of gut microbiome features for schizophrenia Feng Zhu 1,19,20, Yanmei Ju2,3,4,5,19,20, Wei Wang6,7,8,19,20, Qi Wang 2,5,19,20, Ruijin Guo2,3,4,9,19,20, Qingyan Ma6,7,8, Qiang Sun2,10, Yajuan Fan6,7,8, Yuying Xie11, Zai Yang6,7,8, Zhuye Jie2,3,4, Binbin Zhao6,7,8, Liang Xiao 2,3,12, Lin Yang6,7,8, Tao Zhang 2,3,13, Junqin Feng6,7,8, Liyang Guo6,7,8, Xiaoyan He6,7,8, Yunchun Chen6,7,8, Ce Chen6,7,8, Chengge Gao6,7,8, Xun Xu 2,3, Huanming Yang2,14, Jian Wang2,14, ✉ ✉ Yonghui Dang15, Lise Madsen2,16,17, Susanne Brix 2,18, Karsten Kristiansen 2,17,20 , Huijue Jia 2,3,4,9,20 & ✉ Xiancang Ma 6,7,8,20 1234567890():,; Evidence is mounting that the gut-brain axis plays an important role in mental diseases fueling mechanistic investigations to provide a basis for future targeted interventions. However, shotgun metagenomic data from treatment-naïve patients are scarce hampering compre- hensive analyses of the complex interaction between the gut microbiota and the brain. Here we explore the fecal microbiome based on 90 medication-free schizophrenia patients and 81 controls and identify a microbial species classifier distinguishing patients from controls with an area under the receiver operating characteristic curve (AUC) of 0.896, and replicate the microbiome-based disease classifier in 45 patients and 45 controls (AUC = 0.765). Functional potentials associated with schizophrenia include differences in short-chain fatty acids synth- esis, tryptophan metabolism, and synthesis/degradation of neurotransmitters.
    [Show full text]
  • GA-Map Dysbiosis Test Sample Report 1
    Genetic Analysis AS Kabelgata 8 0580 Oslo www.genetic-analysis.com Postboks 243 Økern 0510 Oslo, Norway GA-map® Dysbiosis Test Lx – Test Results SAMPLE ID: Collection Date: Date Received: Report Date: DYSBIOSIS INDEX (DI) * The gut microbiota is 11 2 3 4 5 non-dysbiotic FUNCTIONAL IMBALANCE - BACTERIA PROFILES * FUNCTIONAL PROFILES TEST RESULTS COMMENTS A Butyrate producing bacteria Normal levels of major butyrate producers. B Gut mucosa protective bacteria Normal levels of important gut mucosa protective bacteria. Normal level of Faecalibacterium prausnitzii, a key bacterium that C Gut health marker promotes intestinal health. Gut barrier protective bacteria Balance between selected gut barrier protective and potentially D and potentially harmful bacteria harmful bacteria. E Pro-inflammatory bacteria Low levels of pro-inflammatory bacteria. ® Page * For a more detailed explanation of the results, please refer to pages 3-4, 'GA-map Dysbiosis Test Lx - REPORT FORM SUPPLEMENT'. 1/4 SAMPLE ID: Collection Date: Date Received: Report Date: BACTERIA ABUNDANCES * Normal ** Normal ** Reduced Elevated Reduced Elevated Actinobacteria -3 -2 -1 1 2 3 Firmicutes cont. -3 -2 -1 1 2 3 100 Actinobacteria 315 [Eubacterium] rectale 101 Actinomycetales 316 Eubacterium siraeum 103 Bifidobacterium spp. 317 Faecalibacterium prausnitzii Bacteriodetes 318 Lachnospiraceae 319 Lactobacillus ruminis & 201 Alistipes Pediococcus acidilactici 320 Lactobacillus spp. 202 Alistipes onderdonkii 321 Lactobacillus spp. 2 203 Bacteroides fragilis 322 Phascolarctobacterium sp. 204 Bacteroides pectinophilus 323 Ruminococcus albus & 205 Bacteroides spp. R. bromii Bacteroides spp. & 324 Ruminococcus gnavus 206 Prevotella spp. 325 Streptococcus agalactiae & 207 Bacteroides stercoris [Eubacterium] rectale Streptococcus salivarius ssp. Bacteroides 326 thermophilus & S. sanguinis 208 zoogleoformans 327 Streptococcus salivarius 209 Parabacteroides johnsonii ssp.
    [Show full text]
  • Cecum Microbial Communities from Steers Differing in Feed Efficiency1,2,3
    Published October 2, 2015 Cecum microbial communities from steers differing in feed efficiency1,2,3 P. R. Myer,*4 J. E. Wells,* T. P. L. Smith,* L. A. Kuehn,* and H. C. Freetly*5 *USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE 689336 ABSTRACT: Apart from the rumen, limited knowl- bial populations and operational taxonomic units edge exists regarding the structure and function of revealed significant differences among feed efficien- bacterial communities within the gastrointestinal tract cy groups (P < 0.05). Firmicutes was the dominant and their association with beef cattle feed efficiency. cecal phylum in all groups and accounted for up to The objective of this study was to characterize the 81% of the populations among samples. Populations microbial communities of the cecum among steers were also dominated by families Ruminococcaceae, differing in feed efficiency. Within 2 contemporary Lachnospiraceae, and Clostridiaceae, with sig- groups of steers, individual feed intake and BW gain nificant shifts in the relative abundance of taxa were determined from animals fed the same diet. among feed efficiency groups, including families Within both of 2 contemporary groups, BW was Ruminococcaceae (P = 0.040), Lachnospiraceae regressed on feed intake and 4 steers within each (P = 0.020), Erysipelotrichaceae (P = 0.046), and Cartesian quadrant were sampled (n = 16/group). Clostridiaceae (P = 0.043) and genera Coprobacillus Bacterial 16S rRNA gene amplicons were sequenced (P = 0.049), Parabacteroides (P = 0.044), Blautia (P = from the cecal content using next-generation sequenc- 0.042), Ruminococcus (P = 0.040), Oscillospira (P = ing technology. No significant changes in diversity or 0.042), and Prevotella (P = 0.042).
    [Show full text]
  • Relationships Between Diet-Related Changes in the Gut Microbiome and Cognitive Flexibility
    Relationships Between Diet-Related Changes in the Gut Microbiome and Cognitive Flexibility Magnusson, K. R., Hauck, L., Jeffrey, B. M., Elias, V., Humphrey, A., Nath, R., Perrone, A., & Bermudez, L. E. (2015). Relationships Between Diet-Related Changes in the Gut Microbiome and Cognitive Flexibility. [Article in Press]. Neuroscience. Elsevier Accepted Manuscript http://cdss.library.oregonstate.edu/sa-termsofuse *Manuscript (Clear Copy) Click here to view linked References 1 2 3 4 Title: RELATIONSHIPS BETWEEN DIET-RELATED CHANGES IN THE GUT MICROBIOME 5 AND COGNITIVE FLEXIBILITY 6 7 Authors: K.R. Magnusson 1, 2, L. Hauck1, B.M. Jeffrey1, V. Elias 1,2, A. Humphrey1, R. Nath3, A. 8 1, 2 1 9 Perrone , and L.E. Bermudez 10 11 Affiliations: 1 Dept Biomedical Sciences, College of Veterinary Medicine 12 2 Linus Pauling Institute 13 3 Dept of Human Development and Family Sciences, School of Social and 14 Behavioral Health Sciences 15 Oregon State University, Corvallis, OR 97331 U.S.A. 16 17 18 Email addresses: Magnusson: [email protected] 19 Hauck: [email protected] 20 Jeffrey: [email protected] 21 Elias: [email protected] 22 Humphrey: [email protected] 23 Nath: [email protected] 24 Perrone: [email protected] 25 Bermudez: [email protected] 26 27 28 Corresponding Author: Dr. Kathy Magnusson 29 307 Linus Pauling Science Center 30 Oregon State University 31 Corvallis, OR 97331 U.S.A. 32 Phone: 541-737-6923 33 Fax: 541-737-5077 34 Email: [email protected] 35 36 37 38 39 40 41
    [Show full text]