Research Collection

Doctoral Thesis

Mechanistic study of bifidobacterium thermophilum RBL67 for Salmonella inhibition using in vitro fermentation and in vivo swine models

Author(s): Tanner, Sabine

Publication Date: 2014

Permanent Link: https://doi.org/10.3929/ethz-a-010276721

Rights / License: In Copyright - Non-Commercial Use Permitted

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ETH Library

DISS. ETH NO. 22115

MECHANISTIC STUDY OF BIFIDOBACTERIUM THERMOPHILUM RBL67 FOR SALMONELLA INHIBITION USING IN VITRO FERMENTATION AND IN VIVO SWINE MODELS

A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich)

presented by SABINE Amani TANNER MSc ETH in Food Science born on 13.01.1986 citizen of Basel-Stadt (BS) and Eriswil (BE)

accepted on the recommendation of Prof. Dr. Christophe Lacroix, examiner Prof. Dr. Ismaïl Fliss, co-examiner Dr. Christophe Chassard, co-examiner

2014

Contents

Abbreviations 4 Summary 7 Zusammenfassung 11

Chapter 1 General introduction Gastrointestinal physiology of the pig 18 Salmonella in pigs 38 Microbial interactions in complex intestinal ecosystems 60 Background and objectives of the thesis 66

Chapter 2 In vitro continuous fermentation model (PolyFermS) of the swine proximal colon for simultaneous testing on the same gut microbiota 69

Chapter 3 Synergistic effects of Bifidobacterium thermophilum RBL67 and selected prebiotics on inhibition of Salmonella colonization in the swine proximal colon PolyFermS model 97

Chapter 4 Unraveling the transcriptome response of Salmonella enterica subsp. enterica serovar Typhimurium N-15 and Bifidobacterium thermophilum RBL67 grown in co-culture 121

Chapter 5 Effect of Bifidobacterium thermophilum RBL67 and fructooligosaccharide on the gut microbiota of Göttingen minipigs 151

Chapter 6 General conclusions and perspectives 175

Bibliography 185 Appendix 207 Acknowledgements 255 Curriculum vitae 257

Abbreviations

BCFA Branched-chain fatty acid(s) bp base pairs CDC Centers for Disease Control and Prevention cDNA complementary DNA cfu Colony-forming units DC Dendritic cell(s) DGGE Denaturing gradient gel electrophoresis DNA Deoxyribonucleic acid EDTA Ethylenediaminetetraacetic acid EFSA European Food Safety Authority FAO Food and Agriculture Organization of the United Nations FISH Fluorescent in situ hybridization FOS Fructooligosaccharide GALT Gut-associated lymphoid tissue GIT Gastrointestinal tract GOS Galactooligosaccharide HPLC High performance liquid chromatography

H2S Hydrogen sulfide IgA Immunoglobulin A IL- Interleukin MOS Mannanoligosaccharide mRNA Messenger RNA MRS(-C) Man Rogosa Sharpe (cysteine-HCl) broth NCBI National Center for Biotechnology Information OD Optical density ORF Open reading frame OTU Operational taxonomic unit

Abbreviations

PCR Polymerase chain reaction qPCR Quantitative polymerase chain reaction RDP Ribosomal database project RT Retention time rRNA Ribosomal ribonucleic acid SCFA Short chain fatty acids SD Standard deviation SPI-1/2 Salmonella pathogenicity island 1/2 SRA Sequence read archive TE tris(hydroxymethyl)aniomethane-EDTA TGGE Temperature gradient gel electrophoresis WHO World Health Organization xfp Xylulose-5-phosphate/fructose-6-phosphate phosphoketolase gene YCFA Yeast extract-casein hydrolysate-fatty acid medium

Summary

There is a high prevalence of Salmonella in swine livestock production that negatively impacts animal health and performance. Pigs are also known to be persistent carriers of Salmonella, which can pose a significant health risk to the consumer due to transmission of the pathogen via the food chain. After the ban of in-feed antibiotics in the European Union and in Switzerland, probiotics and prebiotics are promising alternative strategies to ensure animal gut health, thereby maintaining animal welfare and productivity of the swine livestock production. The human fecal isolate Bifidobacterium thermophilum RBL67 (RBL67) has been previously shown to possess remarkable features, including the production of a bacteriocin-like inhibitory substance (BLIS), antagonism to Salmonella and high adhesion capacity to human intestinal cell lines. B. thermophilum is predominantly encountered in the gastrointestinal tract (GIT) of animals, including pigs. The overall aim of this study was to further investigate B. thermophilum RBL67 and its anti-Salmonella activity for potential application in swine livestock production.

To study nutritional additives and microbial interactions in the complex environment of the swine gut, we successfully developed and validated a novel in vitro PolyFermS continuous fermentation model, simulating the swine proximal colon. The porcine PolyFermS model was designed as a two-stage multiple reactors model with a first reactor (inoculum reactor; IR) containing immobilized fecal swine microbiota. IR was used to constantly inoculate five subsequent second-stage reactors, one control and four test reactors, with 10 % effluent, while the remaining 90 % of inflow was fresh medium, formulated to mimic the swine ileal chyme. The novel porcine PolyFermS was validated during 54 days continuous fermentation. A high and stable bacterial composition, diversity and metabolite production was measured in the effluents, akin to in vivo conditions, with the same microbiota in all second-stage reactors. We assume that the swine PolyFermS allows the simultaneous testing of different treatments in parallel and compared to a control.

We then used the porcine PolyFermS model to investigate the effect of B. thermophilum RBL67 alone and combined with fructooligosaccharide (FOS), galactooligosaccharide (GOS) and mannanoligosaccharide (MOS) on Salmonella enterica subsp. enterica serovar Typhimurium N-15 (N-15). FOS and GOS increased total short chain fatty acid (SCFA)

Summary production, mainly acetate and propionate, and inhibited N-15 colonization. The antagonistic effect on Salmonella was enhanced when FOS and GOS were combined with RBL67, while the increase in SCFA production was similar to the prebiotics alone. Our data suggest that increased SCFA production contributes to the inhibition of N-15 colonization in our in vitro model, but that additional antimicrobial effects are present when prebiotics are combined with RBL67. Furthermore, RBL67 combined with FOS stimulated butyrate production, while with FOS alone no butyrate stimulation was observed. This could be of interest for gut health preservation due to the numerous health-related properties attributed to butyrate. RBL67 alone and combined with MOS increased acetate and propionate production, respectively, but showed only limited effect on Salmonella inhibition compared to the control.

In a third step we studied the transcriptome responses when RBL67 and N-15 are co-cultured to gain insights into the mechanisms of probiotic-pathogen interaction on molecular level. RBL67 and N-15 were cultured single and in mixture under pH-controlled (6.0) conditions. Sampling for RNA extraction was done after 4 and 5 hours of growth for N-15 and RBL67 mono- and co-cultures, respectively. Transcriptome analysis was performed by RNA- sequencing and mapping of generated reads (>17 Mio per sample) to the genomes of RBL67 and S. Typhimurium LT2. The growth of RBL67 was stimulated in presence of N-15, but this effect could not be explained. In contrast, growth of N-15 was decreased in presence of RBL67, yielding significantly lower cell numbers of N-15 in co- compared to mono-culture. Transcriptome analysis revealed genes associated with Salmonella virulence (type III secretion systems encoding, fimbrial adherence determinants) higher regulated in the presence of RBL67, while flagellar genes were repressed. Salmonella virulence is subjected to a complex regulatory network, tightly controlling the expression of virulence factors. We hypothesize that RBL67 triggers virulence gene expression of Salmonella N-15 prematurely, which leads to redundant energy expenditure and consequently reduced growth. In the competitive environment of the gut, reduced growth could lead to a loss of competition and enhanced clearance of the pathogen from the gut. Our study provided first insights into probiotic-pathogen interaction under model conditions and suggests a mechanism for probiotic protection of Salmonella infection.

Summary

At last we performed a first swine in vivo study to investigate the impact of RBL67 alone and combined with FOS on gut microbiota composition and activity in Göttingen minipigs. Eight minipigs were randomly allocated into two treatments groups and a cross-sectional study design was applied. Minipigs (four per group) were fed with a basal diet supplemented with 8 g/day probiotic powder (1 x 109 cfu g-1; PRO), 8 g probiotic powder plus 8 g/day FOS (SYN) or 8 g/d skim milk powder in the control group (CON). RBL67 was consistently detected at 105 – 106 copies g-1 in feces, cecum and the colon when feeding with PRO or SYN diets. Compared to the PRO, the SYN diet significantly increased Bifidobacterium numbers in the colon. The highest numerical values of B. thermophilum were measured in the cecum of pigs fed with SYN, which suggests a promotion effect of FOS. Experimental diets did not induce large shifts in gut microbiota composition, supporting the safety of RBL67. Furthermore, our data indicate that the Göttingen minipig holds promise for a novel model organism in gut microbiota research in pigs, but there is need for a more complete characterization of the Göttingen minipig gut microbiota.

In conclusion, we successfully applied a complementary approach by combining in vitro fermentation and in vivo swine models to investigate the colonization ability of RBL67 and its antagonistic effects on Salmonella enterica subsp. enterica serovar Typhimurium N-15 in the complex porcine intestinal ecosystem. This doctoral thesis expands and broadens the knowledge on the probiotic candidate B. thermophilum RBL67 and suggests that RBL67 may be considered in combination with prebiotics in swine livestock production to reduce Salmonella. However, further studies are needed to demonstrate efficacy of RBL67 combined with prebiotics on Salmonella infection in vivo and gain a deeper understanding of the mechanism of RBL67 action.

Zusammenfassung

Salmonellen sind weit verbreitet in der Schweinehaltung und können negative Auswirkungen auf das Tierwohl und die Leistungsfähigkeit der Schweine haben. Schweine, als Träger von Salmonellen, stellen somit auch ein Risiko für die menschliche Gesundheit dar, insbesondere wenn Salmonellen in die Lebensmittelkette gelangen. Nachdem in der Europäischen Union und in der Schweiz Antibiotikazusätze im Tierfutter als Leistungsförderer verboten wurden, sind Probiotika und Prebiotika vielversprechende Alternativen um die Darmgesundheit sicherzustellen und damit das Tierwohl und die Produktivität der Schweineproduktion aufrechtzuerhalten. Bifidobacterium thermophilum RBL67 (RBL67) wurde aus menschlicher Fäzes isoliert und besitzt bemerkenswerte Eigenschaften, wie vor allem die Produktion einer Bakteriozin ähnlichen inhibitorischen Substanz, die antagonistische Wirkung gegenüber Salmonellen und die hohe Adhäsionskapazität an menschliche Zelllinien. B. thermophilum kommt vor allem im Magendarmtrakt von Tieren, unter anderem auch im Schwein, vor. Das Ziel der vorliegenden Studie war es, B. thermophilum RBL67 und seine anti-Salmonellen Aktivität weiter zu untersuchen mit dem Ziel seine mögliche Anwendung in der Schweinehaltung zu prüfen.

Zur Untersuchung von nutritionalen Zusätzen und mikrobiellen Interaktionen in der komplexen Milieu des Schweinedarmes, entwickelten und validierten wir erfolgreich ein neues, kontinuierliches in vitro PolyFermS Fermentationssystem, welches den aufsteigenden Schweinedickdarm simuliert. Das PolyFermS Modell bestand aus einem zweistufigen multiplen Reaktoraufbau, mit einem ersten Reaktor (Inokulumreaktor; IR) der eine immobilisierte fäkale Schweinemikrobiota enthielt. IR wurde gebraucht um die nachfolgenden fünf Zweit-Stufen-Reaktoren, einen Kontrollreaktor und vier Testreaktoren mit jeweils 10 % Effluent zu inokulieren, indes die restlichen 90 % des Zuflusses frisches nährstoffreiches Medium waren, welches so zusammengesetzt war, dass es den ilealen Speisebrei des Schweines imitiert. Das neue porzine PolyFermS Modell wurde während einer 54 tägigen kontinuierlichen Fermentation validiert. Das Modell zeichnete sich durch eine hohe und stabile bakterielle Zusammensetzung, Diversität und Metabolitenproduktion, vergleichbar zum in vivo Zustand, aus. Zudem wurde in allen Zweit-Stufen-Reaktoren die gleiche Mikrobiota reproduziert. Das porzine PolyfermS erlaubt somit die gleichzeitige und

Zusammenfassung parallele Untersuchung von verschiedenen Konditionen untereinander und verglichen mit einer Kontrolle.

Wir nutzten dann das porzine PolyFermS Modell um den Einfluss von B. thermophilum RBL67 alleine und kombiniert mit Fruktooligosaccharid (FOS), Galaktooligosaccharid (GOS) und Mannanoligosaccharid (MOS) auf Salmonella enterica subsp. enterica serovar Typhimurium N-15 (N-15) zu untersuchen. FOS und GOS erhöhten die Produktion von kurzkettigen Fettsäuren, vor allem Acetat und Propionat, und inhibierten die Kolonisation von N-15. Der inhibierende Effekt wurde verstärkt wenn FOS und GOS mit RBL67 kombiniert wurden, während die Zunahme in der Produktion von kurzkettigen Fettsäuren gleich blieb wie wenn nur die Prebiotika zugegeben wurden. Unsere Daten deuten an, dass die Produktion von kurzkettigen Fettsäuren dazu beiträgt die Salmonellen in unserem in vitro Modell zu inhibieren, jedoch zusätzliche antimikrobielle Faktoren präsent sind wenn die Prebiotika mit RBL67 kombiniert werden. Ausserdem stimulierte RBL67 kombiniert mit FOS die Produktion von Butyrat, während nur mit FOS keine Stimulation von Butyrat beobachtet wurde. Das könnte für die Erhaltung einer guten Darmgesundheit wichtig sein, da Butyrat mit zahlreichen Gesundheitseigenschaften in Verbindung gebracht wird. RBL67 alleine und kombiniert mit MOS erhöhten entweder die Produktion von Acetat oder Propionat, hatten jedoch einen deutlich geringeren Effekt auf die Inhibition der Salmonellen verglichen mit der Kontrolle.

In einem nächsten Schritt untersuchten wir die Transkription von RBL67 und N-15 in co- Kultur, um Einblicke in die Interaktion von Probiotikum und Pathogen auf molekularer Ebene zu erhalten. RBL67 und N-15 wurden alleine oder zusammen unter pH kontrollierten Bedingungen (6.0) kultiviert. Proben wurden nach 4, respektive 5 Stunden Wachstum für die N-15 und RBL67 mono- und co-Kultur genommen. Die Analyse der Transkription wurde mittels RNA-Sequenzierung gemacht und die generierten Sequenzen (>17 Mio pro Probe) wurden mit den Genomen von RBL67 und S. Typhimurium LT2 abgeglichen. Das Wachstum von RBL67 war verstärkt in Gegenwart von N-15, jedoch bleibt der Grund dafür unklar. Im Gegensatz dazu wurde das Wachstum von N-15 gehemmt durch die Präsenz von RBL67, was zu einer signifikant geringeren Zellzahl von N-15 in co-Kultur gegenüber der mono-Kultur

Zusammenfassung führte. In Gegenwart von RBL67, zeigte die Transkriptionsanalyse von N-15 eine Überreguliereng von Genen die mit der Virulenz von Salmonellen assoziiert werden (Typ III Sekretionssysteme, fimbriale Adhärenzfaktoren), während umgekehrt die Flagellengene unterdrückt wurden. Die Virulenz von Salmonellen ist einem komplexen regulatorischen System unterworfen, welches die Expression der Virulenz eng kontrolliert. Wir nehmen an, dass RBL67 die Virulenz von Salmonella N-15 vorzeitig induziert, was in einem unnötigen Energieaufwand resultiert und somit zu einem reduzierten Wachstum führt. In dem kompetitiven Umfeld des Darmes kann das reduzierte Wachstum zu einem Verlust der Konkurrenzfähigkeit führen und somit das Beseitigen von Salmonellen begünstigen. Unsere Studie erlaubte erste Einblicke in die Interaktion von Probiotika mit Pathogenen unter Modellbedingungen und zeigt eine Möglichkeit auf wie probiotische Organismen vor einer Salmonellen Infektion schützen können.

Schliesslich führten wir eine erste in vivo Studie durch um den Einfluss von RBL67 alleine und kombiniert mit FOS auf die Darmflorazusammensetzung und –aktivität von Göttingen Minischweinen zu untersuchen. Acht Minischweine wurden zufällig in zwei Behandlungsgruppen eingeteilt und im Ansatz einer Querschnittstudie untersucht. Die Minischweine (vier pro Gruppe) wurden mit einem Grundfutter gefüttert, welches mit 8 g/Tag probiotischem Pulver (1 x 109 KBE g-1; PRO), 8 g probiotischem Pulver plus 8 g/Tag FOS (SYN) oder 8 g/Tag Magermilchpulver in der Kontrollgruppe (CON) ergänzt wurde. RBL67 wurde konsistent mit 105 – 106 Kopien g-1 in Fäzes, Zäkum und Dickdarm detektiert. Im Vergleich zur PRO Nahrung, erhöhte die SYN Nahrung die Anzahl Bifidobakterien im Dickdarm signifikant. Des Weiteren wurden bei den Minischweinen mit der SYN Nahrung die höchsten numerischen Werte von B. thermophilum gemessen, ein Hinweis auf einen unterstützenden Effekt von FOS auf B. thermophilum. Die experimentelle Nahrung führte nicht zu einer drastischen Veränderung der Zusammensetzung der Darmflora, welches eine sichere Anwendung von RBL67 bestätigt. Zudem sind die Göttingen Minischweine ein vielversprechender Modellorganismus für Darmflorastudien in Schweinen, vor allem wenn die Darmflorazusammensetzung von Göttingen Minischweinen noch umfassender charakterisiert wird.

Zusammenfassung

Zusammenfassend haben wir durch die Kombination von in vitro Fermentations- und in vivo Schweinemodellen erfolgreich einen komplementären Ansatz verfolgt um die Kolonisationsfähigkeit und den Antagonismus gegen Salmonella enterica subsp. enterica serovar Typhimurium N-15 von RBL67 in der komplexen Umgebung des intestinalen Ökosystems des Schweines zu untersuchen. Die vorliegende Arbeit erweitert das Wissen über den probiotischen Kandidaten B. thermophilum RBL67 und schlägt vor, dass RBL67 in Kombination mit Prebiotika zur Reduktion von Salmonellen in der Schweineproduktion berücksichtigt werden soll. Weitere Studien sind nötig, um die Wirksamkeit von RBL67 kombiniert mit Prebiotika gegen Salmonellen in vivo zu demonstrieren und um das Verständnis für die Mechanismen der Aktivität von RBL67 zu vertiefen.

Chapter 1

General introduction

Chapter 1

1. Gastrointestinal physiology of the pig

1.1. Digestive system

The digestive system of the pigs is very similar to that of humans and can be considered as a tube starting with the mouth, followed by the pharynx, the alimentary canal (comprising esophagus, stomach, the small and large intestine) and several accessory glands (including major salivary glands, liver and pancreas) (Figure 1.1). While the small intestine is divided into duodenum, jejunum and ileum, the large intestine is composed of the cecum, colon and rectum (Yen, 2001).

Figure 1.1. Digestive system of the pig (Yen, 2001).

The principal function of the digestive system is the mechanical and chemical breakdown of complex food materials to facilitate subsequent absorption and assimilation to the body, providing energy to the host and sustaining life. Mastication and saliva excreted from the salivary gland initiate the digestion process in the oral cavity. The saliva is composed of water, mucus and α-amylase, which initiate the chemical degradation of starch and lubricate the esophagus (Yen, 2001). Via peristaltic movements the moistened food is transported to the stomach where it is broken down by the presence of hydrochloric acid, proteolytic enzymes and gastric lipases. The predominant proteolytic enzymes in pigs include pepsin A and B, gastricsin and chymosin (Yen, 2001). While the mucus secreted from the gastric

Gastrointestinal physiology of the pig glands together with the gastric juice protects the mucosal epithelium from damage through the highly concentrated hydrochloric acid, peristaltic movements of the stomach support the mixing of food with digestive enzymes. The semi-liquid form of food at the end of the digestion process in the stomach is called chyme and is transported via the pyloric sphincter to the duodenum, the first section of the small intestine. The small intestine in the fully grown pig is 16-21 meters long and is the principal site of digestion and absorption of soluble carbohydrate, protein and fat (Yen, 2001). The small intestine is characterized by a specific structural organization of the inner epithelium, covered with fingerlike appendices called villi. By increasing the surface area, the villi contribute substantially to a higher efficiency in digestion and absorption (Ewing, 2008). In the duodenum, the chyme is mixed with excretions from the small intestine, the pancreas and the liver. Bile secreted from the liver emulsifies the fat, which enables pancreatic lipases to degrade fats into fatty acids and glycerol, while pancreatic proteases break down proteins into small peptides and amino acids. Finally, pancreatic amylases cleave carbohydrates into oligosaccharides, which are further broken down by brush border enzymes of enterocytes that line the intestinal mucosa. However, carbohydrates such as dietary fibers and physiologically resistant starch are not digested in the small intestine and pass directly to the large intestine. The large intestine of fully grown pigs is 4 to 6.0 m in length (Patterson et al., 2008) and comprises the cecum and the colon, with the latter being divided into the ascending, transverse and descending colon. In contrast to the small intestine, the villi of the colonic enterocytes do not contain digestive enzymes and the large intestine is less vigorous in its absorptive capacity. However, a vast amount of fluids and electrolytes that have been secreted in the ileum and proximal part of the large intestine are reabsorbed by the surface epithelium along the colon, with highest absorption in the proximal part (Yen, 2001). The principal function of the large intestine is the digestion of dietary carbohydrates and proteins as well as endogenous materials that escape digestion in the small intestine by microbial fermentation. The major end products of bacterial fermentation are short chain fatty acids (SCFA), accounting for approximately 98 % of all organic acids present in the large intestine of pigs (Clemens et al., 1975), and that are readily absorbed in the intestine. The feces finally excreted through the rectum include undigested material and a high load of gut bacteria.

Chapter 1

1.2. The gastrointestinal tract as protective barrier

The mucosal surface of the gastrointestinal tract (GIT) is exposed to a diverse collection of foreign antigens, such as proteins, natural toxins, exogenous microbes including pathogens and the commensal gut flora, rendering the GIT susceptible to infections (Oswald, 2006). Thus, the GIT of pigs constitutes one of the largest immunological organs of the body and the challenge is to maintain the vital structures and functions of the intestine, while conferring response to possible harmful bacteria (Burkey et al., 2009). The primary function of the gastrointestinal immune system is to keep antigens out or prevent them from interaction with the epithelium (Stokes et al., 1994), thus acting as a barrier. It is subdivided into the innate and the adaptive immune system that differ in their mechanisms of pathogen recognition (Kogut and Swaggerty, 2012) and synergistically act together for protecting the host from foreign antigens (Burkey et al., 2009). The innate immune system is a non-specific defense against exogenous invaders, whereas the adaptive immune system is highly specific in its response to exogenous invaders and has been acquired as a consequence of antigen exposure (Johnson et al., 2001).

1.2.1. Innate immune system

The innate immune system constitutes the first line of defense for foreign antigens by supplying anatomic, physiologic, phagocytic and inflammatory barriers (reviewed by Burkey et al. (2009)). The response of the innate immunity is non-specific, arising immediately from primary exposure to the pathogen, and does not confer long-lasting protection from the specific infectious microorganism. Epithelial cells cover the GIT in a monolayer and represent a selective physical barrier between external and internal environment (Oswald, 2006). However, epithelial cells also play a key role in the communications network that transmits signals, generated as response to infection, to cells of the innate and adaptive immune system (reviewed by Kogut and Swaggerty, (2012)). Immune cells that derive from myeloid progenitors are crucial for the innate immune response (Johnson et al., 2001). Neutrophils recognize pathogens via invariant receptors, engulf and kill them, whereas natural killer cells are more prone to kill viruses and certain intracellular pathogens (Johnson

Gastrointestinal physiology of the pig et al., 2001). In addition to kill pathogenic bacteria, macrophages also present antigens to lymphocytes and secrete inflammatory cytokines. The cytokines in turn provoke an inflammatory response and activate B and L lymphocytes, constituents of the adaptive immunity (Johnson et al., 2001). Moreover, cytokines have been recognized to act as hormones, enabling the communication of the innate immune system with other physiological systems and thus redirecting the biological priorities of the pig in response to infection (Johnson et al., 2001).

1.2.2. Adaptive immune system

The adaptive immune system generates a highly specific response to a certain pathogen and has been acquired over time due to previous exposure to the same pathogen (Johnson et al., 2001). The adaptive immune system in the GIT is mediated by gut-associated lymphoid tissues (GALT), harboring the largest collection of immune cells in the body (Mowat and Viney, 1997). The GALT includes appendix, Peyer’s Patches and isolated lymphoid follicles (Scharek and Tedin, 2007), and B and T lymphocytes are its principal cellular components, whereas regulation of the response is conferred by cytokines (Blecha, 2001). T cells can either be cytotoxic, killing the pathogen or, are helper T cells that combat the intracellular pathogen by either activating macrophages or B cells to produce antibodies (Johnson et al., 2001). B cells on the other hand produce specific antibodies, the so-called immunoglobulins that match a specific antigen and consequently contribute to the immunological memory of the adaptive immune system (Johnson et al., 2001). In summary, the GIT of pigs has an important protective function against exogenous invaders that potentially harm the host. The challenge is, however, to differentiate between harmful invaders and the commensal microbiota as well as innocuous macromolecules in the GIT, to maintain the functionality of this highly integrated mucosal system.

1.3. The gastrointestinal tract as microbial habitat

The GIT of pigs is habitat to a vast diversity of bacteria, co-existing in a mutual relationship with their host. The establishment of the gastrointestinal microbiota is successive and influenced by many intrinsic and extrinsic factors (Mackie et al., 1999). While at birth the

Chapter 1

GIT of pigs is sterile, it becomes thereafter rapidly colonized by bacteria from the birth canal, the maternal feces, teats, colostrum and the rearing environment (Yen, 2001). Studies have reported E. coli, Streptococcus spp. and Lactobacillus being the initial colonizers of the pig gastrointestinal tract (Konstantinov et al., 2006, Stewart, 1997) with numbers of up to 108- 1010 cfu g-1. The first colonizers are facultative anaerobic bacteria that create a reduced environment due to oxygen depletion, which is favorable for succession with obligate anaerobes, consisting of Bacteroides, Bifidobacterium and Clostridium (Conway, 1995, Stewart, 1997). During growth of the animal, bacterial density increases rapidly and is subsequently substantially challenged during weaning transition, which is the separation of the piglet from the sow and the introduction of solid feed. The weaning transition is characterized by large changes in structural, functional and barrier functions in the intestine (Lalles et al., 2007, Richards et al., 2005). Consequently, each animal is subjected to different conditions shaping the microbiota composition and activity. The successive process of microbiota establishment ends with the so-called climax community, comprising bacteria that remain in stable association with the host and have a relative population composition that is stable (Isaacson and Kim, 2012). However, even after establishment of the climax community, perturbations such as changes in diet, production stresses and diseases, affect the balance of the gut microbial ecosystem (Isaacson and Kim, 2012).

1.3.1. Spatial distribution of bacteria in the GIT

Bacterial distribution and colonization density is dependent on environmental and physicochemical conditions prevailing at the site-specific location in the gastrointestinal tract and total concentrations increase from proximal to distal GIT (Looft et al., 2014, Richards et al., 2005). In addition to the low pH of 2.5-4.5 in the stomach of adult pigs, caused by the secretion of hydrochloric acid, mucus and proteases (Ewing, 2008), the fast transit time and oxidized condition in the stomach are parameters creating an unfavorable environment for microbial growth (Richards et al., 2005). As a consequence, microbial load in the stomach and proximal part of the small intestine is relatively low with numbers of 103-105 cfu g-1 digesta (Richards et al., 2005). The stomach and proximal small intestine are almost exclusively colonized by facultative anaerobes such as lactobacilli, streptococci and

Gastrointestinal physiology of the pig coliforms (Yen, 2001). With increasing pH and short transit time of digesta, the more distally located ileum harbors already 108-109 cfu g-1 digesta. Based on a culture-independent approach, Looft et al. (2014) reported a predominance of the phyla Firmicutes and Proteobacteria in the ileum (Figure 1.2), with the genera Anaerobacter, Turicibacter, Lactobacillus, Streptococcus, Sporacetigenum and Clostridium being predominant in the lumen ileum. Furthermore, Looft et al. (2014) detected a striking difference in the ileum between the mucosa-associated and lumen-associated bacterial community. While only 13 operational taxonomic units (OTU), which are clusters of 16S rRNA sequences based on their percent sequence identity (Sekirov et al., 2010), were detected in the lumen, 299 OTUs were detected in the mucosal ileum (Looft et al., 2014). Radial differences (lumen vs. mucosa) were also observed for the cecum and colon, however, to a much lesser extent (Looft et al., 2014). The luminal bacterial community is thought to consist of a subset of the mucosal bacterial community, likely due to the epithelial turnover (Leser et al., 2002). Finally, the more distally located cecum and colon of pigs are characterized by the highest bacterial diversity and density in the GIT, reaching concentrations of up to 1010 - 1011 bacteria g-1 content (Gaskins, 2001). The large intestine is further characterized by its low redox potential and high concentrations of SCFA (Gaskins, 2001). The high production of SCFA, points at a large functional capacity harbored by the diverse colonic gut microbiota that is thereby considered to play a key role in host health and disease. Given this importance for host health, the diversity and functional activity of the colonic microbiota as well as techniques to study the complex intestinal ecosystem will be presented in more detail in the following sections.

Chapter 1

Figure 1.2. Spatial distribution of lumen bacterial phyla in the gastrointestinal tract of pigs determined by 16S rRNA gene sequencing. Adapted from Looft et al. (2014).

1.4. The colonic microbiota of pigs

1.4.1. Techniques to study microbiota composition and diversity

The genetic bacterial diversity of the mammalian GIT along with its functional capacity is enormous and its comprehensive description is a large and enduring research area (Isaacson and Kim, 2012). To elucidate and understand the complexity of microbial diversity in the gut, appropriate techniques are inevitable and a tremendous progress was achieved by the use of 16S rRNA gene-based molecular methods (Lamendella et al., 2011). The difficulty of studying the intestinal microbiota constitutes also of the hindered accessibility of gastrointestinal contents and thus, the majority of research to characterize the pig gut microbiota is still based on fecal sample analysis. The following sections will focus on different technical approaches to investigate the composition and diversity of the intestinal microbiota along with highlighting advantages and disadvantages (see also Table 1.1).

1.4.1.1 Culture-dependent techniques

The first studies on the intestinal microbiome were based on culture-dependent techniques, which allowed the phenotypic and morphological description of some of the major constituents (Isaacson and Kim, 2012). Characterization by culture-dependent approaches have revealed that the majority of pig cecal bacteria (~78 %) are Gram-negative bacteria

Gastrointestinal physiology of the pig comprising the genera Prevotella, Bacteroides, Selemonas and Butyrivibrio, whereas the Gram-positive bacteria included the genera Lactobacillus, Eubacterium and Peptostreptococcus (reviewed by Yen, (2001)). The analysis using culture-based approaches allowed a quantitative assessment of the cultivated bacteria, information about metabolic functions and the isolation of specific strains that might be further used as probiotics (Gueimonde and de los Reyes-Gavilàn, 2009). However, cultivation-dependent techniques have important inherent limitations and are restricted by three major factors: (i) only bacteria with known growth substrates and conditions can be cultured, (ii) phenotypic description does not provide phylogenetic information and (iii) culture-dependent techniques are extremely tedious and impractical for studying such a comprehensive ecosystem (Gaskins, 2001). It is estimated that only 20-40 % of the species in the GIT are yet cultivated with the current methods (Walker et al., 2014), leading to an incomplete characterization of the gut microbial diversity by culture-dependent techniques.

1.4.1.2 Culture-independent techniques

The onset of molecular methods has allowed enormous progress in elucidating the taxonomic diversity of gut microbial communities by providing more rapid and accurate techniques. By far the most widely used gene for genotypic classification of bacteria is the 16S rRNA gene, due to its highly conserved regions that allow universal primer binding and highly variable regions for taxonomic assignment on species or even strain level (Gueimonde and de los Reyes-Gavilàn, 2009). However, molecular based methods are not reprieved from limitations, such as specificity of primers and probes, as well as PCR amplification and hybridization bias.

PCR-based DNA profiling techniques. Denaturing gradient gel electrophoresis (DGGE) and terminal restriction length fragment length polymorphism (TRLFP) have been the predominant “fingerprinting” methods used in pigs to assess bacterial succession as well as bacterial community profiling as response to dietary interventions (Janczyk et al., 2007, Konstantinov et al., 2004, Leser et al., 2000, Liu et al., 2012a, Metzler-Zebeli et al., 2010, Wang et al., 2013, Yan et al., 2013). DGGE as well as temperature gradient gel electrophoresis (TTGE) need PCR amplification of the 16S rRNA genes with universal or

Chapter 1 group-specific primers prior to separation of the amplification products using gel electrophoresis (Gueimonde and de los Reyes-Gavilàn, 2009). The resulting pattern is a rough assessment of the microbial diversity in a given sample. The predominant bands revealed by DGGE or TTGE can be excised and sequenced, allowing identification of predominant bacteria. However, DGGE and TTGE do not allow quantification, and are limited by a high detection limit that does not detect phylotypes with a relative abundance < 1 % (Sekirov et al., 2010). Similar to DGGE and TTGE, TRFLP is based on 16S rRNA gene amplification by PCR. However, one of the primers for amplification is fluorescently-labeled, resulting in a PCR product that is labeled on one end only. The labeled PCR product is subsequently digested with one or more endonucleases and the length of the labeled terminal restriction fragments is analyzed by capillary electrophoresis (Gueimonde and de los Reyes- Gavilàn, 2009). PCR-based DNA profiling techniques are a powerful tool to rapidly assess dynamic changes in microbial community profiles. Nonetheless, biases can be introduced by PCR amplification, which is the basis for application of these techniques.

Quantitative real-time PCR (qPCR). Using qPCR, the amplification of the target DNA is monitored in real time with the help of a fluorescent dye, e.g. SYBR green that intercalates in double stranded DNA molecules and is released during specific hybridization with the primers. Absolute quantification is achieved by the construction of standard curves with known DNA or cell concentrations of the type strain of the respective bacterial target strain. In qPCR, bias can be introduced by the amount of 16S rRNA gene copies/genome, which are often target molecules for detection, as well as the efficiency of the qPCR reaction (Gueimonde and de los Reyes-Gavilàn, 2009). In pigs, detection of bacterial groups by qPCR is widely accepted and has been used in several studies, also in combination with other methods (for example Guo et al. (2008a), Pedersen et al. (2013), Zentek et al. (2013)). Although qPCR provides a relatively fast, accurate and quantitative assessment of the gut microbial composition, it requires specific primer design, which does not allow unraveling novel bacterial species (Sekirov et al., 2010). Furthermore, in complex ecosystems it may be difficult to design primers that are specific to the bacterial target group, as also members of closely related groups can be amplified (Sekirov et al., 2010).

Gastrointestinal physiology of the pig

Fluorescent in situ hybridization (FISH). FISH is based on the hybridization of a fluorescently labeled specific probe to its complementary nucleic acid sequence in the intact target cell (Moter and Gobel, 2000). Dyes for probe labeling with a different emission wavelength can be chosen, allowing the simultaneous detection of different target organisms in the same hybridization step (Moter and Gobel, 2000). Different studies have applied FISH to determine changes in gut bacterial communities in pigs upon specific dietary interventions (Fava et al., 2007, Konstantinov et al., 2004, Loh et al., 2006). However, FISH is a very laborious technique and the detection limit is relatively high (Loh et al., 2006), thereby not assessing the complete depth of bacterial diversity, which is detrimental for studying gut microbial diversity.

Microarrays. Based on DNA-DNA hybridization, microarrays represent a fast high- throughput assay technology that allows thousands of genes to be analyzed simultaneously in a single experiment (reviewed by Gong and Yang, (2012)). Specific oligonucleotide probes based on whole genomic DNA or 16S rRNA genes are spotted on an array and hybridized with fluorescently labeled sample DNA (Gong and Yang, 2012). Based on this principle, the human intestinal tract chip (HiTChip) (Rajilic-Stojanovic et al., 2009) and later also the porcine intestinal tract chip (PITChip) were designed (Pérez Gutiérrez, 2010). The PITChip allows the detection of over 627 porcine intestinal phylotypes based on 2985 oligonucleotide probes that target the 16S rRNA gene sequence (Pérez Gutiérrez, 2010). It has been evaluated by comparison to 454 pyrosequencing and qPCR data, showing comparable results for the three different methods applied (Pérez Gutiérrez, 2010). Nonetheless, microarray based methods do not allow quantitative analysis and require a selected probe design, which presumes a certain knowledge of the community to be analyzed (Nocker et al., 2007).

Sequencing-based methods. Most of the sequencing based techniques use the 16S rRNA gene as target molecule. For full-length sequencing the Sanger method is predominantly used and it generates relatively long sequence reads (~ 400-900 bp) (Liu et al., 2012b) that allow maximizing the taxonomic resolution (Gong and Yang, 2012). The application of full-length sequencing following random 16S rRNA amplification and cloning to describe microbial diversity of a pig has first been presented by Pryde et al. (1999). A more comprehensive

Chapter 1 study, comprising gastrointestinal samples of 24 pigs was performed by Leser et al. (2002), where a total of 3.5 Mb DNA were sequenced and the presence of 375 phylotypes using a sequence identity cutoff of 97 % was detected. Although full-length sequencing is very accurate due to the long sequence reads obtained, it is highly laborious to assess the entire intestinal microbial diversity using this method, as it depends on previous cloning or culturing. Owing to considerable development of sequencing technologies, high-throughput sequencing such as pyrosequencing, has provided tremendous insights into abundance and diversity of the intestinal microbiome (Lozupone et al., 2012). Pyrosequencing amplifies selected variable regions of the 16S rRNA gene (Sekirov et al., 2010) and relies on the “sequencing by synthesis” (de novo sequencing) principle, where the release of pyrophosphate upon nucleotide incorporation is detected. Lamendella et al. (2011) have provided a comprehensive study on the fecal pig microbiome using two different 454 pyrosequencing platforms (GS20 and FLX) and very recently, Looft et al. (2014) provided insights into the spatial and radial distribution of bacteria in the gastrointestinal tract of pigs using a 454 Genome Sequencer FLX. Development and optimization in the field of high-throughput sequencing technologies are enduring and while a decade ago the sequence read length using the 454 GS FLX was as little as 100-150 bp, it can to date reach up to 700 bp (Liu et al., 2012b). However, different variable regions of the 16S rRNA gene can be targeted by pyrosequencing and care has to be taken to compare diversities between communities that were determined by using divergent variable 16S rRNA gene regions (Claesson and O'Toole, 2010). To conclude, sequencing-based technologies have provided huge progress in assessing gastrointestinal microbial diversity. Furthermore, high-throughput sequencing technologies are fast evolving and offer the opportunity to generate large datasets of microbial communities, which allows gaining further insights into the complexity of gut microbiota diversity.

Gastrointestinal physiology of the pig

Table 1.1. Advantages and limitations of different methods used to characterize gut microbiota composition and diversity. Adapted from Zoetendal et al. (2004), Gueimonde and de los Reyes- Gavilàn (2009) and Sekirov et al. (2010).

Technique Advantages Limitations culture-dependent  quantitative  laborious

 allows isolation and further  only culturable bacteria can study as source of potential be assessed probiotic  biased community  provides metabolic information representation culture-independent

PCR-based DNA profiling  versatile (either universal or  PCR bias (DGGE, TTGE, TRFLP)a specific primers can be used)  methodologically difficult  comparative analysis  not quantitative

Quantitative real-time PCR  quantitative  no detection of novel species (qPCR)  relatively fast  DNA extraction bias  high resolution (species level)  PCR bias  low detection limit

Fluorescent in situ  quantitative  no detection of novel species hybridization (FISH)  high taxonomic resolution  relatively high detection limit (species level)  laborious  possibility to observe "location" of the bacteria (e.g. position in the mucosa)  no PCR bias

Microarrays  high throughput  cross-hybridization bias  fast  only taxa that hybridize to known oligonucleotides  16S copy number bias

Full length sequencing  high taxonomic resolution  cultivation or isolation prior (Sanger sequencing) to sequencing  DNA extraction, PCR amplification and cloning bias

454 pyrosequencing  high throughput  not quantitative  high taxonomic resolution  laborious bioinformatic  no cloning bias analyses  less susceptible to PCR bias due  DNA extraction bias to shorter PCR amplicons  16S copy number bias  sequencing depth bias a) DGGE: Denaturing Gradient Gel Electrophoresis; TTGE: Temperature Gradient Gel Electrophoresis; TRFLP: Terminal Restriction Fragment Length Polymorphism

Chapter 1

1.4.2. Culture-independent techniques for functional studies of the

gut microbiota

Whereas the methods presented in the previous section allow the assessment of the microbial composition and diversity within the gastrointestinal tract, only restricted information can be drawn on the physiological characteristics of this consortium. However, considering the importance of the gut microbiota for gut health and overall host health, it is crucial to elucidate the role and functionality of the gut microbial community. This has, amongst others, resulted in the development of the “omics” technologies (Gong and Yang, 2012). Metagenomics analyzes the entire genetic potential of a given sample and is able to predict putative functions, whereas metatranscriptomics directly targets the entire RNA pool to assess gene expression (Gong and Yang, 2012). Metaproteomics and metabolomics aim to identify whole community proteomics and the function of the gut microbiota by studying metabolic profiles, respectively (Gong and Yang, 2012, Gueimonde and de los Reyes- Gavilàn, 2009). In pigs, metagenomics has been applied to elucidate the functional capacity of feces, ileum, colon and cecum content (Lamendella et al., 2011, Looft et al., 2014). Further, metatranscriptomics has been applied in the study of Poroyko et al. (2010) using RNA-sequencing for studying differentially expressed genes as response to different feeding practices (mother-fed vs. formula-fed pigs). Due to the relevance of RNA-sequencing in this thesis, this technique will be described in more detail in the following section.

1.4.2.1 RNA-sequencing

RNA-sequencing (RNA-seq) is a major technique used in metatranscriptomics and is a powerful method to gain functional insights into gene expression (Gong and Yang, 2012, Marguerat and Bahler, 2010). It involves total RNA extraction from a given sample, followed by depletion of the rRNA to increase sequence coverage of other transcripts (Croucher and Thomson, 2010). After conversion into cDNA, library preparation and high-throughput sequencing, the resulting reads can be directly mapped onto a reference genome (Croucher and Thomson, 2010). The non-targeted approach of RNA-seq allows the detection of known and unknown gene transcripts and thus differs substantially from previous transcriptional methods (e.g. microarray, RT-qPCR) (Mach et al., 2014). Furthermore, the high sensitivity

Gastrointestinal physiology of the pig and the data output in the form of sequencing reads, allows studying fine-scale variations in gene transcripts (Gong and Yang, 2012, Poroyko et al., 2010). RNA-seq was originally described for eukaryotic gene expression and so far only one study applied RNA-seq to profile gene expression from prokaryotes of the porcine gut microbiota (Poroyko et al., 2010). Moreover, RNA-seq can also be used to study less complex ecosystems or even individual microbe-microbe interactions of a defined microbial consortium isolated from a given ecosystem (Rosenthal et al., 2011). This allows gaining substantial insights into underlying mechanisms of direct microbe-microbe interactions and produces testable hypotheses about functions of different members of a given ecosystem (Rosenthal et al., 2011). Using this approach, Rosenthal et al. (2011) was able to reveal a symbiotic metabolic interaction of two termite-gut species when grown under simplified conditions in co-culture. Despite the distinct advantages of RNA-seq, it is not possible to differentiate between de novo and posttranslational transcripts and RNA-seq also faces bioinformatics challenges (Gong and Yang, 2012). In summary, RNA-seq provides a powerful approach to elucidate gene expression levels in complex ecosystems, as the gut microbiota, without knowing what organisms are present. Furthermore, transcriptional analysis using RNA-seq of a defined microbial consortium could support to formulate testing hypotheses for more complex ecosystems. Due to the decreasing sequencing costs and the ongoing advent in high-throughput sequencing, RNA-seq holds promise to be used as a routine technique for transcriptome studies.

1.4.3. Diversity of the colonic microbiota

High-throughput sequencing technologies have largely confirmed the findings of culture- dependent methods. However, due to their sequencing depth they have allowed gaining a much deeper insight into the large bacterial diversity of the pig intestine (reviewed by Isaacson and Kim, (2012)). Nonetheless, the composition of the porcine gut microbiota is far from being understood and the complete microbial diversity remains to be elucidated. The large intestine of pigs is composed of bacteria belonging to the phyla Firmicutes, Bacteroidetes, Proteobacteria, Spirochaetes, Actinobacteria, Tenericutes and Synergistetes (Buzoianu et al., 2012, Kim et al., 2011, Lamendella et al., 2011, Looft et al., 2014, Poroyko

Chapter 1 et al., 2010). In all mentioned studies more than 90 % of generated sequence reads were derived from the phyla Firmicutes (35-75 %) and Bacteroidetes (12-50 %), followed by the Proteobacteria phylum (3-6 %) (Buzoianu et al., 2012, Kim et al., 2011, Lamendella et al., 2011, Looft et al., 2014, Poroyko et al., 2010). Already culture-dependent methods have identified Gram-negative bacteria and especially the genus Prevotella as predominant member of the pig colonic microbiota (reviewed by Yen, (2001)), which was consistently confirmed using culture-independent approaches. Prevotella constitutes up to 30-40 % of all genera detected and belongs to the phylum Bacteroidetes (Buzoianu et al., 2012, Kim et al., 2011, Lamendella et al., 2011, Looft et al., 2014, Poroyko et al., 2010). Other predominant genera detected in the porcine gut microbiota are mainly members of the phylum Firmicutes, including Clostridium, Lactobacillus, Streptococcus, Sporacetigenum, Blautia, Roseburia, Faecalibacterium, Acidaminococcus, Coprococcus, Megasphaera, Anaerobacter, Oscillibacter and Anaerovibrio. Bacteroides (phylum Bacteroidetes) and Succinivibrio (phylum Proteobacteria) are also predominant (Table 1.2).

Gastrointestinal physiology of the pig

Table 1.2. Predominant genera of the pig gut microbiota detected in selected studies from different sampling sites using culture-independent methods.

Predominant genera Sampling site Reference

Prevotella, Bacteroides, Oscillibacter, Parabacteroides, Anaerovibrio, Clostridium, Alistipes, Turicibacter, cecum Poroyko et al. (2010) Sporacetigenum, Roseburia

Prevotella, Anaerobacter, Streptococcus, Lactobacillus, Coprococcus, Sporacetigenum, Megasphaera, feces Kim et al. (2011) Subdoligranulum, Blautia, Oscillibacter, Faecalibacterium, Pseudobutyrivibrio, Dialister, Sarcina, Roseburia

Prevotella, unclassified Clostridiales, Sporobacter, Lactobacillus, Anaerovibrio, Clostridium, unclassified Bacteroidales, Megasphaera, unclassified feces Lamendella et al. (2011) Porphyromonadaceae, Streptococcus, Treponema, unclassified Lachnospiraceae, unclassified Clostridiaceae

Prevotella, Clostridium, Succinivibrio, Faecalibacterium, cecum Buzoianu et al. (2012) Acidaminococcus, Megasphaera, Oscillospira

Prevotella, Coprococcus, Treponema, Anaerovibrio, cecum, colon, Roseburia, Succinivibrio, Anaerobacter, Oscillibacter, Looft et al. (2014) feces Parabacteroides, Bacteroides, Papillibacter, Streptococcus,

It should be noted, however, that each animal has a specific microbiome and differences between studies can be due to methodological (sequencing and assignment pipeline), sampling-site specific (cecum, colon, feces) and study subject (pig age, pig breed, feeding) discrepancies. The influence of feeding (formula fed vs. mother fed) was for example demonstrated by Poroyko et al. (2010), detecting almost no Prevotella in the cecum of formula fed piglets compared to mother fed piglets where Prevotella was the most abundant genus.

1.4.4. Function of the gut microbiota

The colonic microbiota is composed of a high bacterial diversity and encompasses a tremendous functional capacity, thereby contributing substantially to host health. While the

Chapter 1 host provides a stable niche for the microbiota (Gaskins, 2001), the gut microbiota in turn provides nutritional functions to the host, stimulates host immunity, metabolizes xenobiotics, and prevents pathogens from colonization (reviewed by Richards et al., (2005) and Jacobs and Braun, (2014)). The function of the gut microbiota depends largely on its composition, the available substrate source and the conditions prevailing at the site-specific location (e.g. pH, transit time).

1.4.4.1 Nutritional contributions

One of the major contributions of the commensal gut microbiota is the breakdown of non- digestible dietary components (e.g. oligosaccharides, non-starch polysaccharides, proteins) and endogenous mucus by fermentation, which results in a considerable energy salvation for bacterial proliferation and for the host through absorbable substrates (Guarner and Malagelada, 2003). Fermentation of non-starch polysaccharides and oligosaccharides in the colon by the complex microbial community results predominantly in the formation of SCFA and gases, such as CO2, H2 and CH4 (Figure 1.3). The stepwise fermentation of polysaccharides by colonic bacteria includes the production of intermediates, such as lactate, succinate, ethanol and formate (Blaut, 2013). While H2 and formate can be used by methanogens to produce CH4, H2 and lactate can be used by sulfate-reducing bacteria to produce H2S (Chassard and Lacroix, 2013). H2 can be metabolized by the hydrogenotrophic acetogenic community (e.g. Blautia) to produce acetate and lactate-utilizers (e.g. Eubacterium, Anaerostipes, Propionibacterium) produce propionate and butyrate from lactate (Chassard and Lacroix, 2013). SCFA concentrations in the colon are in the range of 70-100 mM with a typical ratio of 60:25:15 (acetate:propionate:butyrate) (Grieshop et al., 2000). SCFA are rapidly absorbed and it has been estimated that their contribution to the energy requirement of the pig varies between 5-28 % (Grieshop et al., 2000). The major SCFA resulting from microbial fermentation are (in decreasing concentrations) acetate, propionate and butyrate (Varel and Wells, 2005). Acetate serves as a substrate for liver cholesterol synthesis (Wong et al., 2006) and reaches highest concentrations in plasma due to transport to peripheral tissues (Russell et al., 2013, Guarner and Malagelada, 2003). Furthermore, acetate has also been reported to be involved in combating pathogens (Fukuda et al., 2011) and in

Gastrointestinal physiology of the pig controlling inflammation (Maslowski et al., 2009). Propionate has been shown to have manifold health effects including decreased serum cholesterol levels, antilipogenic effects and induction of apoptosis in vitro using colorectal carcinoma cells (reviewed by Hosseini et al. (2011)). Finally, butyrate is the major energy source for colonocytes and is involved in inflammation regulation, cellular differentiation, apoptosis and anticarcinogenesis (reviewed by Russell et al. (2013)). Besides the major SCFA produced from breakdown of non-starch polysaccharides and oligosaccharides, bacterial fermentation also results in the formation of vitamin K and B-vitamins and amino acids (reviewed by Richards et al. (2005)). In contrast to carbohydrate breakdown proteolytic activity of the gut microbiota in pigs is much less studied. The main sources for proteolytic activity include dietary residues, endogenous secretions such as mucus and other glycoproteins and bacterial secretions (Gaskins, 2001). Protein breakdown by the gut microbiota is a step by step process including short peptides, amino acids and results in the formation of branched chain fatty acids (BCFA) and potentially toxic products (Blaut, 2013). The potentially harmful end products from amino acid catabolism, such as amines, ammonia, phenols and indoles, are of particular interest, because they may influence growth of the pig or affect intestinal cell differentiation (Gaskins, 2001).

Figure 1.3. Microbial breakdown of carbohydrates and proteins in the gastrointestinal tract. Adapted from Chassard and Lacroix (2013) and Rist et al. (2013).

Chapter 1

1.4.4.2 Immunological contributions

The intestinal epithelium constitutes the major barrier between external and internal environment and is at the same time the first line of defense against exogenous invaders (Roselli et al., 2005). The microbiota in the GIT is in close association with the epithelial lining, and can stimulate the development of both, the innate and the adaptive immune system (Gaskins, 2001). Thereby the trophic effect of SCFA that influence epithelial cell differentiation and proliferation is notable (Guarner and Malagelada, 2003). Furthermore, studies on germfree animals have shown that the immune system is underdeveloped in the absence of the commensal microbiota (Gaskins, 2001), thus highlighting the importance of the gut microbiota for the development of a competent intestinal immune system. The microbiota and the immune system have co-evolved resulting in a close relationship and the control of the mutual homeostasis (Thaiss et al., 2014). This also explains the tolerance of the intestinal immune system towards the commensal microbiota and antigens deriving from food components.

1.4.4.3 Protective function

The commensal gut microbiota constitutes an important defense barrier against enteropathogens, a mechanism which is known as colonization resistance and has emerged from studies in germfree animals (Richards et al., 2005, Buffie and Pamer, 2013). Colonization resistance can either be conferred by direct or indirect mechanisms, as well as a combination of both (Buffie and Pamer, 2013). Direct antagonism against exogenous invaders can be (i) the competition for adhesion sites and nutrients, whereby the pathogen is prevented from outgrowing the commensal microbiota, (ii) the production of antimicrobial substances, such as bacteriocins, that are known to be produced from different bacteria and elicit inhibition on potential pathogens and (iii) the production of SCFA thereby lowering intestinal pH which generates unfavorable conditions for potential invaders (Buffie and Pamer, 2013). Taken together, the large diversity of the colonic microbiota encompasses a tremendous functional capacity that provides the host with energy derived from nutrient breakdown, is crucial for the development of a competent immune system and confers protective barrier

Gastrointestinal physiology of the pig function against pathogenic organisms. Thus, a considerable interplay between gut microbiota diversity and function and host health and performance exists. Any imbalance of gut microbiota composition or function, for example due to pathogen infection, results in the so-called dysbiosis.

Chapter 1

2. Salmonella in pigs

Disruption of the mucosal integrity can result in the infection with an enteropathogen, such as Salmonella, which will be the focus in the next sections highlighting the prevalence of Salmonella in swine livestock production, the implications to animal and human health and discuss strategies to combat Salmonella at the herd level.

2.1. Salmonella taxonomy

Salmonella is a Gram-negative non-spore forming bacterial genus equipped with peritrichous flagella, allowing its motility, and belongs to the family Enterobacteriaceae (de Busser et al., 2013). The genus Salmonella encompasses two species, S. enterica and S. bongori. These two species are subdivided into 6 subspecies (I-VI): enterica (I), salamae (II), arizonae (IIIa) diarizonae (IIIb), houtenae (IV), and indica (VI) (Abbott et al., 2012). Previously, bongori was subspecies V, but is now considered to be an own species. Further differentiation of Salmonella species according to the Kauffmann-White Scheme is based on their specific somatic (O) or flagellar (H) antigens, which has resulted in more than 2400 different serotypes, isolated from different animals (Boyen et al., 2008a), whereby most of the animal and human isolates belong to the subspecies enterica (Uzzau et al., 2000). Salmonella serotypes can also be classified based on their host range (Uzzau et al., 2000). Host-restricted serotypes encompass for example the serotypes Typhi, Abortusequi, Gallinarum, Typhisuis and Abortusovis that cause systemic disease and are almost exclusively associated with one particular host-species. Host-adapted serotypes, such as serotypes Dublin (cattle) and Choleraesuis (pigs), usually cause systemic disease in a specific host, but can also cause severe infections in other mammalian hosts, including humans (Uzzau et al., 2000). Finally, un-restricted serotypes have a broad host range and comprise the largest collection of different serotypes, including Typhimurium and Enteriditis and can cause severe systemic infections, but usually induce self-limiting gastroenteritis (Uzzau et al., 2000).

Salmonella in pigs

2.2. Salmonella prevalence in pigs

In 2008 the European Food Safety Authority (EFSA) performed a baseline survey on Salmonella prevalence in more than 5000 pig holdings across 24 member states and 2 non- member states (Norway and Switzerland) (EFSA, 2009). Overall Salmonella prevalence in European Union pig holdings was 31.8 %, whereby large variations amongst the member states (0-64 % for breeding holdings and 0-55.7 % for production holdings) were reported (EFSA, 2009). A total of 54 and 88 Salmonella serotypes were isolated in breeding and production holdings, respectively, whereby S. Derby followed by S. Typhimurium were the two most frequently isolated serotypes (EFSA, 2009). In Switzerland, 15.5 % (2250 samples in total) holdings were Salmonella positive and S. Typhimurium followed by S. Derby was the most frequently isolated serotype (EFSA, 2009). In the United States (US) the USDA’s National Animal Health Monitoring System (NAHMS) conducted in 2006 a baseline survey in more than 17 states, representative for approximately 94 % of the U.S. pig inventory (USDA, 2009). From the almost 8000 samples analyzed from 135 different sites, 7.2 % were Salmonella positive. However, 52.6 % of the sites had at least one fecal sample positive for Salmonella (USDA, 2009). A total of 27 serotypes were identified whereby the serotypes (in order of prevalence) Derby, Typhimurium variant Copenhagen, Agona and Anatum represented more than 70 % of the total serotypes detected (USDA, 2009).

2.3. Salmonella and health

2.3.1. Pathogenesis and implications for pig health

The source for Salmonella infection in pigs can be manifold and includes different steps along the pig production chain. Possible sources for Salmonella infection comprise infected purchased pigs, Salmonella contaminated feed and transmission between animals due to cleaning and disinfection strategies and stress-related Salmonella shedding (de Busser et al., 2013). Transmission of Salmonella between pigs is thought to occur mainly via the fecal-oral route, although nose-to-nose and aerosol transmission over short distances was also reported (de Busser et al., 2013).

Chapter 1

During the infection process Salmonella is confronted with a large array of host defense mechanisms, consisting of chemical barriers (low pH, bile salt excretion, lysozyme), mechanical barriers (intestinal epithelium) and the innate and adaptive immune system (Boyen et al., 2008a). The environmental cues encountered during the infection process have been shown to trigger specific and appropriate virulence gene expression, which enables Salmonella to foster different phenotypes due to the variable prevailing conditions (reviewed by Fabrega and Vila, (2013)). The high concentration of bile salt and fast transit time in the upper small intestine, for example, might explain the preferred colonization of the ileum, cecum and colon by Salmonella (Boyen et al., 2008a). Salmonella pathogenesis is a multi- step process including motility, adhesion, invasion and intestinal persistence and its virulence gene expression is under control of a complex regulatory network (reviewed by Fabrega and Vila, (2013)). The majority of the virulence determinants are located within highly conserved Salmonella pathogenicity islands (SPI), the pSLT virulence plasmid or in the chromosome (Fabrega and Vila, 2013). After passage of the stomach, the adherence to the intestinal mucosa is generally the first step in pathogenesis and is mediated by adhesins encoded within SPI-3 and SPI-4 (Boyen et al., 2008a, Fabrega and Vila, 2013). To reach the adhesion site, the expression of flagella and chemotaxis are considered to play a key role (Saini et al., 2010). Subsequent invasion to absorptive enterocytes and M-cells is the consequence of membrane deformation and cytoskeletal rearrangement, a process called membrane ruffling, which leads to the engulfment of Salmonella in large vesicles called Salmonella-containing vacuoles (SCV) (Boyen et al. 2008a, Fabrega and Vila, 2013). SCV allow Salmonella to survive and replicate. SCV transcytose to the basolateral membrane, where Salmonella are phagocytosed and as a consequence can be disseminated through the lymph and the bloodstream (Fabrega and Vila, 2013). The invasion to enterocytes and mesenteric lymph nodes is under control of the Salmonella pathogenicity island 1, which encodes structural components and secreted effector proteins of the type III secretion system (TTSS) (reviewed by Altier, (2005) and Fabrega and Vila, (2013)). The key regulator of the invasion process is HilA (reviewed by Fabrega and Vila, (2013)). A second TTSS is encoded on SPI 2, which has been shown to be particularly important for persistence of Salmonella in macrophages, but is also involved in the invasion process (Fabrega and Vila, 2013). Secreted effector

Salmonella in pigs proteins induce actin rearrangement resulting in the uptake of the bacteria, and further induce cytokine production, which has been reported after only 2 h of oral challenge (Boyen et al., 2008a, Fabrega and Vila, 2013). Cytokine IL-8 is thought to mediate the onset of gastroenteritis, however also other mechanisms for gastroenteritis induction are possible but have not yet been described in pigs (Boyen et al., 2008a). In contrast to the colonization of the lower GIT by Salmonella, little is known about the systemic part of the infection where Salmonella can colonize mesenteric lymph nodes, liver and spleen (Boyen et al., 2008a). The symptoms encountered in pigs during Salmonella infection are largely serotype dependent. The host-restricted serotype Typhisuis causes chronic paratyphoid, ending in the death of the animal, usually within several weeks (Uzzau et al., 2000). Upon infection S. Typhisuis can be found in lymphatic tissues along the gastrointestinal tract, where it progressively produces lesions and leads to dehydration, emaciation and intermittent diarrhea (Uzzau et al., 2000). Furthermore, systemic dissemination of S. Typhisuis results in granulomas and necrosis at the site of infection (Uzzau et al., 2000). The host-adapted serotype Choleraesuis is genetically related to S. Typhisuis and was the first Salmonella serotype isolated from swine (Uzzau et al., 2000). Interestingly, S. Choleraesuis has markedly decreased in prevalence between 1986 and 2006 (Foley et al., 2008) and is not amongst the 10 predominant serotypes detected in the most recent EU or US baseline survey (EFSA, 2009, USDA, 2009). S. Choleraesuis infection causes severe systemic salmonellosis in weanling pigs and can lead to abortion in the pregnant sow (Uzzau et al., 2000). The systemic lesions caused by S. Choleraesuis and the absence of severe enteritis distinguish it from the serotype Typhimurium that induces watery diarrhea as a typical sign of disease (Uzzau et al., 2000). S. Typhimurium infections, however, can also result in a long-term asymptotic carrier state (Boyen et al., 2008a) and stress-induced shedding of S. Typhimurium during transport to the slaughter house has been reported, although the mechanisms remain unclear (Boyen et al., 2008a). Persistent carriage has also been reported for S. Choleraesuis (reviewed by Uzzau et al. (2000)) and recent evidence from S. Typhimurium suggests that persistence can be favored by the reduction of intracellular growth (Monack et al., 2004) and downregulation of the local inflammatory response in pigs (Niewold et al., 2007, Wang et al., 2007b) by Salmonella itself.

Chapter 1

The persistent and asymptotic carriage of host-adapted and un-restricted serotypes in the tonsils, intestines and GALT, pose an important threat to both, animal and human health.

2.3.2. Implications for human health

The EFSA reported Salmonella as a major cause for foodborne outbreaks and salmonellosis was the second most reported zoonosis in the European Union with more than 91’034 confirmed cases in 2012 (EFSA and ECDC, 2014). In the US, the Centers for Disease Control and Prevention (CDC) estimated Salmonella to cause more than 1.2 million illnesses per year with more than 450 deaths (CDC, 2011). The primary cause for salmonellosis is contaminated food products, including eggs, meat (pork, broiler, turkey, beef), dairy products, fruits and vegetables (EFSA and ECDC, 2014). Moreover, the predominant 5 serotypes isolated from clinically healthy animals in 2005 accounted for more than 56 % of human isolates in the U.S. (Callaway et al., 2007). Thereby the consumption of pork meat has a significant contribution to the amount of illnesses as data from 2005-2006 suggest that S. Typhimurium contaminated pork accounted for 7.5 % of human infections in the EU (Pires and Hald, 2010). Statistical models estimate that in the U.S. approximately 100’000 cases of human salmonellosis are associated with pork annually, resulting in associated costs of about 81 million dollar (Miller et al., 2005). Although human non-typhoidal Salmonella infections are usually characterized by mild symptoms (fever, abdominal pain, nausea) and are self- liming, some patients experience severe symptoms and the associated diarrhea can be life- threatening (EFSA and ECDC, 2014). Furthermore, non-typhoidal Salmonella can also cause severe systemic infections that can lead to endovascular or localized infections (Crum Cianflone, 2008). In summary, Salmonella infections can be caused by different serotypes and provoke symptoms with different degrees of severity in both, pigs and humans. Given the fact that Salmonella prevalence in pig holdings is abundant, asymptotic carriage is common and contaminated pork has a considerable contribution to human infections with Salmonella, it is of utmost importance to find potential strategies to combat Salmonella at the herd level.

Salmonella in pigs

2.4. Antibiotics to combat Salmonella prevalence in pigs

Antibiotics have a long history of use in swine industry (Cromwell, 2002); thereby it is important to differentiate between four different ways of antibiotic usage for food production animals. Therapeutic usage refers to the treatment of a clinically sick animal, while metaphylactic implies the treatment of clinically healthy animals allocated in the same pens as clinically sick animals (Aarestrup, 2005). Further, prophylactic usage means treating animals in periods where they are susceptible to infections (e.g. during weaning transition), which is in most countries not legal and finally the continuous inclusion of antibiotics to feed is referred to the use as growth promoters (Aarestrup, 2005). The most frequently used antimicrobials for therapeutic use in swine are tetracyclines, tylosin and sulfamethazine or other sulfonamides (McEwen and Fedorka-Cray, 2002). Whereas therapeutic usage was the primary purpose of antibiotic usage in food animal production in the beginning and still is important for a safe animal production, the initial idea for antibiotic usage was redirected when recognizing the benefits of antibiotics on growth promotion. In 1949, Stokstad et al. (1949) were the first to recognize the practical implications of growth-promoting effects due to antibiotics and in 1950 Jukes et al. (1950) demonstrated enhanced growth of pigs upon inclusion of chlortetracycline to the feed. Thereafter antibiotics were readily included at subtherapeutic levels in the integral feeding program for pigs all over the world. Although some antibiotics included in animal feed at subtherapeutic levels are also approved for therapeutic usage and thus may help to prevent disease (reviewed by McEwen and Fedorka-Cray, (2002)), the primary goal remains the growth promotion of the animals. Despite numerous studies having addressed antibiotic- driven growth promotion, the exact mechanism on how antibiotics improve growth promotion yet has to be elucidated and it seems that it cannot be ascribed to one unifying mode of action (Page, 2006). However, given the fact that dietary supplementation with antibiotics has only little effect on germ-free animals it seems evident that growth promotion in response to antibiotics is driven by the interplay with the complex gut microbial ecosystem (Page, 2006). Besides control of enteric disease and growth promotion, also reproductive

Chapter 1 effects, environmental benefits and improvements in feed efficiency have been ascribed to in- feed antibiotics (Cromwell, 2002). Although there is no data collection that thoroughly describes the specific amounts and types of in-feed antibiotics given to healthy animals (Sapkota et al., 2007), it is estimated that in the US more antibiotics are used for animal food production than for human health care (CDC, 2013). In light of the high prevalence of antibiotic resistant bacteria, the high use of antibiotics at subtherapeutic levels has seriously become questioned. Nowadays it is well- accepted that the tremendous pressure of subtherapeutic levels of antibiotics in animal feed has promoted the emergence of antibiotic resistant bacteria, including relevant human pathogens such as enterococci, streptococci, Salmonella, Campylobacter and E. coli, although it needs to be emphasized that other factors (e.g. human health care, aquaculture, horticulture) have also contributed (Aarestrup, 2005, Kruse and Schlundt, 2006, Wegener, 2006). Furthermore, it should not be forgotten that any improper use of antibiotics, also for therapeutic usage, contributes to the selection of antibiotic resistant bacteria. Based on the increased occurrence of antibiotic resistant bacteria, Sweden was the first country to enunciate a complete ban of antibiotics for growth promotion in 1986. In 1995, Denmark was the first country to ban an antimicrobial growth promoter (Avoparcin) and by 2000 the use of antimicrobials in Denmark was restricted to therapeutic use (Dibner and Richards, 2005). In Switzerland, antibiotics for growth promotion are banned since 1999 (Arnold et al., 2004) and in the EU since 2006 (Simon, 2005). In the U.S. however, antibiotics for growth promotion are still allowed, although in 2013 the Food and Drug Administration (FDA) has published a voluntary phase-out plan for antibiotics used as growth promoters (FDA, 2013). Given the problematic of antibiotic resistant bacteria and the thereto related ban of antibiotics as growth promoters, it is evident that potent alternatives are needed that are able to guarantee animal health and welfare while keeping productivity and efficiency of swine livestock production.

Salmonella in pigs

2.5. Alternative strategies to combat Salmonella in pigs

Different alternative strategies to antibiotics are being discussed to control Salmonella at the herd level and a combination of several strategies seems to be even more efficient (hurdle principle). Amongst the different strategies are general hygiene measures (cleaning and disinfection strategies, rodent control), Salmonella-free feed, antimicrobials (bacteriocins, essential oils), vaccination, phages and nutritional strategies, such as probiotics, prebiotics and organic acids (reviewed by de Busser et al. (2013) and Thacker, (2013)). Some of them will be discussed in the following sections and special emphasis is given to probiotics and prebiotics.

2.5.1. Bacteriocins

Bacteriocins are ribosomally synthesized peptides from bacteria which are active against other bacteria and against which the producing bacteria itself is immune (Cotter et al., 2013). Bacteriocins can have a narrow or broad host range, which is particularly suitable to select a bacteriocin that targets the selected pathogen but does not affect the entire microbial community (Cotter et al., 2013, Doyle and Erickson, 2012). Despite many in vitro studies demonstrating activity of different bacteriocins against pathogens (reviewed by Cotter et al. (2013)), information about in vivo trials in swine using bacteriocins is lacking. Furthermore, resistance against bacteriocins is a crucial point that needs to be addressed when considering bacteriocins as potential alternative to antibiotics (reviewed by Cotter et al. (2013)).

2.5.2. Phage therapy

Phage therapy uses bacteriophages, naturally occurring viruses, to infect a specific bacterium or a narrow group of bacteria (Allen et al., 2013, Doyle and Erickson, 2012). As with bacteriocins, the narrow specificity of bacteriophages is advantageous over antibiotics as it does not affect the overall gut microbiota and thus prevents dysbiosis (Allen et al., 2013). Efficacy of phage therapy is low, as demonstrated in the study of Saez et al. (2011), where Salmonella counts were reduced 1 log in cecal and ileal contents of pigs after in-feed and oral admission of a phage cocktail. The application of phages requires accurate identification of the pathogen, high numbers of the target bacteria and administration soon after infection,

Chapter 1 which is particularly a challenge to adopt it as a potent viable alternative to antibiotics (reviewed by Allen et al. (2013)). Furthermore, use of temperate phages should be avoided, given the potential of transferring resistance genes and virulence traits to the host bacterium. A solution to solve this problem is the use of purified phage products, such as lysins that efficiently target and lyse the host bacterium without infection (reviewed by Allen et al. (2013)).

2.5.3. Vaccines

The aim of vaccination is the improvement of the immune system via introducing antigens to the host. Immune response can be triggered by applying whole killed-microorganisms, purified microbial components, recombinant proteins or live-attenuated microorganisms (Haesebrouck et al., 2004). In general, live-attenuated vaccines are more suited to induce cell-mediated immunity and humoral response and thus are more effective against facultative intracellular bacteria, such as Salmonella (Haesebrouck et al., 2004). To date only one Salmonella Typhimurium live vaccine is commercially available in Europe (SalmoporcΔompD, IDT Biologika), which has shown to induce antibody response and reduce shedding and colonization of host tissue (reviewed by de Busser et al. (2013)). This live vaccine, however, is not compatible with most of the current Salmonella surveillance programs in Europe, as they rely on the detection of antibodies against lipopolysaccharides (LPS) of Salmonella using an O-antigen based ELISA (reviewed by de Busser et al. (2013)). This detection is however not able to differentiate between vaccine-induced antibodies and those induced by infection. Recently, Leyman et al. (2011) presented LPS mutants of Salmonella Typhimurium that are compatible with the current surveillance programs in Europe and provided protection against a virulent Salmonella Typhimurium strain in pigs. de Ridder et al. (2013) demonstrated that one of these LPS mutants of Salmonella Typhimurium (Salmoporc-ΔrfaJ) is able to reduce Salmonella transmission between piglets and is capable with current surveillance programs, thus merits further research. Vaccination demonstrates an important alternative, given the good scientific background and general understanding of vaccine immunology (Allen et al., 2013). However, problems that may arise with the use of vaccination for Salmonella control in pigs are the costs and ease of use, the possible reversion

Salmonella in pigs of live-attenuated strains to virulence and antimicrobial genes that might be transferred from the live-attenuated strain to other bacteria, including pathogens (reviewed by de Busser et al. (2013) and Allen et al. (2013)).

2.5.4. Probiotics

The Russian Elie Metchnikoff was the first to present in 1907 the concept of “harmful bacteria that can be replaced by useful bacteria”. He claimed that bacteria ingested from yoghurt can reduce the amount of toxin-producing bacteria in the intestine and thus improve longevity of the host (Metchnikoff, 1907). In 1965, the term probiotic was first introduced by Lilly and Stillwell, (1965) and after several redefinitions, the nowadays commonly accepted definition for probiotics is: “live microorganisms which when administered in adequate amounts confer a health benefit to the host” (FAO/WHO, 2002). Probiotic strains are expected to have health-promoting characteristics and fulfill safety criteria as well as possessing technological features which enable production of probiotics on large scale (Table 1.3). Furthermore, it is recommended that the concentration of probiotics is approximately 109 cfu per kg of feed (Simon, 2005).

Table 1.3. Functional characteristics, safety criteria and technological features expected from probiotic strains. Based on Gaggia et al. (2010), Ross et al. (2005) and de Vrese and Schrezenmeir (2008). functional characteristics safety criteria technological features  survival and persistence in the  non-toxic and non-pathogenic  high viability GIT bacteria  stability in products  adhesion to epithelium or  accurate taxonomic  easy propagation mucus identification  desirable organoleptic  competition with resident  normal inhabitant of the host properties microbiota  genetically stability  oxygen, acid, bile and  production of antimicrobial heat tolerance substances  antagonism towards pathogens  modulation of immune response  desirable metabolic activities

Chapter 1

The most often used probiotics in humans belong to the genera Lactobacillus and Bifidobacterium, however in animal nutrition Enterococcus, Bacillus and Saccharomyces are more frequently applied (Gaggia et al., 2010, Simon, 2010). Lactobacillus and Bifidobacterium are Gram-positive bacteria that produce lactic acid and are constituents of the normal gut microbiota of humans and animals (de Vrese and Schrezenmeir, 2008, Gaggia et al., 2010). Due to their long history of safe use many species are proposed for the Qualified Presumption of Safety (QPS) status (EFSA, 2007). Enterococci belong to the lactic acid bacteria (LAB) and are normal human and animal commensals. However, enterococci were also related to nosocomial infections and gained special attention due to their role in acquisition and transmission of antibiotic resistance genes (Gaggia et al., 2010). Furthermore, Bacillus are Gram-positive spore-forming microorganisms that are associated with soil, water and air. Due to their ability to form spores and the presence of potentially toxigenic traits in most of the species, Bacillus are controversially being discussed for their use as probiotics (Gaggia et al., 2010). Finally, Saccharomyces is a yeast and common member of the gut microbial community. It is widely used in foods and beverages for its key role in fermentation process (Gaggia et al., 2010). Considering that probiotic traits were attributed to members of different genera and species it is important to consider strain-specificity when evaluating a novel probiotic candidate. The identification of a novel probiotic strain should thus be based on a polyphasic approach, including morphological, physiological and biochemical features as well as aspects of its genetic profile (Donelli et al., 2013).

2.5.4.1 Probiotics in swine livestock

Several probiotic cultures are already in use and commercially available for in-feed application in swine livestock (e.g. BioPlus®YC from Evonik, BONVITAL from Schaumann and BACTOCELL® and LEVUCELL® from Lallemand). Nonetheless, research on investigating effects of probiotic application in swine feed is ongoing, especially targeting the effects on pathogen reduction and/or inhibition. Casey et al. (2007) and Genovese et al. (2000) for example reported reduced shedding of Salmonella Typhimurium and enterotoxigenic E.coli, respectively, using each a different mixture of probiotic strains. Further, Lessard et al. (2009) could demonstrate reduced translocation of enterotoxigenic

Salmonella in pigs

Escherichia coli to mesenteric lymphnodes in pigs treated with Saccharomyces cerevisiae boulardii and Pediococcus acidilactici. A large interdisciplinary study specifically demonstrated reduced pathogen load in healthy piglets and sows upon feeding with Enterococcus faecium NCIMB 10415 (Lodemann et al., 2006, Taras et al., 2006, Vahjen et al., 2007). Other studies have reported reduced incidence or severity of diarrhea (Bhandari et al., 2008, Casey et al., 2007, Shu et al., 2001), possibly related to reduced pathogen load and competitive exclusion mechanisms preventing adherence of pathogens to the epithelium, which in turn is indispensable for the onset of diarrhea. Reduced incidence of diarrhea can, however, additionally be related to the stimulation of health-related bacteria, such as lactobacilli and bifidobacteria, in the gastrointestinal tract. Biavati et al. (2009) reported an increased ratio of bifidobacteria/E.coli, whereas Takahashi et al. (2007) detected higher total lactobacilli concentrations in the gut of weaned pigs after administering the diet of neonatal pigs with a strain of Lactobacillus plantarum. Furthermore, besides the important implications for gut health, probiotic application was also reported to improve average daily weight gain (ADG) and improve feed utilization efficiency (Abe et al., 1995, Konstantinov et al., 2008, Zani et al., 1998). Both are important factors considering an efficient swine livestock production. Despite the manifold positive influences of different probiotics on overall pig health, some studies have also reported no effects or adverse effect upon probiotic addition to swine feed (reviewed by Vondruskova et al. (2010)). While effects of probiotic application are well documented yet there is only little known about the exact mechanisms of action of probiotics.

2.5.4.2 Mechanisms of action of probiotics

Probiotics in general are characterized by their diversified mechanisms of action to modulate the gastrointestinal equilibrium of their host. Probiotics possess an array of mechanisms for competition with enteric pathogens, including modulation of the innate and adaptive immune system, direct bacteria-bacteria interaction and altered metabolic processes (Figure 1.4) (O'Toole and Cooney, 2008; Oelschlaeger, 2010).

Chapter 1

Figure 1.4. Schematic representation of probiotic mechanisms. Adapted from O'Toole and Cooney (2008) and Zihler (2010).

Modulation of the immune response. The modulation of the immune response is an indirect factor by which probiotics can combat enteric pathogens, such as Salmonella. Immunomodulatory effects of probiotics in pigs have been reported in several studies. Upon administering Lactobacillus brevis B1, Zhang et al. (2011) detected an increase in intraepithelial lymphocytes (IEL), IgA-producing cells and a higher expression level of cytokine IL-6, all of which confer to the development of the mucosal immunity in piglets. IgA producing cells were even more increased when Bacillus subtilis RJGP16 was co- administered with L. brevis B1 (Deng et al., 2013). Modulation of IgA concentrations in ileal flushes were also observed when feeding Saccharomyces cerevisiae boulardii or Pediococcus acidilactici to piglets (Lessard et al., 2009). Furthermore, upon feeding young pigs with E. coli Nissle 1917, an increased concentration of mucosal CD8+ cells (cytotoxic T-cells) was

Salmonella in pigs observed in the ascending colon, possibly contributing to the maintenance of porcine intestinal health and prevention of disease (Duncker et al., 2006). Increased CD8+ cells together with higher CD4+ cells and increased ileal IL-8 mRNA expression was observed when feeding a five strain probiotic mixture (4 Lactobacillus strains, 1 Pediococcus strain) to weaned pigs, indicating a probiotic mechanism, at least in part, via immunostimulation (Walsh et al., 2008). The stimulation of the humoral immune response as measured by increased serum IgM and IgA levels was also observed by Szabo et al. (2009) after Enterococcus faecium NCIMB 10415 feeding following challenge with Salmonella Typhimurium DT104, although a more severe infection was recorded for the probiotic treatment group. This observation was later addressed in a follow-up study indicating that probiotic fed piglets had a negatively modulated systemic and intestinal immunity which led to a more severe infection with S. Typhimurium (Mafamane et al., 2011).

Competitive exclusion. The ability of adhesion to intestinal cells is an important factor for probiotic strain selection. It is postulated that by adhesion of the probiotic to the intestinal epithelium other, potentially pathogenic bacteria that need adhesion for infecting the GIT, are prevented from adhesion, a term called competitive exclusion. Competitive exclusion can either be due to direct competition for specific receptors or through steric hindrance (Heo et al., 2013). Furthermore, adhesion to intestinal cells also enables the probiotic to transiently colonize and modulate immunity (Lahtinen and Ouwehand, 2009). Competitive exclusion is difficult to determine in vivo, therefore most of the studies reported incidence for competitive exclusion in in vitro models. For example Collado et al. (2007) studied two well-known human probiotic strains, Lactobacillus rhamnosus LGG and Bifidobacterium lactis Bb12, for adhesion, competitive exclusion and displacement of pathogens (S. Typhimurium, E. coli, Clostridium perfringens and C. difficile) to porcine mucus from different intestinal sections. They showed efficient adhesion for the probiotic strains as well as inhibition of pathogens and displacement of pathogens due to probiotic prevention or treatment, respectively (Collado et al., 2007). Furthermore, Jin et al. (2000) suggested steric hindrance as potential reason for inhibition of E. coli K88 adhesion to porcine intestinal mucus when probiotic Enterococcus faecium 18C23 was added simultaneously. Competitive exclusion of S. Typhimurium and E. coli O157:H7 from HeLa

Chapter 1 cells by Lactobacillus crispatus ZJ001 was reported by Chen et al. (2007), suggesting that S- layer proteins of L. crispatus ZJ001 mainly conferred the observed effects. In an in vivo trial using a competitive exclusion culture derived from a pig, 50 % less Salmonella were recorded on gut tissue compared to the control (Fedorka-Cray et al., 1999). Finally, Walsh et al. (2008) and Riboulet-Bisson et al. (2012) demonstrated colonization of the pig ileum in vivo by two probiotic strains, Lactobacillus salivarius DPC6005 and UCC118, respectively. Although not referred to directly as competitive exclusion, Saccharomyces cerevisiae boulardii was reported to bind Salmonella to its own surface, thereby likely preventing Salmonella from attachment to intestinal cells (Badia et al., 2012). The binding of Salmonella is thought to be due to type I fimbriae that attach to the mannose-rich surface of Saccharomyces cerevisiae boulardii. Type I fimbriae are shared between Salmonella and other enteropathogens, such as E. coli, and usually bind to the mannan units of glycoproteins on the host cell surface, thereby laying the foundation for possible invasion (Kogan and Kocher, 2007).

Secretion of antimicrobials and acids. The ability of probiotics to produce antimicrobial substances such as organic acids, hydrogen peroxide and bacteriocins is an important competitive advantage to directly combat potential harmful bacteria. The production of organic acids, for example, contributes to a lower luminal pH, thus counteracting pathogens susceptible to low pH values. Furthermore, it has been shown in in vitro studies that in particular the undissociated form of acetic acid inhibits Salmonella (Adams and Hall, 1988, van der Wielen et al., 2001, Wilson et al., 2003). Recently, Fukuda et al. (2011) demonstrated that acetate produced by bifidobacteria at least partly contributes to the protection of mice challenged with lethal E. coli O157:H7. Bacteriocins are ribosomally-synthesized small peptides produced by a large variety of bacteria that are active against either closely related bacteria (narrow spectrum) or unrelated genera (broad spectrum) (reviewed by Cotter et al. (2013)). They are a heterogenous group of proteins that differ in their mode of action, spectrum of activity, molecular weight, genetic origin and biochemical properties (Vesterlund, 2009). Furthermore, bacteriocin production by probiotics at the mucosal surface is regarded as an important trait for direct antagonism against other potentially harmful bacteria. Several probiotic strains have been reported to

Salmonella in pigs produce bacteriocins, for example Abp 118 and Salivaricin P by Lactobacillus salivarius UCC118 and DPC6005 (Barrett et al., 2007, Corr et al., 2007), thermophilicin B67 by Bifidobacterium thermophilum RBL67 (von Ah, 2006) and pediocin PA-1 by Pediococcus acididlactici MA18/5M (di Giancamillo et al., 2008). However, despite the known antimicrobial activities of bacteriocins it remains to be elucidated to which extent probiotic bacteriocins can exert their benefits in the gastrointestinal tract, especially in regard to their proteinaceous structure (Vesterlund, 2009).

2.5.4.3 Bifidobacterium thermophilum RBL67

Bifidobacterium thermophilum is a bifidobacterium species that is frequently encountered in the GIT of pigs and belongs to the B. boum group (Figure 1.5) (Turroni et al., 2011). Bifidobacterium thermophilum strain RBL67 was isolated from feces of a 1-week-old breast fed baby (Toure et al., 2003). RBL67 was classified as B. thermophilum by 16S rRNA sequence homology, comparative groEL gene sequence analysis and carbohydrate fermentation patterns (von Ah et al., 2007). RBL67 exhibits interesting growth characteristics compared to other bifidobacterial species. It is moderately oxygen tolerant, growing at low pH down to 4.0 and elevated temperatures of up to 47°C (von Ah et al., 2007). Furthermore, RBL67 was shown to produce a bacteriocin-like substance, thermophilicin B67 (von Ah, 2006). The crude preparation of thermophilicin B67 exerted a narrow inhibition spectrum including strains of Listeria monocytogenes, L. innocua, L. ivanovii and Lactobacillus acidophilus (von Ah, 2006). Purification of thermophilicin B67 is challenging due to high hydrophobicity, low production levels and suggested composition with 2 subunits. No information is available to date on the genetic background of thermophilicin B67 production, despite the genome of RBL67 has been completely sequenced (Jans et al., 2013). The genome of RBL67 is the first completely sequenced and assembled genome of the species B. thermophilum and consists of a single 2’291’643-bp circular molecule with a G+C content of 60.1 % (Jans et al., 2013). It encodes 1’845 coding sequences (CDS), 47 tRNA genes and 4 copies of rRNA operons (Jans et al., 2013). 50 CDS out of the 1’845 do not have homologous in any other species, although 25 of these were <100 amino acids. Furthermore, RBL67 has some interesting phenotypic attributes concerning its role in the GIT. RBL67 adheres to

Chapter 1 human intestinal cell lines (HT-29 and Caco-2) and is as such able to block Listeria monocytogenes invasion (Moroni et al., 2006). This study also showed that RBL67 itself is not invasive (Moroni et al., 2006). RBL67 reduced the severity of rotavirus-associated diarrhea in suckling mice (Gagnon, 2007) and prolonged the lifespan of Caenorhabditis elegans compared to the standard food source E. coli OP50 (Zihler, 2010). RBL67 was also reported to inhibit Salmonella Typhimurium infection in an in vitro continuous fermentation model simulating child colon combined with a human intestinal cell model (Zihler et al., 2011, Zihler et al., 2014). This latter study showed that RBL67 is competitive and active in the complex ecosystem of the GIT. The robust growth characteristics, the protective and antimicrobial characteristics of RBL67 and the highly competitive and adaptive traits in a complex intestinal ecosystem suggest RBL67 as a promising probiotic candidate.

Figure 1.5. Phylogenetic tree of the Bifidobacterium genus based on comparative sequence analysis of the 16S rRNA gene. The B. boum group is highlighted.

Salmonella in pigs

2.5.5. Prebiotics

Another way of modulating the intestinal microbial community and activity profile and combat enteric pathogens, are prebiotics. Already in the 1980’s Japanese researchers have shown that specific non-digestible oligosaccharides are selectively utilized by bifidobacteria and are able to stimulate their growth (reviewed by Crittenden and Playne (2009)). In 1995 the term prebiotic was first introduced by Gibson and Roberfroid (1995) and after several redefinitions dietary prebiotics are nowadays defined as being selectively fermented ingredients that result in speciﬁc changes in the composition and/or activity of the GI microbiota thus conferring beneﬁt(s) upon host health” (Gibson et al., 2010). Prebiotic compounds are mainly carbohydrates and oligosaccharides of different molecular structures that can be found in the normal animal diet (Gaggia et al., 2010). However, before a dietary ingredient can be classified as prebiotic at least three criteria based on scientific evidence have to be fulfilled (Gibson et al., 2004). (i) The prebiotic must resist gastric acidity as well as hydrolysis and absorption, (ii) it should be readily fermentable by the intestinal microbiota and (iii) it should selectively stimulate the growth and/or activity of beneficial bacteria (Gibson et al., 2004). To date, particularly nondigestible oligosaccharides (NDO), such as inulin, fructooligosaccharides (FOS) and galactooligosaccharides (GOS), as well as lactulose, have been shown to fulfill the three prebiotic criteria (Table 1.4), although also a host of other indigestible feed compounds (e.g. lactitol, isomaltooligosaccharides, raffinose, resistant starch) are discussed for their prebiotic potential (Gibson et al., 2004). Furthermore, although mannanoligosaccharides (MOS) were applied in a similar manner as prebiotics, they should not be regarded as a prebiotic because they do not selectively stimulate beneficial bacteria (Gaggia et al., 2010).

Chapter 1

Table 1.4. Characteristics of prebiotics that fulfill the three prebiotic criteria defined by Gibson et al. (2004). Adapted from Forssten et al. (2011).

Prebiotic Monomer Linkage DP1 inulin Glucose-Fructosen β(2→1) 10-60

FOS Glucose-Fructosen, Fructosen β(2→1) 1-9

GOS Glucose-Galactosen, Galactosen mixture of β(1→6), β(1→3), β(1→4) 1-4 lactulose Galactose-Fructose β(1→4) 2 1DP:degree of polymerization

Prebiotics act on the gut microbial equilibrium and contribute to the establishment of a “healthier” microbiota with predominance of bifidobacteria and lactobacilli (Gaggia et al., 2010). Furthermore, prebiotics can also act on the activity of the gut microbiota by increasing and shifting (i.e. towards more butyrate) SCFA production and lowering intestinal pH, both of which are mechanisms well-known for improving host health (Crittenden and Playne, 2009). Prebiotic inclusion into piglet diets is common, particularly with the aim to prevent drastic changes in microbiota composition during weaning transition and to maintain a bifidobacteria dominated flora (Flickinger et al., 2003).

2.5.5.1 Prebiotics in pig diets

Effects attributed to the inclusion of mainly inulin-type fructans (FOS, inulin), but also GOS, in pig diets include decrease in number of pigs shedding pathogens, increase in bifidobacteria along with a decrease in E. coli and Salmonella, stimulation of SCFA production, increased feed intake, average daily weight gain and growth promotion, decrease in mortality and odor metabolites (Table 1.5) (reviewed by Verdonk et al. (2005)). More specifically, after feeding inulin (40 g kg-1 feed), Patterson et al. (2010) reported stimulation of Bifidobacterium spp. and Lactobacillus spp. and a concomitant suppression of Clostridium spp. and members of Enterobacteriaceae. Accordingly, Yan et al. (2013) reported higher bacterial hindgut diversity in pigs fed 40 g kg-1 feed of inulin. Stimulation of Bifidobacterium spp. and Lactobacillus spp. was also observed by Smiricky-Tjardes et al. (2003b), and they additionally reported increase in SCFA production when including GOS (35 g kg-1 diet) to pigs diets. Concomitant stimulation of Bifidobacterium spp. and acetate concentrations was

Salmonella in pigs observed in the proximal colon of pigs feeding a novel GOS (40 g kg-1 feed) mixture (Tzortzis et al., 2005). Mikkelsen and Jensen (2004) reported a decrease in molar acetate proportions along with an increase in molar butyrate proportions with FOS and TOS at an inclusion rate of 40 g kg-1 feed, while Mountzouris et al. (2006) observed a trend to higher metabolic activity in the large intestine with FOS and trans-galactooligosaccharide (TOS) (both at 10 g kg-1 feed). However, despite the many beneficial effects attributed to prebiotic consumption, also no or even adverse effects were described. In the study of Mikkelsen and Jensen (2004) for example no effect on bifidobacteria were observed although metabolic activity was altered. Similar observations were made by Mountzouris et al. (2006), who could not demonstrate any changes in microbial populations after feeding FOS and TOS. Additionally, it was shown that also other bacteria than lactobacilli and bifidobacteria, including Salmonella, Bacteroides and Roseburia can efficiently utilize FOS and GOS as growth substrates (Martin-Pelaez et al., 2008, Scott et al., 2013b, van der Meulen et al., 2006b). Ten Bruggencate et al. (2003) even observed that FOS dose-dependently (30 g kg-1 feed and 60 g kg-1 feed) enhanced translocation of Salmonella in rats, although this awaits confirmation in pigs. Invariable outcomes due to prebiotic feeding were also reported in regard to average daily weight gain, feed intake and immunomodulation (reviewed by Verdonk et al. (2005)). These divergent results from prebiotic application in swine feed can be partly explained by the structure of the prebiotic and its inclusion rate into diets. While 10 g kg-1 feed of FOS or TOS resulted in increased digestibility of carbohydrate components (Mountzouris et al., 2006), higher inclusion rates resulted in a decreased nutrient digestibility (Smiricky-Tjardes et al., 2003a). Considering also economic aspects it was suggested that the inclusion rate in piglet diets varies from 0.1-1 % (Gibson et al., 2004). Furthermore, the type, purity and degree of polymerization of a prebiotic, the age of animals and composition of the basal diet should be considered for a careful evaluation of the prebiotic and its inclusion rate to the diet. To conclude, inconsistent results were obtained when including prebiotics to pig diets. Prebiotics can result in the beneficial modulation of the gut microbiota composition and activity, but also in no or adverse effects, for example enhancement of pathogenic bacteria, decreased nutrient digestibility and average daily weight gain.

Chapter 1

2.5.6. Synbiotics

Synbiotics are defined as “a mixture of probiotics and prebiotics that beneficially affects the host by improving the survival and implantation of live microbial dietary supplements in the gastrointestinal tract” (Gibson and Roberfroid, 1995).

2.5.6.1 Synbiotics in pig diets

Only limited research has been done with synbiotic formulations in pig diets. Bomba et al. (2002) for example observed increased fecal concentrations of total anaerobes and aerobes, bifidobacteria and lactobacilli when combining FOS with a probiotic Lactobacillus paracasei, whereas enterococci concentrations were decreased compared to the probiotic formulation and the control diet. However, due to the lack of a treatment group with only oligofructose, it is difficult to conclude a synergistic effect. In the study of Shim et al. (2005) the synbiotic formulation of a five-strain probiotic mixture and oligofructose (2 g kg-1 feed) increased colonic bifidobacteria, decreased colonic coliform concentrations and increased body weight gain, but no extra effect of the synbiotic was observed compared to oligofructose and probiotic mixture alone. Guerra-Ordaz et al. (2013) reported additive effects of Lactobacillus plantarum and lactulose (10 g kg-1 feed). With concomitant administration of FOS (20 g kg-1 feed) Böhmer et al. (2005) reported improved survival of a probiotic E. faecium strain during transit of the GIT. Furthermore, feed efficiency was positively affected when feeding Lactobacillus salivarius in combination with lactitol (3 g kg- 1) (Piva et al., 2005) and absolute numbers of CD4+, T and B lymphocytes was higher when feeding oligofructose (3 g/day) combined with L. paracasei to weaned piglets compared to the probiotic and control group (Herich et al., 2002). Krause et al. (2010) reported beneficial effects on growth performance, reduced diarrhea incidence and increased microbial diversity in the gut when feeding probiotic E. coli strains combined with raw potato starch to piglets in a challenge trial with pathogenic E. coli K88. Nonetheless, as with probiotics and prebiotics alone, also adverse effects are reported with synbiotic formulations. For example Mair et al. (2010) reported increased enterococci concentrations when feeding a synbiotic formulation to weaned pigs.

Salmonella in pigs

Table 1.5. Benefits reported from probiotics, prebiotics and synbiotics in swine. Based on reviews by Flickinger et al. (2003), Verdonk et al. (2005), Gaggia et al. (2010) and Heo et al. (2013). feed additive beneficial effects

modulation of the intestinal microbiota, immune modulation, stabilization of intestinal barrier function, reduced shedding of pathogens, reduced incidence or probiotics severity of diarrhea, improved average daily weight gain, improved feed utilization efficiency reduced diarrhea and mortality, decrease in pathogen shedding, modulation of the intestinal microbiota, higher bacterial diversity, immune stimulation, increased feed prebiotics intake and average daily weight gain, growth promotion, decrease in odor metabolites

modulation of the intestinal microbiota, reduced incidence in diarrhea, immune synbiotics modulation, increase in feed efficiency, increased average daily weight gain

In summary, probiotics, prebiotics and synbiotics have potential to be used in swine feed to maintain a healthy intestinal microbial equilibrium and thereby positively influence animal health and performance. However, inconsistent results from probiotics, prebiotics and synbiotics imply a thorough evaluation of any potential new feed additive.

Chapter 1

3. Microbial interactions in complex intestinal

ecosystems

Given the huge compositional and functional complexity of the gut microbiota, investigating effects and interactions of potential feed additives with the complex intestinal ecosystem is challenging. It is of utmost importance that an integrated approach of in vitro and in vivo models is pursued to obtain complementary results from both models, helping to distinguish the functionality of gut microbial and host processes (Payne et al., 2012). The following sections will discuss advantages and limitations of different in vivo and in vitro models to study gut microbiota.

3.1. In vivo studies

In vivo animal studies represent the most powerful strategy to investigate dietary ingredients and consequences on pig gut health and performance. Animal models allow studying simultaneously modulatory effects on the gut microbiota composition and activity as well as immune modulation and performance parameters such as feed conversion, average daily weight gain and animal welfare. However, animal research is limited by ethical constraints and costs. The three Rs (3Rs), replacement, reduction and refinement are guiding principles for animal research (Russell and Burch, 1959). In vivo animal studies with the aim to investigate dietary ingredients for swine are largely performed in the pig itself. However, for large screening studies or a first proof-of-concept study, rodents are an interesting cost- effective alternative.

3.1.1. In vivo pig studies

Animal studies allow assessing the fate of dietary ingredients in the frame of the entire host- complexity and environmental conditions, thus are common and of crucial importance. Animals used are of different age and breed, as well as housed in different environments (e.g. commercial vs. experimental facilities) (de Lange et al., 2010). The primary advent of in vivo studies using pigs raised under commercial conditions is that they provide data on efficiency of a feed additive under “real” conditions. However, the

Microbial interactions in complex intestinal ecosystems complexity and number of variables can also represent a hurdle in ascertaining the outcome of the study to the specific variables. Moreover, pigs from commercial units cannot be subjected to infection trials e.g. with pathogenic Salmonella, and are often already subject of uncontrolled pathogenic infections (e.g. Salmonella or E. coli), posing difficulties to investigate controlled interventions. Furthermore, the given complexity in commercial units usually does not allow testing of a wide range of feed additives (de Lange et al., 2010) and only analysis of the fecal sample in course of an experiment or contents at slaughter can be done. Fecal sample analysis can only partly provide conclusive results on gut microbiota composition (Looft et al., 2014) and particularly activity as measured by SCFA concentrations, because absorption of SCFA occurs largely in the proximal colon (Yen, 2001). Experimental facilities allow a highly controlled setting, which enables a range of possibilities proper to experimental facilities. The challenge here is to represent commercial conditions in research units (de Lange et al., 2010). To circumvent, for example, the restriction of fecal samples during the course of an experiment, pigs fitted with an intestinal cannula are a promising solution, enabling a site-specific evaluation of responses obtained from the use of dietary ingredients. Furthermore, specific disease models can be used, which allow the effect of a dietary ingredient to be assessed in the context of a pathogen (de Lange et al., 2010). A general limitation of in vivo pig studies, applying to commercial and experimental units, is the age and number of animals to be used. Given the changes in gut microbiota composition and activity during the lifespan of a pig (Kim et al., 2011), it is of utmost importance to evaluate an eventual feed additive in the actual target group. However, if targeting fattening pigs or even mother sows, this requires tremendous capacity of facilities and ressources.

3.1.2. Minipigs

The general advantages of using small pigs are their facilitated handling and the reduced requirements for food, space and testing substrate (Köhn, 2011). Attempts to breed pigs small in size were first undertaken in the 1940’s in the U.S. with the aim to provide an experimental animal for medical research (Dettmers et al., 1965). Different breeds of minipigs exist,

Chapter 1 including Yucatan, Göttingen and Clawn. The Göttingen minipig breed, the most widely used minipig breed used in research (also used in the frame of this thesis), was developed in 1960 at the University of Göttingen and comprises a breed proportion of 59 % Vietnamese Potbelly Pig, 33 % Minnesota Miniature and 8 % German Landrace (Köhn, 2011). Göttingen minipigs have an average adult weight of 35 kg and reach sexual maturity within 3-5 months (Köhn, 2011). An important characteristic is their predisposition to obesity, which is different from production pigs that were selected to feature low fat content and leanness (Forster et al., 2010). Furthermore, the breeding is under strict control by Ellegaard Göttingen Minipigs A/S (Köhn, 2011). Although minipigs are widely used and accepted in biomedical research and comparative biology studies of minipigs to human and pigs have been done (reviewed by Bode et al. (2010)), yet information about the distal part of the intestine and gut microbiota composition is scarce. In consideration of afore mentioned advantages, the minipig should however be envisaged as a potential alternate pig model in gut microbiota research. Furthermore, the strict control of the breeding line allows a high standardization in experimental research and fistulated animals were shown to remain under good health conditions during long experimentations (Lick et al., 2001).

3.2. In vitro models

In vitro models represent a valuable complementary tool to in vivo animal studies. In vitro gut modeling provides a useful platform to cost-effectively screen a variety of feed additives and to assess compositional and functional changes in the gut microbiota. The experimental capacity of intestinal in vitro models is virtually limitless, as experimentation is not restricted by ethical concerns. Given the large variety in gut microbiota composition, intestinal in vitro models are superior to enable reproducible experimentation under standardized conditions. In vitro systems for gut modeling range from simple batch fermentations over semi-continuous fermentation models and end with complex continuous single- or multistage systems (reviewed by Payne et al. (2012)).

Microbial interactions in complex intestinal ecosystems

3.2.1. Batch fermentation models

Batch fermentations are closed systems with simple and static fermentations that grow single or mixed bacterial cultures in nutritive medium without further medium inflow during the fermentation (Payne et al., 2012, Williams et al., 2005). The fermentation is usually limited to last 24-72 hours and together with the small amount of substrate required, as well as the convenient handling, this allows the screening of a large variety of different compounds in a relatively short time period (Williams et al., 2005). Most of the porcine in vitro models are batch fermentations and aim to evaluate the fermentation capacity of a given substrate by a fecal or collected digesta inoculum (Williams et al., 2005). The large majority of in vitro batch models to determine fermentability of a given substrate use the cumulative gas production technique that relies on the measurement of gas accumulation to calculate specific fermentation rates (reviewed by Williams et al. (2005)). Batch fermentation models using the cumulative gas production technique have for example been used to study the fermentation characteristics of wheat bran (Jha et al., 2011), sugar beet pulp (Zhu et al., 2003) and prebiotics (inulin and oligofructose) (Jonathan et al., 2012). Moreover, compositional and functional changes in the fecal inoculum resulting from fermentation were assessed by means of DGGE (Zhu et al., 2003) and analysis of bacterial metabolites (Jonathan et al., 2012). Furthermore, Martin-Pelaez et al. (2008) studied the fermentation of carbohydrates by fecal inocula and the resulting effects on Salmonella challenge in the reactors. While batch fermentations are usually easy to perform and allow a fast, cost-effective screening of a large amount of different dietary additives (Macfarlane and Macfarlane, 2007), they have also important limitations. Most of the batch fermentations are uncontrolled (e.g. in pH) and due to limited substrate could lead to a selection of non-representative bacterial populations that may distort fermentation profiles (Macfarlane and Macfarlane, 2007, Williams et al., 2005). Furthermore, the inoculation density largely defines growth characteristics and the short duration does not allow generating a steady-state condition in batch fermentations (Payne et al., 2012).

Chapter 1

3.2.2. Semi-continuous fermentation models

Semi-continuous fermentation models are characterized by adding fresh nutritive medium and remove spent culture at certain time intervals (Williams et al., 2005). This prevents substrate from complete depletion and fermentation products from accumulation. The Cositec model is a semi-continuous colon simulation technique for pig hindgut fermentation and was originally designed based on the rumen simulation technique (Stuck et al., 1995). The Cositec was run with caecal and proximal colon inocula to investigate the effect of clindamycin (Stuck et al., 1995), sugar beet pulp (von Heimendahl et al., 2010) or live yeast (Pinloche et al., 2012) on microbial hindgut metabolism.

3.2.3. Continuous fermentation models

Continuous fermentation models represent the most sophisticated systems in gut fermentation modeling. They allow the precise adaptation of various parameters, such as pH, dilution rate, temperature and retention time, which is supportive to generate and maintain optimal growth conditions (Williams et al., 2005). Due to the high freedom in parameter definition virtually all gastrointestinal sections can be modeled depending on the study objective. Given the constant influx of fresh substrate and outflow of fermented effluent, continuous fermentation systems are eligible to simulate the dynamic nature of the upper gastrointestinal tract and further enable the mixed bacterial cultures to reach pseudo steady state conditions. The establishment of a pseudo steady state facilitates reproducibility and the investigation of metabolic and ecological studies over long fermentation periods (reviewed by Payne et al., (2012)). Whereas in human gut microbiota modeling, different continuous fermentation models have been described and validated (PolyFermS (Zihler Berner et al., 2013), SHIME (Alander et al., 1999), TIM-2 (van der Werf and Venema, 2001)), only few porcine continuous fermentation models exist. Ricca et al. (2010) for example investigated changes in fecal bacterial communities over 14 days simulating the proximal large intestine of swine, whereas Messens et al. (2010) studied the effect of medium chain fatty acids on cecal bacterial populations in general and specifically on Salmonella. Both models used free cell suspensions (feces (Ricca et al., 2010) or cecum (Messens et al., 2010)) to inoculate the

Microbial interactions in complex intestinal ecosystems fermentation system. This, however, can cause important limitations, such as limited stability and washout of less dominant or slow growing bacterial species (Payne et al., 2012). To overcome drawbacks associated with free cell cultures, a procedure for fecal microbiota immobilization in polysaccharide gel beads was developed (Cinquin et al., 2004). Fecal microbiota immobilization demonstrated high cell density and allowed the maintenance of a high bacterial diversity and stability over extended fermentation periods (up to 71 days (Le Blay et al., 2010, Payne et al., 2012). Furthermore, the immobilization of feces in the gel matrix is able to reproduce the planktonic (free-cell) and sessile (biofilm-associated) states of bacterial populations in the colon (Payne et al., 2012). As example, fermentation models using immobilized fecal microbiota have been successfully applied to study preventive and therapeutic use of Bifidobacterium thermophilum RBL67 against Salmonella infection (Zihler et al., 2014), the impact of different nutrient load (Payne et al., 2012) and different iron conditions (Dostal et al., 2013) on gut microbiota composition and functionality. A remaining challenge for in vitro gut modeling is the lack of true replication (Payne et al., 2012). Thus, Zihler Berner et al. (2013) developed PolyFermS, a second generation in vitro continuous fermentation model that allows the simultaneous testing of different environmental factors on the same inoculated microbiota in parallel test reactors. However, a major drawback of the PolyFermS was the high pH dependency in the test reactors to reach a metabolic balance for the child microbiota, likely provoked by the two-stage model design for upper and lower section of the child proximal colon (Zihler Berner et al., 2013). Taken together, in vitro gut modeling is a prominent strategy to investigate microbial interactions and metabolic changes of the gut microbiota, without host complexity. Furthermore, the large flexibility of the systems allows designing virtually all gastrointestinal sections, being it single- or multistage systems. The host-effect however, should not be ignored, making it inevitable that combined in vitro and in vivo approaches are emphasized for proper evaluation of environmental impacts (e.g. dietary additives) on gut microbiota composition and functionality.

Chapter 1

4. Background and objectives of the thesis

The porcine gut microbiota comprises a vast amount and diversity of bacteria, encompassing tremendous functional capacity and residing in a mutual relationship with the host. Gut health substantially impacts overall animal health and thereby directly influences animal welfare and performance in swine livestock production. Enteropathogens, such as Salmonella, can impair animal performance and indeed, Salmonella prevalence is high, affecting about one third of the swine breeding and production holdings in the European Union. Moreover, persistent carriage of Salmonella in pigs has been reported, which not only can have detrimental effects on animal performance, but also poses a risk for consumers via transmission of the pathogen through the food chain. Previously, in-feed antibiotics were routinely applied to support overall animal welfare, protect from pathogens and enhance growth. However, due to increased prevalence of antibiotic resistant bacteria, in-feed antibiotics were banned in Switzerland and the EU and recently, also the U.S. has released a guideline for restricted use of in-feed antibiotics on a voluntary basis. Consequently, the ban of in-feed antibiotics has driven research to find potential alternatives that can beneficially impact gut health and therefore sustain animal performance and welfare. Amongst the different alternatives being discussed, nutritional strategies such as probiotics and prebiotics are of particular interest due to their well-known modulatory effects on gut microbiota composition and activity, thereby potentially counteracting enteric pathogens. However, defining and investigating efficacy of potential probiotics and prebiotics is challenging due to ethical constraints and costs related to in vivo studies, and scarcity of appropriate porcine in vitro models simulating the complex intestinal gut microbiota. Yet, previous studies have identified Bifidobacterium thermophilum RBL67, originally isolated from infant feces, as a promising probiotic candidate. B. thermophilum are commensals of the animal tract, including pigs, thus RBL67 could be equally considered for application in swine.

Objectives

4.1. General objective

The general objective was to investigate B. thermophilum RBL67 and its anti-Salmonella properties alone or combined with prebiotics for potential use in swine livestock production.

4.2. Specific objectives

The investigation included the anti-Salmonella activity of B. thermophilum RBL67 alone and combined with prebiotics assessed in an in vitro fermentation model, the impact of RBL67 on Salmonella gene expression under simplified conditions using RNA-sequencing, and a first in vivo study using minipigs to study the effects of RBL67 alone and combined with FOS on gut microbiota composition and activity. Accordingly, the specific objectives were defined as follows:

1. Development and validation of a novel in vitro continuous fermentation model of the PolyFermS platform, simulating the swine proximal colon

2. Investigating the effects of RBL67, prebiotics and combinations, on Salmonella and gut microbiota composition and activity in the novel in vitro model

3. Elucidating the impact of RBL67 on Salmonella gene expression under co-culture conditions using RNA-sequencing to study the transcriptome response

4. Elucidating the effects of RBL67 and combined with FOS on swine gut microbiota composition and colonization of RBL67 in an in vivo minipig model

Chapter 2

In vitro continuous fermentation model (PolyFermS) of the swine proximal colon for simultaneous testing on the same gut microbiota

Sabine A. Tanner, Annina Zihler Berner, Eugenia Rigozzi, Franck Grattepanche, Christophe

Chassard, Christophe Lacroix

Laboratory of Food Biotechnology, Institute of Food, Nutrition and Health, ETH Zurich,

Zurich, Switzerland.

Published in PLoS One (2014) 9(4): e94123.

Chapter 2

Abstract

In vitro gut modeling provides a useful platform for a fast and reproducible assessment of treatment-related changes. Currently, pig intestinal fermentation models are mainly batch models with important inherent limitations. In this study we developed a novel in vitro continuous fermentation model, mimicking the porcine proximal colon, which we validated during 54 days of fermentation. This model, based on our recent PolyFermS design, allows comparing different treatment effects on the same microbiota. It is composed of a first-stage inoculum reactor seeded with immobilized fecal swine microbiota and used to constantly inoculate (10 % v/v) five second-stage reactors, with all reactors fed with fresh nutritive chyme medium and set to mimic the swine proximal colon. Reactor effluents were analyzed for metabolite concentrations and bacterial composition by HPLC and quantitative PCR, and microbial diversity was assessed by 454 pyrosequencing. The novel PolyFermS featured stable microbial composition, diversity and metabolite production, consistent with bacterial activity reported for swine proximal colon in vivo. The constant inoculation provided by the inoculum reactor generated reproducible microbial ecosystems in all second-stage reactors, allowing the simultaneous investigation and direct comparison of different treatments on the same porcine gut microbiota. Our data demonstrate the unique features of this novel PolyFermS design for the swine proximal colon. The model provides a tool for efficient, reproducible and cost-effective screening of environmental factors, such as dietary additives, on pig colonic fermentation.

Introduction

The pig gut microbiome is a complex ecosystem, dominated by members of the phyla Firmicutes and Bacteroidetes (Lamendella et al., 2011). The vast quantity and diversity of the gut bacterial community provides the host with a large set of metabolic functions and is considered to play a key role in host health and disease (Macfarlane and Macfarlane, 2007). Diet is a principal factor shaping the gut bacterial composition and functionality (Russell et al., 2013), thereby also impacting overall animal health. This in turn largely determines productivity and efficiency of swine livestock production. Given the complexity of this interplay, it is of paramount importance to precisely evaluate the effects of specific feed ingredients or additives on composition and functionality of the gut microbiome and further elucidate the role and function of the gut bacterial ecosystem in animal health to improve productivity and efficiency of swine livestock production. It is obvious that animal testing is one of the most prominent strategies to predict effectiveness and impact of dietary additives on the gut microbiota, but ethical concerns and costs can restrict these applications (Macfarlane and Macfarlane, 2007). Intestinal in vitro models are able to partly evade these restrictions by enabling reproducible experimentation under standardized conditions, and more importantly, giving the yet host-uncoupled opportunity to investigate the complexity of gut microbiomes and the functional relatedness of specific bacterial species (Payne et al., 2012). To date, most porcine in vitro models are using simple batch cultures with the aim to examine the fermentation capacity of intestinal ecosystems on a given substrate, using the cumulative gas production technique (Jha et al., 2011, Jonathan et al., 2012, Lin et al., 2011, Zhu et al., 2003). However, batch fermentations are limited in terms of experimental duration and the amount of substrate supply to avoid negative feedback mechanisms (Williams et al., 2005). Batch cultures are also highly dependent on the inoculation density as it directly impacts microbial growth in these closed systems (Payne et al., 2012). In contrast, continuous culture systems are superior in modeling the dynamic nature of the gastrointestinal tract, allowing the adaptation of various parameters, including dilution rate, retention time, pH and temperature, to meet and maintain optimal growth conditions (Williams et al., 2005). Substrate

Chapter 2 replenishment and toxic waste removal, further, is continuous and facilitates studies on the modulation of microbial composition and activity (Payne et al., 2012). So far, only few porcine semi-continuous or continuous intestinal fermentation models inoculated with feces or cecal content have been described, focusing on changes in gut bacterial communities tested over limited fermentation periods (Ricca et al., 2010), the inhibition of Salmonella by medium-chain fatty acids (Messens et al., 2010) or the effect of live yeast on fermentation parameters (Pinloche et al., 2012). Long-term continuous in vitro models of the swine gut are still lacking, likely due to the difficulty of generating highly stable free-cell suspension fermentations, inoculated with fecal extract, over a long experimental period. To overcome possible drawbacks associated with free-cell suspension cultures, such as limited stability and washout of less dominant or slow growing bacterial species, fecal microbiota was immobilized in polysaccharide gel beads to set up stable continuous intestinal fermentation models (Cinquin et al., 2004, Cinquin et al., 2006). The major benefits shown for immobilized fecal microbiota models include high cell density, maintenance of bacterial diversity and high stability over extended fermentation periods, tested for up to 71 days (Le Blay et al., 2010, Payne et al., 2012). Besides stability requirements, reproducibility and parallel testing of treatments with the same gut microbiota are of major importance for gut in vitro research, but difficult to apply with classical continuous models inoculated with fecal microbiota. We have recently set up and validated a novel PolyFermS model of the child proximal colon in a two-stage system for parallel testing of treatments on the same microbiota (Zihler Berner et al., 2013). In this model, a first-stage reactor containing immobilized fecal microbiota and operated with conditions mimicking the first section of the proximal colon was used to constantly inoculate up to four second-stage reactors, operated with conditions mimicking the remaining proximal colon. Our data demonstrated that this PolyFermS model produced reproducible and stable intestinal microbiota over a 38 days test period (Zihler Berner et al., 2013). The microbial diversity of reactor effluents tested with the HITChip phylogenetic array was comparable to the feces of the healthy donor, whereas a high response to pH was demonstrated. However, this study used a two-stage model for upper and lower sections of the proximal colon, shown to be highly dependent on pH in the first

Introduction reactor, which had to be arbitrary set at 5.5 to reach a metabolic balance for the child microbiota. In the present study we aimed to enhance the original design of the PolyFermS model validated with child microbiota (Zihler Berner et al., 2013), and to adapt to a new model of the swine proximal colon. The novel PolyFermS model consisted of two stages; a first stage for the inoculum reactor (IR) seeded with immobilized swine fecal microbiota and used to inoculate at 10 % (v/v) five parallel second-stage reactors, which were also fed with 90 % fresh chyme medium. Each reactor was operated under identical conditions, selected to mimic swine proximal colon, and allowing the parallel testing of multiple treatments on the same gut microbiota. The stability of the complex intestinal microbiota in the multiple reactors was monitored over 54 days of fermentation by analyzing its composition (qPCR and 454 pyrosequencing) and metabolic activity (HPLC).

Chapter 2

Materials and Methods

Ethical statement

No specific permits were obtained for the collection of the fecal sample. The animal was not harmed during fecal sample collection and oral consent for sample collection was obtained from the owner of the farm.

Feces collection and immobilization

Feces from a healthy 5 month old sow (80 kg), raised under farming conditions and not subjected to any antibiotic treatment for the last 3 months, were collected in a sterile 50 mL Falcon tube. Anaerobiosis was maintained using anaerobic gas pack systems (Oxoid AnaeroGen TM, Oxoid AG, Basel Switzerland) during transport to the laboratory and until immobilization was performed. The entire immobilization procedure was carried out under anaerobic conditions (anaerobic chamber; Coy Laboratories, Ann Arbor, MI, USA). Briefly, a 20 % (w/v) suspension of feces in pre-reduced peptone water (0.1 %, pH 7) was prepared, homogenized and further immobilized in a polymer solution consisting of gellan gum (2.5 %, w/v), xanthan (0.25 %, w/v) and sodium citrate (0.2 %, w/v) for production of 1-2 mm diameter gel beads using a two phase dispersion process as described previously (Cinquin et al., 2004; Zihler Berner et al., 2013).

Nutritive medium

The nutritive medium described by Macfarlane et al. (1998) was modified for its carbohydrate and protein concentration to more closely mimic the ileal chyme of a swine (Table S2.1), using a similar approach as described previously (Le Blay et al., 2010). For calculation of ingredient concentrations, a standard cornstarch based diet with corn (641g kg- 1) as main carbohydrate and soybean meal (331 g kg-1) as main N-source was used (Nyannor et al., 2007). Digestibility indices of 97 % for cornstarch (Lee et al., 2011) and 82.5 % for soybean meal (Wang et al., 2011) were applied while considering a 2 kg/day feed intake per pig, resulting in a cornstarch:N-compound (soybean meal) ratio of 25:75. The amount of cornstarch and soy peptone supplied daily to the model was calculated by applying a scale factor of 0.09 to account for the actual volume of the proximal colon in vivo (approx. 2.9 L (Kararli, 1995) compared to the proximal reactor volume of the model (260 mL). The final

Materials and Methods concentration of the two compounds in the chyme medium was estimated for a mean retention time of 9 hours in the reactors, giving a daily chyme medium supply per reactor of 693 mL. To avoid excess of nitrogen compounds, the soy peptone concentration was fixed to 13 g L-1 resulting in a carbohydrate (cornstarch) concentration of 4.3 g L-1. Yeast extract and mucin, mimicking the contribution of endogenous secretion (Cinquin et al., 2004), and the non-starch polysaccharides (pectin, xylan, arabinogalactan and guar gum) were excluded from this calculation. A volume of 0.5 mL L-1 of a filter-sterilized (Minisart pore size 0.2 µm, Sartorius, VWR International AG, Dietikon, Switzerland) vitamin solution described by Michel et al. (1998) was added to the sterilized medium (20 min, 120°C). All components of the nutritive medium were purchased from Sigma-Aldrich Chemie (Buchs, Switzerland), except for soy peptone (Labo-Life Sàrl, Pully, Switzerland), yeast extract (I2CNS GmbH, Urdorf, Switzerland) and

KH2PO4 (VWR International AG). Experimental set-up of the PolyFermS model

The continuous fermentation was carried out for 54 days using a two-stage design with a total of six reactors (Sixfors, Ismatec, Glattbrugg, Switzerland) (Figure 2.1). Each reactor was aimed to simulate conditions of the swine proximal colon fermentation. The inoculum reactor (234 mL) was seeded with 30 % (v/v) swine fecal beads and connected via a peristaltic pump (Masterflex L/S, Fisher Scientific SA, Wohlen, Switzerland) to one control reactor (CR) and four test reactors (TR1-4) operated in parallel. IR was supplied with 100 % fresh nutritive medium (26 mL h-1) whereas the second-stage reactors CR and TR1-4 were continuously supplied with 90 % (26 mL h-1) fresh nutritive medium and 10 % (2.9 mL h-1) effluent from IR for continuous inoculation. The remaining effluent (50 %) from IR was discarded. The inoculation rate of CR and TR1-4 was accurately controlled using an in-house designed distributor device equipped with valves regulated chronometrically.

Chapter 2

Figure 2.1. Experimental reactor set-up and time schedule of the swine PolyFermS model. IR: inoculum reactor, containing immobilized swine feces (30 % v/v); CR: control reactor; TR1-TR4: test reactors 1-4; M: fresh nutritive medium supply; S: effluent sampling; F: flow rate; Stab: stabilization period; T: treatment period; W: wash period.

Fermentation procedure

The PolyFermS model was run under conditions of the swine proximal colon with a controlled constant pH of 6.0 through addition of 2.5 M NaOH. The mean retention time was fixed at 9 hours, while temperature was maintained at 38°C (being in the middle of the range previously applied in porcine in vitro models) (Bindelle et al., 2007, Lin et al., 2011, Messens et al., 2010, Ricca et al., 2010, Sakata et al., 2003, von Heimendahl et al., 2010). Anaerobic conditions were ensured by constantly flushing the headspace of the reactors with

CO2 and constant stirring was performed at 120 rpm. During the first 72 hours, IR was operated in batch mode to colonize the fecal beads and the nutritive medium was replaced by fresh medium every 12 hours. After colonization, continuous operation (26 mL h-1) in IR was started, followed by a stabilization period of 5 days before connection to CR and TR1-4. The entire two-stage system was then stabilized for another 5 days.

Materials and Methods

The 54 days continuous fermentation was split into stabilization, treatment and washing periods (Figure 2.1). IR and CR were operated with constant conditions to assess the temporal stability of the system and were not subjected to any manipulation during the entire fermentation. In addition, CR served as a control reactor for TR1-4 that were subjected to different parallel treatment periods (data not shown). Between two treatment periods, TR1-4 were subjected to a washing procedure with 10 % chlorine to kill microbes and remove any historical effect of the previous periods. Briefly, TR1-4 were disconnected from IR, the entire medium was removed and reactors were filled with 10 % freshly prepared chlorine solution. After stirring for one hour, the reactors were rinsed twice by adding sterile bidistilled water and stirring for another hour. After complete removal of water and chlorine residues, reactors were filled with sterile fresh nutritive medium and reconnected to IR. Thereafter, the system was allowed to stabilize for 3 days until reaching steady state before starting the next treatment period. Effluent samples of all reactors were collected daily. HPLC samples were processed immediately whereas samples for DNA extraction were stored at -80°C.

HPLC analysis for metabolite determination

Short-chain fatty acids (SCFA; acetate, propionate, butyrate, valerate, formate, iso-butyrate and iso-valerate) as well as lactate concentrations in fermentation effluent samples from all reactors were determined by HPLC analysis (Thermo Fisher Scientific Inc. Accela, Wohlen, Switzerland). Briefly, effluent samples were centrifuged (14 000 g) for 10 min at 4°C. The pellet was used for DNA extraction while the supernatant was diluted 1:10 with ultrapure water and filtered directly into vials through a 4 mm HPLC filter with a 0.45 µm nylon membrane (Infochroma AG, Zug, Switzerland). The analysis was run at a flow rate of 0.4 mL min-1 using an Aminex HPX-87H column (Bio-Rad Laboratories AG, Reinach, Switzerland) and 10 mM H2SO4 as eluent. Mean metabolite concentrations were calculated from duplicate analyses and expressed in mM.

DNA extraction and qPCR analyses

Genomic DNA was extracted from effluent samples using the FastDNA SPIN Kit for soil (MP Biomedicals, Illkirch, France). DNA extracts were subjected to quantitative real-time PCR (qPCR) for enumeration of specific bacterial target groups comprising total bacteria,

Chapter 2

Bifidobacterium spp., Bacteroides-Prevotella group, Enterobacteriaceae, Lactobacillus/Pediococcus/Leuconostoc spp. and Clostridium Cluster IV (Table 2.1). In addition, the Succinivibrio dextrinosolvens group was quantified to verify results obtained by 454 pyrosequencing. Standard curves for each target group were prepared as described previously (Dostal et al., 2013). All assays, were performed using the 2 x SYBR Green PCR Master Mix (Applied Biosystems, Zug, Switzerland) in a 25 µl volume and an ABI PRISM 7500-PCR sequence detection system (Applied Biosystems).

454 pyrosequencing

Selected samples were analyzed by 454 pyrosequencing for their microbial 16S rRNA based composition profile. IR and CR (days 19/20, 30/31, 42/43 and 52/53) samples were selected to assess bacterial diversity and temporal stability of the model as well as to compare the microbial composition to the fecal inoculum. To demonstrate the re-establishment of the microbiota after washing, day 25 (last day of 3rd stabilization period) was chosen as a representative day for all reactors. For IR and CR stability samples, effluent from 2 consecutive days were pooled at a ratio of 1:1. DNA was extracted with the FastDNA Spin Kit for soil (MP Biomedicals) and sent to DNAVision SA (Charleroi, Belgium) for 454 pyrosequencing analysis and subsequent taxonomic assignment of 16S rRNA gene reads. 454 pyrosequencing was performed using a 454 Life Science system combined with Titanium Chemistry (Roche) as described previously (Jost et al., 2012). The complete 454 pyrosequencing dataset has been deposited to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number SRP034540.

Materials and Methods

Table 2.1. Primers for detection of specific bacterial groups by qPCR.

Target Primer Sequence 5'-3' Reference Total 16S rRNA genes Eub338F ACT CCT ACG GGA GGC AGC AG Guo et al. (2008b) Eub518R ATT ACC GCG GCT GCT GG Bacteroides-Prevotella Bac303F GAA GGT CCC CCA CAT TG Ramirez-Farias et al. group Bfr-Fmrev CGC KAC TTG GCT GGT TCA G (2009) Lactobacillus/ F_Lacto 05 AGC AGT AGG GAA TCT TCC A Furet et al. (2009) Pediococcus/ R_Lacto 04 CGC CAC TGG TGT TCY TCC ATA TA Leuconostoc spp. Enterobacteriaceae Eco1457F CAT TGA CGT TAC CCG CAG AAG AAG C Bartosch et al. (2004) Eco1652R CTC TAC GAG ACT CAA GCT TGC Bifidobacterium spp. xfp_fw ATC TTC GGA CCB GAY GAG AC Cleusix et al. (2010) xfp_rv CGA TVA CGT GVA CGA AGG AC Clostridium Cluster IV Clep866mF TTA ACA CAA TAA GTW ATC CAC CTG G Ramirez-Farias et Clep1240mR ACC TTC CTC CGT TTT GTC AAC al.(2009) S. dextrinosolvens SucDex1F CGT CAG CTC GTG TCG TGA GA Stevenson and group SucDex1R CCC GCT GGC AAC AAA GG Weimer (2007)

Statistical analysis

All statistical analyses were performed using PASW Statistics for Windows version 18.0 (SPSS Inc., Chicago). qPCR data were log10-transformed and expressed as means ± SD of the last three days of each stabilization period. To assess reproducibility of the microbial composition in CR and TR1-4 prior to a treatment period qPCR data were subjected to the non-parametric Mann-Whitney U test with exact significance and a p-value < 0.05 was considered significant. The same test was used to verify results obtained from 454 pyrosequencing for Succinivibrionaceae. Comparisons were made between qPCR data from before (d11-37) and after (d38-54) appearance in CR. To assess temporal metabolite stability in IR and CR, linear regression of total SCFA, acetate, butyrate, propionate, iso-valerate, valerate and iso-butyrate concentrations versus time were calculated over the time period d11-54 and difference from 0 of slope coefficients was tested using the t-test (P<0.05).

Chapter 2

Results

Microbial activity by HPLC

To assess the microbial activity and temporal stability of the novel PolyFermS model for the pig proximal colon during 54 days continuous fermentation, daily effluents for each reactor were analyzed using HPLC. IR and CR were operated with constant conditions and not subjected to any treatment or washing period during the entire fermentation. Therefore, the microbial activity in these two reactors was used to assess the metabolic stability of the two- stage model. After an initial stabilization period of 10 days for reaching pseudo-steady state conditions, high and stable metabolic activity was measured in IR and CR throughout the fermentation (Figure 2.2). In IR the mean total SCFA concentration (d11-54) was 180.1 ± 5.8 mM with mean values for acetate, propionate and butyrate of 102.3 ± 7.3 mM, 45.1 ± 4.4 mM and 20.4 ± 1.2 mM, respectively. Iso-valerate was produced at 5.9 ± 0.5 mM. In contrast, valerate and iso-butyrate were not detected until day 13 but were measured at concentrations of 4.5 ± 1.3 mM and 1.9 ± 0.6 mM over d13-54, respectively. Lactate and formate, were not detected throughout the fermentation. For CR similar mean total SCFA (174.4 ± 7.6 mM) as well as main SCFA acetate (92.9 ± 7.6 mM), propionate (47.1 ± 3.8 mM) and butyrate (23.1 ± 2.4 mM) concentrations were detected. Detection of minor metabolites, iso-valerate, valerate and iso-butyrate, was delayed for 9, 14 and 13 days in CR, respectively, resulting in mean values of 5.7 ± 1.2 mM (d11-54), 4.2 ± 1.6 mM (d14-54) and 1.8 ± 0.9 mM (d13-54).

Results

Figure 2.2. Daily main SCFA concentrations in fermentation effluents of IR and CR measured by HPLC. Initial stabilization: stabilization period in continuous mode to reach pseudo steady-state; closed symbol: IR; open symbol: CR; (▲, Δ) total SCFA, (●, ○) acetate, (♦, ◊) propionate, and (■, □) butyrate.

To test the effect of culture time on metabolite concentrations, linear regressions of daily concentration data (d11-54) versus time were calculated. Highly significant time effects (P<0.001) were determined for most measured metabolites in IR and CR, except for the total SCFA concentration (Table 2.2). Acetate concentrations in IR and CR (P<0.001) and butyrate in CR (P<0.05) decreased significantly over time whereas propionate, butyrate in IR, iso-valerate, valerate and iso-butyrate showed significant (P<0.001) concentration increases. Corresponding slope coefficients remained small (-0.493 mM/day – 0.265 mM/day) indicating a moderate time effect on tested metabolites.

Chapter 2

Table 2.2. Effect of culture time on metabolite concentrations analyzed by linear regression analysis.

Inoculum reactor (IR) Control reactor (CR)

Ba SE B R2 Ba SE B R2 total SCFA -0.030 0.069 0.005 0.092 0.090 0.024 acetate -0.493** 0.043 0.760 -0.365** 0.072 0.376 propionate 0.265** 0.034 0.597 0.168** 0.038 0.317 butyrate 0.050** 0.012 0.292 -0.090* 0.025 0.238 iso-valerate 0.031** 0.004 0.544 0.063** 0.010 0.486 valerate 0.080** 0.009 0.637 0.093** 0.013 0.542 iso-butyrate 0.037** 0.005 0.593 0.047** 0.007 0.509 a slope coefficients significantly different from 0 are denoted by significance level:* P<0.05 ** P<0.001 B: unstandardized (slope) coefficient (mM/day); SE B: Standard error of B; R2: coefficient of determination

Microbial composition by qPCR

Microbial composition of daily reactor effluents was assessed by analyzing the 16S rRNA gene copy numbers of bacterial target groups using qPCR. IR and CR data were used to assess the time stability of the model.

-1 Total 16S rRNA gene copy numbers mL effluent were approximately 1.6 log10 units higher in the fecal inoculum compared to the reactor effluents from IR and CR (Table 2.3). The fecal inoculum was dominated by the Bacteroides-Prevotella group and the Lactobacillus/Pediococcus/Leuconostoc spp. group, followed by Clostridium Cluster IV, Enterobacteriaceae and Bifidobacterium spp. In reactor effluents from IR and CR, stable copy numbers of the targeted bacterial groups were recorded during continuous fermentation (d11-54) after an initial stabilization as indicated by low standard deviations of the mean. Similar to the fecal inoculum, Bacteroides-Prevotella group was predominant in IR and CR effluents while Bifidobacterium spp. was the least abundant group. Enterobacteriaceae were approximately 1.8 log10 higher in reactor effluents compared to the fecal inoculum and displayed equally high copy numbers as for the Clostridium Cluster IV. In contrast, the

Lactobacillus/Pediococcus/Leuconostoc spp. population was detected at ca. 3 log10 lower copy numbers in reactor effluents during the stabilized period (d11-54) compared to the fecal inoculum.

Results

-1 Table 2.3. Mean concentration (log10 copy numbers mL effluent) of specific bacterial groups measured by qPCR in the fecal inoculum and effluent samples from inoculum reactor (IR) and control reactor (CR) during the stabilized period (d11-54).

fecal inoculum IR CR

total 16S rRNA gene 12.2 10.6 ± 0.2 10.6 ± 0.2

Bifidobacterium spp. 7.8 6.6 ± 0.7 6.3 ± 0.5

Bacteroides-Prevotella group 11.3 10.2 ± 0.2 10.2 ± 0.2

Enterobacteriaceae 8.1 9.9 ± 0.2 9.8 ± 0.2

Lactobacillus/ Pediococcus/ Leuconostoc spp. 11.2 8.3 ± 0.3 8.0 ± 0.3

Clostridium Cluster IV 10.7 9.9 ± 0.2 9.9 ± 0.2

Mean values ± SD for IR and CR were calculated from daily values during the experimental stabilized period corresponding to d11-54.

Microbial diversity by 454 pyrosequencing

Microbial diversity and composition analyses of the fecal inoculum and selected samples from IR and CR (days 19/20, 30/31, 42/43, 52/53) were performed by 454 pyrosequencing. Sequences were aligned with the RDP classifier v 2.1 using a confidence cutoff level of 80 %. After quality check the number of reads per sample was decreased from 11661 ± 2758 to 7762 ± 4585 and mean read length per sample was 256 ± 2 base pairs (bp). Relative abundance detected by 454 pyrosequencing revealed the predominance of three major phyla in all samples tested (Figure S2.1A). The fecal inoculum was predominated by the phylum Firmicutes whereas the Bacteroidetes phylum was most abundant in reactor effluents, followed by the Firmicutes and Proteobacteria phyla, except for samples 19/20 in IR and 30/31 in CR where the two phyla Bacteroidetes and Firmicutes were almost equally abundant. The phylum Proteobacteria was increasing from less than 1 % relative abundance in the fecal inoculum to up to 29 % in sample 52/53 from CR. At family level (Figure 2.3), the highest abundance in IR was recorded for Prevotellaceae (33-66 %), Lachnospiraceae (7- 17 %), Ruminococcaceae (5-14 %) and Enterobacteriaceae (4-8 %), with unclassified reads accounting for 10-21 % of total reads. The same pattern of relative abundances on family

Chapter 2 level was observed in CR, with Prevotellaceae (38-51 %), Lachnospiraceae (13-18 %), Ruminococcaceae (5-13 %) and Enterobacteriaceae (5-9 %) being the most abundant families in all samples and unclassified bacteria accounting for 10-20 % of the reads. The detected families were composed of the predominant genera Prevotella, Escherichia/Shigella, Ruminococcus, Roseburia, Blautia, Bacteroides and Oscillibacter (Figure S2.1B). Remarkably, the family Succinivibrionaceae increased from 0.1 % on day 19/20 in IR to up to 8 % at the end of the fermentation (52/53). In CR the family Succinivibrionaceae even accounted for as much as 21 % of total reads on day 52/53. In contrast, reads assigned to the family Ruminococcaceae decreased in CR from 10 % on day 42/43 to 4 % on day 52/53. This observation was confirmed by qPCR with specific primers targeting the 16S rRNA gene of S. dextrinosolvens. Similarly, the 16S rRNA gene copy numbers for the S. dextrinosolvens

-1 group in CR significantly increased (P< 0.001) from 7.47 ± 0.2 log10 copies mL (mean ±

-1 SD; d11-37) to 9.2 ± 0.3 log10 copies mL (mean ± SD; d38-54). In the fecal inoculum, the family Clostridiaceae (26 %) was most abundant and predominantly represented by the genus Clostridium, followed by Ruminococcaceae and Lactobacillaceae (both 13 %) and Lachnospiraceae (8 %). The unclassified bacteria accounted for 25 % of total reads. Enterobacteriaceae were not detected in the fecal inoculum.

Results

Figure 2.3. Microbial composition in the fecal inoculum (FI), IR and CR measured by 454 pyrosequencing. The relative abundance on family level is shown. Values < 1 % are summarized in the group "others".

Reproducibility of microbial composition and activity

The novel PolyFermS model was designed to allow reproducible testing of different treatments in parallel test reactors compared to a control reactor inoculated with the same microbiota. Consecutive multiple treatment periods could be tested by subjecting the test reactors to a washing procedure using 10 % chlorine and followed by a stabilization period to regenerate comparable microbiota in CR and all test reactors (Figure 2.1). Therefore, to test reproducibility of control and test reactors after a washing procedure, bacterial composition (qPCR data) and activity (HPLC data) in TR1-4 after three day re-stabilization were compared with data measured in CR. No significant differences of bacterial composition of CR and TR1-4 (P<0.05) were detected using the Mann-Whitney U test (Table S2.2). Moreover the analysis of effluent samples of CR and TR1-4 by 454 pyrosequencing on day 25 (last day of 3rd stabilization period) showed similar microbiota composition (Figure S2.2). Metabolite concentrations in TR1-4 progressively re-established at similar levels to that in CR, reaching comparable values three days after restarting the system (Figures 2.4 and S2.3). High and stable bacterial concentrations were measured already after one day re-stabilization

Chapter 2 as indicated by total 16S rRNA gene copy numbers in TR1-4 over three days stabilization periods.

Figure 2.4. Main metabolites in CR and TR1-4 on the last day of each stabilization period. (◊) total SCFA; (○) acetate; (□) propionate; (Δ) butyrate; (―) CR; (―) TR1; (―) TR2; (―) TR3; (―) TR4.

Discussion

The large field of research to evaluate the impact of feed ingredients and additives on the gastrointestinal health and development of pigs (Pluske, 2013) emphasizes the need for potent porcine in vitro fermentation models to study feed-related impacts on gut microbial community and functionality in a fast and reproducible setting. In this study we tested a novel porcine in vitro fermentation model for the proximal colon, advanced from the PolyFermS model presented by Zihler Berner et al. (2013). The porcine in vitro model was designed to generate self-contained parallel fermentations in each of the reactors, simulating the proximal colon, which is the primary site of fermentation in the gastrointestinal tract (Guarner and Malagelada, 2003). The first PolyFermS model validated by Zihler Berner et al. (2013) included an inoculum reactor operated under conditions of the first section of the proximal colon which was used to continuously feed control and test reactors with fermented effluent, operated with conditions selected to mimic the distal section of the proximal colon. Therefore in this model a combination of two stages, IR and CR or TR, was used to model the proximal colon. In contrast, test reactors in the porcine PolyFermS tested in this study were operated in conditions of the proximal colon and constantly inoculated with only 10 % fecal microbiota produced in IR, while 90 % of the feed was fresh medium to simulate the dynamic process of chyme inflow. This design is likely more suitable for studying the fate of dietary treatments on the fermentative capacity of the gut microbial community, since the complete response of the proximal colon microbiota, constantly supplied with fresh substrates from the small intestine, is of interest. In our study, the combination of the reactor set-up and the nutritive medium, adapted to simulate swine chyme, allowed the establishment of six self-contained parallel fermentations for the porcine proximal colon with a high metabolic and compositional stability, diversity and reproducibility throughout 54 days of fermentation. The stable metabolite concentrations obtained during the continuous fermentation, indicate balanced microbial growth and the maintenance of gut microbiota functional capacity. In addition, metabolite ratios for IR and CR (61:27:12 and 57:29:14, acetate:propionate:butyrate) after initial stabilization were very similar and also in agreement

Chapter 2 with values reported for the pig proximal colon (60:25:15 (Grieshop et al., 2000)) and for fecal SCFA ratios (63:25:12 (Bird et al., 2009)) in vivo. Small but significant time effects were recorded for all detected metabolites, except total SCFA concentrations that remained constant during the entire stabilized fermentation. The ability to detect even small time changes is directly related to the sensitivity of the analysis, permitted by continuous operation of a stabilized fermentation system and achieved with a high number of time points analyzed (Mozzetti et al., 2012). The slight but significant increase of propionate concentrations during continuous fermentation may directly be related to the observed increase of the family Succinivibrionaceae, which was already detected in the fecal inoculum but at a low relative abundance of 0.1 % by 454 pyrosequencing. Succinivibrionaceae belong to the γ-subclass of the phylum Proteobacteria and play an important role in starch digestion in sheep and cattle (Hippe et al., 1999). While Kim et al. (2012) assigned the genus Succinivibrio to the group of the less abundant genera in the swine fecal microbiome, other studies in contrast have grouped the genus as a member of the core microbiota of the porcine proximal colon (Li et al., 2012) or porcine cecum (Buzoianu et al., 2012). Different carbohydrate sources can be metabolized by Succinivibrionaceae resulting in the main fermentation products acetate and succinate (Hippe et al., 1999). Further decarboxylation of succinate can lead to propionate (Hosseini et al., 2011), likely due to cross-feeding reactions in the complex intestinal environment. Microbial composition and diversity determined in the model effluents by qPCR and 454 pyrosequencing showed no major changes in the bacterial groups between days 11 and 54 in both, IR and CR, after the initial stabilization period of 10 days. The establishment of a microbial pseudo steady-state is an important factor to gain reliable data on the modulating potential of a specific treatment in order to avoid false positive conclusions related to the microbiota adaptation to in vitro conditions (Possemiers et al., 2004). Compared to the fecal inoculum all bacterial groups targeted by qPCR were reduced in reactor effluents from IR and CR, except for Enterobacteriaceae that displayed higher copy numbers. Changes in microbiota composition and diversity may reflect the transfer from in vivo (feces) to in vitro (proximal colon) conditions, the adaptation to a new environment, which depends on the

Discussion conditions in the host during collection of the fecal sample, and the lack of host effects in vitro (van den Abbeele et al., 2010, Zihler Berner et al., 2013). Additionally, the shift in microbial composition may favor more robust species due to a competitive advantage in adaptation and may open niches, possibly occupied by Enterobacteriaceae, which can explain their increase. Using 454 pyrosequencing, Clostridiaceae exhibited the most remarkable decrease in relative abundance between the fecal inoculum and reactor effluents, whereas Prevotellaceae increased markedly in reactor effluents. The high prevalence of Prevotellaceae was directly linked to an increased Bacteroidetes and decreased Firmicutes ratio in reactor effluents compared to the fecal inoculum. The high occurrence of Bacteroidetes in intestinal in vitro models, has already been reported previously in the Twin- SHIME (van den Abbeele et al., 2010) and TNO intestinal model (Rajilic-Stojanovic et al., 2010) and may be the result of the higher micromolar levels of oxygen and the less adhesive capacity of Bacteroidetes. Studies on the swine fecal and cecal microbiome reported Firmicutes, Bacteroidetes and Proteobacteria as the most prevalent phyla in swine (Buzoianu et al., 2012, Kim et al., 2012, Lamendella et al., 2011, Looft et al., 2012, Poroyko et al., 2010, Riboulet-Bisson et al., 2012), which is in accordance with our study. Firmicutes and Bacteroidetes represented between 80-90 % of assigned reads in all effluent samples, except for CR on day 52/53, when Proteobacteria increased to 29 % and Firmicutes/Bacteroidetes decreased to 70 %, respectively. On family level, Prevotellaceae was the most abundant family detected in all reactor samples, which is in accordance with other studies (Buzoianu et al., 2012, Kim et al., 2011, Lamendella et al., 2011, Li et al., 2012, Looft et al., 2012) and supported by our qPCR data. On genus level the predominant genera in the porcine PolyFermS (mean relative abundance in IR and CR >1 %) were Prevotella, Escherichia/Shigella, Roseburia, Ruminococcus, Oscillibacter, Succinivibrio, Blautia and Bacteroides, genera that have previously been described as members of the cecal (Buzoianu et al., 2012, Haenen et al., 2013, Poroyko et al., 2010) and fecal (Kim et al., 2011, Lamendella et al., 2011) gut microbiota. We thus conclude that the microbial composition is representative for the porcine proximal colon, as demonstrated by the high accordance of bacterial genera between our in vitro study and previous in vivo studies on the cecal porcine microbiota. Furthermore, a

Chapter 2 dominant fraction of glycan degraders was identified, consisting of the predominant genera Prevotella, Ruminococcus and Roseburia. The high prevalence of fibrolytic bacteria and their associated carbohydrate utilization systems in swine has already been reported previously (Lamendella et al., 2011, Leser et al., 2002) and is possibly related to the high amount of complex polysaccharides found in the pig diet. Starch degraders play a key role in gut microbial ecosystems as they represent the top of the trophic chain by providing simple sugars from the breakdown of complex carbohydrates, thus directly rendering them accessible to other members of the microbial community (Chassard and Lacroix, 2013). Assessing the reproducibility of in vitro gut fermentation models is a permanent challenge (Payne et al., 2012) and is difficult to achieve with classical models. In this study, we used a constant 10 % inoculation rate from IR to the subsequent reactors, which allowed reproducing similar and parallel evolving self-contained ecosystems in IR and the second- stage reactors. Due to the consecutive treatment periods with in-between washing of the test reactors, a fast re-establishment of reproducible environment in the test reactors was required. This is of particular importance for studying and more importantly comparing treatment- related responses of the microbial community in vitro, which presumes comparable experimental conditions in the different reactors. While the bacterial groups in TR1-4 targeted by qPCR reached similar numbers to that of CR after only one day of re- stabilization, complete metabolic activity was recovered after a longer time of approximately three days. Such delay of functionality response has also been reported with the Twine- SHIME model (van den Abbeele et al., 2010). The short re-stabilization period of PolyFermS compared to 5-8 days for Twin-SHIME points on the benefits of immobilized fecal microbiota to provide bacterial stability and diversity (Payne et al., 2012). To conclude, in the present study we validated a novel PolyFermS continuous intestinal fermentation model of the swine proximal colon, inoculated with immobilized fecal microbiota. This model operated with a nutritive medium designed to mimic pig chyme allowed to stably reproduce the microbiota and metabolic activity of swine proximal colon for at least 54 days. The particular model set-up allows comparing different treatments and a control, run with the same inoculated microbiota, simultaneously. Furthermore, our data demonstrate a considerable interplay between functionality and taxonomic composition and

Discussion highlight the stringent potential of the model for compositional as well as functionality related studies. This in vitro gut model can further be expanded to simulate multiple stages of the large intestine (proximal, transverse, distal) and a number of consecutive treatment periods. It should be particularly suitable to accurately investigate the effects of dietary factors such as pro-and prebiotics, as well as environmental parameters or drugs on the porcine gut microbiota in highly controlled settings.

Acknowledgements

We kindly thank the Genetic Diversity Center (GDC), ETH Zurich, for providing equipment for quantitative real-time PCR analysis and Eun-Hee Doo, Lukas Meile and Simon Galenda for technical assistance. This work was supported by the Commission for Technology and Innovation (project number: 11962.1).

Chapter 2

Supporting information

Table S2.1. Composition of the nutritive medium simulating the swine ileal chyme.

Ingredients g L-1 Carbohydrates 11.32 Corn starch 4.32 Pectin (citrus) 2.00 Xylan (beechwood) 2.00 Arabinogalactan (larch wood) 2.00 Guar gum 1.00 N-compounds 13.00 Soy Peptone 13.00 Yeast extract 4.50 Mucin (from porcine stomach) 4.00 Salts/Minerals L-cysteine HCl monohydrate 0.80 Bile extract porcine 0.40

KH2PO4 0.50

NaHCO3 1.50 NaCl 4.50 KCl 4.50 -1 MgSO4 anhy. (120.37 g mol ) 0.64 -1 CaCl2*2H2O (147.02 g mol ) 0.15 -1 MnCl2*4H2O (197.91 g mol ) 0.20 Hemin solution (0.05 g mL-1) 0.05 Tween 80 1.00

Supporting Information

-1 Table S2.2. 16S rRNA gene copy numbers (log10 mL of effluent) of specific bacterial groups measured by qPCR in effluent samples from CR and TR1-4. Lactobacillus/ Bacteroides- Bifidobacterium Pediococcus/ Clostridium Prevotella Enterobacteriaceae spp. Leuconostoc Cluster IV group spp.

CR 5.4 ± 0.7 10.5 ± 0.1 9.4 ± 0.2 8.4 ± 0.4 9.4 ± 0.3 TR1 5.1 ± 0.2 10.3 ± 0.1 9.5 ± 0.3 8.0 ± 0.2 9.4 ± 0.2 TR2 4.9 ± 0.3 10.4 ± 0.1 9.4 ± 0.3 7.8 ± 0.4 9.5 ± 0.2 TR3 5.6 ± 0.4 10.3 ± 0.2 9.5 ± 0.4 8.1 ± 0.2 9.4 ± 0.3

Stabilization 1 Stabilization TR4 4.8 ± 0.4 10.2 ± 0.3 9.5 ± 0.2 8.1 ± 0.3 9.3 ± 0.1

CR 5.9 ± 0.3 10.4 ± 0.1 9.6 ± 0.03 8.0 ± 0.2 10.0 ± 0.1 TR1 6.0 ± 0.7 10.5 ± 0.2 9.8 ± 0.1 7.9 ± 0.5 9.9 ± 0.1 TR2 6.0 ± 0.6 10.5 ± 0.1 9.8 ± 0.04 8.4 ± 0.6 10.0 ± 0.2 TR3 5.8 ± 0.8 10.4 ± 0.1 9.6 ± 0.1 7.6 ± 0.7 9.8 ± 0.1

Stabilization 2 Stabilization TR4 6.0 ± 0.6 10.4 ± 0.2 9.8 ± 0.1 7.8 ± 0.6 9.9 ± 0.2

CR 6.1 ± 0.1 10.3 ± 0.1 9.7 ± 0.1 8.3 ± 0.1 10.1 ± 0.1 TR1 5.8 ± 0.4 10.5 ± 0.1 9.5 ± 0.04 8.7 ± 0.3 9.9 ± 0.3 TR2 6.0 ± 0.4 10.5 ± 0.2 9.9 ± 0.1 8.8 ± 0.5 9.9 ± 0.2 TR3 5.8 ± 0.2 10.4 ± 0.1 9.7 ± 0.1 8.3 ± 0.6 9.8 ± 0.2

Stabilization 3 Stabilization TR4 5.9 ± 0.3 10.3 ± 0.1 9.8 ± 0.1 8.7 ± 0.6 9.8 ± 0.1

CR 6.6 ± 0.1 9.9 ± 0.1 9.9 ± 0.03 7.7 ± 0.3 10.0 ± 0.1 TR1 7.3 ± 0.1 10.0 ± 0.2 9.8 ± 0.1 8.4 ± 0.2 10.1 ± 0.1 TR2 6.4 ± 0.1 9.9 ± 0.1 9.9 ± 0.3 7.8 ± 0.1 10.1 ± 0.1 TR3 6.5 ± 0.04 10.0 ± 0.1 10.1 ± 0.2 8.2 ± 0.3 10.2 ± 0.2

Stabilization 4 Stabilization TR4 6.3 ± 0.1 10.0 ± 0.1 9.9 ± 0.2 8.0 ± 0.4 10.2 ± 0.1

CR 6.7 ± 0.2 10.0 ± 0.2 9.9 ± 0.02 8.4 ± 0.2 9.8 ± 0.3 TR1 7.7 ± 0.6 10.1 ± 0.1 9.8 ± 0.1 8.7 ± 0.4 9.6 ± 0.1 TR2 6.3 ± 0.7 10.0 ± 0.1 9.9 ± 0.2 8.6 ± 0.5 9.7 ± 0.3 TR3 6.5 ± 0.6 10.0 ± 0.1 9.9 ± 0.1 8.8 ± 0.5 9.7 ± 0.2

Stabilization 5 Stabilization TR4 6.6 ± 0.6 10.1 ± 0.1 10.0 ± 0.1 8.7 ± 0.5 9.9 ± 0.3

Data are log10 transformed and mean values for the last three days of each stabilization period. No significant differences were observed for all time points tested using the Mann-Whitney U test (P<0.05).

Chapter 2

Figure S2.1. Microbial composition in the fecal inoculum (FI), IR and CR measured by 454 pyrosequencing on (A) phylum level and (B) genus level. Values < 1 % are summarized in the group “others”.

Figure S2.2. Microbial composition on family level in CR and TR1-4 on day 25 (last day of 3rd stabilization period) measured by 454 pyrosequencing. Values < 1 % are summarized in the group “others”.

Supporting Information

Figure S2.3. Mean main metabolite concentrations and total 16S rRNA gene copy numbers from TR1-4 during the last three days of each stabilization period. Data are depicted as mean values ± SD -1 from TR1-4. ( ) total 16S rRNA gene copies mL effluent; (○) acetate; (□) propionate; (Δ) butyrate.

Chapter 3

Synergistic effects of Bifidobacterium thermophilum RBL67 and selected prebiotics on inhibition of Salmonella colonization in the swine proximal colon PolyFermS model

Sabine A. Tanner, Christophe Chassard, Annina Zihler Berner, Christophe Lacroix

Laboratory of Food Biotechnology, Institute of Food, Nutrition and Health, ETH Zurich,

Zurich, Switzerland

Published in Gut Pathogens (2014) 6:44.

Chapter 3

Abstract

Background: Probiotics and prebiotics are promising strategies to counteract Salmonella prevalence in swine. In the present study, we investigated the effects of prebiotics (fructo- (FOS), galacto- (GOS) and mannan- (MOS) oligosaccharides) and the bacteriocinogenic Bifidobacterium thermophilum RBL67 (RBL67) on Salmonella enterica subsp. enterica serovar Typhimurium N-15 (N-15) colonization using the PolyFermS in vitro continuous fermentation model simulating the swine proximal colon.

Materials and Methods: The PolyFermS model was designed with a first-stage reactor containing immobilized fecal pig microbiota. This reactor continuously inoculated five parallel second-stage reactors, a control and four treatment reactors, all operated with proximal colon conditions. FOS and GOS (5.2 g/day), and MOS (half dosage) and RBL67 (108 copy numbers mL-1 applied daily) were tested on the ability of N-15 to colonize reactors, inoculated with the same microbiota. Reactor effluents were collected daily and analyzed for microbial composition (quantitative PCR and 454 pyrosequencing of 16S rRNA gene pool) and main metabolites (HPLC).

Results: RBL67 and N-15 were shown to stably colonize the system. Colonization of N-15 was strongly inhibited by FOS and GOS, whereas addition of RBL67 alone or combined with MOS showed intermediate results. However, the effect of FOS and GOS was enhanced when prebiotics were combined with a daily addition of RBL67. FOS and GOS increased the total short chain fatty acid production, especially acetate and propionate. RBL67 combined with FOS additionally stimulated butyrate production.

Conclusions: Our study demonstrates the suitability of the porcine PolyFermS in vitro model to study nutritional effects of pro- and prebiotics on gut microbiota composition and activity. It can further be used to monitor Salmonella colonization. The inhibition effects of FOS and GOS on N-15 colonization are partly due to an increased acetate production, while further antimicrobial mechanisms may contribute to an enhanced inhibition with prebiotic-RBL67 combinations. A future direction of this work could be to understand the anti-Salmonella effects of Bifidobacterium thermophilum RBL67 in the presence of prebiotics to unravel the mechanism of this probiotic:pathogen interaction.

Introduction

Salmonella are highly prevalent in swine where they affect about one third of all production holdings in the European Union (EFSA, 2009). Salmonella negatively impact pig health and the productivity of livestock. Transmission to humans occurs via the food-chain, leading to severe infections. Therefore, Salmonella control must be initiated at the farm level. Since antibiotics for growth promotion have been banned, alternative strategies to improve gut health are necessary to maintain productivity. Gut microbial composition and activity can be directly influenced via the diet (Scott et al., 2013a). This in turn impacts the colonization ability of enteric pathogens, such as Salmonella, through competitive exclusion mechanisms (Pieper et al., 2009). Probiotics and prebiotics, known for their potential to modulate gut microbial composition and activity, are amongst the promising alternative strategies (Allen et al., 2013). Probiotics are defined as “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host” (FAO/WHO, 2002). Beneficial effects attributed to probiotics in pig feed include reduced incidence and severity of infections and decreased shedding of pathogens (Callaway et al., 2011, Casey et al., 2007, Chang et al., 2013). For example, weaned pigs treated with a five strain probiotic mixture (four

Lactobacillus strains and one Pediococcus strain) showed significantly reduced (> 2 log10 cfu g-1 feces) Salmonella numbers at 15 days post-infection (Casey et al., 2007). Other authors report a lower incidence of diarrhea and fecal coliform numbers when feeding Lactobacillus rhamnosus GG (Zhang et al., 2010), reduced carriage of Escherichia coli with Bifidobacterium lactis HN019 (Shu et al., 2001), or decreased Salmonella counts in feces and tissues after feeding pigs a combination of Lactobacillus acidophilus and Lactobacillus reuteri (Chang et al., 2013). Prebiotics are non-digestible food-ingredients that are readily fermentable in the colon and stimulate potentially health-promoting bacteria, mainly bifidobacteria and/or lactobacilli, thereby beneficially shifting the microbial equilibrium of the host gut (Gibson et al., 2004). For example, Patterson et al. (2010) reported stimulation of Bifidobacterium spp. and Lactobacillus spp. with a concomitant suppression of Clostridium spp. and members of

Chapter 3

Enterobacteriaceae spp. upon feeding of inulin to pigs. Prebiotics can stimulate short chain fatty acid (SCFA) production, known to play a key role in intestinal host health. For example, butyrate, the main energy source for colonocytes, has anti-inflammatory and anti- carcinogenic properties (reviewed by Russell et al. (2013)) and down-regulates the expression of genes associated with Salmonella invasion (Gantois et al., 2006). However, conflicting results have been reported for the effects of prebiotic feeding in pigs. Tzortzis et al. (2005) reported higher acetate concentrations and increased bifidobacteria numbers after feeding GOS to pigs, while Mikkelsen and Jensen (2004) showed increased butyrate production after feeding FOS to piglets. In contrast, no effect was observed with FOS on bifidobacterial populations (Mountzouris et al., 2006) and on fecal SCFA concentrations (Bird et al., 2009). Prebiotics are increasingly combined with probiotics (synbiotics) to enhance probiotic survival and growth. Synbiotic formulations tested in pigs decreased the level of Enterobacteriaceae in pig fecal samples (Bomba et al., 2002), and reduced adherence of Escherichia coli O8:K88 to the jejunal and colonic mucosa (Nemcova et al., 2007). However, synbiotic formulations have been much less studied for pathogen inhibition. Yet, they have a promising potential considering the competitive advantage of the probiotic through simultaneous application of a prebiotic with high specificity (Gaggia et al., 2010, Gibson and Roberfroid, 1995). The species B. thermophilum belongs to the commensals of the pig gut microbiota (Biavati and Mattarelli, 2009). Bifidobacterium thermophilum RBL67 (RBL67) previously isolated from baby feces was shown to produce a bacteriocin-like substance (BLIS) with in vitro activity against Listeria and Salmonella (Toure et al., 2003, von Ah, 2006, Zihler, 2010). Furthermore, we recently showed that RBL67 has antagonistic effects on Salmonella infection in an in vitro continuous intestinal fermentation model simulating the child proximal colon (Zihler et al., 2014). This strain was reported to adhere to human intestinal cell lines (Moroni et al., 2006) and to exert protective effects on epithelial HT29-MTX cell culture integrity upon Salmonella challenge in combined cellular and colonic fermentation models (Zihler et al., 2011). Inulin supplemented in a three-stage continuous intestinal fermentation model of the child induced an increase of B. thermophilum numbers in the proximal, transverse and distal colon sections while SCFA production was shifted towards

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Introduction higher butyrate concentrations (Zihler et al., 2010). However, inulin in the proximal colon environment of the model was also shown to promote Salmonella growth (Zihler et al., 2010), and to increase the efficiency of HT29-MTX cell invasion (Zihler et al., 2011). Finally, RBL67 has technological features of interest for application, such as being moderately oxygen-tolerant, growing at high cell density, low pH and high temperatures of up to 47°C (von Ah et al., 2007). Studying the complex interplay of pro- and prebiotics with the gut microbiota and pathogens is hindered by the inaccessibility of the gastrointestinal tract. Studies are further challenged by ethical limits to conduct in vivo animal infection trials. In this context, in vitro models represent a cost-effective and ethically less constraint strategy (Payne et al., 2012). We recently reported and validated a novel two-stage in vitro continuous fermentation model (PolyFermS) inoculated with immobilized fecal microbiota simulating the swine proximal colon. This model allows the parallel operation of five self-contained independent fermentations to simultaneously test different nutritional factors with the same microbiota (Tanner et al., 2014). In this study, we used this PolyFermS model of the swine proximal colon to investigate the effects of B. thermophilum RBL67 and prebiotics (fructo-, galacto- and mannan-oligosaccharides) on the gut microbiota composition and activity and on the colonization of the enteric pathogen Salmonella enterica subsp. enterica serovar Typhimurium N-15 (N-15).

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Chapter 3

Materials and Methods

Bacterial strains

B. thermophilum RBL67 (LMG S-23614, Laboratory of Food Biotechnology, ETH Zurich) was isolated from human baby feces (Toure et al., 2003). S. Typhimurium N-15 was obtained from a clinical case and was supplied by the National Center for Enteropathogenic Bacteria and Listeria (NENT; University of Zurich, Zurich, Switzerland). RBL67 and N-15 were cultured from a glycerol stock (33 %, -80°C) in serum flasks containing the fermentation medium used to simulate swine chyme (Tanner et al., 2014), at 37°C for 15 h. The headspace of the serum flasks was flushed with an N2:CO2 (3:1) gas mixture before autoclaving to generate anaerobic conditions. Viable cell counts of Salmonella were determined by plating serial 10-fold dilutions in duplicate on CHROMAgar™ Salmonella (Becton Dickinson AG, Allschwil, Switzerland).

Prebiotics

Fibrulose F97 (FOS) (Cosucra Groupe Warcoing S. A., Warcoing, Belgium) contains oligofructose (≥ 97 % [wt/wt]) and minor amounts of free fructose, glucose and sucrose (≤ 5 % [wt/wt]), and has a polymerization degree of 94 % ≤ 20. Vivinal GOS 90 (GOS), composed of 96.5 % GOS, 2 % lactose, 0.7 % glucose and 0.8 % galactose, was supplied by Friesland Campina Domo (Amersfoort, Netherlands). Bio-Mos (MOS) was obtained from Alltech (Sarney, Ireland).

Fermentation set-up

The experimental set-up of the continuous in vitro fermentation model was presented in detail by Tanner et al. (2014). Briefly, the fermentation model consisted of a two-stage reactor set- up, with six reactors operated under conditions of the swine proximal colon (38°C, pH 6.0, retention time 9 h, anaerobiosis by CO2 headspace flushing) (Figure 3.1). The inoculum reactor (IR) containing 30 % (v/v) polysaccharide gel beads immobilizing swine fecal microbiota was used to continuously inoculate five subsequent reactors (one control (CR) and four test reactors (TR1-4)) with 10 % effluent. CR and TR1-4 were additionally fed with 90 % fresh nutritive medium, designed to simulate swine chyme (Tanner et al., 2014). While IR and CR were operated under constant conditions during the entire fermentation period, the

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Materials and Methods test reactors (TR1-4) were used to test N-15 and RBL67 colonization (period 1) and effects of RBL67 and/or prebiotics on N-15 colonization (periods 2-4) (Figure 3.1). Between each period, test reactors were disconnected from the IR, washed with 10 % chlorine solution, reconnected and microbiota composition and activity was re-established for minimum 3 days before application of a new treatment (Tanner et al., 2014).

Figure 3.1. Experimental set-up of the continuous fermentation experiment. IR: inoculum reactor; CR: control reactor; TR: test reactors 1-4; F: flow rate; M: fresh medium inflow; stab: stabilization; prev: prevention; challenge: challenge with Salmonella N-15; N-15: S. Typhimurium N-15; RBL67: B. thermophilum RBL67; R-FOS/GOS/MOS: B. thermophilum RBL67 + respective prebiotic.

Period 1: RBL67-N-15 colonization

Colonization of S. Typhimurium N-15 and B. thermophilum RBL67 was tested during period

1 (Figure 3.1). N-15 was inoculated in TR1 once to reach a cell concentration of 106 cfu mL-1 reactor. RBL67 was added once to TR2 and TR4 for a final gene copy number (CN) of

108 CN mL-1, while TR4 was additionally supplied with 5.2 g of FOS/day. Effluent samples were analyzed after 3, 6, 9, 24, 27, 54, 78 and 96 h for enumeration of Salmonella and B.

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Chapter 3 thermophilum with plate counts and qPCR, respectively. Measured concentrations of N-15 and RBL67 were compared to a theoretical washout curve, calculated with the formula:

(-t/RT) ct = c0 * e , where RT is the mean retention time (9 h), c0 and ct are cell concentrations of bacteria at time point 0 and t, respectively.

Periods 2-4: N-15 treatment periods

The effects of RBL67, FOS, GOS and combinations of RBL67 with FOS (R-FOS), GOS (R- GOS) and MOS (R-MOS) on N-15 colonization were tested during periods 2-4. For each period one reactor served as control (CR) and one reactor was infected with N-15 only (Figure 3.1). Treatment periods were divided into three phases: stabilization (stab) was carried out for 3 days (periods 2 and 4) or 5 days (period 3), prevention (prev) with pro- and/or prebiotics was done for 2 days, and challenge with N-15 was tested for 5 days, while addition of RBL67 and/or prebiotics was pursued. During prevention and challenge periods RBL67 and prebiotics were applied daily (Figure 3.1). All test reactors were infected once with N-15 on the first day of the challenge period. RBL67 and N-15 inoculum was prepared from an overnight culture, which was centrifuged (6000 x g, 5 min) and resuspended in fresh fermentation medium. Reactors were inoculated with a syringe to obtain final concentrations of approximately 108 CN mL-1 for RBL67 and 106 cfu mL-1 for N-15 corresponding to a probiotic:pathogen ratio of approximately 100:1. FOS and GOS were supplied twice daily for a total of 5.2 g/day. This addition level was selected to correspond to approximately 3 % (w/w) of the daily feed for pigs, considering a 2 kg/d feed intake and a scale factor of 0.09 for the ratio of the reactor volume (260 mL) to the pig proximal colon volume in vivo (approx. 2.9 L (Kararli, 1995)). MOS was supplied only once per day and at 1.5 % (w/w, 2.6 g/day), because higher amounts led to blocking of the flow. Reactor effluent samples were collected daily during the entire fermentation and analyzed for bacterial composition and activity.

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Materials and Methods qPCR analyses

Predominant bacterial groups of the swine gut microbiota (Leser et al., 2002) in reactor effluents were enumerated by qPCR. Genomic DNA was extracted using the FastDNA Spin Kit for soil (MP Biomedicals, Illkirch, France) according to the manufacturer’s instructions. qPCR targets were: total bacteria (total 16S rRNA gene copies), Bacteroides-Prevotella group, Enterobacteriaceae, Lactobacillus/Pediococcus/Leuconostoc spp., Clostridium Cluster IV and Bifidobacterium spp. (Table S3.1). Standard curve preparation and reaction conditions were carried out as described by Dostal et al. (2013) using a reaction volume of 25 μl and an ABI PRISM 7500-PCR sequence detection system (Applied Biosystems, Zug, Switzerland). All assays were carried out using the 2 x SYBR Green PCR Master Mix (Applied Biosystems). B. thermophilum enumeration was performed using primers bthermRTF and bthermRTR and the Taqman probe bthermTqm (Table S3.1) (Mathys et al., 2008). The RT-QP2X-03WOULR Mastermix (Eurogentec s.a., Seraing, Belgium) was used and standard curve preparation and reaction conditions were carried out as described previously (Dostal et al., 2013, Mathys et al., 2008). Pyrosequencing

Effluent samples of CR and TRs from periods 2-4 were analyzed using 454 pyrosequencing on the V5-V6 region of the entire 16S rRNA gene pool. Reactor effluents from two consecutive days during the N-15 challenge (day 3 and 4) were pooled in a ratio 1:1, prior to DNA extraction using the FastDNA SPIN Kit for soil (MP Biomedicals). Genomic DNA extracts were sequenced by DNAVision SA (Charleroi, Belgium) on a 454 Life Sciences Genome Sequencer GS FLX instrument (Roche AG, Basel, Switzerland), and subsequent taxonomic assignment of the 16S rRNA gene reads was done as described previously (Jost et al., 2013). Quality-filtered sequencing reads were assigned using the Ribosomal Database Project (RDP) Bayesian classifier (v 2.1) (Wang et al., 2007) and applying a confidence threshold of 80 %. The entire 454 pyrosequencing dataset has been deposited to the National Center for Biotechnology (NCBI) Sequence Read Archive under accession number SRP044728.

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Metabolite analysis

Reactor effluents were analyzed for SCFAs (acetate, propionate and butyrate), BCFAs (valerate, iso-valerate and iso-butyrate), formate and lactate by HPLC (Thermo Fisher Scientific Inc. Accela, Wohlen, Switzerland) (Tanner et al., 2014). Effluent samples were centrifuged (14 000 x g, 10 min, 4°C); the resulting supernatant was diluted 1:10 with ultrapure water and directly filtered through a 0.45 μm nylon filter (Infochroma AG, Zug, Switzerland). The analysis was carried out using an Aminex HPX-87H column (Bio-Rad

Laboratories AG, Reinach, Switzerland) and 10 mM H2SO4 as eluent. Mean metabolite concentrations (mM) were estimated from duplicate analyses. Total SCFA contents correspond to the sum of acetate, propionate and butyrate.

Statistical analysis

All statistical analyses were performed using JMP 10.0 (SAS Institute Inc., Cary, NC). Prior to statistical analysis qPCR data were log10 transformed. HPLC and qPCR data are expressed as means ± SD from three consecutive days (days 2-4) during N-15 challenge periods. Metabolite and qPCR data from each treatment reactor were compared pairwise to the control reactor within the same period using the non-parametric Kruskal-Wallis Test. P-values < 0.05 were considered significant.

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Results

Colonization potential of RBL67 and N-15

To evaluate the colonization ability of RBL67 and N-15 in an in vitro model of the swine proximal colon, we inoculated TRs once with RBL67 with and without FOS or with N-15 during period 1 (Figure 3.1). RBL67 and N-15 concentrations were estimated 96 h after addition and data were compared to the theoretical washout curve (Figure 3.2). The N-15 cell counts initially declined at a rate close to the theoretical washout curve and

-1 stabilized after 27 h at 4.7 ± 0.2 log10 cfu mL until 96 h. RBL67 gene copy numbers (8.1

-1 log10 CN mL ) declined faster than the theoretical washout curve during the first 54 h and

-1 reached a stable value of 4.6 ± 0.2 log10 CN mL between 78 and 96 h. A similar pattern was observed for the treatment of RBL67 combined with FOS, with CN decreasing until 27 h,

-1 followed by stability (5.3 ± 0.3 log10 CN mL , 27-96 h).

Figure 3.2. Salmonella and B. thermophilum in reactor effluents compared to theoretical washout curves during colonization tests. RBL67 was added once to TR2 and TR4 to reach 108 CN mL-1, while TR4 was additionally supplied with 5.2 g of FOS/day. N-15 was added once to TR1 to reach 106 cfu mL-1. Salmonella viable cell counts in reactor effluents was measured by plating on CHROMAgar™. B. thermophilum numbers were estimated by qPCR. Measured concentrations were compared to a theoretical washout curve. (●) Salmonella viable cell counts, given as cfu mL-1 (□) B. thermophilum and (■) B. thermophilum + FOS. B. thermophilum numbers are given as 16S rRNA gene copy numbers mL-1. (--) theoretical washout curve of Salmonella and (─) theoretical washout curve of B. thermophilum.

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Effect of prebiotics and RBL67 on N-15 colonization

Pretreatments with RBL67 and prebiotics were tested during periods 2-4 on N-15. After N-15

-1 infection in period 2, N-15 cell counts declined 1.6 log10 cfu mL during the first 2 days and

-1 stabilized at 5.0 ± 0.2 log10 cfu mL effluent (days 2-5) (Figure 3.3). Unexpectedly, N-15 cell counts in the following periods showed either a limited initial decline phase after the first day

-1 of challenge followed by stability (6.3 ± 0.1 log10 cfu mL , period 3, days 1-5), or a steady

-1 increase until day 2 to reach 7.4 ± 0.1 log10 cfu mL (period 4, days 2-5). The treatments with FOS and GOS during periods 2 and 3 induced a strong inhibition of N-15 colonization, with

-1 N-15 cell numbers decreasing below the detection limit (4.1 log10 cfu mL effluent) 3 days post-infection. When FOS or GOS were combined with RBL67 (R-FOS and R-GOS) during periods 2-4, N-15 counts decreased even more rapidly compared to treatments with the prebiotics alone, reaching non-detectable levels after two days post-infection (periods 2 and

-1 3) or reducing initial N-15 counts by approximately 2 log10 cfu mL (period 4). Intermediate effects were recorded for RBL67 alone (periods 2 and 3) and in combination with MOS (R- MOS, period 4), with a reduction of N-15 counts 2 days post-infection by approximately 1.8

-1 ± 0.3 and 0.7 log10 cfu mL , respectively.

Figure 3.3. Salmonella cell counts determined in test reactors during treatment periods 2-4. Treatment periods 2-4 were used to test the effect of prebiotics (FOS, GOS) or RBL67 or combinations of RBL67 with FOS, GOS or MOS on Salmonella N-15 colonization. RBL67 and/or prebiotics were added to TRs at 108 CN mL-1 and 5.2 g/day, respectively during a 2 days prevention period and for 5 days after N-15 challenge. N-15 was inoculated once at 106 cfu mL-1 and was monitored by plate counts on CHROMAgar™ Salmonella. (─) Salmonella detection limit of 4.1 cfu mL-1. Cell counts at day 0 correspond to the inoculum added to the reactors.

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Results

Effect of prebiotics, RBL67 and N-15 infection on gut microbiota composition

Changes in the microbial community composition were monitored by qPCR and by 454 pyrosequencing. We compared mean copy numbers (days 2-4) of bacterial populations during pseudo-steady states of N-15 challenge periods of control and treatment reactors. Bacteroides-Prevotella and Clostridium Cluster IV were the most prominent groups, followed by Enterobacteriaceae, Lactobacillus/Leuconostoc/Pediococcus and Bifidobacterium (Table 3.1). Total 16S rRNA, Clostridium Cluster IV and Bacteroides- Prevotella gene copy numbers remained stable independent of the tested conditions. Furthermore, the other bacterial groups, except for Bifidobacterium, did not show large

-1 changes (difference to CR < 0.5 log10 CN mL ) upon treatment application. Bifidobacterium

-1 numbers increased by more than 1 log10 CN mL during treatments with RBL67 alone and RBL67 combined with prebiotics (R-FOS, R-GOS, R-MOS). B. thermophilum was detected during daily treatments with RBL67, at concentrations ranging from 7.6 and 8.1 log10 CN mL-1, but not in the other treatments and in CR (Table 3.1). The highest numbers of B. thermophilum were measured for RBL67 and FOS applied in combination (R-FOS). N-15 inoculation in absence of dietary treatments showed no effect on Enterobacteriaceae numbers, but was associated with a slight but significant increase of the group

-1 Lactobacillus/Leuconostoc/Pediococcus (0.6 log10 CN mL , period 2) and of

-1 Bifidobacterium (0.6 log10 CN mL , period 4) compared to CR. Using 454 pyrosequencing of the entire 16S rRNA gene pool, a mean value of 6259 ± 3730 quality-filtered reads per sample was obtained with an average read length of 256 ± 1 bp. All samples revealed the predominance of the 3 phyla, Firmicutes, Bacteroidetes and

Proteobacteria (Figure S3.1). Additionally, Actinobacteria were detected at low levels (< 1

%; except for R-FOS in period 2 with 1.9 %). Firmicutes and Bacteroidetes accounted for more than 80 % of assigned reads in all samples for periods 2 and 3. However, during period

4, Proteobacteria increased to up to 30 % while Firmicutes and Bacteroidetes decreased to approximately 70 % of all reads. The phylum Proteobacteria displayed a steady increase in all reactors during the fermentation, including in CR where no treatment was applied. In general,

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Chapter 3 pro- and prebiotic treatments and N-15 infection did not markedly impact microbiota composition. At the phylum level, Bacteroidetes increased and Firmicutes decreased in the N-

15 (alone) and RBL67 treatments compared to CR (period 3). On the family level a consistent increase of Erysipelotrichaceae was observed with prebiotics, alone (FOS, GOS) or in combination with RBL67 (R-FOS, R-FOS, R-MOS), compared to CR, with highest effect for

R-FOS (6.4 % compared to 0.3 % in CR, period 2 and 4.7 % compared to 0.1 % in CR, period 4) (Figure 3.4). Changes observed at the genus level (Figure S3.2) were consistent with observations at the family level. The genus Sharpea, a member of the family

Erysipelotrichaceae, was highly abundant in the TRs after FOS or GOS treatments and the combined treatments of RBL67 with prebiotics (R-FOS, R-GOS, R-MOS) compared to CR.

The genus increased 6 % (period 2) and 5 % (period 6) after the R-FOS treatment with values

< 0.1 % in CR.

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Table 3.1. 16S rRNA gene copy numbers of bacterial groups by qPCR in reactors during periods 2-4.

-1 Bacterial group, log10 copies mL effluent Lactobacillus/ Bifidobacterium Bacteroides- Clostridium total 16S rRNA Treatment Enterobacteriaceae Leuconostoc/ spp. Prevotella Cluster IV genes Pediococcus spp. PERIOD 2 control 6.4 ± 0.2 10.3 ± 0.1 9.6 ± 0.1 7.6 ± 0.1 10.0 ± 0.1 10.5 ± 0.1 N-15 6.5 ± 0.2 10.3 ± 0.03 9.6 ± 0.1 8.2 ± 0.3* 10.1 ± 0.1 10.5 ± 0.2 RBL67 8.3 ± 0.2* 10.2 ± 0.1 10.0 ± 0.1* 7.6 ± 0.2 10.0 ± 0.1 10.6 ± 0.2 FOS 6.0 ± 0.2* 10.4 ± 0.04 9.9 ± 0.2* 7.4 ± 0.1 10.0 ± 0.03 10.7 ± 0.1 R-FOS 8.7 ± 0.2* 10.2 ± 0.2 9.9 ± 0.2 7.4 ± 0.2* 10.0 ± 0.1 10.7 ± 0.1 PERIOD 3 control 6.7 ± 0.1 10.0 ± 0.03 10.0 ± 0.1 8.2 ± 0.3 10.0 ± 0.02 10.6 ± 0.1 N-15 6.9 ± 0.2 10.1 ± 0.2 9.8 ± 0.1* 8.2 ± 0.2 10.1 ± 0.2 10.6 ± 0.2 RBL67 7.7 ± 0.1* 10.1 ± 0.2 10.2 ± 0.1* 8.6 ± 0.2 10.1 ± 0.1 10.6 ± 0.2 GOS 6.6 ± 0.04 9.9 ± 0.2 9.7 ± 0.2* 8.1 ± 0.2 10.0 ± 0.5 10.5 ± 0.1* R-GOS 8.0 ± 0.1* 10.0 ± 0.2 9.9 ± 0.2 8.3 ± 0.3 10.1 ± 0.3 10.5 ± 0.2 PERIOD 4 control 6.4 ± 0.1 10.0 ± 0.2 10.0 ± 0.2 8.0 ± 0.2 9.5 ± 0.02 10.6 ± 0.3 N-15 7.0 ± 0.1* 9.9 ± 0.1 9.8 ± 0.1 8.1 ± 0.3 9.8 ± 0.1 10.5 ± 0.1 R-MOS 8.1 ± 0.1* 9.8 ± 0.3 9.8 ± 0.2 8.2 ± 0.4 9.5 ± 0.3 10.5 ± 0.4 R-FOS 8.4 ± 0.1* 9.8 ± 0.1 9.9 ± 0.2 8.4 ± 0.2* 9.7 ± 0.1 10.5 ± 0.1 R-GOS 8.3 ± 0.03* 9.9 ± 0.1 10.0 ± 0.2 8.3 ± 0.2 9.8 ± 0.2 10.6 ± 0.3 -1 *Bacterial populations significantly different (P<0.05) from CR within a period; Values are given as means ± SD (log10 copy numbers mL reactor effluent) calculated from three consecutive days (days 2-4) during N-15 challenge periods

Chapter 3

Figure 3.4. Microbial composition in reactors during treatment periods 2-4 measured by 454 pyrosequencing on family level. The microbiota profile in reactor effluents during treatment periods was analyzed by 454 pyrosequencing of the entire 16S rRNA gene pool in the V5-V6 region. Reactor effluents were pooled in a ratio 1:1 from two consecutive days of the N-15 challenge period (days 3 and 4) for genomic DNA extraction and subsequent sequencing on a 454 Life Sciences Genome Sequencer GS FLX instrument. Quality-filtered sequencing reads were assigned using the Ribosomal Database Project (RDP) Bayesian classifier (v2.1) and applying a confidence threshold of 80%. CR: control reactor; values < 1% are summarized in the group others.

Effect of prebiotics, RBL67 and N-15 infection on gut microbiota metabolism

Metabolite concentrations were measured by HPLC and mean values of three consecutive days (days 2-4), corresponding to pseudo-steady states of the N-15 challenge periods, were compared to corresponding data from the CR (Table 3.2). In the CR, the total short chain fatty acid (SCFA) concentration was stable from periods 1 to 3 (162 ± 1 mM), but a slight decrease to 153 ± 1 mM was observed during period 4, corresponding to a switch of the metabolite molar ratio (acetate:propionate:butyrate) from 57:29:14 (period 1-3) to 51:34:15 (period 4). The total SCFA concentration in the TRs was increased by 29 ± 4 % compared to the CR for treatments with FOS and GOS alone and combined with RBL67. Acetate (+ 38 ± 10 %) and propionate (+ 28 ± 4 %) levels were most increased with FOS, GOS, R-FOS and R-GOS, whereas R-FOS also induced a significant increase of butyrate (18 %, 45 % and 7 % for period 1, 2 and 4, respectively) compared to the CR. The total SCFA concentration was also significantly increased in TRs treated with RBL67 alone (+ 5 ± 1%, periods 2-3) or with R-MOS (+ 11 %), although to a lesser extent than for the other prebiotics. Treatment with RBL67 increased acetate (+ 12 ± 2 %, periods 1-

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Results

2) and butyrate concentrations (+ 16 ± 2 %, periods 1-2), while R-MOS mainly stimulated propionate production (+ 25 %). Infection with N-15 (alone) had little effect on metabolite productions, except for an increase in acetate concentration (+ 11 ± 2 %, periods 1 and 4). Branched chain fatty acids (BCFA) were measured at low amounts (< 7 mM) in all reactors. Formate and lactate were not detected throughout the fermentation (data not shown).

Table 3.2. Concentration (mM) and molar ratios (%) of metabolites measured by HPLC during periods 1-4.

ratio Acetate Propionate Butyrate total SCFA (acetate:propionate:butyrate) PERIOD 1 control 94.6 ± 2.3 45.8 ± 1.3 22.5 ± 0.4 162.9 ±1.8 58:28:14 N-15 104.2 ± 1.0* 43.6 ± 1.5 22.9 ± 1.1 170.6 ± 3.0* 61:26:13 RBL67 96.8 ± 6.1 43.0 ± 0.3* 25.7 ± 1.0* 165.5 ± 5.4 58:26:16 R-FOS 146.8 ± 5.9* 52.1 ± 3.3* 26.4 ± 0.9* 225.3 ± 1.8* 65:23:12 PERIOD 2 control 92.4 ± 3.7 48.0 ± 2.0 20.7 ± 0.8 161.0 ± 5.1 57:30:13 N-15 94.0 ± 6.9 45.2 ± 1.1 21.9 ± 1.8 161.1 ± 8.2 58:28:14 RBL67 102.3 ± 2.6* 44.3 ± 1.3* 24.3 ± 1.3* 170.9 ± 5.0* 60:26:14 FOS 126.4 ± 5.2* 58.4 ± 1.1* 21.0 ± 2.4 205.8 ± 6.0* 61:28:10 R-FOS 112.2 ± 1.0* 60.6 ± 0.9* 29.9 ± 1.1* 202.7 ± 1.5* 55:30:15 PERIOD 3

CR 89.5 ± 1.8 46.9 ± 0.1 27.0 ± 1.8 163.4 ± 3.6 55:29:16 SAL 86.1 ± 1.4* 50.0 ± 2.8 22.9 ± 5.2* 159.0 ± 5.2 54:31:14 RBL67 101.3 ± 3.1* 47.3 ± 2.0 22.7 ± 5.9* 171.3 ± 5.9 59:28:13 GOS 122.7 ± 4.8* 61.1 ± 5.5* 21.6 ± 5.0* 205.4 ± 5.0* 60:30:10 R-GOS 117.6 ± 2.0* 60.4 ± 2.7* 27.1 ± 5.9 205.1 ± 6.0* 57:29:13 PERIOD 4

CR 78.7 ± 0.8 51.4 ± 1.0 23.2 ± 0.4 153.3 ± 1.4 51:34:15 SAL 90.1 ± 1.0* 50.9 ± 0.9 22.8 ± 1.7 163.8 ± 1.7* 55:31:14 R-MOS 82.7 ± 3.2* 64.1 ± 1.5* 22.8 ± 2.9 169.3 ± 1.0* 49:38:13 R-FOS 114.6 ±7.6* 65.0 ± 1.3* 24.7 ± 0.6* 204.3 ± 6.0* 56:32:12 R-GOS 118.5 ± 1.1* 68.2 ± 3.0* 20.1 ± 0.3* 206.8 ± 2.9* 57:33:10 *Means significantly different (P<0.05) from CR within a metabolite and period using the Kruskal-Wallis pairwise comparison; reported are mean values (days 2-4 of N-15 challenge periods) ± SD and ratios are given as percentage of total SCFA (acetate, propionate, butyrate); N-15: Salmonella N-15 without pro- or prebiotic; RBL67: B. thermophilum RBL67 alone; FOS: FOS alone; R-FOS: B. thermophilum RBL67 + FOS; GOS: GOS alone; R-GOS: B. thermophilum RBL67 + GOS; R-MOS: B. thermophilum RBL67 + MOS.

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Chapter 3

Discussion

We recently described and validated a novel in vitro continuous fermentation model (PolyFermS) simulating conditions of the swine proximal colon. The model consists of parallel reactors inoculated with the same microbiota (Tanner et al., 2014). In this study, we report the first time application of this swine PolyFermS model to investigate the effects of a probiotic strain, B. thermophilum RBL67, prebiotics (FOS, GOS, MOS) and combinations thereof, on S. Typhimurium N-15 colonization in the presence of a diverse gut microbiota. In a first test, RBL67 and N-15 were shown to colonize the system after one single inoculation. They reached stable and similar numbers after 1 to 2 days. Our in vitro model data suggest competitive and adaptive traits of RBL67 and N-15 in co-culture with the modeled porcine microbiota. These results are in agreement with previous studies done with one- and three-stage chemostat models of the child colon (Le Blay et al., 2009, Zihler et al., 2014). The increasing capacity of N-15 to colonize the model observed from periods 2 to 4, underlines the robustness and/or adaptation of Salmonella in simulated colonic conditions of the swine colon. This suggests that the PolyFermS model is suitable to mimic a Salmonella carrier state of pigs with continuous shedding of Salmonella (de Busser et al., 2013). Moreover, an incomplete removal of N-15 during washing periods of reactors may partly explain the enhanced competition of N-15 over time, because viable cells of Salmonella were detected in the effluents by plating after careful washing with 10 % chlorine for 1 h and prior to N-15 challenge in periods 3 and 4 (data not shown). This persistence of Salmonella could be due to the formation of biofilms in the reactor, which is known to increase sterilization resistance (Hai and Yuk, 2013). This effect may be avoided in the future by replacing the test reactors with sterile units before each new treatment period. We also reported an increase of the family Succinivibrionaceae during the course of the fermentation for the first-stage immobilized cell and all second-stage reactors for the same fermentation test (Tanner et al., 2014). Salmonella and Succinivibrionaceae belong to the γ-subclass of the phylum Proteobacteria (Hippe et al., 1999). Increased numbers of Succinivibrionaceae correlated with the increased capacity of N-15 to grow in the system, suggesting that this group potentially supported N-15 persistence and growth in periods 3 and 4 after washing. Such co-

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Discussion occurrence of related bacteria has been previously reported for Salmonella invasion in a mouse infection model in the presence of high titers of E. coli (Stecher et al., 2010). Colonization of N-15 in the porcine PolyFermS was strongly inhibited by the addition of FOS or GOS. This correlated with an increase of SCFA production, especially acetate and propionate. A 5 mM undissociated acetic acid solution was reported to inhibit Salmonella growth (Adams and Hall, 1988, van der Wielen et al., 2001, Wilson et al., 2003). In our study, concentrations of undissociated acetic acids were calculated to be > 6 mM (pH=6.0) for treatments with FOS and GOS, compared to levels ≤ 5 mM in the reactor spiked with N- 15 alone. RBL67 combined with FOS or GOS showed an enhanced inhibition of N-15 compared to single treatments with pro- or prebiotics. We chose strain RBL67, because it produces BLIS (thermophilicin B67), which exhibits an antagonistic effect against Salmonella and Listeria (Toure et al., 2003, von Ah, 2006, Zihler, 2010). The production of acetate was decreased for R-FOS and R-GOS compared to prebiotics alone (Table 3.2). This suggests that BLIS contributed to N-15 inhibition in combination with organic acids produced by FOS and GOS. The lower dosage of the prebiotic in R-MOS compared to the other combinations and the stimulation of propionate rather than acetate production, may explain the less pronounced effect on N-15 colonization. However, MOS has previously been shown to block enteropathogen adhesion to the mannose-rich surface glycoproteins of epithelial villi via binding of its α-D-Mannan to Type 1 fimbriae of enteropathogens and thus may reduce the risk of infection by this mechanism (Kogan and Kocher, 2007). The antagonistic effect of RBL67 was less pronounced in this study compared to a previous report (Zihler et al., 2014). A strong inhibition of Salmonella and a rapid metabolic rebalancing of the gut microbiota after antibiotic treatments were observed when RBL67 was added before or after infection in an in vitro intestinal fermentation model inoculated with child microbiota (Zihler et al., 2014). In contrast, Zihler et al. (2010) did not detect an anti- Salmonella effect of RBL67. This may be explained by different host microbiota, model set- up and probiotic:pathogen ratios used for all these studies, i.e. 16:1 (this study), 3050:1 (Zihler et al., 2014) and 2:1 (Zihler et al., 2010). FOS has been reported to stimulate butyrate production in some studies with piglets (Mikkelsen and Jensen, 2004, Tsukahara et al., 2003). In our study, we observed an increased

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Chapter 3 butyrate production with the combination of FOS and RBL67. Because bifidobacteria do not produce butyrate (van der Meulen et al., 2006a), we presume that FOS was first degraded e.g. by RBL67, followed by cross-feeding reactions with butyrate-producing bacteria (e.g. Roseburia spp. or Megasphaera; (Levine et al., 2013)). Interestingly, while butyrate has been linked to a series of health-related properties (reviewed by Russell et al. (2013)), it was also shown to repress invasion gene expression of Salmonella (Gantois et al., 2006). The microbiota composition from CR to TR effluents only changed marginally after RBL67 and prebiotic treatments. In particular, we did not observe a growth stimulation of bifidobacteria or lactobacilli in the FOS and GOS treatments, as it was previously shown in vitro with human gut microbiota treated with FOS and inulin (Le Blay et al., 2010, Zihler et al., 2014) or pig microbiota treated with GOS (Martinez et al., 2013, Tzortzis et al., 2005). Divergent results have been reported concerning the effect of FOS and GOS in vivo. Patterson et al. (2010) reported increased numbers of bifidobacteria and lactobacilli in young pigs fed with inulin. In contrast, Mountzouris et al. (2006) and Mikkelsen and Jensen (2004) did not observe a significant stimulation of bifidobacteria and lactobacilli in pigs fed with FOS and transgalactooligosaccharides. These discrepancies may be explained by different prebiotic structures, dosage and methodology (Allen et al., 2013, Flickinger et al., 2003), complicating a direct comparison between the studies. Furthermore, other bacteria of the gut microbiota, including Salmonella and members of Roseburia and Bacteroides, can efficiently utilize FOS and GOS as growth substrates (Martin-Pelaez et al., 2008, Scott et al., 2013b, van der Meulen et al., 2006b) and can directly compete for these nutrients with bifidobacteria and lactobacilli. Using 454 pyrosequencing, we detected a consistent increase in the relative abundance of the genus Sharpea upon addition of prebiotics. This suggests that Sharpea spp. play a role for prebiotic degradation. They belong to the family Erysipelotrichaceae within the Clostridium

Cluster XVII. Members of this genus are heterofermentative and produce lactic acid and CO2 from glucose. They were first isolated from horse feces and are closely related to Eggerthia catenaformis (Morita et al., 2008, Salvetti et al., 2011). Higher net substrate availability upon prebiotic addition may be responsible for a higher abundance of Sharpea spp. Erysipelotrichaceae were also more abundant in pigs with increased feed consumption

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Discussion

(Buzoianu et al., 2012, Walsh et al., 2012), and accounted for a sevenfold higher proportion in mice fed a high energy diet (Fleissner et al., 2010). Yet, the exact role of the genus Sharpea remains unclear and further insights into prebiotic degradation or its involvement in possible cross-feeding reactions should be elucidated in future research.

Conclusion

Our data highlight the suitability of the novel porcine PolyFermS model to discover ecophysiological changes resulting from different nutritional treatments on S. Typhimurium

N-15 colonization. We showed that FOS and GOS distinctively inhibit N-15 colonization in this model, while the effect was enhanced in presence of B. thermophilum RBL67. This was likely due to a combined effect of SCFA and antimicrobial compound production and competition. We showed that RBL67 stimulates butyrate production in the presence of FOS, beneficially impacting swine gut health. Future research should thus focus on elucidating the antagonistic mechanisms of RBL67 towards N-15 in the presence of prebiotics such as FOS and GOS.

Acknowledgements

This work was supported by the Commission for Technology and Innovation (project number: 11962.1). We thank the Genetic Diversity Center (GDC) at ETH Zurich for support for quantitative real time PCR analysis and Dr. Franck Grattepanche, Eugenia Rigozzi, Eun-

Hee Doo, Lukas Meile and Simon Galenda for technical assistance. We further thank Dr.

Fabienne Wichmann for proof reading of the manuscript.

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Supporting information

Table S3.1. Primers and probes used for detection of bacterial target groups with qPCR.

Target primer/probe Sequence 5’-3’ Reference total 16S rRNA genes Eub338F ACT CCT ACG GGA GGC AGC AG Guo et al. Eub518R ATT ACC GCG GCT GCT GG (2008b) Bifidobacterium spp. xfp-fw ATC TTC GGA CCB GAY GAG AC Cleusix et al. xfp-rv CGA TVA CGT GVA CGA AGG AC (2010) Lactococcus/ F_Lacto 05 AGC AGT AGG GAA TCT TCC A Furet et al. Pediococcus/ R_Lacto 04 CGC CAC TGG TGT TCY TCC ATA TA (2009) Leuconostoc spp. Bacteroides-Prevotella Bac303F GAA GGT CCC CCACAT TG Ramirez-Farias et Bfr-Femrev CGC KAC TTG GCT GGT TCA G al. (2009) Enterobacteriaceae Eco1457F CAT TGA CGT TAC CCG CAG AAG AAG C Bartosch et al. Eco1652R CTC TAC GAG ACT CAA GCT TGC (2004) Clostridium Cluster IV Clep866mF TTA ACA CAA TAA GTW ATC CAC CTG G Ramirez-Farias et Clep1240mR ACC TTC CTC CGT TTT GTC AAC al. (2009) Bifidobacterium btherm RTF TTG CTT GCG GGT GAG AGT Mathys et al. thermophilum btherm RTR CGC CAA CAA GCT GAT AGG AC (2008) bthermTqm FAM-ATG TGC CGG GCT CCT GCA T-TAMRA

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Supporting Information

Figure S3.1. Microbial composition in reactors during treatment periods 2-4 measured by 454 pyrosequencing on phylum level. Microbial composition in reactor effluents was analyzed by 454 pyrosequencing of the entire 16S rRNA gene pool in the V5-V6 region. Reactor effluents were pooled in a ratio 1:1 from two consecutive days of the N-15 challenge period (days 3 and 4) for genomic DNA extraction and subsequent sequencing on a 454 Life Sciences Genome Sequencer GS FLX instrument. Quality-filtered sequencing reads were assigned using the Ribosomal Database Project (RDP) Bayesian classifier (v2.1) and applying a confidence threshold of 80%. CR: control reactor; values < 1% are summarized in the group others.

Figure S3.2. Microbial composition in reactors during treatment periods 2-4 measured by 454 pyrosequencing on genus level. Microbial composition in reactor effluents was analyzed by 454 pyrosequencing of the entire 16S rRNA gene pool in the V5-V6 region. Reactor effluents were pooled in a ratio 1:1 from two consecutive days of the N-15 challenge period (days 3 and 4) for genomic DNA extraction and subsequent sequencing on a 454 Life Sciences Genome Sequencer GS FLX instrument. Quality-filtered sequencing reads were assigned using the Ribosomal Database Project (RDP) Bayesian classifier (v2.1) and applying a confidence threshold of 80%. CR: control reactor; values < 1% are summarized in the group others.

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Chapter 4

Unraveling the transcriptome response of Salmonella enterica subsp. enterica serovar Typhimurium N-15 and Bifidobacterium thermophilum RBL67 grown in co-culture

Sabine A. Tanner, Christophe Chassard, Christophe Lacroix, Eugenia Rigozzi, Marc J. A.

Stevens

Laboratory of Food Biotechnology, Institute of Food, Nutrition and Health, ETH Zurich,

Zurich, Switzerland

Manuscript to be submitted.

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Abstract

Bifidobacterium thermophilum RBL67 (RBL67), a human fecal isolate and promising probiotic candidate, showed antagonistic and protective effects against Salmonella and Listeria in vitro. However, the underlying mechanisms fostering these health-related effects remain unknown. In this study, we explored the growth and transcriptome response of RBL67 and Salmonella enterica subsp. enterica serovar Typhimurium N-15 (N-15) in co- culture compared to mono-culture growth. Growth experiments were performed at controlled pH of 6.0 in a complex nutritive medium suitable for balanced growth of both, RBL67 and N- 15. RBL67 growth was slightly enhanced in presence of N-15. Conversely, N-15 growth was affected by the presence of RBL67 as revealed by reduced growth, downregulation of growth-related transcripts and an induction of virulence genes. RBL67 activated virulence genes located on the Salmonella pathogenicity islands 1 and 2 and encoding fimbrial adherence determinants. Flagellar genes, however, were repressed by RBL67. Sequential expression of flagellar, SPI 1 and fimbrial genes is essential for Salmonella infection. This led to the hypothesis that RBL67 triggers the expression of SPI 1 and fimbrial determinants prematurely, leading to a redundant energy expenditure and hence alleviated growth, as observed for N-15 in presence of RBL67. In the competitive environment of the gut such energy expenditure could lead to enhanced clearing of Salmonella. Our study provided first insights into probiotic-pathogen interaction on transcriptional level and suggests a mechanism for how probiotic organisms can protect the host from infections.

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Introduction

Probiotics are gaining attention for their potential to beneficially modulate the gut microbiota of the host, especially in dysbiotic or infected hosts. Such beneficial modulation is exerted via a wide array of mechanisms including direct and indirect antagonism with enteropathogens, improvement of the intestinal barrier function and activation of the mucosal immune system (O'Toole and Cooney, 2008, Walsh et al., 2014). Direct antagonism with enteropathogens is mediated via production of antimicrobial compounds (e.g. organic acids and bacteriocins), competition for nutrients and minerals, and occupation of adhesion sites (O'Toole and Cooney, 2008). Bifidobacteria and lactobacilli are important constituents of the human and animal gut microbiota and are the major genera used for probiotic applications, because they have been associated with a good health status of the host and have a long history of safe use (Gaggia et al., 2010, Walsh et al., 2014). Strains from both genera have been shown to protect against pathogens, with strain specific effects (Gaggia et al., 2010). Bifidobacterium thermophilum is a relatively oxygen tolerant Bifidobacterium species and has been isolated from bovine rumen, sewage, and from piglet, calf and baby feces (Biavati and Mattarelli, 2009, Toure et al., 2003). Peptidoglycan derived from B. thermophilum P2-91 have been shown to protect mice against Escherichia coli infections and improve cytotoxic activity of mice lymphocytes (Sasaki et al., 1994a, Sasaki et al., 1994b). Furthermore, chicken were more resistant to E. coli infection after oral administration of B. thermophilum CL (Kobayashi et al., 2002). The infant feces isolate B. thermophilum RBL67 (RBL67) is a promising probiotic candidate due to its activity against Salmonella and Listeria, robustness and technical properties, such as being moderately oxygen tolerant, growing at low pH down to 4.0 and up to elevated temperatures of 47°C (Toure et al., 2003, von Ah, 2006, von Ah et al., 2007, Zihler et al., 2011, Zihler et al., 2014). Further, the complete genome sequence of RBL67 is available (Jans et al., 2013). RBL67 was shown to decrease S. Typhimurium counts in an in vitro fermentation model of the gastrointestinal tract (Zihler et al., 2014), reduce severity of rotavirus-associated diarrhea in suckling mice (Gagnon, 2007), and block invasion of S. Typhimurium and L. monocytogenes to human intestinal cell lines (Moroni et al., 2006,

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Zihler et al., 2011). However, the underlying mechanism of RBL67-Salmonella interaction is not elucidated yet. Salmonella usually infects humans after ingestion of contaminated food products and is a worldwide leading cause for foodborne illness (EFSA and ECDC, 2014, Fabrega and Vila, 2013). Salmonella pathogenesis depends on multiple factors including motility and chemotaxis, adhesion, invasion and persistence (Fabrega and Vila, 2013). The majority of relevant virulence determinants are located on Salmonella pathogenicity islands (SPI) and are regulated by a complex molecular network that transmits environmental signals of conditions prevailing in the host (Fabrega and Vila, 2013). One of the key regulators for Salmonella invasion is HilA (Ellermeier and Slauch, 2007). HilA expression is affected by regulatory systems and environmental signals, thus enables Salmonella to foster different invasive phenotypes under different conditions (de Keersmaecker et al., 2005, Fabrega and Vila, 2013, Lucas and Lee, 2000). Salmonella invasion is dependent on the gut environment and is positively regulated by low oxygen tension, high osmolarity, neutral pH and acetate, whereas cationic peptides, bile, propionate and butyrate suppress invasion (Altier, 2005, Fabrega and Vila, 2013). The conditions in the gut are set through a complex interaction and metabolic network involving the host, the gut microbiota and the diet (Chassard and Lacroix, 2013). Modulation of the gut environment via pre- and/or probiotic treatments, may alter the gene expression of pathogens such as Salmonella, either indirectly via metabolic activity (e.g. production of short chain fatty acids) or directly via microbe-microbe interactions (O'Toole and Cooney, 2008). Indeed, probiotic strains were reported to modulate the transcriptional response of Salmonella. PhoP, a postulated repressor of hilA expression was activated, whereas HilA was repressed during growth in the presence of supernatant of Lactobacillus rhamnosus GG (de Keersmaecker et al., 2005). However, information about modulation of gene expression in enteropathogens due to direct microbe-microbe interaction is still scarce and unraveling the transcriptomic response of these multifactorial interactions is challenging. RNA-sequencing (RNA-seq) is a powerful tool to determine the transcriptional response of an organism in a complex culture because interference of signals from other organisms is limited (Gong and Yang, 2012). In this study we investigated the potential of B. thermophilum RBL67 to modulate the transcriptome of S. Typhimurium N-15. The response

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Introduction of RBL67 and Salmonella Typhimurium N-15 in the co-culture was compared to mono- cultures using RNA-seq in attempt to provide insight in the protective mechanism of RBL67 against Salmonella infections.

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Materials and Methods

Bacterial strains

Salmonella Typhimurium N-15 was isolated from a clinical case in Switzerland in 2007 and obtained from the National Reference Centre for Enteropathogenic Bacteria and Listeria (NENT; Zurich, Switzerland). Bifidobacterium thermophilum RBL67 (LMG S-23614) (RBL67), originally isolated from infant feces (Toure et al., 2003), was obtained from our own culture collection.

Batch fermentation conditions

Two sets of fermentations were performed, each set consisting of six fermentations. The first set was composed of three RBL67 mono-cultures and three RBL67-N15 co-cultures. The second set consisted of three N-15 mono-cultures and three N-15-RBL67 co-cultures. Bacteria were cultured in 350 mL scale Sixfors bioreactors (Infors AG, Bottmingen, Switzerland) using 310 mL YCFA medium (Duncan et al., 2002) supplemented with 6 g L-1 glucose (Sigma-Aldrich Chemie GmbH, Buchs, Switzerland). Fermentations were performed for 24 h at 38°C with stirring at 200 rpm. A constant pH of 6.0 was maintained by automated addition of 2.5 M NaOH. Anaerobic conditions were ensured by purging the headspace with

CO2. Both, mono- and co-culture fermentations were inoculated with 4 % (v/v) of a 16 h grown pre-culture. Pre-cultures were prepared by twice propagating RBL67 and N-15 in 10 mL YCFA medium in Hungate tubes to adapt the strains to the medium and anaerobic conditions. The pre-cultures were centrifuged (6000 x g, 5 min), washed in 0.1 % peptone water reduced with 0.05 % L-cysteine hydrochloride (VWR International AG, Dietikon, Switzerland) and resuspended in 2 mL peptone water before inoculation to the fermenter.

Growth was monitored by optical density measurements at 600 nm (OD600) using a Biochrom WPA CO8000 cell density meter (Biochrom, Cambridge, United Kingdom). Samples were taken hourly until the stationary growth phase was reached, with a final sample taken after 24 hours. Metabolite concentrations were determined by HPLC analysis (Thermo Fisher Scientific, Wohlen, Switzerland) as described previously (Tanner et al., 2014). Carbon balance was calculated in percentage of mole carbon recovered as organic acids (acetate, lactate, propionate, butyrate, formiate) in relation to glucose consumed. Viable cell counts of

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Materials and Methods

RBL67 were determined by plating appropriate dilutions on MRS agar (Biolife, Milan, Italy), supplemented with 0.05 % L-cysteine hydrochloride. Viable cell counts of N-15 were determined on MacConkey Agar No. 2 (Oxoid AG, Pratteln, Switzerland). Co-culture effluent samples were plated on MRS-C agar supplemented with 5 g L-1 mupirocin (VWR International AG, Dietikon, Switzerland) to select for RBL67 (Rada et al., 1999), and on MacConkey Agar No. 2 to select for N-15. MRS plates were incubated anaerobically using anaerobic gas pack systems (AnaeroGen TM, Oxoid AG) at 37°C for 48 hours. MacConkey Agar plates were incubated aerobically at 37°C for 24 hours. The maximum specific growth rate was calculated for each replication from the slope of the exponential growth phase and is expressed as mean values ± SD of triplicate experiments. Except for RBL67 co-culture, where mean values ± SD of duplicate experiments are reported.

Sampling for RNA extraction

RNA was extracted from culture samples taken after 4 and 5 h (N-15) or 5 h (RBL67) for mono-and co-cultures. RBL67 and N-15 mono- and co-culture samples were subjected to different procedures to allow optimal RNA extraction of both RBL67 and N-15. Mono- and co-culture samples of N-15 cultures (20 mL each) were directly transferred to 20 mL 60 % glycerol (Sigma-Aldrich Chemie GmbH, Buchs, Switzerland) at -40°C, kept on ice for 20 min and centrifuged for 15 min (3220 x g, 4°C). The supernatant was discarded and the resulting pellets were immediately frozen at -80°C until RNA extraction. Mono- and co- culture samples of RBL67 cultures were shortly centrifuged (10000 x g, 20 s). The RBL67 mono-culture pellets were resuspended in 400 μl MRS-C and transferred to a pre-chilled screw cap tube, containing 500 mg glass beads (0.1 mm; Biospec Products Inc., Bartlesville, USA), 500 μl chloroform/phenol (1:1, v/v), 30 μl 3 M Na-acetate (pH 5.2) and 30 μl SDS 10 % (Stevens et al., 2008). The pellet of the RBL67 co-culture was resuspended in 12 mL of RNAprotect® Bacteria Reagent (Qiagen AG, Basel, Switzerland), incubated for 5 min at room temperature and centrifuged again (10000 x g, 20 s). Both samples were then rapidly frozen in liquid nitrogen and stored at -80°C until RNA extraction.

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RNA-extraction and ribosomal RNA depletion

Frozen pellets from N-15 samples were resuspended in 200 μl 10 mM Tris-buffer (pH 8.0). Total RNA was extracted using the High Pure RNA isolation kit (Roche Diagnostics, Rotkreuz, Switzerland), according to the manufacturer’s instructions. Total RNA of RBL67 mono- and co-culture samples was extracted using a phenol/chloroform extraction method (Stevens et al., 2008), followed by a purification using the High Pure RNA isolation kit (Roche Diagnostics). Prior to RNA extraction the sample from the RBL67 co-culture was resuspended in MRS-C medium and transferred to a pre-chilled mix of 500 mg glass beads (Biospec Products Inc.) and TRI Reagent® (Life Technologies Europe BV, Zug, Switzerland). RNA quantity and purity was determined on a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Washington, USA) and RNA integrity was tested with an Agilent 2100 Bioanalyzer (Agilent, Basel, Switzerland). RBL67 samples with a RNA integrity number (RIN) ≥ 9.5 and a 16S/23S-rRNA ratio ≥ 1.6 were used for ribosomal RNA depletion and subsequent RNA-sequencing. Due to the aberrant nature of ribosomal RNA of S. Typhimurium (Winkler, 1979), the RIN value and the 16S/23S-rRNA ratio could not be calculated for N-15. Hence we selected samples which were comparable to the profiles reported previously for Salmonella (Smith et al., 1988), i.e. a straight zero line (indicating no RNA degradation), absence of 23S RNA and two additional peaks neighboring the 16S peak. Depletion of ribosomal RNA from 10 μg total RNA was performed using the MICROBExpress™ Bacterial mRNA Enrichment Kit (Life Technologies Europe BV, Zug, Switzerland) according to the manufacturer’s instructions. Additionally, EDTA (1 mM) was added to chelate divalent cations present in the RNA solution.

RNA-sequencing

RNA-sequencing was performed on an Illumina HiSeq 2000 sequencer (Illumina Inc., California, USA) at the Functional Genomics Center Zurich (FGCZ). Libraries were prepared using the TruSeq Stranded mRNA Sample Prep Kit (Illumina) according to the manufacturer’s protocol. The libraries were qualitatively and quantitatively checked using a Qubit® (1.0) Fluorometer (Life Technologies Europe BV, Zug, Switzerland) and a Bioanalyzer 2100 (Agilent, Basel, Switzerland) and were subsequently normalized at 10 nM

128

Materials and Methods in Tris-Cl (10mM, pH 8.5) containing 0.1 % Tween20. Cluster generation was performed using the TruSeq SR Cluster Kit v3-cBot-HS (Illumina) using 8 pM of pooled normalized libraries on the cBOT and stranded sequencing of 100 bp was done using the TruSeq SBS Kit v3-HS (Illumina). Each set of samples (n=6) was analyzed in a separate sequencing lane.

RNA-seq data analysis

Illumina raw data reads (100 bp) were separated by barcode and mapped against the genome of RBL67 (GenBank accession no. CP004346) or Salmonella Typhimurium LT2 (GenBank accession no. AE006468) using CLC Genomics Workbench 6.5.1 (http://www.clcbio.com), applying the default settings. Maximum allowance of mismatches was set at 2, minimum length fraction at 0.9 and minimum similarity fraction at 0.8. Statistical analysis for differential gene expression of the mono- and co-cultures was done with the statistical software R (http://www.R-project.org) using the GLM method (McCarthy et al., 2012) included in the Bioconductor edgeR software package (Robinson and Smyth, 2007, Robinson and Smyth, 2008, Robinson et al., 2010, Robinson and Oshlack, 2010), based on negative binomial distribution. Low reads (sum of reads of all samples < 3 counts per million (cpm)) and high reads (number of reads > 50’000 cpm in all samples) were filtered out before data normalization. A false discovery rate (FDR) value < 0.05 was used as cut off for significant differentially expressed genes and log2 ratio > 1 and < -1 was used as cutoff for differential transcription for genes higher expressed in mono-culture and co-culture (Rosenthal et al., 2011). Proteins of RBL67 and LT2 were assigned to gene ontology categories (GO) using Blast2GO at standard settings (Conesa et al., 2005). GO categories enrichment analyses were performed and visualized using the BiNGO plugin (Maere et al., 2005) in cytoscape (v.3.0.1) applying the hypergeometric test with Benjamini and Hochberg false discovery rate correction option. The significance cutoff for overrepresented gene ontology categories was a corrected p-value of < 0.05. Virulence factors of Salmonella LT2 were identified by genome wide blast against the virulence factor database (VFDB) (Chen et al., 2012), using a cut off E-value of 1-20. Significant enrichment of virulence factors was calculated using the Fisher's Exact Test Calculator for 2x2 Contingency at www.research.microsoft.com.

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Statistical analysis

Statistical analysis for cell counts (log10 transformation) and growth rates were performed using JMP 10.0 (SAS Institute., Cary, NC). Cell counts and maximum specific growth rates of mono-and co-cultures were tested for significant differences using the non-parametric Kruskal-Wallis (P-value < 0.05).

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Results

Growth characteristics of N-15 and RBL67 in mono- and co-culture

To analyze interactions of B. thermophilum RBL67 and Salmonella N-15, both strains were grown in pH controlled mono- and co-cultures (pH 6.0) and growth characteristics were compared (Figure 4.1).

-1 The maximum specific growth rate of RBL67 in mono-culture (μmax = 0.26 ± 0.05 h ) was

-1 lower than in co-cultures (μmax = 0.33 ± 0.01 h ), while stationary growth phase was reached

-1 after 8 hours for both, with maximum cell counts of 8.88 ± 0.10 log10 cfu mL and 9.12 ±

-1 0.14 log10 cfu mL , respectively (Figure 4.1a). Glucose consumption and metabolite profiles were similar for RBL67 in mono- and co- culture, however a slightly higher acetate (+ 5 mM) concentration was recorded for the mono-culture (Figure 4.1b). Glucose was depleted after 8 hours in both cultures, which corresponds to the onset of the stationary growth phase, indicating growth limitation by the sugar source. Main metabolites produced in mono-cultures were acetate (50 ± 3 mM), lactate (15 ± 1 mM), and formate (9 ± 0.3 mM) with carbon recovery of 103 %. In co-cultures similar levels of acetate (45 ± 2 mM), lactate (16 ± 2 mM) and formate (7 ± 2 mM) were produced, with carbon recovery of 100 %.

Salmonella N-15 had similar maximum specific growth rates in mono- and co-culture (μmax=

-1 -1 0.39 ± 0.02 h and μmax = 0.38 ± 0.04 h , respectively) and reached the stationary growth phase after 8 hours (Figure 4.1c). Maximum cell counts of N-15 in mono-cultures (9.10 ±

-1 -1 0.16 log10 cfu mL ) were higher (P < 0.05) than in co-cultures (8.82 ± 0.08 log10 cfu mL ). Glucose was depleted after 10 and 8 hours for mono- and co-cultures, respectively (Figure 4.1d). Main metabolites produced in the N-15 mono-culture were acetate (27 ± 0.4 mM), lactate (23 ± 2 mM) and formate (12 ± 2 mM), with carbon recovery of 93 %. In co-culture, acetate production was higher (42 ± 4 mM), while lactate (17 ± 2 mM) and formate (8 ± 3 mM) were lower compared to the mono-culture. The carbon mass balance in the co-culture was 99 %.

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Figure 4.1. Cell counts (a+c) and metabolic activity (b+d) of RBL67 (a+b) and N-15 (c+d) in mono- (open symbols) and co-cultures (closed symbols) in YCFA medium. Means ± SD from three biological replicates. * cell counts significantly different between mono- and co-culture with the non- parametric Kruskal-Wallis Test (P<0.05); square: glucose; circle: acetate; triangle: lactate and diamonds: formate.

Global transcriptional response of RBL67 to co-culture with N-15

The transcriptome profiles of B. thermophilum RBL67 in mono- and co-culture were compared to elucidate the response of RBL67 to N-15. Samples were taken after 5 hours of

-1 growth (Figure 4.1), corresponding to 8.07 ± 0.07 and 8.53 ± 0.04 log10 cfu mL and total metabolite concentrations of 34.3 ± 5.0 mM and 42.1 ± 0.5 mM in mono- and co-cultures, respectively. RNA sequencing of RBL67 cultures resulted in a mean read number of

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37’365’651 and 31’752’403 for mono- and co-cultures, respectively. Thereof, 93 % of the reads deriving from the mono-cultures and 79 % of the reads from the co-cultures could be mapped onto the RBL67 genome. Mapping the co-culture reads to the Salmonella Typhimurium LT2 genome resulted in a low number of mapped reads (< 5’000’000, data not shown). Gene expression analysis revealed 57 genes being significantly differentially expressed (FDR < 0.05 and log2 ratio > 1 and < -1) in mono- compared to co-cultures (Table 4.1 and 4.2). Assignation to Gene Ontology (GO) categories did not result in overrepresented categories and therefore we focused directly on the differentially expressed genes. An operon involved in lipid export (D805_0155-D805-0157), sugar transport (D805_1600-D805_1602) and an operon of undefined function (D805_1659-D805_1660), together with its putative regulator of the HxlR family (D805_1658) were higher expressed in co- compared to monocultures (Table 4.1). A stress response was also triggered in co-cultures as revealed by higher expression of the protease ClpB (D805_1594) and its transcriptional regulator HspR (D805_1678), and the SOS-response repressor and protease LexA (D805_0599). Additional functions assigned to RBL67 genes higher expressed in co-cultures with Salmonella N-15 were related to amino acid metabolism (D805_1238 and D805_1530) and metal transport (D805_1209), while seven genes were classified as hypothetical proteins. Twenty-seven genes were higher expressed in mono- compared to co-cultures, of which 12 were classified as hypothetical proteins (Table 4.2). A putative operon encoding glycosyltransferases (ORF bt_0351-bt_0356), three genes involved in amino acid metabolism (D805_0341, D805_0525 and D805_1313) and two genes in metal transport (D805_0345 and D805_0885) were higher expressed.

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Table 4.1. Bifidobacterium thermophilum RBL67 genes higher expressed in co-culture.

ORF Gene Function logFC logCPM FDR

Bt_0058 D805_0058 Oligopeptide transport ATP-binding protein OppF (TC -1.52 4.09 4E-06 3.A.1.5.1) Bt_0077 D805_0077 hypothetical protein -1.17 6.36 4.8E-07 Bt_0155 D805_0155 Transcriptional regulator, MarR family -1.95 6.80 1.2E-17 Bt_0156 D805_0156 hypothetical protein -1.50 3.64 0.00401 Bt_0157 D805_0157 Lipid A export ATP-binding/permease protein MsbA -1.17 8.10 6.2E-06 Bt_0382 D805_0382 hypothetical protein -1.13 7.94 1.4E-06 Bt_0466 D805_0466 FIG00672402: hypothetical protein -1.00 7.21 9.5E-08 Bt_0503 D805_0503 possible conserved integral membrane protein -1.14 3.08 0.01355 Bt_0599 D805_0599 SOS-response repressor and protease LexA (EC 3.4.21.88) -1.24 6.59 7.9E-09 Bt_0600 D805_0600 hypothetical protein -2.14 6.18 2.6E-27 Bt_0707 D805_0707 Inner membrane protein -1.14 6.16 3.4E-06 Bt_1209 D805_1209 Zinc ABC transporter, periplasmic-binding protein ZnuA -1.15 4.69 1.3E-05 Bt_1238 D805_1238 Cystathionine beta-synthase (EC 4.2.1.22) -1.23 6.23 2.7E-09 Bt_1392 D805_1392 putative aminotransferase -1.23 4.66 1.2E-05 Bt_1393 D805_1393 hypothetical protein -1.03 4.30 0.00118 Bt_1530 D805_1530 Glutamate 5-kinase (EC 2.7.2.11) -1.08 8.00 3.9E-09 Bt_1531 obgE COG0536: GTP-binding protein Obg -1.03 9.16 2.5E-06 Bt_1591 D805_1591 DNA recombination protein RmuC -1.27 7.58 3.6E-12 Bt_1594 D805_1594 ClpB protein -1.06 8.68 2.1E-06 Bt_1600 D805_1600 Maltodextrin glucosidase (EC 3.2.1.20) -1.17 6.23 5.6E-08 Bt_1601 D805_1601 ABC-type sugar transport system, permease component -1.85 4.02 3.9E-09 Bt_1602 D805_1602 MSM (multiple sugar metabolism) operon regulatory -1.68 3.28 1.1E-05 protein Bt_1621 D805_1621 Sortase A, LPXTG specific -1.09 3.52 0.00557 Bt_1622 D805_1622 hypothetical protein -1.44 4.42 5.6E-08 Bt_1637 D805_1637 COG family: predicted phosphohydrolases -1.63 6.91 1.8E-21 Bt_1658 D805_1658 Transcriptional regulator, HxlR family -1.07 4.37 0.00624 Bt_1659 D805_1659 Rrf2-linked NADH-flavin reductase -2.01 5.23 2.9E-14 Bt_1660 D805_1660 COG2110, Macro domain, possibly ADP-ribose binding -1.83 4.90 2.4E-16 module Bt_1678 D805_1678 HspR, transcriptional repressor of DnaK operon -1.08 5.37 4.9E-08 Bt_1702 D805_1702 transport protein -1.80 6.58 3.5E-23

ORF: open reading frame; logFC: log2 fold change; logCPM: log2 counts per million; fdr: false discovery rate

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Table 4.2. Bifidobacterium thermophilum RBL67 genes higher expressed in mono-culture.

ORF Gene Function logFC logCPM FDR

Bt_0063 D805_0063 FIG00519111: hypothetical protein 1.76 5.67 1.8E-12 Bt_0064 D805_0064 HTH domain protein 2.18 3.98 4.1E-09 Bt_0075 D805_0075 hypothetical protein 1.04 5.15 0.00011 Bt_0178 D805_0178 Ribonucleotide reductase of class Ib (aerobic), alpha 1.21 7.11 0.04089 subunit (EC 1.17.4.1) Bt_0341 D805_0341 Transcriptional regulator, GntR family domain / Aspartate 1.93 3.72 0.02045 aminotransferase (EC 2.6.1.1) Bt_0345 D805_0345 Manganese transport protein MntH 1.84 3.71 0.04055 Bt_0351 D805_0351 Glycosyl transferase, group 2 family protein 1.49 4.48 0.00041 Bt_0352 D805_0352 Glycosyltransferase( EC:2.4.1.- ) 1.74 4.96 1.3E-07 Bt_0354 D805_0354 glycosyl transferase, group 1 family protein 2.07 4.79 3.0E-10 Bt_0355 D805_0355 hypothetical protein 1.34 6.14 4.5E-06 Bt_0356 D805_0356 Glycosyltransferase (EC 2.4.1.-) 2.14 4.96 1.5E-15 Bt_0512 D805_0512 hypothetical protein 2.01 2.39 0.03026 Bt_0524 D805_0524 D-lactate dehydrogenase (EC 1.1.1.28) 2.85 3.15 0.00049 Bt_0525 D805_0525 Aspartate aminotransferase (EC 2.6.1.1) 1.91 3.11 0.01352 Bt_0652 D805_0652 Oligopeptide transport system permease protein OppC (TC 2.25 2.47 0.00227 3.A.1.5.1) Bt_0656 D805_0656 hypothetical protein 1.57 3.01 0.01267 Bt_0665 D805_0665 hypothetical protein 1.18 5.85 2.0E-05 Bt_0693 D805_0693 Acetyltransferase, GNAT family 2.23 6.96 2.7E-31 Bt_0694 D805_0694 hypothetical protein 2.80 3.46 1.5E-14 Bt_0698 D805_0698 hypothetical protein 1.19 4.02 0.00369 Bt_0837 D805_0837 putative TraA-like conjugal transfer protein 2.75 3.13 0.00038 Bt_0885 D805_0885 Ferric iron ABC transporter, iron-binding protein 1.63 2.52 0.00624 Bt_0928 D805_0928 hypothetical protein 1.90 2.59 0.00374 Bt_0948 D805_0948 hypothetical protein 2.08 2.96 0.00118 Bt_1220 D805_1220 hypothetical protein 2.60 3.03 0.00015 Bt_1313 D805_1313 Methionine ABC transporter permease protein 1.52 2.03 0.04091 Bt_1771 D805_1771 hypothetical protein 1.01 4.56 0.01571

ORF: open reading frame; logFC: log2 fold change; logCPM: log2 counts per million; fdr: false discovery rate

Global transcriptional response of N-15 to co-culture with RBL67

Because RNA-seq analyses of the co-culture after 5 hours growth resulted in low read mapping (data not shown), the transcriptome of N-15 mono-and co-culture with RBL67 was analyzed after 4 hours growth. This time point corresponds to cell counts of 8.42 ± 0.12 and

-1 8.02 ± 0.06 log10 cfu mL and total metabolite concentrations of 20.4 ± 2.4 mM and 27.5 ± 6.9 mM for mono- and co-cultures, respectively. From the total mean read numbers of 38’838’013 (mono-culture) and 30’020’491 (co-culture), 91 % and 52 % could be mapped onto the genome of the sequenced strain Salmonella Typhimurium LT2, respectively. LT2 contains a large plasmid encoding virulence factors, but no reads of N-15 could be mapped to

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Casadesus, 1999). Analysis of differential gene expression (FDR < 0.05 and log2 ratio > 1 and < -1) revealed 701 and 1278 genes higher expressed in mono- and co-cultures, respectively, including 92 and 219 hypothetical proteins (Appendix;Table A1 and A2). Enrichment analyses of GO categories resulted in 88 categories being significantly overrepresented in co-culture (P < 0.05 after Benjamini-Hochberg correction), which could be clustered into biological processes (47 categories), molecular function (29), and cellular component (29). In the cluster of biological processes the GO categories “localization” (GO: 051179), “establishment of localization” (GO:051234) and “transport” (GO:006810) were significantly overrepresented in the co-culture (Table 4.3, Figure 4.2). Comparison of the genes encompassed by these categories revealed all (N=281) genes being identical. On a lower hierarchical level, the “protein secretion by the type III secretion system” was highly overrepresented (GO:030254, N=49 genes) as well as metal and carbohydrate transport systems, including “PEP-dependent sugar phosphotransferase systems” (GO:009401, N=33). Other overrepresented categories included “multi-organism process” (GO:051704, N=47) , “pathogenesis” (GO:009405, N=26) and “interspecies interaction between organisms” (GO:044419, N=35). The majority of the genes (N=26) in the latter category were clustered in GO:052049:” interaction with host via protein secreted by type III secretion system”. The 26 genes assigned to this category were also found to be present in GO:030254: “type III secretion system”, as mentioned above. In the molecular function cluster, “transporter activity” (GO:005215) was significantly (N=215) overrepresented, with transmembrane transporters being highly abundant (Table 4.3), paralleling the results found in the biological process cluster. The cellular component cluster included membrane-associated functions (GO:016020, N=381) including again the overrepresented “type III protein secretion system complex” (GO:030257, N=26), (Table 4.3). Summarizing, the transcriptomic analyses of N- 15 in co-cultures compared to mono-cultures revealed responses mainly involved in transport (mainly carbohydrate and metals) and extracellular biology (secretion, cell wall organization, interaction with other organisms).

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Table 4.3. GO categories of the Salmonella Typhimurium N-15 transcriptome significantly overrepresented in the co-culture with RBL67.

number of GO category p-value description genes

Biological process

GO:051234 4.77E-28 281 establishment of localization GO:006810 4.77E-28 281 transport GO:051179 6.29E-25 281 localization GO:030254 8.34E-15 42 protein secretion by the type III secretion system GO:051704 1.80E-14 47 multi-organism process GO:051701 3.20E-12 35 interaction with host GO:044419 3.20E-12 35 interspecies interaction between organisms GO:044403 3.20E-12 35 symbiosis, encompassing mutualism through parasitism GO:008643 8.15E-11 52 carbohydrate transport GO:046903 4.51E-10 49 secretion GO:032940 4.51E-10 49 secretion by cell GO:009306 4.51E-10 49 protein secretion GO:052047 5.68E-10 26 interaction with other organism via secreted substance involved in symbiotic interaction GO:052049 5.68E-10 26 interaction with host via protein secreted by type III secretion system GO:052048 5.68E-10 26 interaction with host via secreted substance involved in symbiotic interaction GO:052210 5.68E-10 26 interaction with other organism via protein secreted by type III secretion system involved in symbiotic interaction GO:044046 5.68E-10 26 interaction with host via substance released outside of symbiont GO:051649 4.63E-09 49 establishment of localization in cell GO:051641 8.25E-09 49 cellular localization GO:009405 9.76E-08 26 pathogenesis GO:015031 2.55E-06 49 protein transport GO:045184 2.55E-06 49 establishment of protein localization GO:033036 2.96E-06 50 macromolecule localization GO:008104 3.67E-06 49 protein localization GO:009401 8.19E-06 33 phosphoenolpyruvate-dependent sugar phosphotransferase system GO:007047 2.10E-04 12 cellular cell wall organization GO:045229 2.10E-04 12 external encapsulating structure organization GO:071555 7.60E-04 12 cell wall organization GO:009242 2.19E-03 7 colanic acid biosynthetic process GO:052126 2.19E-03 7 movement in host environment GO:052192 2.19E-03 7 movement in environment of other organism involved in symbiotic interaction GO:044409 2.19E-03 7 entry into host GO:046377 2.19E-03 7 colanic acid metabolic process GO:051828 2.19E-03 7 entry into other organism involved in symbiotic interaction GO:022610 3.83E-03 17 biological adhesion GO:007155 3.83E-03 17 cell adhesion GO:030001 6.91E-03 33 metal ion transport GO:006814 8.26E-03 16 sodium ion transport GO:009235 1.23E-02 14 cobalamin metabolic process GO:009236 1.23E-02 14 cobalamin biosynthetic process

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number of GO category p-value description genes

GO:015891 2.34E-02 5 siderophore transport GO:019184 2.34E-02 5 nonribosomal peptide biosynthetic process GO:006811 2.34E-02 47 ion transport GO:015672 2.61E-02 27 monovalent inorganic cation transport GO:006812 3.45E-02 37 cation transport GO:006778 4.86E-02 16 porphyrin metabolic process GO:006779 4.86E-02 16 porphyrin biosynthetic process

Molecular function

GO:005215 4.79E-20 215 transporter activity GO:015144 1.57E-09 39 carbohydrate transmembrane transporter activity GO:022892 1.31E-08 111 substrate-specific transporter activity GO:022891 9.42E-08 99 substrate-specific transmembrane transporter activity GO:051119 9.81E-08 34 sugar transmembrane transporter activity GO:022857 1.61E-07 107 transmembrane transporter activity GO:008324 1.82E-07 64 cation transmembrane transporter activity GO:005402 1.79E-06 29 cation:sugar symporter activity GO:015075 5.82E-06 70 ion transmembrane transporter activity GO:015291 6.89E-06 44 secondary active transmembrane transporter activity GO:015294 6.89E-06 34 solute:cation symporter activity GO:015293 6.89E-06 34 symporter activity GO:015295 6.89E-06 27 solute:hydrogen symporter activity GO:005351 6.89E-06 27 sugar:hydrogen symporter activity GO:022804 5.84E-05 71 active transmembrane transporter activity GO:015082 6.07E-03 13 di-, tri-valent inorganic cation transmembrane transporter activity GO:046873 7.20E-03 23 metal ion transmembrane transporter activity GO:022890 9.53E-03 28 inorganic cation transmembrane transporter activity GO:015149 1.06E-02 8 hexose transmembrane transporter activity GO:015145 1.06E-02 8 monosaccharide transmembrane transporter activity GO:015343 2.20E-02 5 siderophore-iron transmembrane transporter activity GO:042927 2.20E-02 5 siderophore transporter activity GO:005381 2.59E-02 8 iron ion transmembrane transporter activity GO:046915 2.59E-02 11 transition metal ion transmembrane transporter activity GO:042879 2.59E-02 6 aldonate transmembrane transporter activity GO:015128 2.59E-02 6 gluconate transmembrane transporter activity GO:005506 3.93E-02 16 iron ion binding GO:046943 4.82E-02 26 carboxylic acid transmembrane transporter activity GO:005342 4.82E-02 26 organic acid transmembrane transporter activity

Cellular component

GO:016020 6.15E-12 381 membrane GO:030257 3.05E-10 26 type III protein secretion system complex GO:005886 2.84E-08 314 plasma membrane GO:044425 5.28E-06 129 membrane part GO:016021 5.28E-06 124 integral to membrane GO:031224 5.28E-06 124 intrinsic to membrane GO:009279 4.20E-05 47 cell outer membrane

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number of GO category p-value description genes

GO:019867 3.67E-04 47 outer membrane GO:043234 4.76E-04 50 protein complex GO:009289 9.12E-04 15 pilus GO:044462 2.45E-03 104 external encapsulating structure part GO:043190 3.32E-02 4 ATP-binding cassette (ABC) transporter complex

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Figure 4.2. Hierarchical visualization of overrepresented GO categories in the biological process cluster of the S. Typhimurium N-15 transcriptome in co-culture with B. thermophilum RBL67. Data were visualized using the BiNGO plugin in Cytoscape. For better readability, all children nodes from the metabolic process node and one children node from the cellular process node were omitted in this figure. Dark green categories are most significantly overrepresented, white categories are not significantly overrepresented. The area of the node is proportional to the number of genes assigned to the category.

Results

In mono-cultures, 133 GO categories were significantly enriched. 101 GO categories were assigned to biological processes, 6 into molecular functions and 26 into cellular components (Table 4.4). Overrepresented GO categories included “cellular process” in the biological processes cluster (GO:009987; N=276) and “structural molecule activity” in the molecular function cluster, (GO:005198; N=39). In the cellular components cluster the “intracellular parts” (GO:044424; N=391), which includes the categories “ribosome” (GO:005840; N=33) and “flagellum” (GO:019861; N=19), were overrepresented. GO category 019861 (“flagellum”) encompasses the genes flgNHMIKGCLB, fliLSKOEIHGT and cheZ, a chemotaxis regulator. Overall, GO-categories related to cell growth (cellular processes, ribosome) and flagella were significantly overrepresented in the mono-culture transcriptome of Salmonella N-15.

Table 4.4. GO categories of Salmonella Typhimurium N-15 transcriptome significantly overrepresented in the mono-culture.

number GO category p-value description of genes

Biological process

GO:044249 1.69E-20 168 cellular biosynthetic process GO:009058 2.27E-18 174 biosynthetic process GO:010467 1.16E-11 70 gene expression GO:009987 2.05E-11 276 cellular process GO:044237 1.12E-10 236 cellular metabolic process GO:006412 1.07E-09 49 translation GO:044238 2.26E-08 227 primary metabolic process GO:034645 5.65E-08 74 cellular macromolecule biosynthetic process GO:009059 1.43E-07 75 macromolecule biosynthetic process GO:044267 2.58E-07 66 cellular protein metabolic process GO:006633 1.86E-06 12 fatty acid biosynthetic process GO:019538 4.61E-06 82 protein metabolic process GO:044260 5.80E-06 118 cellular macromolecule metabolic process GO:008299 5.80E-06 12 isoprenoid biosynthetic process GO:006720 5.80E-06 12 isoprenoid metabolic process GO:008610 7.04E-06 32 lipid biosynthetic process GO:008152 1.73E-05 286 metabolic process GO:044255 1.80E-05 32 cellular lipid metabolic process GO:043170 1.93E-05 138 macromolecule metabolic process GO:006629 2.55E-05 34 lipid metabolic process GO:006631 4.06E-05 12 fatty acid metabolic process GO:048870 4.72E-04 13 cell motility GO:051674 4.72E-04 13 localization of cell

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number GO category p-value description of genes GO:001539 4.72E-04 13 ciliary or flagellar motility GO:044283 6.74E-04 62 small molecule biosynthetic process GO:006928 8.07E-04 13 cellular component movement GO:006350 9.66E-04 9 transcription GO:043064 1.53E-03 8 flagellum organization GO:009141 2.48E-03 10 nucleoside triphosphate metabolic process GO:016070 2.77E-03 30 RNA metabolic process GO:009108 3.33E-03 17 coenzyme biosynthetic process GO:030030 3.43E-03 8 cell projection organization GO:009142 4.02E-03 9 nucleoside triphosphate biosynthetic process GO:006351 5.55E-03 7 transcription, DNA-dependent GO:040011 5.89E-03 17 locomotion GO:044281 8.06E-03 106 small molecule metabolic process GO:009296 8.41E-03 6 flagellum assembly GO:034641 9.65E-03 117 cellular nitrogen compound metabolic process GO:006139 9.87E-03 77 nucleobase, nucleoside, nucleotide and nucleic acid metabolic process GO:032774 9.87E-03 7 RNA biosynthetic process GO:019720 9.87E-03 7 Mo-molybdopterin cofactor metabolic process GO:032324 9.87E-03 7 molybdopterin cofactor biosynthetic process GO:043545 9.87E-03 7 molybdopterin cofactor metabolic process GO:051189 9.87E-03 7 prosthetic group metabolic process GO:006777 9.87E-03 7 Mo-molybdopterin cofactor biosynthetic process GO:016053 1.01E-02 31 organic acid biosynthetic process GO:046394 1.01E-02 31 carboxylic acid biosynthetic process GO:009219 1.10E-02 4 pyrimidine deoxyribonucleotide metabolic process GO:009394 1.10E-02 4 2'-deoxyribonucleotide metabolic process GO:042180 1.26E-02 58 cellular ketone metabolic process GO:030031 1.55E-02 6 cell projection assembly GO:006732 1.55E-02 20 coenzyme metabolic process GO:042559 1.55E-02 7 pteridine and derivative biosynthetic process GO:042558 1.55E-02 7 pteridine and derivative metabolic process GO:046034 1.55E-02 7 ATP metabolic process GO:015985 1.55E-02 7 energy coupled proton transport, down electrochemical gradient GO:015986 1.55E-02 7 ATP synthesis coupled proton transport GO:006119 1.55E-02 7 oxidative phosphorylation GO:006754 1.55E-02 7 ATP biosynthetic process GO:044271 1.65E-02 52 cellular nitrogen compound biosynthetic process GO:006950 1.72E-02 26 response to stress GO:022607 1.93E-02 16 cellular component assembly GO:044085 2.28E-02 22 cellular component biogenesis GO:009152 2.52E-02 10 purine ribonucleotide biosynthetic process GO:009201 2.53E-02 7 ribonucleoside triphosphate biosynthetic process GO:009206 2.53E-02 7 purine ribonucleoside triphosphate biosynthetic process GO:009145 2.53E-02 7 purine nucleoside triphosphate biosynthetic process GO:006807 2.99E-02 121 nitrogen compound metabolic process GO:019748 3.10E-02 4 secondary metabolic process

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number GO category p-value description of genes GO:009234 3.10E-02 4 menaquinone biosynthetic process GO:009233 3.10E-02 4 menaquinone metabolic process GO:042362 3.10E-02 4 fat-soluble vitamin biosynthetic process GO:042371 3.10E-02 4 vitamin K biosynthetic process GO:042373 3.10E-02 4 vitamin K metabolic process GO:006775 3.10E-02 4 fat-soluble vitamin metabolic process GO:009150 3.10E-02 10 purine ribonucleotide metabolic process GO:006164 3.35E-02 11 purine nucleotide biosynthetic process GO:009165 3.40E-02 15 nucleotide biosynthetic process GO:009205 3.58E-02 7 purine ribonucleoside triphosphate metabolic process GO:009199 3.58E-02 7 ribonucleoside triphosphate metabolic process GO:009144 3.58E-02 7 purine nucleoside triphosphate metabolic process GO:019752 3.59E-02 53 carboxylic acid metabolic process GO:043436 3.59E-02 53 oxoacid metabolic process GO:009211 3.59E-02 3 pyrimidine deoxyribonucleoside triphosphate metabolic process GO:009200 3.59E-02 3 deoxyribonucleoside triphosphate metabolic process GO:009120 3.59E-02 3 deoxyribonucleoside metabolic process GO:046125 3.59E-02 3 pyrimidine deoxyribonucleoside metabolic process GO:009221 3.59E-02 3 pyrimidine deoxyribonucleotide biosynthetic process GO:009263 3.59E-02 3 deoxyribonucleotide biosynthetic process GO:009265 3.59E-02 3 2'-deoxyribonucleotide biosynthetic process GO:009260 3.68E-02 10 ribonucleotide biosynthetic process GO:006163 3.85E-02 11 purine nucleotide metabolic process GO:019438 3.88E-02 12 aromatic compound biosynthetic process GO:015992 3.97E-02 8 proton transport GO:006818 3.97E-02 8 hydrogen transport GO:006082 4.00E-02 54 organic acid metabolic process GO:090304 4.05E-02 52 nucleic acid metabolic process GO:043648 4.38E-02 9 dicarboxylic acid metabolic process GO:016043 4.38E-02 22 cellular component organization GO:009259 4.64E-02 10 ribonucleotide metabolic process GO:032787 4.92E-02 16 monocarboxylic acid metabolic process

Molecular function

GO:005198 2.25E-10 39 structural molecule activity GO:003735 1.05E-08 32 structural constituent of ribosome GO:046983 1.50E-02 10 protein dimerization activity GO:003774 2.16E-02 10 motor activity GO:016810 2.24E-02 16 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds GO:016814 3.39E-02 6 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines

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number GO category p-value description of genes Cellular component

GO:044424 7.14E-22 391 intracellular part GO:005622 8.72E-21 395 intracellular GO:005737 1.19E-16 371 cytoplasm GO:043228 7.58E-16 53 non-membrane-bounded organelle GO:043232 7.58E-16 53 intracellular non-membrane-bounded organelle GO:043229 7.58E-16 57 intracellular organelle GO:043226 7.58E-16 57 organelle GO:005840 1.95E-10 33 ribosome GO:030529 5.54E-10 33 ribonucleoprotein complex GO:044444 1.68E-09 41 cytoplasmic part GO:032991 2.06E-07 59 macromolecular complex GO:019861 1.39E-06 19 flagellum GO:009288 1.47E-05 15 bacterial-type flagellum GO:044422 3.09E-04 16 organelle part GO:033279 9.66E-04 9 ribosomal subunit GO:016469 3.31E-03 8 proton-transporting two-sector ATPase complex GO:042995 7.19E-03 19 cell projection GO:044446 7.85E-03 9 intracellular organelle part GO:015934 8.09E-03 5 large ribosomal subunit GO:009426 1.60E-02 3 bacterial-type flagellum basal body, distal rod GO:009424 1.60E-02 3 bacterial-type flagellum hook GO:009317 1.60E-02 3 acetyl-CoA carboxylase complex GO:044463 3.10E-02 7 cell projection part GO:044461 3.10E-02 7 bacterial-type flagellum part GO:044460 3.10E-02 7 flagellum part GO:030694 4.73E-02 3 bacterial-type flagellum basal body, rod

Effect of RBL67 to the virulence response of N-15

GO enrichment analysis revealed differential expression of pathogenicity genes, for example 49 genes belonging to “protein secretion by the type III secretion system” (GO:030254), were higher regulated in the Salmonella transcriptome when grown in co-culture. Therefore we analyzed the regulation of all putative virulence factors of Salmonella as revealed by comparison of all assigned reads to the virulence database VFDB (Chen et al., 2012). Comparison to VFDB revealed 151 ORFs being putatively involved in virulence. Of these 151 genes, 122 were higher expressed in co-culture compared to only one gene being higher expressed in mono-culture (phoP). This reveals a significant enrichment of expressed virulence genes (P = 7 -39 in Fisher’s test). The large majority of genes were involved in secretion systems (n=66) and fimbrial adherence determinants (n=51). This involved genes

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Results located on SPI 1 (hilA, iagB, sptP, iacP) and encoding the type III secretion system 1 (TTSS- 1) (sipB, sipD, prgIHK, invACBGH, spaSRQPO, sicAP), genes located on SPI 2, including TTSS-2 genes (ssrBA, ssaBCDEIJKLMVNOPRSTU, sseCDEFG, sscAB), as well as the main activation complex of type 1 fimbriae (fimY, fimW and fimZ (the latter not significant)).

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Discussion

Antagonism and protective effects of B. thermophilum RBL67 against Salmonella have been observed in several in vitro studies (Zihler et al., 2011, Zihler et al., 2014). The underlying mechanisms of this antagonism are unknown and could be due to direct interactions or to the production of a bacteriocin-like inhibitory substance by RBL67 (von Ah, 2006). In this study we used RNA-sequencing to investigate the transcriptome response of RBL67 and Salmonella N-15 when grown in co-culture and compared it to their mono-culture growth. To our knowledge we present the first study investigating the interaction of a probiotic Bifidobacterium strain with enteropathogenic S. Typhimurium using RNA-sequencing. RNA-sequencing was previously shown to be a powerful method to investigate genome-wide transcript analysis in co-culture experiments (Rosenthal et al., 2011). In our study we could map over 90 % of the reads obtained from RBL67 mono-culture samples to the RBL67 genome. In parallel, over 90 % of N-15 reads from the mono-cultures could be mapped to the genome of S. Typhimurium LT2. Genomic comparison of Salmonella reveals major differences due to large insertions and presence of large plasmids (Rotger and Casadesus, 1999, Zhao et al., 2013). Indeed, the 94 kB virulence plasmid of LT2 seems absent in N-15. However, N-15 was selected for this study for its high invasion rate into mucus-secreting HT29-MTX cells together with high disruption of the epithelial cell integrity (Zihler et al., 2010). Mapping efficiencies to three other Salmonella genomes were > 90 % and similar to mapping to S. Typhimurium LT2. Furthermore, less than 0.9 % genes were significantly differentially expressed by comparing mapping to two different strains (data not shown). In the co-culture samples mapping efficiency was lower, especially to the Salmonella genome after 5 h of growth. The low mapping efficiency was due to a low abundance of Salmonella RNA compared to RBL67 at this time point, probably because of reduced growth speed of Salmonella from this point onwards. This effect was not observed after 4 hours of growth, which was consequently selected to study transcriptome of N-15. Finally, more than 50 % of generated reads from RNA-Seq could be mapped for both co-cultures. This suggests that RNA-seq is powerful enough to study the transcriptome response from our two-strain consortium, albeit conditions for sampling have to be chosen carefully.

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Discussion

RBL67 growth was enhanced in presence of Salmonella N-15, as shown by a higher maximum specific growth rate and slightly higher maximum cell counts. Also growth of other Bifidobacterium species (B. globosum, B. animalis, B. breve) was shown to be stimulated by S. Typhimurium and S. Enteriditis, albeit under pH uncontrolled conditions (Bielecka et al., 1998). However, the transcriptome response of RBL67 did not provide evidence on the mechanism of increased growth performance, likely because in both mono- and co-cultures, RBL67 was in the exponential growth phase. The stress associated gene ClpB of RBL67 and its transcriptional regulator HspR were higher expressed in co-culture with N-15. The Clp chaperones and proteases are highly conserved and are involved in the general stress response of various bacteria (Kannan et al., 2008, Lourdault et al., 2011, Mozzetti et al., 2012). Further, ClpB is a key player in protein homeostasis under normal growth conditions (Tomoyasu et al., 2001) and presumably the increased growth rate of RBL67 in presence of N-15 results in the higher expression of ClpB. In contrast, Salmonella N-15 was affected by presence of RBL67, as shown by significantly lower end cell numbers and its transcriptome response. Enrichment analyses of GO categories revealed growth-associated GO categories being overrepresented in mono-cultures of N-15, while GO categories involved in extracellular biology, including genes involved in virulence, were overrepresented in co-culture. The only virulence associated gene that was higher regulated in monoculture was phoP, a member of the two-component regulatory system PhoQ-PhoP and a postulated repressor of hilA (Fabrega and Vila, 2013). Regulatory proteins and RNA’s are of major importance in Salmonella virulence, as the allocation of the virulence factors at the right time, correct place and in appropriate amounts is crucial for virulence (Kato and Groisman, 2008). S. Typhimurium cells expressing TTSS-1 exhibited retarded growth compared to their counterparts that were defective in TTSS-1 (Sturm et al., 2011), thus virulence represents a burden at the individual cell level. The observed alleviated growth of Salmonella in presence of RBL67 could be related to the higher expression of virulence determinants and especially genes belonging to TTSS-1. Expression of the Salmonella virulence machinery as response to RBL67 presence could increase infection rate. However, this data disagrees with previous results, showing reduced invasion capacity of Salmonella to HT29-MTX cells in presence of a complex human microbiota containing

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Chapter 4 colonic RBL67, compared to a microbiota without RBL67 (Zihler et al., 2011). Thus, host integrity could be protected by activating Salmonella virulence gene expression in the lumen, leading to reduced growth and simultaneous host protection by other probiotic mechanisms (competition for adhesion sites, steric hindrance). Whether virulence gene expression of Salmonella is triggered directly via microbe-microbe interactions with RBL67 or via the environmental conditions prevailing at the sampling time point is not clear. Different environmental factors including acetate can trigger virulence gene expression in Salmonella (Altier, 2005). Indeed, 15 and 30 mM acetate at pH 6.7 were reported to induce the three invasion determinants hilA, invF and sipC in S. Typhimurium (Lawhon et al., 2002). This induction was shown to be dependent on the presence of acetate kinase (ackA) and phosphotransacetylase (pta) that produce acetyl-phosphate from acetate and acetyl-CoA, respectively (Lawhon et al., 2002). Accordingly, hilA, invF and sipC were significantly (except sipC) higher regulated in co-cultures in our study. However, the difference in acetate concentrations between mono- and co-cultures at the sampling point was only 5 mM and both, ackA (STM2337) and pta (STM2338) were down regulated in co-cultures compared to the mono-cultures. This suggests that acetate was not the trigger for the observed induction of invasion genes and presupposes that other environmental conditions such as minor or secondary metabolites, cell wall components or cell-to-cell contact induces invasion gene expression of N-15 when grown in co-cultures. The regulatory cross talk between flagellar genes, SPI 1 and type 1 fimbrial genes enables a precise timing of gene expression in terms of temporal activation and deactivation (Saini et al., 2010). Hence, expression dynamics of flagellar genes, SPI 1 and type 1 fimbrial genes follows a sequential natural hierarchy (Saini et al., 2010). Remarkably, N-15 in co-cultures expresses genes belonging to SPI 1 and the main activation complex of type 1 fimbriae significantly higher than in mono-cultures, while flagellar genes were significantly higher expressed in mono- than in co-cultures. This expression pattern suggests that N-15 in co- culture is further progressed in the sequential expression dynamics of flagellar, SPI 1 and type 1 fimbrial genes, than in mono-cultures. A type III secretion system-1 (TTSS-1) expressing S. Typhimurium subpopulation is essential for Salmonella infection. However, this subpopulation is also vulnerable to overgrowth by a non-TTSS-1 expressing S.

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Discussion

Typhimurium subpopulation, due to a growth deficit (Sturm et al., 2011). An imbalance in the regulation of TTSS-1 genes in a Salmonella (sub-) population may result in inappropriate fractions of non-TTSS-1 expressing S. Typhimurium and therefore to decreased infection (Diard et al., 2013). The earlier induction of SPI 1 and fimbrial genes in N-15 in the presence of RBL67 may confer an imbalance of virulence gene expression and a lower fitness due to premature energy expenditure, pointing to a possible mechanism of RBL67. In vivo this mechanism may contribute to a clearance of Salmonella from the gut lumen. A reduced invasion of Salmonella to human intestinal cells in presence of an infant microbiota containing colonic RBL67 was previously reported and RBL67 was also shown to reduce Salmonella in an in vitro continuous fermentation model (Zihler et al., 2011, Zihler et al., 2014). Whether these observations can be related to the observed imbalance of virulence gene expression needs further evaluation. Our study provides first insights into the transcriptome response of probiotic RBL67 and S. Typhimurium grown in co-cultures under simplified conditions, shedding light on a possible molecular mechanism of probiotic-pathogen interaction. We showed that RBL67 provokes earlier expression of SPI 1 and fimbrial genes, potentially reducing the competitive advantage of the Salmonella strain which may result in a better clearance of Salmonella from the gut. The trigger of this early expression remains unclear, but is likely not related to the acetate level. Our study suggests that such disbalance in the cascade pathway of virulence represents a novel possible mechanism of how probiotic organisms can protect the host against infections.

Acknowledgements

We thank Dr. Hubert Rehraurer and Dr. Lucy Poveda from the Functional Genomics Center Zurich for RNA-sequencing and support in statistical analysis.

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Chapter 5

Effect of Bifidobacterium thermophilum RBL67 and fructooligosaccharide on the gut microbiota of Göttingen minipigs

Sabine A. Tanner1, Christophe Lacroix1, Christophe Del’Homme2, Christoph Jans, Annina

Zihler Berner1, Annick Bernalier-Donadille2, Christophe Chassard1

1Laboratory of Food Biotechnology, Institute of Food, Nutrition and Health, ETH Zurich,

Zurich, Switzerland

2INRA, UR454 Microbiology Unit, Clermont-Ferrand Research Centre, Saint Genès-

Champanelle, France

Manuscript to be submitted.

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Chapter 5

Abstract

Modulating the gut microbiota via dietary interventions, such as probiotics, is a common strategy to enhance the natural defense mechanisms of the host. Several in vitro studies have highlighted the probiotic potential of Bifidobacterium thermophilum RBL67 (RBL67). The present study aimed to investigate the impact of RBL67 alone and combined with fructooligosaccharide (FOS) on gut microbiota of Göttingen minipigs, as new model for gut microbiota research in pigs. Minipigs were fed with a basal diet supplemented with 8 g/day probiotic powder (1x109 cfu g-1 in skim milk matrix) (PRO), 8 g/day probiotic powder plus 8 g/day FOS (SYN) or 8 g/day skim milk powder (CON), following a cross-sectional study design. Fecal and cecal microbiota compositions were analyzed with 454 pyrosequencing of 16S rRNA genes and qPCR. Metabolic activity in cecum and colon was measured by HPLC. Pyrosequencing revealed the predominance of 4 phyla, Firmicutes, Bacteroidetes, Proteobacteria and Spirochaetes in minipig feces, showing close similarity to pig microbiota. During the treatments and at sacrifice RBL67 was consistently detected in feces, cecum and colon at numbers of 105-106 16S rRNA copies g-1 content after feeding PRO and SYN diets. At sacrifice, significantly higher Bifidobacterium numbers in the cecum and colon of SYN- fed minipigs were measured compared to PRO. No significant effect of experimental diets was observed on metabolic activity. Our data indicate a potential for the Göttingen minipig as model for gut microbiota research in pigs; however, due to a high fraction of unclassified reads using pyrosequencing the gut microbiota composition and diversity should be further characterized. The first in vivo assessment of RBL67 and combined with FOS suggests that RBL67 is safe and that a synbiotic formulation may be required for RBL67 as a potential feed additive against Salmonella in pigs. As a next step, this will need in vivo validation in an animal infection trial.

152

Introduction

The Göttingen minipig holds promise as novel model for gut microbiota studies. It has important advantages compared to pigs, including the high control of breeding and origin, and the small size, allowing easier handling and consequently reducing labor and costs (Forster et al., 2010, Kohn et al., 2007). The Göttingen minipig is already widely accepted as a non-rodent toxicology model and extensive research of the comparative biology of minipigs to humans and pigs was undertaken (reviewed by Bode et al. (2010)). Nonetheless, information about the distal part of the intestine and gut microbiota composition of minipigs is very limited. Pedersen et al. (2013) recently described the cecal, ileal and colonic microbiota composition of lean and obese minipigs (Göttingen and Ossabaw) using Illumina- based sequencing. Probiotics can enhance the natural barrier function of the host by modulating the gut microbiota. Specific characteristics attributed to probiotics include, competition for nutrients and adhesion sites, stimulation of short chain fatty acids (SCFA), production of antimicrobial substances and modulation of the immune response (Gaggia et al., 2010, Ohashi and Ushida, 2009). Probiotic traits are strain specific and must be proven for any single strain. Given their safe history of use, strains of the genera Bifidobacterium and Lactobacillus are widely represented amongst probiotics (Gaggia et al., 2010). Bifidobacterium thermophilum strains have been primarily isolated from animals, such as pig and calf feces and bovine rumen (Biavati and Mattarelli, 2009, Gavini et al., 1991), and were also shown in baby feces (Mathys et al., 2008, Toure et al., 2003). The probiotic potential of the human fecal isolate Bifidobacterium thermophilum RBL67 (RBL67) is supported by several in vitro studies carried out with human gut microbiota. RBL67 produces a bacteriocin-like substance (Toure et al., 2003, von Ah, 2006), adheres to human intestinal cell lines (Moroni et al., 2006) and exhibits protective effects on epithelial integrity in presence of a competing human microbiota (Zihler et al., 2011). Furthermore, RBL67 shows adaptive and competitive traits in a complex human intestinal ecosystem and reduced Salmonella counts in an in vitro continuous fermentation model of the child proximal colon (Zihler et al., 2014). This anti- Salmonella effect of RBL67 is of particular interest for a targeted probiotic approach to

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Chapter 5 reduce Salmonella in pigs. In addition, RBL67 has important technological features, such as being moderately oxygen-tolerant, growing at low pH of 4.0 and up to high temperatures of 47°C and reaching high cell densities (von Ah et al., 2007). Recently, the genome of RBL67 was sequenced, providing information on the genetic background of the strain (Jans et al., 2013). Probiotic effects can be potentiated by the use of prebiotics, a concept known as synbiotic (Gibson and Roberfroid, 1995). Prebiotics are non-digestible food-ingredients that largely escape digestion in the upper gastrointestinal tract. They are readily fermentable in the colon and can stimulate the growth of beneficial bacteria, mainly lactobacilli and bifidobacteria that confer a health benefit to the host (Gibson et al., 2004). Fructans, such as fructooligosaccharide (FOS) and inulin, are amongst the most widely studied and applied prebiotics (Scott et al., 2013). Inulin was shown to specifically enhance B. thermophilum RBL67 growth in an in vitro continuous fermentation model of the child proximal colon (Zihler et al., 2010) and FOS increased molar butyrate proportions in the cecum and proximal colon of piglets, albeit not affecting microbial populations (Mikkelsen and Jensen, 2004). In this work we used the Göttingen minipig as an animal model to investigate the impact of B. thermophilum RBL67 alone or combined with FOS on the gut microbiota composition and activity.

154

Materials and Methods

Ethical statement

The minipig study was conducted at the INRA facilities of Clermont-Ferrand-Theix, France. All experimental protocols were carried out according to the European directives on the protection of animals used for scientific purposes (2010/63/EU), and the laboratory procedures have been approved by the local ethics committee CEMEAA 02.

Preparation of probiotic powder

For production of the probiotic powder, two parallel fermentations with B. thermophilum RBL67 (our culture collection) were carried out. Bioreactors (Multifors, Infors-HT, Bottmingen, Switzerland) were filled with 1 L sterile MRS broth (Table S5.1) supplemented with 0.05 % L-cysteine hydrochloride (VWR International AG, Dietikon, Switzerland) (MRS-C). RBL67 was inoculated at 2 % (v/v) after being propagated twice overnight. Batch fermentations were performed at 37°C and constant flushing of the headspace with CO2 was provided to ensure anaerobic conditions. The pH was maintained at 6.0 by adding 5 M

NH4OH and constant stirring was carried out at 150 rpm. Fermentations were stopped after 14 hours and broth from the two fermentations was mixed. Cells were collected by centrifugation (7000 x g, 10 min, room temperature) and washed in phosphate buffered saline, supplemented with 0.05 % L-cysteine hydrochloride (VWR International AG). After another centrifugation step, cells were resuspended (1 hour under stirring at 4°C) in 20 % (w/v) reconstituted skim milk (RSM; Lonza Ltd., Basel, Switzerland), selected as a carrier. Cells in RSM were stored at -80°C for two days before freeze drying (Christ Alpha 1-4 LD plus, Martin Christ Gefriertrocknungsanlagen, GmbH, Osterode am Harz, Germany), milling (particle size: 600 μm) and blending with skim milk powder (Lonza Ltd., Basel, Switzerland)

-1 to reach an average cell count in the final probiotic powder of log10 9.36 ± 0.07 cfu g . Aliquots of 8 g, corresponding to the 2 % of daily probiotic powder supply in the minipig diet, were prepared under vacuum and stored at 4°C protected from light until usage.

Animals and procedures

The study involved eight female Göttingen minipigs (20-21 months old, average body weight, 28 kg), randomly allocated into two treatment groups (A and B) in a cross-sectional

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Chapter 5 study design (Figure 5.1). All animals were housed in separate units (1 m * 1.50 m) in a ventilated room with controlled temperature (22-23°C). Animals received 400 g/d of basal diet (18 % protein (N x 6.25), 2 % fat, 5 % cellulose, 6 % ash; Porcyprima, SANDERS Nutrition Animale, France) before study start and during washing periods. During treatment periods the basal diet was supplemented with 8 g/d skim milk powder (Lonza Ltd.) (control; CON), 8 g/d probiotic powder (probiotic diet; PRO) or 8 g/d probiotic powder plus 8g/d fructooligosaccharide (FOS; Fibrulose F97, Cosucra Groupe Warcoing S. A., Warcoing, Belgium) (synbiotic diet; SYN), according to the experimental design of the feeding trial (Figure 5.1). Animals had access to water ad libitum throughout the study. Fecal samples were collected daily and immediately stored at -80°C for subsequent DNA extraction and HPLC analyses. At the end of the trial, pigs were euthanized with an injection of pentobarbital (Doléthal, 125 mg kg-1 body weight; Vétoquinol, Paris, France). The first four minipigs (2 minipigs of each group) were euthanized on day 36, while the remaining four minipigs were euthanized on day 37. After euthanization, cecum and colon (proximal, middle, distal) content were collected and immediately stored at -80°C until further analysis.

Figure 5.1. Experimental set-up of the feeding trial using two groups of four Göttingen minipigs. Diets included: 400 g of basal diet supplemented with 8 g/d milk powder (CON), 8 g/d probiotic -1 powder with average B. thermophilum RBL67 cell count of log10 9.36 ± 0.07 cfu g (PRO) and 8 g/d probiotic powder + 8 g/d Fibrulose F97 (SYN). During wash periods, minipigs received 400 g/d of basal diet without supplement. Shaded cases indicate days used for 454 pyrosequencing.

Analysis

Fecal samples and cecum and colonic (proximal, middle, distal) contents were analyzed for bacterial populations by quantitative PCR (qPCR) and metabolite concentrations by HPLC analysis. The microbial profiles of fecal samples before study start (day 1) as well as cecal content samples were also analyzed for their microbial profiles with 454 pyrosequencing.

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Materials and Methods

Microbial composition by qPCR

Genomic DNA for qPCR was extracted with the FastDNA SPIN Kit for soil (MP Biomedicals, Illkirch, France) according to the manufacturer’s instructions. qPCR was performed using an ABI PRISM 7500-PCR sequence detection system (Applied Biosystems, Zug, Switzerland) for enumeration of predominant bacterial groups of swine gut microbiota (Leser et al., 2002), including Bifidobacterium spp., Lactobacillus/Pediococcus/Leuconostoc spp., Bacteroides-Prevotella, Enterobacteriaceae, Roseburia spp./Eubacterium rectale, Faecalibacterium prausnitzii, Streptococcus spp. and Eubacterium hallii, using specific primers (Table S5.2). Standard curves for each target group were prepared as described previously (Dostal et al., 2013) and assays were carried out using the KAPA SYBR® FAST qPCR Kit (Kapa Biosystems Inc., Wilmington, United States) in a reaction volume of 25 μl. A TaqMan assay was used for enumeration of B. thermophilum with specific primers (Table S5.2) using the RT-QP2X-03WOULR Mastermix (Eurogentec s.a., Seraing, Belgium) and reaction conditions set by Mathys et al. (2008).

Microbial composition by 454 pyrosequencing

For 454 pyrosequencing analysis, DNA from individual fecal samples (n=8) before study start (day 1) and cecal samples (n=8) at the end of the study was extracted using the FastDNA SPIN Kit for soil (MP Biomedicals) (Figure 5.1). Extracted DNA was sent to DNAVision (Gosselies, Belgium) for high-throughput sequencing of the hypervariable V5-V6 region of the entire 16S rRNA gene pool using a 454 Life Sciences Genome Sequencer FLX instrument (Roche AG, Basel, Switzerland), according to procedures described previously (Jost et al., 2013). Resulting sequencing reads were quality-filtered according to three criteria: maximum of one mismatch in barcode and primers, at least 240 nucleotide length and a maximum of two undetermined bases per sequence. Sequencing reads passing the quality check were assigned on phylum-, family- and genus-level using the Ribosomal Database Project (RDP) Bayesian classifier (v2.1) (Wang et al., 2007) with a confidence threshold of 80 %. Sequences were further assigned into operational taxonomic units (OTU) based on nearest neighbor clustering using the Mothur software package (Schloss et al., 2009). Chao1 richness and Shannon diversity were calculated based on the numbers of OTU. The complete 454 pyrosequencing dataset has been deposited to the National Center for 157

Chapter 5

Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number SRP044704.

Metabolite analysis

Samples for HPLC analyses were prepared according to the protocol described by Dostal et al. (2014). Briefly, cecum and colonic contents (proximal, middle, distal) samples (100-200 mg) were homogenized with 1 mL of 0.15 M H2SO4, centrifuged (4°C, 9000 x g, 20 min) and resulting supernatants were filtered through a 0.45 μM nylon filter (Infochroma AG, Zug, Switzerland) before injection. Total metabolites (acetate, propionate, butyrate, valerate, iso- valerate, iso-butyrate, formate and lactate) as well as glucose and lactose were quantified in duplicate by HPLC (LaChrome, Hitachi High-Tech, Rotkreuz, Switzerland) using an Aminex HPX-87H column (Bio-Rad Laboratories AG, Reinach, Switzerland) at a flow rate of 0.4 mL

-1 min and 10 mM H2SO4 as eluent. Data are expressed as means from duplicate analyses in mM g-1 cecal and colonic content.

Statistical analysis

All statistical analyses were performed using PASW Statistics for Windows (v.18.0; SPSS

Inc, Chicago). qPCR data were log10 transformed prior to analysis. To define effects of experimental diets in fecal samples, mean qPCR data from three samples during the treatment (T) period (T1: days 5/7/8; T2: days 19/21/22; T3: days 33/35/36 or 33/35/37) (Figure 5.1) for group A and B, were compared using ANCOVA. The bacterial target group was treated as dependent factor, the treatment group as independent factor and the baseline (BL: BL1: day 1; BL2: days 12/14/15; BL3: days 26/27/29) was taken as covariate. qPCR data from fecal samples are expressed as means ± SD from three days for four minipigs of group A (n=12) and B (n=12). A factorial repeated measures analysis of variance (ANOVA) was performed to test the effects of experimental diets (between-subjects factor; PRO, SYN), intestinal segment (within-subject factor; cecum, proximal, middle and distal colon) and their interactions on bacterial groups targeted by qPCR and metabolite concentrations in cecum and colon. If assumption of sphericity was not met, the Greenhouse-Geisser correction was applied and significant effects of intestinal segments were further tested using pairwise comparison, with post hoc Bonferroni’s correction for multiple comparisons.

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Materials and Methods

To compare differences in microbial composition and diversity using 454 pyrosequencing, the relative abundance data at genus and family levels from PRO and SYN in the cecum at sacrifice were compared using the non-parametric Mann Whitney U Test with exact significance. qPCR, HPLC and 454 pyrosequencing data from cecum and colon are expressed as mean ± SD for PRO (n=4) and SYN (n=4). For all statistical tests, results were considered significant with P ≤ 0.05.

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Chapter 5

Results

Animals

All animals remained healthy throughout the study and did not show any sign of disease.

Fecal microbiota composition and diversity of Göttingen minipigs

Fecal microbiota composition and diversity was assessed before study start (baseline 1) with qPCR targeting predominant bacterial groups of the swine gut microbiota and 454 pyrosequencing. At baseline mean (n=8) total 16S rRNA gene copy numbers by qPCR were high at 11.8 ± 0.03 copies g-1 feces (Table 5.1). The most abundant bacterial group targeted was Bacteroides-Prevotella (11.1 ± 0.2 copies g-1), followed by Enterobacteriaceae (10.9 ± 0.1 copies g-1), Lactobacillus/Leuconostoc/Pediococcus (10.3 ± 0.01 copies g-1), Streptococcus (9.6 ± 0.1 copies g-1), F. prausnitzii (9.3 ± 0.01 copies g-1), E. hallii (9.3 ±0.1 copies g-1) and Roseburia/E. rectale (8.5 ± 0.1 copies g-1). Bifidobacterium was least

-1 abundant with 7.4 ± 0.1 copies g , while B. thermophilum was not detected in both groups at baseline. 454 pyrosequencing on the V5-V6 region of the entire 16S rRNA gene pool from fecal samples of each minipig (n=8) before study start generated a total of 72048 quality-filtered reads with a mean of 9991 ± 3864 reads per sample and a mean read length of 258 ± 1. Richness (Chao1 index: 2369 ±769) and diversity (non-parametric Shannon index: 4.50 ± 0.88) were estimated from the number of OTU with similarity cutoff 0.03 % (1068 ± 329). On phylum level mean relative abundance data revealed the predominance of four major phyla (Figure 5.2). The phylum Firmicutes was most abundant (~75 %) followed by Bacteroidetes (~ 14 %), Proteobacteria (~4 %) and Spirochaetes (~3 %), while 4 % of the reads could not be assigned to any phylum. Interindividual variation was observed in all four phyla, mostly in Bacteroidetes and Proteobacteria, with relative abundances ranging from 4- 22 % and 0.16-17 %, respectively. At family level, the most abundant phylum, Firmicutes, encompassed the families Streptococcaceae (~25 %), Ruminococcaceae (~ 11 %), Lactobacillaceae (~ 8 %), Clostridiaceae (~ 5 %), Lachnospiraceae (~ 4 %) and Erysipelotrichaceae (~ 4 %). The phylum Bacteroidetes was almost exclusively represented by the family Prevotellaceae (7 %), while Porphyromonadaceae and Rikenellaceae, the two

160

Results other families detected from this phylum, displayed low relative abundances (1 ± 0.7 % and 1 ± 1 %, respectively). Two other families, Enterobacteriaceae (Proteobacteria phylum) and Spirochaetaceae (Spirochaetes phylum), were detected (both ~3 %) and unclassified reads accounted for approximately 24 % of all reads on family level. Eleven predominant genera represented at least 1 % relative abundance in at least one minipig, while unclassified reads accounted for 44 % (Figure 5.3). The highest relative abundance was observed for the genera Streptococcus (~ 25 %), Lactobacillus (~ 8 %) and Clostridium (~ 5 %). The genus Acinetobacter was detected in only one pig, while the genus Escherichia/Shigella was missing in one pig out of eight. The remaining nine genera were detected in all minipigs. On genus level, inter-individual variations were observed, most prominent within the genera Streptococcus, Lactobacillus and Clostridium.

Figure 5.2. Mean relative 16S rRNA gene abundances detected in feces from Göttingen minipigs on phylum and family level using 454 pyrosequencing. Values are means ± SD for all minipigs (n=8) before study start and with a relative abundance > 1 % in at least one minipig. Relative abundances < 1 % are summarized in the group “others”. P: Proteobacteria; S: Spirochaetes.

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Figure 5.3. Relative 16S rRNA gene abundance detected in feces from Göttingen minipigs on genus level using 454 pyrosequencing. Values are given for each minipig individually before study start. Relative abundances < 1 % are summarized in the group “others”.

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-1 Table 5.1. Bacterial concentrations (log10 copies g ) in feces at different time points during the trial for each treatment group detected by qPCR.

Baseline 1 Treatment 1 Baseline 2 Treatment 2 Baseline 3 Treatment 3 Target a A B CON PRO Aab B SYN CON Aa B PRO SYN total 16S rRNA gene 11.8± 0.2 11.8 ± 0.2 12.0 ± 0.1 12.1 ± 0.1 11.4 ± 0.4 11.4 ± 0.3 11.2 ± 0.4 11.1 ± 0.3 11.4 ± 0.1 11.6 ± 0.4 11.5 ± 0.1 11.6 ± 0.2 copies Bacteroides- 10.9 ± 0.3 11.2 ± 0.6 11.1 ± 0.1 11.0 ± 0.2 10.7 ± 0.5 10.7 ± 0.2 10.7 ± 0.4 10.5 ± 0.2 11.0 ± 0.1 10.9 ± 0.3 11.0 ± 0.1 10.8 ± 0.2 Prevotella

Enterobacteriaceae 10.8 ± 0.7 11.0± 0.8 11.3 ± 0.3 11.2 ± 0.2 11.0 ± 0.3 10.9 ± 0.3 10.8 ± 0.3 10.7 ± 0.3 11.2 ± 0.3 11.2 ± 0.5 11.3 ± 0.2 11.2 ± 0.1

Lactobacillus/Pedio- coccus/Leuconostoc 10.3 ± 0.5 10.4 ± 0.3 9.7 ± 0.5 9.7 ± 0.5 9.3 ± 0.4 8.8 ± 0.7 9.3 ± 0.9 9.2 ± 0.7 9.7 ± 0.4 9.6 ± 0.3 10.5 ± 0.3* 9.8 ± 0.3 spp.

Streptococcus spp. 9.7 ± 0.5 9.6 ± 0.8 9.8 ± 0.4 10.1 ± 0.6 9.8 ± 0.7 10.1± 0.9 9.9 ± 0.5 9.3 ± 0.5 9.8 ± 0.4 10.0 ± 0.6 9.6 ± 0.7 10.3 ± 0.3

B. thermophilum n.d. n.d. n.d. 6.0 ± 0.4d n.d. n.d. 5.8 ± 0.4a n.d. n.d. n.d. 6.3 ± 0.2d 6.1 ± 0.3e

Bifidobacterium 7.4 ± 0.1 7.5 ± 0.3 6.9 ± 0.1 7.0 ± 0.3 7.0 ± 0.3 6.9 ± 0.3 6.8 ± 0.2 6.9 ± 0.2 6.5 ± 0.1 6.7 ± 0.6 6.6 ± 0.1 7.0 ± 0.5 spp. Roseburia spp./ 8.5 ± 0.4 8.6 ± 0.1 8.7 ± 0.2 8.4 ± 0.1 8.6 ± 0.3 8.4 ± 0.4 8.7 ± 0.3 8.6 ± 0.3 8.9 ± 0.1 8.5 ± 0.1 8.6 ± 0.1 8.4 ± 0.3 Eubacterium rectale Faecalibacterium 9.3 ± 0.3 9.3 ± 0.1 9.5 ± 0.1 9.4 ± 0.1 9.4 ± 0.2 9.1 ± 0.3 9.3 ± 0.3 9.1 ± 0.2 9.3 ± 0.4 9.2 ± 0.2 9.0 ± 0.1 9.2 ± 0.3 prausnitzii

Eubacterium hallii 9.2 ± 0.2 9.4 ± 0.1 9.2 ± 0.3 9.3 ± 0.2 9.4 ± 0.4 9.1 ± 0.5 9.4 ± 0.6 9.2 ± 0.3 9.6 ± 0.4 9.4 ± 0.5 9.4 ± 0.2 9.5 ± 0.2

-1 a b Reported are means (log10 copies g feces) from three days during washing periods (=baseline) and treatment periods per group (n=12), unless stated otherwise; n=11; n=10 for Enterobacteriaceae, Roseburia spp./E. rectale, F. prausnitzii and E. hallii; d n=10; e n=9; * means significantly different between groups within a treatment period and for the bacterial target group P ≤ 0.05; n.d. not detected.

Chapter 5

Effect of PRO and SYN on fecal bacterial concentrations

Differences in fecal bacterial concentrations upon feeding experimental diets PRO or SYN were assessed by qPCR targeting predominant bacterial groups of the swine microbiota (Table 5.1). Throughout the study (during wash and treatment periods) high and stable concentrations (>

-1 -1 10.9 log10 copies g feces) of total 16S rRNA gene copies (11.6 ± 0.3 log10 copies g feces),

-1 Enterobacteriaceae (11.1 ± 0.2 log10 copies g feces) and Bacteroides-Prevotella (10.9 ± 0.2

-1 log10 copies g feces) were measured in both groups. Stable concentrations during wash and treatment periods, with no effect of the experimental diet, were also observed for

-1 Streptococcus (9.8 ± 0.2 log10 copies g feces), Lactococcus/Pediococcus/Leuconostoc (9.7 ±

-1 -1 0.5 log10 copies g fecesm except in period 3), F. prausnitzii (9.3 ± 0.2 log10 copies g

-1 feces), E. hallii (9.3 ± 0.2 log10 copies g feces), Roseburia/E. rectale (8.6 ± 0.1 log10 copies

-1 -1 g feces) and Bifidobacterium (6.9 ± 0.3 log10 copies g feces). B. thermophilum was not detected during wash periods and in the control groups (CON), but was detected in the treatment groups that included RBL67 (PRO or SYN). Experimental diets in periods 1 and 2 did not significantly change fecal bacterial numbers of the groups targeted by qPCR compared to the control group. In period 3, fecal Lactobacillus/Pediococcus/Leuconostoc numbers were significantly higher with PRO compared to SYN.

Effects of PRO and SYN on bacterial concentration and composition in cecum and colon

The effect of experimental diets (PRO and SYN) on bacterial composition in the cecum and colon was assessed with qPCR and 454 pyrosequencing (Table 5.2, Figure 5.4). A significant effect of experimental diets was observed for Bifidobacterium (P=0.021) with higher numbers in the SYN group compared to the PRO group. For all other bacterial groups (total 16S rRNA gene copies, Bacteroides-Prevotella, Enterobacteriaceae, Streptococcus, B. thermophilum, Lactobacillus/Leuconostoc/Pediococcus, Roseburia/E. rectale, F. prausnitzii and E. hallii), no significant effect of experimental diets (PRO and SYN) was observed for bacterial gene copy numbers in the cecum and the different colon segments. Moreover, 454 pyrosequencing indicated significantly (P ≤ 0.05) lower relative abundance of Lactobacillaceae (0.15 ± 0.12 %) and Spirochaetaceae (0.27 ± 0.10 %) for minipigs from the

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SYN group compared to the PRO group (Lactobacillaceae 1.48 ± 0.86 %; Spirochaetaceae 2.47 ± 1.79 %) (Figure 5.4). A statistically significant effect of the intestinal segment was obtained for total 16S rRNA gene copies (P=0.004), B. thermophilum (P=0.005) and Roseburia/E. rectale (P=0.028). Post- hoc pairwise comparisons revealed lower total 16S rRNA gene copies in the first two segments (cecum, proximal colon) compared to the third (middle colon), while B. thermophilum concentrations were higher in the cecum compared to the middle colon. For Roseburia/E. rectale pairwise comparisons failed to detect the location effects. For all of the bacterial groups no significant interaction between experimental diets and intestinal segment was observed.

Figure 5.4. Cecal microbiota composition of minipigs in the PRO (n=4) and SYN (n=4) groups analyzed by 454 pyrosequencing. Relative abundance is depicted at family level. Families < 1 % are summarized in the group “others”.

165

-1 Table 5.2. Bacterial concentrations (log10 copies g ) detected by qPCR in cecum and colon (proximal, middle, distal) after sacrifice of PRO and SYN groups.

Target Cecum Proximal Middle Distal PRO SYN PRO SYN PRO SYN PRO SYN total 16S rRNA copies‡ 11.1 ± 0.2 11.3 ± 0.1 11.1 ± 0.2 11.3 ± 0.1 11.4 ± 0.2 11.6 ± 0.1 11.5 ± 0.3 11.7 ± 0.1

Bacteroides-Prevotella 10.4 ± 0.3 10.5 ± 0.2 10.5 ± 0.3 10.5 ± 0.2 10.5 ± 0.1 10.5 ± 0.2 10.6 ± 0.2 10.7 ± 0.2

Enterobacteriaceae 11.1 ± 0.3 11.0 ± 0.3 11.2 ± 0.2 11.0 ± 0.2 11.2 ± 0.2 11.0 ± 0.1 11.1 ± 0.1 11.1 ± 0.1

Lactobacillus/Pediococcus/Leuconostoc 10.8 ± 0.2 10.4 ± 0.4 10.8 ± 0.4 10.5 ± 0.3 10.8 ± 0.4 10.4 ± 0.3 10.9 ± 0.4 10.4 ± 0.3 spp.

Streptococcus spp. 8.0 ± 0.8 9.2 ± 0.1 8.3 ± 1.0 9.2 ± 0.2 8.3 ± 1.0 9.1 ± 0.3 8.3 ± 1.2 9.3 ± 0.3

B. thermophilum‡a 5.8 ± 0.6 6.4 ± 0.7 5.9 ± 0.6 6.0 ± 0.3 5.4 ± 0.5 5.7 ± 0.3 5.9 ± 0.6 6.0 ± 0.4

Bifidobacterium spp.* 6.5 ± 0.3 7.1 ± 0.4 6.6 ± 0.2 7.0 ± 0.3 6.6 ± 0.2 7.0 ± 0.3 6.6 ± 0.3 7.0 ± 0.2

Roseburia spp./ Eubacterium rectale‡ 9.3 ± 0.3 9.7 ± 0.3 9.2 ± 0.2 9.4 ± 0.3 9.3 ± 0.2 9.3 ± 0.3 9.3 ± 0.1 9.2 ± 0.4

Faecalibacterium prausnitzii 9.3 ± 0.3 9.4 ± 0.1 9.3 ± 0.2 9.5 ± 0.2 9.3 ± 0.1 9.4 ± 0.2 9.4 ± 0.2 9.5 ± 0.2

Eubacterium hallii 9.5 ± 0.5 9.7 ± 0.3 9.5 ± 0.4 9.8 ± 0.5 9.6 ± 0.2 9.7 ± 0.4 9.6 ± 0.2 9.7 ± 0.5

-1 a Means ± SD (log10 copies g colonic content); * significant effect of experimental diet P ≤ 0.05; ‡ significant effect of intestinal segment P<0.05 no B. thermophilum detected in one minipig of SYN.

Results

Effect of PRO and SYN on microbial activity in the cecum and colon

Metabolite concentrations were analyzed in the cecum and colon content samples from PRO (n=4) and SYN (n=4) at sacrifice by HPLC. Total SCFA and measured metabolite concentrations did not significantly differ between the two experimental diets (Figure 5.5, Table S5.3). A significant effect of the intestinal segment was recorded for total SCFA (P=0.029), acetate (P=0.032), propionate (P=0.043) and butyrate (P=0.029), reflecting a higher metabolic activity in the cecum (total mean metabolite production of 86 ± 56 mM for PRO and 115 ± 40 mM for SYN), but also large interindividual variations with groups. In the proximal colon total metabolite concentrations decreased to 51 ± 8 mM (PRO) and 53 ± 6 mM (SYN) and remained stable in middle and distal colon regions. The ratios of SCFA were similar for PRO and SYN (acetate 61 ± 1 %: propionate 19 ± 2 %: butyrate 8 ± 1 %) and remained stable along the cecum and colon sections Minor metabolites, isobutyrate, isovalerate and valerate, also exhibited similar ratios for PRO and SYN in the cecum and colon sections (Table S5.3). Interestingly, a considerable amount of residual glucose in the cecum of one minipig from PRO (17 mM) and two from SYN (17mM and 8 mM) was detected, which also displayed the highest total metabolite concentration.

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Figure 5.5. Total metabolite concentrations (mM) and metabolite ratios (% of total metabolites) detected in the cecum and subsequent colon sections (proximal, middle, distal) in PRO and SYN after sacrifice using HPLC. (A) total metabolite concentration, (B) acetate ratio, (C) propionate ratio and (D) butyrate ratio. Depicted are mean values (line) and individual values ( □ PRO and ○ SYN).

168

Discussion

The present study investigated the impact of the probiotic RBL67 alone or combined with FOS on fecal, cecal and colonic microbiota composition and activity of Göttingen minipigs, a potential new model for gut microbiota research in pigs. The Göttingen minipig is particularly advantageous over conventional pig models due to its small size, facilitating handling and reducing costs (Kohn et al., 2007), and its high control in breeding and origin (Forster et al., 2010). Information on the gut microbiota composition of Göttingen minipigs is scarce. Different to the study of Pedersen et al. (2013) that characterized the ileal, cecum and colonic microbiota composition using primers for the V5 16S rRNA gene variable region; we analyzed the fecal microbiota composition by 454 pyrosequencing using a primer set of the V5-V6 variable region and targeted specific bacterial groups by qPCR. The results at baseline obtained by qPCR and 454 pyrosequencing were qualitatively in accordance, i.e. members of the Firmicutes (Streptococcus, F. prausnitzii, E. hallii, Roseburia/E. rectale, Lactobacillus/Leuconostoc/Pediococcus), Bacteroidetes (Bacteroides-Prevotella) and Proteobacteria phylum (Enterobacteriaceae) were dominant. However, quantitative comparison between qPCR and 454 pyrosequencing should be taken with caution due to inherent biases coming with each of the method, such as 16S rRNA gene copy number and primer specificity bias in qPCR and relatively low sensitivity in 454 pyrosequencing. As such, Bifidobacterium with the lowest abundance in qPCR likely fell below the detection limit of 454 pyrosequencing. Thus, qPCR and 454 pyrosequencing should be used in a complementary rather than in a comparative approach. In general, the fecal microbiota composition in Göttingen minipigs revealed by 454 pyrosequencing was in accordance with previous data (Pedersen et al., 2013). Similar occurrence of phyla was recorded in both studies with small differences in relative abundance. Minor differences between the studies can be explained by the site-specificity of the samples (ileum, cecum, colon vs. feces), variations in diet and environment and methodological differences of the two studies (different variable 16S rRNA region targeted, different pipeline for reads assignment) (Claesson and O'Toole, 2010). Our samples displayed higher relative abundance of 75 % Firmicutes compared to 49 % in Pedersen et al. (2013).

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Large variations in relative abundance of Firmicutes have also been reported for studies characterizing the cecal microbiota of weanling pigs ranging from ~37 % (Poroyko et al., 2010) to up to ~ 75 % (Buzoianu et al., 2012). Further, in our study lower relative abundances of Spirochaetes (3.2 % vs. 6 %) and Tenericutes (<1 % vs. 3.8 %) but higher relative abundance of Proteobacteria (3.5 % vs. <1 %), were observed compared to Pedersen et al. (2013). On family and genus level our study revealed a similar microbiota composition to minipigs (Pedersen et al., 2013) and pigs (Buzoianu et al., 2012, Kim et al., 2011, Lamendella et al., 2011, Looft et al., 2014, Poroyko et al., 2010). A remarkable high relative abundance of the genus Streptococcus was recorded in three out of eight fecal samples. Accordingly, high Streptococcus numbers were observed in feces from pigs (Kim et al., 2011, Lamendella et al., 2011, Looft et al., 2014). In contrast, Pedersen et al. (2013) and other studies on pig cecal microbiota (Buzoianu et al., 2012, Looft et al., 2014, Poroyko et al., 2010) did not find a predominance of the genus Streptococcus in the cecum, suggesting a dominance of streptococci in fecal material but not in the proximal colon. The high fraction of unclassified reads (up to 58 % of total) presumes that the diversity of the fecal microbiota in minipigs is by far higher than what is currently known for pigs. Hence, to support the use of the minipigs as model for gut microbiota research, the microbiota composition of minipigs should be further studied. In our study, B. thermophilum was not detected in minipig feces in the control groups and during wash periods. Consequently, we assume that our qPCR test for B. thermophilum detection is mainly enumerating the supplemented strain RBL67. After feeding experimental diets (PRO and SYN), RBL67 was consistently detected in fecal, cecal and colonic content. Enterococcus faecium NCIMB 10415, an authorized probiotic strain for use in pig feed in the European Union (Simon, 2005), was recovered at 1.8 x 105 cfu g-1 wet weight in pregnant sows fed the probiotic for 80 days at approximately 1 x 109 cfu kg-1feed (Macha et al., 2004, Simon, 2005), which is consistent with our data on B. thermophilum. The SYN diet displayed significantly higher total Bifidobacterium numbers in the cecum and colon compared to the PRO group. In addition, the analysis of B. thermophilum after sacrifice revealed highest numerical values of B. thermophilum in the cecum of the SYN group. This suggests that RBL67 is stimulated in the cecum by the concomitant administration of FOS,

170

Discussion implying the synbiotic concept, and may further indicate viability of B. thermophilum RBL67. The simultaneous stimulation of Bifidobacterium by SYN explains why in the cecum the relative percentage of B. thermophilum as proportion of the total Bifidobacterium population is higher in the PRO group (36 %) compared to the SYN group (22 %). FOS has previously been reported to selectively stimulate the growth of bifidobacteria (reviewed by Flickinger et al. (2003)), although it is nowadays established that also other bacteria (e.g. members of the genera Roseburia, Bacteroides, Salmonella) are able to utilize prebiotics (Martin-Pelaez et al., 2008, Scott et al., 2013, van der Meulen et al., 2006). No differences between PRO and SYN were observed in metabolite concentrations measured by HPLC in the cecum and colon. This agrees with a previous study where piglets had ad libitum access to experimental diets including 10 g kg-1 FOS or transgalactooligosaccharide (TOS) (Mountzouris et al., 2006). However, other studies have reported a significant increase in molar butyrate and decrease in molar acetate concentrations when including 4 % FOS (Mikkelsen and Jensen, 2004) or 3 % inulin to piglet diets (Loh et al., 2006). Different factors, including prebiotic substrate (degree of polymerization, inclusion rate), feeding frequency (ad libitum, restricted), pig breed and basal diet (Flickinger et al., 2003) may contribute to the variable outcome of the different studies and complicate comparisons between studies. Furthermore, SCFA concentrations can be considered as a snapshot of the situation (Loh et al., 2006) due to their rapid absorption by the host or utilization by other members of the gut microbiota (Grieshop et al., 2000). The rapid absorption of SCFA is demonstrated by a decrease of SCFA concentrations from the cecum to the proximal colon as reported previously (Haenen et al., 2013) and also shown for our study. It is possible that differences in absorption and high interindividual variations of SCFA profiles have masked the effect of FOS on metabolite concentrations, leading to non-significant differences between treatment groups. Another hypothesis may be that FOS is partly digested in the upper gastrointestinal tract, as demonstrated also by Loh et al. (2006), where 20-50 % of the supplemented inulin was digested in the jejunum of pigs. In conclusion, our data on gut microbiota composition and treatment response from the SYN diet showing stimulation of Bifidobacterium, suggest that Göttingen minipigs may be a suitable model for investigating pig gut microbiota. However, to exploit the potential of this

171

Chapter 5 model, the gut microbiota of Göttingen minipigs should be further characterized. We demonstrated recovery of the probiotic candidate RBL67 by qPCR upon feeding to minipigs with a stimulation effect of FOS on the bifidobacteria population and specifically RBL67 in the cecum.

Acknowledgements

We would like to thank Dr. Franck Grattepanche and Friederike Plata Gröber from ETH Zurich for probiotic powder optimization and production. We further thank Eve Delmas and Benoît Cohade from INRA in Clermont-Ferrand for assistance in sampling and animal care throughout the study. This work was supported by the Commission for Technology and Innovation (project number: 11962.1).

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Supporting information

Table S5.1. MRS composition used for preparation of probiotic powder of B. thermophilum RBL67.

Concentration Ingredient Supplier (g L-1 or mL L-1) Soy peptone 10 Biokar diagnostics, Beauvais, France D-Glucose 10 Cargill, Mechelen, Belgium monohydrate Yeast extract 5 Ohly, Hamburg, Germany

KH2PO4 2 VWR International, Dietikon, Switzerland

MgSO4 0.2 Sigma-Aldrich Chemie GmbH, Buchs, Switzerland MnSO4 0.05 Sigma-Aldrich Chemie GmbH, Buchs, Switzerland Tween 80 1 Sigma-Aldrich Chemie GmbH, Buchs, Switzerland

Table S5.2. Primers and probes used for detection of bacterial groups by qPCR.

primer/ Target Sequence 5’-3’ Reference probe Bifidobacterium spp. xfp-fw ATC TTC GGA CCB GAY GAG AC Cleusix et al. xfp-rv CGA TVA CGT GVA CGA AGG AC (2010)

Lactobacillus/ F_Lacto 05 AGC AGT AGG GAA TCT TCC A Furet et al. Pediococcus/ R_Lacto 04 CGC CAC TGG TGT TCY TCC ATA TA (2009) Leuconostoc spp. Bacteroides-Prevotella Bac303F GAA GGT CCC CCA CAT TG Ramirez-Farias Bfr-Femrev CGC KAC TTG GCT GGT TCA G et al. (2009) Enterobacteriaceae Eco1457F CAT TGA CGT TAC CCG CAG AAG AAGC Bartosch et al. Eco1652R CTC TAC GAG ACT CAA GCT TGC (2004) Roseburia spp./ RrecF GCG GTR CGG CAA GTC TGA Ramirez-Farias Eubacterium rectale Rrec630mR CCT CCG ACA CTC TAG TMC GAC et al. (2009) Faecalibacterium Fprau223F GAT GGC CTC GCG TCC GAT TAG Bartosch et al. prausnitzii Fprau420R CCG AAG ACC TTC TTC CTCC (2004) Eubacterium hallii EhalF GCG TAG GTG GCA GTG CAA Ramirez-Farias EhalR GCA CCG RAG CCT ATA CGG et al. (2009) Streptococcus spp. Tuf-Strep-1 GAA GAA TTG CTT GAA TTG GTT GAA Collado et al. Tuf-Strep-R GGA CGG TAG TTG TTG AAG AAT GG (2009)

Bifidobacterium btherm RTF TTG CTT GCG GGT GAG AGT Mathys et al. thermophilum btherm RTR CGC CAA CAA GCT GAT AGG AC (2008) bthermTqm FAM-ATG TGC CGG GCT CCT GCA T-TAMRA

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Table S5.3. Ratios of minor metabolites (%) and concentrations of sugars (mM) detected with HPLC in cecum and colon (proximal, middle, distal) after feeding PRO or SYN experimental diets.

Metabolite* Cecum Proximal Middle Distal PRO SYN PRO SYN PRO SYN PRO SYN Isobutyrate 4.4 ± 1.9 3.3 ± 2.4 3.1 ± 2.1 4.2 ± 0.6 5.4 ± 0.5 5.2 ± 0.7 4.9 ± 0.3 5.0 ± 0.6 Isovalerate 3.2 ± 2.2 1.4 ± 1.4 4.4 ± 0.5 4.4 ± 0.5 4.5 ± 0.5 4.1 ± 0.5 4.6 ± 0.6 4.3 ± 0.5 Valerate 2.0 ± 1.1 3.2 ± 1.6 1.9 ± 0.7 2.1 ± 0.6 1.9 ± 0.8 1.8 ± 0.3 2.1 ± 0.6 1.9 ± 0.6 Lactate n.d. 2.9a 1.7 ± 0.4b 1.4 ± 0.1c 2.2 ± 0.9c 2.5 ± 1.6 2.0 ± 1.8c 1.8 ± 1.0 Succinate 0.7a n.d. n.d. n.d. n.d. 1.7a n.d. n.d. Formate 1.9a 1.0a n.d. n.d. n.d. n.d. n.d. n.d. ‡ Sugar

Glucose 5.4 ± 7.9 9.0 ± 7.2c 1.5 ± 0.2b n.d. n.d. 1.6 ± 0.8b 2.0 ± 0.5b 1.2 ± 0.2 Lactose n.d. n.d. 0.1 ± 0.0b n.d. n.d. n.d. n.d. n.d. * Data are given as % of total metabolites and means ± SD for PRO (n=4) and SYN (n=4); ‡ concentrations are given in mM and as means ± SD for group A and B; a n=1; b n=2; c n=3

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Chapter 6

Conclusions and perspectives

175

Chapter 6

General conclusions

In swine livestock production Salmonella prevalence is high, which impacts animal health and performance. Pigs are known as persistent carriers of Salmonella, posing a health risk to consumers via transmission of the pathogen through the food chain. The ban of in-feed antibiotics has intensified research on potential alternatives, able to maintain gut health and keep productivity of swine livestock production. In this context, probiotics and prebiotics that are known for their beneficial modulation of the gut microbiota are considered as promising strategies. Bifidobacterium thermophilum RBL67 is a human fecal isolate, featuring probiotic characteristics as demonstrated in several previous in vitro studies carried out with human gut microbiota. In particular, the anti-Salmonella activity of RBL67 is of interest for a targeted probiotic approach to reduce Salmonella in pigs. B. thermophilum is frequently found in feces from animals, including pigs. Thus, RBL67 could be considered as a potential probiotic in pigs. Therefore, the aim of this doctoral thesis was to investigate RBL67 as a promising alternative strategy to counteract Salmonella in swine. To reach our objectives we used a combined approach of swine in vitro and in vivo models with emphasis on RBL67 colonization and anti-Salmonella properties in the porcine gut microbiota environment.

Investigating potential new feed additives in the complex environment of the pig intestinal ecosystem is challenging. In the frame of this doctoral thesis we thus developed and validated a novel porcine PolyFermS in vitro fermentation model (Chapter 2). The lack of host response and interaction is a major limitation when considering in vitro models for gut microbiota research. However, this can equally be considered as an advent, because the absence of confounding influences from the host in in vitro models allows conclusive interpretation of obtained data. This is particularly important for functionality studies that are crucial for the assessment of novel potential feed additives, including probiotics and prebiotics. Because most of the porcine in vitro fermentation models are batch models with important inherent limitations (e.g. substrate and experimental duration limitation), we developed a continuous in vitro fermentation model simulating swine proximal colon (porcine PolyFermS). The porcine PolyFermS model was shown to produce a stable microbiota composition, diversity and metabolic activity akin to in vivo conditions

176

General conclusions over a 54 days fermentation period. Conclusively, the medium formulated to simulate swine chyme and the chosen parameter settings (pH 6.0, 38°C, retention time 9 h) could maintain microbiota composition and activity representative for the in vivo proximal colon. The model set-up and reactor design allowed the simultaneous testing of different treatments in parallel and compared to a control with the same microbiota, which is an advent if screening studies of different feed additives are required, particularly also limiting in vivo trials which come imperatively with high costs and ethical constraints. With the novel PolyFermS model developed in the frame of this doctoral thesis, efficient, fast, reproducible and cost-effective screening of new potential feed additives on pig colonic fermentation is possible.

The PolyFermS model was further used to test antagonism of RBL67 alone and combined with selected prebiotics to Salmonella Typhimurium N-15 (Chapter 3). Both strains, RBL67 and N-15 could establish in the porcine PolyFermS, while RBL67 was supported by FOS. This demonstrates the ability of N-15 and RBL67 to compete with the modeled porcine microbiota. The prebiotics FOS and GOS showed strong antagonistic effects to S. Typhimurium N-15 in the in vitro model and significantly increased SCFA production, mainly acetate and propionate. When combining FOS or GOS with RBL67, total SCFA production was similar to the prebiotics alone, while antagonism to Salmonella was enhanced. Our overall data suggest that an increased SCFA production is partly responsible for Salmonella inhibition, in agreement with previous studies showing inhibitory effects of acetate on Salmonella growth (Adams and Hall, 1988, van der Wielen et al., 2001, Wilson et al., 2003). However, the enhanced antagonism to Salmonella for R-FOS and R-GOS suggests that additional antimicrobial mechanisms are involved, for example the production of BLIS from RBL67. In addition, R-FOS led to a significant increase of butyrate production, which could be of interest for gut health preservation. In conclusion, in our in vitro model RBL67 was shown to be supported in colonization ability and anti-Salmonella effect by prebiotics, while not changing the commensal microbiota composition. Our data reinforce the interest of RBL67 combined with prebiotics as alternative safe strategy to protect swine health by counteracting Salmonella.

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Using RNA-sequencing we aimed to investigate probiotic-pathogen interactions in a co- culture model under simple conditions to gain insights into the mechanism of antagonism of RBL67 to Salmonella at molecular level (Chapter 4). RNA-sequencing was shown to be a robust method allowing the analysis of a whole-genome transcript response in co-culture experiments. Furthermore, despite the lack of the whole genome sequence of S. Typhimurium N-15 the majority of reads generated from the N-15 mono-culture (> 90 %) could be assigned to the genome of S. Typhimurium LT2. Under simple conditions, we observed reduced growth of Salmonella and stimulation of Salmonella virulence determinants in the co- cultures. In contrast, RBL67 showed enhanced growth in presence of Salmonella N-15. Salmonella virulence is under control of a highly complex and tightly controlled regulatory network. Our data suggest that RBL67 triggers virulence gene expression of N-15, leading to redundant energy expenditure, which may contribute to the reduced growth of Salmonella in co- compared to mono-cultures. Reduced growth in a complex environment such as the gut microbiota can be detrimental to competitiveness and favor Salmonella clearance from the gut. It is not clear whether this effect is specific to RBL67, and additional experiments should be performed to confirm this hypothesis, possibly showing a mechanism of probiotic organisms on host protection against Salmonella.

At last we used an in vivo animal model with Göttingen minipigs to test colonization of RBL67 and the effects in combination with FOS on gut microbiota composition and activity (Chapter 5). Göttingen minipigs have a high control of breeding and origin and are small animals allowing easier handling and reducing costs compared to other pig models. Conventional pig models, e.g. piglets, are often characterized by a large diversity and uncontrolled pathogen load. The high control of minipigs is particularly advantageous to reduce confounding influences implied from conventional pig models, while still accounting for the full complexity encountered by the host environment. We successfully applied the Göttingen minipig to show a stimulation of Bifidobacterium numbers in the cecum and proximal colon when feeding RBL67 combined with FOS, in agreement with previous studies in pigs reporting increase of bifidobacterial populations after prebiotic feeding. Furthermore, the highest numerical values

178

General conclusions of B. thermophilum were found in SYN-fed minipigs, suggesting a promotion effect of FOS. In agreement with data in the in vitro PolyFermS model (Chapter 3), RBL67 alone and combined with FOS did not induce significant alterations of the gut microbiota, suggesting that the use of RBL67 is safe, since drastic re-modeling of the gut microbiota may lead to dysbiosis. However, in contrast to our in vitro PolyFermS model we did not detect butyrate stimulation with RBL67 combined with FOS, which may be partly explained by the rapid absorption of SCFA by the host. This suggests that in vitro and in vivo models are useful in a complementary approach for gut microbiota research, to distinguish between functionality of gut microbial and host processes. The fecal microbiota composition of Göttingen minipigs using 454 pyrosequencing revealed a microbiota composition very similar to pigs. However, a large fraction of generated reads could not be assigned to known genera, which suggests that the Göttingen minipig is yet to be studied. Hence, to support the use of the Göttingen minipig as a cost-efficient and highly controlled complementary animal model for gut microbiota research in pigs and to exploit the entire potential of this model, a deeper characterization of the minipig gut microbiota composition and diversity is needed.

In conclusion, this doctoral thesis has allowed expanding and broadening the knowledge on the probiotic candidate RBL67 and provides a first insight into a possible mechanism of how RBL67 mediates protection from Salmonella. We successfully applied a combined approach of in vitro fermentation and in vivo swine models to investigate RBL67 colonization ability and antagonism to Salmonella in the porcine gut microbial ecosystem. Based on the data generated in the frame of this doctoral thesis it is proposed that RBL67 is supported by the concomitant administration of prebiotics in the complex gut environment, which results in improved colonization potential and competitiveness, consequently allowing antagonism to Salmonella. The findings of this doctoral thesis, in addition to previous studies on RBL67, highlight the potential of RBL67 combined with prebiotics to be actively pursued as an alternative strategy to counteract Salmonella prevalence in swine. Table 6.1 summarizes the major findings of this doctoral thesis.

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Chapter 6

Perspectives

So far, the antagonistic activity of B. thermophilum RBL67 against pathogens has been solely demonstrated in in vitro studies using simple antagonistic tests (Toure et al., 2003, von Ah, 2006), intestinal human cell models (Moroni et al., 2006), combined human in vitro fermentation and cellular models (Zihler et al., 2011) and more complex models of human and swine fecal microbiota fermentation systems (Zihler et al., 2014) (Chapter 3). However, to fulfill the requirements of EFSA for the assessment of microbial feed additives, the efficacy of probiotics has to be demonstrated in the host with a minimum of three trials demonstrating a significant outcome (Anadon et al., 2006). Thus, to demonstrate efficacy of RBL67 and to account for the full complexity of the host environment, pig infection trials should be performed. Based on the data obtained from the PolyFermS model (Chapter 3) and the minipig trial (Chapter 5), a synbiotic approach to support colonization, competitiveness and antagonism to Salmonella should be included and tested in a future animal infection trial. Moreover, the combination with FOS induced a stimulation of butyrate in the PolyFermS model. While butyrate has been associated with a number of health-related properties (reviewed by Russell et al. (2013)), it was also shown to downregulate Salmonella invasion associated genes (Gantois et al., 2006). However, in a first animal in vivo study using minipigs, we could not detect a significant stimulation of butyrate by RBL67 in combination with FOS, likely due to a combination of factors such as the high interindividual variation within a treatment group, the dosage of FOS, but also the partial degradation of prebiotics in the upper GIT and the rapid absorption of SCFA by the host (Flickinger et al., 2003; Grieshop et al., 2000). The dosage of the prebiotic should be addressed carefully, especially in regard of the conflicting results obtained from different studies (Flickinger et al., 2003). To account for the complexity of the appropriate prebiotic dosage, a future animal infection trial should test different inclusion rates, while also considering the frequency of feeding (e.g. monitor ad libitum or restrict feed). Thereby the balance of efficacy, nutritional contribution and economical profitability of the product is a challenge. The rapid absorption of SCFA by the host makes it challenging to predict the true effect of a dietary intervention on gut

180

Perspectives microbial metabolic activity, especially when working with fecal samples. Fistulation of Göttingen minipigs is a possible solution, enabling direct access to the main fermentation sites (cecum and colon) and allowing skirting of fixed time-point analysis (e.g. at sacrifice). This would also allow investigating the degradation of prebiotics in more detail. Although, T- cannulas fitted at the terminal ileum of Göttingen minipigs were maintained without problems in a previous study (Lick et al., 2001), fistulation represents a non-neglectable intervention of the animal, increasing ethical constraints and costs. Furthermore, taking advantage of an animal infection trial, performance parameters, such as weight gain, feed intake and feed conversion ratio should be studied. Besides efficacy against Salmonella, these supplementary attributes could enforce the potential of a probiotic or synbiotic product to be marketed. We have observed that RBL67 alone and in combination with FOS does not exert adverse health effects on pigs, giving a first indication on the safety of RBL67 when ingested. However, to broaden the knowledge of health-related aspects when feeding RBL67 alone or combined with prebiotics, host response using immunological parameters should be equally considered in future animal models.

Using RNA-sequencing we proposed a possible mechanism on how probiotic organisms might induce protection of the host from infection. However, this is based solely on transcriptome analysis and is lacking further experimental data. Therefore, the next step is the confirmation of the hypothesis. Co-culture of RBL67 with a mutant defective for TTSS-1 should be performed to observe whether Salmonella growth in presence of RBL67 is indeed not impaired without TTSS-1 expression. To verify that the virulence gene expression has not been triggered by the acetate concentration difference of mono- and co-culture, simple challenge tests with different acetate concentrations at controlled pH could be performed and transcript analysis should be done. However, to avoid high costs for RNA-seq of multiple samples, specific transcripts could be targeted by designing a RT-qPCR approach. Specific transcripts can be selected based on RNA-seq results from this thesis and could include genes encoding for TTSS-1 effector proteins (e.g. sipC), fimbrial adherence determinants (fimWZY) or flagellar genes.

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Chapter 6

These specific transcripts could equally be used to investigate whether the effect observed in this study is strain specific to RBL67, by testing different bifidobacterial or other potential probiotic strains for interaction with Salmonella and analyzing transcript response. In a further step and to account for interaction and response of the host, probiotic-pathogen interaction on transcript level could be studied with intestinal cell lines or even in an infection model of gnotobiotic pigs using a defined two-strain consortium (RBL67 and Salmonella). Using RNA-sequencing we could not detect an effect of the bacteriocin-like inhibitory substance that is produced by RBL67 and might contribute to the antagonism to Salmonella. This can be partly explained by the lack of knowledge on this BLIS, the model settings (cell density, medium, fermentation conditions) and the overall low production level of the BLIS (von Ah, 2006). To further investigate the presence and activity of the BLIS comparative metabolomics and genomics between RBL67 and a non-producing B. thermophilum strain could be performed. Equally, comparative extracellular proteomics using MS/MS may provide evidence for the BLIS. We used a simplified co-culture model to study transcriptome response of RBL67 and N-15. This provides important insights into microbe-microbe interactions and allows formulating hypotheses on functions of different members of a given ecosystem (Rosenthal et al., 2011). However, the ultimate target is to understand microbial interactions in a complex ecosystem, such as the gut. This could be achieved by delineating transcript information from complex ecosystems to single cell or species level. Given the fast evolution of high throughput sequencing techniques, including RNA-seq, advances in data analysis and reduced costs in combination with computational biology holds promise to achieve this target in future years and thus will allow a deeper understanding of microbial interactions in complex ecosystems.

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Table 6.1. Major findings from in vitro and in vivo studies on RBL67 as a potential probiotic candidate in swine livestock production to counteract Salmonella. study major findings PolyFermS in vitro model  Porcine PolyFermS displays stable bacterial composition, diversity and activity, mimicking porcine proximal colon. This allows the (Chapter 2 and 3) simultaneous comparison of different treatments in parallel and studying microbial interactions in the complex porcine intestinal ecosystem.  RBL67 and Salmonella N-15 can compete with the modeled microbiota and establish in the system.  FOS supports RBL67 establishment in vitro.  GOS and FOS inhibit Salmonella and the effect is enhanced in combination with RBL67, while total SCFA concentrations are similar. Salmonella inhibiton likely due to SCFA production and additional antimicrobial effects when prebiotics are combined with RBL67.  RBL67+FOS additionally increase butyrate production, which is of interest for gut health preservation.  RBL67 alone and combined with MOS has little effect on Salmonella. RNA-seq  RBL67 displays enhanced growth in presence of Salmonella N-15. (Chapter 4)  N-15 growth is reduced in presence of RBL67.  Salmonella virulence determinants are higher expressed in co-culture with RBL67 than in mono-culture.  RBL67 represses flagella genes.  We hypothesize that RBL67 triggers induction of Salmonella virulence machinery, leading to energy expenditure and attenuation in growth.  In the competitive gut environment, reduced growth could lead to a loss in competition and thus could favor clearance of Salmonella from the gut. Minipig study  RBL67+FOS significantly increase Bifidobacterium populations in cecum and colon. (Chapter 5)  RBL67 consistently detected in feces, cecum and colon after feeding probiotic or synbiotic diet.  Highest copy numbers per gram cecal content when RBL67 is combined with FOS, suggesting a promotion effect of FOS.  No effect on metabolic activity in the cecum by HPLC and no drastic changes in gut microbiota composition as determined with qPCR and 454 pyrosequencing. This reassures the use of RBL67 alone and combined with FOS, because drastic remodeling could lead to dysbiosis.  High number of unclassified reads by 454 pyrosequencing, which needs further investigation in regard of the Göttingen minipig as animal model in gut microbiota research.

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Appendix

207

Appendix

Table A1. Salmonella Typhimurium N-15 genes higher expressed in mono-culture, mapped onto Salmonella Typhimurium LT2 genome

ORF Gene Function logFC logCPM FDR STM0004 thrC threonine synthase 1.30 7.25 1.32E-02 STM0005 yaaA hypothetical protein 1.57 7.34 5.00E-03 STM0007 talB transaldolase B 2.49 9.46 4.94E-03 STM0008 mogA molybdenum cofactor biosynthesis protein MogA 1.12 6.14 9.07E-03 STM0043 rpsT 30S ribosomal protein S20 2.63 10.23 1.12E-02 STM0045 ribF bifunctional riboflavin kinase/FMNadenylyltransferase 1.82 7.42 1.57E-03 STM0046 ileS isoleucyl-tRNA synthetase 1.73 10.24 3.68E-02 STM0047 lspA lipoprotein signal peptidase 1.59 7.42 1.10E-02 STM0048 slpA FKBP-type peptidyl-prolyl cis-trans isomerase 1.60 6.41 1.95E-04 STM0049 ispH 4-hydroxy-3-methylbu t-2-enyl diphosphatereductase 1.25 6.89 6.29E-03 STM0051 rihC ribonucleoside hydrolase RihC 1.03 6.81 2.47E-02 STM0064 dapB dihydrodipicolinate reductase 1.13 6.44 8.57E-03 STM0092 surA peptidyl-prolyl cis-trans isomerase SurA 1.64 8.28 2.28E-02 STM0095 rluA 23S rRNA/tRNA pseudouridine synthase A 1.21 6.05 2.02E-03 STM0118 fruR DNA-binding transcriptional regulator FruR 1.49 7.58 2.91E-02 STM0121 ftsL cell division protein FtsL 1.21 6.66 1.48E-02 STM0124 murF UDP-N-acetylmuramoyl -tripeptide--D-alanyl-D-alanin e ligase 1.44 7.74 3.88E-02 STM0128 murG undecaprenyldiphosph o-muramoylpentapeptidebeta-N- 2.06E-02 1.18 7.26 acetylglucosaminyltransferase STM0133 ftsZ cell division protein FtsZ 2.17 9.38 8.02E-03 STM0134 lpxC UDP-3-O-[3-hydroxymy ristoyl] N- 8.35E-03 2.35 9.64 acetylglucosaminedeacetylase STM0135 yacA SecA regulator SecM 1.15 6.62 1.99E-02 STM0139 yacF hypothetical protein 1.29 6.79 5.42E-03 STM0151 pdhR transcriptional regulator PdhR 1.64 6.95 2.30E-03 STM0153 aceF dihydrolipoamide acetyltransferase 1.52 8.40 3.54E-02 STM0154 lpdA dihydrolipoamide dehydrogenase 1.54 8.93 4.41E-02 STM0164 STM0164 transcriptional regulator 1.61 6.97 8.32E-03 STM0170 hpt hypoxanthine-guanine phosphoribosyltransferase 1.57 6.88 2.21E-03 STM0171 yadF carbonic anhydrase 1.04 6.83 2.37E-02 STM0180 panD aspartate alpha-decarboxylase 1.96 6.83 1.73E-04 STM0202 hemL glutamate-1-semialde hyde aminotransferase 1.53 7.94 3.92E-02 STM0204.S yadR iron-sulfur cluster insertion protein ErpA 1.92 7.27 3.46E-03 STM0211 yaeH hypothetical protein 1.10 5.19 4.10E-02 STM0213 dapD 2,3,4,5-tetrahydropy ridine-2,6-dicarboxylateN-succ 1.04E-02 2.04 8.28 inyltransferase STM0219 frr ribosome recycling factor 1.88 8.79 2.98E-02 STM0220 dxr 1-deoxy-D-xylulose 5-phosphate reductoisomerase 1.51 7.11 6.19E-03 STM0225 hlpA outer membrane protein OmpH 1.73 9.20 4.83E-02 STM0227 fabZ (3R)-hydroxymyristoy l-ACP dehydratase 2.23 7.18 1.19E-04 STM0228 lpxA UDP-N-acetylglucosam ine acyltransferase 1.67 8.02 2.07E-02 STM0232 accA acetyl-CoA carboxylase carboxyltransferasesubunit alpha 1.94 8.33 1.01E-02 STM0237 rof Rho-binding antiterminator 1.36 6.46 5.83E-03 STM0238 yaeP hypothetical protein 2.21 5.79 1.05E-03 STM0240 yaeJ peptidyl-tRNA hydrolase domain-containingprotein 1.08 4.80 1.35E-02 STM0243 yaeB regulatory protein 1.11 6.05 3.88E-03 STM0244 rcsF outer membrane lipoprotein 1.01 6.54 3.13E-02

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ORF Gene Function logFC logCPM FDR STM0245 metQ DL-methionine transporter substrate-bindingsubunit 1.33 7.41 2.72E-02 STM0258 yafD hypothetical protein 1.49 6.91 1.14E-02 STM0310 gmhA phosphoheptose isomerase 1.19 6.56 8.15E-03 STM0312 yafK hypothetical protein 1.15 6.93 4.76E-02 STM0316 pepD aminoacyl-histidine dipeptidase 1.82 9.62 3.01E-02 STM0327 STM0327 hypothetical protein 1.54 6.07 1.24E-03 STM0386 proC pyrroline-5-carboxyl ate reductase 1.69 6.91 1.38E-03 STM0397 phoB transcriptional regulator PhoB 1.11 6.78 3.16E-02 STM0406 yajC preprotein translocase subunit YajC 2.20 7.76 1.69E-03 STM0408 secF preprotein translocase subunit SecF 1.55 7.97 3.93E-02 STM0415 nrdR transcriptional regulator NrdR 1.63 6.73 6.56E-04 STM0416 ribD bifunctionaldiaminoh ydroxyphosphoribosylaminopyrim 4.75E-03 idinedeaminase/5-amino-6-(5-ph 1.72 7.50 osphoribosylamino)uracilreduct ase STM0418 nusB transcription antitermination protein NusB 1.45 7.02 1.71E-02 STM0423 ispA geranyltranstransfer ase 1.22 7.12 3.45E-02 STM0424 xseB exodeoxyribonuclease VII small subunit 1.82 4.86 2.37E-04 STM0434 apbA 2-dehydropantoate 2-reductase 1.34 6.83 1.22E-02 STM0435 yajQ nucleotide-binding protein 1.78 8.21 2.80E-02 STM0448 clpP ATP-dependent Clp protease proteolytic subunit 1.72 7.62 1.76E-02 STM0449 clpX ATP-dependent protease ATP-binding subunit ClpX 2.39 9.27 3.17E-03 STM0450 lon DNA-binding ATP-dependent protease La 1.79 9.47 2.35E-02 STM0451 hupB transcriptional regulator HU subunit beta 3.26 9.54 5.80E-04 STM0462 glnK nitrogen regulatory protein P-II 2 1.09 4.86 1.94E-02 STM0474 ybaJ hypothetical protein 1.70 6.42 5.34E-04 STM0475 acrB acridine efflux pump 1.61 9.83 4.41E-02 STM0477 acrR DNA-binding transcriptional repressor AcrR 1.13 6.00 1.02E-02 STM0483 apt adenine phosphoribosyltransferase 1.49 6.28 5.70E-03 STM0484 dnaX DNA polymerase III subunits gamma and tau 1.36 7.38 1.17E-02 STM0485 ybaB hypothetical protein 2.32 7.07 2.06E-04 STM0486 recR recombination protein RecR 2.29 7.25 2.42E-04 STM0487.S htpG heat shock protein 90 1.78 10.02 3.60E-02 STM0489 hemH ferrochelatase 2.54 8.64 3.11E-03 STM0495 ybaK hypothetical protein 1.38 5.86 5.25E-04 STM0496 ybaP hypothetical protein 1.11 5.65 6.03E-03 STM0499 cueR DNA-binding transcriptional regulator CueR 1.34 6.26 1.68E-03 STM0500 ybbJ hypothetical protein 1.41 6.00 3.44E-03 STM05020 STM05020 hypothetical protein 2.30 5.84 8.77E-07 STM0504 ybbN thioredoxin protein 1.67 6.85 1.03E-03 STM0516 allR DNA-binding transcriptional repressor AllR 1.07 6.26 1.60E-02 STM05225 STM05225 hypothetical protein 2.44 4.22 5.59E-04 STM0534 purE phosphoribosylaminoi midazole carboxylasecatalytic subunit 1.28 5.22 2.91E-02 STM0541 ybcJ hypothetical protein 2.20 5.60 1.22E-06 STM0542 folD bifunctional 5,10-methylene-tetrahydrofolat 1.48E-02 edehydrogenase/ 5,10-methylene-tetrahydrofolat 1.53 7.42 ecyclohydrolase STM05615 STM05615 hypothetical protein 1.23 4.81 5.14E-03 STM0578 nfnB nitroreductase 1.52 6.94 5.13E-03 STM0608 ahpC alkyl hydroperoxide reductase subunit C 2.30 10.71 4.01E-02

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ORF Gene Function logFC logCPM FDR STM0609 ahpF alkyl hydroperoxide reductase F52a subunit 1.65 8.81 3.16E-02 STM0614 ybdQ universal stress protein 1.93 8.50 3.47E-02 STM0616 rnk nucleoside diphosphate kinase regulator 1.32 5.71 2.84E-03 STM0617 rna ribonuclease I 1.51 7.17 8.48E-03 STM0630 ccrB camphor resistance protein CrcB 1.16 5.69 5.71E-03 STM0632 tatE twin arginine translocase protein E 2.18 7.09 6.31E-04 STM0633 lipA lipoyl synthase 1.74 8.15 3.21E-02 STM0635.S lipB lipoate-protein ligase B 1.16 6.51 1.23E-02 STM0636 ybeD hypothetical protein 1.55 7.19 9.63E-03 STM0637 dacA D-alanyl-D-alanine carboxypeptidase 1.30 7.12 2.24E-02 STM0642 ybeB hypothetical protein 1.69 5.39 2.48E-04 STM0646 holA DNA polymerase III subunit delta 1.67 7.10 3.05E-03 STM0647 rlpB LPS-assembly lipoprotein RlpB 1.57 7.11 7.98E-03 STM0667 ybeX transporter 1.49 7.56 3.31E-02 STM0668 ybeY metalloprotease 1.50 6.21 1.81E-03 STM0669 phoL phosphate starvation-inducible protein 1.42 7.40 3.08E-02 STM0680 asnB asparagine synthetase B 1.71 7.71 1.57E-02 STM0681 nagD UMP phosphatase 1.12 6.25 4.38E-03 STM0694 fldA flavodoxin FldA 1.71 7.59 2.64E-02 STM0695 ybfE LexA regulated protein 1.68 5.31 2.19E-03 STM0708 ybfA hypothetical protein 2.21 5.15 2.68E-06 STM0712 ybgJ carboxylase 1.28 5.85 1.48E-02 STM0713 ybgK carboxylase 1.24 6.82 2.89E-02 STM0731 STM0731 inner membrane protein 1.73 6.10 3.48E-03 STM0737 sucB dihydrolipoamide succinyltransferase 1.30 7.14 1.44E-02 STM0740 cydA cytochrome d terminal oxidase polypeptidesubunit I 2.04 9.69 1.62E-02 STM0741 cydB cytochrome d terminal oxidase polypeptidesubunit II 1.96 8.86 1.23E-02 STM0742 ybgT outer membrane lipoprotein 2.48 6.01 9.77E-04 STM0744 ybgC acyl-CoA thioester hydrolase YbgC 1.67 6.58 3.23E-04 STM0745 tolQ colicin uptake protein TolQ 1.31 7.17 3.84E-02 STM0746 tolR colicin uptake protein TolR 1.26 6.19 9.73E-03 STM0748 tolB translocation protein TolB 2.64 9.24 8.52E-04 STM0749 pal peptidoglycan-associ ated outer membranelipoprotein 2.34 10.25 2.93E-02 STM0750 ybgF tol-pal system protein YbgF 2.13 7.94 9.72E-03 STM0760 aroG phospho-2-dehydro-3- deoxyheptonate aldolase 1.95 7.29 1.73E-03 STM0774 galK galactokinase 1.65 6.96 1.60E-03 STM0780 STM0780 hypothetical protein 2.23 7.46 7.38E-03 STM0781 modA molybdate transporter periplasmic protein 1.85 7.45 8.40E-03 STM0782 modB molybdate ABC transporter permease 1.38 6.24 1.91E-03 STM0783 modC molybdate transporter ATP-binding protein 1.47 6.61 1.38E-03 STM0786 ybhC pectinesterase 1.42 7.83 4.56E-02 STM0802 moaA molybdenum cofactor biosynthesis protein A 1.27 7.03 1.54E-02 STM0803 moaB molybdopterin biosynthetic protein B 1.06 6.82 4.57E-02 STM0804 moaC molybdenum cofactor biosynthesis protein MoaC 1.59 6.64 3.33E-03 STM0805 moaD molybdopterin synthase small subunit 1.56 5.21 3.70E-04 STM0806 moaE molybdopterin guanine dinucleotide biosynthesisprotein 7.45E-05 1.74 6.00 MoaE STM0807 ybhL permease 1.72 7.90 1.70E-02

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ORF Gene Function logFC logCPM FDR STM0822 ybiB glycosyl transferase family protein 1.01 5.69 2.12E-02 STM0823 ybiJ hypothetical protein 1.72 4.85 6.67E-04 STM0846 moeA molybdopterin biosynthesis protein MoeA 1.21 6.65 6.15E-03 STM0847 iaaA isoaspartyl peptidase 1.09 6.14 4.07E-02 STM0872 grxA glutaredoxin 1.86 7.85 1.51E-02 STM0888 artM arginine ABC transporter permease ArtM 1.43 5.97 1.19E-03 STM0889 artQ arginine ABC transporter permease ArtQ 1.58 6.19 2.91E-04 STM0930 orfB hypothetical protein 1.40 5.33 1.80E-03 STM0931 ybjR aminidase 1.59 6.45 4.58E-04 STM0934 ltaA L-threonine aldolase 1.75 6.21 1.17E-03 STM0943 cspD stress response protein 3.31 6.09 9.45E-11 STM0945 clpA ATP-dependent Clp protease ATP-binding subunit 1.90 9.38 2.25E-02 STM0953 infA translation initiation factor IF-1 2.26 8.26 1.04E-02 STM0959 lrp leucine-responsive transcriptional regulator 1.71 7.33 1.98E-02 STM0960 ftsK DNA translocase FtsK 1.42 8.66 4.41E-02 STM0962 ycaJ recombination factor protein RarA 1.38 7.05 6.10E-03 STM0963 serS seryl-tRNA synthetase 2.42 9.38 3.95E-03 STM0970 pflA pyruvate formate lyase-activating enzyme 1 1.53 7.32 2.12E-02 STM0974 focA formate transporter 1.75 8.75 4.17E-02 STM0977 serC phosphoserine aminotransferase 2.10 8.30 1.08E-02 STM0980 cmk cytidylate kinase 1.88 7.43 8.40E-03 STM0982 ihfB integration host factor subunit beta 1.88 7.15 9.72E-03 STM0987 ycaR hypothetical protein 2.23 5.99 2.10E-05 STM0988 kdsB 3-deoxy-manno-octulo sonate cytidylyltransferase 1.67 7.32 3.52E-03 STM0991 smtA metallothionein SmtA 1.08 6.29 7.91E-03 STM0992 mukF condesin subunit F 1.16 6.77 9.72E-03 STM0993 mukE condesin subunit E 1.26 6.15 1.92E-03 STM0995 ycbB murein L,D-transpeptidase 1.75 7.68 1.72E-02 STM0997 ycbL metallo-beta-lactama se 1.53 7.20 3.45E-03 STM0998 aspC aromatic amino acid aminotransferase 1.86 8.52 2.34E-02 STM1004 pncB nicotinate phosphoribosyltransferase 1.02 6.87 2.00E-02 STM1005 STM1005 integrase 1.17 7.43 4.45E-02 STM1066 rmf ribosome modulation factor 1.22 4.20 9.02E-03 STM1067 fabA 3-hydroxydecanoyl-AC P dehydratase 2.01 6.77 3.57E-04 STM1069 ycbG hypothetical protein 1.24 5.08 6.83E-03 STM1076 mgsA methylglyoxal synthase 1.50 5.61 4.03E-04 STM1078 STM1078 hypothetical protein 1.17 5.93 6.83E-03 STM1079 yccV heat shock protein HspQ 1.05 5.91 2.77E-02 STM1111 yccD chaperone-modulator protein CbpM 1.41 6.71 2.14E-02 STM1113 scsA suppression of copper sensitivity protein A 1.07 4.40 1.51E-02 STM1119 wraB TrpR binding protein WrbA 1.67 7.73 4.07E-02 STM1123 STM1123 hypothetical protein 1.53 5.19 2.18E-03 STM1136 ycdX hydrolase 1.47 6.24 6.89E-04 STM1147 STM1147 hypothetical protein 1.11 6.07 1.45E-02 STM1155 htrB lipid A biosynthesis lauroyl acyltransferase 1.18 6.25 2.53E-03 STM1160 solA N-methyltryptophan oxidase 1.69 6.21 6.25E-04 STM1161.S bssS biofilm formation regulatory protein BssS 1.59 5.69 1.38E-03 STM1162 dinI DNA damage-inducible protein I 2.16 5.26 1.47E-06

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ORF Gene Function logFC logCPM FDR STM1164 yceB hypothetical protein 1.50 5.91 3.09E-03 STM1165 grxB glutaredoxin 1.54 6.89 9.29E-03 STM1168 yceH hypothetical protein 1.33 5.99 4.81E-03 STM1169 mviM virulence protein 1.08 6.01 2.13E-02 STM1171 flgN FlgK/FlgL export chaperone 2.27 7.48 3.68E-03 STM1172 flgM anti-sigma-28 factor FlgM 2.47 7.08 1.49E-03 STM1173 flgA flagellar basal body P-ring biosynthesis proteinFlgA 1.84 6.44 1.34E-04 STM1174 flgB flagellar basal body rod protein FlgB 1.80 6.07 4.29E-04 STM1175 flgC flagellar basal body rod protein FlgC 1.16 5.94 1.75E-02 STM1179 flgG flagellar basal body rod protein FlgG 2.00 7.60 7.88E-03 STM1180 flgH flagellar basal body L-ring protein 1.25 6.03 5.05E-03 STM1181 flgI flagellar basal body P-ring protein 1.18 6.86 1.32E-02 STM1183 flgK flagellar hook-associated protein FlgK 2.18 9.43 1.53E-02 STM1184 flgL flagellar hook-associated protein FlgL 2.54 8.94 4.18E-03 STM1190 yceD hypothetical protein 1.83 8.99 4.95E-02 STM1191 rpmF 50S ribosomal protein L32 2.59 8.38 1.70E-02 STM1192 plsX glycerol-3-phosphate acyltransferase PlsX 1.24 7.13 3.59E-02 STM1193 fabH 3-oxoacyl-ACP synthase 1.70 7.67 1.79E-02 STM1194 fabD acyl carrier protein S-malonyltransferase 1.98 8.25 1.74E-02 STM1195 fabG 3-ketoacyl-ACP reductase 1.90 8.93 2.15E-02 STM1197 fabF 3-oxoacyl-(acyl carrier protein) synthase II 2.63 9.83 4.88E-03 STM1200 tmk thymidylate kinase 1.20 6.13 3.11E-03 STM1201 holB DNA polymerase III subunit delta' 1.41 6.01 3.23E-04 STM1203 ptsG PTS system glucose-specific transporter subunitIIBC 2.38 9.98 1.10E-02 STM1206 ycfL outer membrane lipoprotein 2.03 6.52 1.22E-04 STM1210 ycfP hypothetical protein 1.32 6.80 1.58E-02 STM1211 ndh respiratory NADH dehydrogenase 2 1.49 7.79 2.83E-02 STM1213 ycfQ transcriptional repressor 1.73 6.28 3.77E-04 STM1214 ycfR outer membrane protein 2.41 5.63 2.37E-05 STM1216 mfd transcription-repair coupling factor 1.44 8.45 4.15E-02 STM1218 lolD lipoprotein transporter ATP-binding subunit 1.13 5.29 1.65E-02 STM1220 ycfX N-acetyl-D-glucosami ne kinase 1.29 6.30 1.09E-03 STM1221 cobB NAD-dependent deacetylase 1.72 6.70 9.25E-05 STM1229 ycfD hypothetical protein 1.38 6.68 4.09E-03 STM1231 phoP DNA-binding transcriptional regulator PhoP 2.13 7.72 8.29E-03 STM1233 ycfC hypothetical protein 1.70 6.20 1.39E-03 STM1238 icdA isocitrate dehydrogenase 1.12 7.27 4.39E-02 STM1276 STM1276 hypothetical protein 1.36 4.54 8.78E-03 STM1277 yeaO hypothetical protein 1.39 4.95 5.42E-03 STM1280 yeaL inner membrane protein 1.49 4.73 2.89E-03 STM1289 yeaD aldose 1-epimerase 1.65 8.08 3.56E-02 STM1292 yeaC hypothetical protein 1.67 4.82 3.26E-03 STM1297 selD selenophosphate synthetase 1.76 7.21 4.34E-03 STM1300 STM1300 hypothetical protein 2.24 5.78 2.93E-03 STM1301 STM1301 pyrimidine (deoxy)nucleoside 2.56E-05 2.51 6.05 triphosphatepyrophosphohydrola se STM1302 xthA exonuclease III 1.48 6.64 2.41E-03 STM1310 nadE NAD synthetase 1.57 6.51 2.51E-03

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ORF Gene Function logFC logCPM FDR STM1311 osmE DNA-binding transcriptional activator OsmE 1.81 6.10 3.15E-03 STM1322 yniC 2-deoxyglucose-6-pho sphatase 1.57 6.05 3.11E-04 STM1324 STM1324 hypothetical protein 1.25 5.74 1.12E-02 STM1326 pfkB 6-phosphofructokinas e 1.62 6.94 2.89E-03 STM1336 rplT 50S ribosomal protein L20 2.40 10.51 4.04E-02 STM1339 ihfA integration host factor subunit alpha 1.94 7.38 1.71E-02 STM1367 ydiH hypothetical protein 2.53 6.80 1.55E-05 STM1368 STM1368 Na+-dicarboxylate symporter 1.59 7.16 1.75E-03 STM1377 lpp murein lipoprotein 3.39 12.59 1.18E-02 STM1389 orf319 inner membrane protein 1.50 6.47 3.44E-03 STM1426 ribE riboflavin synthase subunit alpha 1.23 6.83 1.07E-02 STM1433 ydhD hypothetical protein 1.91 6.19 1.87E-03 STM1434 rnt ribonuclease T 1.51 6.44 2.42E-03 STM1435 gloA glyoxalase I 2.07 6.69 2.07E-04 STM1448 pdxH pyridoxamine 5'-phosphate oxidase 2.01 7.80 1.22E-02 STM1451 gst glutathionine S-transferase 1.72 6.64 2.58E-03 STM1453 nth endonuclease III 1.14 5.61 5.41E-03 STM1455 ydgP electron transport complex protein RnfG 1.05 5.14 3.73E-02 STM1460 ydgK inner membrane protein 1.65 6.48 1.90E-03 STM1462.S ydgJ oxidoreductase 1.30 7.24 2.31E-02 STM1463 add adenosine deaminase 1.83 6.70 5.05E-05 STM1467 manA mannose-6-phosphate isomerase 1.27 7.31 3.66E-02 STM1479 pntA NAD(P) transhydrogenase subunit alpha 1.92 8.48 1.46E-02 STM1480 pntB pyridine nucleotide transhydrogenase 2.10 7.84 3.02E-03 STM1500 ynfD outer membrane protein 1.06 4.64 2.60E-02 STM1502 speG spermidine N1-acetyltransferase 1.40 6.85 9.63E-03 STM1503 ynfB hypothetical protein 1.28 7.07 4.52E-02 STM1509 ydfZ hypothetical protein 3.38 8.38 4.25E-04 STM1510 ydfH regulatory protein 1.24 5.80 6.97E-03 STM1518 marB hypothetical protein 1.01 3.84 4.76E-02 STM1524 yneI succinate semialdehyde dehydrogenase 1.97 8.32 9.86E-03 STM1525 yneH glutaminase 1.31 5.90 6.77E-04 STM1565 rpsV 30S ribosomal subunit S22 2.72 6.55 9.81E-05 STM1567 adhP alcohol dehydrogenase 1.12 6.96 4.27E-02 STM1572 nmpC outer membrane porin precursor 2.54 12.81 3.62E-02 STM1575 STM1575 transcriptional regulator 1.00 4.68 2.03E-02 STM1592 ydcY hypothetical protein 1.76 4.83 1.36E-03 STM1642 acpD azoreductase 1.09 5.83 4.96E-02 STM1652 ynaF universal stress protein 2.18 7.12 3.73E-04 STM1658 ydaL hypothetical protein 1.74 5.91 6.08E-04 STM1660.S fnr fumarate/nitrate reduction transcriptionalregulator 1.45 7.34 3.78E-02 STM1661 ydaA universal stress protein UspE 1.85 7.65 1.82E-02 STM1662 ynaJ inner membrane protein 1.01 3.50 3.01E-02 STM1682 tpx thiol peroxidase 1.74 6.14 2.07E-04 STM1683 tyrR DNA-binding transcriptional regulator TyrR 1.06 6.97 2.37E-02 STM1686 pspE thiosulfate:cyanide sulfurtransferase 1.40 4.83 2.72E-03 STM1690 pspA phage shock protein PspA 1.28 6.25 9.98E-03 STM1700 fabI enoyl-(acyl carrier protein) reductase 2.14 8.98 1.04E-02

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ORF Gene Function logFC logCPM FDR STM1705 osmB lipoprotein 1.05 3.99 2.18E-02 STM1707 pyrF orotidine 5'-phosphate decarboxylase 1.13 5.84 2.85E-02 STM1709 yciS inner membrane protein 1.44 5.27 6.61E-03 STM1711 ribA GTP cyclohydrolase II 1.17 6.61 1.97E-02 STM1713 cysB transcriptional regulator CysB 1.53 6.33 1.79E-03 STM1714 topA DNA topoisomerase I 1.51 8.11 3.17E-02 STM1715 yciN hypothetical protein 2.41 5.85 6.25E-06 STM1719 yciL 23S rRNA pseudouridylate synthase B 1.20 6.14 6.02E-03 STM1720 yciO hypothetical protein 1.43 6.56 3.00E-03 STM1734 yciC hypothetical protein 1.56 6.26 5.60E-04 STM1737 tonB transporter 1.05 5.37 1.01E-02 STM1738 yciI YciI-like protein 2.20 5.25 2.60E-06 STM1740 STM1740 dsDNA-mimic protein 1.34 5.23 1.61E-02 STM1746.S oppA oligopeptide transport protein 1.86 7.48 1.83E-02 STM1749 adhE bifunctional acetaldehyde-CoA/alcoholdehydr ogenase 2.36 12.69 3.38E-02 STM1753 hnr response regulator of RpoS 1.04 5.97 1.05E-02 STM1755 ychJ hypothetical protein 1.20 5.83 5.64E-03 STM1756 purU formyltetrahydrofola te deformylase 1.13 6.64 2.60E-02 STM1769 ychN sulfur reduction protein 2.20 6.20 4.61E-05 STM1772 kdsA 2-dehydro-3-deoxypho sphooctonate aldolase 1.61 7.97 3.20E-02 STM1775 hemK N5-glutamine S-adenosyl-L-methionine-depend 9.29E-03 1.20 5.71 entmethyltransferase STM1777 hemA glutamyl-tRNA reductase 2.03 8.06 9.21E-03 STM1778 lolB outer membrane lipoprotein LolB 1.39 6.15 5.79E-03 STM1779 ipk 4-diphosphocytidyl-2 -C-methyl-D-erythritolkinase 3.01 7.73 5.45E-05 STM1784 ychF GTP-dependent nucleic acid-binding protein EngD 1.67 8.00 3.92E-02 STM1790 STM1790 hydrogenase-1 operon protein HyaE 1.79 7.46 1.93E-02 STM1794 STM1794 membrane protein 2.29 6.23 3.68E-03 STM1801 ycgO potassium/proton antiporter 1.33 6.82 6.40E-03 STM1805 fadR fatty acid metabolism regulator 1.06 6.18 8.45E-03 STM1806 nhaB sodium/proton antiporter 1.20 6.98 1.07E-02 STM1807 dsbB disulfide bond formation protein B 1.53 5.69 3.74E-04 STM1808 STM1808 hypothetical protein 1.06 4.49 2.64E-02 STM1809 STM1809 hypothetical protein 1.44 6.53 3.21E-02 STM1814 minC septum formation inhibitor 1.61 6.47 1.21E-03 STM1815 minD cell division inhibitor MinD 1.92 7.89 1.17E-02 STM1816 minE cell division topological specificity factorMinE 2.46 6.28 2.56E-06 STM1822 yoaB translation initiation inhibitor 1.32 5.89 7.48E-03 STM1826 sdaA L-serine deaminase I/L-threonine deaminase I 1.40 7.49 2.70E-02 STM1830 manX PTS system mannose-specific transporter subunitIIAB 2.33 9.86 1.21E-02 STM1831 manY PTS system mannose-specific transporter subunitIIC 2.95 9.52 7.13E-04 STM1832 manZ PTS system mannose-specific transporter subunitIID 2.79 9.38 2.61E-03 STM1834 yebN hypothetical protein 1.38 6.77 5.48E-03 STM1837 cspC cold shock-like protein CspC 1.94 7.40 1.43E-02 STM1838 yobF hypothetical protein 2.37 6.42 3.89E-04 STM1844 htpX heat shock protein HtpX 1.62 7.72 2.18E-02 STM1847 yebR nucleotide-binding protein 1.38 6.65 8.61E-03 STM1873 STM1873 hypothetical protein 1.09 5.09 1.57E-02

214

Appendix

ORF Gene Function logFC logCPM FDR STM1884 eda keto-hydroxyglutarat e-aldolase/keto-deoxy-phosphog 1.83E-03 1.87 7.07 luconate aldolase STM1886 zwf glucose-6-phosphate 1-dehydrogenase 1.64 7.99 2.48E-02 STM1888 pykA pyruvate kinase 1.93 9.13 2.20E-02 STM1893 znuB high-affinity zinc transporter membrane protein 1.10 6.06 6.80E-03 STM1894 ruvB Holliday junction DNA helicase RuvB 1.64 6.53 3.56E-04 STM1895 ruvA Holliday junction DNA helicase RuvA 1.17 5.62 8.44E-03 STM1898 ruvC Holliday junction resolvase 2.20 6.11 1.47E-06 STM1900 ntpA dATP pyrophosphohydrolase 1.18 6.46 1.30E-02 STM1901 aspS aspartyl-tRNA synthetase 1.82 9.08 2.89E-02 STM1904 yecN inner membrane protein 1.23 4.88 3.62E-03 STM1905 yecO tRNA (cmo5U34)-methyltransferase 1.35 6.12 4.60E-04 STM1906 yecP tRNA (mo5U34)-methyltransferase 1.27 6.54 3.86E-03 STM1915 cheZ chemotaxis regulator CheZ 2.01 8.26 1.77E-02 STM1916 cheY chemotaxis regulatory protein CheY 1.78 7.30 7.94E-03 STM1918 cheR chemotaxis methyltransferase CheR 1.51 7.11 1.53E-02 STM1919 cheM methyl accepting chemotaxis protein II 2.05 8.77 1.28E-02 STM1920 cheW purine-binding chemotaxis protein 1.52 7.62 4.34E-02 STM1921 cheA chemotaxis protein CheA 2.02 9.47 1.40E-02 STM1922 motB flagellar motor protein MotB 2.04 7.95 8.19E-03 STM1923 motA flagellar motor protein MotA 1.88 8.23 1.70E-02 STM1924.S flhC transcriptional activator FlhC 1.93 7.81 1.42E-02 STM1925 flhD transcriptional activator FlhD 2.15 7.84 1.06E-02 STM1927 yecG universal stress protein UspC 1.91 5.47 1.79E-03 STM1932 ftnB ferritin-like protein 1.67 6.50 1.12E-02 STM1936 yecH hypothetical protein 1.11 5.30 2.43E-02 STM1945 pgsA phosphatidylglycerop hosphate synthetase 2.36 6.33 3.31E-06 STM1946 uvrC excinuclease ABC subunit C 1.47 7.83 2.92E-02 STM1947 uvrY response regulator 2.28 8.06 3.82E-03 STM1949 yecF hypothetical protein 1.75 6.23 5.61E-03 STM1953 yedO D-cysteine desulfhydrase 1.07 5.77 1.17E-02 STM1956 fliA flagellar biosynthesis sigma factor 2.01 9.16 4.73E-02 STM1961 fliS flagellar protein FliS 2.55 6.76 1.04E-04 STM1962 fliT flagellar biosynthesis protein FliT 1.60 6.46 1.56E-03 STM1965 yedE inner membrane protein 1.31 5.87 4.61E-03 STM1966 yedF hypothetical protein 1.51 4.07 5.55E-03 STM1968 fliE flagellar hook-basal body protein FliE 1.27 5.18 4.39E-03 STM1970 fliG flagellar motor switch protein G 1.41 6.91 2.94E-03 STM1971 fliH flagellar assembly protein H 1.29 6.22 9.27E-03 STM1972 fliI flagellum-specific ATP synthase 1.46 6.32 2.65E-04 STM1974 fliK flagellar hook-length control protein 1.66 7.18 2.39E-03 STM1975 fliL flagellar basal body-associated protein FliL 1.59 5.74 5.61E-03 STM1978 fliO flagellar biosynthesis protein FliO 1.42 5.16 2.67E-03 STM1979 fliP flagellar biosynthesis protein FliP 1.30 5.95 1.45E-03 STM2059 yeeX hypothetical protein 2.45 8.71 6.86E-03 STM2070 yeeZ dehydratase 1.23 6.79 1.40E-02 STM2091 rfbG CDP glucose 4,6-dehydratase 1.61 9.32 4.52E-02 STM2121 dcd deoxycytidine triphosphate deaminase 2.05 6.42 6.25E-06

215

Appendix

ORF Gene Function logFC logCPM FDR STM2122 udk uridine kinase 1.12 6.57 2.19E-02 STM2154 mrp ATPase 2.05 8.97 8.99E-03 STM2168 pbpG D-alanyl-D-alanine endopeptidase 1.38 6.75 7.20E-03 STM2183 cdd cytidine deaminase 1.20 6.52 1.46E-02 STM2184 sanA hypothetical protein 1.15 6.36 2.07E-02 STM2193 folE GTP cyclohydrolase I 2.21 7.83 3.54E-03 STM2194 yeiG esterase 1.08 6.37 1.99E-02 STM2202 yeiH inner membrane protein 1.34 6.67 4.39E-03 STM2204 fruA PTS system fructose-specific transporter subunitIIBC 2.38 10.22 7.13E-03 STM2205 fruK 1-phosphofructokinas e 2.05 9.32 1.73E-02 STM2214 spr outer membrane lipoprotein 2.26 7.55 1.22E-03 STM2224 rplY 50S ribosomal protein L25 2.98 9.00 2.63E-03 STM2246 narP transcriptional regulator NarP 1.13 5.83 7.74E-03 STM2262 eco ecotin 1.72 6.16 5.37E-05 STM2279 yfaE 2Fe-2S ferredoxin 1.49 4.85 9.12E-04 STM2303 STM2303 hypothetical protein 1.26 5.60 8.67E-03 STM2305 menE O-succinylbenzoic acid--CoA ligase 1.82 7.50 2.61E-03 STM2306 menC O-succinylbenzoate synthase 1.96 7.88 4.34E-03 STM2307 menB naphthoate synthase 1.73 7.92 2.61E-02 STM2309 menD 2-succinyl-5-enolpyr uvyl-6-hydroxy-3-cyclohexene-1 - 1.66E-02 1.52 7.55 carboxylate synthase STM2314 STM2314 chemotaxis signal transduction protein 1.81 7.87 2.58E-02 STM2316.S nuoN NADH dehydrogenase subunit N 1.50 7.72 2.29E-02 STM2318 nuoL NADH dehydrogenase subunit L 1.90 8.39 8.68E-03 STM2319 nuoK NADH dehydrogenase subunit K 1.40 5.34 1.42E-03 STM2321 nuoI NADH dehydrogenase subunit I 1.45 6.43 1.76E-03 STM2322 nuoH NADH dehydrogenase subunit H 1.49 7.32 7.56E-03 STM2324 nuoF NADH dehydrogenase I subunit F 1.66 7.58 1.23E-02 STM2327 nuoB NADH dehydrogenase subunit B 1.62 6.86 2.82E-03 STM2328 nuoA NADH dehydrogenase subunit A 1.54 6.17 4.29E-04 STM2334 yfbT phosphatase 2.43 7.30 1.03E-04 STM2335 yfbU hypothetical protein 2.26 7.59 5.92E-03 STM2338 pta phosphate acetyltransferase 2.23 11.75 3.47E-02 STM2346 STM2346 NTP pyrophosphohydrolase 1.06 7.04 2.50E-02 STM2347 yfcE phosphodiesterase 1.89 6.84 5.61E-04 STM2354 hisJ histidine ABC transporter substrate-bindingprotein HisJ 1.16 7.07 3.58E-02 STM2363 cvpA colicin V production protein 1.06 6.42 3.78E-02 STM2364 dedD hypothetical protein 1.45 6.76 2.92E-03 STM2366 accD acetyl-CoA carboxylase subunit beta 2.23 7.87 1.91E-03 STM2369 usg semialdehyde dehydrogenase 1.18 7.21 4.34E-02 STM2378 fabB 3-oxoacyl-ACP synthase 2.28 9.21 6.03E-03 STM2380 yfcL hypothetical protein 1.10 5.74 6.13E-03 STM2384 aroC chorismate synthase 1.21 7.32 2.85E-02 STM2387 sixA phosphohistidine phosphatase 1.16 6.59 1.08E-02 STM2390 yfcZ hypothetical protein 2.64 9.06 2.68E-03 STM2392 vacJ lipoprotein precursor 1.51 6.96 3.90E-03 STM2402 yfdZ aminotransferase 1.35 7.53 3.88E-02 STM2414 yfeD negative regulator 1.38 5.57 5.36E-03

216

Appendix

ORF Gene Function logFC logCPM FDR STM2415 gltX glutamyl-tRNA synthetase 1.97 9.75 2.38E-02 STM2428 zipA cell division protein ZipA 2.02 8.55 1.07E-02 STM2430 cysK cysteine synthase A 1.01 6.70 2.35E-02 STM2431 ptsH PTS system phosphohistidinoprotein-hexose 4.81E-04 3.59 9.14 phosphotransferase Hpr STM2432 ptsI phosphoenolpyruvate- protein phosphotransferase 2.08 11.39 3.72E-02 STM2433 crr PTS system glucose-specific transporter subunitIIA 2.65 9.49 5.24E-03 STM2449.S STM2449.S acetyltransferase 1.04 6.17 1.02E-02 STM2483 dapE succinyl-diaminopime late desuccinylase 1.04 6.72 2.18E-02 STM2488 nlpB lipoprotein 2.24 8.86 5.70E-03 STM2489 dapA dihydrodipicolinate synthase 2.00 9.28 2.01E-02 STM2490 gcvR glycine cleavage system transcriptionalrepressor 1.51 6.10 8.92E-03 STM2491 bcp thioredoxin-dependen t thiol peroxidase 1.90 6.76 5.42E-04 STM2492 STM2492 glycerate kinase 1.62 4.85 2.91E-03 STM2495 yfgD arsenate reductase 1.91 5.71 3.07E-05 STM2496 yfgE DNA replication initiation factor 1.26 6.63 3.16E-03 STM2502 ppx exopolyphosphatase 1.18 7.01 1.94E-02 STM2506 STM2506 inner membrane protein 1.51 4.05 8.01E-03 STM2510 guaA GMP synthase 1.83 10.16 4.43E-02 STM2520 yfgL outer membrane protein assembly complex subunitYfgL 2.07 8.54 5.94E-03 STM2523 ispG 4-hydroxy-3-methylbu t-2-en-1-yl diphosphatesynthase 1.67 8.03 2.22E-02 STM2524 yfgA cytoskeletal protein RodZ 1.81 7.53 4.26E-03 STM2536 pepB aminopeptidase B 1.32 7.48 4.21E-02 STM2537 yfhJ hypothetical protein 1.31 5.27 1.01E-02 STM2539 hscA chaperone protein HscA 1.54 8.20 3.33E-02 STM2541 iscA iron-sulfur cluster assembly protein 1.37 7.22 3.69E-02 STM2544 yfhP DNA-binding transcriptional regulator IscR 2.18 6.68 2.35E-05 STM2545 STM2545 tRNA (cytidine/uridine-2'-O-)-methy ltransferaseTrmJ 1.56 6.96 1.22E-03 STM2561 glnB nitrogen regulatory protein P-II 1 1.50 5.43 7.30E-04 STM2563 yfhG hypothetical protein 1.23 6.42 4.90E-03 STM2577 acpS 4'-phosphopantethein yl transferase 1.69 6.82 6.07E-04 STM2578 pdxJ pyridoxine 5'-phosphate synthase 2.17 7.38 3.49E-04 STM2581 rnc ribonuclease III 1.73 7.35 8.90E-03 STM2582 lepB signal peptidase I 1.25 7.18 3.66E-02 STM2640 rpoE RNA polymerase sigma factor RpoE 1.41 6.79 2.46E-03 STM2646 yfiD autonomous glycyl radical cofactor GrcA 2.91 9.74 1.62E-03 STM2647 ung uracil-DNA glycosylase 1.62 6.65 1.59E-03 STM2649 trxC thioredoxin 2 2.37 8.67 1.09E-02 STM2650 yfiP hypothetical protein 1.51 6.69 1.69E-03 STM2660 clpB protein disaggregation chaperone 1.59 10.09 4.81E-02 STM2661 yfiH hypothetical protein 1.08 6.39 1.59E-02 STM2663 yfiO outer membrane protein assembly complex subunitYfiO 1.54 7.90 4.53E-02 STM2665 yfiA translation inhibitor protein RaiA 3.52 9.69 4.38E-03 STM2673 rplS 50S ribosomal protein L19 2.11 9.76 3.67E-02 STM2675 rimM 16S rRNA-processing protein RimM 2.67 9.64 5.94E-03 STM2676 rpsP 30S ribosomal protein S16 2.84 9.06 3.57E-03 STM2681 grpE heat shock protein GrpE 1.56 8.52 4.75E-02 STM2685 smpA hypothetical protein 1.54 7.35 2.01E-02

217

Appendix

ORF Gene Function logFC logCPM FDR STM2686 yfjF hypothetical protein 1.40 5.74 1.18E-03 STM2687 yfjG hypothetical protein 1.57 6.90 2.63E-03 STM2800 STM2800 inner membrane protein 1.88 7.60 1.18E-02 STM2817 luxS S-ribosylhomocystein ase 2.36 7.16 3.06E-04 STM2820 yqaB fructose-1-phosphata se 1.62 7.10 3.63E-03 STM2826 csrA carbon storage regulator 2.72 8.02 3.33E-03 STM2843 hydN electron transport protein HydN 2.11 8.12 8.90E-03 STM2844 STM2844 hypothetical protein 1.35 7.13 5.50E-03 STM2846 hycH hydrogenase 3 large subunit processing protein 1.88 6.43 2.38E-04 STM2847 hycG hydrogenase 2.38 8.03 3.86E-03 STM2849 hycE hydrogenase 3 large subunit 2.25 9.74 7.42E-03 STM2850 hycD hydrogenase 3 membrane subunit 2.20 8.64 4.42E-03 STM2851 hycC formate hydrogenlyase subunit 3 2.43 9.42 2.92E-03 STM2852 hycB hydrogenase-3 iron-sulfur subunit 2.38 8.30 2.19E-03 STM2853 hycA formate hydrogenlyase regulatory protein HycA 2.11 7.98 1.14E-02 STM2855 hypB hydrogenase nickel incorporation protein HypB 1.71 8.04 3.01E-02 STM2857 hypD hydrogenase formation protein 1.86 8.07 1.38E-02 STM2858 hypE hydrogenase formation protein 1.82 7.71 9.43E-03 STM2924 rpoS RNA polymerase sigma factor RpoS 1.54 8.49 4.43E-02 STM2927 surE stationary phase survival protein SurE 1.72 6.93 3.74E-04 STM2929 ispF 2-C-methyl-D-erythri tol 2,4-cyclodiphosphatesynthase 1.01 5.94 1.28E-02 STM2930 ispD 2-C-methyl-D-erythri tol 4-phosphatecytidylyltransferas e 1.19 6.76 2.06E-02 STM2952 eno phosphopyruvate hydratase 2.16 12.42 4.76E-02 STM2955.S STM2955.S transcriptional regulator 1.24 5.57 1.76E-02 STM2965 yqcC hypothetical protein 1.03 4.81 2.67E-02 STM2968 queF 7-cyano-7-deazaguani ne reductase 1.05 6.40 1.34E-02 STM2969 ygdH nucleotide binding 1.99 8.42 1.21E-02 STM2970 sdaC serine transport protein 2.01 9.21 1.66E-02 STM2971 sdaB L-serine dehydratase/L-threonine deaminase 2 1.92 8.56 1.84E-02 STM3001 thyA thymidylate synthase 1.08 7.00 3.73E-02 STM3004 ygdP dinucleoside polyphosphate hydrolase 1.05 6.60 2.08E-02 STM3034 STM3034 antitoxin VapB 1.52 5.27 1.80E-03 STM3040 lysS lysyl-tRNA synthetase 2.08 10.98 2.89E-02 STM3045 fldB flavodoxin FldB 1.36 6.63 3.20E-03 STM3046 ygfX inner membrane protein 1.26 6.27 3.58E-03 STM3047 ygfY hypothetical protein 1.30 5.99 5.09E-03 STM3048 ygfZ global regulator 1.77 7.76 1.43E-02 STM3053 gcvP glycine dehydrogenase 1.81 8.95 2.55E-02 STM3054 gcvH glycine cleavage system protein H 2.33 6.84 2.80E-04 STM3055 gcvT glycine cleavage system aminomethyltransferaseT 1.46 7.88 5.00E-02 STM3057 ubiH 2-octaprenyl-6-metho xyphenyl hydroxylase 2.16 7.84 2.81E-03 STM3058 pepP proline aminopeptidase P II 1.46 8.01 3.83E-02 STM3059.S ygfB hypothetical protein 1.33 6.93 2.39E-02 STM3069 pgk phosphoglycerate kinase 2.41 11.27 1.57E-02 STM3070 epd erythrose 4-phosphate dehydrogenase 1.79 9.00 1.61E-02 STM3072 STM3072 inner membrane protein 1.19 5.39 2.01E-02 STM3076 tktA transketolase 1.90 10.80 3.81E-02 STM3078 speB agmatinase 1.59 7.84 2.94E-02

218

Appendix

ORF Gene Function logFC logCPM FDR STM3095 gshB glutathione synthetase 1.32 7.18 1.21E-02 STM3101 yggT integral membrane protein 1.32 6.86 1.46E-02 STM3103 yggV deoxyribonucleotide triphosphatepyrophosphatase 1.27 6.64 6.84E-03 STM3143 hybG hydrogenase 2 accessory protein HypG 1.31 6.65 9.92E-03 STM3145 hybE hydrogenase 2-specific chaperone 2.17 6.66 7.52E-06 STM3147 hybC hydrogenase 2 large subunit 1.75 9.27 2.55E-02 STM3148 hybB hydrogenase 2 b cytochrome subunit 1.29 7.64 4.52E-02 STM3150 hypO hydrogenase 2 small subunit 1.73 8.48 2.27E-02 STM3162 yghB hypothetical protein 1.03 6.33 9.86E-03 STM3172 sufI repressor protein for FtsI 1.09 7.37 4.39E-02 STM3176 ygiW outer membrane protein 1.61 7.50 3.25E-02 STM3180 ygiN hypothetical protein 1.69 6.34 2.07E-04 STM3182 yqiA esterase YqiA 1.24 6.30 3.26E-03 STM3183 icc cyclic 3',5'-adenosine monophosphatephosphodiesterase 1.54 7.20 3.76E-03 STM3187 ygiB hypothetical protein 2.50 7.28 1.30E-04 STM3188 ygiC glutathionylspermidi ne synthase 1.37 7.74 4.07E-02 STM3208 gcp DNA-binding/iron metalloprotein/AP endonuclease 1.43 6.99 3.78E-03 STM3209 rpsU 30S ribosomal protein S21 3.03 8.68 3.01E-03 STM3211.S rpoD RNA polymerase sigma factor RpoD 1.57 9.54 4.44E-02 STM3229 yqjD inner membrane protein 1.98 6.83 4.40E-04 STM3230 yqjE inner membrane protein 1.33 6.13 4.17E-03 STM3231 yqjK inner membrane protein 2.04 5.94 1.12E-05 STM3234 yhaH inner membrane protein 1.03 5.46 3.69E-02 STM3237 yhaL hypothetical protein 1.33 4.25 1.93E-02 STM3238 yhaN inner membrane protein 2.18 9.17 4.23E-03 STM3273 yhbT lipid carrier protein 1.65 7.43 6.50E-03 STM3282 pnp polynucleotide phosphorylase/polyadenylase 1.67 10.15 4.56E-02 STM3283 rpsO 30S ribosomal protein S15 2.68 8.53 5.36E-03 STM3284 truB tRNA pseudouridine synthase B 1.56 7.37 7.23E-03 STM3285 rbfA ribosome-binding factor A 1.72 6.40 6.67E-04 STM3293 secG preprotein translocase subunit SecG 1.78 8.46 3.81E-02 STM3296 hflB ATP-dependent metalloprotease 2.06 10.51 1.58E-02 STM3297 rrmJ 23S rRNA methyltransferase J 2.37 8.53 4.14E-03 STM3298.S yhbY RNA-binding protein YhbY 1.72 7.00 5.01E-03 STM3299 greA transcription elongation factor GreA 1.60 7.33 1.27E-02 STM3305 ispB octaprenyl diphosphate synthase 1.62 7.80 1.78E-02 STM3308 yrbA transcriptional regulator 1.25 5.48 8.83E-03 STM3309 yrbB hypothetical protein 1.23 6.20 2.11E-02 STM3318 yhbN lipopolysaccharide transport periplasmic proteinLptA 1.27 7.28 4.10E-02 STM3319 yhbG ABC transporter ATP-binding protein 1.24 7.03 2.64E-02 STM3321 yhbH sigma(54) modulation protein 1.65 7.21 1.69E-02 STM3322 ptsN PTS system transporter subunit IIA-likenitrogen-regulatory 2.12E-02 1.16 6.85 protein PtsN STM3323 yhbJ hypothetical protein 1.24 7.33 2.09E-02 STM3327 yhbL isoprenoid biosynthesis protein 1.40 7.19 2.62E-02 STM3341 sspB ClpXP protease specificity-enhancing factor 1.95 7.55 3.25E-03 STM3342 sspA stringent starvation protein A 2.06 8.59 1.01E-02 STM3344 rpsI 30S ribosomal protein S9 2.43 9.99 1.17E-02

219

Appendix

ORF Gene Function logFC logCPM FDR STM3347 yhcB cytochrome d ubiquinol oxidase subunit III 2.31 7.85 3.20E-03 STM3359 mdh malate dehydrogenase 1.47 7.63 3.39E-02 STM3374 mreB rod shape-determining protein MreB 1.67 8.33 2.83E-02 STM3374.1n STM3374.1n hypothetical protein 1.87 5.22 2.02E-05 STM3376 yhdH oxidoreductase 1.25 7.34 2.61E-02 STM3379 accB acetyl-CoA carboxylase biotin carboxyl carrierprotein subunit 1.81 8.04 2.39E-02 STM3402 yrdC ribosome maturation factor 1.67 5.93 3.55E-04 STM3403 yrdD DNA topoisomerase 1.57 6.81 3.62E-03 STM3404 smg hypothetical protein 2.29 7.71 5.67E-03 STM3406 def peptide deformylase 1.51 7.81 4.17E-02 STM3407 fmt methionyl-tRNA formyltransferase 1.52 7.73 3.17E-02 STM3414 rplQ 50S ribosomal protein L17 2.39 10.35 2.14E-02 STM3415 rpoA DNA-directed RNA polymerase subunit alpha 2.34 11.85 2.78E-02 STM3417 rpsK 30S ribosomal protein S11 2.64 10.15 1.23E-02 STM3420 secY preprotein translocase subunit SecY 2.51 11.69 1.10E-02 STM3421 rplO 50S ribosomal protein L15 2.20 10.44 2.60E-02 STM3422 rpmD 50S ribosomal protein L30 2.78 8.60 5.82E-03 STM3423 rpsE 30S ribosomal protein S5 2.40 10.16 1.23E-02 STM3424 rplR 50S ribosomal protein L18 2.64 9.76 1.32E-02 STM3432 rpmC 50S ribosomal protein L29 2.93 9.97 4.65E-03 STM3433 rplP 50S ribosomal protein L16 2.34 10.28 2.27E-02 STM3434 rpsC 30S ribosomal protein S3 2.59 11.25 1.27E-02 STM3435 rplV 50S ribosomal protein L22 2.47 10.47 1.63E-02 STM3437 rplB 50S ribosomal protein L2 2.44 11.28 2.21E-02 STM3439 rplD 50S ribosomal protein L4 2.46 10.92 1.27E-02 STM3440 rplC 50S ribosomal protein L3 2.05 11.17 4.66E-02 STM3447 rpsG 30S ribosomal protein S7 2.22 10.09 3.57E-02 STM3448 rpsL 30S ribosomal protein S12 2.51 10.39 2.23E-02 STM3451 yheN sulfur transfer complex subunit TusD 1.02 5.68 1.21E-02 STM3453 fkpA FKBP-type peptidyl-prolyl cis-trans isomerase 1.74 9.08 3.12E-02 STM3454 slyX hypothetical protein 1.17 5.19 1.22E-02 STM3455 slyD FKBP-type peptidyl-prolyl cis-trans isomerase 1.83 9.08 2.49E-02 STM3465 yhfA hypothetical protein 1.34 6.00 1.06E-03 STM3466 crp cAMP-regulatory protein 2.03 7.82 9.74E-03 STM3481 trpS tryptophanyl-tRNA synthetase 1.42 8.32 4.98E-02 STM3484 dam DNA adenine methylase 1.17 6.95 2.50E-02 STM3486 aroB 3-dehydroquinate synthase 1.49 8.37 4.92E-02 STM3502 ompR osmolarity response regulator 1.03 6.64 1.53E-02 STM3568 rpoH RNA polymerase factor sigma-32 1.75 9.09 2.59E-02 STM3569 ftsX cell division protein FtsX 1.16 7.28 2.40E-02 STM3570 ftsE cell division protein FtsE 1.55 7.26 3.21E-03 STM3575 yhhN inner membrane protein 1.27 6.44 8.80E-03 STM3578 yhhP sulfur transfer protein SirA 1.64 6.03 3.58E-04 STM3596 yhiR hypothetical protein 1.10 6.85 1.48E-02 STM3647 yiaF outer membrane lipoprotein 1.54 8.40 2.95E-02 STM3649 cspA major cold shock protein 2.02 8.56 9.00E-03 STM3655 glyS glycyl-tRNA synthetase subunit beta 1.57 9.20 3.92E-02 STM3656 glyQ glycyl-tRNA synthetase subunit alpha 1.89 7.73 8.20E-03

220

Appendix

ORF Gene Function logFC logCPM FDR STM3683 selA selenocysteine synthase 1.09 6.86 1.21E-02 STM3699 cysE serine acetyltransferase 1.09 7.34 4.39E-02 STM3700 gpsA NAD(P)H-dependent glycerol-3-phosphatedehydrogen ase 1.95 8.12 7.85E-03 STM3701 secB preprotein translocase subunit SecB 2.11 8.70 9.72E-03 STM3703 yibN rhodanese-like sulfurtransferase 1.16 6.91 3.01E-02 STM3704 pmgI phosphoglyceromutase 1.88 9.94 2.64E-02 STM3710 rfaD ADP-L-glycero-D-mann o-heptose-6-epimerase 1.74 8.10 2.12E-02 STM3711 rfaF ADP-heptose--LPS heptosyltransferase 1.70 7.87 1.54E-02 STM3725 coaD phosphopantetheine adenylyltransferase 1.20 6.45 4.36E-03 STM3728 rpmB 50S ribosomal protein L28 2.34 9.45 1.79E-02 STM3731 dut deoxyuridine 5'-triphosphatenucleotidohydro lase 1.27 6.65 1.33E-02 STM3740 gmk guanylate kinase 1.59 7.22 3.41E-03 STM3741 rpoZ DNA-directed RNA polymerase subunit omega 2.49 6.54 1.34E-06 STM3808.S ibpB heat shock chaperone IbpB 1.01 5.85 1.12E-02 STM3839 rpmH 50S ribosomal protein L34 1.82 6.72 1.94E-03 STM3857 pstS phosphate ABC transporter substrate-bindingprotein 2.13 8.76 1.11E-02 STM3861 glmS glucosamine--fructos e-6-phosphateaminotransferase 2.17 10.23 7.91E-03 STM3862 glmU bifunctional N-acetylglucosamine-1-phosphat 2.50E-02 euridyltransferase/glucosamine -1- 1.69 9.21 phosphateacetyltransferase STM3864 atpC F0F1 ATP synthase subunit epsilon 2.42 9.64 1.14E-02 STM3865 atpD F0F1 ATP synthase subunit beta 2.38 11.28 1.21E-02 STM3867 atpA F0F1 ATP synthase subunit alpha 2.31 11.39 1.38E-02 STM3868 atpH F0F1 ATP synthase subunit delta 2.21 9.08 1.13E-02 STM3870 atpE F0F1 ATP synthase subunit C 2.81 9.32 1.79E-03 STM3871 atpB F0F1 ATP synthase subunit A 1.70 8.89 2.40E-02 STM3872 atpI F0F1 ATP synthase subunit I 1.22 7.00 1.46E-02 STM3875 mioC flavodoxin 1.05 6.37 3.15E-02 STM3877 asnA asparagine synthetase AsnA 1.67 8.59 4.34E-02 STM3897 yifA transcriptional regulator HdfR 1.52 7.07 2.67E-02 STM3910 ppiC peptidyl-prolyl cis-trans isomerase C 1.36 5.55 2.10E-03 STM3914 rhlB ATP-dependent RNA helicase RhlB 1.47 8.47 4.17E-02 STM3915 trxA thioredoxin 1.81 8.32 2.63E-02 STM3937 hemD uroporphyrinogen-III synthase 1.22 6.41 3.57E-03 STM3938 hemC porphobilinogen deaminase 1.75 7.64 1.71E-02 STM3943 cyaY frataxin-like protein 1.82 6.19 7.89E-05 STM3947 dapF diaminopimelate epimerase 1.51 7.50 2.12E-02 STM3948 yigA hypothetical protein 1.20 6.84 1.79E-02 STM3950 yigB flavin mononucleotide phosphatase 1.05 6.09 1.71E-02 STM3973 tatA twin arginine translocase protein A 1.36 7.30 1.81E-02 STM3995 yihD hypothetical protein 1.31 6.20 2.09E-02 STM4007 glnA glutamine synthetase 1.91 10.65 3.57E-02 STM4034 fdhE formate dehydrogenase accessory protein FdhE 1.40 6.98 3.57E-03 STM4058 cpxA two-component sensor protein 1.20 7.53 4.67E-02 STM4060 cpxP repressor CpxP 1.18 7.10 3.70E-02 STM4062 pfkA 6-phosphofructokinas e 1.61 9.60 4.18E-02 STM4084 fpr ferredoxin-NADP reductase 1.00 6.69 3.03E-02 STM4088 yiiU hypothetical protein 2.07 8.39 1.48E-02 STM4092 hslV ATP-dependent protease peptidase subunit 1.14 7.28 3.37E-02

221

Appendix

ORF Gene Function logFC logCPM FDR STM4096 rpmE 50S ribosomal protein L31 2.14 8.55 1.66E-02 STM4099 metJ transcriptional repressor protein MetJ 1.25 6.70 1.43E-02 STM4106 katG hydroperoxidase 2.09 10.35 1.29E-02 STM4119 ppc phosphoenolpyruvate carboxylase 1.88 9.47 3.59E-02 STM4120 argE acetylornithine deacetylase 1.40 6.81 3.84E-02 STM4125 oxyR DNA-binding transcriptional regulator OxyR 1.73 8.05 1.65E-02 STM4147 secE preprotein translocase subunit SecE 1.87 8.29 1.56E-02 STM4148 nusG transcription antitermination protein NusG 2.07 8.30 1.11E-02 STM4149 rplK 50S ribosomal protein L11 2.31 10.03 2.56E-02 STM4151 rplJ 50S ribosomal protein L10 2.31 10.71 3.65E-02 STM4152 rplL 50S ribosomal protein L7/L12 2.52 10.43 3.03E-02 STM4153 rpoB DNA-directed RNA polymerase subunit beta 1.85 11.45 3.66E-02 STM4154 rpoC DNA-directed RNA polymerase subunit beta' 2.13 11.76 1.50E-02 STM4171 yjaH inner membrane protein 1.12 6.28 5.48E-03 STM4265 soxS DNA-binding transcriptional regulator SoxS 1.58 6.47 3.22E-03 STM4294 yjdE arginine:agmatin antiporter 1.94 9.63 1.82E-02 STM4325 dcuA anaerobic C4-dicarboxylate transporter 2.21 10.12 8.76E-03 STM4330 groEL chaperonin GroEL 2.23 12.33 2.68E-02 STM4331 yjeI outer membrane lipoprotein 2.78 7.92 1.51E-03 STM4340 frdD fumarate reductase subunit D 2.04 9.17 2.20E-02 STM4341 frdC fumarate reductase subunit C 2.04 7.84 2.91E-02 STM4360 miaA tRNA delta(2)-isopentenylpyrophosph atetransferase 1.56 7.69 2.00E-02 STM4362 hflX GTPase HflX 1.52 8.55 4.40E-02 STM4364 hflC FtsH protease regulator HflC 1.42 8.22 4.61E-02 STM4366 purA adenylosuccinate synthetase 2.24 9.77 5.05E-03 STM4367 yjeB transcriptional repressor NsrR 1.62 6.42 1.88E-03 STM4369 yjfH 23S rRNA (guanosine-2'-O-)-methyltransf erase 1.47 6.85 2.22E-03 STM4379 yjfO biofilm stress and motility protein A 1.02 4.76 2.75E-02 STM4380 yjfP esterase 1.07 6.17 3.80E-02 STM4394 rplI 50S ribosomal protein L9 2.45 9.69 8.61E-03 STM4397 fklB peptidyl-prolyl cis-trans isomerase 1.47 7.09 5.58E-03 STM4411 ytfP hypothetical protein 1.06 6.59 2.03E-02 STM4478 STM4478 hypothetical protein 1.19 2.92 4.75E-02 STM4512 iadA isoaspartyl dipeptidase 1.67 7.43 6.18E-03 STM4532 yjiY carbon starvation protein 1.66 9.43 3.16E-02 STM4544 dnaT primosomal protein DnaI 1.01 6.16 2.60E-02 STM4549 STM4549 hypothetical protein 1.75 6.39 1.26E-03 STM4563 yjjU phosphoesterase 1.29 6.81 3.58E-03 STM4564 yjjV deoxyribonuclease YjjV 1.18 5.70 3.11E-03 STM4570 deoD purine nucleoside phosphorylase 1.44 7.78 4.03E-02 STM4581 yjjK ABC transporter ATP-binding protein 1.57 8.84 3.78E-02 STM4585 gpmB phosphoglycerate mutase 1.41 6.90 4.51E-03 STM4586 rob transcriptional regulator 1.44 7.68 3.73E-02 STM4598 arcA two-component response regulator 1.79 9.23 2.90E-02

ORF: open reading frame; logFC: log2 fold change; logCPM: log2 counts per million; fdr: false discovery rate

222

Appendix

Table A2. Salmonella Typhimurium N-15 genes higher expressed in co-culture, mapped onto Salmonella Typhimurium LT2 genome

ORF Gene Function logFC logCPM FDR STM0010 htgA hypothetical protein -1.26 4.76 8.28E-03

STM0011 yaaI hypothetical protein -2.01 4.23 3.67E-05

STM0014 STM0014 transcriptional regulator -1.40 5.43 2.92E-02

STM0015 STM0015 bacteriophage protein -2.25 3.39 2.43E-04

STM0016 STM0016 hypothetical protein -2.49 4.95 2.35E-04

STM0017 STM0017 hypothetical protein -2.22 4.20 3.28E-04

STM0018 STM0018 exochitinase -1.51 6.41 2.24E-04

STM0019 STM0019 hydroxymethyltransferase -1.90 6.90 6.33E-05

STM0020 STM0020 hypothetical protein -2.63 4.65 2.68E-06

STM0022 bcfB fimbrial chaperone -1.91 4.67 2.36E-04

STM0023 bcfC fimbrial usher -1.83 6.84 2.39E-04

STM0024 bcfD fimbrial subunit -1.68 5.61 2.28E-04

STM0025 bcfE fimbrial subunit -1.72 4.59 3.12E-04

STM0027 bcfG fimbrial chaperone -1.56 4.72 1.62E-03

STM0028 bcfH thiol-disulfide isomerase -1.55 5.51 1.73E-03

STM0028.1n STM0028.1n hypothetical protein -1.40 4.28 5.74E-03

STM0029 STM0029 transcriptional regulator -1.89 3.38 1.09E-03

STM0030 STM0030 transcriptional regulator -2.00 5.25 2.11E-03

STM0031 STM0031 transcriptional regulator -2.31 4.71 4.44E-03

STM0032 STM0032 arylsulfatase -1.91 6.53 1.09E-04

STM0033 STM0033 5'-nucleotidase -1.32 6.37 2.20E-03

STM0034 STM0034 outer membrane/exported protein -2.21 5.07 1.79E-04

STM0035 STM0035 arylsulfatase -1.25 6.44 2.35E-03

STM0036 STM0036 arylsulfatase regulator -1.45 5.73 5.92E-04

STM0037 STM0037 hypothetical protein -2.15 5.64 3.40E-06

STM0038 STM0038 arylsulfatase -1.58 6.55 3.24E-04

STM0041 STM0041 glycosyl hydrolase -1.85 6.36 1.64E-05

STM0042 STM0042 sodium galactoside symporter -2.19 6.06 5.93E-05

STM0044 yaaY hypothetical protein -1.49 2.60 1.21E-02

STM0053 STM0053 signal transduction histidine kinase -1.54 6.62 3.80E-04

STM0055 STM0055 oxaloacetate decarboxylase -1.93 6.26 1.14E-02

STM0056 STM0056 oxaloacetate decarboxylase subunit gamma -2.00 3.59 1.21E-03

STM0057 STM0057 citrate-sodium symporter -2.33 5.49 1.52E-05

STM0058 citC2 citrate lyase synthetase -1.52 5.61 7.65E-04

STM0059 citD2 citrate lyase subunit gamma -2.23 3.44 7.45E-05

STM0060 citE2 citrate lyase subunit beta -2.20 5.17 2.31E-05

STM0061 citF2 citrate lyase subunit alpha/citrate-ACPtransferase -1.90 6.19 1.98E-05

STM0062 citX2 hypothetical protein -2.18 4.43 5.05E-05

223

Appendix

ORF Gene Function logFC logCPM FDR STM0063 citG2 triphosphoribosyl-de phospho-CoA synthase -1.84 5.64 7.32E-05

STM0070 caiD carnitinyl-CoA dehydratase -1.68 4.87 2.62E-04

STM0071 caiC crotonobetaine/carni tine-CoA ligase -1.35 6.44 1.43E-03

STM0074 caiT L-carnitine/gamma-bu tyrobetaine antiporter -1.34 6.32 1.68E-03

STM0075 fixA electron transfer flavoprotein FixA -2.50 5.20 1.80E-06

STM0076 fixB electron transfer flavoprotein FixB -1.67 5.09 6.49E-04

STM0077 fixC oxidoreductase FixC -1.79 5.49 3.94E-04

STM0078 fixX ferredoxin -1.78 3.51 1.05E-03

STM0079 yaaU transporter -1.47 5.91 2.73E-04

STM0081 STM0081 hypothetical protein -1.67 4.91 2.71E-04

STM0084 STM0084 sulfatase -2.12 6.36 2.95E-06

STM0086 kefC glutathione-regulate d potassium-efflux systemprotein KefC -1.41 6.94 4.29E-03

STM0098 STM0098 hypothetical protein -1.22 4.31 8.94E-03

STM0100 STM0100 hypothetical protein -1.96 3.60 1.87E-03

STM0101 araD L-ribulose-5-phospha te 4-epimerase -1.79 4.82 3.12E-04

STM0102 araA L-arabinose isomerase -1.89 5.81 2.45E-05

STM0103 araB ribulokinase -1.24 5.84 5.06E-03

STM0110 leuD isopropylmalate isomerase small subunit -1.41 4.64 2.40E-03

STM0115 leuO leucine transcriptional activator -2.87 5.25 3.54E-08

STM0142 hofC type IV pilin biogenesis protein -1.13 5.90 3.52E-03

STM0143 hofB hypothetical protein -2.22 5.45 2.47E-06

STM0144 ppdD major pilin subunit -2.18 4.07 9.43E-05

STM0149 STM0149 Na+/galactoside symporter -2.20 6.04 5.15E-06

STM0155 STM0155 outer membrane protein -1.52 3.76 4.21E-03

STM0157 yacH outer membrane protein -1.45 6.27 7.54E-04

STM0161 kdgT 2-keto-3-deoxyglucon ate permease -1.90 5.43 3.32E-04

STM0162 STM0162 inner membrane protein -1.68 5.98 1.49E-04

STM0174 stiH fimbrial protein precurosr -1.67 6.43 1.58E-04

STM0175 stiC fimbrial usher -2.24 6.60 3.18E-06

STM0176 stiB fimbrial chaperone -2.49 4.68 2.56E-06

STM0177 stiA fimbrial subunit -1.82 4.39 7.25E-04

STM0191 fhuA ferrichrome outer membrane transporter -1.24 6.98 8.94E-03

STM0192 fhuC iron-hydroxamate transporter ATP-bindingsubunit -1.52 4.49 9.70E-04

STM0193 fhuD iron-hydroxamate transporter substrate-bindingsubunit -1.75 4.51 2.99E-04

STM0194 fhuB iron-hydroxamate transporter permease subunit -1.21 5.95 1.20E-02

STM0195 stfA fimbrial subunit -2.02 4.35 4.28E-05

STM0196 stfC fimbrial outer membrane usher -2.12 6.70 1.23E-05

STM0197 stfD periplasmic fimbrial chaperone -2.10 4.73 4.98E-05

STM0198 stfE minor fimbrial subunit -2.50 5.13 1.97E-04

STM0199 stfF minor fimbrial subunit -1.70 4.24 2.71E-03

STM0200 stfG minor fimbrial subunit -1.29 3.94 2.33E-02

224

Appendix

ORF Gene Function logFC logCPM FDR STM0257 STM0257 drug efflux protein -1.35 5.38 1.15E-03

STM0267 STM0267 hypothetical protein -1.27 4.98 7.19E-03

STM0268 STM0268 hypothetical protein -1.40 5.93 2.92E-03

STM0269 STM0269 hypothetical protein -1.63 3.73 1.28E-03

STM0271 STM0271 hypothetical protein -1.99 5.63 4.46E-05

STM0272 STM0272 chaperone ATPase -1.49 6.57 1.83E-03

STM0273 STM0273 hypothetical protein -1.90 4.36 2.02E-04

STM0274 STM0274 hypothetical protein -1.74 6.04 2.15E-05

STM0274A STM0274A invasol SirA -2.85 2.97 1.54E-05

STM0275.s STM0275.s hypothetical protein -2.21 4.32 2.42E-04

STM0276 STM0276 hypothetical protein -1.47 3.70 1.61E-02

STM0277 STM0277 hypothetical protein -2.47 4.76 1.43E-05

STM0278 STM0278 hypothetical protein -2.32 3.79 3.81E-05

STM0279 STM0279 hypothetical protein -2.64 3.99 5.01E-06

STM0280 STM0280 outer membrane lipoprotein -2.90 4.39 2.68E-06

STM0281 STM0281 hypothetical protein -2.05 5.48 2.34E-04

STM0282 STM0282 hypothetical protein -2.23 4.97 3.86E-06

STM0283 STM0283 inner membrane protein -1.89 5.62 4.93E-04

STM0284 STM0284 Shiga-like toxin A subunit -2.49 5.07 2.34E-04

STM0285 STM0285 inner membrane protein -1.26 6.80 1.67E-02

STM0286 STM0286 hypothetical protein -1.33 5.32 1.09E-02

STM0287 STM0287 hypothetical protein -1.63 5.09 1.28E-03

STM0288 STM0288 hypothetical protein -1.50 5.05 3.01E-03

STM0289 STM0289 hypothetical protein -1.65 6.62 4.82E-04

STM0290 STM0290 hypothetical protein -1.89 3.83 6.73E-04

STM0291 STM0291 RHS-like protein -1.73 5.44 2.34E-04

STM0294 STM0294 hypothetical protein -2.18 4.50 1.24E-02

STM0294.1N STM0294.1N hypothetical protein -3.00 2.64 6.25E-06

STM0295 STM0295 hypothetical protein -2.20 4.14 6.32E-03

STM0299 safA major pilus subunit SafA -1.79 5.01 4.25E-04

STM0300 safB fimbrial assembly chaperone -1.69 4.60 3.80E-04

STM0301 safC fimbrial usher -1.44 7.66 3.39E-02

STM0302 safD fimbrial subunit -1.07 5.08 1.46E-02

STM0303 ybeJ xylanase/chitin deacetylase -1.95 5.15 1.68E-05

STM0304 sinR transcriptional regulator -1.52 5.40 4.39E-03

STM0306 STM0306 adhesin/invasin protein PagN -1.64 4.88 2.65E-04

STM0307 STM0307 VirG-like protein -1.11 4.47 4.18E-02

STM0309 fadE acyl-CoA dehydrogenase -1.28 6.48 2.75E-03

STM0320 phoE outer membrane phosphoporin protein E -1.03 5.73 2.92E-02

STM0325 STM0325 IS3 transposase -1.31 5.42 1.32E-02

STM0328.s STM0328.s permease -2.68 6.18 2.91E-05

225

Appendix

ORF Gene Function logFC logCPM FDR STM0329 STM0329 isopropylmalate isomerase large subunit -2.55 6.06 1.51E-05

STM0330 STM0330 3-isopropylmalate isomerase -1.55 5.19 1.32E-02

STM0331 STM0331 fumarylacetoacetate hydrolase -1.62 5.38 2.88E-04

STM0332 STM0332 hydrolase/acyltransf erase -1.75 5.56 7.91E-05

STM0333 STM0333 transcriptional regulator -1.51 5.90 3.98E-04

STM0334 STM0334 hypothetical protein -1.45 3.84 2.43E-03

STM0335 STM0335 outer membrane protein -1.74 3.60 3.95E-03

STM0336 stbE fimbrial chaperone -1.75 4.54 2.34E-04

STM0337 stbD fimbrial usher -1.54 5.60 6.80E-04

STM0338 stbC fimbrial usher -1.57 6.81 5.65E-04

STM0339 stbB fimbrial chaperone -2.16 4.73 4.64E-04

STM0340 stbA fimbrial major subunit -2.79 3.69 2.95E-06

STM0341 STM0341 inner membrane protein -1.66 4.77 7.43E-04

STM0342 STM0342 hypothetical protein -1.34 3.10 4.03E-02

STM0343 STM0343 hypothetical protein -1.61 6.12 1.33E-04

STM0346 STM0346 outer membrane protein -2.28 4.54 4.51E-04

STM0347 STM0347 response regulator -2.17 4.91 2.48E-04

STM0348 STM0348 inner membrane protein -2.75 3.13 1.08E-05

STM0349 STM0349 outer membrane lipoprotein -1.09 4.15 3.21E-02

STM0350.S STM0350.S outer membrane efflux-like protein -1.81 5.31 9.25E-05

STM0351 STM0351 cation efflux system protein -2.14 6.77 1.00E-05

STM0352.S STM0352.S cation efflux pump -2.23 5.35 6.18E-05

STM0353 STM0353 cation transport ATPase -2.28 6.62 4.26E-06

STM0354 STM0354 transcriptional regulator -1.10 4.46 4.04E-02

STM0356 STM0356 inner membrane protein -2.32 5.56 7.08E-07

STM0360 STM0360 cytochrome BD2 subunit I -2.38 5.57 5.92E-05

STM0361 STM0361 cytochrome BD2 subunit II -1.55 5.62 4.50E-04

STM0362 STM0362 hypothetical protein -1.35 3.58 7.47E-03

STM0364 foxA ferrioxamine receptor -1.46 6.68 6.78E-04

STM0367 prpR prp operon regulator -2.21 5.51 3.52E-06

STM0368 prpB 2-methylisocitrate lyase -1.75 4.68 2.88E-04

STM0369 prpC methylcitrate synthase -2.55 5.29 4.79E-07

STM0370 prpD 2-methylcitrate dehydratase -2.22 5.55 2.68E-06

STM0371 prpE propionyl-CoA synthetase -1.86 6.23 2.59E-05

STM0374 yaiV DNA-binding transcriptional regulator -1.32 5.39 3.24E-03

STM0381 STM0381 inner membrane protein -1.25 4.56 8.53E-03

STM0382 STM0382 permease -1.15 5.47 7.35E-03

STM0403 yajB acyl carrier protein phosphodiesterase -1.12 4.93 1.62E-02

STM0427 phnU 2-aminoethylphosphon ate transporter -1.50 4.57 1.28E-03

STM0431 phnW 2-aminoethylphosphon ate--pyruvate transaminase -1.48 5.22 1.76E-03

STM0432 phnX phosphonoacetaldehyd e hydrolase -1.23 4.71 1.07E-02

226

Appendix

ORF Gene Function logFC logCPM FDR STM0437 STM0437 hypothetical protein -2.06 6.18 1.22E-04

STM0438 STM0438 hypothetical protein -1.86 5.70 1.78E-03

STM0440 cyoD cytochrome o ubiquinol oxidase subunit IV -1.14 4.63 1.67E-02

STM04665 STM04665 hypothetical protein -2.34 3.44 4.73E-04

STM0469 rpmE2 50S ribosomal protein L31 -1.65 3.60 2.67E-03

STM0493 fsr transporter -1.11 5.47 8.76E-03

STM0497 STM0497 hypothetical protein -1.81 5.55 7.53E-04

STM04985 STM04985 hypothetical protein -1.22 4.18 1.76E-02

STM0508 ybbP inner membrane protein -1.09 6.66 1.10E-02

STM0514 ybbS DNA-binding transcriptional activator AllS -1.50 4.82 9.97E-04

STM0517 gcl glyoxylate carboligase -1.76 3.96 2.38E-04

STM0530 ylbE hypothetical protein -2.35 5.16 2.50E-05

STM0531 ylbF hypothetical protein -2.48 4.60 1.47E-06

STM0532 arcC carbamate kinase -2.03 5.40 5.88E-05

STM0539 STM0539 inner membrane protein -1.45 4.79 7.33E-03

STM0540 ybcI membrane-bound metal-dependent hydrolase -1.18 4.95 9.04E-03

STM05445 STM05445 purine nucleoside phosphorylase -1.70 5.06 6.04E-03

STM0550 fimY regulatory protein -1.32 5.42 1.75E-03

STM0551 STM0551 hypothetical protein -1.01 4.12 4.69E-02

STM0552 fimW fimbrial protein -1.51 4.68 9.98E-03

STM05625 STM05625 hypothetical protein -1.05 4.46 2.45E-02

STM0564 STM0564 pyridine nucleotide-disulfide oxidoreductase -1.63 6.37 9.87E-05

STM05645 STM05645 hypothetical protein -1.07 4.28 2.20E-02

STM0566 STM0566 inner membrane protein -1.26 5.21 8.53E-03

STM0571 STM0571 inner membrane protein -1.07 6.57 1.32E-02

STM0572 STM0572 phosphosugar isomerase -1.74 4.98 1.43E-04

STM0573 STM0573 inner membrane protein -1.39 4.64 2.48E-02

STM0574 STM0574 PTS system mannose-specific transporter subunitIID -2.54 4.56 1.64E-05

STM0575 STM0575 PTS system mannose-specific transporter subunitIIC -2.07 4.01 4.38E-05

STM0576 STM0576 PTS system mannose-specific transporter subunitIIAB -2.34 3.34 7.84E-05

STM0577 STM0577 PTS system mannose-specific transporter subunitIIAB -2.24 4.44 7.98E-05

STM0583 ybdK carboxylate-amine ligase -1.46 5.21 7.19E-04

STM0584 entD phosphopantetheinylt ransferase component ofenterobactin -1.78 4.84 8.20E-04

synthase multienzyme complex

STM0585 fepA outer membrane receptor FepA -1.80 6.63 5.87E-05

STM0586 fes enterobactin/ferric enterobactin esterase -2.21 5.28 4.15E-05

STM0587 ybdZ hypothetical protein -2.49 2.35 1.08E-04

STM0588 entF enterobactin synthase subunit F -1.46 6.80 3.16E-03

STM0591 fepG iron-enterobactin transporter permease -1.16 4.77 2.35E-02

STM05910 STM05910 hypothetical protein -2.70 5.31 1.47E-06

STM0592 fepD iron-enterobactin transporter membrane protein -1.60 4.84 1.56E-03

227

Appendix

ORF Gene Function logFC logCPM FDR STM0593 ybdA enterobactin exporter EntS -1.84 5.14 6.89E-05

STM0594 fepB iron-enterobactin transporter periplasmicbinding protein -1.09 4.33 2.33E-02

STM0595 entC isochorismate synthase -1.58 4.80 8.13E-04

STM0596 entE enterobactin synthase subunit E -2.11 5.51 2.10E-05

STM0597 entB 2,3-dihydro-2,3-dihy droxybenzoate synthetase -1.57 4.62 1.28E-03

STM0598 entA 2,3-dihydroxybenzoat e-2,3-dehydrogenase -1.44 4.83 3.86E-03

STM0599 ybdB hypothetical protein -1.49 3.73 2.27E-03

STM0600 cstA carbon starvation protein -1.33 6.62 1.80E-03

STM06000 STM06000 hypothetical protein -1.65 4.29 1.92E-03

STM0610 STM0610 anaerobic dehydrogenase component -2.71 4.19 1.29E-04

STM0611 STM0611 oxidoreductase protein -1.68 6.33 6.10E-05

STM0612 STM0612 e-S-cluster-containi ng hydrogenase subunit 1 -1.86 4.18 1.22E-04

STM0615 ybdR dehydrogenase -1.66 5.32 2.06E-04

STM0618 citT citrate/succinate transport antiport protein -1.44 6.04 3.15E-04

STM0620 citX 2-(5''-triphosphorib osyl)-3'-dephosphocoenzyme-Asy nthase -1.46 3.21 4.74E-03

STM0621 citF citrate lyase subunit alpha/citrate-ACPtransferase -1.86 5.36 1.88E-03

STM0623 citD citrate lyase subunit gamma -1.54 2.77 3.90E-03

STM0624 citC citrate lyase synthetase -2.26 4.77 2.00E-05

STM0625 dpiB sensory histidine kinase -1.76 6.17 2.97E-05

STM0649.S STM0649.S hypothetical protein -2.45 3.56 1.23E-05

STM0650 STM0650 hypothetical protein -2.05 5.17 2.67E-05

STM0651 STM0651 2-keto-3-deoxyglucon ate permease -1.38 5.76 1.83E-03

STM0654 ybeQ hypothetical protein -1.20 5.56 5.72E-03

STM0655 ybeR hypothetical protein -2.54 4.88 2.68E-06

STM0656 ybeS molecular chaperone -2.19 5.61 5.13E-06

STM0657 ybeU hypothetical protein -1.62 5.08 3.41E-03

STM0658 ybeV molecular chaperone -1.60 5.76 1.56E-04

STM0687 ybfM chitoporin -1.69 5.14 1.90E-04

STM0689 citA citrate-proton symporter -1.60 5.46 3.84E-04

STM0690 citB citrate utilization protein b -2.51 4.91 6.09E-07

STM0691 STM0691 tricarballylate dehydrogenase -1.37 5.07 2.32E-03

STM0704 kdpC potassium-transporti ng ATPase subunit C -1.69 5.14 3.23E-04

STM0705 kdpB potassium-transporti ng ATPase subunit B -1.54 5.80 3.72E-04

STM0706 kdpA potassium-transporti ng ATPase subunit A -2.05 5.75 1.39E-04

STM0717 STM0717 inner membrane protein -1.37 3.73 1.06E-02

STM0718 STM0718 hypothetical protein -1.84 4.40 6.17E-04

STM0719 STM0719 UDP-galactopyranose mutase -1.69 4.54 3.33E-03

STM0720 STM0720 glycosyl transferase family protein -2.10 4.43 1.22E-03

STM0721 STM0721 glycosyl transferase family protein -1.90 5.13 1.45E-02

STM0722 STM0722 ABC transporter permease -2.53 4.96 1.87E-04

STM0723 STM0723 polysaccharide/polyo l phosphate ABC transporterATPase -2.26 4.86 3.11E-04

228

Appendix

ORF Gene Function logFC logCPM FDR STM0724 STM0724 glycosyltransferase -2.23 5.67 2.67E-03

STM0725 STM0725 glycosyltransferase -2.74 4.25 6.36E-04

STM0726 STM0726 glycosyl transferase family protein -2.13 5.93 9.89E-03

STM0762 STM0762 fumarate hydratase -1.58 5.25 8.59E-04

STM0763.s STM0763.s transcriptional regulator -1.47 4.44 2.10E-02

STM0764 STM0764 transcriptional regulator -1.49 4.79 2.30E-03

STM0765 STM0765 cation transporter -2.66 5.35 1.58E-05

STM0766 dcoC oxaloacetate decarboxylase subunit gamma -2.05 3.37 2.93E-03

STM0769 STM0769 hypothetical protein -1.53 3.76 2.30E-03

STM0770 STM0770 iron ABC transporter permease -1.75 6.11 3.34E-04

STM0777 STM0777 inner membrane protein -2.26 4.70 1.64E-05

STM0791 hutH histidine ammonia-lyase -1.40 5.60 8.70E-04

STM0793 bioA adenosylmethionine-- 8-amino-7-oxononanoateaminotra -2.03 5.06 8.53E-06

nsferase

STM0794 bioB biotin synthetase -1.93 5.09 1.06E-04

STM0795 bioF 8-amino-7-oxononanoa te synthase -1.36 5.12 4.29E-03

STM0796 bioC biotin biosynthesis protein BioC -2.11 4.05 1.18E-04

STM0808 ybhM integral membrane protein -2.07 4.36 7.87E-05

STM0809 STM0809 inner membrane protein -2.50 5.35 4.32E-04

STM0810 STM0810 inner membrane protein -2.60 4.24 5.13E-06

STM0811 ybhN hypothetical protein -1.73 5.02 9.85E-05

STM0812 ybhO cardiolipin synthase 2 -1.87 4.80 2.73E-04

STM0813 ybhP hypothetical protein -1.51 4.40 4.44E-03

STM0836 ybiR transporter -1.02 6.07 2.47E-02

STM0839 STM0839 inner membrane protein -2.30 4.90 5.24E-05

STM0843 pflF pyruvate formate lyase -1.38 6.04 1.34E-03

STM0844 pflE pyruvate formate lyase activating enzyme -1.70 4.78 4.07E-04

STM0850 STM0850 glutathione ABC transporter permease GsiC -1.07 5.37 2.27E-02

STM0854 STM0854 hypothetical protein -1.78 3.77 1.67E-02

STM0855 STM0855 electron transfer protein subunit beta -2.63 4.80 3.52E-06

STM0856 STM0856 electron transfer protein subunit alpha -2.36 4.77 1.32E-05

STM0857 STM0857 acyl-CoA dehydrogenase -1.87 5.35 1.94E-04

STM0858 STM0858 dehydrogenase -2.21 5.51 1.18E-04

STM0859 STM0859 transcriptional regulator -1.35 5.25 2.94E-03

STM0860 STM0860 inner membrane protein -2.20 4.28 2.35E-05

STM0869 STM0869 regulatory protein -1.08 4.86 1.70E-02

STM0877 potF putrescine ABC transporter substrate-bindingprotein -1.01 5.10 4.92E-02

STM0878 potG putrescine ABC transporter ATP-binding protein -2.02 5.39 7.82E-06

STM0879 potH putrescine ABC transporter permease -1.61 4.98 2.01E-03

STM0885 STM0885 inner membrane protein -1.73 3.62 2.13E-03

STM0886 STM0886 sulfatase -1.26 5.38 2.79E-03

229

Appendix

ORF Gene Function logFC logCPM FDR STM0907 STM0907 chitinase -1.09 5.09 9.96E-03

STM0908 STM0908 hypothetical protein -2.16 4.05 2.15E-05

STM0935 poxB pyruvate dehydrogenase -1.04 5.60 1.43E-02

STM0947 STM0947 integrase -2.04 4.62 1.30E-04

STM0951 STM0951 hypothetical protein -1.07 4.43 1.91E-02

STM0969 ycaM amino-acid transporter -1.36 6.09 1.17E-03

STM0976 ycaP inner membrane protein -1.75 5.11 1.08E-04

STM0983 ycaI hypothetical protein -1.34 6.33 1.53E-03

STM1002 STM1002 diaminopropionate ammonia-lyase -2.23 4.93 1.18E-05

STM1003 STM1003 transcriptional regulator -2.56 5.41 3.68E-05

STM1007 STM1007 hypothetical protein -1.23 3.84 3.58E-02

STM1008.S STM1008.S hypothetical protein -1.09 6.70 9.48E-03

STM1009 STM1009 exodeoxyribonuclease -1.60 6.84 6.20E-04

STM1010 STM1010 hypothetical protein -1.74 4.41 5.27E-04

STM1010.1n STM1010.1n hypothetical protein -1.57 4.16 1.38E-03

STM1013 STM1013 regulatory protein -1.68 2.57 2.28E-03

STM1014 STM1014 regulatory protein -2.15 3.91 4.09E-05

STM1015 STM1015 replication protein -1.76 4.05 1.21E-03

STM1016 STM1016 hypothetical protein -2.16 2.53 2.21E-04

STM1017 STM1017 hypothetical protein -1.50 2.72 8.75E-03

STM1021 STM1021 hypothetical protein -2.42 3.97 4.73E-06

STM1022 STM1022 molecular chaperone -2.40 4.94 3.18E-06

STM1025 STM1025 hypothetical protein -1.21 4.69 1.07E-02

STM1026 STM1026 hypothetical protein -1.30 4.68 7.97E-03

STM1028 STM1028 lysozyme -1.70 4.38 4.37E-04

STM1029 STM1029 hypothetical protein -1.34 4.75 1.69E-02

STM1030 STM1030 hypothetical protein -1.12 4.28 1.80E-02

STM1031 STM1031 hypothetical protein -1.55 6.42 2.21E-04

STM1032 STM1032 hypothetical protein -1.33 5.52 5.01E-03

STM1033 STM1033 Clp protease-like protein -1.20 6.73 6.29E-03

STM1034 STM1034 recombinase A -1.19 2.80 3.19E-02

STM1035 STM1035 ATP-binding sugar transporter-like protein -1.75 4.49 2.23E-04

STM1036 STM1036 minor tail protein -1.55 4.26 2.14E-03

STM1037 STM1037 minor tail protein -1.88 4.29 9.06E-04

STM1038 STM1038 major tail protein -1.68 5.17 1.77E-04

STM1039 STM1039 minor tail protein -1.24 3.81 1.83E-02

STM1040 STM1040 minor tail protein -1.29 3.18 2.35E-02

STM1041 STM1041 minor tail protein -1.48 7.02 2.14E-03

STM1045 STM1045 minor tail protein -1.68 4.95 2.01E-04

STM1046 STM1046 tail assembly protein -1.29 4.66 3.97E-03

STM1048 STM1048 host specificity protein J -1.07 6.81 2.96E-02

230

Appendix

ORF Gene Function logFC logCPM FDR STM1048.1N STM1048.1N hypothetical protein -1.39 4.82 2.37E-03

STM1049 STM1049 tail fiber protein -1.10 6.57 1.00E-02

STM1050 STM1050 tail fiber assembly like-protein -1.56 4.70 7.12E-04

STM1051 sseI secreted effector protein -2.10 5.01 8.44E-05

STM1053 STM1053 hypothetical protein -1.57 5.36 3.22E-04

STM1056 STM1056 MsgA-like protein -1.98 3.13 4.30E-03

STM1077 yccT hypothetical protein -1.09 4.10 2.66E-02

STM1088 pipB secreted effector protein -1.42 5.22 3.29E-02

STM1090 pipC pathogenicity island-encoded protein C -1.73 3.47 3.48E-04

STM1091 sopB inositol phosphate phosphatase SopB -2.24 5.85 2.32E-06

STM1094 pipD dipeptidase -1.05 5.84 6.64E-03

STM1098 hpaC 4-hydroxyphenylaceta te catabolism -2.02 3.26 1.07E-04

STM1099 hpaB 4-hydroxyphenylaceta te catabolism -2.20 5.28 3.09E-05

STM1101 hpaG 4-hydroxyphenylaceta te catabolism -2.40 5.07 2.47E-06

STM1102 hpaE 5-carboxymethyl-2-hy droxymuconate -1.57 5.40 9.70E-04

semialdehydedehydrogenase

STM1103 hpaD 4-hydroxyphenylaceta te catabolism -1.64 4.66 1.84E-03

STM1104 hpaF 4-hydroxyphenylaceta te catabolism -2.13 3.46 6.88E-05

STM1105 hpaH 2-oxo-hepta-3-ene-1, 7-dioic acid hydratase -1.67 4.42 4.40E-04

STM1106 hpaI 4-hydroxyphenylaceta te catabolism -1.21 4.07 4.03E-02

STM1107 hpaX 4-hydroxyphenylaceta te catabolism -1.53 5.10 1.13E-03

STM1108 hpaA 4-hydroxyphenylaceta te catabolism -1.87 4.13 3.73E-04

STM1109 STM1109 hypothetical protein -1.02 4.63 2.28E-02

STM1114 scsB suppression of copper sensitivity protein -1.37 5.95 2.34E-03

STM1115 scsC copper sensitivity suppression protein -1.54 3.69 1.92E-03

STM1125 putP major sodium/proline symporter -1.23 5.65 2.67E-03

STM1128 STM1128 sodium/glucose cotransporter -1.51 5.76 5.64E-04

STM1129 STM1129 N-acetylmannosamine- 6-phosphate 2-epimerase -1.94 4.10 5.67E-04

STM1130 STM1130 N-acetylneuraminic acid mutarotase -1.54 4.86 8.94E-03

STM1131 STM1131 outer membrane protein -2.65 4.31 9.50E-04

STM1132 STM1132 sialic acid transporter -2.03 5.92 1.39E-04

STM1133 STM1133 dehydrogenase -1.05 5.75 1.07E-02

STM1139 csgG curli operon transcriptional regulator -1.45 4.81 2.82E-03

STM1140 csgF curli assembly protein CsgF -3.08 3.03 1.34E-06

STM1141 csgE curli assembly protein CsgE -3.08 2.88 1.71E-06

STM1142 csgD DNA-binding transcriptional regulator CsgD -2.26 3.45 3.74E-04

STM1143 csgB curlin minor subunit -2.10 3.07 4.25E-04

STM1145 csgC autoagglutination protein -1.40 3.46 8.79E-03

STM1156 yceA hypothetical protein -1.43 4.75 5.33E-03

STM1157 yceI hypothetical protein -1.13 4.28 1.59E-02

STM1158 STM1158 inner membrane protein -2.11 3.97 2.14E-04

231

Appendix

ORF Gene Function logFC logCPM FDR STM1204 fhuE ferric-rhodotorulic acid outer membranetransporter -1.52 5.93 5.91E-04

STM1224 sifA secreted effector protein SifA -2.13 4.87 3.23E-04

STM1239 STM1239 hypothetical protein -1.76 4.57 1.46E-03

STM1240 envF envelope lipoprotein -2.60 4.37 1.20E-05

STM1252 STM1252 hypothetical protein -1.26 5.19 2.93E-03

STM1263 STM1263 hypothetical protein -1.38 4.61 2.67E-03

STM1265 STM1265 response regulator -2.04 3.75 2.50E-04

STM1269 STM1269 chorismate mutase -1.75 3.86 4.37E-03

STM1273 STM1273 nitric oxide reductase -1.24 4.91 6.32E-03

STM1278 yeaN amino acid/amine transport protein -1.29 4.90 8.87E-03

STM1285 yeaG serine protein kinase -1.01 5.88 1.06E-02

STM1287 STM1287 arylsulfatase regulator -1.44 5.16 7.50E-04

STM1304 astA arginine succinyltransferase -1.92 4.68 4.89E-05

STM1305 astD succinylglutamic semialdehyde dehydrogenase -1.72 5.22 2.02E-04

STM1306 astB succinylarginine dihydrolase -1.99 5.45 2.92E-05

STM1307 astE succinylglutamate desuccinylase -1.97 4.83 7.48E-05

STM1313 celB PTS system N,N'-diacetylchitobiose-specif ictransporter -2.09 5.38 9.47E-06

subunit IIC

STM1314 celC PTS system N,N'-diacetylchitobiose-specif ictransporter -1.11 4.14 1.92E-02

subunit IIA

STM1315 celD DNA-binding transcriptional regulator ChbR -1.21 4.36 1.53E-02

STM1316 celF phospho-beta-glucosi dase/cellobiose-6-phosphatehyd rolase -1.69 5.14 9.89E-04

STM1318 katE hydroperoxidase II -1.22 6.14 1.84E-03

STM1328 STM1328 lipid A modifying protein -1.41 5.35 1.28E-03

STM1329 STM1329 inner membrane protein -2.29 3.23 2.06E-04

STM1345 ydiU hypothetical protein -1.07 5.61 1.14E-02

STM1350 ydiD short chain acyl-CoA synthetase -1.71 5.51 1.18E-04

STM1352 ydiS hypothetical protein -1.69 5.02 3.60E-04

STM1353 ydiR electron transfer flavoprotein subunit YdiR -1.33 4.80 2.75E-03

STM1354 ydiQ electron transfer flavoprotein YdiQ -1.21 4.25 1.36E-02

STM1355 ydiP transcriptional regulator -1.63 4.11 1.14E-03

STM1356 ydiO acyl-CoA dehydrogenase -1.36 5.20 2.43E-03

STM1357.S ydiF acetyl-CoA/acetoacet yl-CoA transferase subunitbeta -2.02 5.14 2.58E-04

STM1360 ydiN transporter -2.59 5.32 4.28E-05

STM1361 ydiM transporter -2.34 4.93 5.17E-05

STM1362 ydiL hypothetical protein -2.75 3.24 2.34E-05

STM1369 sufA iron-sulfur cluster assembly scaffold protein -1.16 3.55 2.11E-02

STM1370 sufB cysteine desulfurase activator complex subunitSufB -1.56 5.30 2.84E-04

STM1371 sufC cysteine desulfurase subunit ATPase -1.46 4.46 2.30E-03

STM1373 sufS bifunctional cysteine desulfurase/selenocysteinelyas e -1.05 5.34 2.41E-02

STM1374 ynhA cysteine desufuration protein SufE -1.30 4.29 1.45E-02

232

Appendix

ORF Gene Function logFC logCPM FDR STM1379 orf48 amino acid permease -1.38 5.39 1.56E-03

STM1381 orf245 hypothetical protein -1.72 3.75 1.27E-03

STM1382 orf408 regulatory protein -2.55 4.80 7.15E-06

STM1383 ttrA tetrathionate reductase complex subunit A -1.83 6.30 2.10E-05

STM1384 ttrC tetrathionate reductase complex subunit C -1.84 4.31 7.91E-04

STM1385 ttrB tetrathionate reductase complex subunit B -1.81 3.89 5.91E-04

STM1391 ssrB transcriptional activator -1.25 4.27 5.83E-03

STM1392 ssrA sensor kinase -1.98 6.30 2.68E-06

STM1393 ssaB secreted effector protein -2.19 3.82 1.95E-03

STM1394 ssaC outer membrane secretin precursor -2.14 5.06 3.85E-04

STM1395 ssaD virulence protein -2.38 5.87 2.67E-05

STM1396 ssaE secretion system effector -1.88 3.72 5.40E-04

STM1399 sscA secretion system chaperone -1.90 4.12 1.01E-04

STM1400 sseC translocation machinery protein SseC -2.31 6.16 1.85E-06

STM1401 sseD translocation machinery protein SseD -1.80 4.25 3.57E-04

STM1402 sseE secreted effector protein -1.80 3.94 5.65E-04

STM1403 sscB secretion system chaperone -1.95 3.65 8.81E-04

STM1404 sseF secreted effector protein -1.75 4.18 4.96E-04

STM1405 sseG secreted effector protein -1.47 4.13 4.94E-03

STM1408 ssaI type III secretion system apparatus protein -2.07 2.56 4.29E-04

STM1409 ssaJ needle complex inner membrane lipoprotein -2.62 4.20 2.34E-04

STM1410 STM1410 hypothetical protein -1.94 4.65 3.90E-03

STM1411 ssaK type III secretion system apparatus protein -2.04 3.86 3.49E-04

STM1412 ssaL type III secretion system apparatus protein -1.55 5.26 1.60E-03

STM1413 ssaM type III secretion system apparatus protein -1.54 3.26 1.09E-02

STM1414 ssaV secretion system apparatus protein SsaV -2.52 6.30 1.46E-06

STM1415 ssaN type III secretion system ATPase -1.73 6.04 2.89E-05

STM1416 ssaO type III secretion system apparatus protein -1.52 3.39 4.12E-03

STM1417 ssaP type III secretion system apparatus protein -1.49 4.05 2.47E-03

STM1419 ssaR type III secretion system protein -1.38 4.48 4.42E-03

STM1420 ssaS type III secretion system apparatus proteinSsaS -2.39 3.54 3.75E-04

STM1421 ssaT type III secretion system apparatus protein -2.80 4.68 8.06E-06

STM1422 ssaU secretion system apparatus protein SsaU -1.34 5.37 2.68E-02

STM1472 STM1472 hypothetical protein -1.77 5.55 1.18E-04

STM1473 ompN outer membrane protein N precursor -2.09 4.87 1.52E-05

STM1477 ydgI amino acid transporter -1.03 5.70 5.00E-02

STM1482 ydgF multidrug efflux system protein MdtJ -1.11 3.72 3.47E-02

STM1492 STM1492 ABC transporter permease -1.83 4.18 4.55E-04

STM1493 STM1493 ABC transporter substrate-binding protein -2.43 4.64 6.49E-06

STM1494 STM1494 transport system permease component -2.68 4.16 6.94E-06

STM1505 rspA bifunctional D-altronate/D-mannonatedehydra tase -2.36 4.89 1.69E-05

233

Appendix

ORF Gene Function logFC logCPM FDR STM1506 rspB dehydrogenase -2.06 4.61 3.13E-05

STM1514 ydeJ competence damage-inducible protein A -2.02 3.78 2.27E-04

STM1515 ydeI hypothetical protein -1.18 3.87 2.06E-02

STM1528 STM1528 outer membrane protein -1.33 4.24 3.31E-03

STM1530 STM1530 outer membrane protein -1.61 5.49 4.58E-04

STM1531 STM1531 hydrogenase -1.25 3.84 2.15E-02

STM1536 STM1536 hydrogenase maturation protease -1.04 4.65 3.85E-02

STM1537 STM1537 Ni/Fe hydrogenase 1 b-type cytochrome subunit -1.01 4.79 2.55E-02

STM1538 STM1538 hydrogenase-1 large subunit -1.66 5.42 1.74E-04

STM1539 STM1539 hydrogenase-1 small subunit -1.49 5.33 3.32E-04

STM1542 STM1542 zinc-binding dehydrogenase -1.41 5.43 1.09E-03

STM1543 STM1543 transporter -2.68 4.82 3.71E-05

STM1544 pqaA PhoPQ-regulated protein -2.13 5.60 4.64E-04

STM1545 STM1545 multidrug efflux protein -2.04 5.30 1.97E-05

STM1546 STM1546 hypothetical protein -1.39 5.43 9.70E-04

STM1551.1n STM1551.1n hypothetical protein -1.73 4.01 1.44E-03

STM1552 STM1552 hypothetical protein -1.06 4.95 1.75E-02

STM1554 STM1554 coiled-coil protein -2.53 3.95 2.75E-05

STM1555 STM1555 transcriptional regulator -3.38 3.63 2.45E-09

STM1556 STM1556 Na+/H+ antiporter -1.55 5.49 2.71E-04

STM1557 STM1557 aminotransferase -1.42 5.19 1.45E-03

STM1559 STM1559 glycosyl hydrolase -1.07 5.92 7.85E-03

STM1560 STM1560 alpha amylase -1.52 5.71 5.92E-04

STM1571 yddG hypothetical protein -1.18 5.04 6.11E-03

STM1576 narU nitrate extrusion protein -1.86 5.01 1.48E-04

STM1577 narZ nitrate reductase 2 subunit alpha -1.68 6.35 1.83E-04

STM1578 narY nitrate reductase 2 subunit beta -1.84 5.47 1.10E-04

STM1579 narW nitrate reductase 2 subunit delta -2.25 4.80 2.42E-05

STM1580 narV nitrate reductase 2 subunit gamma -1.37 4.56 3.40E-03

STM1587 yncD outer membrane receptor -1.20 5.67 3.62E-03

STM1588 yncC DNA-binding transcriptional regulator -1.79 3.77 5.42E-04

STM1601 ugtL hypothetical protein -1.68 3.81 1.62E-03

STM1602 sifB secreted effector protein -2.35 4.53 2.41E-03

STM1612 STM1612 cellulase protein -1.26 5.19 5.04E-03

STM1614 STM1614 PTS system transporter subunit IIC -1.94 5.03 3.31E-05

STM1620 STM1620 (S)-2-hydroxy-acid oxidase -2.08 5.01 3.30E-04

STM1621 STM1621 hypothetical protein -1.72 3.46 1.94E-03

STM1624 STM1624 hypothetical protein -1.06 5.08 1.90E-02

STM1625 ydcI transcriptional regulator -1.04 4.23 3.13E-02

STM1630 STM1630 inner membrane protein -3.10 4.53 4.56E-04

STM1631 sseJ secreted effector protein SseJ -1.95 5.13 3.73E-03

234

Appendix

ORF Gene Function logFC logCPM FDR STM1633 STM1633 extracellular solute-binding protein -2.72 4.39 1.74E-04

STM1634 STM1634 ABC transporter permease -3.20 3.40 1.49E-06

STM1635 STM1635 polar amino acid ABC transporter ATPase -2.43 3.85 4.40E-04

STM1636 STM1636 ABC transporter membrane protein -2.46 4.01 3.70E-04

STM1637 STM1637 inner membrane protein -2.56 5.84 2.95E-06

STM1657 STM1657 methyl-accepting chemotaxis protein -1.08 5.27 1.56E-02

STM1667 STM1667 thiol peroxidase -1.24 3.41 4.60E-02

STM1668 STM1668 antivirulence protein ZirS -2.46 3.48 7.50E-04

STM1669 STM1669 invasin-like protein -2.27 5.69 2.60E-06

STM1670 STM1670 lipoprotein -1.53 3.93 4.00E-03

STM1671 STM1671 regulatory protein -2.10 4.16 7.22E-05

STM1698 STM1698 effector kinase SteC -1.50 5.54 5.71E-03

STM1701 yciW hypothetical protein -1.47 4.89 2.42E-03

STM1703 yciR RNase II stability modulator -1.05 5.86 1.68E-02

STM1723 trpE anthranilate synthase component I -1.25 4.65 5.48E-03

STM1724 trpD bifunctional glutamineamidotransferase/anth ranilate -1.07 4.76 4.73E-02

phosphoribosyltransferase

STM1729 yciF hypothetical protein -2.65 3.29 1.90E-05

STM1730 yciE hypothetical protein -2.15 3.81 1.38E-03

STM1765 narK nitrite extrusion protein -1.81 5.50 7.04E-05

STM1771 chaA calcium/sodium:proto n antiporter -1.63 5.10 7.47E-04

STM1797 ymgE transglycosylase-ass ociated protein -1.95 3.13 1.17E-03

STM1827.S STM1827.S diguanylate cyclase/phosphodiesterase -1.41 5.44 1.04E-03

STM1836 STM1836 penicillin-binding protein 3 -1.02 5.62 1.43E-02

STM1843 STM1843 transporter -1.64 5.32 2.37E-04

STM1853 pphA serine/threonine protein phosphatase 1 -1.36 4.36 6.84E-03

STM1855 sopE2 type III-secreted effector protein -1.85 4.23 4.47E-04

STM1859 STM1859 hypothetical protein -1.33 2.98 1.13E-02

STM1862 pagO integral membrane protein -2.07 4.73 1.90E-04

STM1863 STM1863 inner membrane protein -1.96 2.20 1.03E-03

STM1864 STM1864 inner membrane protein -1.62 4.04 1.15E-03

STM1868 mig-3 phage-tail assembly-like protein -1.26 5.01 5.16E-03

STM1869 STM1869 phage-tail assembly-like protein -2.27 4.16 5.04E-05

STM1869A STM1869A hypothetical protein -2.17 4.21 2.67E-05

STM1870 STM1870 hypothetical protein -2.47 5.03 1.84E-05

STM1896 STM1896 hypothetical protein -1.65 4.68 4.71E-03

STM1910 STM1910 penicillin-binding protein -1.15 5.78 4.82E-03

STM1929 otsB trehalose-6-phosphat e phosphatase -1.37 4.90 8.75E-03

STM1940 STM1940 cell wall-associated hydrolase -1.20 5.76 2.84E-03

STM1957 tnpA_2 transposase for IS200 -1.82 8.46 1.97E-02

STM1982 rcsA colanic acid capsular biosynthesis activationprotein A -1.86 4.90 1.42E-04

235

Appendix

ORF Gene Function logFC logCPM FDR STM1986 yedP mannosyl-3-phosphogl ycerate phosphatase -1.19 4.62 1.30E-02

STM1990 yedA hypothetical protein -1.08 4.97 1.24E-02

STM1995 ompS porin -1.13 4.92 1.90E-02

STM1997 umuC DNA polymerase V subunit UmuC -1.19 5.90 2.47E-03

STM2007 STM2007 hypothetical protein -2.03 5.87 1.55E-05

STM2008 STM2008 hypothetical protein -1.80 5.26 1.82E-04

STM2013 yeeO hypothetical protein -1.11 6.02 4.59E-03

STM2021 cbiQ vitamin B12 biosynthetic protein -1.50 4.74 1.41E-03

STM2022 cbiN cobalt transport protein CbiN -1.26 2.99 3.69E-02

STM2023 cbiM cobalt transport protein CbiM -1.50 5.33 2.39E-03

STM2024 cbiL cobalt-precorrin-2 C(20)-methyltransferase -1.38 4.03 3.50E-03

STM2025 cbiK vitamin B12 biosynthetic protein -1.31 4.23 6.16E-03

STM2026 cbiJ cobalt-precorrin-6x reductase -1.56 4.79 2.06E-03

STM2027 cbiH precorrin-3B C(17)-methyltransferase -1.59 4.47 7.22E-04

STM2028 cbiG cobalamin biosynthesis protein CbiG -1.40 4.76 2.47E-03

STM2029 cbiF vitamin B12 biosynthetic protein -1.32 4.48 5.11E-03

STM2030 cbiT cobalt-precorrin-6Y C(15)-methyltransferase -1.69 3.89 4.97E-04

STM2031 cbiE cobalt-precorrin-6Y C(5)-methyltransferase -2.09 3.71 3.30E-04

STM2032 cbiD cobalt-precorrin-6A synthase -1.61 4.74 3.42E-04

STM2033 cbiC cobalt-precorrin-8X methylmutase -2.15 3.91 9.22E-05

STM2034 cbiB cobalamin biosynthesis protein -1.68 4.66 5.67E-04

STM2035 cbiA cobyrinic acid a,c-diamide synthase -1.33 5.86 2.53E-03

STM2037 pduF propanediol diffusion facilitator -1.55 4.79 1.22E-03

STM2038 pduA polyhedral body protein PduA -1.88 3.32 1.04E-03

STM2039 pduB polyhedral body protein -1.86 4.24 9.59E-05

STM2040 pduC propanediol dehydratase large subunit -2.19 5.72 1.47E-06

STM2041 pduD propanediol dehydratase medium subunit -1.56 4.19 5.25E-03

STM2042 pduE propanediol dehydratase small subunit -1.91 3.33 5.83E-04

STM2043 pduG propanediol dehydratase reactivation protein -1.61 5.20 9.12E-04

STM2044 pduH propanediol dehydratase reactivation protein -2.12 2.71 4.14E-04

STM2045 pduJ polyhedral body protein -1.63 2.53 3.22E-03

STM2046 pduK polyhedral body protein -2.06 3.43 7.45E-05

STM2047 pduL phosphate propanoyltransferase PduL -1.93 3.30 3.88E-04

STM2048 pduM propanediol utilization protein PduM -1.67 2.61 3.33E-03

STM2050 pduO propanediol utilization protein -1.33 4.23 9.58E-03

STM2051 pduP CoA-dependent propionaldehyde dehydrogenase -1.81 5.37 3.22E-05

STM2052 pduQ propanol dehydrogenase -1.42 4.35 1.40E-03

STM2053 pduS polyhedral body protein -1.45 4.48 2.09E-03

STM2054 pduT polyhedral body protein -1.65 3.57 7.76E-04

STM2055 pduU polyhedral body protein -1.45 4.13 1.22E-02

STM2056 pduV propanediol utilization protein -1.86 2.87 8.77E-04

236

Appendix

ORF Gene Function logFC logCPM FDR STM2057 pduW propionate kinase -1.74 4.67 2.91E-04

STM2066 sopA E3 ubiquitin-protein ligase SopA -1.17 6.57 5.44E-03

STM2099 wcaM colanic acid biosynthesis protein -1.90 5.95 2.01E-04

STM2100 wcaL glycosyl transferase family protein -2.06 5.03 1.88E-05

STM2101 wcaK pyruvyl transferase -2.02 4.99 3.90E-05

STM2102 wzxC colanic acid exporter -2.47 5.41 2.31E-06

STM2103 wcaJ UDP-glucose lipid carrier transferase -2.16 4.99 3.85E-05

STM2104 cpsG phosphomannomutase -1.63 5.20 1.32E-02

STM2105.S manC mannose-1-phosphate guanylyltransferase -1.97 5.38 7.55E-04

STM2106 wcaI glycosyl transferase family protein -2.14 4.79 8.79E-06

STM2107 wcaH GDP-mannose mannosyl hydrolase -1.59 3.68 2.45E-02

STM2108 wcaG GDP-fucose synthetase -1.52 4.49 2.32E-03

STM2109 gmd GDP-D-mannose dehydratase -2.70 5.50 1.54E-05

STM2110 wcaF colanic acid biosynthesis acetyltransferaseWcaF -2.14 3.78 1.86E-04

STM2111 wcaE glycosyl transferase family protein -2.85 4.07 2.68E-06

STM2112 wcaD colanic acid biosynthesis protein -2.18 4.97 5.00E-04

STM2113 wcaC glycosyl transferase family protein -1.92 4.74 2.06E-04

STM2114 wcaB colanic acid biosynthesis acetyltransferaseWcaB -1.74 3.38 1.04E-03

STM2115 wcaA glycosyl transferase family protein -1.72 4.47 2.24E-04

STM2116 wzc tyrosine kinase -2.03 6.04 4.21E-06

STM2117 wzb tyrosine phosphatase -2.45 3.70 1.66E-05

STM2118 wza outer membrane polysaccharide export protein -2.37 5.30 1.21E-03

STM2125 yegD chaperone -1.58 4.93 8.92E-04

STM2127 yegN multidrug efflux system subunit MdtB -1.18 6.22 4.36E-03

STM2128 yegO multidrug efflux system subunit MdtC -1.62 6.38 1.64E-04

STM2129 yegB multidrug efflux system protein MdtE -1.03 5.31 2.28E-02

STM2133 STM2133 hypothetical protein -2.58 4.62 5.55E-05

STM2134 STM2134 inner membrane protein -1.61 3.64 8.86E-03

STM2135 STM2135 inner membrane protein -1.39 5.90 6.73E-04

STM2137 STM2137 hypothetical protein -1.95 5.02 4.84E-04

STM2138 STM2138 hypothetical protein -1.88 4.15 2.05E-03

STM2139 STM2139 inner membrane protein -1.50 3.51 3.33E-03

STM2139.2n STM2139.2n hypothetical protein -3.13 3.11 1.46E-06

STM2142 yegT transporter -1.61 5.57 1.14E-03

STM2149 stcD outer membrane lipoprotein -2.23 5.35 4.82E-04

STM2150 stcC outer membrane protein -1.79 6.26 4.74E-04

STM2151 stcB periplasmic chaperone protein -2.56 4.31 8.38E-06

STM2152 stcA fimbrial-like protein -1.73 4.30 5.29E-04

STM2156 yehR lipoprotein -1.97 3.75 2.33E-03

STM2162 yehW proline/glycine betaine ABC transporterpermease -1.01 4.71 3.59E-02

STM2163 yehX proline/glycine betaine ABC transporter ATPase -1.32 4.89 3.74E-03

237

Appendix

ORF Gene Function logFC logCPM FDR STM2164 yehY proline/glycine betaine ABC transporterpermease -1.33 5.36 2.04E-03

STM2169 yohC transporter -1.62 3.75 6.84E-03

STM2172 yohG multidrug resistance outer membrane proteinMdtQ -1.64 5.47 3.49E-04

STM2175 STM2175 salicylate hydroxylase -1.09 5.42 1.70E-02

STM2176 STM2176 glutathione S-transferase -1.96 4.77 4.85E-05

STM2177 STM2177 flutathione S-transferase -2.05 4.19 4.80E-05

STM2178 STM2178 1,2-dioxygenase -2.03 4.79 2.05E-04

STM2179 STM2179 sugar transporter -2.14 4.90 5.20E-04

STM2188 mglC beta-methylgalactosi de transporter innermembrane protein -1.96 5.28 9.36E-05

STM2189 mglA galactose/methyl galaxtoside transporterATP-binding protein -2.00 5.16 8.06E-04

STM2190 mglB galactose-binding transport protein -1.32 5.18 1.83E-03

STM2191 galS DNA-binding transcriptional regulator GalS -1.13 5.06 1.10E-02

STM2197 STM2197 phosphoserine phosphatase -2.54 4.31 2.95E-06

STM2198 STM2198 regulatory protein -1.97 4.99 2.08E-03

STM2199 cirA colicin I receptor -1.48 6.15 4.25E-04

STM2207 setB proton efflux pump -1.10 5.76 3.54E-02

STM2220 yejG hypothetical protein -1.01 5.03 2.48E-02

STM2231 STM2231 virulence protein -2.42 3.60 1.67E-05

STM2232 oafA O-antigen acetylase -1.52 5.96 4.26E-03

STM2233 STM2233 hypothetical protein -2.39 4.21 1.50E-04

STM2234 STM2234 phage tail fiber assembly protein -1.97 4.27 2.84E-04

STM2235 STM2235 hypothetical protein -1.69 6.18 4.39E-05

STM2236 STM2236 hypothetical protein -2.09 4.22 6.52E-05

STM2237 STM2237 inner membrane protein -2.33 2.87 5.08E-05

STM2239 STM2239 phage antiterminator -1.64 4.56 2.54E-03

STM2240 STM2240 hypothetical protein -1.53 7.14 2.49E-03

STM2242 STM2242 phage tail fiber protein -2.73 3.08 7.08E-06

STM2243 STM2243 tail fiber protein of phage -2.01 4.34 5.31E-05

STM2255 napC cytochrome c-type protein NapC -1.57 4.78 5.83E-04

STM2256 napB citrate reductase cytochrome c-type subunit -1.15 5.24 1.87E-02

STM2257 napH quinol dehydrogenase membrane component -1.80 5.80 2.06E-04

STM2260 napD assembly protein for periplasmic nitratereductase -1.30 3.29 1.55E-02

STM2261 napF ferredoxin-type protein -1.11 4.20 3.30E-02

STM2263 yojI multidrug transporter membraneprotein/ATP-binding -1.46 5.82 4.07E-04

component

STM2273 STM2273 dehydratase -1.95 5.91 2.02E-04

STM2274 STM2274 permease -2.59 5.43 2.67E-05

STM2275 STM2275 regulatory protein -1.83 4.39 4.58E-04

STM2280 STM2280 permease -1.13 5.70 7.45E-03

STM2287 sseL deubiquitinase SseL -1.97 4.85 6.90E-05

STM2290 yfaV transporter -1.23 5.53 5.07E-03

238

Appendix

ORF Gene Function logFC logCPM FDR STM2291 yfaW galactonate dehydratase -1.12 6.38 6.33E-03

STM2292 yfaX transcriptional regulator -1.39 4.74 2.25E-03

STM2315 yfbK hypothetical protein -1.22 6.12 3.42E-03

STM2329 STM2329 hypothetical protein -2.53 3.28 1.43E-05

STM2340 STM2340 transketolase -1.28 5.04 4.28E-03

STM2341 STM2341 transketolase -1.76 4.63 3.10E-04

STM2343 STM2343 hypothetical protein -1.90 3.39 1.49E-03

STM2344 STM2344 PTS system transporter subunit IIA -1.80 4.41 6.11E-03

STM2358 STM2358 hypothetical protein -1.47 5.06 1.79E-03

STM2359 STM2359 amino acid transporter -1.71 5.44 1.22E-04

STM2360 STM2360 diaminopimelate decarboxylase -1.80 5.57 4.61E-05

STM2373 STM2373 hypothetical protein -2.21 3.57 5.04E-05

STM2375 STM2375 hypothetical protein -1.75 3.02 5.11E-03

STM2376 STM2376 hypothetical protein -1.55 4.50 2.08E-03

STM2377 STM2377 inner membrane protein -1.68 5.24 2.07E-02

STM2389 fadI 3-ketoacyl-CoA thiolase -1.76 5.51 3.55E-03

STM2393 yfdC hypothetical protein -1.73 5.31 3.96E-04

STM2395 pgtE outer membrane protease -1.91 5.28 3.08E-05

STM2396 pgtA activator -1.29 6.45 1.61E-03

STM2398 pgtC phosphoglycerate transport regulatory proteinprecursor -1.52 5.23 6.73E-04

STM2399 pgtP phosphoglycerate transporter -2.29 5.62 6.42E-07

STM2400 STM2400 inner membrane protein -1.35 3.93 4.91E-03

STM2420 xapR DNA-binding transcriptional activator XapR -1.94 5.82 2.36E-04

STM2421 xapB xanthosine permease -2.11 5.81 1.25E-05

STM2423 yfeN hypothetical protein -2.43 5.13 2.35E-05

STM2441 cysA sulfate/thiosulfate transporter subunit -1.11 5.45 8.37E-03

STM2442 cysW sulfate/thiosulfate transporter permeasesubunit -2.20 4.91 3.59E-06

STM2444 cysP thiosulfate transporter subunit -1.63 4.87 8.13E-04

STM2455 eutK carboxysome structural protein -1.30 4.59 4.88E-03

STM2456 eutL carboxysome structural protein -1.18 4.46 1.81E-02

STM2458 eutB ethanolamine ammonia-lyase heavy chain -1.76 5.99 3.85E-05

STM2459 eutA reactivating factor for ethanolamine ammonialyase -1.28 4.90 8.29E-03

STM2460 eutH transporter -1.56 5.70 5.65E-04

STM2462 eutJ ethanolamine utilization protein -1.35 4.65 1.14E-02

STM2463 eutE aldehyde oxidoreductase -1.55 5.24 7.96E-04

STM2464 eutN ethanolamine utilization protein EutN -1.29 3.59 1.95E-02

STM2466 eutD phosphotransacetylas e -1.28 4.52 3.81E-02

STM2467 eutT cobalamin adenosyltransferase -1.10 4.21 2.49E-02

STM2468 eutQ ethanolamine utilization protein -1.82 4.46 2.43E-04

STM2469 eutP ethanolamine utilization protein -1.61 3.50 4.51E-03

STM2470 eutS carboxysome structural protein -2.13 2.78 3.13E-04

239

Appendix

ORF Gene Function logFC logCPM FDR STM2473 talA transaldolase A -1.35 5.44 1.62E-03

STM2476 ypfG hypothetical protein -1.02 5.59 1.22E-02

STM2503 STM2503 diguanylate cyclase -1.26 6.41 2.59E-03

STM2508 STM2508 hypothetical protein -1.30 3.61 1.58E-02

STM2509 STM2509 transposase -1.46 4.21 3.45E-03

STM2513 shdA AIDA autotransporter-like protein -1.40 7.88 4.34E-02

STM2516 sinI outer membrane protein -2.07 5.38 1.97E-05

STM2517 sinH intimin-like protein -2.53 6.07 1.22E-06

STM2556 hmpA nitric oxide dioxygenase -1.12 5.40 5.65E-03

STM2566 STM2566 hypothetical protein -2.14 3.40 1.18E-04

STM2570 STM2570 PTS system transporter subunit IIB -1.12 6.29 5.01E-03

STM2573 STM2573 2-dehydropantoate 2-reductase -1.30 5.61 1.86E-03

STM2574 STM2574 permease -2.60 6.05 9.69E-05

STM2575 STM2575 transcriptional regulator -2.00 5.19 1.07E-04

STM2584 gogB hypothetical protein -1.15 6.45 2.73E-02

STM2586 STM2586 phage tail assembly-like protein -1.37 4.47 8.53E-03

STM2587 STM2587 phage tail assembly-like protein -1.52 5.22 1.43E-03

STM2589 STM2589 host specificity protein-J-like -1.56 7.54 1.20E-02

STM2591 STM2591 tail assembly protein K-like -1.51 4.91 1.67E-03

STM2592 STM2592 phage tail component L-like protein -1.07 5.83 8.20E-03

STM2593 STM2593 phage tail component M-like protein -1.20 4.19 1.23E-02

STM2600 STM2600 minor tail protein Z-like -1.07 6.15 5.50E-03

STM2601 STM2601 minor capsid protein FII -1.05 4.82 1.35E-02

STM2602 STM2602 DNA packaging-like protein -1.21 4.57 1.07E-02

STM2606 STM2606 head-tail preconnector-like protein -1.02 7.01 2.98E-02

STM2609 STM2609 DNA packaging-like protein -1.48 4.75 8.43E-04

STM2611.S STM2611.S endopeptidase-like protein -1.33 4.24 5.11E-03

STM2614 STM2614 hypothetical protein -1.73 3.33 3.84E-03

STM2623 STM2623 hypothetical protein -1.48 2.71 8.46E-03

STM2624 STM2624 hypothetical protein -1.84 2.54 2.40E-03

STM2625 STM2625 replication protein -1.93 4.08 1.05E-03

STM2626 STM2626 replication protein 15-like -2.04 4.04 3.10E-05

STM2627 STM2627 cI-like protein -1.94 2.59 6.99E-04

STM2629 STM2629 hypothetical protein -1.26 3.04 2.50E-02

STM2632 STM2632 exodeoxyribonuclease VIII-like protein -1.77 6.79 1.29E-04

STM2654 kgtP alpha-ketoglutarate transporter -1.67 6.20 6.18E-05

STM2655 STM2655 hypothetical protein -1.80 3.98 1.56E-04

STM2691 STM2691 ABC transporter ATP-binding protein -2.29 6.34 6.10E-05

STM2692 STM2692 HlyD family secretion protein -1.41 5.66 1.09E-03

STM2705 STM2705 hypothetical protein -1.57 4.19 2.32E-03

STM2706 STM2706 phage tail-like protein -1.31 4.44 3.78E-03

240

Appendix

ORF Gene Function logFC logCPM FDR STM2740 STM2740 integrase-like protein -1.51 5.64 1.64E-03

STM2741 STM2741 hypothetical protein -2.23 5.12 8.23E-06

STM2745 STM2745 inner membrane protein -1.11 6.22 4.76E-02

STM2746 STM2746 ATPase -1.98 4.96 3.48E-03

STM2747 STM2747 hypothetical protein -2.25 4.63 1.09E-03

STM2753 STM2753 dehydrogenase -1.11 6.02 1.02E-02

STM2754 STM2754 hexulose 6 phosphate synthase -2.44 5.79 7.95E-04

STM2755 STM2755 hexulose 6 phosphate synthase -2.66 5.47 2.05E-06

STM2756 STM2756 sugar phosphate aminotransferase -2.16 4.50 2.20E-04

STM2757 STM2757 hypothetical protein -2.31 5.26 3.49E-05

STM2758 STM2758 PTS system transporter subunit IIBC -2.33 6.05 3.42E-06

STM2759 STM2759 dipeptide/oligopepti de/nickel ABC-type ABCtransporter -1.58 5.91 1.90E-04

substrate-binding protein

STM2760 STM2760 integrase -2.02 4.23 1.09E-04

STM2761 STM2761 inner membrane protein -2.07 5.78 7.50E-04

STM2762 STM2762 inner membrane protein -1.72 5.38 2.73E-03

STM2770 fljA phase-1 flagellin repressor -1.63 4.16 2.08E-03

STM2772 hin DNA-invertase Hin -1.36 4.56 3.78E-03

STM2773 iroB glycosyl transferase family protein -1.51 5.78 4.83E-04

STM2774 iroC ABC transporter ATP-binding protein -1.56 7.14 3.60E-03

STM2775 iroD enterochelin esterase-like protein -1.84 5.31 1.74E-03

STM2776 iroE hydrolase -1.94 5.00 2.67E-05

STM2777 iroN outer membrane receptor FepA -1.54 6.36 4.40E-04

STM2780 pipB2 secreted effector protein PipB2 -1.37 5.72 1.14E-03

STM2785 tctD regulatory protein TctD -1.67 5.43 1.53E-04

STM2786 STM2786 tricarboxylic transport -1.68 4.72 4.37E-04

STM2787 STM2787 tricarboxylic transport -1.50 3.09 6.89E-03

STM2789 STM2789 hypothetical protein -2.61 5.27 2.18E-07

STM2790 ygaF hydroxyglutarate oxidase -1.98 5.53 4.05E-05

STM2791 gabD succinate-semialdehy de dehydrogenase I -1.94 5.34 1.01E-04

STM2793 gabP gamma-aminobutyrate transporter -2.22 5.37 2.64E-05

STM2794 ygaE DNA-binding transcriptional regulator CsiR -1.68 4.37 1.01E-03

STM2795 ygaU LysM domain/BON superfamily protein -1.78 4.78 1.35E-03

STM2804 STM2804 hypothetical protein -1.09 3.45 3.47E-02

STM2804.1n STM2804.1n hypothetical protein -1.37 2.92 2.14E-02

STM2806 nrdI ribonucleotide reductase stimulatory protein -2.63 3.67 1.46E-06

STM2807 nrdE ribonucleotide-dipho sphate reductase subunitalpha -1.58 6.05 9.55E-05

STM2808 nrdF ribonucleotide-dipho sphate reductase subunitbeta -1.30 5.52 2.72E-03

STM2809 proV glycine betaine transporter ATP-binding subunit -1.17 5.74 3.13E-02

STM2810 proW glycine betaine transporter membrane protein -1.23 5.24 5.02E-03

STM2811 proX glycine betaine transporter periplasmic subunit -1.76 5.52 1.22E-04

241

Appendix

ORF Gene Function logFC logCPM FDR STM2816 STM2816 glycoporin -2.00 6.01 1.55E-05

STM2832 srlA PTS system glucitol/sorbitol-specifictran sporter subunit IIC -2.07 4.54 1.73E-03

STM2833 srlE PTS system glucitol/sorbitol-specifictran sporter subunit IICB -1.74 5.07 1.44E-04

STM2834 slrB PTS system glucitol/sorbitol-specifictran sporter subunit IIA -1.70 3.45 1.63E-03

STM2836 gutM DNA-binding transcriptional activator GutM -1.38 3.09 9.74E-03

STM2840 STM2840 anaerobic nitric oxide reductaseflavorubredoxin -1.97 5.90 7.50E-06

STM2861 sitA periplasmic binding protein -1.63 4.85 3.58E-04

STM2862 sitB ATP-binding protein -1.45 5.21 8.92E-04

STM2863 sitC permease -1.80 5.48 3.14E-05

STM2871 prgK needle complex inner membrane lipoprotein -1.07 6.11 1.46E-02

STM2873 prgI needle complex major subunit -1.13 4.71 4.96E-02

STM2874 prgH needle complex inner membrane protein -1.48 6.78 1.47E-03

STM2876 hilA invasion protein regulator -1.89 6.55 7.58E-05

STM2877 iagB invasion protein precursor -1.11 4.43 2.68E-02

STM2878 sptP protein tyrosine phosphatase/GTPase activatingprotein -1.10 7.17 2.88E-02

STM2879 sicP secretion chaperone -1.09 5.36 7.66E-03

STM2881 iacP acyl carrier protein -1.50 4.28 1.62E-03

STM2882 sipA cell invasion protein SipA -1.68 7.74 1.10E-02

STM2883 sipD cell invasion protein SipD -1.59 6.11 2.98E-04

STM2885 sipB cell invasion protein SipB -1.35 7.75 4.18E-02

STM2886 sicA secretion chaperone SicA -1.38 5.30 2.72E-03

STM2887 spaS surface presentation of antigens protein SpaS -2.29 5.61 1.37E-04

STM2888 spaR needle complex export protein -1.90 5.05 1.97E-05

STM2889 spaQ needle complex export protein -1.50 3.88 1.87E-02

STM2890 spaP surface presentation of antigens protein SpaP -2.06 5.77 1.22E-06

STM2891 spaO surface presentation of antigens protein SpaO -1.51 6.50 8.13E-04

STM2892 invJ needle length control protein -2.03 6.75 2.24E-05

STM2893 invI needle complex assembly protein -2.33 5.74 4.90E-06

STM2894 invC ATP synthase SpaL -1.09 6.54 1.00E-02

STM2895 invB secretion chaperone -1.64 5.70 3.30E-04

STM2896 invA needle complex export protein -1.81 7.28 6.38E-04

STM2897 invE invasion protein -1.80 6.42 1.64E-05

STM2898 invG outer membrane secretin InvG -1.88 7.15 4.32E-04

STM2899 invF invasion regulatory protein -1.25 5.95 2.43E-03

STM2900 invH needle complex outer membrane lipoproteinprecursor -1.51 5.62 6.93E-04

STM2907 pphB serine/threonine-spe cific protein phosphatase 2 -1.02 5.27 2.98E-02

STM2908 STM2908 hypothetical protein -1.70 4.91 5.34E-04

STM2911 STM2911 permease -1.64 5.85 2.85E-04

STM2913 STM2913 permease -1.29 6.42 2.54E-03

STM2914 STM2914 nucleoside-diphospha te-sugar epimerase -2.13 5.25 6.59E-04

STM2915 ygbM hypothetical protein -2.51 4.97 4.62E-06

242

Appendix

ORF Gene Function logFC logCPM FDR STM2916 ygbL aldolase -2.53 4.21 2.05E-06

STM2917 ygbK tRNA synthase -1.79 4.98 2.10E-03

STM2918 ygbJ 3-hydroxyisobutyrate dehydrogenase -2.19 4.75 1.75E-04

STM2921 STM2921 3-octaprenyl-4-hydro xybenzoate carboxy-lyase -1.64 4.57 5.45E-03

STM2922 STM2922 3-polyprenyl-4-hydro xybenzoate decarboxylase -1.78 6.06 1.52E-05

STM2933 cysC adenylylsulfate kinase -1.27 4.83 7.94E-03

STM2935 cysD sulfate adenylyltransferase subunit 2 -1.99 5.04 2.50E-05

STM2937 ygbF hypothetical protein -1.74 5.03 9.00E-04

STM2938 STM2938 hypothetical protein -1.29 5.36 2.35E-03

STM2939 ygcH hypothetical protein -1.14 4.95 1.09E-02

STM2942 STM2942 transposase -1.34 5.01 2.99E-03

STM2943 STM2943 hypothetical protein -1.76 6.71 1.44E-04

STM2944 ygcB helicase -2.00 6.85 5.40E-05

STM2945 sopD secreted effector protein SopD -2.24 5.42 1.94E-04

STM2946 cysH phosphoadenosine phosphosulfate reductase -1.45 4.26 4.38E-03

STM2947 cysI sulfite reductase subunit beta -1.43 5.89 1.91E-03

STM2948 cysJ sulfite reductase subunit alpha -2.09 6.15 6.68E-06

STM2950 STM2950 metal-dependent hydrolase -1.46 6.04 2.38E-04

STM2961 ygcY D-glucarate dehydratase -1.30 5.53 1.26E-03

STM2962 gudT D-glucarate permease -1.87 6.09 7.77E-06

STM2973 fucO L-1,2-propanediol oxidoreductase -1.31 6.06 3.99E-03

STM2974 fucA L-fuculose phosphate aldolase -2.07 4.76 4.76E-05

STM2976 fucI L-fucose isomerase -2.13 5.84 5.13E-06

STM2977 fucK L-fuculokinase -1.57 5.16 4.24E-04

STM2978 fucU L-fucose-binding protein -1.32 3.75 7.35E-03

STM2992 argA N-acetylglutamate synthase -1.18 6.08 2.93E-03

STM2997 ppdC hypothetical protein -1.84 4.59 1.03E-04

STM2998 ygdB hypothetical protein -1.33 4.79 4.38E-03

STM2999 ppdB hypothetical protein -1.49 4.72 4.00E-03

STM3000 ppdA hypothetical protein -2.05 4.08 4.58E-04

STM3004.1n STM3004.1n hypothetical protein -3.51 2.82 5.95E-08

STM3013 lysA diaminopimelate decarboxylase -1.20 6.02 2.92E-03

STM3016 araE L-arabinose/proton symport protein -1.89 5.96 8.93E-06

STM3017 kduD 2-deoxy-D-gluconate 3-dehydrogenase -1.41 5.72 1.02E-03

STM3018 kduI 5-keto-4-deoxyuronat e isomerase -1.40 5.22 1.38E-03

STM3019 yqeF acetyl-CoA acetyltransferase -1.75 6.02 2.51E-04

STM3023 yohL hypothetical protein -1.07 4.25 2.06E-02

STM3024 yohM nickel/cobalt efflux protein RcnA -1.13 5.66 1.26E-02

STM3025 STM3025 hypothetical protein -1.38 5.58 3.31E-03

STM3025.1N STM3025.1N hypothetical protein -2.09 4.05 5.78E-05

STM3026 STM3026 outer membrane protein -2.05 5.69 5.68E-04

243

Appendix

ORF Gene Function logFC logCPM FDR STM3027 stdC fimbrial chaperone -2.51 4.68 7.12E-07

STM3028 stdB outer membrane usher protein -1.88 6.21 6.89E-05

STM3029.S stdA fimbrial-like protein -2.36 5.09 3.29E-05

STM3030 STM3030 hypothetical protein -1.71 5.27 1.42E-04

STM3031 STM3031 Ail/OmpX-like protein -1.81 4.59 1.72E-04

STM3052 STM3052 outer membrane protein -1.92 5.77 2.12E-03

STM3066 yggA arginine exporter protein -1.74 5.43 7.60E-05

STM3079.S STM3079.S hydrolase/acyltransf erase -2.20 5.40 1.92E-06

STM3080 STM3080 mannitol dehydrogenase -1.61 4.31 2.18E-02

STM3081 STM3081 malate/L-lactate dehydrogenase -2.13 5.16 2.15E-05

STM3082 STM3082 zinc-binding dehydrogenase -1.57 5.34 6.25E-04

STM3083 STM3083 mannitol dehydrogenase -2.12 5.83 3.40E-06

STM3085 STM3085 outer membrane lipoprotein -1.82 4.78 3.96E-04

STM3089 yqgD inner membrane protein -2.63 3.22 6.68E-06

STM3099 yggR twitching motility protein -2.00 5.31 2.10E-05

STM3105 yggM hypothetical protein -2.07 5.50 2.59E-05

STM3115 yqgA inner membrane protein -1.19 5.32 5.38E-03

STM3118 STM3118 acetyl-CoA hydrolase -1.66 6.36 1.29E-04

STM3119 STM3119 monoamine oxidase -1.28 4.73 8.92E-03

STM3121 STM3121 transcriptional regulator -1.18 5.86 3.64E-03

STM3123 STM3123 arylsulfatase regulator -2.15 5.35 3.54E-06

STM3124 STM3124 response regulator -1.80 5.06 2.48E-04

STM3125 STM3125 hypothetical protein -1.70 5.00 1.53E-04

STM3126 STM3126 amino acid transporter -1.92 6.78 4.76E-05

STM3128 STM3128 oxidoreductase -1.03 6.02 1.78E-02

STM3129 STM3129 NAD-dependent aldehyde dehydrogenase -1.22 6.12 2.30E-03

STM3132 STM3132 xylanase/chitin deacetylase -2.14 5.60 2.95E-06

STM3133 STM3133 amidohydrolase -3.00 5.02 6.77E-05

STM3142 STM3142 periplasmic ferrichrome-binding protein -1.21 5.86 8.15E-03

STM3166.S STM3166.S cation transporter -2.41 5.51 5.67E-05

STM3169 STM3169 periplasmic dicarboxylate-binding protein -2.58 5.02 1.34E-06

STM3170 STM3170 inner membrane protein -2.89 4.18 3.61E-07

STM3193 STM3193 disulfide bond formation protein DsbL -2.28 4.92 1.52E-05

STM3194 STM3194 disulfide oxidoreductase -1.37 5.25 1.30E-03

STM3198 STM3198 inner membrane protein -1.79 4.10 1.03E-03

STM3199 yqiK hypothetical protein -1.37 6.99 3.63E-03

STM3217 aer aerotaxis sensor receptor -1.27 6.57 6.44E-03

STM3218 oat putrescine--2-oxoglu tarate aminotransferase -2.16 6.41 2.68E-06

STM3219 fadH 2,4-dienoyl-CoA reductase -2.07 6.40 3.26E-05

STM3222 ygjQ integral membrane protein -1.27 4.80 4.80E-03

STM3240 tdcG L-serine deaminase -1.04 5.96 1.06E-02

244

Appendix

ORF Gene Function logFC logCPM FDR STM3241 tdcE pyruvate formate-lyase 4/2-ketobutyrateformate-lyase -1.28 6.75 7.14E-03

STM3242 tdcD propionate/acetate kinase -2.18 5.74 2.98E-05

STM3243 tdcC threonine/serine transporter TdcC -1.58 6.51 3.06E-04

STM3244 tdcB threonine dehydratase -1.70 5.25 1.51E-03

STM3245 tdcA DNA-binding transcriptional activator TdcA -1.95 5.78 7.05E-06

STM3247 garK glycerate kinase -1.19 6.19 6.37E-03

STM3250 garD galactarate dehydratase -1.33 5.95 1.08E-03

STM3254 STM3254 fructose-1-phosphate kinase -3.22 4.84 6.68E-06

STM3255 STM3255 PTS system fructose-specific transporter subunitIIB -2.22 6.09 6.49E-06

STM3256 STM3256 PTS system mannitol/fructose-specifictran sporter subunit -2.00 5.54 3.11E-05

IIA/phosphocarrier protein FPr

STM3257 STM3257 tagatose 6-phosphate kinase 1 -1.91 6.01 7.15E-06

STM3258 STM3258 PTS system galactitol-specific transportersubunit IIA -2.17 4.32 1.97E-05

STM3259 STM3259 PTS system galactitol-specific transportersubunit IIB -2.12 4.13 9.15E-04

STM3260 STM3260 PTS system galactitol-specific transportersubunit IIC -2.21 5.98 2.17E-06

STM3261 STM3261 galactitol-1-phospha te dehydrogenase -1.85 5.72 3.37E-05

STM3277 STM3277 inner membrane protein -2.42 5.05 1.10E-04

STM3278 STM3278 hypothetical protein -1.94 4.26 3.78E-03

STM3329 yhcC FeS oxidoreductase -1.30 5.86 2.37E-03

STM3332 yhcG hypothetical protein -2.13 5.73 5.01E-06

STM3338 nanT sialic acid transporter -1.26 6.14 2.42E-03

STM3339 nanA N-acetylneuraminate lyase -2.20 5.46 1.03E-04

STM3343 STM3343 hypothetical protein -1.37 5.73 1.13E-03

STM3350 STM3350 inner membrane protein -1.34 5.64 2.89E-03

STM3351 oadB sodium ion pump oxaloacetate decarboxylasesubunit beta -1.30 5.51 4.51E-02

STM3352 oadA oxaloacetate decarboxylase -1.90 4.72 3.56E-04

STM3354 STM3354 L(+)-tartrate dehydratase subunit beta -1.94 5.40 1.62E-03

STM3355 STM3355 tartrate dehydratase subunit alpha -2.21 5.58 3.07E-05

STM3356 STM3356 cation transporter -2.74 6.05 2.42E-05

STM3364 yhcP p-hydroxybenzoic acid efflux subunit AaeB -1.55 6.90 7.82E-04

STM3365 yhcQ p-hydroxybenzoic acid efflux subunit AaeA -1.08 5.57 2.23E-02

STM3366 yhcR hypothetical protein -1.66 3.12 2.79E-03

STM3386 yhdJ methyltransferase -1.67 5.81 2.13E-04

STM3388 STM3388 signal transduction protein -1.54 6.63 2.76E-04

STM3389 envR DNA-binding transcriptional regulator EnvR -2.33 4.59 7.15E-04

STM3390 acrE acriflavine resistance protein E -1.68 5.39 3.75E-04

STM3391 acrF multidrug transport protein -1.83 7.49 2.52E-03

STM3442 hopD leader peptidase HopD -2.30 4.59 7.15E-06

STM3458 yheR glutathione-regulate d potassium-efflux systemancillary -1.31 4.94 1.48E-02

protein KefG

STM3470 fic cell filamentation protein Fic -1.06 4.79 1.44E-02

245

Appendix

ORF Gene Function logFC logCPM FDR STM3476 nirC nitrite transporter NirC -1.66 5.45 1.28E-04

STM3478 bigA surface-exposed virulence protein -1.48 8.35 3.60E-02

STM3488 hofQ outer membrane porin HofQ -1.46 6.12 2.36E-04

STM3489 yrfA inner membrane protein -1.99 5.37 5.65E-04

STM3490 yrfB inner membrane protein -2.18 4.42 9.21E-05

STM3491 yrfC inner membrane protein -2.08 5.36 7.32E-05

STM3492 yrfD hypothetical protein -1.67 5.05 7.33E-04

STM3499 yhgE inner membrane protein -1.27 6.75 4.21E-03

STM3512 gntT high-affinity gluconate permease -1.17 5.95 8.16E-03

STM3527 STM3527 hypothetical protein -1.25 5.68 4.09E-03

STM3528 STM3528 phosphate-binding protein -1.04 7.08 3.17E-02

STM3531 STM3531 dihydroxyacid dehydratase -1.97 6.56 1.82E-05

STM3532 STM3532 dihydrodipicolinate synthetase -1.11 6.08 7.18E-03

STM3541 gntU low affinity gluconate transporter -1.50 5.97 2.06E-04

STM3542 gntK gluconate kinase -1.82 4.90 1.07E-04

STM3547.Sc STM3547.Sc transcriptional regulator -2.59 5.44 1.04E-05

STM3548 STM3548 hypothetical protein -2.37 5.12 2.15E-05

STM3549 STM3549 inner membrane protein -2.56 5.80 6.68E-06

STM3550 STM3550 phosphotriesterase -2.09 6.00 8.23E-06

STM3551 ggt gamma-glutamyltransp eptidase -1.84 6.48 3.07E-05

STM3554 ugpC glycerol-3-phosphate transporter ATP-bindingsubunit -1.14 6.48 8.90E-03

STM3555 ugpE glycerol-3-phosphate transporter membraneprotein -2.08 5.39 2.02E-05

STM3556 ugpA glycerol-3-phosphate transporter permease -2.31 5.00 1.64E-05

STM3557 ugpB glycerol-3-phosphate transporter periplasmicbinding protein -1.17 6.06 1.18E-02

STM3558 STM3558 death-on-curing protein -1.68 4.89 1.12E-03

STM3560 livF leucine/isoleucine/v aline transporterATP-binding subunit -1.21 5.00 6.29E-03

STM3561 livG leucine/isoleucine/v aline transporterATP-binding subunit -2.08 5.60 1.49E-06

STM3562 livM leucine/isoleucine/v aline transporter permeasesubunit -1.73 6.00 4.76E-05

STM3563 livH branched-chain amino acid transporter permeaseLivH -1.64 5.37 2.65E-04

STM3564 livK high-affinity branched-chain amino acidtransporter -1.33 5.77 8.75E-04

STM3582 yhhT permease -2.10 6.22 1.98E-05

STM3599 STM3599 anaerobic C4-dicarboxylate transporter -2.92 6.06 2.05E-06

STM3600 STM3600 sugar kinase -1.84 5.37 2.24E-04

STM3601 STM3601 phosphosugar isomerase -1.52 5.67 5.60E-04

STM3605 STM3605 phage endolysin -1.89 4.03 2.37E-04

STM3606 yhjB transcriptional regulator -2.12 4.63 1.74E-04

STM3614 dctA C4-dicarboxylate transporter DctA -1.37 6.39 6.33E-04

STM3623 yhjT inner membrane protein -1.74 3.93 2.08E-03

STM3625 yhjV transporter -2.54 6.83 3.54E-05

STM3626 dppF dipeptide ABC transporter ATP-binding subunitDppF -1.21 5.67 1.53E-02

STM3627 dppD dipeptide ABC transporter ATP-binding subunitDppD -1.57 5.75 1.28E-04

246

Appendix

ORF Gene Function logFC logCPM FDR STM3628 dppC dipeptide ABC transporter permease DppC -1.60 5.50 1.31E-04

STM3629 dppB dipeptide ABC transporter permease DppB -1.57 5.90 3.73E-04

STM3631 STM3631 xanthine permease -1.86 6.16 7.45E-05

STM3632 STM3632 hypothetical protein -1.45 6.10 2.13E-03

STM3635 yhjW phosphoethanolamine transferase -1.02 6.73 1.88E-02

STM3636 lpfE long polar fimbrial minor protein -1.79 5.34 2.01E-04

STM3637 lpfD long polar fimbrial protein -1.45 6.27 4.07E-04

STM3638 lpfC long polar fimbrial outer membrane usherprotein -1.61 7.24 2.21E-03

STM3639 lpfB long polar fimbrial chaperone precursor -2.14 4.43 1.34E-04

STM3640 lpfA long polar fimbrial protein A precursor -2.50 5.32 1.22E-06

STM3648 yiaG transcriptional regulator -1.24 4.20 1.71E-02

STM3653 STM3653 acetyltransferase -1.69 4.92 2.67E-03

STM3658 yiaH inner membrane protein -2.22 5.81 1.41E-06

STM3659 yiaB inner membrane protein -2.28 4.74 8.07E-05

STM3660 xylB xylulokinase -1.16 6.10 3.90E-03

STM3661 xylA xylose isomerase -2.09 6.42 4.09E-06

STM3662 xylR xylose operon regulatory protein -1.50 6.06 1.16E-03

STM3664 malS periplasmic alpha-amylase -2.15 7.09 1.18E-04

STM3668 yiaK 2,3-diketo-L-gulonat e reductase -2.38 5.23 7.11E-06

STM3669 yiaL hypothetical protein -1.48 4.46 2.10E-02

STM3670 STM3670 hypothetical protein -2.14 5.69 1.82E-06

STM3671 yiaM 2,3-diketo-L-gulonat e TRAP transporter smallpermease YiaM -2.19 4.98 3.23E-04

STM3672 yiaN hypothetical protein -2.23 5.69 1.95E-04

STM3673 yiaO periplasmic dicarboxylate-binding protein -2.49 5.91 1.14E-03

STM3674 lyxK L-xylulose kinase -1.95 6.78 5.45E-03

STM3675 sgbH 3-keto-L-gulonate-6- phosphate decarboxylase -2.64 4.55 8.35E-05

STM3676 sgbU L-xylulose 5-phosphate 3-epimerase -2.53 4.87 2.06E-05

STM3677 sgbE L-ribulose-5-phospha te 4-epimerase -1.67 5.13 6.17E-04

STM3678 STM3678 regulatory protein -1.95 5.00 4.61E-05

STM3679 STM3679 hypothetical protein -1.69 6.26 8.16E-05

STM3680 aldB aldehyde dehydrogenase B -1.50 6.24 2.03E-04

STM3681 STM3681 transcriptional regulator -1.29 5.44 3.20E-03

STM3690 STM3690 inner membrane lipoprotein -2.56 5.63 2.47E-03

STM3691 STM3691 trimeric autotransporter adhesin -1.96 8.48 5.30E-03

STM3692 lldP L-lactate permease -2.30 6.10 1.46E-06

STM3693 lldR DNA-binding transcriptional repressor LldR -2.11 5.07 4.76E-05

STM3694 lldD L-lactate dehydrogenase -1.41 5.83 9.12E-04

STM3697 STM3697 L-talarate/galactara te dehydratase -2.14 5.89 1.99E-06

STM3707 yibD glycosyl transferase family protein -1.24 6.15 2.56E-03

STM3729 radC DNA repair protein RadC -1.55 5.18 3.75E-04

STM3736 STM3736 transcriptional regulator -1.18 6.32 2.83E-03

247

Appendix

ORF Gene Function logFC logCPM FDR STM3737 STM3737 Zn-dependent hydrolase -1.89 5.85 2.59E-05

STM3739 ligB NAD-dependent DNA ligase LigB -1.27 7.32 1.72E-02

STM3749 yicI alpha-xylosidase -1.42 7.00 3.41E-03

STM3750 yicJ transporter -2.46 6.24 2.56E-06

STM3756 rmbA hypothetical protein -2.59 4.35 1.65E-04

STM3757 misL autotransporter -1.79 7.51 4.38E-03

STM3758 fidL inner membrane protein -1.45 4.50 4.30E-03

STM3759.S marT transcriptional regulator -1.79 5.02 9.70E-04

STM3763 mgtB Mg2+ transporter -1.95 7.64 4.20E-03

STM3764 mgtC protein MgtC -1.69 4.92 2.30E-03

STM3767 STM3767 hypothetical protein -1.03 5.71 3.69E-02

STM3768 STM3768 selenocysteine synthase -1.67 5.65 2.45E-04

STM3769.S STM3769.S PTS system mannose-specific transporter subunitIID -1.76 5.39 2.92E-04

STM3770 STM3770 PTS system mannose-specific transporter subunitIIC -2.22 5.45 4.41E-06

STM3771 STM3771 PTS system mannose-specific transporter subunitIIB -1.93 4.68 2.39E-04

STM3772 STM3772 PTS system mannose-specific transporter subunitIIA -1.77 3.70 1.21E-03

STM3774 STM3774 inner membrane protein -2.11 3.14 2.07E-04

STM3776 nepI ribonucleoside transporter -1.18 6.45 4.05E-03

STM3779 STM3779 PTS system phosphocarrier protein HPr -2.07 3.47 2.01E-03

STM3781 STM3781 sugar kinase -2.19 6.83 7.84E-05

STM3782 STM3782 PTS system mannitol/fructose-specifictran sporter subunit IIC -1.97 6.50 7.61E-05

STM3783 STM3783 PTS system mannitol/fructose-specifictran sporter subunit IIB -1.92 4.66 8.13E-04

STM3784 STM3784 PTS system mannitol/fructose-specifictran sporter subunit IIA -1.31 4.99 5.83E-03

STM3786 yicN inner membrane protein -1.82 5.77 4.16E-04

STM3787 uhpT sugar phosphate antiporter -1.42 6.40 4.18E-03

STM3788 uhpC regulatory protein UhpC -1.18 6.07 3.09E-02

STM3791 STM3791 hypothetical protein -1.41 5.91 7.08E-04

STM3792 STM3792 L-fucose permease -2.06 6.21 5.13E-06

STM3793 STM3793 sugar kinase -1.78 5.25 4.29E-04

STM3795 ilvN acetolactate synthase 1 regulatory subunit -1.05 4.48 1.86E-02

STM3821 torD chaperone protein TorD -1.81 5.25 4.17E-04

STM3822 torA trimethylamine N-oxide reductase subunit -1.65 7.05 1.26E-03

STM3823 torC trimethylamine N-oxide reductase cytochromec-like subunit -1.75 6.20 8.03E-05

STM3827 dgoT D-galactonate transport protein -2.26 6.33 1.34E-06

STM3828 dgoA galactonate dehydratase -2.21 6.04 2.68E-06

STM3829 dgoK 2-oxo-3-deoxygalacto nate kinase -1.86 5.64 1.16E-04

STM3832 STM3832 permease -2.76 5.89 3.61E-07

STM3833 STM3833 mandelate racemase -2.05 6.17 5.54E-06

STM3847 yidY multidrug efflux system protein MdtL -1.59 6.86 8.13E-04

STM3858 STM3858 PTS system fructose-specific transporter subunitIIBC -1.09 6.99 3.47E-02

STM3863 STM3863 permease -1.40 6.15 2.55E-03

248

Appendix

ORF Gene Function logFC logCPM FDR STM3881 rbsD D-ribose pyranase -1.29 5.31 1.79E-03

STM3882 rbsA D-ribose transporter ATP-binding protein -1.48 6.72 8.43E-04

STM3887 yieO tranport protein -1.19 7.13 1.81E-02

STM3899 yifB ATP-dependent protease -1.06 6.70 1.21E-02

STM3906 STM3906 hypothetical protein -1.02 5.13 1.57E-02

STM3911 STM3911 hypothetical protein -2.06 4.21 3.49E-04

STM3912 rep ATP-dependent DNA helicase Rep -1.03 7.25 4.45E-02

STM3940 STM3940 inner membrane protein -1.53 4.62 4.85E-03

STM3941 STM3941 inner membrane protein -2.52 4.44 2.68E-06

STM3942 STM3942 hypothetical protein -1.69 4.59 3.49E-04

STM3944 STM3944 inner membrane protein -1.69 4.46 5.77E-04

STM3953 yigF inner membrane protein -2.05 4.51 3.57E-03

STM3954 yigG hypothetical protein -1.79 4.30 1.40E-02

STM3956 yigI hypothetical protein -1.87 4.56 1.70E-04

STM3963 yigM transporter -1.30 5.82 3.51E-03

STM3964 metR metE/metH regulator -1.12 5.30 1.13E-02

STM3965 metE 5-methyltetrahydropt eroyltriglutamate/homocysteine S- -1.50 7.42 9.57E-03

methyltransferase

STM3966 STM3966 arylsulfatase regulator -1.42 5.99 7.36E-04

STM3980 STM3980 outer membrane protein -1.66 4.55 1.84E-03

STM3981 STM3981 hypothetical protein -1.52 6.42 2.98E-04

STM3982 fadA 3-ketoacyl-CoA thiolase -2.01 6.27 1.59E-05

STM3983 fadB multifunctional fatty acid oxidation complexsubunit alpha -2.40 6.58 6.94E-06

STM3998 yihG acyltransferase -1.01 6.39 2.47E-02

STM4010 STM4010 hydrolase -1.24 5.52 2.68E-03

STM4011 STM4011 inner membrane protein -2.34 5.38 2.73E-06

STM4012 STM4012 coproporphyrinogen III oxidase -2.29 5.45 7.77E-06

STM4013.S STM4013.S membrane-associated metal-dependent hydrolase -2.24 5.60 2.37E-05

STM4014 STM4014 hypothetical protein -2.61 4.88 4.79E-07

STM4015 STM4015 hypothetical protein -1.76 5.90 3.11E-04

STM4016 ompL outer membrane porin L -2.38 4.84 3.23E-04

STM4017 yihO GPH family transport protein -2.06 6.69 2.01E-03

STM4018 yihP GPH family transport protein -2.00 6.35 7.82E-06

STM4019 yihQ alpha-glucosidase -1.82 6.71 1.18E-04

STM4020.S yihR aldose-1-epimerase -1.47 5.70 6.13E-04

STM4021 yihS isomerase -1.95 6.16 1.46E-05

STM4022 yihT aldolase -1.78 4.94 2.48E-04

STM4023 yihU oxidoreductase -2.22 5.48 7.85E-05

STM4024.S yihV sugar kinase -1.93 6.24 6.68E-06

STM4039 STM4039 inner membrane lipoprotein -1.62 5.72 1.38E-04

STM4040 yiiG hypothetical protein -3.07 5.61 1.23E-05

249

Appendix

ORF Gene Function logFC logCPM FDR STM4041 STM4041 inner membrane protein -1.88 3.17 1.22E-03

STM4045 rhaD rhamnulose-1-phospha te aldolase -1.62 5.84 2.10E-03

STM4046 rhaA L-rhamnose isomerase -2.28 5.40 5.13E-06

STM4047 rhaB rhamnulokinase -2.45 6.04 1.49E-06

STM4048 rhaS transcriptional activator RhaS -1.97 5.67 2.54E-05

STM4049 rhaR transcriptional activator RhaR -1.73 5.45 1.01E-04

STM4050 rhaT rhamnose-proton symporter -2.06 6.17 2.50E-05

STM4051 STM4051 outer membrane protein -2.19 5.04 3.12E-04

STM4052 STM4052 C4-dicarboxylate transport system -2.10 6.25 2.27E-06

STM4053 STM4053 C4-dicarboxylate transport system -2.46 4.35 2.50E-05

STM4054 STM4054 periplasmic dicarboxylate-binding protein -2.45 5.68 1.47E-05

STM4065 STM4065 Na+/galactoside symporter -2.48 6.66 1.49E-04

STM4066 STM4066 aminoimidazole riboside kinase -2.14 6.34 7.87E-05

STM4070 STM4070 hypothetical protein -1.21 3.00 1.74E-02

STM4071 STM4071 mannose-6-phosphate isomerase -1.06 3.71 4.64E-02

STM4072 ydeV autoinducer-2 (AI-2) kinase LsrK -1.67 6.24 1.13E-04

STM4073 STM4073 transcriptional repressor LysR -1.29 6.00 5.94E-03

STM4074 STM4074 autoinducer 2 import sysem ATP-binding proteinLsrA -1.69 6.04 5.45E-04

STM4075 STM4075 autoinducer 2 import system permease LsrC -2.15 6.11 2.89E-04

STM4076 STM4076 autoinducer 2 import system permease LsrD -2.22 5.90 4.78E-06

STM4077 STM4077 autoinducer 2-binding protein LsrB -1.85 5.91 2.02E-05

STM4079.S STM4079.S autoinducer-2 (AI-2) modifying protein LsrG -1.65 4.70 2.29E-03

STM4087 glpF glycerol diffusion protein -1.56 6.11 4.54E-04

STM4098 STM4098 arylsulfate sulfotransferase -2.02 6.26 5.54E-06

STM4102 STM4102 mechanosensitive channel protein -1.60 5.70 4.90E-04

STM4103 STM4103 hypothetical protein -1.39 5.89 2.60E-03

STM4104 STM4104 5'-nucleotidase -1.73 6.74 2.08E-04

STM4110 ptsA PEP-protein phosphotransferase -1.04 7.10 3.87E-02

STM4112 frwC PTS system fructose-like transporter subunitIIC -1.73 5.79 7.45E-05

STM4113 frwB PTS system fructose-like transporter subunitIIB -1.39 5.25 1.69E-03

STM4141 STM4141 hypothetical protein -2.18 3.91 1.90E-04

STM4156 STM4156 hypothetical protein -1.62 3.60 6.77E-03

STM4157 STM4157 hypothetical protein -1.48 6.14 5.47E-03

STM4159 thiH thiamine biosynthesis protein ThiH -1.83 6.36 9.39E-05

STM4160 thiG thiazole synthase -1.43 5.19 2.05E-02

STM4161 STM4161 sulfur carrier protein ThiS -1.56 2.34 1.01E-02

STM4162 thiF thiamine biosynthesis protein ThiF -2.11 4.78 3.13E-05

STM4163 thiE thiamine-phosphate pyrophosphorylase -1.81 4.41 1.33E-04

STM4164 thiC hydroxymethylpyrimid ine phosphate synthase ThiC -1.42 6.87 1.53E-03

STM4172 zraP zinc resistance protein -1.06 5.04 1.66E-02

STM4182 metA homoserine O-succinyltransferase -1.50 6.40 2.42E-04

250

Appendix

ORF Gene Function logFC logCPM FDR STM4183 aceB malate synthase -1.99 6.37 7.84E-05

STM4184 aceA isocitrate lyase -1.85 5.83 1.64E-05

STM4185 aceK bifunctional isocitrate dehydrogenasekinase/phosphatas e -1.55 6.67 4.93E-04

protein

STM4196 STM4196 hypothetical protein -1.03 5.64 1.57E-02

STM4198 STM4198 hypothetical protein -1.18 4.62 1.56E-02

STM4199 STM4199 hypothetical protein -2.19 5.70 7.32E-06

STM4200 STM4200 phage tail fiber protein H -1.57 7.06 7.85E-03

STM4201 STM4201 phage tail protein -2.27 4.66 2.27E-04

STM4202 STM4202 phage baseplate protein -1.98 4.83 4.38E-05

STM4203 STM4203 phage baseplate protein -2.47 4.07 2.01E-06

STM4204 STM4204 inner membrane protein -1.53 6.66 1.07E-03

STM4205 STM4205 phage glycosyltransferase -1.59 5.78 4.83E-04

STM4207 STM4207 phage baseplate component -1.64 5.44 3.78E-04

STM4208 STM4208 hypothetical protein -2.05 5.44 7.46E-06

STM4209 STM4209 inner membrane protein -2.13 3.24 3.78E-04

STM4210 STM4210 methyl-accepting chemotaxis protein -2.21 6.11 7.92E-06

STM4211 STM4211 phage tail protein -2.01 6.23 1.40E-05

STM4212 STM4212 phage tail core protein -1.91 4.86 2.99E-04

STM4213 STM4213 phage tail sheath protein -1.85 6.07 1.97E-05

STM4214 STM4214 hypothetical protein -2.06 2.54 6.59E-04

STM4215 STM4215 hypothetical protein -2.12 3.85 9.39E-05

STM4216 STM4216 inner membrane protein -2.13 3.84 2.29E-03

STM4217 STM4217 soluble lytic murein transglycosylase -1.55 4.27 7.75E-03

STM4218 STM4218 inner membrane protein -1.70 3.36 2.72E-03

STM4219.S STM4219.S hypothetical protein -1.03 4.67 2.70E-02

STM4223 yjbF outer membrane lipoprotein -2.65 4.55 1.44E-06

STM4224 yjbG hypothetical protein -1.79 4.96 3.80E-04

STM4225 yjbH outer membrane lipoprotein -1.55 7.34 5.07E-03

STM4226 yjbA phosphate-starvation -inducible protein PsiE -1.35 5.01 3.90E-03

STM4227 malG maltose transporter permease -1.02 5.55 2.44E-02

STM4228 malF maltose transporter membrane protein -1.75 6.36 3.05E-05

STM4230 malK maltose/maltodextrin transporter ATP-bindingprotein -1.59 6.29 4.29E-03

STM4231 lamB maltoporin -1.30 6.02 1.42E-03

STM4257 STM4257 hypothetical protein -3.10 5.92 3.59E-10

STM4258 STM4258 methyl-accepting chemotaxis protein -2.67 6.64 5.51E-07

STM4259 STM4259 ABC exporter outer membrane protein -2.27 6.57 1.34E-05

STM4260 STM4260 cation efflux pump -2.41 6.03 1.44E-06

STM4262 STM4262 bacteriocin/lantibio tic ABC transporter -2.14 6.43 1.31E-04

STM4264 yjcC diguanylate cyclase/phosphodiesterase -1.07 6.85 3.34E-02

STM4273 actP acetate permease -1.40 6.78 8.38E-03

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Appendix

ORF Gene Function logFC logCPM FDR STM4274 yjcH inner membrane protein -2.80 3.88 2.60E-06

STM4275 acs acetyl-CoA synthetase -1.97 6.39 7.64E-06

STM4277 nrfA cytochrome c552 -1.27 6.35 4.17E-03

STM4280 nrfD formate-dependent nitrate reductase -1.63 5.80 3.55E-04

STM4281 nrfE formate-dependent nitrite reductase -1.68 7.10 1.91E-03

STM4297 melR DNA-binding transcriptional regulator MelR -1.59 5.84 2.24E-04

STM4298 melA alpha-galactosidase -2.42 6.30 2.97E-06

STM4299 melB melibiose:sodium symporter -1.70 6.58 1.10E-04

STM4305.S STM4305.S anaerobic dimethylsulfoxide reductase subunit A -1.02 6.92 2.75E-02

STM4306 STM4306 anaerobic dimethylsulfoxide reductase subunit B -1.32 5.99 2.47E-03

STM4309 STM4309 hypothetical protein -2.15 6.27 3.79E-06

STM4310 STM4310 inner membrane protein -1.08 6.00 2.27E-02

STM4312 STM4312 hypothetical protein -1.64 4.53 6.89E-04

STM4313 STM4313 hypothetical protein -1.09 4.13 1.91E-02

STM4314 STM4314 regulatory protein -1.77 3.99 5.45E-03

STM4315 STM4315 DNA-binding protein -1.21 6.20 2.84E-02

STM4345 yjeM amino-acid transport protein -1.81 6.42 1.22E-04

STM4346 yjeO inner membrane protein -1.74 5.43 3.13E-05

STM4370 yjfI hypothetical protein -2.59 3.59 1.54E-05

STM4371 yjfJ phage shock protein A -1.96 4.67 5.65E-04

STM4372 STM4372 potassium channels -2.40 5.97 6.29E-06

STM4373 yjfK hypothetical protein -2.67 4.98 7.20E-07

STM4374 yjfL inner membrane protein -1.82 3.99 6.57E-04

STM4375 yjfM inner membrane protein -1.81 5.00 3.90E-05

STM4376 yjfC glutathionylspermidi ne synthase -1.77 5.78 2.64E-05

STM4377 aidB isovaleryl CoA dehydrogenase -1.17 6.69 7.19E-03

STM4382 yjfR L-ascorbate 6-phosphate lactonase -1.85 5.67 1.85E-04

STM4384 sgaB PTS system L-ascorbate-specific transportersubunit IIB -1.84 3.82 3.58E-04

STM4385 ptxA PTS system L-ascorbate-specific transportersubunit IIA -1.95 4.57 1.07E-04

STM4386 ulaD 3-keto-L-gulonate-6- phosphate decarboxylase -1.17 5.09 6.83E-03

STM4388 sgaE L-ribulose-5-phospha te 4-epimerase -1.59 5.60 1.75E-03

STM4390 STM4390 hypothetical protein -2.15 3.03 4.25E-04

STM4395 yifZ permease -1.69 5.61 8.40E-05

STM4399 ytfE iron-sulfur cluster repair di-iron protein -1.47 4.79 2.82E-03

STM4400 ytfF cationic amino acid transporter -1.83 5.20 5.67E-05

STM4401 ytfG reductase -1.41 5.23 3.52E-03

STM4413 STM4413 metallo-dependent hydrolase -2.31 6.24 6.99E-06

STM4418 STM4418 sugar transporter -1.93 6.52 1.33E-05

STM4419 STM4419 sugar transporter -2.14 6.94 4.38E-05

STM4420 STM4420 inner membrane protein -1.02 6.30 1.03E-02

STM4423 STM4423 DNA-binding protein -1.69 5.90 9.76E-04

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Appendix

ORF Gene Function logFC logCPM FDR STM4424.S STM4424.S endonuclease -2.69 5.10 5.27E-04

STM4425 STM4425 dehydrogenase -2.32 6.37 1.47E-06

STM4426 srfJ lysosomal glucosyl ceramidase-like protein -1.81 5.86 4.51E-05

STM4427 STM4427 endonuclease -1.12 6.38 6.03E-03

STM4428 STM4428 major facilitator superfamily transporter -1.07 5.43 1.64E-02

STM4433 STM4433 myo-inositol 2-dehydrogenase -2.13 6.13 6.68E-06

STM4434 STM4434 permease -1.95 6.42 2.83E-03

STM4435 STM4435 hypothetical protein -2.41 5.06 6.23E-06

STM4436 STM4436 endonuclease -1.95 6.11 1.50E-04

STM4440 STM4440 hypothetical protein -2.52 3.45 7.16E-06

STM4441 STM4441 hypothetical protein -1.94 3.59 5.77E-04

STM4442 STM4442 hypothetical protein -1.97 3.64 2.85E-04

STM4443 STM4443 inner membrane protein -1.85 5.05 1.64E-04

STM4444 STM4444 inner membrane protein -2.00 5.04 3.81E-04

STM4445 STM4445 dihydroorotase -1.81 5.85 1.76E-04

STM4446 STM4446 selenocysteine synthase -1.90 6.03 1.81E-04

STM4447 STM4447 hypothetical protein -1.38 4.85 3.21E-03

STM4448 STM4448 PTS system mannitol/fructose-specifictran sporter subunit IIA -1.10 7.24 3.24E-02

STM4453 treC trehalose-6-phosphat e hydrolase -1.69 5.99 8.58E-05

STM4456 mgtA magnesium-transporti ng ATPase MgtA -1.42 7.30 7.56E-03

STM4463 STM4463 arginine repressor -1.76 4.13 1.41E-03

STM4464 STM4464 arginine repressor -1.32 6.23 1.87E-03

STM4465 STM4465 ornithine carbamoyltransferase -2.25 5.06 5.67E-05

STM4466 STM4466 carbamate kinase -1.85 5.06 1.11E-04

STM4467 STM4467 arginine deiminase -2.73 5.95 3.94E-06

STM4469 argI ornithine carbamoyltransferase subunit I -1.76 5.66 3.42E-05

STM4472 ytgA inner membrane protein -1.90 4.65 9.41E-04

STM4474 yjgN inner membrane protein -1.99 6.61 9.85E-06

STM4481 idnR L-idonate regulator -1.11 5.92 9.70E-03

STM4482 idnT L-idonate transport protein -1.68 5.57 1.64E-04

STM4483 idnO gluconate 5-dehydrogenase -2.04 4.79 5.88E-05

STM4484 idnD L-idonate 5-dehydrogenase -1.88 5.38 2.50E-05

STM4485 idnK D-gluconate kinase -1.66 4.48 7.35E-04

STM4486 yjgB alcohol dehydrogenase -1.03 5.51 1.14E-02

STM4488 STM4488 integrase -1.30 3.18 1.70E-02

STM4502 STM4502 hypothetical protein -2.25 5.28 2.02E-05

STM4503 STM4503 inner membrane protein -1.30 5.83 3.28E-03

STM4504 STM4504 hypothetical protein -2.23 5.23 1.64E-05

STM4505 STM4505 hypothetical protein -1.15 5.51 4.54E-03

STM4516 yjiN inner membrane protein -1.11 5.67 7.13E-03

STM4517 yjiO transporter -1.09 6.46 2.09E-02

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Appendix

ORF Gene Function logFC logCPM FDR STM4519 STM4519 NAD-dependent aldehyde dehydrogenase -1.21 5.97 2.65E-03

STM4521 yjiS hypothetical protein -1.51 2.77 9.16E-03

STM4522 STM4522 inner membrane protein -2.72 5.51 1.07E-04

STM4523 yjiW endoribonuclease SymE -1.33 3.43 1.32E-02

STM4534 STM4534 transcriptional regulator -1.45 7.31 9.63E-03

STM4535 STM4535 PTS system mannose-specific transporter subunitIIA -2.66 4.30 3.29E-05

STM4536 STM4536 PTS system mannose-specific transporter subunitIIB -2.12 4.51 6.33E-03

STM4537 STM4537 PTS system mannose-specific transporter subunitIIC -2.56 4.95 2.42E-04

STM4538 STM4538 PTS system mannose-specific transporter subunitIID -2.27 5.15 3.40E-06

STM4539 STM4539 glucosamine-fructose -6-phosphateaminotransferase -2.16 5.33 2.98E-04

STM4540.S STM4540.S glucosamine-fructose -6-phosphateaminotransferase -1.30 5.72 1.21E-03

STM4547 yjjQ transcriptional regulator -2.80 4.94 3.09E-05

STM4561 osmY hypothetical protein -1.07 5.22 1.23E-02

STM4572 stjB fimbrial usher protein -1.44 6.89 2.01E-03

STM4573 stjC periplasmic chaperone protein -2.37 5.03 3.18E-06

STM4575 STM4575 outer membrane protein -1.52 5.91 7.19E-04

STM4590 creD hypothetical protein -1.14 6.20 4.37E-03

STM4591 sthE major fimbrial subunit -1.74 6.03 2.67E-05

STM4593 sthB fimbrial usher protein -2.25 6.75 6.68E-06

STM4594 sthA fimbrial chaperone -1.82 5.15 5.27E-03

STM4595 STM4595 fimbrial chaperone -1.96 4.28 2.48E-04

STM4596 STM4596 inner membrane protein -1.09 5.26 1.13E-02

ORF: open reading frame; logFC: log2 fold change; logCPM: log2 counts per million; fdr: false discovery rate

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Acknowledgements

First of all, I would like to thank Prof. Dr. Christophe Lacroix for giving me the opportunity to perform this PhD project under his supervision. His scientific expertise and support during the last years were extremely valuable. I especially acknowledge his careful review of my thesis. Sincere thanks go to Dr. Christophe Chassard for co-examining this thesis. I truly appreciated his tremendous commitment, support and constant encouragement during my PhD. Un grand merci! I further thank Prof. Dr. Ismaïl Fliss for co-examining this thesis.

I am deeply grateful to Dr. Annina Zihler Berner who encouraged me to do this PhD and acted as supervisor during the first 2 years. Her help and advice in the beginning of my PhD were very helpful and I am thankful for her constant motivation, understanding and friendship, even after she left our laboratory.

I thank Dr. Marc J. A. Stevens for his enormous support in RNA-seq data analysis and for help in drafting the RNA-seq manuscript. I really do not know what I would have done without your help! Thanks for answering all my questions, encouraging me and saving my weekends now and then with good results. Dankjewel!

I would like to thank Dr. Annick Bernalier-Donadille for allowing me to conduct the minipig study at her laboratory at INRA in Clermont-Ferrand. Special thanks to Dr. Christophe Del’Homme and his team, especially Eve Delmas and Benoît Cohade, for the commitment to my study, the animal handling and all other assistance they provided throughout the study. I appreciated the warm welcome and nice working atmosphere at INRA.

I further thank our industrial partner and all project co-workers at ETH for their contribution, assistance and collaboration in the project.

Special thanks go to Prof. Leo Meile for having introduced me to this lab almost 7 years ago. He always showed great interest and support during all these years.

Many thanks to all present and former members of the Laboratory of Food Biotechnology for the pleasant working atmosphere and all activities inside and outside the lab! I appreciated our scientific exchange as well as the less-scientific discussions when having a drink together. I especially thank Dr. Alexandra Dostal and Sophie Fehlbaum for their continuous

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Acknowledgements friendship, support, encouragement, the scientific exchange and our jogging tours for freeing the mind of lab stuff. Thanks also to Dr. Martina Haug for a first insight into science and an excellent supervision during my master thesis, Dr. Christoph Jans for support during the practical course and for patiently answering all questions, Dr. Saskia Malang for the nice office atmosphere, Dr. Franck Grattepanche, Eun-Hee Doo and Kiki for their support and help during fermentation and Alfonso Die for assistance in HPLC analyses.

A lot of extremely valuable work in this thesis was conducted by my students, Nicole Karmann, Eugenia Rigozzi and Sandra Gadient, who contributed substantially to the outcome of this project with their excellent theses. Special thanks to Eugenia Rigozzi for her dedication to her projects and the hard and precise work during her semester and master theses.

Thanks also to the entire group of the Food Microbiology for all the chats on the B-floor and troubleshooting whenever needed.

Heartfelt thanks goes to all my friends who have endured me during the last 3.5 years, each of them having supported me throughout my PhD. I deeply appreciate every moment I spent with them, which helped me to gain the required distance from work. Thanks for listening to all my lab-stories and being there when I needed you. Thanks to Catrina, Fabienne, Franca, Jessie, Joséphine, Kathrin, Katrin, Lea, Leslie, Lucie, Mirjam, Sabina, Sarah O. and Sarah U.

My deepest gratitude goes to my parents, who never failed to support me. Thanks for teaching me to be curious and open minded and guiding me with your everlasting support and love through my life. I deeply appreciate everything you have done for me. Sincere thanks also to my brother and my sister for being there whenever I needed them. It would not have been possible to reach this goal without the everlasting support of my family.

Most heartfelt thanks go to Django. For his patience, faith, love and support during my PhD. For all the moments that have helped me to gain distance from work and for showing me every day how wonderful life is.

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