Publication #1 Loss of NHE3 Alters Gut Microbiota

M.A. Engevik 2014

Ion transport and the gut microbiota

A Dissertation Submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the Department of Molecular and Cellular Physiology, Systems Biology & Physiology

Ph.D. program, of the College of Medicine

Melinda Anne Engevik

B.S. Biology 2004 Biola University, La Mirada, CA

M.S. Microbiology 2008 California State University Long Beach, CA

Committee Chair:

Roger T. Worrell, Ph.D.

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ABSTRACT

The mechanisms bacteria use to proliferate and alter the normal gut bacterial composition remain unknown. Changes in the intestinal microbiota have been linked to diabetes, obesity, inflammatory bowel disease, and Clostridium difficile (C. difficile)-associated disease. The ability to link changes in the intestinal micro-environment, such as ion composition and pH, and macro-environment, such as diet, to bacterial proliferation is clinically advantageous for diseases that involve an altered gut microbiota. To address these gaps in knowledge, mouse models of altered ion transport (Na+/H+ exchangers, NHE2 and NHE3) and mice fed prebiotic GHF7K were used to examine changes in ion composition and pH along the length of the intestine and correlate these changes to alteration of the gut microbiota. In addition to mice, patients with C. difficile-infection (CDI), which exhibit a diarrhea similar to NHE3-/- mice, were also examined to determine if altered ion transport affects C. difficile proliferation. The hypothesis was that ion transport-induced change in the intestinal environment would lead to alteration of the microbiota. The data presented herein demonstrate the gut microbiota is highly sensitive to intestinal ions (Na+, K+, and Cl-), pH, prebiotics, and pathogen toxin production. In the NHE2-/- mouse model, an acidic luminal pH was associated with increased mucosa-associated gram-positive bacterial phyla in a region-specific manner. In contrast, in the NHE3-/- mouse model, an alkaline luminal pH and high [Na+] correlated with increased gram-negative Bacteriodetes members in both luminal and mucosa-associated bacteria. An alkaline luminal pH and high [Na+] environment was observed in patients with CDI which correlated with inhibition of NHE3. These changes in the intestinal environment likewise correlated with increased gram-negative Bacteroidetes members. These studies clearly demonstrate the connection between the gut micriobiota and the intestinal environment set by ion transport. In addition to genetically altered mice, WT mice fed a dietary supplement of GHF7K showed significantly altered composition of luminal populations at the level of the phyla, with region-specific differences. This dissertation has advanced the field of gut microbiology by showing (1) altered ion transport, and thus environment, can alter the gut microbiota in region-specific manner. These changes correlate with the specific environmental change; (2) shifts in specific bacterial groups correlate with changes in mucus fucosylation and/or glycosylation; (3) diet can dramatically affect the luminal bacteria, while leaving the mucosa-associated bacteria mostly intact; (4) ion transport deficient mouse models and intestinal organoids provide key tools for dissecting the host-microbiota interaction; both models mirror closely changes observed in patients under the same conditions. Together these data indicate that changes in ion transport and prebiotics induce region-specific bacterial changes, which alter host mucus oligosaccharide patterns. These host-bacterial interactions provide a possible mechanism of niche-development and provide insights on how certain groups proliferate in changing environments and maintain their proliferation by altering the host. Therapies addressing this new host-microbiota interaction could potentially be used to rebalance the intestinal microbiota after a shift, or microbial dysbiosis.

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ACKNOWLEDGMENTS

I would like to gratefully acknowledge my two sisters for their support, encouragement, advice and willingness to discuss scientific and other challenges encountered during the course of my PhD work. Additionally, I would like to thank my husband, Kenny Stavert, for his unfailing love and support.

M.A. Engevik 2014

TABLE OF CONTENTS

INTRODUCTION ...... 1

I. THE GUT MICROBIOTA ...... 1 II. REGULATION OF THE INTESTINAL ENVIRONMENT ...... 3 A. The intestinal structure ...... 3 B. Ion Transport ...... 7 III. THE GUT MICROBIOTA AND HOST CROSS-TALK ...... 11 A. Impact of the gut microbiota on the host ...... 11 B. Impact of the host environment on the gut microbiota ...... 13 1. Host ion transport ...... 13 2. Antibiotics ...... 14 3. Host diet ...... 15 i. Whole diet ...... 15 ii. functional foods: probiotics and prebiotics ...... 16 IV. THE ROLE OF THE HOST MUCUS IN SHAPING THE GUT MICROBIOTA NICHE ...... 18 A. Host mucus structure ...... 18 B. Mucus oligosaccharides ...... 21 V. APPROACHES TO EXAMINE THE MICROBIOTA ...... 22 VI. SIGNIFICANCE OF THESIS WORK ...... 23 VII. CENTRAL HYPOTHESIS AND AIMS OF THESIS ...... 23

EXPANDED METHODS ...... 24

I. MOUSE WORK ...... 24 A. Genotype and maintenance ...... 24 B. Diet ...... 24 C. Organ Collection ...... 25 II. PATIENT WORK ...... 25 A. Patient information ...... 25 B. Stool mucus extraction ...... 27 III. HISTOLOGY ...... 28 IV. ION AND PH MEASUREMENT ...... 30 V. ORGANOIDS ...... 31 A. Mouse intestinal organoids ...... 31 B. Human intestinal organoids (HIOs) ...... 32 C. Organoid microinjections ...... 32 VI. BACTERIAL STRAINS AND CULTURE CONDITIONS ...... 33 VII. QUANTITATIVE REAL TIME –PCR (QRT-PCR) ...... 35 A. 16S sequences ...... 35 B.mRNA ...... 36 VIII. STATISTICS ...... 36

PUBLICATION 1. LOSS OF NHE3 ALTERS GUT MICROBIOTA COMPOSITION AND INFLUENCES BACTEROIDES THETAIOTAOMICRON GROWTH ...... 49

ABSTRACT ...... 50 INTRODUCTION ...... 51 METHODS ...... 53 RESULTS ...... 59 DISCUSSION ...... 67

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PUBLICATION 2. ACIDIC CONDITIONS IN THE NHE2-/- MOUSE INTESTINE RESULTS IN AN ALTERED MUCOSA-ASSOCIATED BACTERIAL POPULATION WITH CHANGES IN MUCUS OLIGOSACCHARIDES ...... 92

ABSTRACT ...... 93 INTRODUCTION ...... 94 METHODS ...... 97 RESULTS ...... 100 DISCUSSION ...... 106

MANUSCRIPT 1. HUMAN CLOSTRIDIUM DIFFICILE INFECTION: INHIBITION OF NHE3 AND MICROBIOTA PROFILE ...... 122

ABSTRACT ...... 123 INTRODUCTION ...... 124 METHODS ...... 126 RESULTS ...... 132 DISCUSSION ...... 136

MANUSCRIPT 2. HUMAN CLOSTRIDIUM DIFFICILE INFECTION: ALTERED MUCUS PRODUCTION AND COMPOSITION ...... 147

ABSTRACT ...... 148 INTRODUCTION ...... 149 METHODS ...... 151 RESULTS ...... 156 DISCUSSION ...... 160

PUBLICATION 3. PREBIOTIC PROPERTIES OF GALURSAN HF 7K ON MOUSE GUT MICROBIOTA ... 177

ABSTRACT ...... 178 INTRODUCTION ...... 179 METHODS ...... 181 RESULTS ...... 184 DISCUSSION ...... 188

DISCUSSION ...... 200

I. THESIS CONCLUSIONS ...... 200 II. REMAINING ISSUES AND FUTURE STUDIES ...... 206 A. Improvement of gut microbiota analysis ...... 206 B. Of mice and men ...... 207 C. Intestinal organoids ...... 210 D. Limitations of gut microbiota analysis ...... 211 E. Future directions ...... 212

REFERENCES ...... 214 APPENDICES ...... 276

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ABBREVIATIONS

AAD antibiotic-associated diarrhea APC antigen-presenting cells BBM apical membrane BLM basolateral membrane cAMP cyclic adenosine monophosphate CDAD Clostridium difficile-associated disease CDI Clostridium difficile infection CF cystic fibrosis CFTR cystic fibrosis transmembrane conductance regulator cGMP cyclic guanosine monophosphate cH+/K+-ATPase colonic hydrogen potassium ATPase exchanger ClC chloride channel type CLCA Ca2+-activated Cl- channels CT cholera toxin DC dendritic cells DGGE denaturing gradient gel electrophoresis DRA down-regulated in adenoma protein EGF epidermal growth factor ENaC epithelial sodium channel ETEC enterotoxigenic E. coli FISH fluorescent in-situ hybridisation FOS fructooligosaccharides GalNAc N-acetylgalactosamine GI gastrointestinal GlcNAc N-acetylglucosamine GLUT2 glucose transporter 2 IBD inflammatory bowel disease IEC intestinal epithelial cell IFN interferon IL interleukin IlFs isolated lymphoid follicles ISC intestinal stem cell LP lamina propia LTi lymphoid tissue inducer cells M cell microfold cell MLN mesenteric lymph nodes Mlns mesenteric lymph nodes MUC mucin NDOs non-digestible oligosaccharides NHE1 sodium Hydrogen Exchanger isoform 1 NHE2 sodium Hydrogen Exchanger isoform 2 NHE3 sodium Hydrogen Exchanger isoform 3 NK natural killer NKCC1 sodium Potassium Chloride Cotransporter 1 PAPS 3’-phosphoadenosine-5’-phosphate PAT1 putative anion transporter PKA phosphokinase A PMNs polymorphonucleocytes PMNs polymorphonucleocytes

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PTS proline-, threonine-, and serine-rich qPCR quantitative Polymerase Chain Reaction qRT-PCR quantitative real time-Polymerase Chain Reaction SCFA short-chain fatty acids SEA sperm protein, enterokinase and agrin domain SGLT1 sodium-glucose transporter 1 TGGE temperature gradient gel electrophoresis TLR toll-like receptor TOS transgalactooligosaccharides T-RFLP terminal-restriction fragment length polymorphism vWD von Willebrand D domain YPM Yersinia pseudotuberculosis mitogen

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INTRODUCTION:

The intestinal microbiota and host physiology are intimately associated. This interconnection is demonstrated by the fact that distinct anatomical regions along the gastrointestinal (GI) tract are characterized by their own physicochemical conditions, which exert selective pressure on the gut microbiota (Gerritsen et al. 2011). The microbiota, in turn, are also capable of altering the intestinal environment through production of bacterial byproducts and altering the host (ion transport, mucus composition/production, tight junctions, protein expression, etc.). Very few studies have examined the effect of ion composition and pH on the intestinal microbiota. Furthermore, the majority of studies focus on fecal microbiota and do not fully analyze the gut microbiota along the length of the intestine or differentiate between luminal and mucosa-associated bacterial populations. I have attempted to address these gaps in knowledge using both mouse and human gut microbiota samples focusing on the effect of altered ion transport in shaping the microbiota composition and host interaction.

I. The Gut microbiota

The gut microbiota is estimated to contain approximately 1 × 1014 commensal bacteria (Wells et al. 2011; Gill et al. 2006; Qin et al. 2010) and comprises more than 800 bacterial species (Backhed et al.

2005) and 10,000 different phylotypes (Harris et al. 2012; Frank et al. 2007; Hattori and Taylor 2009).

This bacterial load is approximately 10 to 20 times greater than the total number of eukaryotic cells in the human body (Sonnenburg et al. 2004; Xu and Gordon 2003) and represents 150-fold larger gene set than the set of human genes (Qin et al. 2010). Collectively the gut microbiota and encoded genes are known as the human gut microbiome (Greenblum et al. 2012). The gut microbiota is involved in nutrient fermentation, amino acid and vitamin biosynthesis, and dietary energy harvesting (Greenblum et al. 2012;

Turnbaugh et al. 2007), all of which benefit the host. The interplay between the gut microbiota and host has been shown to be critical for immunity, nutrition, development and health (Dethlefsen et al. 2008;

Dethlefsen et al. 2007; Eckburg et al. 2005).

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The GI microbiota can be classified into two groups: autochthonous and allochthonous.

Autochthonous bacteria represent resident microbiota that occupy a given ecological niche, while allochthonous bacteria represent transitory microbiota that pass through the gut without occupying a niche

(Finegold et al. 1977) . It has been hypothesized that autochthonous bacteria thrive in their GI niche due to the production of specific capsular polysaccharides (Liu et al. 2008) which are recognized and tolerated by the host (Hord 2008). It has also been suggested that autochthonous microbiota act as a barrier against allochthonous bacteria by competing for mucosal adherence and nutrients (Dunne 2001; Holzapfel et al.

1998) or through production of metabolites, which prolong the allochthonous bacteria lag phase and prevent colonization (Gaskins et al. 2008). Interestingly, specific bacteria may be considered autochthonous in one region of the intestine and allochthonous in another (Dubos et al. 1965).

Allochthonous bacteria can also establish a niche when the normal gut microbiota has been disrupted.

Bacteria capable of this transition include Clostridium difficile, Escherichia coli, Enterococcus faecalis,

Enterococcus faecium, and Bacteroides fragilis (Tannock 1999; Dingle et al. 2010; Bauer and van Dissel

2009; Owens et al. 2008; Rolfe et al. 1981; Naaber et al. 1998; Badger et al. 2012).

Several studies have demonstrated that the mammalian gut microbiota are normally dominated by the phyla Firmicutes and Bacteriodetes, while Proteobacteria, Actinobacteria, Fusobacteria and

Verruomicrobia phylum are present in lower abundance (Backhed et al. 2005; Xu and Gordon 2003;

Eckburg et al. 2005; Mariat et al. 2009; Guarner and Malagelada 2003; Suau et al. 1999; Kostic et al.

2013). Firmicutes represents the largest bacterial phylum and contains greater than 200 genera (Gerritsen et al. 2011). GI Firmicutes fall into two main genera: Clostridium coccoides group (Clostridium cluster

XIVa) and Clostridium leptum group (Clostridium cluster IV) (Gerritsen et al. 2011; Mariat et al. 2009;

Collins et al. 1994). These two genera encompass Clostridium, Eubacterium and Ruminococcus genera members. GI Bacteroidetes is primarily comprised of the class Bacteroidetes, containing the well-known, abundant genera Bacteroides and Prevotella (Gerritsen et al. 2011). The dominance of specific GI phyla has been suggested to be a result of the limits placed on the microbiota by the human genome (Cho and

Blaser 2012).

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The microbiota composition varies in total number and species representation corresponding to changes in intestinal location, architecture, and function (Berkes et al. 2003; Talbot and Lytle 2010;

Salzman et al. 2010). However, very few researchers analyze the gut microbiota along the length of the intestine under given conditions in the same study, leaving several gaps in knowledge. The microbiota also varies in composition across the diameter of the intestine with different groups located in the intestinal lumen, or luminal bacterial population, and those adhered or closely associated with the intestinal mucus and epithelium, or mucosa-associated bacterial population (Salzman et al. 2010; Berg

1996; Mackie et al. 1999; Hooper et al. 2000). Unfortunately, most studies select either luminal or mucosal bacterial populations or simply combine both populations when analyzing the gut microbiota. I attempt to reconcile these inconsistencies in analysis by examining the gut microbiota along the length of the intestinal tract and by examining both luminal and mucosa-associated bacterial populations in a single study.

II. Regulation of the intestinal environment

A. The intestinal structure

The GI epithelium sets the

intestinal environment thereby regulating

the gut microbiota. The architecture of

the GI tract facilitates digestion, uptake

of nutrients and water, regulation of the

intestinal environment, preservation of

immune homeostasis and maintenance of

the gut microbiota (Huffnagle and

Noverr 2008). The GI tract can be Figure 1. Architecture of the gut mucosal wall. Four- layered (mucosa, submucosa, muscularis mucosa, divided by cross-section and length. In and serosa) organization of the digestive tract. From Rao 2010.

M.A. Engevik 2014 terms of cross-section, the GI tract is divided into four layers: (1) the mucosa (epithelium, lamina propria, and muscular mucosae), (2) the submucosa, (3) the muscularis propria (inner circular muscle layer, intermuscular space, and outer longitudinal muscle layer), and (4) the serosa (Rao and Wang 2010) (see

Figure 1). The mucosa is the innermost layer and is the site of absorption and secretion. The mucosa can be further subdivided into three layers. The first layer is the epithelium which faces the intestinal lumen and consists of epithelial cells attached to a basement membrane. Due to the presence of villi, microvilli, crypts and folds, the adult human epithelium covers a surface area of approximately 300 m2 (Brandtzaeg

2011, 2009), which maximizes the surface area and provides for optimal nutrient/water absorption. The epithelium controls the intestinal environment, thus influencing the gut microbiota composition. This single layer of epithelium overlays the second layer, the lamina propria (LP), which consists of subepithelial connective tissue and lymph nodes. The LP supports the intestinal architecture and contains immune cells such as B cells (sIgA-producing plasma cells), T cells, stromal cells, and antigen-presenting cells (APC) such as quiescent macrophages and dendritic cells (DC) (Wells et al. 2011). Underneath the

LP is the third layer called muscularis mucosae, which consists of a continuous sheet of smooth muscle cells (Rao and Wang 2010). This three layered mucosa lies atop the submucosa. The submucosa consists of a variety of inflammatory cells, lymphatics, autonomic nerve fibers, and ganglion cells. This area is also a branching and distribution zone for arteries and small venous channels. Beneath the submucosa lies the muscularis propria. The muscularis propria contains smooth muscle cells organized into a tightly coiled, inner circular layer and outer longitudinal layer. These smooth muscle cells are arranged in parallel arrays with prominent autonomic neural fibers and ganglionic clusters that form a myenteric plexus in between the layers. The major functions of the muscularis propria are to propel food through the gut by contractile peristaltic waves initiated and regulated by various neural and hormonal events (Wells et al. 2011; Montgomery et al. 1999). Intestinal flow, and thus intestinal transit time, is regulated by peristaltic movements and by sphincters located in the upper esophagus, in the distal portions of the esophagus, stomach, ileum and in the anus (Rao 2004). The final outermost layer is the serosa, which consists of a continuous sheet of squamous epithelial cells, the mesothelium, separated from the

M.A. Engevik 2014 underlying longitudinal muscle layer by a thin layer of loose connective tissue. The serosal layer forms a natural barrier preventing the spread of inflammatory and malignant processes (Rao and Wang 2010).

The intestine can also be divided length-wise based on GI tract location. The intestine is divided into small and large intestine based on architecture and function. The small intestine is approximately (~)

20 feet long in humans and ~350 mm in mice (Wolczuk et al. 2011). In terms of structure, the small intestine consists of a simple columnar epithelium with crypts of Lieberkuhn, composed primarily of proliferating cells, and villi projections which contain the majority of differentiated absorptive cells (see

Figure 2) (Rao and Wang 2010; Montgomery et al. 1999). The adult small intestinal epithelium is composed of four differentiated phenotypes: absorptive enterocytes (~90% of cells), mucus-secreting goblet cells (~5%), hormone secreting enteroendocrine cells (~1%) and antimicrobial secreting Paneth cells (~3%) (Wells et al. 2011; Rao and Wang 2010; Pinto et al. 2003; Clavel and Haller 2007; Salzman et al. 2007; Vieira et al. 2004). Enterocytes, enteroendocrine, and goblet cells are located in the intestinal villi, while Paneth cells are located in the crypts. Intestinal stems cells (ISCs) and a proliferating

Figure 2. Histology of mouse small intestine and colon stained by H&E. Images depict villi and crypt regions of the small intestine and the crypts of the large intestine. Proliferative and ISC cells are located at the base of the crypts in both intestine locations. Scale bar is 50 μm.

M.A. Engevik 2014 progenitor compartment are found at the base of the crypts (1%), which provide long-term self-renewal of the intestine. (Pinto et al. 2003; Clavel and Haller 2007; Potten 1998; Potten and Loeffler 1990).

Proliferation of these pluripotent stems cells results in renewal of the epithelium every 3-6 days in humans (Wells et al. 2011) and 2-3 days in mice (Creamer et al. 1961). The small intestinal epithelium is responsible for the digestion of nutrients and transport of nutrients, water, and ions which regulates the intestinal environment.

The small intestine can be further subdivided into duodenum, jejunum and ileum. The duodenum begins distal t the stomach pyloric sphincter. Chyme, or partially processed food material, from the stomach enters the duodenum at the pyloric sphincter and with the addition of bile, pancreatic juice and digestive enzymes, is further digested for absorption by the intestine. The duodenum is largely responsible for the enzymatic breakdown of food. Carbohydrates enzymatically digested into monosaccharides can be absorbed throughout the small intestine. Fats are broken down in the duodenum by lipase from the pancreas into fatty acids. The duodenum is also the site of iron, calcium, Vitamin A,

Vitamin B12, amino acid, fatty acid, monoglycerides, phosphorus, and monosaccharide absorption (Rao and Wang 2010). In addition to digestion, the duodenum also regulates the rate of gastric emptying via the hormones secretin and cholecystokinin. The jejunum is adjacent to the duodenum and is a site for the absorption of amino acids, monosaccharides, fatty acid particles, vitamins, minerals, electrolytes and water (Rao and Wang 2010). The final section of the small intestine is the ileum which is responsible for absorption of fats, and bile salts, which are a component of bile. The ileum is also designed for vitamin

B12 absorption. Nutrients absorbed by villi of the small intestine enter the blood stream to be delivered to the appropriate sites. The ileum joins the large intestine at the ileocecal valve, which prevents the back flow of materials from the colon back into the small intestine (Rao and Wang 2010).

The large intestine, or the colon, is responsible for reclaiming luminal water and electrolytes. The colon is ~5 feet in length in humans (Rao and Wang 2010) and ~110 mm in mice (Wolczuk et al. 2011).

The colon can be further subdivided based on anatomic divisions in humans to the cecum, ascending colon, transverse colon, splenic flexure, descending colon, sigmoid colon, rectum, and anus (Rao and

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Wang 2010). In mice, the colon is commonly divided into two functional parts: proximal and distal colon

(Talbot and Lytle 2010). In contrast to the small intestine, the mucosa of the large intestine is not covered with villous projections. The colonic epithelium consists of deep crypts (Wells et al. 2011) that increase in depth toward the rectum and extend as far as the muscularis mucosa (Rao and Wang 2010). These crypts are rapidly renewed by ISCs located at the crypt base (Sato et al. 2009). Colonic mucosal epithelial cells include absorptive cells, goblet mucus cells, undifferentiated columnar crypt cells, and Paneth cells which are almost identical to those cells present in the small intestine. Intestinal epithelium homeostasis is maintained by multiple cell types undergoing continual renewal while maintaining precise interrelationships (Rao and Wang 2010; Stappenbeck et al. 1998; Wong and Wright 1999; Jankowski et al. 1994; Majumdar 2003; Johnson 1988). Together the combination of digestion, nutrient absorption, ion absorption and/or secretion determines the pH and ion composition of the intestinal fluid and set the backdrop for the growth of the intestinal microbiota. The ability to connect the intestinal environment changes with shifts in the gut microbiota represents a tool for potential therapeutics. However currently few studies have correlated changes in the intestinal environment to changes in the complex microbiota.

B. Ion transport

The differences in microbiota composition reflect the differences in the local environments: the microbiota is exposed to varying pH conditions (acidic stomach and alkaline intestine), various ion concentrations, redox potential, nutrient supplies, and host secretions (e.g. hydrochloric acid, digestive enzymes, bile juice, pancreatic secretion and mucus) (Gerritsen et al. 2011; Dunne 2001; Falk et al. 1998;

Booijink et al. 2007). This varying intestinal environment is created by complex patterns of ion transporters along the length of the intestine (Talbot and Lytle 2010; Gawenis et al. 2002; Schultheis and

Baldwin 1999; Engevik et al. 2013a; Engevik et al. 2013b; Engevik et al. 2013c). Intestinal ion transport is characterized by a net absorption of NaCl, nutrients, short-chain fatty acids (SCFA), and water with secretion of mucus, bicarbonate, and KCl (Rao 2004; Kunzelmann and Mall 2002). Ion transport is critical for water absorption. The intestines process more than 9 liters of fluid/day with the small intestine

M.A. Engevik 2014 absorbing over 80% of this fluid and the colon absorbing > 18%. This absorption/secretion balance produces feces low in salt and water content (< 2%). The intestinal architecture is designed to achieve the opposing functions of absorption and secretion. This is accomplished in general by the villi/surface cells acting as the main site of absorption and the crypts acting as the main site of secretion (Rao 2004;

Kunzelmann and Mall 2002; Field 2003; Sangan et al. 2002; Chu et al. 2002). Transport proteins located in both crypt and villi surfaces are responsible for the actions of both absorption and secretion. The polarized distribution of transport proteins in enterocyte apical (facing the lumen) and basolateral (facing the blood) membranes allow for transport in both directions (Kunzelmann and Mall 2002). Absorptive and secretory functions are tightly coordinated by the epithelia and/or luminal, neuronal, immune or systemic stimulation (Rao 2004).

Multiple transport mechanisms are regionally distributed and regulated to create regional microenvironment differences in the intestine. The small intestine is responsible for the majority of nutrient transport in the intestine (see Figure 3A). Nutrient digestion occurs via the actions of luminal α- amylases and proteases and surface hydrolases which break down larger molecules. Glucose enters the

Figure 3. Transport mechanisms in the small intestine (A) and colon (B) from Rao 2004. Ion transporters are distributed in different regions and result in differences in the microenvironment.

M.A. Engevik 2014 enterocyte via the apical membrane (BBM) Na+-glucose transporter 1 (SGLT1) and glucose transporter 2

(GLUT-2) and exits the basolateral membrane (BLM) via GLUT-2 (Rao 2004). Amino acids enter the enterocyte via BBM Na+-coupled and/or H+/dipeptide co-transporters and exit via various BLM amino acid transporters. Small intestinal fluid absorption predominantly occurs in the jejunum and ileum and is driven by the net movement of Na+. This is accomplished either by Na+ driven in coordination with other molecules, such as by SGLT1, or by Na+/H+ Exchangers (NHEs) (Rao 2004). There are three NHEs located in the intestine: NHE1-3. NHE1 is located on the basolateral membrane and plays a role in pH and cell volume regulation (Rao 2004; Counillon and Pouyssegur 2000; Wakabayashi et al. 1997). In contrast, NHE2 and NHE3 are located on the apical membrane but differ in distribution. BBM NHE3 is expressed almost exclusively in absorptive surface cells and is responsible for Na+ absorption, pH and cell volume regulation (Rao 2004; Gawenis et al. 2002; Counillon and Pouyssegur 2000; Wakabayashi et al.

1997; Schultheis et al. 1998). In contrast BBM NHE2 is expressed in both the surface cells and in the crypts (Chu et al. 2002; Bachmann et al. 2004; Bookstein et al. 1997; Guan et al. 2006; Dudeja et al.

1996; Hoogerwerf et al. 1996). NHE2 has been demonstrated to be the major apical NHE in the colon crypts and only minimally contributes to Na+ absorption (Guan et al. 2006). NHE2 has been shown to be activated by luminal acid (Muthusamy et al. 2013) and high concentration of the short chain fatty acids

(SCFAs) (Chu et al. 2002) ,which generates an acidic luminal environment in the colon. Under these

+ conditions, activation of NHE2 is thought to lead to extrusion of H and maintenance of pHi balance, enabling the cells to tolerate the external acidic conditions (Muthusamy et al. 2013). The different functions of BBM NHE2 and NHE3 are reflected in different intestinal environments in mice lacking either NHE2 or NHE3 (Gawenis et al. 2002; Schultheis and Baldwin 1999; Engevik et al. 2013a; Engevik et al. 2013c). NHE3-/- mice exhibit an alkaline intestinal fluid compared with wild type (WT) littermates while NHE2-/- mice exhibit an acidic intestinal fluid due to the upregulation of NHE3 and NHE8

(Gawenis et al. 2002; Schultheis and Baldwin 1999; Engevik et al. 2013a; Engevik et al. 2013c; Xu et al.

2011). The NHE3-/- and NHE2-/- mouse studies demonstrate NHE3 is the primary Na+ -absorbing NHE3 and that host compensation results in the altered intestinal environment observed in the NHE2-/- mice.

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In the small intestine, fluid secretion (driven by Cl- transport), involves the BLM Na+/K+ pump, which drives the uphill entry of Cl- into the enterocyte by the BLM Na+-K+-2Cl- cotransporter (NKCC1)

(Haas and Forbush 2000). Fluid secretion is also supported by BLM K+ channels, which repolarize the cell and maintain the driving force for Cl- exit. Cl- can exit the enterocyte by BBM CFTR (CLC family

Cl- channels)(Frizzell 1999), and/or the CLCAs (Ca2+-activated Cl- channels) (Kunzelmann and Mall

2002; Fuller and Benos 2000; Jentsch et al. 2002). Secretion of bicarbonate plays a role in intestinal pH and in the duodenum this component is crucial for luminal alkalization. Bicarbonate secretion in the small intestine involves the anion exchanger down-regulated in adenoma protein (DRA) (Jacob et al. 2002) and the putative anion transporter PAT1 (Wang et al. 2002) in the villus cells. Together these ion transporters orchestrate intestinal secretion.

In the colon there is a lack of the major nutrient transporters found in the small intestine (see

Figure 3B). Since glucose transporters are absent, Na+ absorption is the major mechanism for water absorption (Rao 2004). The colon is capable of both actively secreting and absorbing K+ by apical K+ and colonic H+/K+-ATPase (Rao 2004). The proximal portion of the colon expresses NHE2 and NHE3 which play a role in absorbing residual water and maintaining intestinal pH. The more distal colon segments can also express NHE2, NHE3, and epithelial Na+ channel (ENaC) but this depends on hormone status

(Kunzelmann and Mall 2002). In addition, ENaC can be down-regulated by CFTR (Jentsch et al. 2002).

DRA is predominant in the distal colon (Talbot and Lytle 2010).

Ion transporters have been shown to be localized to specific intestinal locations. For example,

PAT1 is highly expressed in the small intestine compared to the colon while DRA is highly expressed in the colon compared to the small intestine (Rao 2004). In addition to differential expression in small vs. large intestine, certain ion transporters are located in different regions within intestinal segments. For example, in mice NHE3 is highly expressed in the proximal colon with decreasing expression in the distal colon, while DRA is highly expressed at the most distal end of the colon with decreasing expression toward the proximal colon (Talbot and Lytle 2010). These very specific locations of ion transporters likely reflect specific intestinal environment regulation and dictate potential environmental changes.

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The major anions in the colon are SCFAs, which are organic fatty acids with 2 to 6 carbon atoms.

SCFAs are produced by bacterial fermentation of polysaccharide, oligosaccharide, proteins, peptide and glycoprotein precursors (Miller and Wolin 1979; Cummings and Macfarlane 1991; Hijova and

Chmelarova 2007). SCFAs are produced in a 3:1:1 ratio of acetate>propionate>butyrate (Hijova and

Chmelarova 2007; Cummings 1981; Cummings et al. 1987; Topping and Clifton 2001). The production of these SCFA depends on the gut microbiota (Roberfroid 2005), available colonic substrate (Cook and

Sellin 1998) and intestinal transit time (Hijova and Chmelarova 2007). Colonic enterocytes absorb and metabolize SCFA. SCFA uptake is enhanced with Na+ absorption and bicarbonate secretion and appears to be higher in the distal colon compared to the proximal colon (Hijova and Chmelarova 2007; Cook and

Sellin 1998). The different SCFAs (acetate, propionate, and butyrate) are absorbed in different regions of the colon at comparable rates (Hijova and Chmelarova 2007). SCFAs serve as a nutrient source for the colonic enterocytes, as regulators of proliferation, differentiation and gene expression and as modulators of intracellular and colonic pH, cell volume and ion transport (Hijova and Chmelarova 2007; Cook and

Sellin 1998). In addition to use by colonic enterocytes, certain bacterial species, such as Bifidobacterium and Lactobacillus, are capable of using SCFA to enhance growth (Roy et al. 2006). Collectively intestinal ions regulate both host homeostasis and microbiota composition, although the mechanisms of ion induced microbial alterations remains unclear.

III. Gut microbiota and host cross-talk

A. Impact of the gut microbiota on the host

The gut microbiota has been shown to significantly influence host physiology, biochemistry, immunology and disease resistance (Dunne 2001). Multiple studies have shown that the gut microbiota members, particularly pathogens, are capable of altering host ion transport. Pathogens can either directly modulate ion transport or indirectly affect ion transport via inflammation, neuropeptides or loss of absorptive surface (Hodges and Gill 2010). Addition of Yersinia pseudotuberculosis mitogen (YPM) to

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T84 cells (a human colonic secretory cell line) inhibited forskolin-stimulated secretion, but increased permeability. In addition YPM-treated mouse colon had diminished responsiveness to cAMP-mediated secretagogues and nerve stimulation (Donnelly et al. 1999). Shigella and Salmonella species have been shown to interact with polymorphonucleocytes (PMNs) which regulate intestinal absorption by cytokine secretion (IL-1β) and secrete a precursor to adenosine which activates CFTR, resulting in Cl- secretion

(Hodges and Gill 2010). Vibrio chlorea produces cholera toxin (CT) which regulates adenylate cyclase and stimulates cAMP production. cAMP activates PKA which activates CFTR, resulting in Cl- secretion

(Hodges and Gill 2010; Basu and Pickett 1969 ). In addition to increased Cl- secretion, Na+ absorption is also affected via a cAMP-dependent mechanism which decreases both NHE3 and NHE2 (Subramanya et al. 2007). Enterotoxigenic E. coli (ETEC) elevates cAMP and elevates cGMP, both of which stimulate

CFTR, resulting in Cl- secretion (Hodges and Gill 2010). Studies have suggested that C. difficile indirectly modulates ion transport via cytokine secretion (TNFα, IL-1 and IL-6) and activation of enteric nerves by neuropeptides (Hodges and Gill 2010). C. difficile toxin B has further been shown to inhibit

RhoGTPases, which decreases NHE3 apical expression in cell lines (Hayashi et al. 2004). Inhibition of

NHE3 by C. difficile was previously un-documented in humans, but I have addressed this gap in knowledge by demonstrating that NHE3 is inhibited in patients with C. difficile infection (CDI) in this thesis.

In addition to modulation of the host ion transport, the gut microbiota also affects the host through production of bacterial byproducts. Bacterial byproducts, such as the formation of SCFA, are of major relevance to the host. The daily production of SCFA has been estimated to be in the range of 400 mmol (Blaut and Clavel 2007). Since SCFAs allow the host to salvage energy from otherwise unusable dietary fiber this represents a large source of host fuel. Various tissues in the body are able to oxidize

SCFA for energy generation (Blaut and Clavel 2007) with butyrate being the preferred colonic fuel. It has been estimated that colon epithelial cells derive 70% of their energy from the oxidation of butyrate

(Roediger 1980a, b). Bacterial oxidative or reductive deamination of amino acids leads to the formation of ammonia (Vince et al. 1973a; Vince et al. 1973b) and high ammonia concentrations have been shown

M.A. Engevik 2014 to act as intestinal tumor promoters (Clinton et al. 1988) and to inhibit cAMP-regulated intestinal Cl- transport (Prasad et al. 1995). These lines of evidence suggest that bacteria, directly or through byproducts, have the capacity of influencing the host and shaping the intestinal environment.

B. Impact of the host environment on the gut microbiota

1. Host ion transport

Although the role of the GI microbiota on host ion transport has been well documented, very few studies have focused on the reciprocal interaction: the role of ion transport on the gut microbiota. In vitro studies have demonstrated the requirement of Na+ and K+ to specific GI microbiota species (Caldwell and

Arcand 1974), but this has not been shown in vivo. Prior to my work, the cystic fibrosis (CF) mouse was the only mouse model used to examine the loss of ion transport on the gut microbiota. In the CF mouse, the chloride transporter CFTR was knocked out resulting in increased luminal Cl- , altered mucus secretion, and an altered gut microbiota (Thomsson et al. 2002; De Lisle 2007; Lynch et al. 2013). CF mice exhibit an increase in total bacteria in the terminal ileum (De Lisle 2007) with decreased community diversity driven by species overgrowth (Lynch et al. 2013). CF mice exhibit increased Bacteroidaceae

(phylum Bacteroidetes), Mycobacteriaceae and Pseudonocardiaceae (phylum Actinobacteria) compared with WT mice, with Bacteroidaceae having the largest increase. Changes in Bacteroidaceae were driven by increased B. fragilis (Lynch et al. 2013). Bacteroides members are known mucus oligosaccharide scavengers (Bry et al. 1996) and changes in B. fragilis were associated with increased fut2 mRNA and fucosylation in the CF mouse terminal ileum. These studies demonstrate using a murine model that CFTR exerts a substantial influence on GI bacterial community composition. However the authors did not fully determine the extent of the altered intestinal environment (ion composition, pH, SCFA, etc) or examine what factors could be potentially driving these gut microbiota changes. In addition, the authors did not address if B. fragilis was capable of eliciting the increased fucosylation observed in the CF mouse terminal ileum. These studies also failed to address if B. fragilis had a competitive advantage over other bacterial members due to the altered intestinal environment. I have attempted to address the role of altered

M.A. Engevik 2014 ion transport, and the specific factors of the intestinal environment, on the gut microbiota using transporter specific knockout mice NHE2 and NHE3, the data of which is presented in this thesis. Using the organoid model I am able to further determine which groups are sufficient to elicit host changes.

2. Antibiotics

Antibiotic use can lead to an altered gut microbiota (termed microbial dysbiosis) that is either a transient shift or the new stable state (Cho and Blaser 2012). Exposure to antibiotics has been shown to induce major shifts in the microbiota and the host intestinal phenotypes (Wlodarska et al. 2011), but not alter the total bacterial number (Brandl et al. 2008). Metronidazole, amoxicillin-clavulanic acid, vancomycin, clindamycin, ancomycin, amipenem, and neomycin have been shown to decrease Firmicutes and Bacteroidetes members and increase Proteobacteria (Hussey et al. 2011). In addition to shifting the microbiota composition, antibiotic use also predisposes animals and humans to pathogen colonization.

This has been demonstrated with C. difficile (Stecher and Hardt 2008), Clostridium perfringens (Bartlett

2002), Salmonella typhimurium (Croswell et al. 2009), and Citrobacter rodentium (Wlodarska et al.

2011). Antibiotic-induced changes in the gut microbiota are also linked to disruption in the microbiota- related intestinal environment. A recent study demonstrated that vancomycin treatment in mice results in disruption of carbohydrate fermentation, increased unfermented fecal oligosaccharides and reduction of

SCFA (Yap et al. 2008). These changes are likely a secondary effect of alterations of the normal gut microbiota. However it is clear that antibiotic-disruption of the gut microbiota opens a niche for pathogen colonization. The colonization of the pathogen C. difficile, which is responsible for the majority of antibiotic-induced diarrhea (Badger et al. 2012), remains unclear. I have addressed the details of C. difficile colonization in this thesis and generated a working model which involves inhibition of NHE3 altering the intestinal environment,

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3. Host diet

Diet is a well-known niche-determining factor in the human and animal intestine. The gut microbiota is highly influenced by diet. In animal models it has been shown that diet is responsible for

57% of the gut microbiota variation in comparison to genetic background which accounts for 12% of the variation (Harris et al. 2012; Zhang et al. 2010). Most studies of diet, based on fecal samples, have demonstrated that dietary substrates are able to influence the bacterial population number and/or population size (Hoyles and Vulevic 2008).

i. Whole Diet.

Whole diet differences have been shown to affect the composition of human fecal microbiota. A study by Hayashi et al. demonstrated that the fecal microbiota from individuals on a vegetarian diet was markedly different than individuals on an omnivore diet (Hayashi et al. 2002). Consistent with these diet findings, individuals from different regions that consume a different diet have also been shown to differ in their gut microbiota composition (Finegold et al. 1977). In mice it has been shown that a change in diet from a low-fat, plant polysaccharide-rich diet to a high-fat, high-sugar ‘‘Western’’ diet alters the gut microbiota (Clemente et al. 2012; Turnbaugh et al. 2009). Although studies with broadly defined diets

(e.g. ‘Japanese’ versus ‘Western’) or manipulation of the proportion of food categories (e.g. ‘vegetarian’ vs ‘omnivore, or ‘high protein’ vs ‘low protein’) found only moderate effects involving few genera, studies with chemically defined diet components have shown effects on particular taxa (Dethlefsen et al.

2007). Several dietary components have been demonstrated to have specific affects. Addition of dietary inulin and related fibers increase beneficial Bifidobacteria (phylum Actinobacteria) (Roberfroid 2005;

Kolida et al. 2002) and dietary sulfate supports growth of sulfate-reducing bacteria over methanogenic archaea (Dethlefsen et al. 2007; Gibson and Roberfroid 1995). Studies have demonstrated that patients on a diet high in animal fat have a higher Bacteroides whereas patients on a carbohydrate-rich diet have higher Prevotella (Clemente et al. 2012; Wu et al. 2011). Dietary fibers derived from plants, such as gum arabic, pectin, celfur, glucomannan, curdlan, guar gum and sugar beet, have been shown to ferment in the

M.A. Engevik 2014 large intestine thereby affecting the colonic microbiota composition (Hoyles and Vulevic 2008). Diet has been demonstrated to change the gut microbiota in mice and humans within one day (Turnbaugh et al.

2009; Wu et al. 2011). A study by Wu et al. demonstrated that in humans a change in diet from a high- fat/low-fiber diet to a low-fat/high-fiber diet resulted in changes in the gut microbiota within 24 hours

(Clemente et al. 2012; Wu et al. 2011). The rapid alteration in the gut microbiota composition suggests that addition or modification of diet may provide new avenues for intestinal therapeutics.

ii. Functional foods: probiotics and prebiotics

In addition to whole diet and dietary fiber, functional foods, or foods that beneficially affect specific bodily functions to improve health, have gained attention in recent years. These functional foods include non-essential food component, such as prebiotics, or a food component without nutritive value

(live microbes), such as probiotics.

Probiotics are live microbial food supplements also designed to modulate the microbiota and immune system. As allochthonous members of the microbiota, probiotics are considered to be non- colonizing and require continual replenishment to maintain their numbers (Hord 2008). Probiotics can exist as single species or combinations of multiple species. The most common probiotics are the lactic acid producing genera Bifidobacteria (phylum Actinobacteria) and Lactobacillus (phylum Firmicutes).

Species include Lactobacillus rhamnosus GG (=ATCC 53103), Lactobacillus rhamnosus LC 705,

Lactobacillus fermentum, Bifidobacterium lactis Bb12, Bifidobacterium breve BB99, and

Bifidobacterium infanis (Weston et al. 2005). Streptococcus species and Enterococcus species (phylum

Firmicutes) and Escherichia coli strain Nissle 1917 (phylum γ-Proteobacteria) have also been used as probiotics (Tannock 1999; Gomes and Malcata 1999; Hoerr and Bostwick 2000). Probiotics are thought to act by modifying microbiota development, composition, activity and/or directly interact with the host epithelium (Salminen et al. 2006). Probiotics have been demonstrated to modulate of various signaling pathways which induces some beneficial change such as enhancement of tight junction functioning (Seth et al. 2008), production of mucus or defensis (Schlee et al. 2007) , or inhibition of apoptosis (Yan et al.

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2007), and modulation of the host immune response (Lebeer et al. 2010). Probiotics have been shown to have the following beneficial effects: reduction of diarrhea, lactose intolerance, blood cholesterol levels, immune regulation and cancer (Schaafsma et al. 1998).

As probiotics are increasing in popularity, questions have arisen about the safety and efficacy of probiotics. Although probiotics are thought to be safe for healthy individuals (Vyas and Ranganathan

2012; D'Souza et al. 2002), it is unclear whether probiotics provide benefits for unhealthy patients. To this end, NIH has released a probiotic safety assessment stating the need for increased reporting of studies using probiotics (United States Department of Health and Human Services 2011). Currently probiotics are required to adhere to the following standards: be of human origin; be resistant to gastric acid and bile; adhere to intestinal mucus and/or epithelium (although may not colonize); be nonpathogenic; produce antimicrobial substances; modulate immune response, and be able to persist in the intestine, regardless of the time period (Reid 1999). However, these standards are contradictory to our current knowledge of probiotics. Currently, it has not been demonstrated that probiotics are able to persist in the intestine as they are allocthonous, and if they do persist and colonize, no data is available on where these bacterial groups colonize. In addition, little is known about how probiotics interact with the resident microbiota.

Due to the cofounding issues with probiotics, prebiotics have also been examined as a method for regulating the gut microbiota composition.

Prebiotics are non-digestible food ingredients that selectively stimulate the growth and/or activity of beneficial commensal bacterial groups (Hoyles and Vulevic 2008; Gibson and Roberfroid 1995). The majority of prebiotics are non-digestible oligosaccharides (NDOs). These oligosaccharides, consisting of between 2 and 20 saccharide units, largely escape small intestinal enzymatic digestion and are fermented in the colon. Interestingly, NDOs have been shown to be largely absent in the feces (<5%) (Molis et al.

1996), indicating their fermentation in the colon. NDOs can be present in breast milk, foods such as carrot, leek, asparagus, garlic, onion, chicory, wheat, oat and soybean, or plant material such as lectins

(Hoyles and Vulevic 2008). Specific prebiotics have been shown in numerous studies to increase beneficial bacteria, such as Bifidobacteria and Lactobacilli (Gibson and Roberfroid 1995; Bouhnik et al.

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1997; Kleessen et al. 2001; Vulevic et al. 2004) which have been correlated to beneficial modulation of the immune system by suppressing inflammation (Hoyles and Vulevic 2008; Hosono et al. 2003;

Nakamura et al. 2004; Kelly-Quagliana et al. 2003; Nagendra and Venkat 1994). Certain prebiotics have been shown to increase mucin production (Fontaine et al. 1996), although whether this is a direct or indirect modulation remains unclear. Changes in mucus production likely correlate with prebiotic-induced changes in the gut microbiota since bacterial SCFA production, specifically butyrate has been shown to alter mucin production (Finnie et al. 1995; Gaudier et al. 2004).

Prebiotics may also provide benefit by mimicking host mucus oligosaccharides which serve as a binding site for bacteria (Hoyles and Vulevic 2008) as prebiotics have been shown to reduce GI infections and allergic symptoms (Roberfroid 2005). In addition, several prebiotics have been shown to decrease mortality rates in animal models challenged with GI pathogens (Buddington et al. 2002). Breast milk transgalactooligosaccharides (TOS) have been shown to decrease Enteropathogenic Escherichia coli

(EPEC) and Salmonella enterica serovar Thyphimurium adhesion (Tzortzis et al. 2005) and are capable of toxin neutralization (Hoyles and Vulevic 2008). Prebiotics provide one method to manipulate the gut microbiota in a beneficial manner. Current literature suggests that prebiotics offer a more selective microbiota management tool than probiotics. However, few studies have examined how prebiotics alter the gut microbiota as a whole along the length of the entire intestinal tract. To address this question, in this thesis I have examined the effect of oligo(2-7)-galacturonic acid on the gut microbiota from duodenum to distal colon, analyzing both the luminal and mucosa-associated bacterial populations.

IV. The role of host mucus in shaping the gut microbiota niche

A. Host mucus structure

The GI tract is swathed in mucus which serves as both a barrier and an interface between the luminal environment and the host (Pullan et al. 1994). The mucus layer provides protection from the gut microbiota and lubrication for stool transit (Deplancke and Gaskins 2001). The GI mucus has been shown to consist of a firmly attached inner mucus layer, devoid of bacteria, and a loose outer mucus layer, which

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is colonized by bacteria (Johansson et al. 2011; Johansson et al. 2008), (see Figure 4). Mucus has been

shown to increase in thickness from the small to the large intestine ranging in humans from 48 to 273 µm

(Pullan et al. 1994). The differences in mucus production have been suggested to be the direct result of

the demands of the tissue and microbiota: as such the colon which contains the largest density of bacteria

also contains the thickest mucus layers. Bacterial growth and mucus shedding are in a constant state of

equilibrium. The IECs turnover every 3-6 days and as a result attached mucus is shed into the lumen at

the same rate (Poulsen et al. 1995). In order for bacteria to maintain colonization, they must replicate

faster than the mucus is shed into the lumen (Cohen et al. 1985). Studies have demonstrated that some

bacteria are capable of adhering to intestinal mucus and/or epithelium (Probert and Gibson 2002).

Mucus is composed of mucin glycoproteins which consist of a peptide backbone and alternating

Figure 4. Diagram of mucus production in the small and large intestine from Gendler et al 1995 depicting the outer loose layer of mucus and adherent firm layer of mucus. The magnified view (right) displays microvilli protruding from the apical membrane of the enterocyte. Together the different layers of mucus act as a protective barrier to separate the intestinal lumen from the immune system.

M.A. Engevik 2014 glycosylated and nonglycosylated domains (Andrianifahanana et al. 2006). Mucins contain serine-, threonine- and proline-rich repeated sequences, so called PTS domains, which serve as attachment sites for O-linked oligosaccharides (Deplancke and Gaskins 2001). The four primary mucin oligosaccharides are N-acetylglucosamine (GlcNAc), N-acetylgalactosamine (GalNAc), fucose, and galactose (Forstner

1995). The initial oligosaccharide of the mucin chain is typically galactosamine, to this residue there can be up to 10 or more oligosaccharides attached (Rhodes 1989). These sugars can vary in linkage and branching which gives the oligosaccharide side chains heterogeneity (Rhodes 1989). Sialic acid (usually as N-acetylneuraminic acid) or sulfate groups often terminate the mucin oligosaccharide chains, which offer protection from bacterial enzymes and create the polyanionic nature of mucins at a neutral pH

(Deplancke and Gaskins 2001; Forstner 1995). Oligosaccharides are sequentially added by membrane- bound glycosyltransferases that transfer monosaccharides from nucleotide sugar donors in the Golgi apparatus (Brockhausen et al. 1998). Sulfate is also added in the Golgi from 3’-phosphoadenosine-5’- phosphate (PAPS) by Golgi sulfotransferases (Deplancke and Gaskins 2001; Brockhausen et al. 1998).

Attached oligosaccharide chains give the mucin domain its “bottle brush” structure and account for approximately 50-90% of the total mass (Loomes et al. 1999; Kim and Ho 2010). These oligosaccharides also make the mucin protease-resistant which protects mucins from bacterial degradation (Kim and Ho

2010).

Mucins are divided into two functionally different groups: the transmembrane and the gel- forming mucins (Moniaux et al. 2001). Intestinal transmembrane mucin span the membrane of normal secretory enterocytes (Kufe 2009) and include MUC1, MUC3, MUC4, MUC12, MUC13, and MUC17

(Linden et al. 2008). The majority of these transmembrane mucins contain a mucin and EGF and SEA domains (Hollingsworth and Swanson 2004). The cytoplasmic ends of transmembrane mucins, which extend over 100 nm from the cell into the lumen (Kufe 2009), are involved in intracellular signaling cascades which allow bacteria to signal to the host (Hattrup and Gendler 2008). Secreted intestinal gel- forming mucins form complex polymer structures and include MUC2, MUC5AC and B, MUC6 (Gum et al. 1992). Gel-forming mucins assemble by C- and N-terminal ends, which contain conserved von

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Willebrand D (vWD) domains, into polymeric molecules (Perez-Vilar and Hill 1999). Gel-forming mucins are secreted from goblet cells by baseline secretion and/or compound exocytosis (Deplancke and

Gaskins 2001). Baseline secretion involves continuous exocytosis of mucin granules, while compound exocytosis involves secretion of mucin granules when stimulated by a mucin secretagogue (Forstner

1995). Mucin secretagogues include neuropeptides, hormones, lipids, and cytokines (Deplancke and

Gaskins 2001). In the intestine the primary transmembrane mucin is MUC1 and the primary secretory gel- forming mucin is MUC2. Loss of these two mucins in mice (MUC1 and MUC2 knockout mice) both result in inflammation and colitis (Johansson et al. 2008), demonstrating the role of mucus in creating a protective barrier.

B. Mucus oligosaccharides

Intestinal mucus also serves as a fuel source for the gut microbiota. It has been suggested that bacteria which are capable of using mucus oligosaccharides have a colonization advantage (Probert and

Gibson 2002). Mucosa-associated bacteria which can use mucus oligosaccharides have a high degree of stability in comparison to luminal bacteria which are affected by diet (Probert and Gibson 2002). It has been calculated that only a small percentage of the gut microbiota are able to secret oligosaccharide- degrading enzymes (Rhodes 1989). Bacteria that are capable of cleaving oligosaccharides include the genera Ruminococcus, Bacteroides, Bifidobacterium, Lactobacillus and Clostridium (Deplancke and

Gaskins 2001). Since certain bacteria have oligosaccharide-specific enzymes, oligosaccharide mucin degradation likely requires the participation of several bacterial species (Hooper et al. 2002). However when oligosaccharides are released, they can be used by degrading bacteria or by other resident bacteria which do not contain mucin-degrading enzymes (Gusils et al. 2003). Released oligosaccharides can shape the gut microbiota and in turn, the microbiota can also shape the oligosaccharide composition of the mucins (Gheri et al. 1999) and the production of mucins (Mack et al. 2003; Mack et al. 1999). A study examining carbohydrate cleaving enzymes of the gut microbiota found that these enzyme profiles are similar in different regions of the body, suggesting that the carbohydrate composition influences the

M.A. Engevik 2014 bacterial community composition (Vyas and Ranganathan 2012). However the relationship between glycosylation and fucosyslation mucus patterns and changes in the gut microbiota has not been fully elucidated.

V. Approaches to examine the microbiota

Understanding the complex interaction between the gut microbiota and the host has been a challenge in the past because of the limitations placed by culturing. Cultivation-based techniques have been limited by 3 factors: (1) unknown nutritional growth requirements for a large number of bacteria; (2) undeterminable phylogenetic identification; and (3) laborious and impractical culturing techniques

(Gaskins et al. 2008). Conventional culture-based methods have isolated and identified over 400 bacterial species from the human GI tract (Rajilic-Stojanovic et al. 2007), but this represents only a fraction of the

GI microbiota. The recent use of DNA-technology has shed light on the composition and diverse functions of the gut microbiota. 16S ribosomal DNA sequencing has revealed the relative abundance of different taxonomic groups in the gut microbiota (Ley et al. 2008). Clone libraries have been used to sequence and identify the gut microbiota to the species level. In addition, fingerprinting techniques, such as temperature or denaturing gradient gel electrophoresis (TGGE or DGGE) and terminal-restriction fragment length polymorphism (T-RFLP), have been used to compare diversity between samples. Dot- blot hybridization, fluorescent in situ hybridization (FISH), and quantitative PCR have been used to target certain bacteria groups to reveal abundance of specific microbes (Gaskins et al. 2008). These techniques have allowed researchers to identify similarities and differences between the gut microbiota of different species and the microbiota differences associated with various host phenotypes (Hartman et al. 2009).

Recently metagenomic studies, including shotgun sequencing of community DNA and gene-centric comparative approaches (White et al. 2009), have further shed light on the gut microbiota composition and function (Greenblum et al. 2012). Metagenomic studies demonstrate that a high functional uniformity exists across samples and only a small set of genes or pathways appear to be associated with certain host states (Greenblum et al. 2012).

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VI. Significance of thesis work

Despite the potential impact of microbial communities on human health and disease, our understanding of how normal intestinal bacteria levels are maintained remains incomplete. We have just begun to understand the dynamics of the intestinal microbiome. Although much is known about basic host physiology and stool gut microbiota composition, more studies are needed for a better understanding of the interplay between the gut microbiota and host environment set by ion transport. Since the majority of studies focus on fecal microbiota the gut microbiota has not been fully analyzed along the length of the intestine or differentiated between the luminal and mucosa-associated bacterial populations. I have attempted to address this gap in knowledge using both mouse and human gut microbiota analysis focusing on the effect of altered ion transport in shaping the microbiota composition and host interaction.

Knowledge of how the intestinal environment affects specific bacteria will likely aid in the development of future therapies for disease with abnormal bacterial composition. The overall aim of the thesis work was to provide a deeper understanding of the effects of the intestinal environment, set by ion transport, diet and pathogens, on the gut microbiota and the reciprocal interaction with the host.

VII. Central hypothesis and aims of thesis

A. Central hypothesis

I hypothesize that ion transport-induced and diet-induced change in the intestinal environment will lead to alteration of the microbiota.

B. Aims of thesis

The overall aim of the thesis work was to provide a deeper understanding of the effects of the intestinal environment, set by ion transport, diet and pathogens, on the gut microbiota and the reciprocal interaction with the host.

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EXPANDED METHODS

I. Mouse Work

A. Genotype and maintenance

Animal protocol was approved by the University of Cincinnati Animal Care and Use Committee and complied with National Institutes of Health guidelines. FVB/N (Friend virus B-type susceptibility)

NHE3-/- and NHE2-/-mice were generated as previously described (Taketo et al. 1991; Schultheis PJ 1998;

Schultheis et al. 1998). Mice were developed from targeted disruption (knockout) of genes encoding

Slc9a2 (NHE2) and Slc9a3 (NHE3) and heterozygous breeding pairs were maintained on an FVB/N background. Genotyping was performed on all offspring at 3–4 wk of age by PCR analysis of genomic

DNA isolated from tail biopsies. The PCR reaction used two gene-specific primers and one neomycin resistance gene-specific primer (Table 1). Mice were identified as wild-type (WT), heterozygous or knockout and only WT and knockout mice were used for experiments. NHE3-/- mice were housed with their mothers for an addition 2-3 weeks to avoid dehydration. Experiments were performed on intestinal tissues obtained from weaned mice at 6-8 weeks post weaning.

B. Diet

WT FVB/N, NHE2-/- and NHE3-/- mice were maintained on a normal mouse diet (7922 NIH-07

Mouse diet, Harlan Laboratories, Indianapolis, IN) with water added ad libitum. In a separate experiment

FVB/N WT mice (Taketo et al. 1991) at 6-8 weeks post-weaning, mice were maintained on either a control mouse diet (CT, 7922 NIH-07 Mouse diet, Harlan Laboratories, Indianapolis, IN) or a GFH7K supplemented diet (Richter Pharma AG; Wels, Austria), which consists of 2% GFH7K added to the control diet chow and drinking water. The CT diet consisted of 22.5% crude protein, 5.2% fat, 3.7% crude fiber, 3.1 kcal/g energy density, 29% calories from protein, 15% calories from fat and 56% calories from carbohydrates. The diet was fed for 14 days and water was replaced daily. This experiment was repeated separately two times (1st: CT n=8, GHF7K n=7, 2nd: CT n=6, GHF7K n=4).

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C. Organ Collection

At 6-8 weeks post weaning, mice were euthanized by CO2 and intestines were dissected on a cold

(approximately -10°C) cutting board to prevent any protein degradation or bacterial replication. Intestines were separated into duodenum, jejunum, terminal ileum (< 10 cm proximal to the cecum, hereafter referred to as ileum), cecum, and colon (proximal and distal) from WT and/or knockout littermates.

Individual intestinal segments were flushed with 500 µl sterile PBS (pH 7.4) into microfuge tubes. Then intestinal segments were cut open length wise, washed thoroughly with PBS and glass slides were used to scrape the epithelia and mucus layer (Frantz et al. 2012; Norkina et al. 2004a, b; Deplancke et al. 2002).

Luminal flushes were processed for total DNA and mucosal scrapings were processed either for total

DNA or for RNA. Sample wet weight was determined and tissue homogenized with a Tissue Tearor homogenizer (Biospec Products Inc, Bartlesville, OK) for 1 min. RNA was extracted in Trizol and stored at -80°C until examined by quantitative real time PCR (qRT-PCR). DNA was extracted and stored at -

20°C until examined by quantitative PCR (qPCR). Immediately after dissection, WT, NHE3-/- and NHE2-

/-mouse intestine was fixed in ice cold carnoy’s fixative, embedded in paraffin and processed for histology.

II. Patient Work

A. Patient information

All patients and healthy volunteers at the University of Cincinnati Medical Center Hospital,

Cincinnati, OH provided informed consent approved by the University of Cincinnati IRB. C. difficile infection (CDI) was defined as a new onset of diarrhea (> 3 loose stools/day for more than 24 hours) and at least one positive C. difficile laboratory test. Diagnosis of CDI was determined by at least one ELISA positive toxin test or a positive LAMP test. Over the course of fecal collections, two types of toxin tests were used. From November 2010 – August 2011, the EIA for toxins A and B was used. After August

2011, the Meridian Illumigene® LAMP test was used. This shift in toxin testing represents a switch to in house testing, lowering the cost, and an upgrade to a more sensitive method. Initial CDI cases, defined as

M.A. Engevik 2014 only one C. difficile positive laboratory test with no prior history of CDI, were not included in this study.

Recurrent CDI was defined as onset of new diarrhea after a symptom-free period of >3 days, more than one C. difficile positive laboratory test and completion of at least one round of antibiotic treatment.

To access gut microbiota, pH and ion composition, feces were collected from patients that were healthy or had recurrent CDI. Fecal samples were collected from 12 recurrent CDI patients with an average age of 56, age range 32-76. This group included 8 females and 4 males. Selected patients did not have history of Inflammatory Bowel Disease (IBD), small bowel obstruction, diverticulosis, colostomy, or cancer. Fecal samples were also collected from 12 healthy volunteers with an average age of 41, age range 28-61. This group included 7 females and 5 males. Healthy volunteers were without previous or current GI symptoms, history of chronic disease or cancer. All stool was processed for total DNA, ion concentration, pH and stored at −20°C.

To access changes in host morphology, colon biopsies were collected from five healthy volunteers were obtained by consent and fixed in neutral buffered formalin and paraffin-embedded.

Healthy patients had an average age of 52, patient age range of 45-63 and included 3 females and 2 males.

Healthy volunteers were without previous or current GI symptoms, history of chronic disease or cancer.

Paraffin sections were obtained from 5 de-identified patients who had been biopsied or had intestinal tissue removed due to diagnoses consistent with CDI and had a current C. difficile-positive toxin test. The average patient age 44, patient age range of 28-65, and included 2 females and 3 males. Selected patients did not have history of Inflammatory Bowel Disease (IBD), small bowel obstruction, diverticulosis, colostomy, or cancer. Confirmation of C. difficile infection was performed by staining with C. difficile specific antibody as described below. For comparison, paraffin sections from one colonic resections and two other biopsies were obtained from patients who exhibited colitis but no C. difficile positive toxin test.

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B. Stool mucus extraction.

Crude mucus was extracted from human feces as previously described (Roos et al. 2000;

Juntunen et al. 2001; Ouwehand et al. 1999). Briefly, stool was diluted in 4 volumes ice-cold PBS (PBS;

10 mM phosphate, pH 7.2) containing: 0.5 µg/µl sodium azide to prevent bacterial growth and 1 mM phenylmethylsulfonyl fluoride, iodoacetamide and 10 mM EDTA to inhibit proteases. The suspension was shaken for 1 h at 43°C and centrifuged for 20 min at 4°C at 14,000g to pellet stool. From the clear supernatant, the mucus was precipitated twice with ice cold 60% ethanol and resuspended in ultrapure water. Mucus was then lyophilized and reconstituted based on weight. Mucus was stored at -80C until used. Crude mucus protein concentration was determined by Bio-Rad protein assay performed according to the instructions of the manufacturer (Bio-Rad Laboratories, Richmond, CA). The original un- lyophilized crude mucus contained different amounts of proteins: healthy mucus contained 0.4 ± 0.2

µg/µl and CDI mucus contained 2.2 ± 0.5 µg/µl. Pooled lyophilized crude mucin preparation was reconstituted to a total protein of 7 µg/µl. Mucus oligosaccharide composition was determined by periodic acid-Schiff’s reagent (PAS) in a microtiter plate as previously described (Kilcoyne et al. 2011).

C. difficile mucus binding was examined in two ways: microtiter plate and slide. For the microtitre plate assy, 100 µl mucus was immobilized in polystyrene microtitre plate wells (Maxisorp, Nunc) by overnight incubation at 4°C as previously described (Ouwehand et al. 1999), which covers the well with sufficient mucus. The wells were washed twice with PBS to remove excess mucus. The microtitre plate was then placed in an anaerobic hood for 2 hr to create an anaerobic environment. Once anaerobic, 100 µl fresh C. difficile ATTC-1870 culture (106 CFU) was added to each well and incubated for 3 hr at 37°C inside the anaerobic hood. After incubation, the wells were washed twice with PBS to remove unattached bacteria.

Mucus was then scrapped off the microtite plate and grown on C. difficile selective agar plates (Fisher

Scientific). For the slide binding assay, 50 µl mucus (385 µg protein) was added to glass slides and spread by an inoculating loop. Slides were allowed to air-dry and then heat fixed using a Bunsen burner. Slides were blocked with blocked with PBS containing 10% serum. An ImmEdge pen (Fisher Scientific) was used to select areas for C. difficile binding. Slides were placed in an anaerobic chamber for 2 hr and then

M.A. Engevik 2014

100 µl of C. difficile (106 CFU) was added to each well and incubated for 3 hr at 37°C within the anaerobic chamber. Slides were then washed 3x in PBS to remove unattached bacteria and slides were incubated with an anti-C. difficile antibody (dilution 1:100, ab93728, ABCAM) for 1 hr at room temperature. Slides were then washed three times in PBS, incubated with goat-anti-rabbit IgG Alexa

Fluor® 488 secondary antibody (1:100 dilution) (Life Technologies), cover slipped, and analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710) and Image J software (NIH).

III. Histology

Histology was performed on mouse and human samples to examine intestinal architecture, mucus production, mucus oligosaccharide composition, bacterial binding and ion transporter expression. All mouse tissue was fixed in carnoy’s fixative (60% ethanol, 30% chloroform, 10% glacial acetic acid) and human tissue was fixed in 10% formalin. Tissue was fixed overnight at 4°C, embedded in paraffin and serial 6–7 m thick sections were applied to glass slides. These slides were stained with hematoxylin and eosin (H&E) for intestine architecture (performed at Children’s Hospital of Cincinnati Pathology Core).

Slides were stained with Periodic Acid-Schiff’s/Alcian blue (PAS-AB) for goblet cells and mucus stain.

Briefly, slides were de-paraffinized through a series of xylene and ethanol, and 3% acetic acid was added for 30 sec and washed. Slides were then incubated with Alcian blue for 3 min, 1% Periodic Acid solution for 10 minutes, Shiff Reagent for 1 minute, and Na2S205 solution for 3 min with washing in between incubations. Slides were mounted with permount and imaged using an Olympus BH2 microscope and an

Olympus Magnafire digital image capture system with AxioVision Release 4.8.1 software.

For slides with human tissue, expression of NHE3 was examined with rabbit anti-human NHE3 antibody (dilution 1:100, NBP1-82574, Novus Biologicals, Littleton CO), MUC1 examined with an anti- human MUC1 antibody (dilution 1:100, RB-9222, Thermo Fisher Scientific), MUC2 examined with an anti-human MUC2 antibody (dilution 1:100, MS-1037, Thermo Fisher Scientific), and C. difficile binding was examined with rabbit anti-C. difficile Cell Surface Protein (dilution 1:50, ab93728, ABCAM,

Cambridge, MA). The anti-C. difficile antibody was tested against pure cultures of C. difficile and

M.A. Engevik 2014 cultures of Firmicutes member Lactocaillus acidophilus, with staining observed only with pure C. difficile cultures. Sections were deparaffinized and incubated for 40 min at 97°C with Tris–EDTA–SDS buffer as previously (Syrbu and Cohen 2011). Briefly, 250 mM Tris–HCl (Tris (hydroxymethyl)amino-methane), pH 8.5, 10 mM EDTA (Ethylenediamine Tetraacetate), 0.5% (w/v) sodium dodecyl sulfate (SDS) buffer was diluted in distilled water at a 1:10 dilution. This solution was preheated to 97 ± 1°C. Slides were deparaffinized in a series of xylene and ethanol, then immersed in Tris-EDTA-SDS buffer for 40 min at

97°C. After incubation, slides were removed and allowed to cool for 10–20 min at room temperature. In a humidified chamber, sections were blocked with PBS containing 10% serum, and stained with primary antibody overnight at 4°C. Sections were then washed three times in PBS, incubated with goat-anti-rabbit

IgG Alexa Fluor® 488 secondary antibody (dilution 1:100; Life technologies, Grand Island, NY) for 1 hr at room temperature and counterstained with Hoechst (0.1 µg/ml) (Fisher Scientific). For MUC1 and

MUC2 stains, MUC1 was stained with goat-anti-rabbit IgG Alexa Fluor® 488 (dilution 1:100) and

MUC2 was stained with donkey anti-mouse IgG Alexa Fluor® 633 (dilution 1:100) and counterstained with Hoechst (0.1 µg/ml) (Fisher Scientific). Sections were analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710). Digital images of slides were evaluated by tabulating mean pixel intensity of the respective color channel on each image using Image J software (NIH). Five regions of interests per image, four images per slide, and n=5 healthy and CDI patients were used for semi- quantitation of stain intensity.

Mucus oligosaccharides were examined using a panel of FITC-conjugated lectin stains: Ulex europaeus agglutinin-1 (UEA-1) for terminal fucose; Concanavalin A (CONA) for mannose, Dolichos biflorus agglutinin (DBA) for N-Acetylgalactosamine, Peanut agglutinin (PNA) for galactose and Wheat

Germ Agglutin (WGA) for N-Acetylglucosamine (Vector Laboratories, Burlingame, CA) as previously described (Hooper et al. 1999; Magalhaes et al. 2009; Hooper et al. 2000). Sections were deparaffinized through a series of xylene and alcohol and blocked with PBS containing 10% BSA at room temperature for one hour. Sections were then stained with FITC-labeled lectin (10 g/ml) for 1 h at room temperature in a humidified chamber. Sections were washed three times in PBS, mounted using Vectashield mounting

M.A. Engevik 2014 medium with DAPI (Vector Laboratories), and analyzed by confocal laser scanning microscopy (Zeiss

LSM Confocal 710, Carl Zeiss, Germany). Digital images of slides were evaluated by tabulating mean pixel intensity of the respective color channel on each image using Image J software (NIH) for semi- quantitation of stain intensity.

IV. Ion and pH measurements

The ion composition of intestinal fluid and stool was determined for mice and humans. To determine the intestinal ion composition of WT, NHE3-/- and NHE2-/- mice and mice fed GHF7K, intestinal segments were flushed with 100 µl of double deionized water using a blunt syringe needle.

Flushes were performed on the same size intestinal segments as those used to collect bacterial content.

Flushes were collected in microfuge tubes and weighed and the intestinal volume was calculated from the weight of the flush balanced against 100 µl of water. GHF7K diet and NHE2-/- did not alter the intestinal fluid volume, although NHE3-/- exhibited differences in intestinal volume. Flushes were centrifuged at

3,000 rpm for 10 min at 4°C to pellet intestinal solids. For examination of human intestinal environment,

0.3 g of human stool/ stool liquid was added to tubes and 300 μl of ddi-water was added and vortexed thoroughly. The samples were centrifuged at 3,000 rpm for 10 min at 4°C to pellet solids. For both mouse and human resulting supernatant Na+ and K+ concentrations were determined using a digital Flame photometer (Single-Channel Digital Flame Photometer Model 02655-10; Cole-Parmer Instrument

Company Vernon Hills, IL). Flame Emission Photometry relies on the fact that the Na+ and K+ ions emit light at different wavelengths (Na+: 589 nm, K+: 766 nm) when excited in a gas flame. The intensity of the light produced is proportional to the concentration of the element. Briefly, intestinal flush samples were diluted 1:200 in lithium for a total of 800 μl. All 800 μl was used for flame photometry. Cl- ion concentration was determined by a digital Chloridometer (Model 4425100, Labconco Kansas City, MO).

Chloridometry is based on the quantitative reaction between silver ions and chloride ions which creates an insoluble precipitate of silver chloride (AgCl). This reaction is carried out at a constant rate by passing a fixed direct current between a pair of silver electrodes immersed in an acid solution. As the equivalence

M.A. Engevik 2014 point of the reaction is reached, an increase in current between a pair of separate indicator electrodes is detected. Briefly, intestinal flush samples were diluted 1:400 in chloridometer Acid Reagent (Labconco) to a volume of 4 ml. All values were normalized to weight. pH measurements were performed via an pH meter (Orion Model 720A; Thermo Fisher Scientific Waltham, MA).

V. Organoids

A. Mouse intestinal organoids

The terminal ileum (< 10 cm proximal to the cecum) was dissected from WT and NHE3-/- mice, flushed with PBS, cut open and washed in ice-cold Ca/Mg-free DPBS. Diced tissue was incubated in a 15 ml falcon tube with 5 ml of 2 mM EDTA in PBS for 30 min at 4 ºC. Tubes were centrifuged at 150 x g at

4°C for 5 minutes and a much liquid as possible was removed. 5 ml of PBS with 2% sorbitol was added and tubes were quickly shaken by hand for 2 min. Presence of crypts were analyzed under 40x magnification on a microscope. Dissociated tissues were centrifuged at 150 x g at 4°C for 10 minutes and embedded in Matrigel (BD Biosciences). After the Matrigel was polymerized, Advanced DMEM/F12 medium supplemented with R-spondin 1 (500 ng/ml R&D Minneapolis, MN), penicillin/streptomycin

(100/100 U/ml), HEPES (10 mM), Glutamax (2 mM), N2 and B27 (Broomall, PA) was overlaid with

Noggin (100 ng/ml, PeproTech, Rocky Hill, NJ) and EGF (50 ng/ml, PeproTech) as previously described

(Sato et al. 2009). Organoid culture medium, was changed every three days and organoids were passaged every week. To passage, the matrigel was removed by addition of 500 µl ice cold PBS and pipetting.

Pooled organoids were centrifuged at 150 x g at 4°C for 10 minutes and the old matrigel and PBS were aspirated off. To the organoid pellet, 1 ml of PBS was added. This volume was taken up by syringe and organoids were pushed through a 27 ½ gauage needle to physically break apart organoids. The presence of individual organoid crypts were analyzed under 40x magnification on a microscope as described.

Based on density, the appropriate matrigel volume was added to the organoid pellet and placed into new

12 well-polystryrene plates. After polymerization of matrigel, overlaying media was added as described.

M.A. Engevik 2014

B. Human Intestinal organoids (HIOs)

Human intestinal organoids (HIOs) were generated by the Cincinnati Children’s Hospital Medical

Center (CCHMC) Pluripotent Stem Cell Facility through directed differentiation of human pluripotent stem cells (hPSC). HIOs were obtained in matrigel and exhibited three-dimensional growth. These organoids have been previously been shown to contain the major intestinal epithelial lineages: enterocytes

(villin), goblet cells (mucin), paneth cells (lysozyme), and enteroendocrine cells (chromogranin A) (Wells and Brugman 2013). HIOS were used within 3 days after being received.

C. Organoid microinjection

Terminal mouse ileum organoids (or enteroids) ranged in size from 200 to 350 µm in approximate diameter (~5 days after passage), while HIOS ranged from 1.5 to 2.2 mm in diameter.

Organoids were assumed to be roughly a sphere and organoid volume was calculated as 4/3πr2 based on diameter. Mouse and human organoids were microinjected with bacteria to analyze host-microbe interactions. Injection needles were pulled on a horizontal bed puller (Sutter Instruments, Novato, CA) and the tip ends broken under stereoscope view to obtain a tip diameter of ~10-15 μm. Microinjection was accomplished using a Nanoject microinjector (Drummon Scientific Company, Broomall, PA). Injection volume was determined by the predicted organoid luminal volume. Injection volumes of ~10% or less of the organoid luminal volume were used in order to minimize stretch effects on epithelial cells and tight junctions. Under these conditions no observable leak is detected from the lumen of the organoid or from the injection site upon injection needle removal. Mouse organoids were injected with either broth alone or with cultures of B. thetaiotaomicron. HIOs with injected with cultures of Clostridium difficile or stool.

For stool, 0.5g of healthy or CDI stool was added to 4.5ml TSB in an anaerobic hood. Samples were vortexed well and centrifuged at 150g for 10 min to pellet solid materials. Stool supernatant was injected into HIOs. Mouse organoids were incubated overnight and fixed with 4% paraformaldehyde for 20 min at room temperature. Organoids were then permeabilized with 0.1% Triton X-100 in PBS for 20 min at room temperature and washed with PBS. 10 µg/ml of UEA-1 FITC (Vector Laboratories) was incubated

M.A. Engevik 2014 with the organoids for 1 hr at room temperature and counterstained Hoechst (0.1 µg/ml) for 10 min.

Organoids were washed three times with PBS and imaged by confocal microscopy (Zeiss LSM Confocal

710, Carl Zeiss). HIOs were incubated overnight and fixed with 4% carnoy’s for 30 min at room temperature. HIOs were washed in PBS and transferred to sucrose (30% in PBS) and incubated overnight at 4°C. The next day, HIOs were placed in OCT embedding medium and frozen at -80°C for 1 day. Serial

7µm sections were cut on a cryostat and applied to slides. Slides were stained with rabbit anti-human

NHE3 antibody (dilution 1:100, NBP1-82574, Novus Biologicals), anti-human MUC1 antibody (dilution

1:100, RB-9222, Thermo Fisher Scientific), or anti-human MUC2 antibody (dilution 1:100, MS-1037,

Thermo Fisher Scientific) overnight at 4°C. Sections were countered stained with ALEXA 488 or 633 secondary antibodies for 1 hr at room temperature and counterstained with Hoechst(0.1 µg/ml) (Fisher

Scientific) for 10 min at room temperature. Expression of mucus oligosaccharides in HIOs were examined with 10 µg/ml FITC-conjugated lectins: Concanavalin A (ConA, mannose), Dolichos biflorus agglutinin (DBA, N-Acetylgalactosamine), Peanut Agglutinin (PNA, galactose), and Wheat germ agglutinin (WGA, N-acteylglucosamine) (Vector Laboratories, Burlingame, CA). Ulex Europaeus agglutinin 1 (UEA-1, fucose) was not used since the obtained HIOs were a non-secretor phenotype (as determined by karyotype). Sections were stained for 1 hr at room temperature followed by Hoechst (0.1

µg/ml) (Fisher Scientific) for 10 min at room temperature. All slides were analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710, Carl Zeiss).

VI. Bacterial strains and culture conditions

Standard curves of pure bacterial cultures were used to correlate qPCR CT values to CFU (see

Table 2). Bacteria were obtained and grown in pure cultures in various broths under aerobic or anaerobic conditions. All bacteria were grown overnight (~16hrs) at 37°C shaking at 150rpm. Anaerobic conditions were created using a Coy Systems, dual-port anaerobic chamber (Coy Lab Products, Grass Lake, MI). For all cultures, 3 ml of media was aliquoted in 15 ml polystyrene culture tubes. To examine growth under various conditions, B. thetaiotaomicron, L. acidophilus and C. difficile were grown to examine the

M.A. Engevik 2014 optimal [Na+] and pH for growth. B. thetaiotaomicron, Lactobacillus acidophilus and C. difficile were grown in media where sodium chloride (NaCl) was either removed or replaced with cesium chloride

(CsCl) as previously described (Caldwell et al. 1973a; Caldwell and Arcand 1974; Caldwell et al. 1973b).

Initial cultures of B. thetaiotaomicron were grown in TSB broth and C. difficile and L. acidophilus were grown in TYG. TYG and TSB media was made without NaCl, or replaced with CsCl or KCl. Low Na+ media was mixed with normal media at various dilutions to obtain varying concentrations of Na+ for optimal bacterial growth determination. Media Na+ and K+ concentrations were confirmed by flame photometry (Single-Channel Digital Flame Photometer Model 02655-10; Cole-Parmer Instrument

Company) and Cl- concentration measured by chloridometry (Digital Chloridometer Model 4425100,

Labconco). Bacteria were grown under anaerobic conditions at 37°C with shaking (150rpm) to early stationary phase (O.D. ~1) in normal media and used to inoculate media containing varying Na+ concentrations. Growth of B. thetaiotaomicron and L. acidophilus were measured as the optical density

(O.D. 600nm) and growth of C. difficile was measured as the optical density (O.D. 560nm) using an

Amersham Biosciences Ultospec 3100 Spectrophotometer (GE Healthcare Life Sciences, Pittsburgh, PA) versus uninoculated medium. Cell concentration was determined by bacterial cell counts using a Petroff

Hauser chamber (Hausser Scientific; Horsham, PA) and colony forming units (CFU) (Hooper et al. 1999;

Caldwell et al. 1973a; Caldwell and Arcand 1974; Caldwell et al. 1973b).

To access the optimal pH for growth, C. difficile, B. thetaiotaomicron and L. acidophilus were grown in media containing either normal media or low Na+ media at various pH ranges as determined electrochemically using a pH meter (Orion Model 720A; Thermo Fisher Scientific). To determine ability of mucus oligosaccharides to promote C. difficile growth, fucose, N-acetylglucosamine (GlcNAc), N- acetylgalactosamine (GalNAc) (Thermo Fisher Scientific), mannose and galactose, (NANA) (Sigma

Aldrich) were added to TYG media at a final concentration of 1 mM in either normal media, low Na+ media at pH 6.0 or 7.0 and analyzed for growth in the same manner as above.

M.A. Engevik 2014

VII. Quantitative Real Time-PCR (qRT-PCR)

A. 16S sequences

In order to determine the gut microbiota composition of mouse and human samples, total DNA was isolated with the QIAamp DNA Stool kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. Briefly, 220 mg stool samples were lysed in Buffer ASL at a temperature of

95°C and incubated with lysozyme (10 mg/ml, 37°C for 30 min) to improve bacterial cell lysis as previously described (Norkina et al. 2004a; Castillo et al. 2006; Fite et al. 2004; Salzman et al. 2010).

After lysis, DNA-damaging substances and PCR inhibitors present in the stool sample were adsorbed in

InhibitEX matrix (Qiagen). The InhibitEX matrix was pelleted by centrifugation and proteins were digested and degraded during a 70°C incubation with proteinase K in buffer AL (Qiagen). Resulting DNA supernatant was then washed and purified on QIAamp Mini spin column silica membrane. Purified, concentrated DNA was eluted from the QIAamp Mini spin column in low-salt buffer equilibrated to room temperature. DNA was stored at –20°C until used. The abundance of total bacteria and specific intestinal bacterial phyla, class, genus and species was measured by qPCR using a Step One Real Time PCR machine (Applied Biosystems (ABI) Life Technologies, Grand Island, NY) with SYBR Green PCR master mix (ABI) and bacteria-specific primers (Table 3) in a 20 µl final volume. Bacterial numbers were determined using standard curves from the pure bacterial cultures as previously described (Salzman et al.

2010; Barman et al. 2008) which correlated cycle of threshold values (CT) to calculated bacteria number.

Bacterial phyla composition was determined using calculated CFU values for each bacterial phylum as a percentage of the total bacteria. Total bacteria were calculated using the universal bacterial primer and represent the total number of bacteria per intestinal segment examined.

M.A. Engevik 2014

B. mRNA

To examine message level total RNA was extracted from mouse intestine mucosal scrapings with

TRIzol Reagent (Invitrogen Life Technologies, Grand Island, NY) according to the manufacturer’s instructions. 400 µl of Trizol was added to mucosal scrapings and chloroform was used to extract only the

RNA. Reverse transcription was performed using 50 µg/ml oligo(dT) 20 primer and SuperScript reverse transcriptase (Invitrogen) according to the manufacturer’s instructions. Amplification reactions were performed with SYBR Green PCR master mix (ABI), 200 ng sample cDNA in a 20 µl final volume on the Step One Real Time PCR Machine (ABI). Gene specific qRT-PCR primers derived from previous literature were used (Table 4). Data was reported as delta delta CT using GAPDH as the standard.

Differences in mRNA expression were determined by qRT-PCR and expressed as the CT relative fold difference.

VIII. Statistics. The data are presented as the mean ± SEM. Comparisons between groups were made with either one or two way analysis of variance (ANOVA) using SigmaPlot (Systat Software Inc, San

Jose, CA). One way ANOVA was used for analyzing data with one factor, while two way ANOVA was used for analyzing two factors. When a two analysis of variance was used, the Holm-Sidak post-hoc test was selected to determine significance between pairwise comparisons. Parametric tests were used for all analysis. A P < 0.05 value was considered significant for all statistical tests. n is number of experiments.

M.A. Engevik 2014 Table 1: PCR primer sequences for genotyping.

Target Forward Primer (5'-3') WT Reverse Primer (5'-3') KO Reverse Primer (5'-3') Paper

(Gawenis NHE3 CTTTTGCGGCATCTGCTGTCAG ACTACTAAGAGTGCTCCTAGCTCTCACC GCATGCTCCAGACTGCCTTG et al. 2002)

(Gawenis NHE2 CATCTCTATCACAAGTTGCCCACAATCGTG GTGACTGCATCGTTGAGCAGAGACTCG GCATGCTCCAGACTGCCTTG et al. 2002)

45 M.A. Engevik 2014 Table 2: Bacteria grown for standard curves and primer testing

Phyla Primer Bacteria Source Media Conditions Firmicutes Firmicutes Phyla Staphylococcus aureus Daniel Hassett Luria-Burtani (LB) aerobic Ruminococcus productus ATCC Tryptone Yeast Glucose C. coccoides group 27340D-5 ATTC (TYG) anaerobic Faecalibacterium prausnitzii ATCC Tryptone Yeast Glucose C. leptum group 27766 ATTC (TYG) anaerobic Carolina Lactobacillus group Lactobacillus acidophilus Biologicals Luria-Burtani (LB) aerobic Clostridium difficile ATTC BAA- Benjamin Tryptone Yeast Glucose C. difficile 1870 Darrien (TYG) anaerobic Bacteroides thetaiotaomicron Trypton Soy Broth Bacteroidetes Bacteroidetes phyla ATCC 29741 Fisher Scientific (TSB) anaerobic Trypton Soy Broth Prevotella Prevotella melaninogenica Fisher Scientific (TSB) anaerobic Bacteroides thetaiotaomicron Trypton Soy Broth Bacteroides ATCC 29741 Fisher Scientific (TSB) anaerobic Bacteroides thetaiotaomicron Trypton Soy Broth Bacteroides thetaiotaomicron ATCC 29741 Fisher Scientific (TSB) anaerobic Actinobacteria Actinobacteria phyla Micrococcus luteus Daniel Hassett Luria-Burtani (LB) anaerobic Carolina Proteobacteria α-Proteobacteria Rhibozium legaminosarum Biologicals Luria-Burtani (LB) aerobic β-Proteobacteria Burkholdena cepacia Daniel Hassett Luria-Burtani (LB) aerobic γ-Proteobacteria Escherichia coli Daniel Hassett Luria-Burtani (LB) aerobic

46 M.A. Engevik 2014 Table 3: qPCR primer sequences for bacterial groups.

Type Bacteria Forward (5'-3') Reverse (5'-3') Reference (Barman et al. 2008; Fierer et Total Universal (Total Bacteria) ACTCCTACGGGAGGCAGCAG ATTACCGCGGCTGCTGG al. 2005) (Guo et al. 2008) Phyla Bacteriodetes GGCGACCGGCGCACGGG GRCCTTCCTCTCAGAACCC (Guo et al. 2008) Phyla Firmicutes GGAGYATGTGGTTTAATTCGAAGCA AGCTGACGACAACCATGCAC (Fierer et al. 2005) Phyla Actinobacteria CGCGGCCTATCAGCTTGTTG ATTACCGCGGCTGCTGG (Fierer et al. 2005) Phyla α-proteobacteria ACTCCTACGGGAGGCAGCAG TCTACGRATTTCACCYCTAC (Fierer et al. 2005) Phyla β-proteobacteria CCGCACAGTTGGCGAGATGA CGACAGTTATGACGCCCTCC (Fierer et al. 2005) Phyla y-Proteobacteria GAGTTTGATCATGGCTCA GTATTACCGCGGCTGCTG (Lee et al. 2009) Clostridium coccoides cluster Class ACTCCTACGGGAGGCAGC GCTTCTTAGTCAGGTACCGTCAT (Salzman et al. 2010) XIVa Class Clostridium leptum cluster IV GTTGACAAAACGGAGGAAGG GACGGGCGGTGTGTACAA (Salzman et al. 2010) Class Lactobacillus/Enterococcus AGCAGTAGGGAATCTTCCA CACCGCTACACATGGAG (Salzman et al. 2010) Genus Bacteriodes GGTTCTGAGAGGAGGTCCC CTGCCTCCCGTAGGAGT (Salzman et al. 2010) Genus Prevotella CCAGCCAAGTAGCGTGCA TGGACCTTCCGTATTACCGC (Dalwai et al. 2007) Genus Mouse Inestinal Bacteria (MIB) CCAGCAGCCGCGGTAATA CGCATTCCGCATACTTCTC (Salzman et al. 2010) Genus Bifidobacterium CTCCTGGAAACGGGTGG GGTGTTCTTCCCGATATCTACA (Collado et al. 2009) Species Bacteriodes thetaiotaomicron GGTAGTCCACACAGTAAACGATGAA CCCGTCAATTCCTTTGAGTTTC (Sonnenburg et al. 2005) Species Clostridium difficile TTGAGCGATTTACTTCGGTAAAGA CCATCCTGTACTGGCTCACCT (Sjogren et al. 2009)

47 M.A. Engevik 2014

Table 4: qRT-PCR primer sequences for mRNA.

Gene Forward Reverse Reference

Muc1 CCAGACCCCTGCACTCTGAT CGCTTGACAAAGGGCATGA (Fu et al. 2011)

Muc2 TGCCCACCTCCTCAAAGAC TAGTTTCCGTTGGAACAGTGAA (Fu et al. 2011)

Muc3 TGGTCAACTGCGAGAATGGA TACGCTCTCCACCAGTTCCT (Fu et al. 2011)

Muc4 CAATGCCCTCCACAAAAAGT CTGTGTGTTGGCAATTTCTG (Fu et al. 2011)

Muc5ac TGGTTTGACACTGACTTCCC TCCTCTCGGTGACAGAGTCT (Fu et al. 2011)

fut-1 AGTCTTCGTGGTTACAAGCAAC TGGCTGGTGAGCCCTCAATA (Meng et al. 2007)

fut-2 CAGCTCTGCCTGACATTTCTG AGCAGGTGATAGTCTGAACACA (Meng et al. 2007)

GAPDH CCTGCACCACCAACTGCTTA ATGACCTTGCCCACAGCCT (Meng et al. 2007)

48 M.A. Engevik 2014

Publication #1

Loss of NHE3 alters gut microbiota composition and

influences Bacteroides thetaiotaomicron growth

Melinda A. Engevik 1,3, Eitaro Aihara1,3, Marshal H. Montrose1,3,

Gary E. Shull 2,3, Daniel J. Hassett2 and Roger T. Worrell 1,3*

1Department of Molecular and Cellular Physiology

2 Department of Molecular Genetics, Biochemistry and Microbiology

University of Cincinnati College of Medicine

Cincinnati, OH 45267

3Digestive Health Center of Cincinnati Children’s Hospital, Cincinnati, OH 45229

49 M.A. Engevik 2014

Abstract:

Changes in the intestinal microbiota have been linked to diabetes, obesity, IBD, and Clostridium difficile-associated disease. Despite this, it remains unclear how the intestinal environment, set by ion transport, affects luminal and mucosa-associated bacterial composition. Na+/H+-Exchanger isoform 3

(NHE3), a target of C. difficile toxin B, plays an integral role in intestinal Na+ absorption. Thus, the

NHE3-deficient mouse model was chosen to examine the effect of pH and ion composition on bacterial growth. We hypothesized that ion transport-induced change in the intestinal environment would lead to alteration of the microbiota. Region-specific changes in ion composition and pH correlated with region- specific alteration of luminal and mucosal-associated bacteria with general decreases in Firmicutes and increases in Bacteroidetes members. Bacteroides thetaiotaomicron increased in NHE3-/- terminal ileum and was examined in vitro to determine if altered Na+ was sufficient to affect growth. Increased in vitro growth of B. thetaiotaomicron occurred in 43 mM Na+ correlating with the NHE3-/- mouse terminal ileum

[Na+]. NHE3-/- terminal ileum displayed increased fut2 mRNA and fucosylation correlating with B. thetaiotaomicron growth. Inoculation of B. thetaiotaomicron in WT and NHE3-/- terminal ileum organoids displayed increased fut2 and fucosylation, indicating that B. thetaiotaomicron alone is sufficient for the increased fucosylation seen in vivo. These data demonstrate that loss of NHE3 alters the intestinal environment leading to region-specific changes in bacteria and shed light on the growth requirements of some gut microbiota members, which is vital for creating better treatments of complex diseases with an altered gut microbiota.

Keywords: NHE3, Clostridium difficile, Bacteroides thetaiotaomicron, fucosylation, ileum

50 M.A. Engevik 2014

Introduction:

The alteration of normal gut microbiota is known to play a role in a variety of complex diseases such as obesity (Turnbaugh et al. 2009; Ley et al. 2005) (Ley et al. 2006; Turnbaugh et al. 2006; Backhed et al. 2004; Turnbaugh et al. 2008; Backhed et al. 2007), diabetes (Larsen et al. 2010), Inflammatory

Bowel Disease (Frank et al. 2007), (Garrett et al. 2007; Sartor 2008; Manichanh et al. 2006; Scanlan et al.

2006), Antibiotic-associated diarrhea (Young and Schmidt 2004) and Clostridium difficile-associated disease (Chang et al. 2008). These complex diseases are increasing in frequency and are associated with significant health care costs. Although these diseases each demonstrate dramatic shifts in the gut microbiota at the phylum level, it remains mechanistically unclear how certain bacterial groups are able to proliferate and maintain an altered composition. It is well documented that changes in the gut microbiota are able to impact epithelial ion transport (Donnelly et al. 1999; Li et al. 2002; Lu et al. 2003; Borenshtein et al. 2009). However, little is known about the effect of epithelial ion transport processes on the intestinal microbiota. Ion transporters are expressed in distinctive patterns based on their intestinal location, and are required for maintaining intestinal homeostasis (Talbot and Lytle 2010). Likewise, microbiota composition varies in total number and species representation along the intestinal length (Berkes et al.

2003). An understanding of the relationship of ion transport status to the microbiota is important for a clear understanding of regional microbiota changes.

Transporter null mouse models offer the unique ability to examine the microbial populations and epithelial cross-talk along the length of the intestine. We have used the Na+/H+-Exchanger isoform 3 null

(NHE3-/-) mouse model to access region-specific bacterial changes in the intestine (Schultheis PJ 1998).

NHE3 plays a critical role in intestinal sodium and water absorption and pH regulation (Schultheis PJ

1998; Gawenis et al. 2002). Due to decreased sodium and water absorption and H+ secretion, NHE3-/- mice have an enlarged cecum and colon and exhibit alkaline diarrhea (Schultheis PJ 1998). This particular mouse model is clinically relevant in that the pathogen Clostridium difficile is responsible for the majority of antibiotic associated diarrhea (Endt et al. 2010). Clostridium difficile interacts with the mucus layer

(Deneve et al. 2009; Tasteyre et al. 2001), and delivers two exotoxins, toxin A (TcdA) and toxin B

51 M.A. Engevik 2014

(TcdB) (Endt et al. 2010; Jank and Aktories 2008; Voth and Ballard 2005). These toxins result in glucosylation of Rho family GTPases, resulting in disorganization of the host cell cytoskeleton (Deneve et al. 2009) and internalization of the NHE3 (Hayashi et al. 2004), which leads to subsequent diarrhea. C. difficile infection has also been shown produce an altered microbiota composition (Young and Schmidt

2004; Chang et al. 2008). The clinical relevance of C. difficile infection highlights the importance of looking more in depth into how ion transport affects the gut microbiota.

We hypothesized that the intestinal bacterial composition of NHE3-/- mice is changed in response to an altered micro-environment. Our research focused on examination of both luminal and mucosa- associated bacterial populations along the length of the intestine and identifies specific patterns of bacterial populations in each region. These populations correlated with both environmental and epithelial changes. Although these changes are likely many, herein we focus on one example; Bacteroides thetaiotaomicron (B. thetaiotaomicron). B. thetaiotaomicron proliferation in the NHE3-/- terminal ileum appears to be driven by a sodium concentration optimum (~43 mM) and maintained by increased host fut2 mRNA and fucosylation. Together, our data links changes in ion composition and pH, as a result of altered ion transport, to changes in bacterial composition. We demonstrate that NHE3-/- mice exhibit an altered intestinal microbiota and with region-specific changes.

52 M.A. Engevik 2014

Methods:

Mice. Animal protocol was approved by the University of Cincinnati Animal Care and Use Committee and complied with National Institutes of Health guidelines. FVBN NHE3-/- mice were generated as previously described (Schultheis PJ 1998). Mice were maintained on a normal mouse diet (7922 NIH-07

Mouse diet, Harlan Laboratories, Indianapolis, IN). At 6-8 weeks post weaning, terminal ileum (<10 cm proximal to the cecum, hereafter referred to as ileum), cecum, and colon (proximal and distal) segments were collected from WT and NHE3-/- littermates. Individual intestinal segments were flushed with PBS

(pH 7.4) and mucosal scrapings collected as previously described (Frantz et al. 2012; IJssennagger et al.

2012; Deplancke et al. 2002; Norkina et al. 2004). Briefly, intestinal segments were flushed with 500 µl

PBS. The segments were then opened lengthwise, washed thoroughly with PBS and glass slides were used to scrape the epithelia and mucus layer. Luminal flushes were processed for total DNA and mucosal scrapings were processed either for total DNA or for RNA. Sample wet weight was determined and tissue homogenized with a Tissue Tearor homogenizer (Biospec Products Inc, Bartlesville, OK) for 1 min. RNA was extracted and stored at -80°C until examined by quantitative real time PCR (qRT-PCR). DNA was extracted and stored at -20°C until examined by quantitative PCR (qPCR).

Bacterial strains and culture conditions. Staphylococcus aureus, Escherichia coli, Micrococcus luteus,

Peptostreptococcus anaerobius, and Burkoholdena cepacia were from Dr. Daniel J. Hassett.

Bacteroidetes thetaiotaomicron ATCC 29741and Prevotella melaninogenica ATCC 25845 were purchased from Fisher Scientific (Thermo Fisher Scientific, Waltham, MA). Lactobacillus acidophilus and Rhibozium legaminosarum were purchased from Carolina Biological Supply Company (Carolina

Biological Supply Company, Burlington, NC). Ruminococcus productus ATCC 27340D-5 and

Faecalibacterium prausnitzii ATCC 27766 were purchased from ATCC (American Type Culture

Collection, Manassas, VA). E. coli, S. aureus, M. luteus, B. cepacia, L. acidophilus and R. legaminosarum were grown in Luria–Burtani (LB; Thermo Fisher Scientific) broth at 37 °C in a shaking incubator. B. thetaiotaomicron and P. melaninogenica were grown in TSB (Tryptone Soy broth; Thermo

53 M.A. Engevik 2014

Fisher Scientific), R. productus and F. prausnitzii were grown in TYG (Tryptone-Yeast extract-Glucose broth; Thermo Fisher Scientific), and Peptostreptococcus anaerobius was grown in Brain-heart-infusion

(BHI; Thermo Fisher Scientific) broth at 37 °C in a Coy Systems, dual-port anaerobic chamber (Coy Lab

Products, Grass Lake, MI). These cultures were used to generate qPCR standard curves.

For characterization of the optimal [Na+] for growth of B. thetaiotaomicron and Lactobacillus acidophilus bacteria were grown in media where sodium chloride (NaCl) was either removed or replaced with cesium chloride (CsCl) as previously described (Caldwell et al. 1973). B. thetaiotaomicron was grown in TSB broth. Briefly, low Na+ media was mixed with normal media at various dilutions to obtain the optimal concentration of Na+ for bacterial growth. Actual Na+ and K+ concentrations were confirmed by flame photometry (Single-Channel Digital Flame Photometer Model 02655-10; Cole-Parmer

Instrument Company Vernon Hills, IL) and Cl- concentration measured by chloridometry (Digital

Chloridometer Model 4425100, Labconco Kansas City, MO). Bacteria were grown under anaerobic conditions at 37°C with gentle shaking to early stationary phase (O.D.600 nm ~1) in normal media and used

+ to inoculate media containing varying Na levels. Growth was measured as the optical density (O.D. 600nm) using an Amersham Biosciences Ultospec 3100 Spectrophotometer (GE Healthcare Life Sciences,

Pittsburgh, PA) versus uninoculated tubes of medium. Cell concentration was determined by bacterial cell counts using a Petroff Hauser chamber (Hausser Scientific; Horsham, PA) and colony forming units

(CFU) (Caldwell et al. 1973; Caldwell and Arcand 1974; Hooper et al. 2000).

Histology. WT and NHE3-/- mouse terminal ileum, cecum, proximal and distal colon segments were fixed for 4 h at 4°C in Carnoy’s fixative and embedded in paraffin. Serial 6–7 m thick sections were applied to glass slides and stained with hematoxylin and eosin (H&E) for intestine architecture. Goblet cells were analyzed semi-quantitative by counting goblet cells per 5 regions of interest/slide, 3 slides, n=4 mice.

Expression of fucosyl glycoconjugates on the mucosal surface was examined using FITC-conjugated

Ulex europaeus agglutinin-1 (UEA-1, Vector Laboratories, Burlingame, CA) as previously described

(Hooper et al. 1999; Magalhaes et al. 2009). Briefly, sections were deparaffinized, blocked with PBS

54 M.A. Engevik 2014 containing 10% BSA, and stained with FITC-labeled lectin (10 g/ml) for 1 h at room temperature.

Sections were then washed three times in PBS, mounted using Vectashield mounting medium with DAPI

(Vector Laboratories), and analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710,

Carl Zeiss, Germany). Digital images of slides were evaluated by tabulating mean pixel intensity of the respective color channel on each image using Image J software (NIH). Five regions of interests of fixed size per slide, 3 slides per mouse, and n=4 mice were used for semi-quantitation of stain intensity.

Expression of TLR4 was examined with rat anti-mouse CD284 antibody (BioLegend, San Diego, CA) from frozen terminal ileum segments embedded in OTC. Briefly frozen sections were blocked with 10% rabbit serum in 1% BSA for 1 hr at room temperature followed by incubation with the anti-mouse CD284 antibody overnight at 4°C. Sections were washed and stained with rabbit-anti-rat ALEXA 488 secondary antibody (Life Technologies, Grand Island, NY) for 1 hr at room temperature, washed and mounted with

Vectasheild with DAPI.

Quantitative Real Time-PCR amplification of 16S sequences. Total DNA was isolated with the

QIAamp DNA Stool kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The lysis temperature was increased to 95°C and an incubation with lysozyme (10 mg/ml, 37°C for 30 min) was added to improve bacterial cell lysis as previously described (Norkina et al. 2004; Castillo et al.

2006; Fite et al. 2004; Salzman et al. 2010). The abundance of total bacteria and specific intestinal bacterial phyla, class, genus and species was measured by qPCR using a Step One Real Time PCR machine (Applied Biosystems (ABI) Life Technologies, Grand Island, NY) with SYBR Green PCR master mix (ABI) and bacteria-specific primers (Table 1) in a 20 µl final volume. Bacterial numbers were determined using standard curves from the pure bacterial cultures as previously described (Salzman et al.

2010; Ott et al. 2004; Barman et al. 2008) which correlated cycle of threshold values (CT) to calculated bacteria number.

55 M.A. Engevik 2014 qRT-PCR of mRNA. Total RNA was extracted from mucosal scrapings with TRIzol Reagent (Invitrogen

Life Technologies, Grand Island, NY) according to the manufacturer’s instructions. Reverse transcription was performed using 50 µg/ml oligo(dT) 20 primer and SuperScript reverse transcriptase (Invitrogen) according to the manufacturer’s instructions. Amplification reactions were performed with SYBR Green

PCR master mix (ABI), 200 ng sample cDNA in a 20 µl final volume on the Step One Real Time PCR

Machine (ABI). Data was reported as the delta delta CT using GAPDH as the standard. Differences in mRNA expression were determined by qRT-PCR and expressed as the CT relative fold difference.

Primers for fucosyltransferase and GAPDH were used as previously described (Meng et al. 2007):

GAPDH forward, 5'-CCTGCACCACCAACTGCTTA-3', GAPDH reverse, 5'-

ATGACCTTGCCCACAGCCT-3'; fut2 forward, 5'-AGTCTTCGTGGTTACAAGCAAC-3', reverse, 5'-

TGGCTGGTGAGCCCTCAATA-3'; fut1 forward, 5'-CAGCTCTGCCTGACATTTCTG-3', and reverse,

5'-AGCAGGTGATAGTCTGAACACA-3'; TLR4 forward: 5'-ACCTGGCTGGTTTACACGTC-3'; and

TLR4 reverse: 5'-CTGCCAGAGACATTGCAGAA-3' (Renshaw et al. 2002): MUC1 forward: 5'-

CCAGACCCCTGCACTCTGAT-3'; MUC1 reverse: 5'-CGCTTGACAAAGGGCATGA- 3' (Fu et al.

2011).

Ion and pH measurements. Intestinal flushes of WT and NHE3-/- mice were performed with 100 double deionized water. Samples were weighed, centrifuged at 3,000 rpm for 10 min at 4°C to pellet intestinal solids and the supernatant Na+ and K+ concentrations were determined using a digital Flame photometer (Single-Channel Digital Flame Photometer Model 02655-10; Cole-Parmer Instrument

Company Vernon Hills, IL). Cl- ion concentration was determined by a digital Chloridometer (Model

4425100, Labconco Kansas City, MO) and normalized to intestinal volume. pH measurements were performed electrochemically via an electronic pH meter (Orion Model 720A; Thermo Fisher Scientific

Waltham, MA) .

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Organoid culture: The terminal ileum (<10 cm proximal to the cecum) was dissected from WT and

NHE3-/- mice and washed in ice-cold Ca/Mg-free DPBS. Tissue was incubated in 2 mM EDTA for 30 min at 4 ºC. Dissociated tissues were centrifuged at 150 x g for 10 minutes and embedded in Matrigel

(BD Biosciences). After the Matrigel was polymerized, Advanced DMEM/F12 medium supplemented with R-spondin 1 (500ng/ml R&D Minneapolis, MN), penicillin/streptomycin (100/100 U/ml), HEPES

(10 mM), Glutamax (2 mM), N2 and B27 (Broomall, PA) was overlaid with Noggin (100 ng/ml,

PeproTech, Rocky Hill, NJ) and EGF (50 ng/ml, PeproTech) as previously described (Sato et al. 2009).

Organoid microinjection: We have previously used microinjection to inject cRNA into Xenopus oocytes (Cunningham et al. 1992; Drumm et al. 1991). Given that the organiods are of similar size to

Xenopus oocytes used for expression studies, a similar technique to that used for oocytes (Hitchcock et al.

1987) was used to inject the lumen of the growing organoids. Organoids derived from terminal mouse ileum tissue ranging in size from 200 to 350 µm in approximate diameter (~5 days after passage) were used. Injection needles were pulled on a horizontal bed puller (Sutter Instruments) and the tip ends broken under stereoscope view to obtain a tip diameter of ~10-15 μm. Microinjection was accomplished using a

Nanoject microinjector (Drummon Scientific Company, Broomall, PA). Injection volume was determined by the predicted organoid luminal volume. Injection volumes of ~10% or less of the organoid luminal volume were used in order to minimize stretch effects on epithelial cells and tight junctions. Under these conditions no observable leak is detected from the lumen of the organoid or from the injection site upon injection needle removal. Organoids were injected with either TBS broth alone or with TBS broth containing B. thetaiotaomicron (106 CFU). Organoids were incubated overnight, fixed with 4% paraformaldehyde, permeabilized with PBS-Triton X (0.1%) and stained with UEA-1 FITC (Vector

Laboratories) and Hoechst and imaged by confocal microscopy.

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Statistics. The data are presented as the mean ± SEM. Comparisons between two groups were made with unpaired t-tests and comparisons between genotype and region were performed using two way analysis of variance (ANOVA) and the Holme-Sidak post-hoc test to determine significance between pairwise comparisons using SigmaPlot (Systat Software Inc, San Jose, CA). P < 0.05 was considered significant while n is number of experiments.

58 M.A. Engevik 2014

Results:

NHE3-/- mice exhibit an altered intestinal environment with increased luminal Na+ and alkaline pH

NHE3-/- mice display increased fluid retention and intestinal size (Schultheis PJ 1998). In order to determine if NHE3-/- mice preserve normal intestinal architecture, H&E (Figure 1A) were done for ileum, cecum, proximal and distal colon. No gross morphological changes are observed in NHE3-/- mice compared to WT littermates. Although there appeared to be a slight decrease in goblet cell number in the

NHE3-/- distal colon (WT: 9.3 +/-0.7 vs NHE3-/-: 7.7 +/- 0.4 goblet cells/crypt) significance was not reached (P = 0.097). To examine the intestinal environment Na+ and K+ concentrations were determined by flame photometry and Cl- concentration was determined by chloridometry (Figure 2, see also Table

2). In comparison to WT mice, NHE3-/- mice had increased luminal Na+ concentration in terminal ileum, cecum, proximal and distal colon (Figure 2A). K+ concentration was increased in the cecum, proximal and distal colon but not in the terminal ileum (Figure 2B). Cl- concentration increased only in the cecum of NHE3-/- mice (Figure 2C). Although we did not directly measure bicarbonate or short chain fatty acids

(SCFA), they represent the bulk of non-Cl anions. The non-Cl anion gap was calculated by [Na+] + [K+] −

[Cl−] and is shown in Figure 2D. NHE3-/- mice exhibited a higher non-Cl anion gap than is seen in WT mice for all intestinal segments studied, representing increased bicarbonate and/or SCFA content. In addition, the NHE3-/- mice had increased pH in all segments (Table 2). These data indicate that NHE3-/- mice have an altered intestinal environment.

NHE3-/- mice exhibit altered microbiota composition in the luminal and mucosa-associated bacteria at the phylum level.

In order to determine if total bacterial numbers were increased in correlation with increased intestinal size, total DNA was extracted from luminal flushes and mucosal scrapings and analyzed by qPCR. As shown in Figure 3A, total luminal bacteria in the NHE3-/- mouse ileum, cecum and distal colon was significantly increased. No significant change was observed in the proximal colon of the NHE3-/- mice, indicating a regional difference for this intestinal segment. Total mucosa-associated bacteria

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(Figure 3B) were significantly increased in NHE3-/- mouse ileum, cecum, proximal colon and distal colon. These data indicate that there is bacterial over-representation in most of the luminal and all the mucosa-associated bacterial populations.

To further characterize the luminal and mucosa-associated bacterial populations, the major intestinal bacterial phyla were compared as a percentage of total bacteria by qPCR. As shown in the bacterial phyla representation in Figure 4A and Table 3, there was an expansion of members of the

Bacteroidetes and a contraction of those within the Firmicutes in the NHE3-/- ileum, cecum and distal colon luminal fluid. The major phylum Firmicutes decreased in the terminal ileum (13.2%), cecum

(23.1%) and distal colon (22.5%) of NHE3-/- mice, whereas the phylum Bacteroidetes were increased

(13%, 22.8%, and 25.6%, respectively). Smaller changes were also observed in the less dominant phyla

Proteobacteria in the distal colon (2.8% decrease) (Figure 4A). No significant changes were observed in phylum from the lumen of the proximal colon. These profiles reveal that there is a region-specific microbiota changes in the NHE3-/- luminal bacterial population. In the mucosa-associated bacterial population representation (Figure 4B and Table 3), there was a contraction of Firmicutes in all NHE3-/- intestinal segments (Ileum: 40.3%, Cecum: 48.9%, Proximal Colon: 58.1%, Distal Colon: 59.9%) and an expansion of Bacteroidetes, such that it became the dominant phyla (I: 44.1%, C: 44.1%, PC: 47.4%, DC:

52.5%). The over-representation of Bacteroidetes phylum members in the mucosa-associated population exceeds the expansion observed in the lumen. In addition to changes in Firmicutes and Bacteroidetes, significant changes were observed in Proteobacteria in the colon (PC: 4.2% increase, DC: 2.5% increase) and Actinobacteria in the cecum (3.2% increase) and colon (PC: 7.3% increase, DC: 5.2% increase).

These data demonstrate that the NHE3-/- mouse mucosa-associated bacterial composition is significantly transformed in response to altered ion transport status. These data also highlight the differences between the luminal and mucosa-associated bacterial population representation. Taken together, our data indicate that the NHE3-/- mouse intestine exhibits significant region-specific changes in the luminal and mucosa- associated bacterial population.

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NHE3-/- mice exhibit altered microbiota composition in the luminal and mucosa-associated bacteria at the subgroup level.

The subgroups of Firmicutes and Bacteroidetes were further examined to determine the groups responsible for the significant changes observed in the Bacteroidetes and Firmicutes phyla (Figure 4C and D). In the luminal population (Figure 4C and Table 4), changes were observed in all NHE3-/- intestinal segments. From the phylum Firmicutes, C. coccoides cluster XIVa was decreased in all the

NHE3-/- segments (I: 13.7%, C: 47.5%, PC: 9.1%, DC: 26.7%) and C. leptum cluster IV decreased in the cecum (8.0%) and colon (PC: 8.4%, DC 13.8%). The Lactobacillus/Enterococcus group was increased only in the cecum (46.5%) and another Firmicutes group was changed in the cecum (14.1% decrease) and colon (PC: 17.1% increase, DC: 18.3% increase). From the phylum Bacteroidetes, Prevotella was increased in all NHE3-/- segments (I: 3.4%, C: 4.8%, PC: 23.5%, DC: 14.2%), while Bacteroides was only increased in the NHE3-/- ileum (5.1%). Interestingly, Bacteroides was decreased in the NHE3-/- cecum and colon (C: 4.0%, PC: 14.6%, DC: 11.9%). Mouse Intestinal Bacteroidetes (MIB) was increased in the

NHE3-/- ileum (11.0%) and cecum and decreased in the NHE3-/- proximal colon (14.2%). Other

Bacteroidetes subgroups were decreased in the NHE3-/- ileum (7.3%) and increased in the NHE3-/- cecum

(3.0%) and colon (PC: 4.8%, DC: 21.4%). Although the overall phylum did not change in the NHE3-/- proximal colon lumen, there were changes in Firmicutes and Bacteroidetes subgroups, indicating that the microbiota composition is altered in all NHE3-/- segments.

In the mucosa-associated bacterial population (Figure 4D and Table 4), C. coccoides cluster

XIVa (I: 11.5%, C: 35.4%, PC: 25.0%; DC: 44.0%), C. leptum cluster IV (I: 2.6%, C: 4.2%, PC: 6.8%,

DC: 8.1%), and Lactobacillus/Enterococcus group (I: 16.9%, C: 4.2%, PC 3.6%, DC: 3.1%) decreased in all NHE3-/- segments. Likewise, Bacteroides was decreased in the NHE3-/- ileum (7.6%) and Prevotella was decreased in the NHE3-/- ileum (3.6%), cecum (1.9%) and proximal colon (3.3%). However

Prevotella increased in the NHE3-/- distal colon (5.5%) and MIB increased in representation in all the

NHE3-/- segments (I: 40.5%, C: 46.3%, PC 40.8%, DC: 50.7%). In addition another unspecified

Bacteroidetes subgroup was increased in the NHE3-/- ileum (14.8%) and proximal colon (10.0%) while

61 M.A. Engevik 2014 decreased in the distal colon (3.3%). These data indicate that, in the mucosa-associated bacterial population, Clostridium clusters and Lactobacillus/Enterococcus groups are responsible for the decrease in the Firmicutes phyla while the MIB group is largely responsible for the significant increase in the

Bacteroidetes phyla.

The genus Bacteroides makes up a substantial portion of Bacteroidetes in the gastrointestinal tract and the species Bacteroides thetaiotaomicron is a well characterized Bacteroides member found in both the human and mouse intestine (Hooper et al. 2000; Bjursell et al. 2006; Bry et al. 1996; Comstock and

Coyne 2003; Moore and Holdeman 1974; Hansen et al. 2012; Xu et al. 2003). Based on the increased luminal [Na+], we sought to determine if B. thetaiotaomicron was responsible for the increase in luminal

Bacteroides in the NHE3-/- terminal ileum. Figure 5A shows that B. thetaiotaomicron is indeed increased in NHE3-/- ileum. No increase in B. thetaiotaomicron was observed in the cecum, proximal or distal colon. B. thetaiotaomicron accounted for 11.3% in WT and 31.7% in NHE3-/- of the genus Bacteroides which represents 33% of the observed Bacteroides species increase. B. forsythus, B. fragilis, B. distasonis and B. vulgaris were not increased in the NHE3-/- terminal ileum (Figure 5B). These data indicate that

Bacteroides, specifically B. thetaiotaomicron proliferation, is partially responsible for the Bacteroidetes overgrowth in the NHE3-/- terminal ileum lumen.

In vitro B. thetaiotaomicron growth is significantly enhanced at [Na+] observed in NHE3-/- mouse terminal ileum

In order to determine if B. thetaiotaomicron over-representation was indeed due to the altered luminal ion composition caused by loss of NHE3, B. thetaiotaomicron growth was tested in vitro. TSB at varying Na+ concentrations, intended to mimic the intestinal Na+ concentration observed in WT and

NHE3-/- mice, was used to determine B. thetaiotaomicron growth. Na+ and K+ concentration were measured in the modified TSB by flame photometry. Na+ concentrations used were 19, 26, 33.6, 43.5,

57.1, 65.9, and 106.5 mM, values that closely resembled the in vivo concentrations (Table 2). K+ concentration averaged 43.2±0.1 mM (ranging 42.8 to 43.6 mM) for all solutions. After 20 hrs of growth,

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bacterial content was examined by O.D.600 nm, cell counts and CFU (O.D.600 nm data are shown in Figure

5C). The data shows that B. thetaiotaomicron has optimal growth at 43 mM Na+, and that the greatest increase in growth occurs over the range of [Na+] measured in the terminal ileum of WT and NHE3-/- mice (annotated by the bars and arrows). Slight growth rate changes were observed over the [Na+] range from other intestinal segments in WT and NHE3-/- mice. The specific effect of [Na+] on B. thetaiotaomicron growth was further confirmed by varying the Na+ concentration in TSB by cation replacement with Cs+ (Figure 5D and E). The NHE3-/- mouse intestinal fluid is more alkaline than WT littermates (Table 2). Thus, we examined the effect of varying pH on B. thetaiotaomicron growth at WT

(33 mM Na+) and NHE3-/- (43 mM Na+) concentrations. Whereas increased B. thetaiotaomicron growth correlated well with terminal ileum [Na+], no significant correlation was seen with varying pH (Figure

5F). These data indicate that both in vivo and in vitro B. thetaiotaomicron is capable of using Na+ to enhance its growth and proliferation in the terminal ileum and that 43 mM Na+ provides an optimal concentration for its growth. It is important to note that this change occurs within the physiologic range of intestinal Na+, thus supporting the notion that ion transport status plays a role in microbial niche development.

Increased B. thetaiotaomicron correlates with increased fut2 mRNA expression and surface fucosylation in vivo in NHE3-/- ileum and in vitro in mouse organoids

Luminal B. thetaiotaomicron is able to secrete α-fucosidases which extract fucose residues from mucus glycans (Hooper et al. 1999; Xu et al. 2003) and stimulates fut2 mRNA transcription and fucosyltransferase activity to increase mucus fucosylation, which is used as an energy source by the bacterium (Hooper et al. 1999; Meng et al. 2007; Coyne et al. 2005). We hypothesized that increased B. thetaiotaomicron in NHE3-/- mouse terminal ileum would stimulate increased fucosylation, and thus provide a positive nutrient feedback mechanism that would further enhance B. thetaiotaomicron growth.

To determine if B. thetaiotaomicron was altering host fucoysltransferase activity, we examined fut mRNA by qRT-PCR. As shown in Figure 6A and B, no significant changes were observed in fut1 mRNA,

63 M.A. Engevik 2014 however the dominant form fut2 mRNA was increased in NHE3-/- terminal ileum (6 +/- 1 relative fold change). No significant change in fut2 mRNA was observed in NHE3-/- cecum, proximal or distal colon compared to WT littermates. Thus increased B. thetaiotaomicron in the NHE3-/- terminal ileum correlates with increased fut2 mRNA levels. In order to determine if increased fut2 mRNA levels reflect increased fucosyltransferase activity a α-1,2-fucose specific lectin, UEA-1, conjugated to FITC was used to assess fucosylation in intestinal segments. Indeed, fucosylation was increased only in NHE3-/- terminal ileum

(Figure 6C). No significant changes in fucosylation were observed in the cecum or colon of NHE3-/- verses WT littermates.

To confirm that B. thetaiotaomicron was indeed responsible for the fucosylation changes observed in the NHE3-/- ileum, B. thetaiotaomicron was injected into WT and NHE3-/- terminal ileum organoids and fut2 mRNA and fucosylation were examined (Figure 8). WT ileum organoids closely resemble mouse ileum tissue: both contain mRNA for ion transporters such as CFTR, NHE1, NKCC1,

NBC1, DRA, AE2, NHE3, GLUT 1, GLUT 2 and GLUT 5, in addition to mucins (MUC 1, MUC2, MUC

5AC) and fut1 and fut2 mRNA (Figure 7A-C). The organoids also secrete MUC2 mucus glycoproteins

(Figure 7D). Although the NHE3-/- terminal ileum shows elevated fucosylation, as seen in Figure 6C,

NHE3-/- organoids did not have high baseline fucosylation as a direct result of NHE3 loss (Figure 8A and

B). It is noteworthy, that in the preparation of the organoid culture the endogenous microbiota are removed. Injection of B. thetaiotaomicron into WT as well as NHE3-/- organoids resulted in increased fut2 mRNA and fucosylation, confirming that increased B. thetaiotaomicron in the NHE3-/- ileum is sufficient for the observed in vivo fucosylation. Fucosylation represents a mechanism of host-bacteria interaction and increased fucosylation reflects the ability of B. thetaiotaomicron to adjust to intestinal environment and other bacterial composition changes.

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Increased B. thetaiotaomicron and TLR4 correlates with IFNγ mRNA and MUC1 mRNA expression in

NHE3-/- ileum

Gram negative bacteria, such as Bacteroides, have been shown to stimulate Toll-Like Receptor 4

(TLR4), a transmembrane receptor which recognizes specific repetitive patterns of bacterial products

(Spiller et al. 2008). Certain bacteria have been found to upregulate of TLR4 at the intestinal epithelium

(Furrie et al. 2005). We hypothesized that the MD exhibited by the NHE3-/- mice may drive upregulation of TLR4. Analysis of TLR4 mRNA by qRT-PCR (Figure 9A) and protein by immunohistochemistry

(Figure 9B) revealed that both mRNA and protein are increased in NHE3-/- terminal ileum. Recent work has shown that TLR recognize commensal bacteria under normal conditions and that this interaction is required for both normal intestinal maintenance and for protection against gut injury (Rakoff-Nahoum et al. 2004).

Activation and upregulation of TLR4 is associated with cytokine production, specifically IFNγ

(Spiller et al. 2008; Soliman et al. 2010). In conditions such as IBD and parasite Toxoplasma gondii infection, increased Gram-negative bacteria, such as E. coli and Bacteroides, signal through Toll-Like

Receptor 4 (TRL 4), which increases IFNγ in terminal ileum (Heimesaat et al. 2006a). To determine if upregulation of TLR4 in the NEH3-/- terminal ileum correlated with IFNγ levels, mRNA was examined by qRT-PCR. IFNγ mRNA was found to be increased in the NHE3-/- ileum (Figure 9C). The previous

NHE3 study has shown that NHE3-/- mice do exhibit elevated blood IFNγ (Woo et al. 2002). IFNγ has been shown signal through STAT1 to stimulate increased MUC1 production (Jonckheere and Van

Seuningen 2010). MUC1 mRNA was examined by qRT-PCR and was found to be increased in the NHE3-

/- ileum (Figure 9D). Increased MUC1 has been shown to orchestrate a number of functions, including inhibition of apoptosis (McAuley et al. 2007), regulating other mucins (Malmberg et al. 2006), regulating

TLR expression (Ueno et al. 2008), and maintaining intestinal barrier function (McAuley et al. 2007). It has been suggested that released MUC1 and its oligosaccharides may act as a decoy ligand for bacterial adhesins, thus limiting bacterial attachment (McAuley et al. 2007). Increased MUC1 transcription in the

NHE3-/- ileum may act to prevent the increased mucosa-associated bacteria from interacting with the

65 M.A. Engevik 2014 epithelium. Taken together, these data demonstrate that changes in ion transport can alter the intestinal environment and thereby result in changes in bacteria representation or MD. These bacteria are then capable of changing the epithelia in a manner that allows for further niche development thus maintaining the MD. All this data depicts a story of host-bacterial interaction (Figure 10) in the NHE3-/- terminal ileum where changes in intestinal Na+ results in increased gram negative bacteria B. thetaiotaomicron. B. thetaiotaomicron which then stimulates fut2 mRNA and mucus fucosylation. In addition activation of

TLR4 stimulates IFNγ which stimulation increased MUC1. MUC1 likely acts to limit bacterial-epithelial interaction, thereby maintaining the appropriate bacterial-immune interaction. Together these results demonstrate that changes in ion transport can alter the intestinal environment and thereby result in changes in bacteria representation. Certain bacteria are also capable of modifying the epithelia in a manner that allows for further proliferation and niche maintenance.

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Discussion:

Recent studies have shown that the intestinal epithelium is not simply a static barrier; the epithelium is in a constant “cross talk” with the gut microbiota and the external environment (Berkes et al.

2003). Environment and bacterial cues signal changes in ion transport, pH regulation, tight junction function, and activation of inflammatory cascades (Berkes et al. 2003). Although pathogens from a variety of phyla have been shown to alter certain host functions, like ion transport, the overall role of an altered intestinal environment on bacteria, pathogens and commensal bacteria alike, remains largely undetermined. This study provides a seminal examination of changes in the gut microbiota as a result of altered ion transport. Our data demonstrate that single transporter null mice represent a valid model to better understand mechanisms of host bacterial niche development and region specific intestinal microbiota changes. In addition we demonstrate that B. thetaiotaomicron in vitro is Na+ sensitive in the physiologic range and increased B. thetaiotaomicron directly correlates with increased fut2 mRNA and fucosylation, in vivo and in vitro, revealing host–bacterial interactions which further enhance B. thetaiotaomicron bacterial proliferation. Collectively our work indicates that altered bacterial growth occurs in response to an altered intestinal environment and that Na+ and pH play a key role in shaping the gut microbiota.

Very few studies have focused on the effect of ion transport on the gut microbiota. An altered gut microbiota was demonstrated in a mouse model of cystic fibrosis (CF) (Thomsson et al. 2002) where the chloride transporter CFTR was knocked out. This model displayed decreased members of Acinetobacter lwoffii and several Lactobacilliales members in addition to significant enrichment of Mycobacteria and

Bacteroides in the ileum. Additionally increased fut2 mRNA and fucosylation were observed in the small intestine of the CF mice (Thomsson et al. 2002) and in humans with CF (Thiru et al. 1990). In the CF mice, there was an increase in total bacteria in the small intestine and the authors hypothesized that bacteria-host interactions were responsible for the induction of increased fut2 and fucosylation. Data presented herein conclusively identifies increased B. thetaiotaomicron and increased fut2 and fucosylation in a mouse model with a complex microbiota. While CFTR is crucial for trans-epithelial anion transport,

67 M.A. Engevik 2014 loss of chloride secretion also results in mucus accumulation and may provide a new niche for abnormal bacterial colonization.

NHE3-/- mice do experience diarrhea and some of the changes in the gut microbiota could be a result of diarrhea; however based on previous literature we believe that the dominant changes in the gut microbiota are in fact due to changes in ion transport. In the mouse model of non-typhoidal Salmonella

Diarrhea, Endt et al. demonstrated that Salmonella infection and diarrhea resulted in decreased Firmicutes members and decreased Bacteroidetes members, with increased Proteobacteria members (Endt et al.

2010). Barman et al. (Barman et al. 2008) also demonstrated decreased Eubacterium rectale/Clostridium coccoides group (Firmicutes) and Bacteroides (Bacteriodetes) in the cecum and colon of mice infected with Salmonella for 7 days. In addition Enterobacteriaceae (Proteobacteria) was found to be increased in the cecum and colon. No changes were observed in Lactobacillus or MIB groups. In humans, osmotic diarrhea from oral ingestion of PEG was found in general to increase Firmicutes members in the stool and decrease Bacteroidetes members in the mucosa-associated bacterial population. Moreover, diarrhea led to a relative increase in the abundance of Proteobacteria (Gorkiewicz et al. 2013). The data herein demonstrate that NHE3-/- mice have increased Bacteroidetes and decreased Firmicutes phyla both in the luminal and mucosa-associated bacterial populations. Since NHE3-/- bacterial composition differs from the bacterial composition seen in diarrhea, the data are consistent with the bacteria in the NHE3-/- being predominantly affected by changes in the intestinal environment as a result of the loss of NHE3.

The increases observed in the luminal and mucosa-associated subgroups of Bacteroidetes indicate that the intestinal environment set by NHE3 regulates the Bacteroidetes phylum significantly. We show that ion transport status alters the intestinal microenvironment in ways that are advantageous for certain bacterial groups. pH changes have been shown to affect bacterial growth. High (alkaline) pH resulting from Streptomycin use has been shown to result in increased gram-negative Bacteroidetes and decreased gram-positive Firmicutes (Schjorring and Krogfelt 2011; Sekirov et al. 2008), similar to the profile observed in the NHE3-/- intestine. GI bacteria have been shown to rely on bacterial cation/proton antiporters when exposed to alkaline pH conditions. Studies of gram-negative E. coli have demonstrated a

68 M.A. Engevik 2014 key role in Na+ extrusion and proton capture at high pH homeostasis (Pinner et al. 1993; Padan et al.

2004; Padan 2008). As one might expect, these Na+-coupled ATPase transporters are increased in response to elevated pH or elevated Na+ (Kakinuma 1998; Ikegami et al. 1999). A myriad of studies have also demonstrated that Na+ can be used by bacteria for many aspects of their fundamental physiological processes (Skulachev 1991, 1985), (Skulachev 1989; Speelmans et al. 1993; Wilson and Wilson 1987;

Avetisyan et al. 1993; Hilpert et al. 1984). Bacteroides spp. require Na+ for growth (Caldwell et al. 1973) and Na+ and K+ ions were found to independently affect the growth rate and growth yield of B. amylophilus (Caldwell et al. 1973), B. ruminicola, B. oralis, and B. succinogenes S-85, respectively

(Caldwell and Arcand 1974). Mutational studies of B. thetaiotaomicron has shown that mutating genes involved in sensing Na+ gradients resulted in a loss of bacterial fitness (Goodman et al. 2009). In the terminal ileum, where there is low expression of oligosaccharides, an additional proliferator factor, such as Na+ may provide B. thetaiotaomicron a competitive edge.

Data presented herein also demonstrate that mucosa-associated bacterial populations respond more dramatically to changes in sodium and pH caused by the loss of NHE3 than the luminal bacteria. It is probable that the mucosa-associated bacteria might be more influenced by the host genetics as they are in closer physical proximity to the host epithelia. However, clearly ion transport status affects both luminal and mucosa-associated representation of bacteria. Dramatic changes in the mucosa-associated bacterial population are also observed in mouse models with genetic host alterations (Frantz et al. 2012;

Salzman et al. 2010; Thomsson et al. 2002; Bergstrom et al. 2010; Van der Sluis et al. 2006). We hypothesize that the luminal bacterial population may be more affected by diet induced changes

(IJssennagger et al. 2012; Everard et al. 2011; Serino et al. 2012) and luminal microenvironment, whereas the mucosa associated population may be more influenced by host changes, such as immunity and antimicrobial concentration (Frantz et al. 2012; Salzman et al. 2010), mucus production (Frantz et al.

2012; Bergstrom et al. 2010; Van der Sluis et al. 2006) or changes in ion transport (Thomsson et al.

2002).

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It should be noted that NHE3-/- mice from our facility do not display overt intestinal inflammation, whereas NHE3-/- mice of the same source (Gary E. Shull) housed elsewhere do show signs of active inflammation (Laubitz et al. 2008). Our NHE3-/- mice do exhibit increased IFNγ and iNOS mRNA in the ileum and IL-1β mRNA in the distal colon (Figure 9C and Figure 11) and have elevated blood IL-1β and IFNγ (Woo et al. 2002), but there is no increase in proinflammatory cytokine mRNA of

IL-18, TNF-α, NOS2, and IL-10 (Laubitz et al. 2008; Larsen et al. 1990). The cytokines observed in the

NHE3-/- mouse may also represent a sub-threshold immune response due to increased gram negative bacteria as observed in other models (Bergstrom et al. 2010; Van der Sluis et al. 2006; Heimesaat et al.

2006b). In conditions such as IBD and parasite Toxoplasma gondii infection, increased Gram-negative bacteria, such as E. coli and Bacteroides, signal through Toll-Like Receptor 4 (TRL 4), which increases

IFNγ and iNOS in terminal ileum (Heimesaat et al. 2006b). Studies with mono-associated B. thetaiotaomicron have also demonstrated increased cytokine production (Larsen et al. 1990; Peterson et al. 2007). In contrast, increased MIB has been linked with protection against inflammation with S.

Typhimurium colonization (Ferreira et al. 2011). Increased MIB in the NHE3-/- intestine may dampen down large scale inflammatory responses, contributing to the low level of inflammatory response observed in our mice. The altered intestinal microbiota exhibited by the NHE3-/- may make these mice vulnerable to subsequent pathogen infection in other mouse facilities. However, for the mice housed at our University’s animal facility, the analysis of the gut microbiota are not complicated by an active, robust inflammatory response. Nevertheless, careful comparative study of differences between similar mice with or without overt inflammation should prove valuable in determining the key factors (e.g., bacterial species) leading to an inflammatory response or pathogen susceptibility.

NHE3 is also clinically relevant for analysis as it is a target of Clostridium difficile toxin B

(Hayashi et al. 2004). C. difficile is a leading cause of nosocomial enteric infections. According to the

CDC, C. difficile affects over half a million people in America yearly and the incidence of non-hospital acquired C. difficile infection is increasing. Since NHE3 is involved in Na+ and water regulation, a large percentage of C. difficile infected patients suffer from diarrhea (Kelly and LaMont 1998; Mylonakis et al.

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2001). In humans, increased Bacteroidetes and decreased Firmicutes have been shown to be associated with C. difficile associated disease (CDAD) (Young and Schmidt 2004; Chang et al. 2008). In mice infected with C. difficile, increased Bacteroides coordinately occurred in the ileum (Naaber et al. 1998).

Such alterations resemble the microbial changes observed in the NHE3-/- mouse model. Thus, it is likely that C. difficile toxin B inhibition of NHE3 serves to alter the intestinal microbiota and thus create a more suitable environment for C difficile infection. Further studies are necessary, and underway, to determine if the changes in the gut microbiota seen in C. difficile-infection is predominately due to inhibition of NHE3 rather than C. difficile-bacterial interaction. Taken together the results presented herein propose that ion transport plays a key role not only in regulating the intestinal environment but also establishing bacterial niches. This can be clinically advantageous because a number of ion transporter drugs could be used in various disease states to alter the intestinal environment (regionally specific to transporter expression) in a manner that promotes or represses growth of certain bacterial groups. Such therapy could potentially be used to rebalance the intestinal microbiota after a shift with minimal effect on propagating anti-biotic resistant bacterial strains.

Acknowledgments. We sincerely would like to thank our undergraduate volunteers Khushboo Patel,

Christine Ciriaco, Kristen Engevik, Robert Phan, Katie Anglin, and Fatma Rah for their assistance. This work is in partial fulfillment of the Ph.D. degree for Melinda A. Engevik. Supported in part by NIH

DK079979 to RTW and DK050594 to GES.

71 M.A. Engevik 2014 Table 1. Primer sequences for qRT-PCR of total bacteria and specific bacterial phyla and groups. Type Bacteria Forward Reverse Reference (Barman et al. 2008), (Fierer et Total Universal (Total Bacteria) ACTCCTACGGGAGGCAGCAG ATTACCGCGGCTGCTGG al. 2005), (Guo et al. 2008) Phyla Bacteriodetes GGCGACCGGCGCACGGG GRCCTTCCTCTCAGAACCC (Guo et al. 2008) Phyla Firmicutes GGAGYATGTGGTTTAATTCGAAGCA AGCTGACGACAACCATGCAC (Fierer et al. 2005) Phyla Actinobacteria CGCGGCCTATCAGCTTGTTG ATTACCGCGGCTGCTGG (Fierer et al. 2005) Phyla α-proteobacteria ACTCCTACGGGAGGCAGCAG TCTACGRATTTCACCYCTAC (Fierer et al. 2005) Phyla β-proteobacteria CCGCACAGTTGGCGAGATGA CGACAGTTATGACGCCCTCC (Fierer et al. 2005) Phyla y-Proteobacteria GAGTTTGATCATGGCTCA GTATTACCGCGGCTGCTG (Lee et al. 2009) (Salzman et al. Class Clostridium coccoides cluster XIVa ACTCCTACGGGAGGCAGC GCTTCTTAGTCAGGTACCGTCAT 2010) (Salzman et al. Class Clostridium leptum cluster IV GTTGACAAAACGGAGGAAGG GACGGGCGGTGTGTACAA 2010) (Salzman et al. Class Lactobacillus/Enterococcus AGCAGTAGGGAATCTTCCA CACCGCTACACATGGAG 2010) (Salzman et al. Genus Bacteriodes GGTTCTGAGAGGAGGTCCC CTGCCTCCCGTAGGAGT 2010) (Dalwai et al. Genus Prevotella CCAGCCAAGTAGCGTGCA TGGACCTTCCGTATTACCGC 2007) (Salzman et al. Genus Mouse Inestinal Bacteria (MIB) CCAGCAGCCGCGGTAATA CGCATTCCGCATACTTCTC 2010) (Sonnenburg et al. Species Bacteriodes thetaiotaomicron GGTAGTCCACACAGTAAACGATGAA CCCGTCAATTCCTTTGAGTTTC 2005) (Malinen et al. Species Bacteroides fragilis GAAAGCATTAAGTATTCCACCTG CGGTGATTGGTCACTGACA 2003) Species Bacteroides vulgatus GCATCATGAGTCCGCATGTTC TCCATACCCGACTTTATTCCTT (Wang et al. 1996) Species Bacteroides distasonis GTCGGACTAATACCGCAT TTACGATCCATAGAACCTTCAT (Wang et al. 1996) (Fujigaki et al. Species Bacteroides forsythus TCACTATTGTGTCTCGCTG TCTCTCCGATTGTGGTTA 1999)

Table 2. Luminal ion concentration (mM) and pH in WT and NHE3-/- intestinal segments. 72 M.A. Engevik 2014

Terminal Ileum Cecum Proximal Colon Distal Colon Measurement WT NHE3-/- WT NHE3-/- WT NHE3-/- WT NHE3-/- Na+ 32.7 ± 1.2 42.8 ± 0.5* 54.0 ± 1.8 109 ± 1* 23.8 ± 1.7 36.6 ± 4.7* 16.0 ± 1.8 24.5 ± 2.2* K+ 11.8 ± 0.7 10.6 ± 0.9 39.7 ± 1.9 49.8 ± 2.4* 10.7 ± 0.9 19.0 ± 0.7* 10.2 ± 1.0 24.2 ± 0.8* Cl- 16.0 ± 1.1 18.6 ± 1.0 16.1 ± 0.7 34.4 ± 0.7* 17.1 ± 2.9 15.9 ± 1.1 11.3 ± 0.3 9.5 ± 0.9 Anion Gap# 19.0 ± 2.2 36.0 ± 3.1* 71.4 ± 4.0 121 ± 11* 16.0 ± 2.5 53.0 ± 4.8* 8.9 ± 4.7 47.1 ± 4.3* pH 7.2 ± 0.1 8.0 ± 0.0* 6.9 ± 0.1 7.8 ± 0.1* 7.3 ± 0.2 8.0 ± 0.1* 7.2 ± 0.2 7.7 ± 0.0*

# + + - - - -/- Calculated as (Na +K -Cl ) ion concentrations, the value predominately represents HCO3 and SCFA anions. n=8 for WT and n=6 for NHE3 groups. An interaction exists between genotype and region for Na+ (P = 0.007), Cl- (P<0.001), and pH (P<0.001) but not for K+ (P=0.382) or Anion Gap (P = 0.703), although the concentrations are statistically different (K+ P=0.006; Anion Gap P=0.002). 2 WAY ANOVA, Holme-Sidak *P<0.005.

73 M.A. Engevik 2014 Table 3. Bacterial phyla statistics between WT and NHE3-/- intestinal segments. NS = not significant.

Luminal Mucosa-associated

Δ of Δ of Bacteria Region P Value Interaction P Value Interaction means means Firmicutes Ileum 13.2 <0.001 40.3 <0.001 Cecum 23.1 <0.001 P = <0.001 48.9 <0.001 P = <0.001 Proximal Colon 1.1 NS 58.1 <0.001 Distal Colon 22.5 <0.001 59.9 <0.001 Bacteroidetes Ileum 13.0 <0.001 44.1 <0.001 Cecum 22.8 <0.001 P = <0.001 44.1 <0.001 P = 0.001 Proximal Colon 0.1 NS 47.4 <0.001 Distal Colon 25.6 <0.001 52.5 <0.001 Actinobacteria Ileum 0.1 NS 1.2 NS Cecum 0.1 NS NS 3.2 <0.001 P = <0.001 Proximal Colon 0.3 NS 7.3 <0.001 Distal Colon 0.4 NS 5.2 <0.001 Proteobacteria Ileum 0.7 NS 0.3 NS Cecum 0.1 NS P = <0.001 0.1 NS P = <0.001 Proximal Colon 0.5 NS 4.2 <0.001 Distal Colon 2.8 <0.001 2.5 <0.001 Unspecified Ileum 0.8 NS 2.3 NS Cecum 0.2 NS NS 1.8 NS NS Proximal Colon 1.2 NS 0.8 NS Distal Colon 0.7 NS 0.4 NS

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Table 4. Bacterial subgroup statistics between WT and NHE3-/- intestinal segments. NS = not significant

Lumin Mucosa-associated al

Δ of P Interacti Δ of P Interacti Bacteria Region Phyla means Value on means Value on C. coccoides cluster Ileum Firmicutes XIVa 13.7 <0.001 11.5 <0.001 P = P = Cecum 47.5 <0.001 <0.001 35.4 <0.001 <0.001 Proximal

Colon 9.1 <0.001 25.0 <0.001 Distal Colon 26.7 <0.001 44.0 <0.001 C. leptum cluster IV Ileum 1.6 NS 2.6 <0.001 P = P = Cecum 8.0 <0.001 <0.001 4.2 <0.001 <0.001 Proximal

Colon 8.4 <0.001 6.8 <0.001 Distal Colon 13.8 <0.001 8.1 <0.001 Lactobacillus/Enter Ileum NS ococcus 1.0 16.9 <0.001 P = P = Cecum 46.5 <0.001 <0.001 4.2 <0.001 <0.001 Proximal NS Colon 0.9 3.6 <0.001 Distal Colon 0.3 NS 3.1 <0.001 Other Firmicutes Ileum 2.1 NS 9.1 <0.001 P = P = Cecum 14.1 <0.001 <0.001 5.1 <0.001 <0.001 Proximal

Colon 17.1 <0.001 22.6 <0.001 Distal Colon 18.3 <0.001 4.7 <0.001 Bacteroidetes Prevotella Ileum 3.4 <0.001 3.6 <0.001 P = P = Cecum 4.8 <0.001 <0.001 1.9 NS <0.001 Proximal

Colon 23.5 <0.001 3.3 <0.001 Distal Colon 14.2 <0.001 5.5 <0.001 Bacteroides Ileum 5.1 <0.001 7.6 <0.001 P = P = Cecum 4.0 <0.001 <0.001 0.6 0.195 <0.001 Proximal

Colon 14.6 <0.001 0.1 1.000 Distal Colon 11.9 <0.001 0.3 1.000 MIB Ileum 11.0 <0.001 40.5 <0.001 P = P = Cecum 19.0 <0.001 <0.001 46.3 <0.001 <0.001 Proximal 14.2 <0.001 40.8 <0.001

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Colon Distal Colon 1.9 NS 50.7 <0.001 Other Bacteroidetes Ileum 6.5 <0.001 14.8 <0.001 P = P = Cecum 3.0 <0.001 <0.001 0.3 NS <0.001 Proximal

Colon 4.8 <0.001 10.0 <0.001 Distal Colon 21.4 <0.001 3.3 <0.001

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Figures:

Figure 1. NHE3-/- mice have normal histology. Mucosal morphology shown in H&E stained sections from WT and NHE3-/- mouse terminal ileum, cecum, proximal and distal colon demonstrating no gross alteration of the mucosal architecture. Micrographs are representative of observations from all mice n=4.

Scale bar = 50 µM.

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Figure 2. Ion concentrations in luminal fluid from WT (black bar) and NHE3-/- (white bar) mouse intestinal segments. A) Sodium concentration as determined by flame photometry was significantly increased in all measured segments from NHE3-/- verses WT littermates. B) Potassium concentration as determined by flame photometry was significantly increased in cecum and colon but not terminal ileum of

NHE3-/- verses WT littermates. C) Chloride concentration as determined by chloridometry was significantly increased only in the cecum of NHE3-/- verses WT littermates. D) Calculated Anion Gap + bicarbonate ([Na+] + [K+] − [Cl−]) was significantly increased in all measured segments from NHE3-/- verses WT littermates. n=5 for WT and n=3 for NHE3-/- . * P<0.05.

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Figure 3. NHE3-/- mice exhibit region-specific increases in total bacteria. Total bacteria were quantified by qPCR using a universal bacterial 16S DNA sequence. Calculated bacterial cell number was calculated using an E. coli standard curve normalized to intestinal flush volume. A) Luminal bacterial levels in WT

(black bar) and NHE3-/- (white bar) littermates (n=6). Significant increases in total bacteria in NHE3-/- samples were observed in all but the proximal colon segment. B) Mucosa-associated (adherent) bacterial levels in WT (black bar) and NHE3-/- (white bar) littermates (n=6). Significant increases in total bacteria were observed all in NHE3-/- all mucosal regions tested. * P>0.005.

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Figure 4. NHE3-/- mice exhibit an altered gut microbiota in luminal and mucosa-associated bacterial populations. Relative bacterial phyla abundance was calculated as the percentage of bacterial phyla in comparison to total bacteria for luminal A) and mucosa-associated bacteria B). NHE3-/- mice showed a disproportionate amount of Bacteriodetes relative to Firmicutes phyla compared to WT littermates (n=6).

In the luminal bacterial population significant interaction between genotype and region was observed in

Firmicutes (P = <0.001), Bacteroidetes (P<0.001) and Proteobacteria (P<0.001). No interaction was observed in Actinobacteria (P=0.641) or unspecified (P= 0.409). In the mucosa-associated bacterial population significant interaction between genotype and region was observed in Firmicutes (P = <0.001),

Bacteroidetes (P=0.001), Actinobacteria (P<0.001), Proteobacteria (P<0.001) and unspecified bacteria

(P<0.001). Relative abundance of luminal C) and mucosa-associated D) bacterial subgroups of Firmicutes and Bacteroidetes phyla. Relative abundance was calculated as the percentage of bacterial subgroup in comparison to total bacteria. Regional changes were observed in the Firmicutes subgroup C. coccoides cluster XIVa, C. leptum cluster IV and Lactobacillus/Enterococcus group. Changes were also observed in the Bacteroidetes subgroup Prevotella, Bacteroides, and MIB in the NHE3-/- mouse luminal and mucosa- associated bacterial populations. Significant interaction was observed between genotype and region for all groups (P<0.001) n=6 for WT and NHE3-/-. 2 way ANOVA, Holme-Sidak * P>0.005.

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Figure 5. In vivo and in vitro growth of B. thetaiotaomicron. A) Calculated bacterial cell number for the

Bacteroides species Bacteroides thetaiotaomicron (B. thetaiotaomicron). Bacterial cell numbers were calculated from a standard curve using a pure culture of B. thetaiotaomicron. B. thetaiotaomicron was significantly increased only in NHE3-/- terminal ileum verses WT littermates. This increase accounts for

33% of the increased Bacteriodetes. n=6 for WT (black bar) and n=6 for NHE3-/- (white bar) *P>0.005.

B) In vivo growth of Bacteroides members. Calculated bacterial cell number for the Bacteroides species.

No changes were observed for any Bacteroides species between WT (black bar) and NHE3-/-(white bar)

(n=6). C) Growth of B. thetaiotaomicron in TSB broth at varying concentrations of Na+ which mimic those seen in vivo for NHE3-/- and WT intestinal fluid (Table 2). The steepest change in B. thetaiotaomicron growth is observed at a [Na+] range that correlates directly with that seen in the WT and

NHE3-/- terminal ileum (n=3). [Na+] ranges for each intestinal segment in WT and NHE3-/- are displayed as bars along the x-axis. Arrows indicate in vivo values for terminal ileum. C and D) In vitro growth of B. thetaiotaomicron in TSB broth at varying concentrations of Na+ by addition of CsCl or KCl. The steepest change in B. thetaiotaomicron growth is observed at a [Na+] range that correlates with NHE3-/- terminal ileum, similar to Figure 6B (n=3). E) Growth of B. thetaiotaomicron in TSB broth at varying pH which mimics that seen in vivo for NHE3-/- and WT intestinal fluid (Table 2). Growth was determined at 33 mM

Na+ () mimicking WT terminal ileum and 43 mM Na+ (○) mimicking NHE3-/- ileum. No significant difference was observed in B. thetaiotaomicron growth within the pH range seen in WT and NHE3-/- terminal ileum (indicated by the bar). n=3. Bars and arrows indicate in vivo values.

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Figure 6. Increased fut2 and fucosylation are observed in the NHE3-/- terminal ileum. mRNA expression was determined by qRT-PCR and expressed as the CT relative fold difference. A) No significant difference is observed in fut1 mRNA expression between NHE3-/- (white bar) and WT (black bar) littermate mice. B) fut2 mRNA is significantly increased only in NHE3-/- (white bar) mouse terminal ileum verses WT (black bar) littermates. No significant difference in fut2 expression is observed in mucosal segments distal to the terminal ileum. This directly correlates with B. thetaiotaomicron levels shown in Figure 5A. n=6. * P<0.05. C) Significant increase in fucosylation was only observed in terminal ileum of NHE3-/- verses WT mice with fucosylated residues apparent from cypt to villus tip

(depicted by the arrows). This increase directly correlates with increased fut2 mRNA and Bacteroides levels in the terminal ileum of the NHE3-/- mice. Fucosylation was determined by UEA-1-FITC lectin binding (red). Nuclei stained with DAPI (blue). Representative micrographs of observations from n=4 mice. Scale Bar = 50 µM. D) Semi-quantitative analysis of fucosylation. There is a significant interaction between genotype and segment (P = <0.001). NHE3-/- vs. WT Ileum P<0.001. No significant differences were observed between WT and NHE3-/- cecum (P=0.896), proximal (P=0.511) and distal colon

(P=0.720) by 2 Way ANOVA, Holme-Sidak.

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Figure 7. Mouse terminal ileum organoids express transporters, fucosylation enzymes and mucin mRNA which are seen in native terminal ileum tissue. Organoids express (A) ion transporters, (B) fut1 and fut2 and (C) MUC1, 2 and 5ac mRNA. D) In addition organoids also express MUC2 at the level of protein.

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Figure 8. B. thetaiotaomicron induced host epithelial changes in mouse terminal ileal organoid. A) fut2 mRNA in WT and NHE3-/- mouse ileum organoids (Black bar = ileum organoid injected with TSB broth;

White bar = ileum organoid injected with B. thetaiotaomicron culture). B) Confocal images from WT and NHE3-/- mouse ileum organoids depicting fucosylation by UEA-1-rhodamine lectin (red) binding.

Nuclei stained with DAPI (blue). Shown from top to bottom are projection images, x-y plane midsection slice, and transmitted light image. Images reveal increased fucosylation in organoids infected with B. thetaiotaomicron. These data demonstrate that B. thetaiotaomicron alone is sufficient to induce fut2 which correlates to increased fucosylation in terminal mouse ileum. n=5 *P < 0.05. Scale bar = 50 µm.

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Figure 9. B. thetaiotaomicron and TLR4 expression correlates with increased INF-γ and MU1 mRNA. A)

-/- TLR4 mRNA was analyzed by qRT-PCR and expressed as the ΔΔCT relative fold difference in NHE3

(white bar) ileum compared with WT (black bar) (n=6). B) Confocal images of TLR4 (purple) and DAPI

(blue) for WT and NHE3-/- ileum demonstrate increased TLR4 expression (n=4). C, D) INF-γ and MUC1

-/- mRNA were analyzed by qRT-PCR and expressed as the ΔΔCT relative fold difference in NHE3 (white bar) ileum compared with WT (black bar) littermate mice (n=6).

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Figure 10. Potential mechanism of bacteria-host interaction in NHE3-/- terminal ileum. Loss of NHE3 results in altered Na+ which stimulates the proliferation of specific bacteria such as gram negative B. theta. B. theta stimulates TLR4, which is also increased, resulting in fut2 mRNA expression and increased mucus fucosylation. TLR4 also stimulates IFNγ mRNA and protein production which increased

MUC1 mRNA expression. Increased MUC1 may function to limit bacteria-host

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Figure 11. NHE3-/- mice have increased iNOS and IL-1β mRNA. mRNA was analyzed by qRT-PCR and

-/- expressed as the ΔΔCT relative fold difference in NHE3 (white bar) compared with WT (black bar)

(n=6). iNOS was increased only in the terminal ileum and IL-1β only in the distal colon of NHE3-/- mice.

*P < 0.05

91 M.A. Engevik 2014

Publication #2

Acidic conditions in the NHE2-/- mouse intestine results in an altered mucosa-associated bacterial population with changes

in mucus oligosaccharides

Melinda A. Engevik 1,3, Annelies Hickerson1, Gary E. Shull 2,3, and Roger T. Worrell 1,3*

1Department of Molecular and Cellular Physiology

2 Department of Molecular Genetics, Biochemistry and Microbiology

University of Cincinnati College of Medicine

Cincinnati, OH 45267

3Digestive Health Center of Cincinnati Children’s Hospital, Cincinnati, OH 45229

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Abstract:

The mechanisms bacteria use to proliferate and alter the normal bacterial composition remain unknown.

The ability to link changes in the intestinal micro-environment, such as ion composition and pH, to bacterial proliferation is clinically advantageous for diseases that involve an altered gut microbiota, such as Inflammatory Bowel Disease, obesity and diabetes. In human and mouse intestine, the apical Na+/H+ exchangers NHE2 and NHE3 affect luminal Na+, water, and pH. Loss of NHE2 results in acidic luminal pH. Since acid resistance systems in gram-positive bacteria are well documented, we hypothesize that gram-positive bacteria would increase in representation in the acidic NHE2-/- intestine. Although total luminal and mucosa-associated bacteria were unchanged in NHE2-/- intestine, gram-positive bacterial phyla were increased in the mucosa-associated bacterial population in a region-specific manner. The genera Clostridium and Lactobacillus were increased in the cecum and colon which corresponded to changes in NHE2-/- mucus oligosaccharide composition of mannose, N-acetyglucosamine, N- acetygalactosamine and galactose. Together these data indicate that changes in ion transport induce region-specific bacterial changes, which alter host mucus oligosaccharide patterns. These host-bacterial interactions provide a possible mechanism of niche-development and shed insight on how certain groups proliferate in changing environments and maintain their proliferation by altering the host.

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Introduction:

Although it is well established that the gut microbiota can influence health and disease, the mechanism(s) which promote bacterial homeostasis or instability remain unknown. Alteration in the composition of gut microbiota, or microbial dysbiosis, has been implicated in the pathogenesis of various disease conditions including antibiotic-associated diarrhea (AAD) (Young and Schmidt 2004), Clostridia difficile-associated disease (CDAD) (Chang et al. 2008), inflammatory bowel disease (IBD) (Frank et al.

2007; Sartor 2008), diabetes (Larsen et al. 2010), and obesity (Turnbaugh and Gordon 2009). A number of studies have demonstrated the effect of host genotype on fecal and cecal samples; however limited data exist on how bacteria inhabiting specific locations of the gut establish a niche and proliferate. Knowledge of how the intestinal environment affects specific bacteria will aid in the development of future therapies for disease with abnormal bacterial composition.

The fundamental mechanisms that govern the intestinal microbiota concentration and composition remain poorly understood. Antimicrobial peptides, mucus production, age, immune status, luminal pH, available fermentable materials and general living conditions all have been hypothesized to affect the gut microbiota (Spor et al. 2011). Environmental factors and host genetics interact to control the acquisition, maintenance, and stability of a healthy gut microbiota. Changes in the microbiota, host genetics, or environment could result in dysbiosis and diseases (Spor et al. 2011). Intestinal pH and ion concentration could be key factors in shaping the intestinal environment. All members of the GI microbiota have specific external pH growth ranges, but some members have developed mechanisms for adaptation to varying pH. Acid resistance systems in gram-positive bacteria have been well documented; these acid resistance systems allow these bacteria to proliferate in various acidic environments (Cotter and

Hill 2003). Gram-positive bacteria use a combination of constitutive and inducible strategies which result in the removal of protons (H+) by ATPase-dependent or ATPase-independent proton transport

(Slonczewski et al. 2009), changes in the composition of the cell envelope such as increased production of cyclopropane fatty acids (CFAs) (Grogan and Cronan 1997), production of general shock proteins and chaperones, expression of transcriptional regulators, and responses to changes in cell density (Cotter and

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Hill 2003). For example, gram-positive E. hirae (formerly Streptococcus faecalis) primarily uses the

F1F0-ATPase to expend ATP to expel excess protons (Slonczewski et al. 2009; Kobayashi and Unemoto

1980; Kobayashi 1982; Suzuki et al. 1993) and low external pH increases the expression and activity of this ATPase (Suzuki et al. 1988). The ATPase is also the key acid removal system for gram-positive

Lactococci (Hutkins and Nannen 1993) and for gram-positive Streptococci (Kuhnert and Quivey Jr 2003), supplemented by catabolic mechanisms such as amino acid decarboxylases (Slonczewski et al. 2009;

Curran et al. 1995). In preference to more acidic conditions, gram-positive bacteria have been shown to produce more lactic acid than the gram-negative bacteria and to lower the ambient pH of their environment in vitro (Sheedy et al. 2009). These studies indicate that gram-positive bacteria are capable of proliferating in more acidic environments.

In addition to reacting to the host environment, bacteria are capable of altering the host mucus oligosaccharide composition to suit their proliferation needs. Bacteria have been shown to interact with the outer loose layer of mucus composed primarily of highly glycosylated MUC2 mucin (Johansson et al.

2011a; Johansson et al. 2011b). Glycans make up approximately 80% of the mucin mass and contain different sugar residues: N-acetylglucosamine (GlcNAc), galactose (Gal), N-acetylgalactosamine

(GalNAc), fucose (Fuc), N-acetylneuraminic acid (NeuNAc), sialic acid, mannose, glucose, and xylose.

Only a small percentage of the gut microbiota are able to secrete glycan-degrading enzymes that sequentially release monosaccharides from mucus glycan chains (Johansson et al. 2011a; Salyers 1979;

Schwab and Ganzle 2011; Hoskins and Boulding 1981). The genera Ruminococcus, Bacteroides,

Bifidobacterium, Lactobacillus and Clostridium have been shown to degrade mucus oligosaccharides, which are then used as a bacterial carbon source (Hoskins and Boulding 1981; Salyers et al. 1977; Hooper et al. 2002; Ruas-Madiedo et al. 2008; Bry et al. 1996; Macfarlane et al. 2001; Deplancke and Gaskins

2001). Studies have demonstrated that mucin degradation often requires the participation of several bacterial species which expresses some or all of the required glycosidases (Hooper et al. 2002). Once oligosaccharides have been released, other resident bacteria which do not contain mucin-degrading enzymes are also able to use these released sugars, which further shapes the microbial composition

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(Kirjavainen et al. 1998; Gusils et al. 2003). Cleavage of these oligosaccharides by the gut microbiota has been linked to subsequent host modulation of mucus glycosylation (Bry et al. 1996; Freitas et al. 2002;

Gheri et al. 1999; Freitas et al. 2005) and mucin gene expression (Mack et al. 1999; Mack et al. 2003).

Very few studies exist which combine altering the intestinal environment, monitoring the bacterial population and correlating microbiota changes with changes in mucus and oligosaccharide patterns. We hypothesized that mucosa-associated bacterium that prefer acidic pH (gram positive bacteria) will proliferate in the NHE2-/- mouse intestine and will maintain their proliferation by increasing glycan oligosaccharide foraging options. Herein we show that the NHE2-/- mouse displays acidic luminal pH in all intestinal segments, which correlates with increased mucosa-associated gram-positive bacteria.

Increases in Clostridium/Ruminoccocus and Lactobacillus are associated with changes in mannose, galactose, N-acetylgalacosamine and N-acetylglucosamine, but not fucose.

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Methods:

Mice. All experimental protocols were approved by the University of Cincinnati Animal Care and Use

Committee and complied with National Institutes of Health guidelines. FVBN NHE2-/- mice were generated as previously described (Schultheis et al. 1998a). Littermate mice were co-housed in the same cage to ensure a common microbial environment. Mice were maintained on a normal mouse diet (7922

NIH-07 Mouse diet, Harlan Laboratories, Indianapolis, IN). At 6-8 weeks post weaning, terminal ileum, cecum, and colon (proximal and distal) segments were collected from WT and NHE2-/- littermates.

Individual intestinal segments were flushed with PBS (pH 7.4) and mucosal scrapings were collected for total DNA analysis as previously described (Frantz et al. 2012; N et al. 2012; Deplancke et al. 2002;

Norkina et al. 2004). Briefly, intestinal segments were flushed with 500 µl PBS. The segments were then opened lengthwise, washed thoroughly with PBS and glass slides were used to scrape the epithelia and mucus layer. Luminal flushes were processed for DNA and mucosal scrapings were processed either for

DNA or for RNA. Sample wet weight was determined and homogenized with a Tissue Tearor homogenizer (Biospec Products Inc, Bartlesville, OK) for 1 min. RNA or DNA was extracted and stored at -80°C or -20°C until the samples were evaluated by quantitative real time PCR (qRT-PCR).

Histology. WT and NHE2-/- mouse terminal ileum, cecum, proximal and distal colon segments were fixed for 4 h at 4°C in Carnoy’s fixative and embedded in paraffin. Serial 6–7 m thick sections were applied to glass slides and stained with hematoxylin and eosin (H&E) for intestine architecture or Periodic Acid-

Schiff’s/Alcian blue (PAS-AB) for goblet cells and mucus. Glycoconjugates on the mucosal surface was examined using a panel of FITC-conjugated lectins: Ulex europaeus agglutinin-1 (UEA-1) for terminal fucose; Concanavalin A (CONA) for mannose, Dolichos biflorus agglutinin (DBA) for N-

Acetylgalactosamine, Peanut agglutinin (PNA) for galactose and Wheat Germ Agglutin (WGA) for N-

Acetylglucosamine (Vector Laboratories, Burlingame, CA) as previously described (Hooper et al. 1999;

Magalhaes et al. 2009). Briefly, sections were deparaffinized, blocked with PBS containing 10% BSA, and stained with FITC-labeled lectin (10 g/ml) for 1 h at room temperature. Sections were then washed

97 M.A. Engevik 2014 three times in PBS, mounted using Vectashield mounting medium with DAPI (Vector Laboratories), and analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710, Carl Zeiss, Germany). Digital images of slides were evaluated by tabulating mean pixel intensity of the respective color channel on each image using Image J software (NIH). Five regions of interests of fixed size per slide, 3 slides per mouse, and n=4 mice were used for semi-quantitation of stain intensity.

qPCR amplification of 16S sequences. Total DNA was isolated with the QIAamp DNA Stool kit

(Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The lysis temperature was increased to 95°C and an incubation with lysozyme (10 mg/ml, 37°C for 30 min) was added to improve bacterial cell lysis as previously described (Norkina et al. 2004; Castillo et al. 2006; Fite et al. 2004;

Salzman et al. 2010). The abundance of total bacteria and specific intestinal bacterial phyla, class, genus and species was measured by qPCR using a Step One Real Time PCR machine (Applied Biosystems,

Carlsbad, California USA) with SYBR Green PCR master mix (Applied Biosystems) and bacteria- specific primers (Table 1) in a 20 µl final volume. Bacterial numbers were determined using standard curves from the pure bacterial cultures as previously described (Salzman et al. 2010; Barman et al. 2008) which correlated cycle of threshold values (CT) to calculated bacteria number.

qRT-PCR of MUC mRNA. Total RNA was extracted from mucosal scrapings with TRIzol Reagent

(Invitrogen) according to the manufacturer’s instructions. Reverse transcription was performed using 50

µg/ml oligo(dT) 20 primer and SuperScript reverse transcriptase (Invitrogen) according to the manufacturer’s instructions. Amplification reactions were performed with SYBR Green PCR master mix

(Applied Biosystems), 200 ng sample cDNA in a 20 µl final volume on the Step One Machine (ABI).

Data was reported as the delta delta CT using GAPDH as the standard. Primers for MUC and GAPDH were used as previously described: MUC 1 Forward: CCAGACCCCTGCACTCTGAT, MUC 1 Reverse:

CGCTTGACAAAGGGCATGA; MUC 2 Forward: TGCCCACCTCCTCAAAGAC; MUC 2 Reverse:

TAGTTTCCGTTGGAACAGTGAA; MUC 3 Forward: TGGTCAACTGCGAGAATGGA; MUC 3

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Reverse: TACGCTCTCCACCAGTTCCT; MUC 4 Forward: CAATGCCCTCCACAAAAAGT; MUC 4

Reverse: CTGTGTGTTGGCAATTTCTG; MUC 5ac Forward: TGGTTTGACACTGACTTCCC; MUC

5ac Reverse: TCCTCTCGGTGACAGAGTCT; GAPDH Forward: CATGGCCTTCCGTGTTCCTA and

GAPDH Reverse: CCTGCTTCACCACCTTCTTGAT (Yu et al. 2009; Fu et al. 2011; Bergstrom et al.

2012).

Ion and pH measurements. Intestinal flushes of WT and NHE2-/- mice were performed with 100 µl of double deionized water. Samples were weighed, centrifuged at 3,000 rpm for 10 min at 4°C to pellet the stool and the supernatant Na+ and K+ concentrations were determined using a digital Flame photometer

(Single-Channel Digital Flame Photometer Model 02655-10; Cole-Parmer Instrument Company Vernon

Hills, IL). Cl- ion concentration was determined by a digital Chloridometer (Model 4425100, Labconco

Kansas City, MO) and normalized to intestinal volume. pH measurements were performed electrochemically via an electronic pH meter (Orion Model 720A; Thermo Fisher Scientific Waltham,

MA) .

Statistics. The data are presented as the mean ± SEM. Comparisons between two groups were made with unpaired t-tests and comparisons between more than two groups were performed using ANOVA. A

P<0.05 value was considered significant while n is number of experiments.

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Results:

NHE2-/- mice exhibit altered intestinal environment with regional changes in K+ and Cl- and an acidic pH

Na+/H+ exchange across the apical membrane of enterocytes is critical for Na+ and fluid absorption and intestinal pH regulation (Schultheis et al. 1998a; Biemesderfer et al. 1993; Schultheis et al.

1998b; Guan et al. 2006). Na+/H+ Exchanger (NHE; gene locus symbol, Slc9aX) 1, 2, 3 and 8 are located in the intestine (Zachos et al. 2005; Xu et al. 2005). NHE2 (Slc9a2) and NHE3 (Slc9a3) are located on the apical membranes of enterocytes in the intestine (Bookstein et al. 1994; Bookstein et al. 1997;

Hoogerwerf et al. 1996) but have unique expression patterns. NHE3 is located primarily in the absorptive intestinal cells, while NHE2 is located both in the absorptive cells and in the intestinal crypts (Guan et al.

2006). NHE3 has been shown to be critical for both Na+ and fluid absorption in the intestine as demonstrated by chronic diarrhea in NHE3-/- mice (Schultheis et al. 1998b). Our previous work has shown NHE3-/- mice have an alkaline intestinal lumen high in Na+ and a proliferation of gram-negative

Bacteroidetes in the luminal and mucosa-associated bacterial populations (Guan et al. 2006). In contrast to NHE3-/- mice, NHE2-/- mice lack the diarrhea phenotype (Gawenis et al. 2002) and NHE3/NHE2 double-knockout mice do not have an aggravated diarrhea phenotype (Ledoussal et al. 2001). This may be due in part to upregulation by NHE3 (Guan et al. 2006; Bachmann et al. 2004) and NHE8 (Xu et al. 2011) in NHE2-/- mice. NHE2-/- mice have no apparent changes in morphology as determined by H&E staining

(Figure 1).

In order to examine the ion and pH changes which may occur in the NHE2-/- intestine, Na+ and K+ concentrations were examined by flame photometry and Cl- concentration by chloridometry (Figure 2).

Consistent with other reports (Gawenis et al. 2002), NHE2-/- intestinal pH was significantly decreased

(more acidic) in terminal ileum, cecum, proximal and distal colon compared to WT littermates (Figure

2A). NHE2-/- mice had no significant change in Na+ concentration (Figure 2B), which is consistent with

NHE3 being the main apical Na+-absorbing NHE. K+ concentration was increased in NHE2-/- ileum, but unchanged in the cecum and colon (Figure 2C). Cl- concentration was decreased only in cecum and distal colon of NHE2-/- mice (Figure 2D). We hypothesized that differences in intestinal ion composition

100 M.A. Engevik 2014 and pH in NHE2-/- intestine would cause region specific bacterial proliferation. Since gram-positive bacteria have been shown to be preferential to acidic conditions, we speculated that gram-positive bacteria would be generally increased in the NHE2-/- mouse.

NHE2-/- mice exhibit luminal and mucosa-associated MD at the phylum and subgroup level.

In order to determine if total bacterial numbers were changed as a result of an altered intestinal environment, luminal and mucosa-associated bacterial gDNA was extracted from luminal flushes and mucosal scrapings and analyzed by qPCR. As shown in Figure 3A, no changes were observed in total luminal bacteria in the NHE2-/- mouse ileum, cecum, proximal and distal colon. In addition no significant changes were observed in the total mucosa-associated bacteria (Figure 3B) of the NHE2-/- mouse. In contrast to the NHE3-/- intestine, which demonstrates increased intestinal size and bacterial overgrowth, the NHE2-/- intestine exhibits no changes in total bacteria.

Next we sought to determine if an alteration in the normal bacterial composition was occurring in

NHE2-/- luminal and mucosa-associated bacterial populations. The mouse and human gut microbiota is dominated by the phyla Firmicutes and Bacteroidetes and contains lesser amounts of Proteobacteria,

Actinobacteria, Fusobacteria and Verruomicrobia (Guarner and Malagelada 2003; Suau et al. 1999;

Eckburg et al. 2005; Backhed et al. 2005; Ley et al. 2008a; Ley et al. 2008b; Zoetendal et al. 2002).

Firmicutes and Actinobacteria are gram-positive groups while Bacteroidetes and Proteobacteria are gram- negative groups. In order to examine if NHE2-/- mice exhibit an altered microbiota composition, the major mouse intestinal bacterial phyla were compared as a percentage of total bacteria. As shown in the bacterial phyla representation in Figure 4A, other than a slight increase of gram-positive Firmicutes and a decrease in gram-negative Proteobacteria in the terminal ileum, no changes occurred in the NHE2-/- luminal bacterial population.

In contrast, large changes in the major phyla were observed in the NHE2-/- mucosa-associated bacterial population as seen in Figure 4B. In the NHE2-/- terminal ileum, there was an increase in gram- positive Actinobacteria (39.1%) and a decrease in gram-positive Firmicutes (11.5%) and gram-negative

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Bacteroidetes (25.1%). In the NHE2-/- cecum there was a decrease in Firmicutes (6.9%) and an increase in

Proteobacteria (4.3%), Actinobacteria (1%) and unspecified bacteria (2.1%). In the NHE2-/- proximal and distal colon there was an increase in Firmicutes (PC: 14.1%; DC: 6.8%), and a decrease in Bacteroidetes

(14.9%; 6.8%). These data together indicate that the NHE2-/- mucosa-associated bacterial population is significantly transformed in response to an altered ion transport status and that in general gram-positive bacteria are proliferating and competing more effectively than others.

Since gram-positive Firmicutes and gram-negative Bacteroidetes compose the dominant microbial phyla, subgroups of these phyla were further examined in the mucosa-associated bacterial population to determine the groups responsible for shifts in the phyla (Figure 4C). Firmicutes subgroups

Bacillales (specifically lactobacillus/enterococcus) and Clostridium (clusters XIVa and IV) were examined in addition to in Bacteroidetes genera Bacteroides, Prevotella, Porphyromonas and Mouse

Intestinal Bacteroidetes (MIB) were analyzed by qPCR and presented as a percentage of total bacteria. In the mucosa-associated bacterial population (Figure 4C), changes were observed at the subgroup level in every segments. Gram-positive Firmicutes members C. coccoides cluster XIVa was increased in the

NHE2-/- cecum (11.0%), proximal colon (4.2%) and distal colon (6.7%) while C. leptum cluster IV was increased in the proximal (29.4%) and distal (5.5%) colon. Lactobacillus/Enterococcus group was increased only in the cecum (4.9%) and proximal colon (4.8%). Interestingly, increases in the examined gram-positive Firmicutes subgroups were accompanied by decreases in other unspecified Firmicutes groups. The gram-negative Bacteroidetes member Prevotella was unchanged in all segments while

Bacteroides was decreased in the NHE2-/- ileum (4.2%) and increased in the cecum (3.9%), proximal colon (5.1%) and distal colon (7.1%). Additionally MIB was decreased in all the NHE2-/- intestinal segments (I: 10.3%, C: 11.0%, PC 11.9%, DC: 9.7%). Decreases were also observed in unspecified

Bacteroidetes subgroups. These data indicate that in the mucosa-associated bacterial population

Clostridium clusters and Lactobacillus/Enterococcus group are responsible for the changes in the

Firmicutes phyla in the NHE2-/- cecum, proximal colon and distal colon.

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NHE2-/- mice exhibit altered mucus oligosaccharides with no change in MUC2

Bacteria that are able to metabolize mucus oligosaccharides display an adaptive advantage for survival and colonization in the intestine (Ruas-Madiedo et al. 2008). These bacteria have access to the host as an energy source and have the ability to recruit other monosaccharide-using bacteria. Together this interaction shapes the nutrient environment and the gut microbiota. Bacteria from the gram-positive genera Ruminococcus, Clostridium, Lactobacillus and Enterococcus have been shown to degrade mucin

(Hoskins and Boulding 1981; Salyers et al. 1977; Hooper et al. 2002; Ruas-Madiedo et al. 2008).

Ruminococcus species are found in both C. coccoides cluster XIVa and C. leptum cluster IV,

Lactobacillus and Clostridium have all been shown to release a number of glycosidases (Macfarlane et al.

2001; Deplancke et al. 2002; Hoskins et al. 1985; Andersen et al. 2011), releasing monosaccharaides, such as galactose, mannose, N-acetylglucasamine, which then supplement bacterial growth. Gram- negative Bacteroides has also been shown to contain some mucin degrading enzymes (Bry et al. 1996;

Freitas et al. 2002; Hooper et al. 2000), and to use intestinal fucose (Bry et al. 1996; Hooper et al. 2000;

Freitas and Cayuela 2000). Since NHE2-/- mice have increased Clostridium/Ruminococcus and

Lactobacillus/Enterococcus groups in the cecum and colon we sought to determine host mucus oligosaccharides changes which may occur as the result of the altered microbiota.

Although gram-negative Bacteroides was increased in the cecum and colon, no changes were observed in host fucosylation in any of the NHE2-/- intestinal segments (Figure 5A and B). Bacteroides has been shown to have a culture-dependent induction of fucosylation (Bry et al. 1996; Hooper et al.

2000) and since the distal intestine has high levels of fucosylation it is possible that fucosylation is already at maximum and cannot be further induced. In NHE2-/- cecum, gram-positive C. coccoides and

Lactobacillus were increased while in NHE2-/- distal colon C. coccoides and C. leptum were increased.

Correlating to the changes in these known mucin degrading groups, we observed mucin changes in the

NHE2-/- cecum and distal colon (Figures 6B-9B). Increases were observed in N-acetylgalactosamine

(Figure 6), N-acetylglucosamine (Figure 7), mannose (Figure 8) and galactose (Figure 9) in the NHE2-/- cecum. In addition, decreased terminal galactose was observed in the distal colon (Figure 9A and B). No

103 M.A. Engevik 2014 changes were observed in NHE2-/- terminal ileum or proximal colon. Galactose transporters and galactosidases have been found in Clostridium species (Gutierrez and Maddox 1996; Ichinose et al. 2006;

Ordobadi et al. 2012; Mitchell et al. 1987). Decreased galactose in the colon may be due in part to the increase in both Clostridium clusters, which may deplete the galactose as observed in Figure 9.

Changes in mucus oligosaccharides can be either a direct result of changes in glycosyltransferases or mucus production. Mucin glycoproteins are encoded by MUC genes (Rhodes 1989). MUC1, MUC3, and MUC4 mucins represent adherent mucus since their carboxy terminus contains transmembrane domains. In contrast MUC2, MUC5AC, and MUC5B represent secreted mucins, which comprise the loose outer layer of mucus which bacteria primarily interact (Johansson et al. 2011b; Rose and Voynow 2006).

Mucus production was examined by PAS-AB Stains (Figure 10) and MUC mRNA (Figure 11). PAS-AB stains reveal no gross changes in mucus goblet cell number or mucus production. Likewise, no change was observed in MUC2 mRNA (Figure 11B). No change was also observed in MUC3, MUC4 or MUC5a mRNAs (Figure 11 C-E). However decreased MUC1 mRNA was observed in the NHE2-/- cecum and colon (Figure 11A) with no changes in the terminal ileum. This indicates that although there does not appear to be any changes in the secreted mucus, there is a decrease in adherent mucus production.

Adherent mucins have been hypothesized to play a role in mucosal defense since mice lacking the Muc1 cell surface mucin are predisposed to GI infection (Linden et al. 2008; McGuckin et al. 2007; McAuley et al. 2007). Altered MUC1 expression could be a result of the altered bacterial composition of the NHE2-/- mice since bacterial adhesion and bacterial products have been shown to stimulate mucus discharge

(Linden et al. 2008; Fischer et al. 1995; Smirnova et al. 2003; Enss et al. 2000). Despite the decreased

MUC1 mRNA levels, the NHE2-/- mice do not have any overt signs of inflammation.

Together these data suggest that the acidic environment of the NHE2-/- mouse intestine correlates to an increase in gram-positive bacteria in the mucosa-associated bacteria population, which in turn use mucus oligosaccharides to drive their proliferation. This information points to pH change as a driving force for shifting the bacterial composition and provides data on which groups may be pH sensitive under

104 M.A. Engevik 2014 in vivo conditions. A better understanding of which groups are capable of proliferating in an altered intestine environment is key for developing better therapies for microbial-dysbiosis associated diseases.

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Discussion:

The factors involved in regional microbiota niche establishment and maintenance remain largely unknown. The NHE2-/- mouse offers a simplified model of altered intestinal environment and alterations in the gut microbiota. In this study we have shown that in general, gram-positive bacteria are increased in the NHE2-/- intestine which correlate with acidic environmental conditions. Our data demonstrates that increased Lactobacillus and Clostridium/Ruminococcus groups correlate with acidic environmental conditions, which correlates to altered mucus glycoprotein patterns. Previous literature has shown that these gram-positive bacteria are pH sensitive. Lactobacillus, for example, contains genes and proteins involved in pH homeostasis and acid adaption (Cotter and Hill 2003). Lactobacillus species have been found to participate in malolactic fermentation in which the dicarboxylic malic acid is converted to monocarboxylic lactic acid (Champomier-Verges et al. 2001; Olsen et al. 1991) (Renault et al. 1988). In malolactic fermentation, a lactate-malate antiporter or an electrogenic uniporter exports L-lactate, thereby allowing the synthesis of ATP at low pH (Cotter and Hill 2003). Clostridial groups have been shown to have transmembrane proton transport which allows them to adapt to a range of pH (Speelmans et al.

1993). Previous studies have shown that cell density plays a role in the acid resistance of gram positive bacteria (Li et al. 2001). Increases in gram-positive bacteria in the NHE2-/- intestine may be responsible for the overall acid adaption of these organisms in the low intestinal pH environment. Interestingly, the changes in the NHE2-/- bacteria are dramatically different from the NHE3-/- mouse which have an alkaline intestinal environment. In the NHE3-/- mouse, gram-negative Bacteroidetes proliferate, particularly in the mucosa-associated bacterial population. This proliferation of Bacteroidetes is primarily caused by a large increase in mucosa-associated Mouse Intestinal Bacteroidetes (MIB) in all the intestinal segments [59]. In contrast, mucosa-associated MIB is decreased in all the NHE2-/- segments. MIB is found in both mouse and human intestine (Salzman et al. 2010; Kibe et al. 2007) and from the data present herein it appears that MIB is influenced by pH.

Ion composition can also be used a mechanism of pH adaption. For gut bacteria which are normally exposed to a neutral environment, intracellular changes in bacterial pH typically require use of

106 M.A. Engevik 2014 bacterial cation/proton antiporters. pH homeostasis in the model bacteria E. coli has revealed a major role in K+ extrusion and proton capture for pH regulation (Slonczewski et al. 2009; Bakker and Mangerich

1981; Roe et al. 2000; Buurman et al. 2004). The NHE2-/- mice have increased K+ concentration in the terminal ileum compared to WT mice. This is also the only location where gram-positive Actinobacteria has increased to become the dominant phyla. Studies have shown that Actinobacteria member

Corynebacterium glutamicum contains an active potassium channel which contributes to maintenance of internal pH and membrane potential of the bacteria, thereby allowing survival of the bacteria at low pH

(Ochrombel et al. 2011). These results indicate that active potassium transport systems correlate with an improved potassium-dependent pH homeostasis in at least one Actinobacteria species and this may be why we observed both increased K+ and Actinobacteria in the NHE2-/- ileum.

Bacteria have been shown to interact with the outer mucus layer and this interaction provides bacterial-host “cross talk” which allows the microbiota to modify the host, creating favorable bacterial niches for proliferation (Johansson et al. 2011a). Oligosaccharides on mucus glycoproteins provide both a signaling mechanism for the host and a foraging option for bacteria. Mucus glycosylation differs depending on intestinal location. In the proximal and distal colon the mucus has increased sialylation

(terminal sialic acid N acetyl neuraminic acid residues) and increased sulphation (terminal ester sulphate residues). Increased sialyation and sulphate increases the charge of the mucus, and thereby the tenacity, aiding in resistance of the mucus to bacterial enzymatic attack (Rhodes 1989; Mian et al. 1979). As a result bacteria have to produce sialidase (neuraminidase) and sulphatase which can remove the sialic acid and ester sulphate before further oligosaccharide degradation can proceed (Rhodes 1989; Corfield et al.

1988; Rhodes et al. 1985a; Rhodes et al. 1985b; Rhodes et al. 1985c). Gram positive gut bacteria from the genera Clostridium and Streptococcus have been found to produce more than one sialidase as isoenzymes

(Kim et al. 2011). Sialidases from Clostridium species Clostridium septicum, Clostridium sordellii,

Clostridium chauvoei, and Clostridium tertium have a relatively high hydrolysis activity toward substrates with the α(2,3)-linkage (Kim et al. 2011; Hoyer et al. 1991), which has been found to be the dominant intestinal linkage in cultured colonic epithelial cells (Ulloa and Real 2001). It is possible that increased

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Clostridium in the NHE2-/- distal colon produces more sialidases which in turn make the colonic mucus oligosaccharides more prone to enzymatic attack, resulting in decreased galactose residues. Interestingly oligosaccharides degradation by colonizing bacteria in the postnatal gut has been shown to stimulate increased oligosaccharide expression (Hooper et al. 2000; Meng et al. 2007; Lenoir et al. 1995; Chu and

Walker 1986). If the cecum does not contain the same degree of sialylation and sulphation bacteria may be able to directly interact with the mucus oligosaccharides, thereby triggering increased oligosaccharide production. Alteration in intestinal glycosylation has been observed in disease states such as Ulcerative

Colitis (UC), Crohn’s Disease (CD) and cancer (Hoskins et al. 1985; Rhodes 1989; Reid et al. 1984; Furr et al. 2010; Raouf et al. 1992; Parker et al. 1995; Clamp et al. 1981). Although the exact mechanism by which these changes take place is unknown, the gut microbiota has been shown to be altered in IBD patients with a disproportionate increase in select mucolytic bacteria, particularly from the Ruminococcus genera (Frank et al. 2007; Sartor 2008; Png et al. 2010), indicating a link between the oligosaccharide changes and the bacterial composition. Based on these studies and the study presented herein there is a correlation between the microbiota and the host glycosylation. Our study demonstrates that an altered intestinal environment is capable of disrupting the bacterial composition and increase mucolytic bacteria, which then affect the mucus oligosaccharide composition as well as the mucin gene expression.

Acknowledgments. We thank our undergraduate volunteers Priscilla Vuong, Stacie Huang, Crystal

Nguyen, and Charles McCombs for their assistance. This work is in partial fulfillment of the Ph.D. degree for Melinda A. Engevik. Supported in part by NIH DK079979 to RTW and DK050594 to GES.

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Table 1. Primer sequences for qRT-PCR of total bacteria and specific bacterial phyla and groups.

Type Bacteria Forward Reverse Reference (Barman et al. 2008), (Fierer Total Universal (Total Bacteria) ACTCCTACGGGAGGCAGCAG ATTACCGCGGCTGCTGG et al. 2005), (Guo et al. 2008) Phyla Bacteriodetes GGCGACCGGCGCACGGG GRCCTTCCTCTCAGAACCC (Guo et al. 2008) GGAGYATGTGGTTTAATTCGAAG Phyla Firmicutes AGCTGACGACAACCATGCAC (Fierer et al. 2005) CA Phyla Actinobacteria CGCGGCCTATCAGCTTGTTG ATTACCGCGGCTGCTGG (Fierer et al. 2005) Phyla α-proteobacteria ACTCCTACGGGAGGCAGCAG TCTACGRATTTCACCYCTAC (Fierer et al. 2005) Phyla β-proteobacteria CCGCACAGTTGGCGAGATGA CGACAGTTATGACGCCCTCC (Fierer et al. 2005) Phyla y-Proteobacteria GAGTTTGATCATGGCTCA GTATTACCGCGGCTGCTG (Lee et al. 2009) Clostridium coccoides cluster GCTTCTTAGTCAGGTACCGTC Class ACTCCTACGGGAGGCAGC (Salzman et al. 2010) XIVa group AT Clostridium leptum cluster IV Class GTTGACAAAACGGAGGAAGG GACGGGCGGTGTGTACAA (Salzman et al. 2010) group Lactobacillus/Enterococcus Class AGCAGTAGGGAATCTTCCA CACCGCTACACATGGAG (Salzman et al. 2010) group Genus Bacteriodes GGTTCTGAGAGGAGGTCCC CTGCCTCCCGTAGGAGT (Salzman et al. 2010) Genus Prevotella CCAGCCAAGTAGCGTGCA TGGACCTTCCGTATTACCGC (Dalwai et al. 2007)

Genus Mouse Inestinal Bacteria (MIB) CCAGCAGCCGCGGTAATA CGCATTCCGCATACTTCTC (Salzman et al. 2010)

GGTAGTCCACACAGTAAACGATG Species Bacteriodes thetaiotaomicron CCCGTCAATTCCTTTGAGTTTC (Sonnenburg et al. 2005) AA

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Figures:

Figure 1. The intestinal tract of NHE2-/- mice has normal histology. Mucosal morphology in WT and

NHE2-/- mouse ileum, cecum, proximal and distal colon demonstrating no gross alteration of the mucosal architecture in any segment. Similar villi number and length, crypt number and length, paneth cell number, and granules (observed in red) are present in WT and NHE2-/- intestinal segments. Micrographs are representative H&E stained sections (n=4). Scale bar = 50

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Figure 2. NHE2-/- mice have an acidic intestinal environment with regional changes in K+ and Cl-. Ion concentrations in luminal fluid from WT (black bar) and NHE2-/- (white bar) mouse intestinal segments.

A) pH as determined by pH probe was significantly decreased in all NHE2-/- intestinal segments. B)

Sodium concentration as determined by flame photometry was not changed in any intestinal segment of

NHE2-/- verses WT littermates. C) Potassium concentration as determined by flame photometry was significantly increased in ileum but not in cecum or colon of NHE2-/- mice vs. WT littermates. D)

Chloride concentration as determined by chloridometry was significantly decreased in the cecum and distal colon of NHE2-/- mice vs. WT littermates. n=11 for WT and n=6 for NHE2-/- . * P<0.05.

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Figure 3. NHE2-/- mice show no significant change in total bacteria. Total bacteria quantified by qRT-

PCR using a universal bacterial 16S DNA sequence and “calculated bacterial cell” number calculated from an E. coli standard curve normalized to intestinal flush volume. A) Bacteria contained within the luminal flushes of WT (black bar) and NHE2-/- (white bar) littermates. B) Mucosa-associated (adherent) bacterial levels between WT (black bar) and NHE2-/- (white bar) littermates. NHE2-/- luminal and mucosa-associated bacterial populations demonstrated no change in total bacteria compared to WT littermates. n=6, * P>0.005.

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Figure 4. NHE2-/- mice exhibit region-specific luminal and mucosa-associated MD. Relative abundance was calculated as the percentage of bacterial phyla in comparison to total bacteria for luminal and mucosa-associated bacteria. A) Luminal bacterial showed no changes in phyla composition except in terminal ileum which had increased Firmicutes. B) Mucosa-associated bacteria showed changes in bacterial phyla in every NHE2-/- segment. C) Relative abundance of mucosa-associated bacterial subgroups of Firmicutes and Bacteroidetes phyla. Relative abundance was calculated as the percentage of

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bacterial subgroup in comparison to total bacteria. Regional changes were observed in the Firmicutes subgroup C. coccoides cluster XIVa, C. leptum cluster IV and Lactobacillus/Enterococcus group.

Changes were also observed in the Bacteroidetes subgroup Bacteroides and MIB in the NHE2-/- mouse mucosa-associated bacterial populations. n=6, * P>0.005.

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Figure 5. Immunostain of fucose residues in intestinal segments. No significant change in fucosylation was observed in any NHE2-/- segment. Fucosylation was determined by UEA-1-FITC lectin binding (red) and tissue sections counter stained with DAPI (blue, cell nuclei) A) Representative micrographs from n=4 mice of each genotype B) Semi-quantitative analysis of oligosaccharide stains analyzed by Two Way ANOVA n=4, * P>0.005.

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Figure 6. Immunostain of N-acetylgalactosamine residues in intestinal segments. NHE2-/- mice have increased N-acetylgalactosamine only in the cecum compared to WT littermates. N-acetylgalactosamine was determined by DBA-FITC lectin binding (purple) and tissue sections counter stained with DAPI

(blue, cell nuclei). A) Representative micrographs from n=4 mice of each genotype. Scale Bar = 50

B) Semi-quantitative analysis of oligosaccharide stains analyzed by Two Way ANOVA n=4, * P>0.005.

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Figure 7. Immunostain of N-acetylglucosamine residues in intestinal segments. NHE2-/- mice have increased N-acetylglucosamine only in the cecum compared to WT littermates. N-acetylglucosamine was determined by WGA-FITC lectin binding (orange) and tissue sections were counter-stained with DAPI

B) Semi-quantitative analysis of oligosaccharide stains analyzed by Two Way ANOVA n=4, * P>0.005.

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Figure 8. Immunostain of mannose residues in intestinal segments. NHE2-/- mice have increased mannose only in the cecum compared to WT littermates. Mannose was determined by ConA-FITC lectin binding

(green) and tissue sections counter stained with DAPI (blue, cell nuclei). A) Representative micrographs from n=4 mice of each genotype. Scale Bar = 50 . B) Semi-quantitative analysis of oligosaccharide stains analyzed by Two Way ANOVA n=4, * P>0.005.

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Figure 9. Immunostain of galactose residues in intestinal segments. NHE2-/- mice have increased galactose in the cecum and decreased galactose in the distal colon compared to WT littermates. Galactose was determined by PNA-FITC lectin binding (yellow) and tissue sections counter stained with DAPI

(blue, cell nuclei). A

B) Semi-quantitative analysis of oligosaccharide stains analyzed by Two Way ANOVA n=4, * P>0.005.

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Figure 10. PAS-AB stain of mucins in intestinal segments. NHE2-/- mice have normal mucus production.

No changes were observed in mucus morphology and production in WT and NHE2-/- mouse ileum, cecum, proximal and distal colon. Micrographs are representative sections (n=4 mice of each genotype) stained with PAS-AB, which stains acidic mucins blue and neutral mucins magenta. There were no apparent differences in the expression of acidic and neutral mucins. Scale bar = 50

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Figure 11: NHE2-/- mice have decreased MUC1 mRNA whereas normal MUC2, 3, 4 and 5 mRNA is unchanged. MUC mRNA was analyzed by qRT-PCR and expressed as the CT relative fold difference.

Decreased MUC1 mRNA (A) was observed in NHE2-/- (white bar) cecum and colon compared with WT

(black bar) littermate mice. B-E) No significant change was observed in MUC2, 3, 4, or 5 mRNA. n=8 for WT and n=6 for NHE2-/- . * P < 0.05.

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Manuscript #1

Human Clostridium difficile infection: Inhibition of NHE3

and microbiota profile

Melinda A. Engevik 1,5, Kristen Engevik1, Mary Beth Yacyshyn3, Jiang Wang4, Daniel J. Hassett2, ,

Benjamin Darrien6, Bruce Yacyshyn3,5, Roger T. Worrell 1,5

1Department of Molecular and Cellular Physiology

2Department of Molecular Genetics, Biochemistry and Microbiology

3Department of Medicine Division of Digestive Diseases

University of Cincinnati College of Medicine

Cincinnati, OH 45267

4Department of Pathology and Lab Medicine

University of Cincinnati Medical Center

5Digestive Health Center of Cincinnati Children’s Hospital, Cincinnati, OH 45229

6 University Wisconsin-Madison

237 Animal Health and Biomedical Sciences

1656 Linden Drive

Madison, WI 53706

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ABSTRACT.

Clostridium difficile infection (CDI) is principally responsible for hospital acquired, antibiotic-induced diarrhea and colitis and represents a significant financial burden on our healthcare system. Little is known about C. difficile proliferation requirements and a better understanding of these parameters are critical for development of new therapeutic targets. In cell lines, C. difficile toxin B has been shown to inhibit Na+/H+

Exchanger 3 (NHE3) and loss of NHE3 in mice results in an altered intestinal environment coupled with a transformed gut microbiota composition. However, this has yet to be established in vivo in humans. We hypothesize that C. difficile toxin inhibits NHE3, resulting in alteration of the intestinal environment and gut microbiota. Our results demonstrate that CDI patient biopsies have decreased NHE3 expression and

CDI stool has elevated Na+ and are more alkaline compared to stool from healthy individuals. CDI stool microbiota have increased Bacteroidetes and Proteobacteria and decreased Firmicutes phyla compared with healthy patients. In vitro, C. difficile grows optimally in the presence of elevated Na+ and alkaline pH, conditions which correlate to changes observed in CDI patients. To confirm that inhibition of NHE3 was specific to C. difficile, human intestinal organoids (HIOs) were injected with C. difficile or healthy and CDI stool supernatant. Injection of C. difficile and CDI stool decreased NHE3 mRNA and protein expression compared with healthy stool and control HIOs. Together this data demonstrates that C. difficile inhibits NHE3 in vivo which creates an altered environment favored by C. difficile.

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INTRODUCTION

Clostridium difficile is a gram-positive anaerobic bacterium from the phylum Firmicutes that is responsible for the majority of antibiotic-associated diarrhea (Dingle et al. 2010). C. difficile infection

(CDI) affects thousands of patients each year and treatment cost of over 1 billion dollars in the United

States (Savidge et al. 2003; Campbell et al. 2009; Dubberke et al. 2008; O'Brien et al. 2007).

Furthermore, C. difﬁcile-related deaths have been steadily rising since 1999 (Redelings et al. 2007), and will likely remain a problem, especially in the face of current antibiotic regimens. CDI has been associated with a spectrum of symptoms ranging from mild to watery diarrhea and abdominal pain, to life-threatening pseudomembranous colitis (PMC) and toxic megacolon (Borriello 1998). Although most of the symptoms of CDI have been linked to C. difﬁcile toxin production (Jafari et al. 2013; Lyras et al.

2009; Kuehne et al. 2010), the mechanism of C. difficile colonization is still unclear. Thus, a better understanding of C. difficile pathogenesis is critical for developing new therapeutics.

C. difficile pathogenesis has been hypothesized to be a three-step process: (1) antibiotic disruption of the normal gut microbiota provides a potential niche for growth from its normal gut spore form; (2) the colonization phase, which includes bacterial-host interaction and adhesion; and (3) multiplication which maintains high numbers of vegetative C. difficile and toxin production, both of which exacerbates the infectious process (Janoir et al. 2013; Deneve et al. 2009). Antibiotic use has been shown to decrease the dominant gut microbiota bacterial phyla Bacteroidetes and Firmicutes (Ley et al. 2008) and increase

Proteobacteria (Antonopoulos et al. 2009; Sekirov et al. 2008; Jernberg et al. 2010; Hazenberg et al. 1983;

Dethlefsen et al. 2008; Manichanh et al. 2010; Croswell et al. 2009), resulting in increased gut susceptibility to C. difficile infection (Bauer and van Dissel 2009; Owens et al. 2008; Rolfe et al. 1981;

Badger et al. 2012; Kelly et al. 1994; Wilson et al. 1986; Stecher and Hardt 2008). Once C. difficile binds to the gastrointestinal (GI) mucus layer (Deneve et al. 2009; Tasteyre et al. 2001), the bacterium can deliver two exotoxins, toxin A (TcdA) and toxin B (TcdB) (Dingle et al. 2010; Jank and Aktories 2008;

Voth and Ballard 2005). The Tcd toxins bind to uncharacterized host receptors and are then internalized into the enterocyte cytoplasm, where they become enzymatically active and glycosylate the Rho family of

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GTPases (Dubberke et al. 2011; Hayashi et al. 2004). Inhibition of such GTPases has been shown to have several effects including: (1) disorganization of the host actin cytoskeleton, (2) loss of cellular tight junctions, (3) disruption of signaling cascades, and (4) arrest of cell cycle progression (Deneve et al.

2009; Dubberke et al. 2011; Barbieri et al. 2002; Jank et al. 2007). In addition, toxin B inhibition of Rho

GTPase in cell lines leads to the internalization of the Na+/H+ exchanger isoform 3 (NHE3) (Hayashi et al.

2004), but this has yet to be established in vivo in animals or in humans. Inhibition of NHE3 in mice results in chronic diarrhea (Gawenis et al. 2002; Schultheis et al. 1998), elevated Na+ and alkaline luminal fluid, and an altered microbiota composition with decreased members of Firmicutes and increased

Bacteroidetes (Engevik et al. 2013a). It has been suggested that the diarrhea associated with CDI is a result of damage to the host epithelium or a response designed to “flush out” the pathogen. However, we hypothesize that C. difficile toxin production inhibits NHE3, creating an altered intestinal microenvironment and gut microbiota composition which favor C. difficile proliferation and colonization of the mucosal lining. In this study, we demonstrate that patient biopsies with CDI have decreased NHE3 with increased Bacteroidetes and decreased Firmicutes phyla in their stool. In vitro, C. difficile growth depends on the high [Na+] and a more alkaline environment which can be caused by downregulation of NHE3.

This study is the first to demonstrate downregulation of NHE3 and an altered luminal environment in patients with CDI.

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METHODS

Patient information. All patients and healthy volunteers at the University of Cincinnati Medical Center

Hospital, Cincinnati, OH provided informed consent approved by the University of Cincinnati IRB. CDI was defined as a new onset of diarrhea (> 3 loose stools/day for more than 24 hours) and at least one positive C. difficile laboratory test. Diagnosis of CDI was determined by at least one ELISA positive toxin test or a positive LAMP test. Samples were evaluated from patients with recurrent C. difficile infection (CDI). Initial CDI cases, defined as only one C. difficile positive laboratory test with no prior history of CDI, were not included in this study. Recurrent CDI was defined as onset of new diarrhea after a symptom-free period of >3 days, more than one C. difficile positive laboratory test and completion of at least one round of antibiotic treatment. Over the course of fecal collections, two types of toxin tests were used. From November 2010 – August 2011, the EIA for toxins A and B was used. After August 2011, the Meridian Illumigene® LAMP test was used. This shift in toxin testing represents a switch to in house testing, lowering the cost, and an upgrade to a more sensitive method.

Fecal samples were collected from 12 recurrent CDI patients with an average age of 56, age range

32-76. This group included 8 females and 4 males. Selected patients did not have history of Inflammatory

Bowel Disease (IBD), small bowel obstruction, diverticulosis, colostomy, or cancer. Fecal samples were also collected from 12 healthy volunteers with an average age of 41, age range 28-61. This group included 7 females and 5 males. Healthy volunteers were without previous or current GI symptoms, history of chronic disease or cancer. All stool was processed for total DNA, ion concentration, pH and stored at −20°C.

Colon biopsies were collected from five healthy volunteers were obtained by consent and fixed in neutral buffered formalin and paraffin-embedded. Healthy patients had an average age of 52, patient age range of 45-63 and included 3 females and 2 males. Healthy volunteers were without previous or current

GI symptoms, history of chronic disease or cancer. Paraffin sections were obtained from 5 de-identified patients who had been biopsied or had intestinal tissue removed due to diagnoses consistent with CDI and had a current C. difficile-positive toxin test. The average patient age 44, patient age range of 28-65, and

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included 2 females and 3 males. Selected patients did not have history of Inflammatory Bowel Disease

(IBD), small bowel obstruction, diverticulosis, colostomy, or cancer. Confirmation of C. difficile infection was performed by staining with C. difficile specific antibody as described below. For comparison, paraffin sections from one colonic resections and two other biopsies were obtained from patients who exhibited colitis but no C. difficile positive toxin test.

Histology. Healthy and CDI biopsies were obtained from the transverse colon and fixed overnight at 4°C in neutral-buffered formalin and embedded in paraffin. Serial 6–7 m thick sections were applied to glass slides and intestinal architecture was examined by H&E staining. Expression of NHE3 was examined with rabbit anti-human NHE3 antibody (dilution 1:100, NBP1-82574, Novus Biologicals, Littleton, CO) and C. difficile binding was examined with rabbit anti-C. difficile cell surface protein antibody (dilution

1:100, ab93728, ABCAM, Cambridge, MA). Briefly, sections were removed of paraffin and incubated for

40 min at 97°C with Tris–EDTA–SDS buffer as previously described (Syrbu and Cohen 2011). Sections were then blocked with PBS containing 10% serum, and stained with primary antibody overnight at 4°C.

Sections were then washed three times in PBS, incubated with goat-anti-rabbit IgG Alexa Fluor® secondary antibody (dilution 1:100) (Life Technologies, Grand Island, NY) for 1 hr at room temperature and counterstained with Hoechst (0.1 µg/ml) (Fisher Scientific). Sections were analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710, Carl Zeiss). Digital images of slides were evaluated by tabulating mean pixel intensity of the respective color channel on each image using Image J software

(NIH) and reported as relative fluorescence. Five regions of interest per image, four images per slide, and n=5 healthy and CDI patients were used for semi-quantification of stain intensity normalized to healthy patients and referred to as relative fluorescence.

Human intestinal organoids (HIOs) and microinjection. Human intestinal organoids (HIOs) used in this study were generated by the Cincinnati Children’s Hospital Medical Center (CCHMC) Pluripotent

Stem Cell Facility through directed differentiation of human pluripotent stem cells (hPSC). HIOs were

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obtained in matragel and exhibited three-dimensional growth. These organoids have been previously shown to contain all major intestinal epithelial cell types: enterocytes (villin), goblet cells (mucin), paneth cells (lysozyme), and enteroendocrine cells (chromogranin A) (Wells and Brugman 2013). The luminal space of HIOs was microinjected with bacteria and stool supernatant to analyze host-microbe interactions as previously described (Engevik et al. 2013a). Injection needles were pulled on a horizontal bed puller

(Sutter Instruments) and the tip cut to a tip diameter of ~10-15 μm. HIOs were injected with C. difficile

ATTC 1870 and stool from healthy and CDI patients. C. difficile ATTC 1870 was grown in Tryptone yeast TY broth as previously described (Fang et al. 2009). For stool, 0.5 g of healthy or CDI stool was added to 4.5 ml Tryptic Soy Broth (TSB) (Fisher Scientific) in an anaerobic hood. Samples were vortexed and centrifuged at 150 x g for 10 min to pellet solid materials. Stool supernatant, C. difficile or C. butryicum cultures and TSB broth were injected into HIOs via a Nanoject microinjector (Drummon

Scientific Company, Broomall, PA). To minimize stretch effects on epithelial cells injection volumes of

~10% or less of the organoid luminal volume were used. Under these conditions no leakage of cultures from the HIOs was observed. HIOs were processed either for RNA or immunostaining. For RNA, organoids were homogenized in Triazol and extracted with chloroform according the manufacturers instructions (Invitrogen). For staining, HIOs were incubated overnight after microinjection and fixed with

4% paraformaldehyde for 30 min at room temperature. HIOs were washed in PBS and transferred to sucrose (30% in PBS) and incubated overnight at 4°C. The next day, HIOs were placed in OCT embedding medium and frozen at -80°C for 1 day. 7 µm sections were cut on a cryostat. Slides were stained with rabbit anti-human NHE3 antibody (dilution 1:100, NBP1-82574, Novus Biologicals) and analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710, Carl Zeiss).

Bacterial strains and culture conditions. C. difficile ATTC BAA-1870 was purchased from ATCC

(American Type Culture Collection, Manassas, VA). Micrococcus luteus, Staphylococcus aureus,

Escherichia coli, and Burkholderia cepacia were locally available in the Hassett laboratory (University of

Cincinnati College of Medicine). Bacteroidetes thetaiotaomicron ATCC 29741 was purchased from

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Fisher Scientific (Thermo Fisher Scientific, Waltham, MA). Lactobacillus acidophilus, Rhizobium leguminosarum and C. butryicum were purchased from Carolina Biological Supply Company (Carolina

Biological Supply Company, Burlington, NC). S. aureus, M. luteus, L. acidophilus, B. thetaiotaomicron,

E. coli, B. cepacia, and R. legaminosarum were used to generate qPCR standard curves as previously described (Engevik et al. 2013a). E. coli, S. aureus, M. luteus, B. cepacia, L. acidophilus and R. legaminosarum were grown in Luria–Burtani (LB; Thermo Fisher Scientific) broth at 37°C in a shaking incubator. B. thetaiotaomicron was grown in TSB (Fisher Scientific) and C. difficile was grown in TYG

(Tryptone-Yeast extract-Glucose broth; Thermo Fisher Scientific) at 37°C in a Coy Systems, dual-port anaerobic chamber (Coy Lab Products, Grass Lake, MI).

To determine the optimal [Na+] for growth, C. difficile was grown in media where sodium chloride (NaCl) was either removed or replaced with cesium chloride (CsCl) or potassium chloride (KCl) as previously described (Engevik et al. 2013a; Caldwell and Arcand 1974; Caldwell et al. 1973). Briefly, low Na+ media was mixed with normal media at various ratios to obtain varying concentrations of Na+ for bacterial growth measurements. Actual Na+ and K+ concentrations were confirmed by flame photometry

(Single-Channel Digital Flame Photometer Model 02655-10; Cole-Parmer Instrument Company Vernon

Hills, IL) and Cl- concentration measured by chloridometry (Digital Chloridometer Model 4425100,

Labconco Kansas City, MO). Bacteria were grown under anaerobic conditions at 37°C to early stationary

+ phase in normal TYG media (12 hrs, O.D. 560nm ~1) and used to inoculate media containing varying [Na ].

Growth was measured as the optical density (O.D. 560nm) with an Amersham Biosciences Ultospec 3100

Spectrophotometer (GE Healthcare Life Sciences, Pittsburgh, PA). C. difficile and C. butryicum titers were determined by bacterial cell counts using a Petroff-Hauser chamber (Hausser Scientific; Horsham,

PA) and also by colony forming units (CFU) (Caldwell and Arcand 1974; Caldwell et al. 1973). No differences in growth patterns were observed between 4 (early exponential phase), 12 (early stationary phase), 24 or 48 (stationary phase) hr time points (see Figure 4A). As a result, all data are represented as the OD560nm at the 24 hr time point. To determine the optimal pH for growth, C. difficile and C. butryicum were grown in TYG media containing either normal media or low Na+ media adjusted to pH values

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ranging from 5.5 to 7.0 as determined electrochemically using a pH meter (Orion Model 720A; Thermo

Fisher Scientific, Waltham, MA).

Quantitative real time-PCR amplification of 16S sequences. QIAamp DNA Stool kit (Qiagen,

Valencia, CA, USA) was used to isolate total DNA from stool of healthy patients or patients with recurrent CDI. To improve bacterial cell lysis, the temperature was increased to 95°C and incubation with lysozyme (10 mg/ml, 37°C for 30 min) was used as previously described (Engevik et al. 2013a;

Castillo et al. 2006; Fite et al. 2004; Salzman et al. 2002; Norkina et al. 2004; Salzman et al. 2010). qPCR was used to access the abundance of total bacteria and specific intestinal bacterial phyla using a Step One

Real Time PCR machine (Applied Biosystems (ABI) Life Technologies) with SYBR Green PCR master mix (ABI) and bacteria-specific primers (Table 1) in a 20 µl final volume. Cycle of threshold values (CT) were correlated to the calculated bacteria number using standard curves from the pure bacterial cultures as previously described (Engevik et al. 2013a; Salzman et al. 2002; Ott et al. 2004; Barman et al. 2008).

Total bacteria were calculated using a universal bacterial primer that recognizes all bacterial groups and represents the total stool microbiota. Percent bacterial phyla were determined with the calculated CFU values for each bacterial phylum represented as a percentage of the total bacteria.

Quantitative real time-PCR amplification of mRNA. To examine NHE3 message level, total RNA was extracted from HIOs with TRIzol Reagent (Invitrogen Life Technologies, Grand Island, NY) according to the manufacturer’s instructions. Briefly the matragel surrounding the HIOs was removed by the addition of ice-cold PBS and 400 µl of Trizol was added to the HIOs and homogenized. RNA was extracted by the addition of chloroform and reverse transcription was performed using 50 µg/ml oligo(dT) 20 primer and

SuperScript reverse transcriptase (Invitrogen) according to the manufacturer’s instructions. Amplification reactions of NHE3 mRNA were performed using SYBR Green PCR master mix (ABI) on a Step One

Real Time PCR Machine (ABI). The following gene specific qRT-PCR primers derived from previous literature were used: human NHE3 Forward 5'- GAGCTGAACCTGAAGGATGC -3', NHE3 Reverse 5'-

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AGCTTGGTCGACTTGAAGGA -3', human GAPDH Forward 5'- TGCACCACCAACTGCTTAGC -3' and GAPDH Reverse 5'- GGCATGGACTGTGGTCATGAG -3' (Quinkler et al. 2005). Data were reported as the delta delta CT using GAPDH as the standard. Differences in mRNA expression were determined by qRT-PCR and expressed as the CT relative fold difference.

Ion and pH measurements. Stool fluid ion composition was analyzed by flame photometry and

- water was added and vortexed thoroughly. The samples were centrifuged at 3,000 rpm for 10 min at 4°C to pellet solids and the supernatant Na+ and K+ concentrations determined using a digital Flame photometer (Single-Channel Digital Flame Photometer Model 02655-10; Cole-Parmer Instrument

Company Vernon Hills, IL). Cl- ion concentrations were determined by a digital Chloridometer (Model

4425100, Labconco Kansas City, MO). All values were normalized to weight. pH measurements were performed electrochemically via a pH meter (Orion Model 720A; Thermo Fisher Scientific Waltham,

MA).

Statistics. Data are presented as mean ± SEM. Comparisons between groups were made with either One or Two Way Analysis of Variance (ANOVA), and the Holm-Sidak post-hoc (parametric) test used to determine significance between pairwise comparisons using SigmaPlot (Systat Software, Inc., San Jose,

CA). A P < 0.05 value was considered significant while n is the number of experiments performed.

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RESULTS

NHE3 has been shown to be essential for intestinal absorption of Na+, and, therefore, water

(Gawenis et al. 2002; Schultheis et al. 1998). Work in cell lines (LLC-PK1: pig kidney, OK: opossum kidney and BeWo: human placenta) have demonstrated that C. difficile toxin B inhibits NHE3 by dephosphorylation and redistribution of ezrin, which normally anchors NHE3 to the cytoskeleton, resulting in the loss of NHE3 from the apical membrane (Hayashi et al. 2004). To determine if NHE3 was inhibited in CDI patients, intestinal architecture was examined by H&E staining (Fig. 1A) and NHE3 expression was examined by immunofluorescence (Fig. 1B). Colonic biopsies demonstrate normal healthy crypts in healthy patients (Fig. 1A). Consistent with CDI pathology, colon segments demonstrated pronounced thickening of the colonic wall (black arrowheads) and pseudomembranes (grey arrowheads) as previously described (Valiquette et al. 2009). To confirm the presence of C. difficile in patients with CDI, slides were stained with an anti-C. difficile antibody. As expected, healthy colonic tissue and adjacent areas did not contain any C. difficile. In patients with CDI, C. difficile was found primarily in the expelled mucus layer (89% ± 3.3) and occasionally in the crypts close to the host epithelium (11%± 1.9) (n=5) (Fig. 1A).

In healthy patients, NHE3 is located along the apical membrane of absorptive enterocytes (Fig.

1B and C). In contrast, CDI patient biopsies demonstrated varying levels of NHE3. In CDI biopsies, there are areas with intact NHE3 and areas with decreased or no NHE3; together CDI biopsies demonstrate a 48% decrease in NHE3 compared with healthy colon samples (Fig. 1C). C. difficile toxin production has been demonstrated to disrupt intestinal actin cytoskeleton, which is thought to lead to cell death (Mitchell et al. 1987). Loss of cell integrity could also result in decreased NHE3 in diseased segments of the intestine. However, varying levels of NHE3 in CDI tissue demonstrates that NHE3 loss is not due solely to altered cell integrity. This is the first study that demonstrates that NHE3 is inhibited in patients infected with C. difficile.

Mice lacking NHE3 have increased intestinal [Na+] and an alkaline pH (Gawenis et al. 2002;

Schultheis et al. 1998; Engevik et al. 2013a). We predicted that loss of NHE3 activity in patients would

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likewise result in increased [Na+] and an altered intestinal pH. In order to determine if CDI patients had an altered ion composition, stool from healthy and CDI patients were examined by flame photometry and chloridometry (Fig. 2A). Patients with CDI had higher [Na+] (Healthy: 3 ± 0.5, CDI: 85.3 ± 7.3 mM) and chloride concentrations (Healthy: 2.5 ± 0.4, CDI: 28.4 ± 10.4 mM) with no change in potassium concentration (Healthy: 45.3 ± 2.3, CDI: 39.9 ± 4.0 mM) (Fig. 2A). Loss of Na+ absorption, and concomitant water absorption, in combination with increased [Cl-], likely results in the diarrhea observed in CDI patients. Non-Cl anion gap calculations ([Na+] + [K+] − [Cl−]) demonstrated an increase in bulk non-Cl- anions (Healthy: 45.8± 1.4, CDI: 96.8 ± 3.3). This suggests an increase in bicarbonate or short chain fatty acids (SCFA) that may also have some influence on the composition of gut microbiota as well.

In addition, CDI patients had a more alkaline stool (pH 6.9 ± 0.3) compared with healthy patients (pH 6.0

± 0.1) (Fig. 2B). These data demonstrate CDI patients, similar to NHE3-/- mice, have an altered intestinal environment high in [Na+] and more alkaline compared to that of healthy patients.

This altered intestinal environment may also promote the growth of a different subset of gut microbiota that may not inhibit growth of C. difficile. Mice lacking NHE3 exhibit an altered gut microbiota with increased Bacteroidetes and decreased Firmicutes phyla (Engevik et al. 2013a). To address whether patients with recurrent CDI exhibit a similar profile, stool microbiota extracted from nine healthy and nine CDI patients was examined by qPCR. Total stool bacteria remained unchanged between the groups and C. difficile represented >2% of total bacteria in CDI (Fig. 3A). In healthy patients, the gut bacterial phylum Firmicutes constituted the most abundant group, followed by Bacteroidetes (Fig. 3B), consistent with other reports (Chang et al. 2008). In contrast, patients with CDI had increased

Bacteroidetes and decreased Firmicutes (P<0.001, 2 WAY ANOVA) (Fig. 3C). CDI patients also had increased αβγ-Proteobacteria (P= 0.02) that may result from antibiotic use, since antibiotics have been shown to increase Proteobacteria titers (Antonopoulos et al. 2009; Sekirov et al. 2008; Jernberg et al.

2010; Hazenberg et al. 1983; Dethlefsen et al. 2008; Manichanh et al. 2010; Croswell et al. 2009). In order to determine if resident Clostridial groups (Firmicutes phylum) were changed in CDI, C. coccoides cluster XIVa and C. leptum cluster IV titers were examined (Fig. 3D) and both were decreased compared

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to healthy patients. In a healthy patient, C. coccoides and C. leptum account in total for approximately

50% of the total stool (Hayashi et al. 2006; Sghir et al. 2000; Lay et al. 2005) and decreases in these groups represents a significant decrease in Firmicutes phylum. Since C. difficile is able to survive and proliferate in the presence of this altered gut microbiota composition (high in Bacteroidetes), our data indicates that Bacteroides in general do not inhibit C. difficile. These observations point to the proclivity of C. difficile in generating an altered intestinal environment.

To address whether C. difficile prefers the environmental conditions mediated by inhibition of

NHE3, C. difficile ATTC BAA-1870 was grown in vitro anaerobically in TYG media containing various

+ + [Na ] (8-106 mM Na ). Growth was examined by OD560nm and CFU enumeration at 4 (early exponential phase), 12 (early stationary phase), 24 or 48 (stationary phase) hr time points as previously described

(Waligora et al. 1999). Growth patterns were found to be the same for all time points and the data are

+ represented as OD560nm at the 24 hr time point. C. difficile was found to grow optimally at >16 mM [Na ]

(media pH 6.0), indicating that C. difficile is influenced by [Na+] (Fig. 4A). This experiment was repeated using KCl or CsCl replacement, and C. difficile again was noted to grow more efficiently at higher [Na+]

(Fig 4B). C. difficile was also grown in vitro at [Na+] and pH values designed to mimic human stool

(healthy: Na+ 8 mM, pH 6.0; CDI: Na+ 75 mM pH 7.0, refer to Fig. 2). As shown in Fig. 4C, C. difficile also grew better at pH 7.0 vs. pH 6.0 (P = 0.003) at both [Na+], indicating that C. difficile is also influenced by pH. The resident Clostridial member C. butryicum was also grown in TYG media in high and low Na+ at pH 6.0 and 7.0 to determine if all Clostridial groups preferred the similar environment conditions as C. difficile (Fig. 5). Clostridium butyricum is a resident bacteria that has been as a probiotic

(Seki et al. 2003; Sato and Tanaka 1997) and has been shown to prevent experimental colitis via an IL-

10-dependent mechanism (Hayashi et al. 2013). C. butyricum grew well at lower [Na+], but proliferation significantly dropped at high [Na+]. These data demonstrate that C. difficile is distinct in its preference for a high [Na+], alkaline pH environment, adding credence to the hypothesis that C. difficile prefers the altered intestinal environment achieved by a loss of NHE3 function. Of note, at low [Na+], C. butyricum

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proliferated to much higher levels compared to C. difficile, even at the high [Na+]. This suggests that under healthy conditions, C. butyricum would be able to out-compete C. difficile for a given niche.

Although CDI patients demonstrate decreased NHE3 expression, it could be argued that a number of different bacterial groups could be responsible for changes in NHE3 levels. We have previously used intestinal organoids to address microbial-host interaction (Engevik et al. 2013a). To determine if C. difficile alone was sufficient to decrease NHE3, human intestinal organoids (HIOs) were used. HIOs have been shown to mimic native tissue: the cellular diversity and architecture is similar to tissue; HIOs contain all the intestine cell lineages; secretory and absorptive functions are present; HIOs also contain a significant degree of epithelial and mesenchymal complexity, and secrete mucus (Wells and Brugman

2013). In order to confirm that decreases in NHE3 were C. difficile-specific, HIOs were injected with C. difficile, C butryicum and stool supernatant from healthy and CDI patients (Fig. 6). mRNA levels of

NHE3 (Fig. 6A) demonstrate that C. butryicum and healthy stool does not inhibit NHE3 expression.

However, injection of C. difficile and CDI stool supernatant resulted in a substantial decrease in NHE3 mRNA compared to broth injected (control) organoids. This inhibition was also observed at the protein level (Fig. 6B and C), demonstrating that C. difficile is sufficient for NHE3 inhibition in patients with

CDI. Taken together, these data indicate that C. difficile is capable of altering the host intestinal environment by decreasing NHE3 expression in vivo. The altered intestinal environment results in the suppression of resident Clostridial members from the phyla Firmicutes and provides an optimal environment (high Na+, alkaline pH) for C. difficile proliferation.

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DISCUSSION

C. difficile represents an ever increasing public concern as the major cause of antibiotic-induced diarrhea and colitis. The incidence of CDI has increased in the last decade, and, with the emergence of more virulent strains, C. difficile will likely persist as a major health concern. Herein, we have demonstrated several novel aspects of CDI including: (1) CDI patients exhibit decreased NHE3 expression in the apical membrane of intestinal enterocytes and higher [Na+] and alkaline stool pH compared to healthy patients. The altered gut intestinal environment correlates with changes in the dominant bacterial phyla, Firmicutes and Bacteroidetes with decreased C. coccoides and C. leptum, (2) C. difficile has increased proliferation at Na+ concentrations greater than 16 mM and at more alkaline pH level in vitro, a pattern of proliferation that is not observed in resident C. butryicum; and (3) C. difficile alone and in combination with a complex microbiota (CDI stool) is capable of decreasing NHE3 expression in HIOs. These new findings shed light on several novel aspects of the C. difficile colonization phase. This study represents the first in vivo analysis of NHE3 inhibition in response to C. difficile infection. Targeted disruption of the normal intestinal environment via regulation of ion transport explains both the diarrhea phenotype and how C. difficile establishes a proliferative niche.

We propose that in healthy individuals, the luminal and mucosa-associated gut microbiota compete for a C. difficile niche. In an antibiotic microbiota-depleted environment, C. difficile toxin inhibition of NHE3 alters the intestinal environment producing a high [Na+], and a more alkaline fluid, which enhances C. difficile proliferation. This altered intestinal environment further shapes the gut microbiota so that non-inhibitory Bacteroidetes members proliferate. Altered gut microbiota may also play a role in further shaping the intestinal environment, making it more favorable for C. difficile colonization. C. difficile toxin B inhibition of NHE3 was demonstrated in cells lines (Hayashi et al. 2004), but this is the first study that has demonstrated that this inhibition of NHE3 occurs in infected human patients. Loss of NHE3 in mice appears to mimic the effects of C. difficile toxin production in humans as

NHE3-/- mice have higher [Na+], alkaline intestinal fluid and a distal colon microbiota that are higher in

Bacteroidetes and lower in Firmicutes (Engevik et al. 2013b). NHE3-/- mice also exhibited increased

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[Na+], alkaline fluid and Bacteroidetes in the small intestine (terminal ileum) in addition to changes in the colon. This suggests that the altered intestinal environment, due in part to the loss of NHE3, may occur upstream as well as in the colon. Although C. difficile studies have focused on the colon, C. difficile infection has also been reported in the small intestine (Taylor et al. 1981; Wee et al. 2009; Navaneethan and Giannella 2009) and can cause small bowel disease (Testore et al. 1986; Tsutaoka et al. 1994; Smith et al. 1997). These studies suggest that C. difficile infection is not localized solely to the colon and may provide keys areas for the initial pathogenesis of C. difficile. Knowledge of C. difficile colonizing in the intestine (either small or large intestine) is critical for developing better therapies against CDI. Regulating ion transport activity now offers a potential new therapeutic target. For example, were NHE3 to be upregulated, this may provide a means to re-establish the normal intestinal environment and thus shift the microbiota toward one that is considered normal. The effects of normal commensal microbiota on NHE3 expression and function may prove valuable in this regard. Lactobacillus has been used as a probiotic treatment for CDI (Orrhage and Nord 2000; Biller et al. 1995; Naaber et al. 1998) and Lactobacillus acidophilus has been shown to upregulate NHE3 (Singh et al. 2012). In addition, Lactobacillus has been shown to produce lactic acid that inhibits C. difficile growth (Naaber et al. 2004). Since normal gut microbiota has been shown to out-compete and inhibit C. difficile growth, control of ion transport can provide a novel therapeutic for CDI.

Acknowledgements:

I would like to thank Lindsey Ferreira and Stacy Huang for their technical assistance with this project.

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Table 1. qPCR primer sequences for of total bacteria, bacterial phyla and C. difficile.

Type Bacteria Forward Reverse Reference

(Barman et al. 2008;

Total Universal (Total Bacteria) ACTCCTACGGGAGGCAGCAG ATTACCGCGGCTGCTGG Fierer et al. 2005; Guo et

al. 2008)

Phyla Bacteriodetes GGCGACCGGCGCACGGG GRCCTTCCTCTCAGAACCC (Guo et al. 2008)

Phyla Firmicutes GGAGYATGTGGTTTAATTCGAAGCA AGCTGACGACAACCATGCAC (Fierer et al. 2005)

Phyla Actinobacteria CGCGGCCTATCAGCTTGTTG ATTACCGCGGCTGCTGG (Fierer et al. 2005)

Phyla α-proteobacteria ACTCCTACGGGAGGCAGCAG TCTACGRATTTCACCYCTAC (Fierer et al. 2005)

Phyla β-proteobacteria CCGCACAGTTGGCGAGATGA CGACAGTTATGACGCCCTCC (Fierer et al. 2005)

Phyla y-Proteobacteria GAGTTTGATCATGGCTCA GTATTACCGCGGCTGCTG (Lee et al. 2009)

Class C. coccoides cluster XIVa ACTCCTACGGGAGGCAGC GCTTCTTAGTCAGGTACCGTCAT (Salzman et al. 2010)

Class C.leptum cluster IV GTTGACAAAACGGAGGAAGG GACGGGCGGTGTGTACAA (Salzman et al. 2010)

Species Clostridium difficile TTGAGCGATTTACTTCGGTAAAGA CCATCCTGTACTGGCTCACCT (Sjogren et al. 2009)

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Figures:

Figure 1. CDI biopsies exhibit altered intestinal structure and decreased apical NHE3 expression. A)

H&E stains of healthy and CDI patient biopsies demonstrate that these CDI patients have regions of pseudomembranes (composed of inflammatory cells, necrotic epithelium and mucus) (grey arrows) and areas of thickened intestine wall (black arrows). Scale Bar = 500 μM Presence of C. difficile was confirmed with an anti-C. difficile antibody (green). Healthy tissue contained no C. difficile stain, while

CDI biopsies contained C. difficile at the level of the mucus (89% ± 3.3) and epithelium (11%± 1.9)

(n=5). B) Confocal images from healthy and CDI patient biopsies depicting NHE3 (red) and nuclei (blue) stained with Hoechst. Scale Bar = 50 μM. Representative micrographs of observations from n = 5 healthy and CDI patient biopsies. NHE3 was found to varying degrees in CDI patients, representing a 48% decrease in NHE3 expression compared to healthy colon. C) Semi-quantitative analysis of surface NHE3 expression in healthy colon, CDI biopsy non-diseased adjacent tissue, and CDI biopsy diseased tissue.

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Data presented as relative fluorescence normalized healthy NHE3 expression. *P < 0.005 Two Way

ANOVA.

Figure 2. Stool from CDI patients have increased Na+ and a more alkaline pH compared to healthy patients. A) Ion concentrations from healthy (black bar) and CDI (white bar) stool. Sodium (Na+) concentration and potassium (K+) as determined by flame photometry. Na+ was significantly increased in

CDI compared to healthy patient stool, while no changes were observed in K+. Chloride concentration as determined by chloridometry was significantly increased in CDI compared to healthy patient stool samples. B) The pH was significantly increased in CDI compare to healthy patient stool samples (n=12).

* P < 0.05. One Way ANOVA.

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Figure 3. CDI patients have an altered gut microbiota compared to healthy patients. No differences are observed in total bacteria (A) between patients with CDI (black bar) and healthy patients (white bar).

Relative phyla percentage was calculated as the percentage of bacterial phylum compared to total bacteria for healthy stool (B) and CDI stool (C). Each patient (1-18; n=9 per group) is presented as a single bar to highlight inter-person variation. CDI stool microbiota are significantly different from healthy stool microbiota (P < 0.001). CDI stool had increased Bacteroidetes (P < 0.001) and Proteobacteria (P = 0.02) and decreased Firmicutes (P < 0.001) (n = 9). No changes were observed between Actinobacteria (P =

0.768) or unspecified bacteria (P = 0.980). Two Way ANOVA, Holme-Sidak. (D). CDI patients have decreased resident C. coccoides (Δ 21%) and C. leptum (Δ 19%) levels. One Way ANOVA, Holme-Sidak

*P < 0.05.

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Figure 4. In vitro growth of C. difficile ATTC BAA-1870 in varying Na+ and pH conditions. [Na+] ranges for healthy and CDI stool are displayed as bars along the x-axis. A) Growth (O.D.560 nm) of C. difficile in

TYG broth at varying [Na+] which mimic those seen in vivo for healthy and CDI stool (Fig. 2A) at 4 (),

12 (○) and 24 (▼) hrs. C. difficile grew optimally >16mM Na+ (pH 6.0) which is above the in vivo

+ concentration of 3mM Na for healthy patient stool. B) Growth (O.D.560 nm) of C. difficile in TYG broth at varying [Na+] using CsCl () and KCl (○) replacement at 24 hrs. Similar to Figure 4A, C. difficile grew optimally >16mM Na+ (pH 6.0). C) Growth of C. difficile in TYG broth at varying pH which mimics that seen in vivo for healthy and CDI stool (Fig. 2B). Growth was determined at 8 mM Na+ () mimicking

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healthy stool and 75 mM Na+ (○) mimicking CDI stool. There is a significant difference between 8mM

Na+ and 75mM Na+ at pH 5.5 (P = 0.001), 6.0 (P < 0.001) and 6.3 (P = 0.001)*. In addition there is a significant difference between growth at pH 5.5 vs 6.0-6.5 (P < 0.001) and pH 6.0-6.5 and pH 7.0 (P <

0.001) for both 8mM Na+ and 75mM Na+. ** P < 0.05. Two Way ANOVA, Holme-Sidak.

+ Figure 5. In vitro growth of C. butryicum in a range of Na and pH conditions. Growth (O.D.560 nm) of C. butyricum in TYG broth at varying [Na+] which mimic that seen in vivo for healthy (pH 6, black dot) and

CDI stool (pH 7, white dot) (Fig. 2A) at 24 hrs. C. butryicum grew optimally from 7-40 mM Na+ (pH 6.0 and 7.0) * P < 0.05. Two Way ANOVA, Holme-Sidak.

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Figure 6. Human intestinal organoids (HIOs) grown in three-dimensional culture microinjected with bacterial or stool supernatant. Organoid culture in matragel left hand panel with injection needle. A)

NHE3 mRNA levels indicate decreased expression in HIOs injected with CDI stool and C. difficile. No changes were observed between healthy stool or C. butryicum culture (White asterisk designates lumen).

B). NHE3 protein expression as determined by immunofluorescence is decreased in HIOs injected with

CDI stool and C. difficile compared to control, healthy and C. butryicum infected HIOs. C) Semi- quantitative analysis of NHE3 florescence. * P < 0.05. Two Way ANOVA, Holme-Sidak.

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Manuscript #2

Human Clostridium difficile infection: altered mucus

production and composition

Melinda A. Engevik 1, 5, Mary Beth Yacyshyn3, Jiang Wang4, Benjamin Darrien6, Daniel J.

Hassett2,5 Bruce Yacyshyn3,5, Roger T. Worrell 1,5

1Department of Molecular and Cellular Physiology

2 Department of Molecular Genetics, Biochemistry and Microbiology

3 Department of Medicine Division of Digestive Diseases

University of Cincinnati College of Medicine

Cincinnati, OH 45267

4 Department of Pathology and Lab Medicine

University of Cincinnati Medical Center

5 Digestive Health Center of Cincinnati Children’s Hospital, Cincinnati, OH 45229

6 University Wisconsin-Madison

237 Animal Health and Biomedical Sciences

1656 Linden Drive

Madison, WI 53706

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ABSTRACT

The majority of antibiotic-induced diarrhea is caused by Clostridium difficile (C. difficile).

Hospitalizations for Clostridium difficile (C. difficile) infection (CDI) have tripled in the last decade, emphasizing the need to better understand how the organism colonizes the intestine and maintains infection. The mucus provides an interface for bacterial-host interactions. and changes in intestinal mucus have been linked host health. To assess mucus production and composition in healthy and CDI patients, the main mucins MUC1 and MUC2 and mucus oligosaccharides were examined. In comparison to healthy patients, CDI patients demonstrated decreased MUC2 with no changes in surface MUC1.

Although MUC1 did not change at the level of the epithelia, MUC1 was the primary constituent of secreted mucus in CDI patients. CDI mucus also exhibited decreased N-Acetylgalactosamine (GalNAc), increased N-Acetylglucosamine(GlcNAc) and increased terminal galactose residues. Increased galactose in CDI biopsies are of particular interest since galactoses are known C. difficile toxin A receptor in animals. In vitro, C. difficile is capable of metabolizing fucose, mannose, galactose, GlcNAc, and

GalNAc for growth under healthy stool conditions (low [Na+], pH 6.0). Injection of C. difficile into human intestinal organoids (HIOs) demonstrated that C. difficile alone is sufficient to reduce MUC2 production, but it is not capable of altering the host mucus oligosaccharide composition. We also demonstrate that C. difficile binds preferentially to mucus extracted from CDI patients compared with healthy patients. Our results provide insights into a mechanism of C. difficile colonization and may provide novel target(s) for the development of alternative therapeutic agents.

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INTRODUCTION

Clostridium difficile infection (CDI) represents an increasing public health problem as it is a primary cause of antibiotic-induced diarrhea and colitis. Since the cause and current treatment of C. difficile is antibiotics, CDI will likely persist as a major health concern. Multiple studies have focused on the effects of C. difficile toxin production on the intestinal epithelium, however the human C. difficile mucus interaction and the process of colonization remains unknown. GI mucus consists of a firmly attached inner mucus layer, devoid of bacteria under normal conditions, and a loose outer mucus layer, which is colonized by bacteria (Johansson et al. 2011a; Johansson et al. 2008). The intestinal mucus is composed primarily of MUC2 and secreted MUC2 forms a gel-like structure which provides a physical barrier to protect the intestinal epithelium from the gut microbiota (Johansson et al. 2011a; Johansson et al. 2008; Johansson et al. 2011b; Zarepour et al. 2013; Berg 1996; Bergstrom et al. 2010; Kim and Ho

2010). The colon harbors the densest bacterial population and contains the thickest intestinal mucus as a result of MUC2 secretion (Sheng et al. 2012). Secreted and adherent mucus are continuously renewed and can be rapidly secreted by the host limit host-bacterial interaction (McGuckin et al. 2011; Linden et al.

2008). In addition to host-modulated mucus changes, several bacterial species are also capable of altering host mucus (Sheng et al. 2012; McGuckin et al. 2011; Linden et al. 2008; Mattar et al. 2002; Dohrman et al. 1998; Lencer et al. 1990; Epple et al. 1997). Secretion of MUC2 mucus has been proposed to be one mechanism used by commensal bacteria to improve barrier function and exclude pathogens (Mattar et al.

2002; Ohland and Macnaughton 2010; Mack et al. 2003). If bacteria are able to penetrate the secreted

MUC2 mucus layer they are able to interact with the glycocalyx consisting primarily cell-surface mucins, such as MUC1 (Sheng et al. 2012).

In order to reach the epithelium, GI pathogens must develop specific virulence strategies to address the presence of mucus (McGuckin et al. 2011). The mucus barrier has been shown to provide partial protection against several enteric bacterial pathogens, including Yersinia enterocolitica, Shigella flexneri, Citrobacter rodentium, and Salmonella enterica Serovar Typhimurium (Zarepour et al. 2013;

Bergstrom et al. 2010; Mantle et al. 1989; Nutten et al. 2002). Although multiple GI pathogens have been

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shown to evade or alter the host mucus barrier (Zarepour et al. 2013; Bergstrom et al. 2010; Mantle et al.

1989; Nutten et al. 2002), no studies have examined how C. difficile infection affects host mucus production or composition. It has been well documented that antibiotic disruption of the gut microbiota provides an open niche which favors C. difficile colonization and toxin production (Badger et al. 2012;

Bauer and van Dissel 2009; Borriello and Barclay 1986; Borriello et al. 1979; Borriello et al. 1987;

Borriello and Larson 1981; Britton and Young 2012). C. difficile has been shown to bind to mucus in cell lines and animal models (Calabi et al. 2002; Eveillard et al. 1993; Hennequin et al. 2003; Juge 2012;

Krivan et al. 1986; Smith et al. 1997; Waligora et al. 1999; Waligora et al. 2001), but data on C. difficile mucus binding and colonization in humans is scarce. Adherence of GI pathogens to the intestinal mucus has been hypothesized to allow for optimal delivery of toxins to the host. C. difficile toxin A has been shown to bind to α-Gal-(1,3)-β-Gal-(1,4)-β-GlcNAc in animal models (Krivan et al. 1986; Smith et al.

1997; Castagliuolo et al. 1996; Clark et al. 1987; Eglow et al. 1992; Greco et al. 2006; Ho et al. 2005;

Pothoulakis et al. 1996a; Pothoulakis et al. 1996b; Genth et al. 2008), but the receptors in humans have not been identified. Furthermore it is unknown whether C. difficile is able to manipulate the mucus oligosaccharide composition thereby exposing or stimulating production of binding and toxin receptors. A better understanding of C. difficile pathogenesis, especially during the colonization phase, is critical for developing new therapeutics for treatment of prevention of CDI.

In this study, we demonstrate that patients with CDI secrete acidic mucus that consists primarily of MUC1. Patients with CDI have decreased MUC2 expression indicating a mucus barrier defect.

Injection of human intestinal organoids (HIOs) with C. difficile demonstrates that C. difficile alone is sufficient to decrease MUC2 production. In addition, patients with CDI exhibit an altered mucus oligosaccharide composition and in vitro C. difficile is able to use free oligosaccharides in media for proliferation. C. difficile injection into HIOs reveals that C. difficile alone is not sufficient to alter the mucus oligosaccharide composition. However HIOs injected with CDI stool supernatant is sufficient to elicit the changes observed in biopsies from patients with CDI. Finally, we demonstrate that C. difficile binds better to mucus in CDI patients compared with control patient mucus.

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METHODS

Patient information: Fecal samples and biopsies were obtained as previously described (manuscript #1).

Histology. Healthy and CDI biopsies were obtained from transverse colon and fixed overnight at 4°C in neutral-buffered formalin and embedded in paraffin. Serial 6–7 m thick sections were applied to glass slides and intestine architecture was examined by H&E stain. Mucus composition was examined by periodic acid-Schiff (PAS)-Alcian Blue (AB) stain. Expression of mucus oligosaccharides was examined using FITC-conjugated lectins: Ulex europaeus agglutinin 1 (UEA-1, fucose), Concanavalin A (ConA, mannose), Dolichos biflorus agglutinin (DBA, N-Acetylgalactosamine), Peanut Agglutinin (PNA, galactose), and Wheat germ agglutinin (WGA, N-acteylglucosamine, Vector Laboratories, Burlingame,

CA) were used as previously described (Engevik et al. 2013a; Hooper et al. 1999; Magalhaes et al. 2009).

Briefly, sections were deparaffinized, blocked with PBS containing 10% BSA, and stained with FITC- labeled lectin (10 g/ml) for 1 h at room temperature. Sections were then washed three times in PBS, counterstained with Hoechst (Fisher Scientific), and analyzed by confocal laser scanning microscopy

(Zeiss LSM Confocal 710, Carl Zeiss, Germany).

Expression of MUC1 examined with an anti-human MUC1 antibody (dilution 1:100, RB-9222,

Thermo Fisher Scientific), MUC2 examined with an anti-human MUC2 antibody (dilution 1:100, MS-

1037, Thermo Fisher Scientific) and C. difficile binding was examined with rabbit anti-C. difficile cell surface protein antibody (dilution 1:100, ab93728, ABCAM, Cambridge, MA). Briefly, sections were stripped of paraffin and incubated for 40 min at 97°C with Tris–EDTA–SDS buffer as previously described (Syrbu and Cohen 2011). Sections were then blocked with PBS containing 10% serum, and stained with primary antibody overnight at 4°C. Sections were then washed three times in PBS, incubated with goat-anti-rabbit IgG Alexa Fluor® 488 secondary antibody (1:100 dilution) (Life Technologies,

Grand Island, NY) for 1 hr at room temperature and counterstained with Hoechst (Fisher Scientific). For

MUC1 and MUC2 stains, MUC1 was stained with goat-anti-rabbit IgG Alexa Fluor® 488(1:100 dilution)

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and MUC2 was stained with donkey anti-mouse IgG Alexa Fluor® 633(1:100 dilution) and counterstained with Hoechst (Fisher Scientific). Sections were analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710). Digital images of slides were evaluated by tabulating mean pixel intensity of the respective color channel on each image using Image J software (NIH) and reported as relative fluorescence. Five regions of interests per image, four images per slide, and n=5 healthy and CDI patients were used for semi-quantitation of stain intensity.

Human Intestinal organoids (HIOs) and microinjection. Human intestinal organoids (HIOs) used in this study were generated by the Cincinnati Children’s Hospital Medical Center (CCHMC) Pluripotent

Stem Cell Facility through directed differentiation of human pluripotent stem cells (hPSC). HIOs were injected with broth (control), C. difficile culture (grown in Tryptone yeast TY broth (Fang et al. 2009)) and healthy or CDI patient stool and incubated overnight as previously described (manuscript #1). For

RNA, organoids were homogenized in Triazol and extracted with chloroform according the manufacturers instructions (Invitrogen). For staining, HIOs were fixed with 4% Carnoy’s fixative for 30 min at room temperature. HIOs were washed in PBS and transferred to sucrose (30% in PBS) and incubated overnight at 4°C. The next day, HIOs were placed in OCT embedding medium and frozen at -

80°C for 1 day. Seven µm sections were cut on a cryostat. Slides were stained with anti-human MUC1 antibody (dilution 1:100, RB-9222, Thermo Fisher Scientific), or anti-human MUC2 antibody (dilution

1:100, MS-1037, Thermo Fisher Scientific) overnight at 4°C. Sections were countered stained with Alexa

Fluor® 488 or 633 secondary antibodies for 1 hr at room temperature and also counterstained with

Hoechst (0.1 µg/ml) (Fisher Scientific) for 10 min at room temperature. Expression of mucus oligosaccharides in HIOs was examined with 10 µg/ml FITC-conjugated lectins: Concanavalin A (ConA, mannose), Dolichos biflorus agglutinin (DBA, N-Acetylgalactosamine), Peanut Agglutinin (PNA, galactose), and Wheat germ agglutinin (WGA, N-acteylglucosamine) (Vector Laboratories, Burlingame,

CA). Sections were stained for 1 hr at room temperature followed by Hoechst staining (Fisher Scientific) for 10 min at room temperature. All slides were analyzed by confocal laser scanning microscopy (Zeiss

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LSM Confocal 710, Carl Zeiss) and the relative fluorescence intensity was quantified using Image J software (NIH) and reported as relative fluorescence. Five regions of interests per image, four images per slide, n=3, were used for semi-quantitation of stain intensity.

qRT-PCR of mRNA. To examine MUC1 and MUC2 message level, total RNA was extracted from HIOs with TRIzol Reagent (Invitrogen Life Technologies, Grand Island, NY) according to the manufacturer’s instructions and as previously described (manuscript #1). qRT-PCR was performed using SYBR Green

PCR master mix (ABI) on a Step One Real Time PCR Machine (ABI) with the following gene specific qRT-PCR primers derived from previous literature: Muc1 Forward 5'- CCAGACCCCTGCACTCTGAT-

3', Muc1 Reverse 5'- CGCTTGACAAAGGGCATGA-3'; Muc2 Forward5'-

TGCCCACCTCCTCAAAGAC-3'; Muc2 Reverse 5'- TAGTTTCCGTTGGAACAGTGAA-3' (Fu et al.

2011); human GAPDH Forward 5'- TGCACCACCAACTGCTTAGC -3' and GAPDH Reverse 5'-

GGCATGGACTGTGGTCATGAG -3' (Quinkler et al. 2005). Data were reported as the delta delta CT using GAPDH as the standard. Differences in mRNA expression were determined by qRT-PCR and expressed as the CT relative fold difference.

Stool mucus extraction. Crude mucus was extracted from human feces as previously described (Roos et al. 2000; Juntunen et al. 2001; Ouwehand et al. 1999). Briefly, stool was diluted in 4 volumes of ice-cold

PBS (PBS; 10 mM phosphate, pH 7.2) containing 0.5 µg/µl sodium azide (prevents bacterial growth), 1 mM phenylmethylsulfonyl fluoride, iodoacetamide and 10 mM EDTA to inhibit proteases. The suspension was shaken for 1 h at 43°C and centrifuged for 20 min at 4°C at 14,000 x g to pellet stool solids. From the clear supernatant, the mucus was precipitated twice with ice cold 60% ethanol and resuspended in ultrapure water. Mucus was then lyophilized and reconstituted based on weight. Mucus was stored at -80°C until used. Crude mucus protein concentration was determined by Bio-Rad protein assay performed according to the instructions of the manufacturer (Bio-Rad Laboratories, Richmond,

CA). The original non-lyophilized crude mucus contained different amounts of proteins, with healthy

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mucus containing 0.4 µg/µl and CDI mucus 2.2 µg/µl. Pooled lyophilized crude mucin preparation was reconstituted to 7 µg/µl protein. Mucus oligosaccharide composition was determined by periodic acid-

Schiff’s reagent (PAS) in a microtiter plate as previously described (Kilcoyne et al. 2011). C. difficile mucus binding was examined in two ways: microtiter plate and slide. For the microtiter plate assay, 100

µl mucus was immobilized in polystyrene microtiter plate wells (Maxisorp, Nunc) by overnight incubation at 4°C as previously described (Ouwehand et al. 1999), which covers the well with sufficient mucus. The wells were washed twice with PBS to remove excess mucus. The microtiter plate was then placed in an anaerobic hood for 2 hr to create an anaerobic environment. Once anaerobic, 100 µl of C. difficile ATTC-1870 culture (106 CFU) was added to each well and incubated for 3 hr at 37°C within the

Coy anaerobic chamber. After incubation, the wells were washed twice with PBS to remove unattached bacteria. Mucus was then scrapped off the microtiter plate and grown on C. difficile selective agar plates

(Fisher Scientific). For the slide binding assay, 50 µl mucus (385 µg protein) was added to glass slides and spread by an inoculating loop. Slides were allowed to air-dry and then heat fixed using a Bunsen burner. Slides were blocked with blocked with PBS containing 10% serum. An ImmEdge pen (Fisher

Scientific) was used to select areas for C. difficile binding. Slides were placed in an anaerobic chamber for 2 hr and then 100 µl of C. difficile (106 CFU) was added to each well and incubated for 3 hr at 37°C within the anaerobic chamber. Slides were then washed 3x in PBS to remove unattached bacteria and slides were incubated with an anti-C. difficile antibody (dilution 1:100, ab93728, ABCAM) for 1 hr at room temperature. Slides were then washed three times in PBS, incubated with goat-anti-rabbit IgG Alexa

Fluor® 488 secondary antibody (1:100 dilution) (Life Technologies), cover slipped, and analyzed by confocal laser scanning microscopy (Zeiss LSM Confocal 710) and Image J software (NIH).

Bacterial strains and culture conditions. C. difficile ATTC BAA-1870 was grown in media with 8 mM

Na+, pH 6.0 mimicking healthy conditions 75 mM Na+, pH 7.0 mimicking CDI conditions (see manuscript #1, Figure 2). To determine the ability of mucus oligosaccharides to promote C. difficile growth, fucose, N-acetylglucosamine (GlcNAc), N-acetylgalactosamine (GalNAc) (Thermo Fisher

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Scientific), mannose or galactose (Sigma-Aldrich) were added to TYG media at a final concentration of 1 mM and their presence was then analyzed for an influence on bacterial growth. Bacteria were grown under anaerobic conditions at 37°C to early stationary phase in normal TYG media (12 hrs, O.D. 560 nm ~1) and used to inoculate media containing varying [Na+]. Growth was measured as the optical density (O.D.

560 nm) with an Amersham Biosciences Ultospec 3100 Spectrophotometer (GE Healthcare Life Sciences,

Pittsburgh, PA) after 24 hrs. C. difficile titers were determined by bacterial cell counts using a Petroff-

Hauser chamber (Hausser Scientific; Horsham, PA) and also by colony forming units (CFU) (Caldwell and Arcand 1974; Caldwell et al. 1973).

Statistics. Data are presented as mean ± SEM. Comparisons between groups were made with either One or Two Way Analysis of Variance (ANOVA), and the Holm-Sidak post-hoc test used to determine significance between pairwise comparisons using SigmaPlot (Systat Software, Inc., San Jose, CA). A P <

0.05 value was considered significant while n is the number of experiments performed.

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RESULTS

Mucus provides both an interface and barrier between luminal bacteria and the host (Pullan et al.

1994). Studies have demonstrated that some bacteria are capable of adhering to intestinal mucus and/or epithelium (Probert and Gibson 2002) and changing the rate of mucus secretion (Ohland and

Macnaughton 2010; Leitch 1988; Deplancke and Gaskins 2001). Although it has been demonstrated that several GI pathogens alter mucus production, little data exists on C. difficile-mucus alteration in the patient setting. H&E stains of healthy and CDI colon reveal the presence of mucus and the formation of pseudomembranes in patients with CDI compared to healthy (Fig. 1A black arrows). Periodic acid-Schiff

(PAS)-Alcian Blue (AB) stains of CDI biopsies depict the secretion of acidic mucins (dark blue) (Fig.

1A). To determine the type of mucin present in CDI mucus, intestinal mucins MUC1 (cell-surface) and

MUC2 (secreted) were examined in human biopsies by immunofluorescence (Fig. 1B and C). Although

MUC1 secretion was observed, no changes were observed in MUC1 levels at the epithelium.

Interestingly, MUC2 levels were significantly decreased in CDI patients compared to healthy patients (P

< 0.001). It is important to note that since the colonic tissue was fixed in formalin, the secreted mucus architecture is not fully retained (Johansson et al. 2011a). Thus the mucus observed in healthy and CDI biopsies likely represents compressed secreted and adhered mucus composition. The only way to preserve the mucus layers is to use a fixative such as Carnoy which maintains the mucus structure. To assess whether MUC1 was the predominant mucus in CDI stool, mucus was extracted from healthy and CDI patient stool and MUC1 and MUC2 was analyzed by immunofluorescence. CDI stool was found to be composed almost entirely of MUC1 (Fig. 1D and E). This data indicates that CDI patients have decreased secreted MUC2 and the secretion of the normally adherent MUC1.

To determine if C. difficile is capable of altering host mucus production, C. difficile, CDI and healthy stool supernatants were injected into HIOs (Fig. 2A-C). HIOs were fixed in Carnoys fixative to maintain normal mucus architecture. Injection of C. difficile and CDI stool supernatant both resulted in decreased MUC2 expression at the level of mRNA (Fig 2A) and protein (Fig 2B and C). This indicates that C. difficile alone is sufficient to inhibit MUC2 expression. Other GI pathogens have been shown to be

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inhibited by the presence of MUC2 (Deplancke and Gaskins 2001), so inhibition of MUC2 in CDI patients may reflect increased access of bacteria, including C. difficile, to the epithelium that may augment CDI induced colitis.

In addition to serving as a barrier, the mucus serves as an interface between the bacteria and the host: the mucus provides binding sites for both commensal and pathogenic bacteria (Lillehoj et al. 2001;

Rajkumar et al. 1998) and bacterial energy/food sources (Zarepour et al. 2013; Berg 1996; Deplancke and

Gaskins 2001; Aristoteli and Willcox 2003). Gut mucus oligosaccharides N-acetylglucosamine (GlcNAc), galactose (Gal), N-acetylgalactosamine (GalNAc), fucose (Fuc), N-acetylneuraminic acid (NeuNAc), and mannose (Johansson et al. 2011a; Johansson et al. 2008; Johansson et al. 2011b) are attached to mucin glycoproteins and can be used by bacteria to maintain their relative niches. Although C. difficile has been shown to be devoid of oligosaccharide-cleaving enzymes (Wilson and Perini 1988), we hypothesized that an altered gut microbiota in CDI patients may modify the mucus oligosaccharide composition to create a favorable environment for C. difficile. To determine if CDI patients have altered intestinal mucus composition, fucose, mannose, galactose, GalNAc, and GlcNAc levels were examined by FITC-lectin

(Fig. 3A and B). CDI patients exhibited increased GlcNAc (P = 0.003) and galactose (P < 0.001) residues and decreased levels of GalNAc (P<0.001). No changes were observed in fucose or mannose levels between healthy and CDI patients. These data indicate that CDI patients have an altered intestinal mucus composition. The increase in galactose residues is of particular interest because α-galactose has been shown to be a receptor for C. difficile toxin A in mice, hamsters, rabbits and pigs (Greco et al. 2006; Ho et al. 2005; Genth et al. 2008; Pothoulakis 1996). The normal human colon contains few terminal galactose residues. Thus, it has been previously postulated that another receptor must be responsible for toxin binding. These data provide evidence that mucus galactose residues are exposed in CDI patients and may, thus, be the toxin receptor in humans as well as in the aforementioned animal models.

To determine if C. difficile is capable of altering the host mucus oligosaccharide composition or mucus production, broth (control), C. difficile, CDI and healthy stool supernatant were injected into HIOs

(Fig. 4A and B). Injection of C. difficile alone did not change HIOs oligosaccharide composition. In

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contrast, injection of whole CDI stool supernatant resulted in changes similar to those observed in the mucus oligosaccharide composition of patients with CDI. These data point to another factor, likely another bacterial group within the altered gut microbiota in CDI patients, which is responsible for exposure or production of altered mucus oligosaccharide composition.

In order to better determine if C. difficile could use cleaved mucus oligosaccharides for growth,

C. difficile ATTC BAA-1870 was grown in vitro in conditions mimicking healthy patients (8 mM Na+, pH 6.0) and CDI patients (75 mM Na+, pH 7.0) with the addition of oligosaccharides found in the mucus

(Fig. 5A and B). Under conditions which mimic a healthy colon (8 mM Na+, pH 6.0), the addition of mannose, fucose, N-acetylglucosamine, N-acetylgalactose and galactose were all found to enhance C. difficile growth at low [Na+] (P=0.004) (Fig. 5). At pH 7.0, no differences were observed between high

[Na+] and addition of oligosaccharides (Fig. 5). This data points to C. difficile use of oligosaccharides before the inhibition of NHE3. These data indicate that C. difficile is able to use mucus oligosaccharides to drive proliferation at lower [Na+] and pH, which mimic healthy patients, yet oligosaccharides do not influence C. difficile growth at higher pH levels. This shift from using oligosaccharides at low [Na+] and a more acidic pH to not using oligosaccharides at high [Na+] with a more alkaline pH may represent the shift in C. difficile gene expression from colonization to virulence as previously described (Janoir et al.

2013). Transformation from a colonization phase (utilization of oligosaccharides) to virulence phase

(toxin production) may alter expression of bacterial oligosaccharide transporters thereby minimizing the need for oligosaccharides for growth under CDI conditions (high [Na+] and pH 7.0).

C. difficile has been shown to be capable of binding to intestinal mucus in cell lines (Eveillard et al. 1993; Waligora et al. 1999), but limited data exist on the C. difficile mucus binding location in humans. Immunofluorescence revealed that C. difficile appeared to localize near the epithelium in CDI patients and in the secreted mucus (89%) (Fig. 1, manuscript #1). C. difficile only comprises >2% of CDI stool microbiota (C. difficile CFU 9.9 x 104 ± 2.4 x 102) and thus represents a minor component of the luminal bacterial population. However C. difficile mucus binding indicates that the organism is present as a member of the mucosa-associated bacterial population. To address if there is a binding site exposed in

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the mucus from patients with CDI, C. difficile binding was examined in mucus extracted from stool of healthy and CDI individuals. C. difficile binding was found to be substantially higher in CDI mucus compared to healthy patients when compared by CFU (2.4-fold increase) and immunofluorescence (Fig

6A and B). C. difficile mucus oligosaccharide binding may allow the bacteria to penetrate deeper into the intestinal mucus and interact with the host in a manner similar to other pathogens (McGuckin et al. 2011;

Juge 2012; Deplancke and Gaskins 2001; de Repentigny et al. 2000; Vimal et al. 2000). C. difficile binding to stool and tissue mucus which is composed primarily of MUC1 indicate that cell-surface mucus

MUC1 may be a key binding site. The data presented herein demonstrate a number of new details about

CDI infection and points to the intestinal environment and mucus as potential targets for the treatment of

CDI.

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DISCUSSION

Little is known about the mechanism of C. difficile colonization, host binding, and mucus-

C.difficile interaction. In this study, we demonstrate the following: (1) Patients with CDI secrete acidic mucins composing of MUC1 (as detected in tissue and stool). At the level of the tissue, CDI patients have decreased MUC2 levels and C. difficile alone is sufficient to decrease MUC2 expression as demonstrated by HIOs injected with C. difficile. (2) Patients with CDI exhibit an altered mucus oligosaccharide composition with increased levels of GlcNAc and galactose, decreased levels of GalNAc and no changes in fucose or mannose levels. In vitro C. difficile growth can use fucose, mannose, galactose and GlcNAc,

GalNAc under healthy conditions (8 mM Na+, pH 6.0) to drive proliferation, but these residues do not contribute in elevated growth at high [Na+] and pH 7.0. HIOs injected with C. difficile or stool supernatant confirm that C. difficile alone is not sufficient to alter mucus oligosaccharide composition. (3)

C. difficile binds better to CDI mucus which is composed of secreted MUC1. Figure 7 highlights our current working model of C. difficile colonization based on the data presented herein. Normally,

Firmicutes is the dominant member of the human colon at the level of the luminal (our data) and mucosa- associated bacterial populations (Chen et al. 2012). The introduction of antibiotics has been shown to alter the gut microbiota by decreasing the resident Firmicutes and increasing Proteobacteria (Hill et al. 2010;

Perez-Cobas et al. 2013). Introduction of antibiotics has also been shown to decrease MUC2 production to some degree (Hill et al. 2010). In this antibiotic depleted environment, C. difficile is able to use oligosaccharides cleaved by antibiotic-resistant bacteria to proliferate. C. difficile also decreases MUC2 production to gain closer access to host. C. difficile then binds to cell adhesive MUC1 and delivers its toxin load, which inhibits NHE3, likely in a similar manner as described in cells lines (Hayashi et al.

2004) by preventing the phosphorylation of ezrin which normally anchors NHE3 into the cell membrane.

Loss of NHE3 alters the intestinal environment making it high in Na+ and more alkaline in pH. This altered intestinal environment further shapes the gut microbiota resulting in increased Bacteroidetes members and maintaining decreased Firmicutes members. This altered intestinal environment also further promotes C. difficile proliferation and prevents other competitive Clostridial groups from colonizing the

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same niche. We predict that the host attempts to prevent bacterial-host interaction by the secretion of normally adherent MUC1 mucus but this is limited in effectiveness as C. difficile is able localize near some host crypts. Exposure and/or production of terminal galactose mucus toxin receptors may also perpetuate the C. difficile infection by introducing toxin load to more host cells, continuing the altered environment and C. difficile proliferation.

Under normal conditions, the secreted mucin Muc2 is the predominant mucus mucin (Johansson et al. 2011a; Johansson et al. 2008; Johansson et al. 2011b) and forms a gel-like and viscous layer which protects the epithelium from both commensal bacteria and pathogens (Zarepour et al. 2013; Bergstrom et al. 2010; Kim and Ho 2010). Several pathogens have been shown to alter mucus production as a mechanism of infection (Zarepour et al. 2013; Bergstrom et al. 2010; Mantle et al. 1989; Nutten et al.

2002). However prior to this study, no studies had addressed the interaction of C. difficile and the host mucus. We show that CDI patients have decreased secreted MUC2 and that C. difficile alone is capable of eliciting this host change. Loss of MUC2 has consequences for both pathogens and commensal bacteria.

MUC2-/- mice have been shown to have enhanced interaction commensal bacteria with the epithelium

(Bergstrom et al. 2010). Loss of MUC2 in CDI patients may also result in increased interaction of both C. difficile and commensal bacteria with the epithelium in patients which may exacerbate intestinal inflammation.

Decreased expression of MUC2 in patients with CDI likely allows for increased C. difficile binding to cell-surface mucins such as MUC1. Since adherence of GI pathogens to the intestinal mucus likely allows toxin delivery (Finlay and Falkow 1989). C. difficile mucus binding may likewise serve for introduction of toxin A and B to the epithelium. Toxin A has been shown to bind to the linear B type α-

Gal-(1,3)-β-Gal-(1,4)-β-GlcNAc in animal models (Krivan et al. 1986; Smith et al. 1997; Castagliuolo et al. 1996; Clark et al. 1987; Eglow et al. 1992; Greco et al. 2006; Ho et al. 2005; Pothoulakis et al. 1996a;

Pothoulakis et al. 1996b; Genth et al. 2008). Although anti-Gal IgGs are present in humans (Pothoulakis et al. 1996a; Galili et al. 1988; Hamadeh et al. 1992), it has been hypothesized that C. difficile has an alternate receptor in humans since α-galactosyl epitopes are minimal in normal human intestine. This

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work is the first to demonstrate that CDI patients present with increased terminal galactose residues. This work is consistent with that of Smith et al. which demonstrated that toxin A can bind human colonic biopsies, but pre-treatment of cells with α- or β- galactosidases, which cleave terminal galactose residues, reduced toxin binding (Smith et al. 1997). Increased galactose residues in patients with CDI may indicate increased toxin A receptor, but this has yet to be shown. Interestingly mice overexpressing β-1,4- galactosyltransferase I (βGalT1), an enzyme involved in adding galactose to mucus glycoproteins, are resistant to TNF induced systemic inflammatory response syndrome (SIRS) and Dextran Sodium Sulfate

(DSS)-induced colitis (Vanhooren et al. 2013). Vanhoorren et al. hypothesized that the addition of galactose was a host response to select for Firmicutes members (vs. Bacteroidetes), which may protect against inflammation (Vanhooren et al. 2013). Since CDI patients have intestinal inflammation (Ng et al.

2010) increased galactose may represent a host response designed to protect against inflammation and/or alter the gut microbiota back towards high Firmicutes and lower Bacteroidetes. Alternatively, since CDI patients have an altered gut microbiota that is capable of altering the mucus oligosaccharide composition, increased galactose may also represent increased exposure as a result of degradation by other bacterial members in CDI patients. Regardless of the mechanism, our data suggests that C. difficile is able to modify the environment, through inhibition of NHE3, and consequently the gut microbiota, resulting in an indirect pathological galactose residue increase.

Intestinal mucus is involved in protection of the enterocytes from bacteria and also provides a binding site, nutrient source and matrix for bacterial growth (Juntunen et al. 2001; Vanhooren et al. 2013).

Studies have demonstrated that several bacterial groups are capable of altering and using the mucus oligosaccharide (Engevik et al. 2013a; Hooper et al. 1999; Deplancke and Gaskins 2001; Engevik et al.

2013b; Bry et al. 1996; Freitas et al. 2002; Hooper et al. 2002). Release of oligosaccharides and mucin degradation is a multistep process involving a variety of microbial proteases, which degrade mucin oligosaccharides based on the size, branching, linkage and presence or absence of terminal sialic acid or sulfate groups (Deplancke and Gaskins 2001; Miner-Williams et al. 2013). Interestingly, C. difficile does not appear to synthesize oligosaccharide hydrolytic enzymes, but does require oligosaccharides to prevent

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being out-competed by other bacteria (Wilson and Perini 1988). The lack of enzymatic activity in C. difficile and work with HIOs injected with C. difficile suggests that C. difficile uses oligosaccharides cleaved by other bacterial group(s). Only certain bacteria groups are capable of secreting oligosaccharide degrading enzymes (Johansson et al. 2011b; Engevik et al. 2013b; Salyers 1979; Schwab and Ganzle

2011; Hoskins and Boulding 1981) (Engevik et al. 2013a) including the genera Ruminococcus

(Firmicutes), Bacteroides (Bacteroidetes), Bifidobacterium (Actinobacteria), Lactobacillus (Firmicutes) and Clostridium (Firmicutes) (Deplancke and Gaskins 2001; Bry et al. 1996; Hooper et al. 2002; Salyers

1979; Corfield et al. 1992; Salyers et al. 1977; Ruas-Madiedo et al. 2008; Macfarlane et al. 2001).

Released oligosaccharides can then be used by non-mucin degrading bacteria, including C. difficile. It is unclear which bacterial group(s) are responsible for the altered oligosaccharide composition, however C. difficile has been shown in a mouse model to use mucus oligosaccharides released by Bacteroides thetaiotaomicron (Ng et al. 2013). In the NHE3-/- mouse terminal ileum, increased B. thetaiotaomicron resulted in increased mucus fucose concentrations (Engevik et al. 2013a). However B. thetaiotaomicron is not increased in distal colon of NHE3-/- mice (Engevik et al. 2013a) or in the stool of CDI patients (B. thetaiotaomicron CFU, Healthy patients: 4.3 x 109 ± 1.8 x 102, CDI: 3.5 x 109 ± 3.1 x 102). It may be possible that B. thetaiotaomicron is increased in the ileum of patients with CDI, similar to NHE3-/- mice, but further studies will be required to test this hypothesis. Although Bacteroidetes is increased in recurrent

CDI patients, the genus Bacteroides is decreased in CDI patients stool (Tvede and Rask-Madsen 1989;

Hopkins and Macfarlane 2002), which is consistent with data showing specific Bacteroides members can inhibit C. difficile growth (Tvede and Rask-Madsen 1989). This suggests that a bacterial group other than

Bacteroides is responsible for releasing or exposing C. difficile toxin epitopes and oligosaccharides. CDI patients have increased Mouse Intestinal Bacteroidetes (MIB) (MIB CFU, Healthy patients 1.4 x 103 ± 1.1 x 101, CDI: 3.7Ex 104± 2.4 x 101), which is closely related to Bacteroides and found in both mice and humans (Salzman et al. 2002; Kibe et al. 2007). It is possible that MIB may contribute to some of the changes observed in the CDI patient mucus, but this remains to be established. In mice, antibiotic treatment alters the gut microbiota (with increased Proteobacteria) (Antonopoulos et al. 2009; Sekirov et

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al. 2008; Hazenberg et al. 1983) and increases free sialic acid oligosaccharides which can be used by C. difficile (Ng et al. 2013). This suggests that members of the antibiotic-induced disrupted gut microbiota may also play a role in C. difficile colonization. These studies implicate an altered gut microbiota and subsequent alteration in mucus oligosaccharide availability in C. difficile colonization and infection.

Taken together, the findings presented herein shed light on potential mechanisms of C. difficile pathogenesis and colonization. This work also demonstrates the production and exposure of potential CDI toxin receptors. Increased knowledge of these binding and/or toxin oligosaccharides could be used to improve current CDI therapeutic regimens.

Acknowledgements:

I would like to thank Lindsey Ferreira, Stacy Huang, Amy Engevik and Kristen Engevik for their technical assistance with this project.

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Figure 1. CDI biopsy mucus production. A) H&E stains of healthy and CDI patient biopsies demonstrate that CDI patients have regions of pseudomembranous colitis (necrotic epithelium and mucus) (black arrows) and altered morphology. PAS-AB stains demonstrate that CDI patients secrete acidic mucus

(dark blue). Scale bar 500 µm. B) Confocal images MUC1 (green) and MUC2 expression (red) counterstained with Hoechst (blue). CDI patient have similar surface MUC1 expression (green), but decreased MUC 2 (red). Representative micrographs of observations from n = 5 healthy and CDI patient biopsies. Scale Bar = 50 μM. C) Semi-quantitative analysis of MUC1 and MUC2 stain in healthy (black bar) and CDI (white bar) biopsies. D) Confocal images MUC1 (green) and MUC2 expression (red) in stool extracted mucus. CDI stool mucus consists primarily of MUC1 (green). Representative micrographs of observations from n = 6 healthy and CDI patient stool. Scale Bar = 50 μM. E) Semi-quantitative analysis of MUC1 and MUC2 stain in healthy (black bar) and CDI (white bar) stool mucus. *P < 0.005

Two Way ANOVA, Holme-Sidak.

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Figure 2. HIOs injected with C. difficile and CDI stool supernatant have decreased MUC2 expression. A)

Muc1 and Muc2 mRNA mRNA levels indicate decreased expression of Muc2in HIOs injected with CDI stool and C. difficile. B) Confocal images MUC1 (green) and MUC2 expression (red) counterstained with

Hoechst (blue) in HIOs. HIOs have similar surface MUC1 expression (green), but decreased MUC 2(red).

Representative micrographs of observations from n = 5 healthy and CDI patient biopsies. Scale Bar = 50

μM. C) Semi-quantitative analysis of MUC1 and MUC2 stain for control (broth injected), C. difficile culture, healthy stool and CDI stool. *P < 0.005 Two Way ANOVA, Holme-Sidak.

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Figure 3. CDI patients have an altered mucus oligosaccharide composition. A) Confocal images of healthy and CDI patient biopsies stained with a panel of lectins to identify mucus oligosaccharides fucose

(red), mannose (green), N-acetylglucosamine (GlcNAc) (orange), N-acetylgalactosamine (GalNAc)

(purple) and galactose (yellow) counterstained with hoechst (blue). Representative micrographs of observations from n = 5 healthy and CDI patient biopsies. Scale Bar = 50 μM. B) Semi-quantitative

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analysis of lectin stain where florescence of oligosaccharide was calculated relative to unstained tissue for healthy (black bar) and CDI (white bar) biopsies. *P< 0.005 Two Way ANOVA, Holme-Sidak.

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Figure 4. HIOs injected with CDI stool supernatant have altered mucus oligosaccharide composition. A)

Confocal images of HIOs stained with a panel of lectins to identify mucus oligosaccharides mannose

(green), N-acetylglucosamine (GlcNAc) (orange), N-acetylgalactosamine (GalNAc) (purple) and galactose (yellow) counterstained with hoechst (blue). Representative micrographs of observations from n

= 3. Scale Bar = 50 μM. B) Semi-quantitative analysis of mannose, GlcNAc, GalNAc, and galactose stain for control (broth injected), C. difficile culture, healthy stool and CDI stool. *P < 0.005 Two Way

ANOVA, Holme-Sidak.

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Figure 5. In vitro growth of C. difficile ATTC BAA-1870 in the presence of oligosaccharides. Growth of

C. difficile in TYG broth at 8 mM Na+ at pH 6.0 mimicking healthy stool and 75 mM Na+ at pH 7.0 mimicking CDI stool with the addition of fucose, mannose, galactose, GlcNAc and GalNAc at 24 hr time point. There is a significant increase in growth between 8mM Na+ without oligosaccharides and the addition of 1 mM fucose, mannose, galactose, GlcNAc and GalNAc (P < 0.001). In contrast, no significant changes were observed between 75 mM Na+ and with or without oligosaccharides. * P < 0.05.

Differences were observed healthy (at 8 mM Na+ at pH 6.0) and CDI (75 mM Na+ at pH 7.0) conditions

(P < 0.001), however there was no further effect of oligosaccharide addition on growth (P = 0.194). Two

Way ANOVA, Holme-Sidak.

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Figure 6. C. difficile mucus binding. A) CDI patients have C. difficile bound to mucus in patients with

CDI. Confocal images from CDI patients depicting C. difficile (green) and nuclei (blue) stained with

Hoechst. Representative micrographs of observations from n=5 CDI patient biopsies. Healthy patients showed no C. difficile binding while CDI patients have C. difficile localized in secreted mucus and near the epithelium. Scale Bar = 50 μM.

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Figure 7. Current working model of C. difficile colonization. (1) Firmicutes is the dominant member of the human colon at the level of the luminal (our data) and mucosa-associated bacterial populations (Chen et al. 2012). (2) Antibiotic-use, which decreases Firmicutes, increase Proteobacteria (Hill et al. 2010;

Perez-Cobas et al. 2013) and decreases MUC2 to some degree (Hill et al. 2010) opens a niche for C. difficile. (3) C. difficile uses oligosaccharides cleaved by antibiotic-resistant bacteria to proliferate and decreases MUC2 production. (3) C. difficile then binds to cell adhesive MUC1 and delivers its toxin load, which inhibits NHE3, likely by preventing the phosphorylation of ezrin which normally anchors NHE3 into the cell membrane (Hayashi et al. 2004). (4) Loss of functional NHE3 results in a high in Na+ and more alkaline pH luminal environment which shapes the gut microbiota to be increased in Bacteroidetes and decreased in Firmicutes. It remains unknown what changes are occurring in the mucosa-associated bacteria under these conditions. High Na+ and more alkaline pH favors C. difficile proliferation. (5)

MUC1 mucus is secreted by the host to limit bacterial-host interaction, but is relatively ineffective. (6)

Exposure and/or production of terminal galactose mucus toxin receptors by an alternate bacteria group

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likely continues C. difficile infection by introducing toxin to new host cells, continuing the altered environment and C. difficile proliferation.

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Publication #3

Prebiotic properties of Galursan HF 7K on mouse

gut microbiota

Melinda A. Engevik 1,2, Carla J. Faletti3,

Markus Paulmichl3, and Roger T. Worrell 1,2,*

1Department of Molecular and Cellular Physiology

University of Cincinnati College of Medicine

Cincinnati, OH 45267

2Digestive Health Center of Cincinnati Children’s Hospital, Cincinnati, OH 45229

3Institute of Pharmacology & Toxicology,

Helga & Erich Kellerhals Laboratories for Novel Therapeutics,

Center for Pharmacogenetics and Pharmacogenomics,

Paracelsus Medical University Strubergasse 21

A-5020 Salzburg, Austria

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Abstract:

Introduction: With the rise of antibiotic resistance, new alternatives are being sought to effectively modulate the characteristics of gut microbiota to obtain pathogen resistance without the use of antibiotics.

In the past, an oligosaccharide derivative of carrots, galursan HF 7K (GHF7K), has been used clinically in

Austria and recently in the fowl-industry to promote health. This study examined the potential role of

GHF7K as a prebiotic to alter the gut microbiota in mice. Methods: Mice were fed either a control diet

(CT) or a diet containing 2% GHF7K in the water and chow for 2 weeks, and weight, food and water consumption, gut microbiota and ion composition of the intestinal fluid were examined. Results: Dietary supplement of GHF7K did not alter mouse weight or daily food consumption. Additionally, no changes were observed in the total number of luminal or mucosa-associated bacteria populations in GHF7K-fed mice. GHF7K supplementation significantly altered the composition of luminal, and to a less extent, mucosa-associated bacterial populations at the level of the phyla, with region-specific differences. Similar to antibiotic use, Proteobacteria number was increased in the ileum and colon of GHF7K–fed mice, with no changes in the number of beneficial Lactobacillus and Bifidobacterium genera of Firmicutes.

Corresponding with the altered gut microbiota, changes in the ion composition of the intestinal fluid were observed. An increased Cl- concentration was observed in the duodenum and jejunum, while the Na+ concentration was increased in the cecum of GHF7K-fed mice. Decreases were observed in the K+ concentration in the cecum and distal colon. Conclusions: Dietary supplement of GHF7K is capable of altering the gut microbiota, which correlates to changes in the intestinal environment. These data suggest that GHF7K dietary supplement can purposefully be used to alter the gut microbiota, and thus could potentially represent an alternative approach to prophylactic antibiotic use.

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Introduction:

The intestinal microbiota contains over 1014 bacteria and plays a role in health and disease (Qin et al. 2010; Backhed et al. 2005; Matsumoto et al. 2001). The microbiota-host interaction plays a key role in maintaining intestinal homeostasis. Alterations in either the gut microbiota or the host genetics can result in alterations in the normal gut flora, termed microbial dysbiosis. In an attempt to control the gut microbiota, antibiotic use has become a routine procedure for treating both humans (Patrick et al. 2004) and livestock (Mathew et al. 2007). Studies have shown that antibiotic treatment of chickens, cattle and swine reduces Salmonella and E. coli infection (Abou-Youssef et al. 1979; Barrow et al. 1998; Mathers et al. 2004; Chadfield and Hinton 2003; Callaway et al. 2002). However, antibiotics have been shown to increase Proteobacteria, a phylum which contains a large number of pathogenic bacteria (Antonopoulos et al. 2009; Sekirov et al. 2008; Jernberg et al. 2010; Hazenberg et al. 1983; Dethlefsen et al. 2008;

Manichanh et al. 2010). In addition, long term use of antibiotics has also been shown to contribute to the increased prevalence of antibiotic-resistant bacteria (Mathew et al. 2007). Additional risk lies in the potential transfer of antibiotic resistance genes to resident microflora, resulting in propagation of antibiotic-resistant bacteria. Livestock in particular serve as a reservoir of bacteria that are resistant to antibiotics. These resistant bacteria tend to spread from manure, commonly used as fertilizer, to the water supply and plants which are consumed by other animals or humans (Biernasiak et al. 2011 ).

As a result of the adverse effects of continued antibiotic use, studies have begun to explore alternative methods of controlling and maintaining the normal gut microbiota while preventing infection.

Dietary supplements have gained attention in recent years. Soluble dietary oligosaccharides extracted from various sources have been shown to promote the proliferation of specific beneficial bacterial groups and thus are termed prebiotics (Neyrinck et al. 2011; Roberfroid et al. 2010; Sousa et al. 2011; Roberfroid and Slavin 2000; Biol et al. 1981; Cani et al. 2007). Examples of prebiotics include fructooligosaccharides, galactooligosaccharides, arabinose, galactose, inulin, raffinose, mannose, lactulose, stachyose, mannanoligosaccharides, xylooligosaccharides, palatinose, lactosucrose, glycooligosaccharides, isomaltooligosaccharides, and soybean oligosaccharides (Sousa et al. 2011; Saier

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and Mansour 2005; Bosscher et al. 2006; Gibson and Fuller 2000). A dietary supplement approach has been used throughout history for the prevention and treatment of diarrhea. Ernst Moro, an Austrian physician and pediatrician, demonstrated in 1908 that a carrot soup dramatically decreased the infant death rate by diarrhea in Germany (Weirich and Hoffmann 2005; Kunz 2012). Preliminary studies with an oligosaccharide derivative of carrot, oligo(2-7)-galacturonic acid, have suggested that in fowl farming can substitute for antibiotics used to prevent gastrointestinal tract (GI) infections (data not shown). These polysaccharides are hypothesized to be responsible for the anti-diarrheal effect of the carrot soup. The aim of this study was to examine the ability of a related carrot compound, galursan HF 7K (GHF7K) to alter the intestinal microbiota, by determining both the luminal and mucosa-associated microbiota profiles from the duodenum to the distal colon in mice.

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Methods:

Mice. All experimental protocols were approved by the University of Cincinnati Animal Care and Use

Committee and complied with National Institutes of Health guidelines. FVB/N (Friend virus B-type susceptibility) WT mice (Taketo et al. 1991) were used for all experiments. At 6-8 weeks post-weaning, mice were maintained on either a control mouse diet (CT, 7922 NIH-07 Mouse diet, Harlan Laboratories,

Indianapolis, IN) or a GFH7K diet (obtained with permission from Markus Paulmichl), which consists of

2% GFH7K added to the control diet chow and drinking water. The CT diet consisted of 22.5% crude protein, 5.2% fat, 3.7% crude fiber, 3.1 kcal/g energy density, 29% calories from protein, 15% calories from fat and 56% calories from carbohydrates. The diet was fed for 14 days and water was replaced daily.

This experiment was repeated separately two times (1st: CT n=8, GHF7K n=7, 2nd: CT n=6, GHF7K n=4).

On day 15, ~5cm of duodenum, jejunum, terminal ileum (hereafter referred to as ileum), cecum, and colon (proximal and distal) segments were collected from CT and GFH7K diet fed mice. Individual intestinal segments were flushed with phosphate buffered saline (PBS, pH 7.4) and mucosal scrapings were collected as previously described (Engevik et al. 2013; Frantz et al. 2012; IJssennagger et al. 2012;

Norkina et al. 2004). Briefly, intestinal segments were flushed with 500 µl PBS. The segments were then opened lengthwise, washed thoroughly with PBS and glass slides were used to scrape the epithelia and mucus layer. Luminal flushes and mucosal scrapings were processed for total DNA which was stored at -

20 °C until the samples were evaluated by quantitative real time PCR (qPCR).

Bacterial strains and culture conditions. Pure cultures of bacterial strains were used to generate standard curves to correlate bacterial cell number with qPCR cycle threshold (CT) values. Micrococcus luteus, Peptostreptococcus anaerobius, Staphylococcus aureus, Escherichia coli, Faecalibacterium prausnitzii and Burkoholdena cepacia were a gift from Dr. Daniel J. Hassett. Lactobacillus acidophilus and Rhibozium legaminosarum were purchased from Carolina Biological Supply Company (Carolina

Biological Supply Company, Burlington, NC), Bacteroidetes thetaiotaomicron ATCC 29741 and

Prevotella melaninogenica ATCC 25845 were purchased from Fisher Scientific (Thermo Fisher

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Scientific, Waltham, MA) and Ruminococcus productus ATCC 27340D-5 was purchased from ATCC

(American Type Culture Collection, Manassas, VA). E. coli, S. aureus, M. luteus, B. cepacia, L. acidophilus and R. legaminosarum were grown in Luria–Burtani (LB; Thermo Fisher Scientific) broth at

37 °C in a shaking incubator. R. productus and F. prausnitzii were grown in tryptone-yeast extract- glucose (TYG; Thermo Fisher Scientific) broth, P. melaninogenica and B. thetaiotaomicron were grown in tryptone-soy broth (TSB; Thermo Fisher Scientific), and Peptostreptococcus anaerobius was grown in brain-heart-infusion (BHI; Thermo Fisher Scientific) broth at 37 °C in a dual-port anaerobic chamber

(Coy Systems, Coy Lab Products, Grass Lake, MI). Bacterial cell counts were determined via a Petroff

Hauser chamber (Hausser Scientific; Horsham, PA) and by colony forming units (CFU) (Engevik et al.

2013; Caldwell and Arcand 1974; Caldwell et al. 1973; Hooper et al. 2000).

qPCR amplification of 16S sequences. QIAamp DNA Stool kit (Qiagen, Valencia, CA, USA) was used to extract total DNA according to the manufacturer’s instructions and as previously described with the addition of lysozyme (10 mg/ml, 37 °C for 30 min) and 95 °C lysis temperature (Engevik et al. 2013;

Norkina et al. 2004; Castillo et al. 2013; Salzman et al. 2010). qPCR was used to analyze the amount of total bacteria and bacterial phyla, class, genus and species using a Step One Real Time PCR machine

(Applied Biosystems, Carlsbad, California USA) with SYBR Green PCR master mix (Applied

Biosystems) and bacteria-specific primers (Table 1) in a 20 µl final volume. Standard curves were generated from pure bacterial cultures and used to correlate bacterial number with cycle of threshold values (CT) to calculated bacteria number (Salzman et al. 2010; Barman et al. 2008). Bacterial phyla composition was determined using calculated CFU values for each bacterial phyla as a percentage of the total bacteria. Total bacteria were calculated using the universal bacterial primer and represent the total number of bacteria per intestinal segment examined.

Ion concentration measurements. were used to analyze the ion composition of the intestinal content of CT and GHF7K diet fed mice as

182 M.A. Engevik 2014 previously described (Engevik et al. 2013). Flushes were performed on the same size intestinal segments as those used to collect bacterial content. Flushes were weighed and the intestinal volume was calculated from the weight of the flush balanced against 100 fluid volume. Flushes were then centrifuged at 1,400 g for 10 min at 4 °C to pellet intestinal solids. Flame photometry was used to determine supernatant Na+ and K+ concentrations (digital Flame photometer,

Single-Channel Digital Flame Photometer Model 02655-10; Cole-Parmer Instrument Company Vernon

Hills, IL). Chloridometry was used to determine the Cl- ion concentration (digital Chloridometer, Model

4425100, Labconco Kansas City, MO). Calculated ion concentration was normalized to intestinal volume and presented as mM. An electronic pH meter (Orion Model 720A; Thermo Fisher Scientific Waltham,

MA) was used to measure pH electrochemically.

Statistics. The data presented herein as the mean ± SEM. Differences between groups determined by two factors (diet and gut region) were determined using the two-way analysis of variance (ANOVA). The

Holme-Sidak post-hoc test was applied to determine significance between pairwise comparisons, using

SigmaPlot (Systat Software Inc, San Jose, CA). P < 0.05 was considered significant and n is the number of experiments.

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Results:

WT FVB/N mice were fed either a control diet (CT) or a diet supplemented with 2% GHF7K in the water and chow for a period of 2 weeks. Mouse weight was examined at the beginning and end of the study. No significant changes in weight were observed during the course of the study (Table 2). In addition, mice supplemented with GHF7K consumed the same food and water quantity as CT diet fed mice, indicating that the sugar itself did not stimulate increased food or water consumption or weight gain. In order to examine if the total number of bacteria was changed in mice supplemented with GHF7K,

DNA was extracted from luminal flushes and mucosal scrapings and analyzed by qPCR using a universal

16S RNA primer. No changes were observed in the total number of luminal bacteria (total bacterial load) in any intestinal segment (Figure 1A). As with the luminal bacterial population, no changes were observed in the mucosa-associated total bacterial load (Figure 1B). This indicates that no general overgrowth or depletion of bacteria occurred with the GHF7K supplemented diet. This is advantageous since increased total bacteria have been associated with increased bacterial translocation, sepsis and inflammation (Berg 1992; Amar et al. 2011; Chichlowski and Hale 2008; Zaidel and Lin 2003; Naaber et al. 1998; Quigley and Quera 2006; Swidsinski et al. 2002). As a result, minimal changes in the total luminal and mucosa-associated bacterial populations is likely beneficial because it minimizes potentially detrimental epithelia- immune response interactions.

In order to determine if GHF7K was able to alter the gut microbiota in a similar manner to antibiotics, the bacterial composition was analyzed by qPCR using bacterial phyla specific primers.

Studies have shown that the mouse and human GI tract is dominated by Firmicutes and Bacteroidetes, while the Actinobacteria, Proteobacteria, Fusobacteria and Verruomicrobia phyla are present in lower abundance (Backhed et al. 2005; Guarner and Malagelada 2003; Suau et al. 1999; Eckburg et al. 2005;

Ley et al. 2008; Ley et al. 2006; Zoetendal et al. 2002; Sghir et al. 2000). The major mouse intestinal bacterial phyla were compared as a percentage of total bacteria as shown in Figure 2. For the duodenum or cecum (Figure 2A) there were no significant changes in the luminal bacterial population from due the

GHF7K-diet. However, there was a decrease in Firmicutes (Δ23.1 ± 3.7% between CT and GHF7K) and

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M.A. Engevik 2014 an increase in Bacteroidetes (22.3 ± 4.1%) in the jejunum from GHF7K-fed mice. Similarly, the ileum of

GHF7K-fed mice showed a decrease in Firmicutes (19.8 ± 2.4%) and an increase in Bacteroidetes (11.2 ±

2.3%), while in the proximal colon there was a decrease in Bacteroidetes (15.8 ± 1.2%) but no significant change in Firmicutes (0.9 ± 0.2 %). In the distal colon there was a decrease in both Firmicutes (10.7 ±

2.2%) and Bacteroidetes (14.0 ± 3.0%) in the experimental group. Actinobacteria did not change in any of the GHF7K-fed mouse intestinal segments. Interestingly, -Proteobacteria was increased in ileum

(8.6 ± 1.8%), proximal (15.0 ± 2.8%) and distal colon (24.2 ± 4.7%). This increase in Proteobacteria is similar to that observed with antibiotic use (Antonopoulos et al. 2009; Sekirov et al. 2008; Jernberg et al.

2010; Hazenberg et al. 1983; Dethlefsen et al. 2008; Manichanh et al. 2010). In the GHF7K fed mice, the increase in Proteobacteria was due to an increase in γ-Proteobacteria (ileum: 8.46 ± 1.9%, proximal colon:

-Proteobacteria (distal colon: 21.1 ± 3.8%). Proteobacteria, particularly γ-Proteobacteria, harbor a number of pathogenic bacteria (Williams et al. 2010), such as

Escherichia coli, Salmonella, Yersinia, Vibrio, and Pseudomonas. The increased γ-Proteobacteria in the

GHF7K-fed mice may minimize growth of non-resident pathogenic species, and thus infection, via competition since members of the same phyla occupy the same niche.

In contrast with the luminal bacterial population, no significant changes were observed in the mucosa-associated bacterial population for the lower intestinal segments (Figure 2B). However, changes were observed in the upper intestinal segments, duodenum and jejunum. In the GHF7K-fed mice, there

-Proteobacteria (3.0 ± 1.5%) and a modest increase in Firmicutes (2.1 ± 0.7%) in the duodenum. In jejunum, there was an increase in Actinobacteria (4.2 ± 1.1%) with a decrease in other unspecified bacteria species (5.9 ± 1.1%). This indicates that GHF7K primarily affects the luminal bacterial population, and that those effects are region-specific.

Next, subgroups of the major phyla from the luminal bacterial population were examined by qPCR using subgroup-specific primers and bacterial cell number calculated from comparison to standard curves generated from each individual species. Antibiotics have been shown to decrease the majority of the Clostridium groups including C. coccoides cluster XIVa and C. leptum cluster IV (Croswell et al.

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2009; Young and Schmidt 2004). In addition, antibiotics have been shown to decrease Bacteroides

(Croswell et al. 2009), Prevotella (Mozes et al. 2013), Lactobacillus (Sekirov et al. 2008; Croswell et al.

2009), Bifidobacteria (Kheadr et al. 2007; Moubareck et al. 2005; Hussey et al. 2011) and Prevotella, and

Mouse Intestinal Bacteroidetes (MIB, see (Salzman et al. 2010; Kibe et al. 2007) references for classification) (Croswell et al. 2009). These groups were examined within the bacterial luminal population of GHF7K fed mice to determine if GHF7K decreases these groups, similarly to antibiotics.

No changes were observed in the Firmicutes subgroup Lactobacillus or the Actinobacteria subgroup

Bifidobacterium in any of the GHF7K-fed mice intestinal segments (Figure 3). Lactobacillus and

Bifidobacterium are commonly used as probiotics because of their therapeutic and prophylactic properties

(Dunne 2001; Dunne et al. 2001; Dunne et al. 1999; Klein et al. 1998; Sanders 1998; Gomes and Malcata

1999; Saxelin et al. 1999; Ouwehand et al. 1999; Vaughan and Mollet 1999). Studies have shown that probiotic bacteria can be beneficial for treatment against diarrhea (Dunne et al. 1999; Millar et al. 1993;

Sepp et al. 1993; Isolauri et al. 1991; Isolauri 1999) and pathogen infection (Castillo et al. 2013; Dani et al. 2002). Therefore, it is beneficial that the GHF7K diet did not decrease either of these valuable bacteria.

When the Firmicutes subgroup C. coccoides cluster XIVa and C. leptum cluster IV were examined, changes were observed with the GHF7K diet (Figure 4). Decreased C. coccoides was observed in GHF7K-fed mice jejunum while increased C. coccoides occurred in the cecum, proximal and distal colon. C. leptum was found to be significantly decreased in both duodenum and jejunum of the

GHF7K-fed mice. The Bacteroidetes subgroups Bacteroides, Prevotella, and MIB were also examined

(Figure 5). MIB was decreased in the proximal and distal colon and Prevotella was significantly decreased in the duodenum of GHF7K fed mice. No changes were observed in Bacteroides in any intestinal segment. With the exception of decreased MIB, the changes in the Firmicutes and Bacteroidetes subgroups do not directly mirror changes observed with antibiotics. Together, this data suggests that the

GHF7K diet can alter the gut microbiota at the level of the phyla and subgroups.

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Certain bacterial groups have the capacity to alter ion transport (Donnelly et al. 1999; Li et al.

2002; Borenshtein et al. 2009). To determine if GHF7K affected luminal ion concentration either directly or indirectly, Na+ and K+ concentrations of the intestinal content were determined by flame photometry and Cl- concentration was determined by chloridometry as depicted in Figure 6. In comparison to CT diet fed mice, GHF7K fed mice showed increased luminal Na+ concentration in the cecum with no changes in any other segment (Figure 6A). The K+ concentration was also affected: K+ was decreased in the cecum and distal colon of the experimental group (Figure 6B). The Cl- concentration was increased only the in duodenum and jejunum of GHF7K fed mice (Figure 6C). The differences in the Na+ and K+ concentrations in the cecum and Cl- concentrations in the upper portion of the small intestine observed between CT and GH7FK fed mice could represent changes in intestinal reabsorption. However, these changes were likely resolved by the colon, since no differences in [Na+] or [Cl-] were observed in this segment. Decreases were observed in the K+ concentration in the distal colon, which may indicate decreased K+ secretion. It is not clear whether this effect is host or bacterial mediated, but does represent another level of bacterial-host interaction. Changes in ion composition can affect the gut microbiota

(Engevik et al. 2013; Caldwell and Arcand 1974; Caldwell et al. 1973; Thomsson et al. 2002; Thiru et al.

1990; Lynch et al. 2013), and an altered ion composition may represent a mechanism by which newly proliferating bacteria are able to maintain a niche. These data indicate that the GHF7K dietary supplement can alter luminal ion composition along with altered bacterial composition. No changes in intestinal pH were observed in any intestinal segment of the GHF7K-fed mice (Figure 6D), similar to results found in dogs fed galacturonic acid (Werch et al. 1942). Taken together, these studies demonstrate that a GHF7K supplemented diet can induce regional specific changes in the gut microbiota on the phyla and subgroup level and ion composition of the intestinal content, which correspond to changes in the intestine environment.

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Discussion

It has been well documented that oligosaccharides are decomposed in the intestinal tract of mammals (Werch et al. 1942), and can act as a prebiotic by modifying the host microbiota in a beneficial manner (Gerritsen et al. 2011). The concept of modulating gut health through diet has been used throughout history, but recently, scientific advances have begun to provide mechanistic insight on how diet supplementation is capable of benefiting the host. The aim of the present work was to assess the prebiotic properties of the acidic oligosaccharide GHF7K. Our data demonstrate that GHF7K diet supplementation increases the phylum Proteobacteria by increasing α- and γ-Proteobacteria. Dietary supplement of GHF7K also changes Firmicutes and Bacteroidetes subgroup members in a region-specific manner. This substance is of particular interest for the livestock industry, which currently relies on antibiotics for animal health purposes. If GHF7K can reproduce the beneficial effects observed with antibiotics while maintaining constant levels of beneficial bacteria, then it could serve as an antibiotic alternative. This would be of great importance to reduce the propagation of antibiotic resistance within the food chain. In this paper, we demonstrate that GHF7K mimics antibiotic use in that it increases

Proteobacteria in a mouse model and could potentially be used as an antibiotic alternative.

Bacteroidetes and Firmicutes are the two dominant phyla in all vertebrates (Ley et al. 2008;

Rongvaux et al. 2013; Kostic et al. 2013). Studies have demonstrated that mice and humans share similar composition at the high-taxonomic levels (phyla, class, order), but differ greatly at the lower-taxonomic levels (genera, species, subspecies) (Ley et al. 2008; Kostic et al. 2013; Cho and Blaser 2012). Despite lower-taxonomic differences, broad trends exist at the phylum level (Kostic et al. 2013). Additionally, the gut microbiota of higher vertebrates (including humans, mice, and livestock) have been shown to be similar in core functions (Cho and Blaser 2012), adding the value of using mouse models to extrapolate large trends in the gut microbiota. Interestingly, no changes were observed in the cecum from mice fed

GHF7K. The cecum represents a “bioreactor” and has been shown to be a relatively stable environment

(Mead 1997; Meimandipour et al. 2009). It may be possible that conditions within the cecum prevent dramatic changes from occurring, thereby preserving the gut microbiota. The cecum has been shown to

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harbor a complex microbial community (Mead 1997; Meimandipour et al. 2009; Zhu et al. 2002) and although changes were not observed in the phyla or major subgroups in this area, it is possible that changes do occur at the level of the species. However, sequencing would be required to fully address these changes.

Prebiotics have been shown to selectively stimulate the growth and/or activity of specific bacteria

(Roberfroid et al. 2010; Roberfroid and Slavin 2000; Schrezenmeir and de Vrese 2001; Marteau and

Boutron-Ruault 2002). Pre-clinical studies indicate that prebiotics have the potential to be used in disease treatment (Szilagyi 1998) and for the prevention of intestinal infections (Marteau and Boutron-Ruault

2002; Dai and Walker 1999). Data presented herein and it the literature suggest that prebiotics predominantly affect the luminal bacterial population, whereas the mucosa-associated bacteria appear to be more affected by modifications at the level of the host epithelia (Engevik et al. 2013; Frantz et al.

2012; Norkina et al. 2004; Salzman et al. 2010). In addition to prebiotics and probiotics, antibiotics have also been used to modify the intestinal gut microbiota (Sekirov et al. 2008; Jernberg et al. 2010;

Hazenberg et al. 1983; Dethlefsen et al. 2008). Multiple antibiotics have been shown to decrease the beneficial bacteria Lactobacillus (Sekirov et al. 2008; Croswell et al. 2009) and Bifidobacteria (Kheadr et al. 2007; Moubareck et al. 2005; Hussey et al. 2011). It is advantageous that GHF7K does not decrease these two groups because Lactobacillus and Bifidobacteria have been shown to reduce luminal pH, produce short-chain fatty acids, secrete antimicrobial compounds (bacteriocins), induce production of antimicrobial compounds (defensins) by the host epithelium, prevent pathogenic bacterial adhesion to epithelial cells, and actively compete for nutrients that might be used by pathogenic bacteria (Gerritsen et al. 2011; Fooks and Gibson 2002a, b; Ng et al. 2009; Gibson and Wang 1994; Hudault et al. 1997).

Previous studies have shown that supplementation of acidic oligosaccharides, such as galacturonic acids, in formula does not affect levels of Bifidobacteria or Lactobacillus in infants (Fanaro et al. 2005a; Fanaro et al. 2005b). This is consistent with the current study, which demonstrates no change in Bifidobacteria or

Lactobacillus in GHF7K-fed mice.

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GHF7K administration also resulted in region-specific changes in the phyla Bacteroidetes and

Firmicutes, and in the subgroups C. coccoides, C. leptum, Prevotella and MIB. Whether or not these changes are beneficial, detrimental or neutral is not clear. Dietary changes have been shown to cause rapid changes in intestinal metagenomics (Cho and Blaser 2012), which represents all the host and gut microbial genes and corresponding functions (Qin et al. 2010). However, as long as the core metagenomic functions are conserved within a host, the changes in specific species are counterbalanced and have no detrimental effect (Cho and Blaser 2012). At this time, we can speculate that GHF7K does not have a negative impact on the gut microbiota as it did not alter the gross morphology of the intestine or result in large scale changes, such as weight gain, in the GHF7K-fed mice. However, further experiments are warranted to fully address this question.

Vancomycin, metronidazole, amoxicillin-clavulanic acid, clindamycin, ancomycin, amipenem, and neomycin have all been shown to increase the phylum Proteobacteria (Antonopoulos et al. 2009;

Sekirov et al. 2008; Jernberg et al. 2010; Manichanh et al. 2010; Croswell et al. 2009; Young and

Schmidt 2004; Mozes et al. 2013; Kheadr et al. 2007; Moubareck et al. 2005; Hussey et al. 2011).

Although Proteobacteria represent a minor taxon in the gut microbiota, this group has been shown to play a significant, active role in overall gut metabolism and host interaction (Perez-Cobas et al. 2012). This study has demonstrated that GHF7K alters the gut microbiota by increasing the levels of α and γ-

Proteobacteria, providing evidence of its application as a potential antibiotic alternative.

A potential downside to antibiotic use is that antibiotics can result in increased susceptibility to further pathogen infection (Sekirov et al. 2008). Whether GHF7K increases pathogen susceptibility or protects against pathogen colonization is currently unkown. In the future, it would be interesting to challenge GHF7K fed mice with a pathogen such as Salmonella typhymurium, Escherichia coli, or

Clostridium difficile, in order to determine if it actually provides benefit. Although GHF7K does increase

Proteobacteria, it does not mirror antibiotic use completely, indicating that there might be some potential for benefit. Additionally, early studies with oligo(2-7)-galacturonic acids in fowl farming (personal communication, Markus Paulmichl) and traditional application of Moro’s carrot soup, suggest that

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GHF7K may provide benefit to the host and may prevent and/or resolve pathogen infection. It is therefore plausible that dietary supplement of GHF7K may prevent infection by either (1) causing the proliferation of resident Proteobacteria, thereby limiting further colonization by other pathogenic Proteobacteria or (2) that the addition of GHF7K is a preferred binding substrate of Proteobacteria members and luminal

GHF7K may prevent pathogenic γ-Proteobacteria from binding to oligosaccharides on the host mucosa.

In addition to the potential for GHF7K to be used as an antibiotic alternative, GHF7K may be beneficial for other disease states associated with an altered intestinal environment, or microbial dysbiosis. One example of this is seen in human gastric bypass surgery as a treatment for obese patients.

Gastric bypass surgery has been shown to promote the proliferation of γ -Proteobacteria with a corresponding decrease in Firmicutes and methanogens that are responsible for increased energy consumption in obese individuals (Ley et al. 2006; Zhang et al. 2009; Samuel and Gordon 2006). Herein we demonstrate that GHF7K acts in a similar manner as gastric bypass surgery: decreasing Firmicutes in the small intestine and increasing γ –Proteobacteria. This suggests that GHF7K could also be used as a food additive for obese patients and may produce similarly “beneficial” effects as those observed following gastric bypass. Although many oligosaccharides have been shown to alter the gut microbiota, it is important to note that each oligosaccharide has its own properties and not all act on the same bacterial groups or species. The data in this study suggest that the Galursan HF 7K sugar could potentially be used to alter the gut microbiota in a beneficial manner with defined changes in the luminal bacterial populations.

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Table 1. Primer sequences for qPCR of total bacteria and specific bacterial phyla and groups.

Type Bacteria Forward Reverse Reference (Barman et al. 2008; Fierer et Total Universal (Total Bacteria) ACTCCTACGGGAGGCAGCAG ATTACCGCGGCTGCTGG al. 2005; Guo et al. 2008) (Guo et al. Phylum Bacteriodetes GGCGACCGGCGCACGGG GRCCTTCCTCTCAGAACCC 2008) (Fierer et al. Phylum Firmicutes GGAGYATGTGGTTTAATTCGAAGCA AGCTGACGACAACCATGCAC 2005) (Fierer et al. Phylum Actinobacteria CGCGGCCTATCAGCTTGTTG ATTACCGCGGCTGCTGG 2005) (Fierer et al. Phylum α-proteobacteria ACTCCTACGGGAGGCAGCAG TCTACGRATTTCACCYCTAC 2005) (Fierer et al. Phylum β-proteobacteria CCGCACAGTTGGCGAGATGA CGACAGTTATGACGCCCTCC 2005) Phylum y-Proteobacteria GAGTTTGATCATGGCTCA GTATTACCGCGGCTGCTG (Lee et al. 2009) Clostridium coccoides cluster (Salzman et al. Class ACTCCTACGGGAGGCAGC GCTTCTTAGTCAGGTACCGTCAT XIVa group 2010) Clostridium leptum cluster IV (Salzman et al. Class GTTGACAAAACGGAGGAAGG GACGGGCGGTGTGTACAA group 2010) (Collado et al. Genus Lactobacillus GGAAACAGATGCTAATACCG CACCGCTACACATGGAG 2009) (Salzman et al. Genus Bacteroides GGTTCTGAGAGGAGGTCCC CTGCCTCCCGTAGGAGT 2010) (Dalwai et al. Genus Prevotella CCAGCCAAGTAGCGTGCA TGGACCTTCCGTATTACCGC 2007) Mouse Intestinal Bacteria (Salzman et al. Genus CCAGCAGCCGCGGTAATA CGCATTCCGCATACTTCTC (MIB) 2010) (Collado et al. Genus Bifidobacterium CTCCTGGAAACGGGTGG GGTGTTCTTCCCGATATCTACA 2009)

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Table 2. Weight, food and water consumption for mice on control and GHF7K diet.

Control GHF7K Weight (g) 20.1 ± 1.2 21.4 ± 1.7 Food (g food/mouse/day) 2.9 ± 0.3 3.1 ± 0.4 Water (ml water/mouse/day) 5.2 ± 0.3 5.4 ± 0.4

Weight was recorded at the end of the study for mice on the control diet and GHF7K diet. Dry food was weighed and water quantity was recorded daily to determine the total volume of food and water consumed by mice on the control diet and GHF7K diet. n=14 for control and n=11 for

GHF7K diet. No significant differences were observed between the control and GHF7K diet. Analyzed by 2-Way ANOVA with Holme-Sidak post-hoc test.

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Figures:

Figure 1. GHF7K-fed mice have no changes in total bacteria compared to control diet fed mice. Total bacteria were quantified by qPCR using a universal bacterial 16S DNA sequence. The bacterial cell number was calculated from a standard curve and normalized to intestinal flush volume. (A) Bacteria contained within the luminal flushes of control (n=14, black bars) and GHF7K diet (n=11, white bars) fed mice. (B) Mucosa-associated (adherent) bacterial levels between control (n=14, black bars) and GHF7K diet (n=11, white bars) mice. As expected, the total bacteria number different among segment (P =

<0.001) confirming that more bacteria are present in the colon than the small intestine. However GHF7K diet did not result in any differences in total bacteria number for any segment (P = 0.665), indicating that

GHF7K diet does not change the total bacteria in the mouse intestine. Analyzed by 2 WAY ANOVA,

Holme-Sidak.

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Figure 2. GHF7K-fed mice exhibit region-specific alterations in the luminal and mucosa-associated gut microbiota. Relative abundance was calculated as the percentage of bacterial phyla in comparison to total bacteria for luminal (A) and mucosa-associated bacteria (B). Differences at the level of segment were observed for all bacterial populations (P = <0.001), indicating different colonization patterns depending on region. More dramatic changes were found in the luminal bacterial population compared to the mucosa-associated bacterial population. In the luminal population, Firmicutes had significant changes in 195

M.A. Engevik, 2014 jejunum (P=0.029), ileum (P=0.030), and distal colon (P= 0.048). Bacteroidetes had significant changes in the jejunum (P=0.042), ileum (P=0.029), proximal (P<0.001) and distal colon (P<0.001). No

Proteobacteria had significant increases in the ileum (P=0.031), proximal (P=0.022) and distal colon (P =

<0.001). In the mucosa-associated bacterial population, changes were observed in Firmicutes (P=0.033)

-Proteobacteria in the duodenum (P=0.025), and Actinobacteria (P=0.014) and unspecific bacteria (P= 0.02) in the jejunum. n=14 for control and n=11 for GHF7K diet fed mice. Analyzed by 2

WAY ANOVA, Holme-Sidak.

Figure 3. GHF7K diet does not alter beneficial bacterial groups Lactobacillus and Bifidobacterium.

Bacterial number was examined from luminal flushes from CT and GHF7K-fed mice by qPCR. No changes were observed in the levels of the beneficial bacterial groups Lactobacillus (A) and

Bifidobacterium (B) in the luminal population of control (n=14, black bars) or GHF7K (n=11, white bars) diet fed mice. Analyzed by 2 WAY ANOVA, Holme-Sidak.

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Figure 4. GHF7K diet alters Clostridial groups in the colon. Bacterial number was examined from luminal flushes from CT and GHF7K-fed mice by qPCR. Regional changes were observed in the levels of

Firmicutes members (A) C. coccoides and (B) C. leptum. C. coccoides was decreased in jejunum, but increased in the cecum and colon of GHF7K diet fed mice (n=11, white bars). C. leptum was decreased only in the duodenum and jejunum of GHF7K diet fed mice (n=11, white bars) compared to control mice

(n=14, black bars). * P<0.05, 2 WAY ANOVA with Holme-Sidak post-hoc test.

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Figure 5. GHF7K diet alters MIB and Prevotella, but does not alter Bacteroides. Bacterial number was examined from luminal flushes from CT and GHF7K-fed mice by qPCR. No changes were observed in

Bacteroidetes member Bacteroides (B), but regional changes were observed in the levels of (C)

Prevotella and (A) MIB. GHF7K (n=11, white bars), control (n=14, black bars). * P<0.05, 2 WAY

ANOVA with Holme-Sidak post-hoc test.

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Figure 6. GHF7K diet alters the intestinal environment with regional changes in Na+, K+ and Cl- concentrations, but does not change pH. Ion concentrations in luminal fluid from control (black bars) and

GHF7K (white bars) diet fed mice intestinal segments. (A) The Na+ concentration, as determined by flame photometry, was increased only in the GHF7K-fed mice cecum. (B) The K+ concentration, as determined by flame photometry, was significantly decreased only in GHF7K-fed mice cecum and distal colon. C) The Cl- concentration, as determined by chloridometry, was significantly increased in the

GHF7K-fed mice duodenum and jejunum. D) No changes in pH along the length of the intestinal tract between GHF7K and control diet fed mice were observed. GHFK (n=11, white bars), control (n=14, black bars). * P<0.05, 2 WAY ANOVA with Holme-Sidak post-hoc test.

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DISCUSSION:

I. Thesis Conclusions

These studies provide an in-depth examination of changes in the gut microbiota as a result of altered ion transport. Prior to these studies little was known regarding endogenous changes by the host, such as ion transport, pH, mucus, and exogenous changes set by diet, prebiotics, probiotics, pathogen toxin production, etc. and their effect on the gut microbiota. The data presented herein demonstrates that endogenous ion transport (luminal Na+, K+, Cl-, pH) sets the microenvironment permissible for particular bacterial species. In addition, the data demonstrates how an alteration in transport (null mouse manipulation of NHE2 and NHE3 and inhibition by C. difficile toxin in CDI patients) disrupts ion composition and thus the resident microbiota. This thesis work also examines the relationship between the microbiota-environment relationship in the setting of a complex microbiota (whole microbiota in the mouse and human setting) and as single species (B. thetatiotaomicron and C. difficile grown in vitro under various conditions and injected into intestinal organoids). My work is the first to (1) analyze different bacterial populations in all of the regions of the intestine; (2) separate the luminal and mucosa- associated bacterial populations for analysis in the same study; and (3) inject bacteria into intestinal organoids as a model to study microbe-host interactions.

Most studies examine only one region of the intestine or if multiple regions are examined the studies combined luminal and mucosa-associated bacterial populations. This is the first report to examine both regional differences in bacterial populations in the intestine and separate luminal and mucosa- associated bacteria. Consistent with previous data the data presented demonstrates that WT mice are dominated by Firmicutes and Bacteroidetes with lesser amounts of Actinobacteria, Proteobacteria and unspecified bacteria (Engevik et al. 2013a; Engevik et al. 2013b; Engevik et al. 2013c) (see Figure 1).

Firmicutes is the dominant group in WT duodenum, jejunum, ileum, cecum and distal colon, while

Bacteroidetes is the dominant group in WT proximal colon. Although Firmicutes and Bacteroidetes are dominant, each intestinal region has a unique phyla profile. My data clearly shows that WT FVB/N mice

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harbor distinct microbiota composition which differ depending on intestinal region and population

(luminal vs. mucosa-associated). These differences emphasize that knowledge of the stool microbiota does not accurately reflect intestinal changes upstream or mucosa-associated bacterial populations which dramatically change in the setting of altered host ion transport. Since it is difficult to obtain human small intestinal samples, mouse models provide an option for correlating environment to microbiota composition.

Figure 1. Diagram depicting WT FVB/N mouse intestine. The ileum contains crypts and villi and harbors less MUC1 (dark green) and MUC2 (light green) mucus compared to the large intestine. The cecum and colon contain crypts and have greater abundance of MUC1 and MUC2. At the phyla level, WT mouse ileum, cecum and distal colon luminal & mucosa associated bacterial populations are dominated by Firmicutes, while the proximal colon luminal population is dominated by Bacteroidetes and the mucosa-associated population by Firmicutes. These populations represent unique regional compositions which differ from each other.

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I have demonstrated that NHE2 and NHE3-deficient mice have an altered gut microbiota (see

Figure 2). These mice have different intestinal environments accompanied by differences in microbiota

composition. NHE2-/- mice exhibit upregulation of NHE3 (Guan et al. 2006; Bachmann et al. 2004) and

NHE8 (Xu et al. 2011) which results in an acidic intestinal fluid compared to WT littermates (Engevik et al. 2013c). Correlating with this change in pH, gram-positive bacteria in the mucosa-associated bacterial population are increased in the NHE2-/- intestine. Previous literature has shown that certain gram-positive

bacteria are pH sensitive (Cotter and Hill 2003; Champomier-Verges et al. 2001; Speelmans et al. 1993).

Figure 2. Diagram depicting alterations in the gut microbiota at the level of the phyla in response to genetic manipulation of ion transport or introduction of prebiotics. For WT comparison see Figure 1. In general NHE3-/- exhibit increased luminal and mucosa-associated Bacteroidetes. These differences are correlated with increased Na+ and a more alkaline luminal fluid in all segments. Specifically increased Bacteroidetes member B. thetaiotaomicron in the ileum corresponds with changes in host ileum fucose levels. In contrast, Firmicutes and Actinobacteria members are increased in the mucosa-associated bacteria population which is correlated with an acidic luminal pH in all segments and with increase K+ in the ileum and decreased Cl- in the cecum and distal colon. Increased Firmicutes members Lactobacillus and Clostridium/Ruminococcus correlate with changes in host glycosylation patterns. In mice fed GH7FK prebiotic, changes were observed primarily in the luminal bacterial population with increases in Proteobacteria in the ileum, proximal and distal colon. These bacterial changes are correlated with changes in Na+ and K+.

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My data indicates that gram positive bacteria within the phyla Firmicutes and Actinobacteria in the presence of a complex gut microbiota are pH sensitive within the physiological range (Engevik et al.

2013c). The gut microbiota of NHE2-/- mice were shown to be dramatically different from that seen in the

NHE3-/- mouse. NHE3-/- mice had an alkaline intestinal environment with proliferation of gram-negative

Bacteroidetes evident in both the luminal and mucosa-associated bacterial populations. This proliferation of Bacteroidetes is primarily caused by a large increase in mucosa-associated Mouse Intestinal

Bacteroidetes (MIB) in all the intestinal segments (Engevik et al. 2013a), which is decreased in all the

NHE2-/- segments (Engevik et al. 2013c). The increased abundance of MIB in NHE3-/- mice and decreased abundance in NHE2-/- mice suggest that MIB is influenced by high [Na+] and an alkaline luminal fluid.

Increased Bacteroidetes member B. thetaiotaomicron was also demonstrated to correlate with high [Na+] in the NHE3-/- mice (Engevik et al. 2013a). These studies have furthered scientific knowledge by demonstrating that gram negative Bacteroidetes is highly sensitive to [Na+] and alkaline luminal pH

(Engevik et al. 2013a), while gram positive Firmicutes and Actinobacteria prefer an acidic luminal pH

(Engevik et al. 2013c). It is possible that certain bacterial groups within the NHE2-/- and NHE3-/- mice may be sensitive to other environmental changes, such as changes in K+ or Cl-, and this is an area that can be further examined in the future.

Changes in the specific bacterial groups in the NHE3-/- model, specifically B. thetaiotaomicron, and in the NHE2-/- model, specifically Lactobacillus and Clostridium/Ruminococcus, correlate with changes in host mucus oligosaccharide composition, adding another layer of previously unexplored host- microbe interaction. Since certain bacterial groups are able to use mucus oligosaccharides as a carbon source, these changes represent either (1) the host in selecting bacterial groups or (2) the microbiota in stimulating increased fuel sources for proliferation needs. This interaction has been only minimally examined. This thesis work contributes to the scientific knowledge base in the area of mucus-microbe interaction by demonstrating that certain bacterial species are able to stimulate the host to produce more oligosaccharides. I have demonstrated in vivo that changes in host fucosylation correlate with B.

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thetaiotaomicron in NHE3-/- mice and in vitro in WT and NHE3-/- mouse ileum organoids. In the NHE2-/- mouse model increased Lactobacillus and Clostridium/Ruminococcus correlates to changes in mannose,

GalNAc, GlcNAc and galactose. To definitely determine which groups are responsible for specific changes, these bacterial groups could be injected into organoids and further examined. Together these studies provide new information on host-mucus interactions and provide another method to examine host- bacterial cross-talk.

Using the NHE3-/- mouse model as a foundation, I have demonstrated that C. difficile toxin inhibition of NHE3 expression occurs in patients infected with C. difficile (CDI) (see Figure 3). Although

C. difficile toxin inhibition of NHE3 has been demonstrated in renal and placental cell lines in one publication (Hayashi et al. 2004), this work is the first to demonstrate C. difficile inhibition of NHE3 in the presence of a complex microbiota in vivo in human CDI patients. Due to the decreased expression of

Figure 3. A diagram depicting the human distal colon microbiota-host relationship in CDI. Normally the host distal colon is dominated by Firmicutes and Bacteroidetes members. Introduction of antibiotics wipes out resident bacterial members, while leaving Proteobacteria intact, opening a niche C. difficile colonization (Owens 2008). C. difficile toxin production inhibits NHE3, which alters the intestinal environment increasing luminal Na+ and increasing pH compared with healthy patients. The altered environment correlates with microbiota restructuring with increased Bacteroidetes members and altered mucus oligosaccharide composition and production.

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NHE3, patients with CDI exhibit an altered intestinal environment comparable to NHE3-/- mice with increased [Na+] and a more alkaline stool fluid compared to healthy patients. This altered intestinal environment corresponds with increased Bacteroidetes and decreased Firmicutes phyla, similar to the microbiota composition of NHE3-/- mice. Previously it has been hypothesized that diarrhea induced by pathogens was either a mechanism of the host to flush out the pathogen or a mechanism of the bacteria to propagate and infect other hosts. However this work suggests the novel hypothesis that GI pathogens alter epithelial transport in order to create a more optimal growth environment within the intestine. My data shows that C. difficile by inhibition of NHE3 creates an alkaline and high [Na+] intestinal environment which is more favorable for its growth. This novel finding provides a new view on microbiota-host interactions and stresses the importance of the need to understand how the host environment affects the microbiota and vice versa.

In addition to changes in host intestinal environment due to genetics and pathogens, I have also demonstrated that the luminal gut microbiota is highly influenced by prebiotics. Supplement of the carrot- derived oligosaccharide GH7FK prebiotic results in region-specific increases in Proteobacteria (Engevik et al. 2013b). Changes in gut microbiota also correlate with changes in ion composition. Since GHF7K increases γ-Proteobacteria in the same regions as antibiotics, GHF7K could potentially be used as alternative to antibiotics. Thus, the luminal gut microbiota can be influenced by diet. This suggests that we may be able to selectively manipulate the gut microbiota by use of specific prebiotics to favor certain bacterial species within a particular region of the intestine. This work provides new tools to address diseases involved with microbial dysbiosis.

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II. Remaining issues and future studies

A. Improvement of gut microbiota analysis

My data has clearly shown the importance of analyzing the microbiota regionally along the length of the intestine and by population (luminal or mucosa-associated) to determine mechanistic interactions.

Studies of luminal bacteria have traditionally focused on stool due to ease of access (Booijink et al. 2007;

Gerritsen et al. 2011). It is widely accepted that feces do not accurately reflect the diversity or composition of the intestinal tract (Tannock 1999; Kasper 1998; Vaughan and Mollet 1999; Dunne 2001), demonstrating the need for improved techniques to examine the entire intestinal tract. Currently the only human data on the luminal bacterial population of the small intestine comes from ileal effluent or post- mortem samples (Booijink et al. 2007; Gerritsen et al. 2011; Leimena et al. 2013; Hartman et al. 2009;

Booijink et al. 2010). Although these studies provide a first glimpse into the luminal ileum microbiota composition, this data may not accurately represent the intact gut microbiota due to the nature of the sample collection. Since the patients in the study required surgery, they likely do not represent healthy patients. Therefore, our current knowledge of the gut microbiota may not accurately reflect the healthy gut microbiota composition.

The microbiota present in the intestinal lumen also differ significantly from that attached to and imbedded in the intestinal mucus layer, or mucosa-associated bacteria (Gerritsen et al. 2011). Since mucosa-associated bacteria live in close contact with host cells, it is likely they execute different functions within the GI ecosystem compared with luminal microbiota (Gerritsen et al. 2011). In humans, knowledge of the mucosa-associated bacteria comes from tissue samples from surgical sectioning, biopsies, fecal swabs or post-mortem analysis (Ahmed et al. 2007; Baumgart et al. 2007; Wang et al.

2003; Willing et al. 2009; Hayashi et al. 2005). The current techniques for examination of human mucosa-associated microbiota are filled with methods that alter the resident microbiota, producing results that may not accurately mirror the intestinal microbiota. Studies involving biopsies are typically obtained from humans undergoing standard colonoscopy, which in general is preceded by a laxative preparation in

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order to clean the GI tract. Several laxative preps have been shown to remove some of the outer mucus layer and thus mucosa attached microbes minimizing the bacterial diversity (Bucher et al. 2006; Mai et al.

2010). Additionally, the colon physiology may also be affected by laxatives which has been shown to affect markers of proliferation in intestinal epithelium (Croucher et al. 2008). If the colon physiology is altered, it is likely that the closely connected mucosa-associated microbiota may be affected as well (Mai et al. 2010). Currently the full influence of this laxative procedure on the luminal and mucosa-associated microbiota remains unknown, but it is likely that it affects the microbiota composition (Mai et al. 2010).

Surgical sections, as previously mentioned, likely do not represent the normal gut microbiota. Fecal swabs, although convenient for sampling purposes, likely suffer from luminal content contamination and do not accurately reflect the microbiota composition or activities in the proximal large intestine or small intestine. These issues remain to be resolved and should be considered for future studies in humans. These difficulties in sample collection point to the use of mice in future examination of the intestinal microbiota because the entire intestinal length can be fully analyzed in a region specific manner.

B. Of Mice and Men

Mouse models have been widely used to examine the interaction between the host and the gut microbiota. Mouse models provide the ability to remove the entire intestine; a method that is not valid in the human population. The mouse and human gut microbiota are both dominated by the phyla

Bacteroidetes and Firmicutes (Ley et al. 2008a; Ley et al. 2008b; Suau et al. 1999; Eckburg et al. 2005;

Backhed et al. 2005). Although they share a common phyla and class composition, mice do differ from humans at the genera and species levels (Ley et al. 2008a; Ley et al. 2008b; Kostic et al. 2013; Cho and

Blaser 2012). However mouse and human microbiota do share similar core functions (Cho and Blaser

2012), adding to the value of using mouse models to extrapolate large trends in the gut microbiota. The publications and manuscripts presented herein add to the existing data by further demonstrating

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similarities between mice and humans at the phyla level and validate the use of mouse models to study human disease processes.

Mice can be genetically manipulated to provide insight into how genetics and the intestinal environment influence the microbiota. Manipulation of host genetics in mice has revealed genes involved in host response to the microbiota. This has allowed researchers to show that the gut microbiota is highly influenced by the host-set environment (Kostic et al. 2013; Natividad et al. 2012; Petnicki-Ocwieja et al.

2009; Chang et al. 2007; Garrett et al. 2010). Although mouse models can be useful, mice differ from humans in some key aspects. Mouse skin, fur, orapharyneal structures, diet, gastrointestinal tract segments, and behavior (e.g., nocturnal behavior, grooming practices, and coprophagia) clearly differ from humans (Kostic et al. 2013; Rongvaux et al. 2013). These differences likely influence the gut microbiota composition and confound analysis. Furthermore the mouse immune system differs from the human immune system (Rongvaux et al. 2013), which affects the way the host responds to the gut microbiota. As a result, mouse disease does not always reflect human disease. Despite these differences mouse models can still be a useful tool for unraveling mechanisms of host–microbiota interactions. Kostic et al. eloquently stated “acknowledging this complexity and the potential pitfalls is not meant to suggest that using mice for host–microbiota studies is a flawed approach; rather, the point is to highlight that studying host–microbiota interactions in mice requires careful experimental design”(Kostic et al. 2013).

Mouse genetic background has been shown to impact the composition, diversity, and richness of the gut microbiota in both WT and knockout mice (Esworthy et al. 2010; Buchler et al. 2012). In a study by Campbell et al. eight core inbred strains were examined by 16S rRNA. Effects were shown to exist in the gut microbiota based on litter, cohousing and in some mouse strains, gender (Campbell et al. 2012). It should be noted that we did not observe any gender-dependent differences in our mice studies, but since gender can be a factor influencing the gut microbiota in some mouse strains and potentially in humans, it should be examined in future studies. All the mice used in this thesis were FVB/N mice, which have been shown to differ in gut microbiota composition compared to other genetic backgrounds, like C57BL/6 and

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129/SvEv (Kostic et al. 2013; Willing et al. 2011; Gulati et al. 2012). Effects of mouse background can provide an advantage for selecting specific traits and determining how they influence the gut microbiota.

However it can also confound data analysis, and as such mouse background should be considered in experimental design.

The majority of past genetic manipulation studies in mice have focused on genetic immune perturbations or obesity models; however my results clearly demonstrate that altered ion transport also has a large impact on the gut microbiota. My work presented herein demonstrates that mice offer a simplified model of altered intestinal environment, via ion transport, and can shed insight into how the gut microbiota responds to physiological changes in the intestinal environment (Engevik et al. 2013a;

Engevik et al. 2013c). At the level of the phyla, NHE3-/- mice show similar trends to NHE3 inhibition in

CDI patients. NHE3-/- mice and human CDI patients showed decreased Firmicutes and increased

Bacteroidetes, with high Na+ and alkaline pH in the distal colon/stool and exhibit diarrhea (Engevik et al.

2013a). Although mice do not represent a perfect model for human disease, work with NHE3-/- mice have provided a foundation for examining changes in CDI and thus have been extremely beneficial. The work presented herein speaks to the need to further elucidate the effects of the intestinal environment set by ion transport on the gut microbiota and suggests that mice offer a platform to approach this research.

To improve future studies with mice, human feces or cells can be transplanted into mice.

Inoculating mice with human feces or specific gut bacteria can create “humanized microbiome mice”

(HMM) (Kostic et al. 2013; Faith et al. 2010). HMM have been used to examine the functionality of the gut microbiome and establish that human gut microbiomes can alter the metabolic capacity of mice

(Ridaura et al. 2013). Mice transplanted with feces from obese human donors developed some of their host's phenotypes including weight gain, increased adiposity, and insulin resistance (Ridaura et al. 2013;

Turnbaugh et al. 2006; Koren et al. 2012). Smith et al. demonstrated that feces from human donors with severe malnutrition transplanted into mice resulted in substantial loss of weight (Smith et al. 2013). These studies support the idea that HMM can be a useful tool to understand the complexity of the human gut

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microbiota. One downfall with fecal transplanted HMM is that the host does confer some specificity as to which members of the human microbiota can colonize the mouse small intestine (Chung et al. 2012).

These differences are linked to immune phenotypes (Chung et al. 2012). To compensate for the host immune system, an alternate approach would be to combine the human microbiota with human-to-mouse- xenotransplantation models in which functional elements of the human hemato-lymphoid system (HHLS) are implemented in mice (Rongvaux et al. 2013; Brehm et al. 2010). Studies have demonstrated that Hu-

SRC-SCID mice engrafted with human hematopoietic stem cells (Lapidot et al. 1992) and SCID-hu mice engrafted with human fetal liver and thymus (McCune et al. 1988) result in functional human immune systems (Brehm et al. 2010). These alternate approaches may provide new avenues for examining the effects of the gut microbiota on mammalian physiology and the mechanisms that permit and regulate relationships between animals and their microbiota.

C. Intestinal Organoids

Mouse and human organoids also represent a novel method for analyzing the gut microbiota-host interaction. My work demonstrates the innovative use of human intestinal organoids to examine the interaction between the gut microbiota and the host. In the past immortalized intestinal cell lines (T84,

Vero, Caco-2 and HT29-MTX cells) have been used to examine microbe-GI interaction. However, these cell lines are tumor derived and often do not accurately reflect primary tissue (Pan et al. 2009). The use of intestinal organoids allows us to circumvent the limitations of both mouse models and cell lines. Intestinal organoids are derived from crypt stem cells and differentiate into the intestinal cell lineages. Intestinal organoids closely mimic intact physiology: organoids include self-renewing Lgr5+ stem cells, antimicrobial producing Paneth cells, enterocytes, goblet cells, enteroendocrine cells, crypt domains and villus-like domains that line the central lumen (Sato et al. 2009). These organoids provide long-term growth and can be grown from any region of the human intestine. Since few studies have been able to focus on the human small intestine due to limited access, this technique allows for new insight on

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microbe-host interaction in different anatomical regions. I have demonstrated that injection of

Bacteroides thetaiotaomicron in mouse ileum organoids leads to induction of host fut2 mRNA and increased fucosylation similar to patterns seen in vivo in NHE3-/- mouse ileum (Engevik et al. 2013a). I have also shown that injection of C. difficile into human intestinal organoids resulted in decreased expression of NHE3 and MUC2 production similar to levels seen in vivo in CDI biopsies. The data presented herein provides the first reports of intestinal organoids injected with commensal and pathogenic bacteria. Injection of intestinal organoids with bacteria provides a simplified model to examine bacterial host interaction and demonstrate that bacteria are capable of altering the host to create a more favorable environment. Although organoids represent a reductionist approach to examining the host-microbe interaction, it should be noted that these cells do not represent the full spectrum of in vivo physiology.

However my experiments demonstrate that organoids have the capacity of reflecting several in vivo host- microbiota alterations and could be used in the future to improve our current understanding of host- microbiota interactions, especially with regards to causality.

D. Limitations of gut microbiota analysis

To fully understand how the gut microbiota interacts and affects the host both the phylogenetic profile of human microbial communities and the functional capacity of their members must be characterized. Several non-culturing DNA-based technologies have been used to examine the gut microbiota including PCR probing for specific genes (qRT-PCR, DGGE, TGGE, and TRFLP), 16S sequencing, shotgun metagenomic sequencing, and chemical profiling of microbial metabolites have provided data towards this end (Moore et al. 2011). These approaches have yielded insights ranging from shifts in prevalent bacterial phylotypes and altered metabolic profiles in disease settings, to variations in the gut microbiota composition with diet, host body habitus, developmental changes and alterations in host genetics (Kostic et al. 2013; Turnbaugh et al. 2006; Moore et al. 2011; Qin et al. 2010; Turnbaugh et al. 2007; Turnbaugh and Gordon 2008). However these techniques are not without their limitations. qRT-

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PCR, DGGE, TGGE, and TRFLP require selected primers and are thus limited to only reporting selected bacterial groups. An alternate approach to primer-based methods is 16S sequencing which provides a complete list of the bacterial groups present in a given sample. This includes data for phyla, order, class, genus and species. While this information is beneficial, it only provides information on “who is there” and does not provide any information on “what they are doing” (e.g. core functions). Recently, metagenomic analysis has been used to determine phylogenetic composition as well as genetic and metabolic potential. This combination is essential to understand the dynamics and possible mechanisms of the cause/effect relationships between gut microbiota and pathology. Metagenomics has emerged as one of the most powerful sequence-driven approaches to study the composition and the genetic potential of this complex ecosystem (Maccaferri et al. 2011). Although metagenomics represents the new gold standard for analysis of the gut microbiota, this technique is costly and requires complex computational data analysis. As technologies for sequencing and bioinformatics improve, scientific priorities will likely include elucidating the ‘core’ metagenome that occupies a specific human niche and discerning the differences between normal and diseased hosts.

E. Future directions

The gut microbiota is a “metabolically adaptable and rapidly renewable organ of the body, the composition and activities of which can affect both intestinal and systemic physiology” (Dunne 2001).

My work clearly identifies the intestinal environment (including prebiotics, ion composition and pH) as a driving force in shaping the intestinal microbiota and thus the host. Given this broad impact, it is important to establish the connection between the intestinal micro-environment and changes in composition of the gut microbiota. This has the potential to target specific bacterial groups for selective manipulation of the gut microbiota.

This work has provided a comprehensive analysis of the microbiota along the length of the intestinal tract, both in the luminal and mucosa-associated bacterial populations. My data highlights

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differences between the small and large intestine and luminal and mucosa-associated bacterial populations. This information may serve as a guide for future studies examining the gut microbiota. My data has contributed to our understanding of the composition of the “normal” microbiota composition in different regions and locations within the intestine and so pave the way for future efforts to study its perturbations in disease. I have associated changes in alkaline luminal pH with growth of gram positive bacterial members and acidic luminal pH with growth of gram negative Bacteroides. In addition, I have begun to correlate changes in the intestinal ion composition with specific changes in ion-using bacteria, such as B. thetaiotaomicron and C. difficile. I have further examined the host-intestinal cross-talk by examining host glycosylation and fucosylation patterns in vivo in mouse and human tissues and in vitro in organoids. I have also demonstrated that prebiotic supplement of GH7FK in mice increases Proteobacteria members and may provide an alternative supplement in place of antibiotics. Knowledge of the growth preferences of commensal bacteria will help aid the development of therapeutics or dietary supplements that target specific components of the intestinal microbiota. In the field, much work remains to be done.

We are moving from a phylogenetic definition of our microbiota and are beginning to appreciate its functionality and to acquire a dynamic view of our gut as an ecosystem. In order to facilitate expansion of our current knowledge, further effort must be contributed to examining the entire length of the intestine and examining both luminal and mucosa-associated bacterial populations. The microbiome holds great potential for breakthroughs in translational medicine, including the promise of precision tools that will allow us to “sculpt” the microbiome with diet, prebiotics, probiotics, and targeted antibiotics to prevent and treat disease. Going forward, model systems will be essential for conducting the experimental perturbations and interventions required to understand the complexities of host interactions with the microbiota.

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APPENDICES

Manuscripts to be submitted

1. Ammonium Secretion in Colon Involves NKCC1and Rh glycoproteins

Abstract: Ammonia transport in the gastrointestinal tract is poorly understood. Identification of the

Rhesus glycoprotein family (SLC42s, RhGs) as ammonia transporters has offered new insight into this

+ process. Ammonia secretion occurs in colonic epithelial cells via basolateral NH4 uptake mediated by

NKCC1 and Rhesus glycoprotein B (RhBG). Apical ammonia exit likely occurs via ion trapping involving the combined action of Rhesus glycoprotein C (RhCG) and proton transport. Quantitative real time PCR (qRT-PCR) and laser capture microdissection (LCM) were used to define RhBG/RhCG expression along the length of the colon and along the crypt-surface axis in murine colon. In WT mice,

RhBG and RhCG mRNA expression was significantly higher in distal verses proximal colon. To more accurately define the RhBG/RhCG distribution, the colon was divided into five equal sections, RhBG and

RhCG expression was highest in the proximal/distal border region. To define RhBG/RhCG expression along the crypt-surface axis, LCM was used to separate surface, mid-crypt and crypt base. RhBG and

RhCG expression was found to be significantly higher in the mid-crypt section, relative to the surface and crypt base sections. In NKCC1-/- mice, RhBG and RhCG expression was significantly increased in the two most distal segments of colon relative to WT littermates. Increased mRNA correlated to increased protein as observed by immunohistochemistry. These data collectively add to the current understanding of ammonia transport and offer evidence for compensatory upregulation of RhBG and RhCG in the absence of NKCC1.

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2. Ammonium ion trapping in mouse distal colon: Role of NHE3

Abstract: Despite its critical role in the development of hepatic encephalopathy, ammonium transport in the gastrointestinal tract is poorly understood. It is well known that overall in the gut net ammonium absorption occurs. However, we have shown that net secretion occurs in the distal colon of mice and thus

+ limits overall net absorption. Ammonium secretion occurs in colonic epithelial cells via basolateral NH4 uptake mediated by NKCC1 and Rhesus glycoprotein B (RhBG). Our data suggests apical ammonia exit occurs via Rhesus glycoprotein C (RhCG) combined with ion trapping via H+ transport on cH/K-ATPase and/or NHE2/3. In the human colonic cell line T84, inhibition of NHE2/3 by amiloride produced a 50%

+ -/- decrease in secretory NH4 flux, implicating NHE2/3 in ion trapping of ammonia. In NHE3 mice portal vein ammonium levels were 1.9±0.4 fold higher than in WT, while no significant change was observed in

NHE2-/- or cH/K-ATPase-/- mice. qRT-PCR and Western blot were used to define distal colon transporter expression in various transporter null mice to establish co-operative function in ammonium secretion. In the NHE3-/- mice, cH/K-ATPase was up-regulated at the mRNA level but not at the level the protein.

NKCC1was decreased in both mRNA and protein while RhBG was only decreased at the mRNA level. In

NHE2-/- mice NHE3 mRNA and protein were up-regulated in the distal colon and in cH/K-ATPase-/- mice

NHE3, RhBG and NKCC1 were up-regulated at the level of the mRNA but only NHE3 protein was upregulated in the proximal colon. Taken together these data suggest that NHE3 plays a principle role in ammonia ion trapping.

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3. Iron metabolism in a mouse model lacking gastric H+/K+ATPase

Abstract:

Conditions with decreased gastric acid, such as gastrectomies, H. pylori infection and use of proton pump inhibitors, have been associated with decreased iron absorption, leading to the belief that gastric acid is essential for iron transport. In this study, we explored the role of gastric acid in iron absorption by examining iron metabolism in the gastric H+/K+ ATPase-null mouse (Atp4a−/−). Wild type and

Atp4a−/− mice 6 weeks post-weaning were placed on a normal mouse diet or a low-iron diet. Liver, heart, and spleen were examined for nonheme iron, and blood was examined for hematological variables and heme iron. RNA extracted from liver and duodenum was analyzed by qRT-PCR for iron absorptive gene expression. No differences in liver nonheme iron levels were found between male WT and Atp4a−/−mice or in females on a normal diet. Decreased liver nonheme levels were observed in female Atp4a−/−mice on a low iron diet and differences were found in Atp4a-/- tissue nonheme iron stores on normal diet and low iron diet. This suggests that liver and other tissue nonheme iron store are affected by the absence of gastric acid and low iron diet. Despite a reduction in the nonheme iron stores, Atp4a-/- mice do not exhibit anemia as determined by hematological variables and blood smears. qRT-PCR revealed that loss of gHKA is compensated for by downregulation of liver hepcidin (Hamp1) and upregulation of duodenal iron-absorptive systems cytochrome b(561) (Cybrd1), DMT1, and Ferroportin (Fpn1) in female mice on a low iron diet. No changes were observed in copper transporter Ctr1, metalloreductase Steap2 or the sodium-hydrogen exchanger isoform 3 (NHE3). No compensatory changes were observed in female mice on a normal iron diet. Together this data indicates that although gastric acid is not required for iron absorption on a normal diet, iron storage is affected by the combination of no gastric acid and a low iron diet.

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4. Loss of parietal cell Sonic Hedgehog in mice results in an altered gut microbiota and epithelial morphology

Abstract: Hedgehog signaling regulates embryonic gut differentiation and adult gastrointestinal (GI) homeostasis. Indian Hedgehog (Ihh) is primarily expressed by colonic enterocytes, while Sonic Hedgehod

(Shh) is primarily express by stomach parietal cells. Loss of intestinal Ihh and combined inhibition of Hh signaling has been shown to result in intestinal inflammation. Mice expressing a parietal cell-specific deletion of Shh (HKCre/ShhKO) develop both hypochlorhydria and hypergastrinemia, but no intestinal phenotype has been linked to Shh-depletion. In order to determine if Shh plays a role in intestinal homeostasis HKCre/ShhKO mouse intestinal architecture and gut microbiota were analyzed. Histological evaluation revealed that HKCre/ShhKO mice developed altered small intestine morphology, with short, broad and irregular villi and inflammatory infiltrate compared to control mice. In addition, HKCre/ShhKO mice develop atypical colonic crypts (aberrant crypt foci). qPCR examination of the gut microbiota from luminal flushes and mucosal scrapping of stomach, terminal ileum, cecum, proximal and distal revealed increased total bacteria in the stomach and ileum. In the ileum, HKCre/ShhKO mice displayed significant decreases in gamma-Proteobacteria and increases in unspecified bacteria. In the colon, increased

Actinobacteria levels occurred in both the proximal and distal colon of HKCre/ShhKO with decreases in

Bifidobacteria in distal colon. Mucosal scrapings from HKCre/ShhKO mice also showed an increase in decreases occurred in Actinobacteria in ileum and in beta-Proteobacteria in the distal colon. These data suggest that loss of parietal cell Shh results in disrupted gastrointestinal morphology and altered microbiota in stomach and small and large intestine.

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