MICROBIOME INTERACTIONS WITH DIETARY CHOLINE A Dissertation Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Paula Edith Banuelos May 2025 © 2025 Paula Banuelos MICROBIOME INTERACTIONS WITH DIETARY CHOLINE Paula Edith Banuelos, Ph. D. Cornell University 2025 Choline is an essential nutrient crucial in synthesizing neurotransmitters, lipid transport, regulation of osmolyte levels, and one-carbon metabolism. The deficiency of choline can lead to several health issues, such as liver disease, muscle atrophy, and neurodegenerative diseases. The recommended daily choline intake may vary depending on age and genetics. Recently, it has been appreciated that the body’s physiological response to choline is dependent on the metabolic capacity of the gut microbiome. Gut microbes with the ability to transform choline to trimethylamine (TMA) via the CutC gene cluster are important modifiers of dietary choline intake. Despite the role of choline metabolizing microbes in contributing to processes ranging from cardiovascular function to body odor, dietary choline recommendations do not actively consider the significant and quantifiable role of gut microbes in interacting with diet-derived choline. Moreover, despite a well-defined role for gut microbes in modifying dietary choline through the production of TMA, choline is a versatile metabolite that has the potential to interact with the microbiome in a CutC- independent fashion to produce bioactive microbiome-derived metabolites other than TMA. The discovery of other choline-dependent microbiome-derived metabolites will help in our understanding of how choline consumption can have varied effects on host health that are highly specific to the personalized metabolic potential of one’s microbiome. The following chapters explore the interactions between dietary choline and the gut microbiome. In Chapter 1, we employed a biorthogonal click chemistry-based workflow to identify novel gut microbes that utilize dietary choline. This approach led to discovering previously uncharacterized choline-utilizing gut microbes, including Limosilactobacillus reuteri (L. reuteri). In Chapter 2, we expanded upon this methodology to confirm the utilization of dietary choline by L. reuteri, both in vitro and in vivo. In addition, we used choline-sufficient and deficient rodent diets to characterize the effects of dietary choline-L. reuteri interaction on host physiology. This work identified novel gut microbes that utilize dietary choline and examined the effects of the choline-utilizing microbe L. reuteri on host physiology. The findings presented in this dissertation lay the groundwork for understanding how these newly identified choline-utilizing microbes influence dietary choline metabolism in the host and, in turn, how this affects their health. v BIOGRAPHICAL SKETCH Paula Bañuelos is a Ph.D. candidate in the Graduate Field of Biomedical and Biological Sciences (BBS) at Cornell University. She joined the program in 2020, driven by a passion for understanding complex biological systems and their implications for human health. Before starting her doctoral studies, Paula earned a bachelor’s degree in microbiology from California State University, Los Angeles (CSULA), where she first developed an interest in host-microbe interactions. At Cornell, Paula conducted her dissertation research under the mentorship of Elizabeth Johnson, PhD, focusing on the utilization of dietary choline by the gut microbiome. This work aimed to identify novel choline-utilizing microbes, characterize the effects of diet-microbiome interaction on host health, and discover microbiome-dependent choline-derived metabolites. Paula demonstrated a strong commitment to scientific research and mentorship throughout her graduate training. In addition to her research activities, she actively contributed to enhancing graduate education as the president of the BBS Graduate Student Council. She also served as the graduate student representative for the Belonging at Cornell Framework. Paula has presented her research findings at national conferences and received several awards, including the 2020 Cornell University Initiative to Maximize Student Development (Cornell-IMSD) and the 2022 Cornell- Corning Inc. GEM Fellowship. vi Para mi familia (For my family), especially To my daughter Elsa, even if you are too young to understand these words now, this work is a promise to you that, with love and perseverance, anything is possible. To my life partner, Eric, thank you for being my rock through every challenge and triumph. From the start, you have stood by my side, not only as my biggest supporter but also as someone who has grown, learned, and built a life alongside me. To my mom, Esther, and my dad, Toño, thank you for your courage and the sacrifices you made to build a new life in the United States. Your journey from Mexico, driven by hope and love, has allowed me to pursue my dreams and earn this Ph.D. To my brothers Tony, Richi, Elvis, and Miguel, and my sister-in-law Grisel- each of you has shaped me in your own way. Thank you for your constant encouragement, humor, and unwavering presence. To my cousins and friends, your support and care have meant the world to me. Thank you for sharing this journey with me. To my dog, Molly, your companionship, comfort, and joyful spirit made even the most stressful days a little brighter. This work reflects our shared strength, resilience, and love. I carry you all with me in every step. vii ACKNOWLEDGMENTS First and foremost, I would like to acknowledge my advisor, Dr. Elizabeth Johnson, for fostering a lab environment where I felt safe, supported, and able to focus solely on science. Your guidance and compassion have meant everything to me, especially as I balanced motherhood and research. Thank you to Dr. Min-Ting Lee, a previous PhD candidate in the Johnson lab, for being the most talented and generous mentor. Your technical and conceptual insights laid a strong foundation for my growth as a microbiome scientist. To Sharon, your expertise in RNA and shotgun sequencing was vital to my project, and your support, especially during the final stretch, helped me stay grounded. Janine, thank you for your contributions to the mass spectrometry data analysis, which provided impactful insights for my research. Your expertise, dedication, and support have been instrumental in helping me reach the finish line of my PhD. I want to thank my labmates for their assistance with mouse work and sample collections and for the camaraderie we shared in the break room, which made this journey much easier. I could not have asked for a better group of colleagues with whom to share this chapter of my life. I would also like to thank my special research committee members, Drs. Ilana Brito, Martha Field, and Kimberly O’Brien, for their insightful feedback and advice on my research projects. I want to thank the Cornell-IMSD program, especially Drs. Avery August and Melanie Ragin for their support as I navigated my PhD. Finally, I thank my undergraduate research mentor, Dr. Edith Porter, for inspiring me to pursue a PhD and the Cal State LA Minority Opportunities in Research (MORE) Program for supporting me as I prepared to embark on my doctoral journey. viii TABLE OF CONTENTS ABSTRACT ................................................................. Error! Bookmark not defined. BIOGRAPHICAL SKETCH .......................................................................................... v ACKNOWLEDGMENTS ............................................................................................ vii LIST OF FIGURES ........................................................................................................ x LIST OF ABBREVIATIONS ....................................................................................... xi CHAPTER 1 ................................................................................................................. 13 INTRODUCTION Gut microbial metabolism provides functional contributions to host biology ......... 13 Microbial Metabolites: Mediators of Host-Microbe Interactions............................. 14 Diet as a Modulator of Gut Microbiome Function ................................................... 15 Dietary choline: An Essential Nutrient with Known Interactions with the Gut Microbiome .............................................................................................................. 16 Preview of Chapters ................................................................................................. 22 CHAPTER 2 ................................................................................................................. 24 DIETARY CHOLINE UTILIZATION BY THE GUT MICROBIOME Abstract ..................................................................................................................... 24 Introduction .............................................................................................................. 24 Results ...................................................................................................................... 31 Discussion ................................................................................................................. 44 Materials and Methods ............................................................................................. 46 CHAPTER 3 ................................................................................................................. 52 MECHANISTIC EXPLORATION OF THE INTERACTION BETWEEN DIETARY CHOLINE AND LIMOSILACTOBACILLUS REUTERI AND ITS RELATION TO HOST PHYSIOLOGY Introduction .............................................................................................................. 52 Results ...................................................................................................................... 53 Discussion ................................................................................................................. 69 Materials and Methods ............................................................................................. 74 CHAPTER 4 ................................................................................................................. 83 Conclusion ................................................................................................................ 83 Outlook ..................................................................................................................... 84 ix References ................................................................................................................ 85 x LIST OF FIGURES CHAPTER 1 Figure 2.1. The BioOrthogonal Labeling-Sort-Sequence-Spectrometry (BOSSS) workflow schematic. ..................................................................................................... 30 Figure 2.2. Propargylcholine is effective in visualizing gut microbiome choline interactions. .................................................................................................................. 33 Figure 2.3. Increasing propargylcholine dosage does not increase detection of microbiome choline utilization in the cecal gut microbiome. ...................................... 35 Figure 2.4. Gut Microbiome Choline Utilization is Higher in the Ileum. .................... 38 Figure 2.5. Fluorescence-activated cell sorting gating strategy to identify choline- utilizing microbes from the ileum of mice. .................................................................. 40 Figure 2.6. Isolation and identification of choline-utilizing and non-choline-utilizing bacteria. ........................................................................................................................ 41 Figure 2.7. Imaging of the incorporation of dietary propargylcholine into host tissues. ...................................................................................................................................... 43 Figure 3.1 Choline utilization by L. reuteri in vitro. .................................................... 55 Figure 3.2 L. reuteri utilizes dietary propargylcholine choline in a gnotobiotic mouse model. ........................................................................................................................... 58 Figure 3.3 Dietary choline-dependent effects on hepatic physiology. ......................... 62 Figure 3.4 Significantly differentially expressed hepatic genes in response to L. reuteri and dietary choline interactions. ................................................................................... 64 Figure 3.5 L. reuteri-dependent upregulation of lysophosphatidylcholine. ................. 66 Figure 3.6 A metabolite derived from choline produced by L. reuteri in vitro is detected in the liver of mice. ........................................................................................ 69 xi LIST OF ABBREVIATIONS Acaca – Acetyl-CoA carboxylase alpha AF647 azide – AlexaFluor 647 azide AI – Adequate Intake B. theta – Bacteroides thetaiotaomicron B. thetaPAA – B. theta treated with palmitic acid-alkyne BADH – Betaine aldehyde dehydrogenase BHMT – Betaine-homocysteine methyltransferase BOSSS – BioOrthogonal-Sort-Sequence-Spectrometry CDH – Choline dehydrogenase cutC – Gene encoding choline trimethylamine-lyase cutD – Gene encoding the activating protein for choline trimethylamine-lyase (cutC) FACS – Fluorescence-Activated Cell Sorting Fasn – Fatty acid synthase FMO3 – Flavin-containing monooxygenase 3 FSC-A – Forward Scatter Area FSC-H – Forward Scatter Height FSC-W – Forward Scatter Width H&E – Hematoxylin and Eosin staining IOM – Institute of Medicine I.P. – Intraperitoneal injection LC-MS – Liquid Chromatography–Mass Spectrometry L. reuteri – Limosilactobacillus reuteri LPC – Lysophosphatidylcholine MRS – Man, Rogosa, and Sharpe media MUFAs – Monounsaturated fatty acids NAFLD – Nonalcoholic fatty liver disease PAA – Palmitic acid-alkyne PBS – Phosphate-buffered saline PEMT – Phosphatidylethanolamine N-methyltransferase xii PC – Phosphatidylcholine PLA1 – Phospholipase A1 PLA2 – Phospholipase A2 PCA – Principal component analysis Scd1 – Stearoyl-CoA desaturase 1 SCFAs – Short-chain fatty acids SSC-A – Side Scatter Area SSC-H – Side Scatter Height SSC-W – Side Scatter Width TMA – Trimethylamine TMAO – Trimethylamine N-oxide 13 CHAPTER 1 INTRODUCTION Gut Microbial Metabolism Provides Functional Contributions to Host Biology The gut microbiome is a complex community of trillions of microorganisms, including bacteria, archaea, fungi, and viruses.1,2 These microbes reside throughout the gastrointestinal tract, forming complex communities.3 Gut bacteria have garnered recent attention due to their high abundance in the colon and potentially dominant contributions to host health through their metabolic activity.4,5 Remarkably, the bacteria present in the gastrointestinal tract collectively possess more than 100 times the number of genes found in the human genome, enabling metabolic capabilities far beyond the host’s genome.6,7 This extensive genetic capacity allows gut bacteria to perform functions essential to host health.8 Functionally, the gut microbiome is integral to host health through its involvement in numerous physiological systems. Of specific interest is the facilitation of nutrient metabolism by the gut microbiome through the fermentation of indigestible dietary fibers9, synthesis of essential vitamins such as B₁₂ and K₃10, and absorption of minerals like calcium and magnesium.11 Defining the interactions of the gut microbiome with dietary inputs is critical in discovering effective ways to modulate gut microbiome function for the benefit of host health. For example, a recent study demonstrated that individuals who were placed on a non-industrialized diet supplemented with the microbe Limoosilactobacillus reuteri (L. reuteri) experienced weight loss, decreased plasma fasting cholesterol, fasting glucose, and C-reactive protein levels. These changes were linked to notable 14 cardiometabolic benefits.12 Microbial Metabolites: Mediators of Host-Microbe Interactions One of the gut microbiome's most compelling functions is its ability to produce bioactive metabolites. Microbiome-derived metabolites are small molecules produced or modified by gut microbes as they metabolize dietary components.13,14 These metabolites play critical roles in mediating the crosstalk between the gut microbiota and host physiology.15 Microbial-derived metabolites influence host metabolism16, immunity17, and brain18 function through local and systemic mechanisms.19,20 Key classes of microbiome-derived metabolites include: Short-chain fatty acids (SCFAs): Produced through microbial fermentation of dietary fibers, SCFAs such as acetate, propionate, and butyrate serve as colonocyte energy sources, modulate immune responses, and regulate lipid and glucose metabolism.21 Secondary bile acid metabolites: Gut microbes deconjugate and convert primary bile acids into secondary bile acids like deoxycholic acid. These derivatives modulate signaling through receptors such as FXR and TGR5, influencing lipid digestion, glucose metabolism, and inflammatory responses.22 Cholesterol Sulfate: Bacteroides species play a crucial role in converting cholesterol to cholesterol sulfate through the action of the enzyme BT_0416.23 This metabolic process is significant because it facilitates the utilization of cholesterol by these bacteria and has implications for the host's cholesterol homeostasis. By transforming cholesterol into cholesterol sulfate, Bacteroides sp. influences the host's free cholesterol levels and modulates various 15 physiological processes. Trimethylamine (TMA): TMA microbial production is the most extensively studied microbial pathway related to dietary choline. Anaerobic bacteria that possess the cutC/D gene complex convert choline into TMA. CutC is the gene encoding the enzyme choline trimethylamine-lyase, responsible for microbial conversion of choline into TMA. CutD is the gene encoding the activating protein for choline trimethylamine-lyase (CutC). TMA is then absorbed, and in the liver, it is oxidized by the flavin-containing monooxygenase 3 (FMO3) enzyme to form trimethylamine N-oxide (TMAO). Elevated levels of TMAO are linked to conditions such as atherosclerosis, thrombosis, and kidney dysfunction.24 Diet as a Modulator of Gut Microbiome Function Diet is a powerful influence on the gut microbiome, affecting its composition and metabolic function. While the development of the gut microbiome starts at birth and is influenced by factors such as the mode of delivery and early antibiotic exposure,25,26 dietary choices throughout life continue to shape its structure and metabolic activity.27,28 Research has shown that the gut microbiome can respond to dietary changes in as little as 24 to 48 hours, highlighting how responsive the gut microbiome is to what we eat.29,30 These alterations extend beyond taxonomic shifts to changes in the metabolic capabilities of the gut microbiome. For example, in one study, individuals adhering to vegan or vegetarian diets experienced a shift in their gut microbiome towards a composition associated with reduced production of TMA.31 This resulted in lower levels of TMAO, a compound linked to several cardiovascular diseases.32 Conversely, diets rich in animal products may 16 encourage the growth of TMA-producing bacteria, resulting in elevated TMAO levels.31 These results underscore the significant impact diet has on the metabolic functions of the gut microbiome and emphasize the importance of making dietary choices that promote health. Dietary choline: An Essential Nutrient with Known Interactions with the Gut Microbiome Choline is an essential, water-soluble nutrient that plays multiple roles in human health. It is a precursor for synthesizing phospholipids such as phosphatidylcholine and sphingomyelin, crucial components of cell membranes.33 Choline also contributes to synthesizing acetylcholine, a neurotransmitter, and betaine, a methyl group donor for homocysteine remethylation.34-36 Dietary choline intake is essential as endogenous synthesis via the phosphatidylethanolamine N-methyltransferase (PEMT) pathway is insufficient to meet physiological needs.37 Choline-rich foods primarily come from animal sources, such as chicken liver, which offers 247 mg of choline per 3 ounces, and eggs, which provide 125 mg per large egg.38 Additional contributions to daily choline intake can be found in various meats, poultry, and fish.38 For those on a plant-based diet, quinoa is a key source of choline, delivering 43 mg per cooked cup.39 Other options include wheat germ, prevalent in whole grain products, select nuts like almonds and walnuts, and vegetables such as cauliflower and broccoli,38 all of which can also help meet dietary choline requirements. Among the various choline-containing compounds found in foods, the most abundant is phosphatidylcholine, commonly referred to as lecithin40. Lecithin 17 is important for cell membrane structure and function and is often used as an emulsifier in food products.37,38 Dietary choline deficiency can negatively impact cognitive function41, cardiovascular health42, cause congenital disabilities43, and contribute to liver disease.44 One well-documented effect of choline deficiency is hepatosteatosis, also known as fatty liver, which occurs when triglycerides accumulate in hepatocytes.45 If the deficiency persists, it can progress to nonalcoholic fatty liver disease (NAFLD) and, in severe cases, develop into steatohepatitis and liver fibrosis.37,44 The Adequate Intake (AI) guidelines for choline, established by the Institute of Medicine (IOM), now the National Academies of Science, Engineering, and Medicine, NASEM) in 199846, were based on studies indicating that insufficient dietary choline leads to the development of liver disease.47 These studies were conducted in adult men47 and later adjusted for groups such as women and children. These guidelines indicate specific daily intake levels, which are influenced by factors such as gender, hormonal status, and age. The recommended daily intake is 550 mg for adult men and 425 mg for adult women.40 Pregnant and lactating women's recommended daily intake is increased to support fetal and infant brain development.38 Interestingly, premenopausal women are less susceptible to choline deficiency.48 A possible explanation for this could be the role of the hormone estrogen, which is known to upregulate the activity of PEMT, a key enzyme related to choline metabolism.49,50 Notably, these guidelines have not been updated since 1998 and thus do not 18 reflect the scientific advancements in our understanding of choline metabolism that have occurred since. For instance, genetic variations in key enzymes related to choline metabolism, such as those in PEMT, choline dehydrogenase (CHDH), and betaine-homocysteine methyltransferase (BHMT), can impair the body's ability to produce choline.51 As a result, individuals may require a greater reliance on dietary sources of choline.52 The gut microbiome also plays a role in modulating choline bioavailability. Certain bacterial taxa, such as Desulfovibrio, Clostridium, and Enterococcus, metabolize free choline to TMA using the enzyme choline TMA-lyase, encoded by cutC and cutD.53 This microbial competition for choline has been shown to reduce the amount available for host absorption54, potentially contributing to choline deficiency, especially in individuals with marginal intake or increased physiological demand.54 Choline Absorption and Host Utilization Choline exists in the diet in water-soluble forms like free choline and phosphocholine and lipid-soluble forms like phosphatidylcholine and sphingomyelin54. Due to its quaternary amine structure, choline carries a permanent positive charge and cannot passively diffuse across cell membranes.55 As such, its absorption is mediated by specific transporter proteins in the small intestine, primarily in the jejunum and ileum.56,57 The primary dietary form of choline is phosphatidylcholine (PC), a major component of cell membranes and lipoproteins.58 Upon ingestion, PC undergoes partial hydrolysis in the lumen of the small intestine. Pancreatic phospholipase A2 cleaves PC into lysophosphatidylcholine (LPC) and a free 19 fatty acid.59 Enterocytes absorb LPC via passive diffusion or via facilitation by membrane proteins.60,61 Once within the enterocyte, LPC is reconverted to PC through the Lands cycle and incorporated into chylomicrons. These chylomicrons are secreted into the lymphatic system and eventually reach systemic circulation. This process enables the transport of lipid-bound choline to peripheral tissues independently of the portal vein.61 Choline Metabolism by Bacteria Choline is a substrate for various microbial metabolic pathways. Several key products of microbial choline metabolism include: Trimethylamine (TMA) production The microbial transformation of choline into TMA is well-studied, primarily involving anaerobic bacteria that carry the cutC/D gene cluster.62 The cutC gene encodes choline TMA-lyase, which is the key enzyme that catalyzes the conversion of choline into TMA and acetaldehyde.62 The cutD gene encodes an activating protein that aids the activation of cutC by introducing a glycyl radical, which is essential for enzymatic function.63 This cutC/cutD system is part of the glycyl radical enzyme (GRE) family.24,53,54 Once produced, TMA can be absorbed by the host's gastrointestinal tract and transported to the liver.64 In the liver, TMA undergoes oxidation by the enzyme FMO3, forming trimethylamine N-oxide (TMAO).65 TMAO has garnered significant attention in recent research for its potential link to cardiovascular disease24 and neurological disorders66,67, suggesting that this microbial pathway may have important implications for human health and disease risk.68-70 20 Glycine betaine production by bacteria Some bacteria, including Escherichia coli71,72 and Staphylococcus aureus73, can oxidize choline to produce glycine betaine, which serves as an osmoprotectant for these organisms. 72,74 The conversion of choline to betaine occurs through a two-step process. In the first step, the enzyme choline dehydrogenase (CDH), which is encoded by the betA gene, oxidizes choline into betaine aldehyde. In the second step, betaine aldehyde dehydrogenase (BADH), encoded by the betB gene, further oxidizes betaine aldehyde to produce glycine betaine.75 This pathway is activated in response to osmotic stress.76 In humans, glycine betaine is an important osmolyte and methyl donor in one-carbon metabolism.77,78 However, the production of betaine by members of the gut microbiome and its physiological impact on the host remain underexplored. Lysophosphatidylcholine (LPC) production by the gut microbiome Certain gut microbes and pathogens, such as Bacteroides ovatus79 and Legionella pneumonia80, are known to produce LPC during their interactions with host membranes or when they remodel their own membrane phospholipids.81,82 The primary pathway for LPC production involves the enzymes phospholipase A2 (PLA2) and phospholipase A1 (PLA1).80 This process converts phosphatidylcholine (PC) into LPC and a free fatty acid. PLA2 removes the fatty acid, while PLA1 removes the sn-1 acyl group.83 The microbial hydrolysis of PC can yield LPCs, which are implicated in immune signaling and vascular function. 84-88 Additionally, recent studies have associated microbiota-derived LPC with improved Alzheimer’s disease pathology by suppressing ferroptosis.79 Further, certain microbes, such as 21 Sinorhizobium meliloti,89 integrate choline into their membrane as PC. This integration enhances bacterial survival, colonization abilities, and interactions with the host immune system.90,91 Although these strategies highlight the biological significance of microbial choline utilization, existing methodological constraints still limit our insight into dietary choline-microbe interactions. The Methodology to Characterize Choline-Microbiome Interactions is Limited. Despite the potential for diverse uses of choline by the gut microbiome, most research efforts have focused on the microbial transformation of choline to TMA. There is a substantial body of work dedicated to studying TMA production from dietary choline and carnitine, primarily due to its connections with cardiovascular diseases.24 This growing body of research has mainly concentrated on identifying choline-utilizing microbes through comparative genomics, where bacteria are predicted to metabolize choline based on the presence of the cutC/cutD gene cluster, which is responsible for converting choline to TMA.53 While this approach has helped identify choline-utilizing microbes, it constrains our understanding of microbial choline metabolism by concentrating solely on a single metabolic pathway. The gut microbiome likely has a broader, more complex network of choline metabolism beyond the well- studied cutC gene that warrants further exploration to understand its impact on host physiology. In the Johnson lab, we use bioorthogonal labeling to characterize how nutrients interact with gut microbes. This powerful technique can track dietary choline 22 utilization by the gut microbiome without interfering with natural biological processes.92 This approach introduces a chemically distinct or "orthogonal" functional group into the biological system, ensuring it remains unreactive with any native biochemical pathways or molecules present in the organism.93 In this work, we use propargylcholine, a modified form of choline, to track its interactions with the microbiome. Propargylcholine can be traced using techniques such as fluorescence or mass spectrometry,94-96 which allows us to identify how the microbiome metabolizes dietary choline. Preview of Chapters In Chapter 1, we leveraged a biorthogonal click chemistry-based workflow, BioOrthogonal-Sort-Sequence-Spectrometry (BOSSS), to investigate the metabolism of dietary choline by the gut microbiome. Using this workflow, we identified novel choline-utilizing probiotic bacteria, including Limosilactobacillus reuteri. In Chapter 2, we build upon our findings from Chapter 1 and explore the bacterial utilization of dietary propargylcholine. We employed biorthogonal labeling of choline-utilizing bacteria L. reuteri to confirm the utilization of dietary propargylcholine both in vitro and ex vivo. Additionally, we utilized a germ-free gnotobiotic mouse model of L. reuteri and modified rodent diets to be either choline-deficient or choline-sufficient. 23 This allowed us to examine the effects of L. reuteri and dietary choline on host physiology. Using novel methodologies in comparative metabolomics, our findings revealed that various LPC metabolites were uniquely elevated in the presence of L. reuteri and dietary choline. Moreover, we identified a novel dietary choline-derived metabolite produced by L. reuteri, which was also detected in the liver of mice, implying that this may be a new diet-dependent microbial metabolite that has bioactive functions in the host. In conclusion, this dissertation presents specific uncharacterized dietary choline-microbiome interactions that could be precisely targeted to modulate host biology. 24 CHAPTER 2 DIETARY CHOLINE UTILIZATION BY THE GUT MICROBIOME Abstract Diet is an important driver in the function of the gut microbiome, which, in turn, has significant implications for host health due to the production of diet-derived microbial metabolites that affect host physiological processes. Choline is a nutrient that influences gut microbiome metabolic function, impacting the host through metabolites derived from the diet. While dietary choline is essential for overall health, there has been limited research characterizing the diverse metabolites produced by the gut microbiome from dietary choline. This knowledge gap largely arises from focusing on a single known pathway through which choline and the microbiome interact, limiting our understanding of the health-related effects resulting from these interactions. More comprehensive and unbiased methodologies are needed to understand how gut microbes shape choline metabolism and their impact on human health. This is necessary not only to identify the specific microbes that metabolize dietary choline, but also to uncover the physiology- relevant metabolites produced from the interactions between dietary choline and the microbiome. In this study, we leveraged the BOSSS workflow to study the utilization of propargylcholine by the gut microbiome. We identified novel dietary choline- utilizing microbes, including several strains with probiotic properties, including Limosilactobacillus reuteri (L. reuteri). We conclude that the BOSSS workflow offers a comprehensive platform to study dietary choline gut microbiome metabolism. Introduction The human gastrointestinal tract is home to a vast and intricate ecosystem of microorganisms known as the gut microbiome. This community includes trillions of 25 microbes, mainly bacteria, that are crucial for host physiology. They contribute to nutrient metabolism, regulate the immune system, and produce bioactive metabolites.97,98 These gut microbes can take up dietary nutrients for their own fitness, and their metabolism of these nutrients can result in the production of metabolites that have systemic effects on the host. Various bacterial metabolites are known to influence various processes, such as glucose regulation, lipid metabolism, and neurological function.21,23,99 One essential dietary component that interacts intimately with the gut microbiome is choline. Choline is a water-soluble nutrient required for synthesizing phospholipids (e.g., phosphatidylcholine), the neurotransmitter acetylcholine, and the methyl group donor betaine.35 Dietary choline plays critical roles in maintaining membrane integrity, liver function, neurodevelopment, and one-carbon metabolism.100,101 Although the human liver can synthesize choline via the PEMT pathway, endogenous production is insufficient to meet total body requirements, necessitating dietary intake.38 In addition to the dietary requirement for choline in mammalian hosts, dietary choline is also consumed by certain members of the gut microbiome. As mentioned previously, one well-characterized choline-microbiome pathway involves the conversion of choline to TMA, which is subsequently modified to TMAO. Elevated levels of TMAO in circulation have been associated with an increased risk of cardiovascular disease, chronic kidney disease, and metabolic dysfunction.24,31The microbial contribution to host TMAO levels was elucidated using germ-free mouse models. These models, when compared with conventionalized mice, demonstrated that trimethylamine-lyase activity, which derived TMAO production, is attributable exclusively to the gut microbiome.24 Further studies indicated that the microbial genes responsible for TMA production were CutC and CutD.62 Comparative genomic analysis enabled the discovery of TMA- 26 producing microbes by searching for CutC and CutD homologs in the bacterial genomes of gut microbes. Taxa known to possess cutC/D gene clusters include Desulfovibrio, Clostridium, and Enterococcus.53,54 However, there is significant untapped potential for choline to alter gut microbial metabolism, independent of trimethylamine-lyase activity. Despite extensive research on TMA-producing pathways, our current understanding of microbial choline metabolism remains incomplete. For example, even though certain microbes, such as Bacillus subtilis (B. subtilis) and Escherichia coli (E. coli), can convert choline into betaine, research on the effects of this metabolite on host physiology has, to the best of our knowledge, not yet been explored.72,79,84,102-104 Some bacteria, such as Streptococcus pneumoniae (S. pneumoniae), also incorporate exogenous choline into their membrane phospholipids, which supports cell structure and biofilm formation.91,105 Given the versatility of choline and preliminary evidence that choline-derived betaine and phospholipids have been observed in vitro77, the potential to investigate dietary choline transformations independent of TMA production is an attractive research avenue. Further work is needed to understand whether dietary choline contributes to microbial metabolism, as observed in culture. To advance our understanding of the extent of dietary choline-microbiome interactions, novel approaches are needed in the discovery of microbes that interact with dietary choline in a cutC/D independent manner. A major limitation in the field is the reliance on comparative genomics approaches focused primarily on identifying homologs of known genes, such as cutC/D, to infer choline utilization potential.53 While these methods have expanded our knowledge of microbial TMA production, they inherently bias discovery toward this single previously characterized pathway and overlook novel or alternative mechanisms of choline metabolism. As a result, we lack a comprehensive view of the diversity and prevalence of choline-utilizing bacteria. There is also huge 27 potential to utilize and manipulate choline-microbiome interactions to improve host health by incorporating microbiome contributions into personalized and national dietary guidelines, but a more comprehensive understanding of how the microbiome modifies dietary choline is needed. To address these gaps, the present study employs an unbiased screening approach, the BioOorthogonal Labeling-Sort-Sequence-Spectrometry (BOSSS) workflow106 developed by the Johnson Lab to identify novel nutrient-utilizing bacteria in the gut microbiome. The BOSSS workflow comprises four key steps, each critical to our investigation. First, this innovative method leverages biorthogonal labeling, a technique that facilitates the study of biological processes with minimal disruption to natural biological systems. This approach involves introducing a small orthogonal (chemically distinct) functional group which does not interact with any native biochemical pathways or molecules within the organism.93,95 We utilized this methodology to meticulously track and isolate specific microbial populations that utilize dietary propargylcholine, an analog of choline where a three-carbon propargyl group replaces one methyl group (Fig 1A). Second, after completing the experiment and collecting samples, fluorescence-based sorting (Sort) is used to isolate the microbes that have successfully interacted with propargylcholine (Fig. 1B). This step utilizes one of the most well-known biorthogonal labeling reactions, specifically click chemistry, which involves the copper(I)-catalyzed azide-propargyl cycloaddition. In this reaction, the propargyl group of propargylcholine is "clicked" to the azide group of a detection reagent attached to a fluoroprobe (AlexaFluor 647 azide – AF647 azide). This reaction covalently links a fluoroprobe to any microbe that takes up propargylcholine, causing those microbes to fluoresce red. This allows us to differentiate choline-utilizing bacteria (AF647+) from the general non- 28 choline-utilizing microbial population (AF647-) using flow cytometry. These two populations are then separated into different tubes using Fluorescence-Activated Cell Sorting (FACS). Third, we apply shotgun metagenomic sequencing (Sequence) to characterize the sorted choline-utilizing (AF647+) microbial community, facilitating the identification and classification of the specific bacteria involved in choline utilization (Fig. 1C). This sequencing step uses cutting-edge technologies to identify previously uncharacterized gut microbes capable of metabolizing dietary choline. Finally, we conduct a comparative metabolomics analysis (Spectrometry) to investigate the range of choline-derived metabolites produced by the previously identified microbes (Fig. 1D). Here, we do not attach an azide-bearing detection reagent to propargylcholine; instead, we leverage the mass difference of propargylcholine (Molecular weight: 128.19) created by the propargyl group compared to native choline (Molecular weight: 104.17). This mass difference (24) allows us to identify and differentiate choline-derived metabolites using mass spectrometry. This chapter focuses on identifying novel-choline-interacting gut microbes using the BioOrthogonal- labeling, Sort, and Sequence steps of the BOSSS methodology. Chapter 3 focuses on the comparative metabolomics (mass Spectrometry) portion of the BOSSS workflow to characterize functional metabolites produced from the uptake of dietary choline by the gut microbiome. Previously, the BOSSS workflow has successfully identified novel sphingolipid and cholesterol-utilizing microbes.23,106 These discoveries highlight the complexity of microbiome-nutrient interactions and emphasize the need for more unbiased approaches to identify nutrient-metabolizing microbes and assess the full extent of their metabolic 29 outputs. This is the first time the BOSSS workflow has been used to understand dietary interactions with the gut microbiome from a nutrient that is not a dietary lipid. Investigating the micronutrient choline using the BOSSS workflow opens the potential for widespread discovery of a wide variety of uncharacterized nutrient-microbe interactions using this approach. In the current work, this novel methodology successfully enabled us to track how the gut microbiome utilizes dietary choline. By using experimental techniques beyond gene homology and predicted function, this work aims to capture a broader spectrum of choline-metabolizing taxa and characterize previously unrecognized metabolic pathways that may have implications for host health. Additionally, the BOSSS workflow addresses current challenges in identifying choline- utilizing bacteria. It allows for precise detection of microbes that metabolize choline without relying on predictive choline utilization genes, which are often nonfunctional.53 The development of this unbiased screening method to identify microbes capable of metabolizing dietary choline is crucial for understanding the interactions between dietary choline and the microbiome. Objectives Given the increasing acknowledgment of the gut microbiome's role in dietary choline utilization and its impact on host health, a comprehensive understanding of the intricate interactions between dietary choline and gut microbiome metabolism is essential. This 30 study aims to advance the field by identifying novel choline-utilizing bacteria and examining the effects of bacterial choline metabolism on host health. We hypothesize that diet-derived choline is taken up by previously uncharacterized gut microbes that can be identified using the BOSSS workflow. This chapter aims to answer the following questions: Figure 2.1. The BioOrthogonal Labeling-Sort-Sequence-Spectrometry (BOSSS) workflow schematic. (A) A modified form of choline, propargylcholine, is introduced to trace diet microbiome interactions. (B) Choline-utilizing microbes are isolated using fluorescence-based sorting method. (C) The identification of choline- utilizing microbes is achieved through shotgun metagenomic sequencing. (D) Comparative metabolomics is performed to identify metabolites produced from the interactions between diet and the microbiome. 31 1. Determine the effectiveness of the BOSSS workflow in investigating uptake of dietary choline by the host and gut microbiome. 2. Identify novel choline-utilizing gut microbes Significance of the Study This research has important implications for precision nutrition, therapeutic modulation of the microbiome, and dietary recommendations. By elucidating how microbes drive the production of choline-derived metabolites and their systemic effects, this study lays the foundation for personalized strategies to manage diseases related to inflammation, metabolism, and cardiovascular health. This could include probiotic supplementation of choline-utilizing microbes that make beneficial metabolites, restriction of choline to individuals with microbes that produce harmful choline-derived metabolites, and/or direct supplementation of choline-derived microbial metabolites that have beneficial health outcomes. Ultimately, it enhances our understanding of the complex relationship between diet, microbiota, and the host. Such knowledge could lead to the development of targeted microbiome interventions designed specifically to enhance beneficial microbial choline metabolism while minimizing harmful byproducts such as TMAO. Results Gut Microbiome utilization of propargylcholine We first sought to confirm the use of propargylcholine by intestinal microbes in vitro and to validate our BOSSS workflow with this compound. To do this, we prepared a slurry from the colon contents of a female Swiss Webster mouse (n=1) and inoculated multiple Petri dishes containing glass coverslips. The Petri dishes were filled with 2 mL of media, with one set supplemented with 60 mM of propargylcholine (n=3) and a no- treatment control group (n=3). Coverslips from each treatment group were collected at 32 3, 6, and 18 hours. After collection, the coverslips were washed and click-stained to attach an azide-bearing fluoroprobe (AF647) to observe the gut microbiome's utilization of propargylcholine through microscopy (Figure 2A). Propargylcholine was utilized by colon-isolated microbes from the colon three hours after inoculation (indicated by red fluorescence), with more pronounced effects observed at six and eighteen hours. As anticipated, negative control microbes without propargylcholine supplementation did not demonstrate red fluorescence (Figure 2B). This affirmed the accuracy and effectiveness of our methodology and indicated that propargylcholine can be utilized to examine direct gut bacterial interactions with dietary choline. Propargylcholine in vivo optimization of experimental conditions To investigate the interaction between intestinal bacteria and dietary choline, we designed an experiment to track the fate of orally administered dietary propargylcholine in the murine gut microbiome. We first sought to determine the ideal concentration for tracking propargylcholine utilization by the gut bacteria. For this, we administered either 0.2 mg or 2 mg of propargylcholine, dissolved in 100 µL of PBS, to twelve-week- old female Swiss Webster mice (n=4) via oral gavage once a day for four consecutive days. The mice were euthanized post-treatment, and their cecal contents were collected. As described above, we isolated microbes from the cecal contents, and utilized click chemistry to attach an azide-bearing fluoroprobe (AF647-azide) to observe the gut microbiome's utilization of propargylcholine using microscopy (Fig. 3A). Microscopy imaging of the cecal gut microbes from mice treated with 0.2 mg (Fig. 3B- I) and 2 mg (Fig. 3B-II) of propargylcholine showed no staining with the AF647 fluorescent probe, indicating a lack of choline utilization by the bacteria isolated from cecal contents. As expected, the no-treatment controls also showed no staining for 33 AF647-azide (Fig. 3B-III). These results showing a lack of bacterial interactions between dietary choline in the cecum at both tested concentrations indicate that this may not be the ideal section of the intestine to study dietary choline gut microbiome interactions. We also investigated the ability of host tissues to incorporate Figure 2.2. Propargylcholine is effective in visualizing gut microbiome choline interactions. (A) Schematic of experiment to determine the feasibility of using propagylcholine for the BOSSS methodology. Colon contents from a female Swiss Webster mouse were homogenized in PBS. The resulting slurry was used to inoculate a Petri dish containing a glass coverslip. The Petri dish contained 2 ml of media either supplemented with 60 mM of propargylcholine (n=3) or no supplementation as the control group (n=3). Coverslips from each treatment group were collected at 3, 6, and 18 hours. After collection, the coverslips were washed and click-stained to attach an azide-bearing fluoroprobe (AF647). (B) Fluorescent microscopy images of colon-derived microbes grown in glass coverslips. Scale bar 20 μm. Red indicates AF647 staining, and blue indicates DNA staining 34 propargylcholine. When we stained a portion of the small intestine tissue of the mice that received propargylcholine, we observed staining throughout the isolated host tissues at both 0.2mg (Fig 3C-I) and 2 mg (Fig 3C-II) concentrations. Negative controls not treated with propargylcholine showed no AF647 signal (Fig 3C-III). These results indicated propargylcholine is readily incorporated by the host into the small intestine and that a concentration of 0.2 mg is suitable for studying host-choline interactions. Given the prior observations in the cecum and that dietary choline is primarily absorbed in the small intestine, we next investigated the presence of choline-utilizing microbes in the ileum, the distal section of the small intestine. To compare the percentage of isolated choline utilizing microbes in each region of the intestinal tract, we isolated gut microbes from the ileum, cecum, and colon of mice that were gavaged with 2 mg of propargylcholine via oral administration. As before, we used click chemistry to attach AF647 azide to the microbes that took up diet-derived propargylcholine (Fig. 4A). We then imaged the ileal, cecal and colon-derived microbes using microscopy. Microscopy images showed significant levels of AF647 staining in the ileal contents, and no AF647 staining was observed in the cecum, indicating no choline utilization and low levels of AF647 staining were observed in the colon contents, indicating low levels of choline 35 utilization by the microbes in this region (Fig. 4B). We then quantified the percent of Figure 2.3. Increasing propargylcholine dosage does not increase detection of microbiome choline utilization in the cecal gut microbiome. Schematic of experimental conditions used to determine propargylcholine utilization in vivo. (A) Twelve-week-old female Swiss Webster mice (n=4) were given either 0.2 or 2 mg of propargylcholine dissolved in 100 μL of PBS via oral gavage. The gut microbiome was isolated from the cecal contents and click-stained with azide-bearing fluoroprobe (AF647-azide). (B) Fluorescent microscopy images of cecal microbes from mice treated with 0.2 mg of propargylcholine (B-I), 2 mg of propargylcholine (B-II), or no treatment (B-III). Scale bar = 20 μm. (C) Fluorescent microscopy of small intestine sections from mice that were treated with 0.2 mg of propargylcholine (C-I), with 2mg of propargylcholine (C-II), or no treatment (C-III). Scale bar = 400 μm. Red indicates propargyl-containing metabolite detection (red, AF647-azide) staining, and blue indicates microbial or host cell presence through DNA staining (blue, Hoechst 33342) 36 AF647 intensity observed in by the microbes isolated from this region using flow cytometry. The ileal-derived microbes had 33.2 % of the population with positive AF647 fluorescent intensity, the cecal-derived microbes showed 4.81 % of the population with positive AF647 fluorescent intensity, and the colon contents showed 31.3 % of the population with positive AF647 fluorescent intensity (Fig 4 C-D), indicating the highest choline utilization by gut microbes in the ileum. Fluorescence-activated cell sorting to identify choline utilizing microbes To identify choline-utilizing microbes in the ileum of mice, we isolated this specific population using Fluorescence-Activated Cell Sorting (FACS). Because the ileum biomass is low, we combined four ileum samples into a single tube. Our gating strategy to sort the choline-utilizing microbes involved creating a gate to separate gut microbes from debris based on two parameters: the Forward Scatter Area (FSC-A), which indicates cell size, and the Side Scatter Area (SSC-A), which reflects the complexity of the bacterial cells (Fig 5A). To ensure the accurate sorting of single cells, a gating strategy was implemented using both the Side Scatter Width (SSC-W) and the Side Scatter Height (SSC-H). SSC-W indicates the width of the side scatter signal, providing insights into cell shape and assisting in the identification of doublets 37 38 or cell clumps. In contrast, SSC-H represents the height or intensity of the side scatter signal, which offers information about the granularity and internal complexity of the cells (Fig. 5B). Additionally, another gate was created to differentiate further between single cells and doublets or cell aggregates by utilizing the Forward Scatter Width (FSC- W) and Forward Scatter Height (FSC-H). FSC-W reflects the width of the forward scatter signal, which is related to cell shape and can help identify doublets. Meanwhile, FSC-H represents the intensity (height) of the forward scatter signal, which is connected to cell size (Fig. 5C). Together, these parameters are most effective for identifying and excluding doublets or cell aggregates from flow cytometry data, ensuring that only single cells are analyzed. To identify the choline-utilizing bacteria, we utilized the SSC-A parameter, which categorizes bacteria based on their size and granularity. Additionally, we used the AF647 parameter, which refers to a fluorescence signal detected from the azide-bearing fluoroprobe (AF647-azide) used to label the choline-utilizing microbes. Figure 2.4. Gut Microbiome Choline Utilization is Higher in the Ileum. (A) Schematic of the experimental setup to determine choline utilization across the intestinal tract. Female Swiss Webster mice (n=4) received 2 mg of propargylcholine via oral gavage daily for five days. After the treatment, the mice were sacrificed, and gut microbiomes from the ileum, cecum, and colon were collected for further analysis. Microbes that utilize propargylcholine were stained through click-chemistry using an AlexaFluor 647 azide (AF647-azide) detection reagent that attaches to the terminal propargyl group of propargylcholine. (B) Fluorescent microscopy images displayed click-stained gut microbes from the ileum, cecum, and colon. Scale bar = 20 μm. Red indicates AF647+, and blue represents the DNA stain. (C) Flow cytometry density plots of click-stained microbes from the ileum (left), cecum (center), and colon (right) are shown. SSC = Side Scatter Area, and AF647 indicates fluorescent intensity. The gate labeled "negative" includes non-choline-utilizing microbes, while the gate labeled "positive" contains choline-utilizing microbes. (D) A bar graph illustrating different regions of the gastrointestinal tract (x-axis) and the percentage of the microbial population from the lumen of that GI region that was positive beyond the background for AF647 (red)fluorescence (choline-utilization) (y-axis) 39 We utilized Bacteroides thetaiotaomicron (B. theta) treated with palmitic acid-alkyne (PAA), referred to as B. thetaPAA, as a control to establish the positive gate for AF647 staining. The alkyne-modified portion of PAA resembles the propargyl group found in propargylcholine, which allows us to click-stain the azide-bearing AF647 fluoroprobe to B. thetaPAA. This bacterium is known to metabolize PAA, enabling us to attach the azide-bearing fluoroprobe to PAA, which serves as the positive control for AF647 staining (Fig. 5D). Additionally, a no-stain sample of B. theta was used to set the negative gate for AF647 detection (Fig. 5E). Furthermore, the positive gate for AF647 was adjusted using a vehicle (PBS) control ileum sample to establish the lower boundary of the gate (Fig. 5F). The mixed ileum sample from mice treated with propargylcholine was analyzed using this gating strategy. The negative gate identified 35.7% of the isolated microbial population as non-choline-utilizing, while the positive gate (AF647) revealed that 60.5% of the isolated microbial population was choline-utilizing in the ileum (Fig. 5G). Identification of choline-utilizing bacteria Next, we sought to identify the sorted population of choline-utilizing microbes. To do this, we again followed the described workflow, beginning with treating mice with 2 mg of propargylcholine via oral gavage daily for five days, then performing click staining of the ileal contents (Fig. 6A). In order to sort non-interactors from interactors, we utilized the above-described methodology of flow cytometry. This allowed the differentiation between the two populations of gut microbiota: choline-utilizing bacteria, which were classified as AF647 positive, and the non-choline-utilizing bacteria, classified as AF647 negative (Fig. 6B). Following this initial identification, we utilized fluorescence-activated cell sorting (FACS), as described 40 above, to isolate the choline-utilizing bacteria (Fig. 6C-D). Flow cytometry was repeated on a subsample of both the positive and negative sort, with 97.1% purity in the AF647+ cells and 97.1% purity in the negative cells, confirming the collected samples were free from contamination (Fig. 6C). Finally, we sought to identify the microbial taxa present within the AF647+ sorted cells and the negative sorted cells. To do this, Figure 2.5. Fluorescence-activated cell sorting gating strategy to identify choline- utilizing microbes from the ileum of mice. Flow cytometry plots showing (A) gating strategy to distinguish microbes from debris. SSC-A: Side Scatter Area, FSC-A: Forward Scatter Area. (B&C) gating strategy to distinguish single cells from doublets and isolates. SSC-W: Side Scatter Width, SSC-H: Side Scatter Height, FSC-W: Forward Scatter Width, and FSC-H: Forward Scatter Height. (D) B. theta treated with palmitic acid-alkyne (PAA), referred to as B. thetaPAA, as a control to establish the positive gate for AF647. (E) A no-stain sample of B. theta was used to set the negative gate for AF647. (F) A vehicle (PBS) control ileum sample was used to establish the lower boundary of the gate. (G) Pooled ileum sample from four mice treated with propargylcholine. AF647: fluorescence signal detected from the azide-bearing fluoroprobe (AF647-azide) used to label the choline-utilizing microbes. 41 DNA was extracted from both the AF647 positive and negative samples, and shotgun Figure 2.6. Isolation and identification of choline-utilizing and non-choline-utilizing bacteria. (A) Schematic of the approach to label choline-utilizing bacteria in vivo. Female Swiss Webster mice (n=4) were given 2 mg of propargylcholine via oral gavage daily for five days. The mice were then sacrificed, and the gut microbiome was collected for further analysis. (B) The flow cytometry density plot illustrates two distinct populations of gut microbiome bacteria: the choline-utilizing bacteria, which are classified as AF647 positive, and the non-choline-utilizing bacteria, which are classified as AF647 negative. (C&D) These two bacterial populations were isolated through Fluorescence-Activated Cell Sorting (FACS). Flow cytometry density plots demonstrate the purity of the sorted bacterial populations. (E) Taxa summary of relative abundance of non-choline-utilizing (negative) and choline-utilizing (positive) bacterial species isolated from ileal contents and sorted for AF647 intensity. 42 metagenomic sequencing was performed using the Illumina platform. Within the positive sorted sample, several human intestinal microbiome-associated taxa were identified as choline-utilizers including Limosilactobacillus reuteri, Lactobacillus johnsonii, Lactobacillus intestinalis, Bifidobacterium pseudolongum, and Bacteroides intestinalis among others (Fig. 6E). The negative sorted sample contained taxa which overlapped with the positive sorted sample including Limosilactobacillus reuteri and L. johnsonii, suggesting perhaps strain differences in choline utilization (Fig. 6E). Choline uptake by host tissues The incorporation of dietary choline into various host physiological processes is a critical area of research. While previous studies have demonstrated the incorporation of propargylcholine into mouse organs via intraperitoneal (I.P.) injection 95, they did not explore how propargylcholine is assimilated from dietary sources. Here, we aimed to determine the host uptake and integration of dietary-derived choline into various tissues via the use of our propargylcholine BOSSS workflow. We hypothesized that similar to native choline, dietary propargylcholine would undergo metabolism and be assimilated into peripheral host tissues beyond that of the small intestine. To test this, we administered a controlled dose of 2 mg of propargylcholine to mice through oral gavage over five consecutive days. Following this supplementation, mice were euthanized, the intestine, liver, and kidney were harvested, and cryostat sectioning was done in each tissue. Cryostat tissue sections were stained with the AF647-azide fluorophore to apply the previously described click staining reaction to visualize dietary choline incorporation into peripheral host tissues. Upon imaging the stained tissue sections, we observed significant fluorescence of propargylcholine within the intestine (Figure 4A), liver (Figure 4B), and kidneys (Figure 4C). This fluorescence 43 indicates the incorporation of dietary choline in the form of propargylcholine across Figure 2.7. Imaging of the incorporation of dietary propargylcholine into host tissues. Cryostat sections of Swiss Webster mouse organs were analyzed from two groups: one group received 2 mg of propargylcholine via oral gavage daily for 5 days, and a control group received no propargylcholine. The incorporation of propargylcholine is indicated in red (AF647-azide), while the DNA stain is shown in blue (Hoechst 33342). (A). Small intestine. (B). Liver. (C). Kidney (scale bar = 200 ) 44 these key organs, suggesting effective absorption and metabolism of the compound within the host. and a powerful methodology to also measure the consequences of host dietary choline consumption. Discussion This chapter leveraged the BOSSS workflow to label and identify novel propargyl- modified-choline utilizing microbes in the murine gut microbiome. By treating an in vitro culture of microbes derived from the colon contents of mice with propargylcholine, followed by click ligation of the azide-bearing detection fluoroprobe AF647, we were able to observe via fluorescent microscopy the utilization of propargylcholine by gut bacteria. This work confirmed that the BOSSS workflow is capable of examining the direct interactions between gut bacteria and dietary choline. To investigate this further, we introduced propargylcholine to mice through oral gavage, allowing us to track the utilization of dietary choline by the gut microbiome in vivo. Initially, we focused on method development and determined that the optimal concentration of propargylcholine for feeding the mice was 2 mg per mouse. Additionally, we found that the majority of choline-utilizing microbes are located in the ileum, a section of the small intestine. Our findings are consistent with a previous study that utilized propargylcholine to label intact E. coli in vitro, allowing for the detection of choline incorporation into the outer membrane.94 However, unlike the previous study, we have demonstrated for the first time that dietary propargylcholine can label gut-derived microbes in vivo. To our knowledge, this is the first time click chemistry, a type of biorthogonal reaction, has been leveraged to study dietary choline-gut microbiome interactions. Next, we sought to reveal the identity of the gut microbiome members that utilize dietary choline. To this end, we sorted the choline-utilizing gut microbes from the ileum of mice that were administered 2 mg of propargylcholine via oral gavage. We then extracted 45 their DNA and shotgun metagenomic sequencing was performed. Through this sequencing process, we successfully identified previously unknown dietary choline- utilizing microbes, including several strains with probiotic properties including Limosilactobacillus reuteri (L. reuteri), which could have significant implications for gut health and overall well-being. Additionally, the absorption of dietary propargylcholine was not limited to the gut microbiome; it was effectively used to label the intestine, kidney, and liver in vivo. These results align with a previous study that used propargylcholine to label mouse tissues via IP injection.95 However, this study was able to trace the incorporation of dietary propargylcholine into host tissues instead of through IP injection. Understanding how dietary propargylcholine is utilized by host tissues may provide valuable insights into a new role of dietary choline in health and disease. This study demonstrates the effectiveness of the propargylcholine BOSSS workflow as a tool for conducting a more comprehensive and unbiased approach to characterizing dietary choline utilization by the gut microbiome. Overall, these findings pave the way for further research in the role of microbiome choline utilization and its effects on host health and disease. This study demonstrates the effectiveness of the propargylcholine BOSSS workflow as a valuable tool for conducting a comprehensive and unbiased characterization of dietary choline utilization by the gut microbiome. Overall, these findings open the door for further research into the role of microbiome choline utilization and its effects on host health and disease. 46 Materials and Methods Animal experiments All mouse experiments were conducted in accordance with a protocol approved by the Cornell University Institutional Animal Care and Use Committee (protocol no. 2010- 0065). In vitro staining of colon-derived microbes A colon sample from a female Swiss Webster mouse was homogenized in PBS. The resulting slurry was used to inoculate multiple Petri dishes containing a glass coverslip. The Petri dishes contained 2 ml of media either supplemented with 60 mM of propargylcholine (n=3) or no supplementation as the control group (n=3). Coverslips from each treatment group were collected at 3, 6, and 18 hours. After collection, the coverslips were washed and click-stained to attach an azide-bearing fluoroprobe (AF647-azide). In vivo dietary choline uptake Female, 9-week-old, conventionally raised murine pathogen-free Swiss Webster mice were purchased from Tactonic Bioscience. The mice were randomly assigned to one of two treatment groups: mice fed 2 mg of propargylcholine or vehicle control (PBS) via oral gavage for five days. Mice were housed in four per cage in a climate-controlled environment with 12-hour light and dark cycles and fed a breeder diet (LabDiet 5021) with ad libitum access autoclaved water. For propargyl choline treated mice, mice were given 2mg of propargylcholine dissolved in 100 μL of PBS using a 20-gauge gavage needle (Fine Science Tools). Mice were gavaged once daily with either propargyl 47 choline or PBS for five days. On day five, mice received the final gavage and were then fasted for 3 hours before euthanasia via decapitation. Intestinal contents and tissues were carefully collected and rapidly snap-frozen using liquid nitrogen to preserve their integrity. The samples were then stored at -80 °C, ensuring their long-term stability for subsequent analysis and processing. Isolation and fixation of microbial cells from intestinal content samples. The procedure was modified from the workflow demonstrated in Lee et al. 106 Intestinal contents were removed from -80°C and allowed to thaw at room temperature for 2-3 minutes. 1 ml of sterile PBS was aliquoted directly into the sample tube. Samples were then vortexed until homogenized and sonicated for 10 seconds, alternating 1 second on pulse and 2-second off pulse at amplitude 1 (Qsonica Ultrasonic Processor, model Q700, with a water bath adaptor, model 431C2) to separate the microbiome from debris. The samples were then allowed to settle at room temperature for 2 minutes and centrifuged at 200 g for 30 seconds. The sample was then filtered into a 5 mL round bottom polystyrene test tube with a 35 µm cell strainer snap cap (Falcon,352235). The supernatant was transferred to a new 1.5 mL microcentrifuge tube (VWR, 1615-5500) and centrifuged for 5 minutes at 8,000 g. The bacterial pellets were washed twice with 1% BSA in PBS. The supernatants were discarded, and the bacterial cell pellet was resuspended in 10% formalin solution for 10 min. Bacterial cells were then washed with 1% BSA/PBS, and 0.1% Triton X-100 in PBS was added for permeabilization for 10 min at room temperature. Bacterial cells were washed with 1% BSA/PBS. Cu(I)-catalyzed azide-alkyne cycloaddition staining After the cells were permeabilized and washed, bacterial cells containing propargylcholine were labeled with 5 μM AF647-azide using a Click-&-Go™ click 48 chemistry reaction buffer kit, following the manufacturer's instructions (Click Chemistry Tools, Scottsdale, AZ). A reaction cocktail was prepared by mixing the reaction buffer, copper(II) sulfate, a reducing agent, and 5 μM of AF647-azide. For the bacterial pellets, 500 μL of the reaction cocktail was added, mixed by pipetting, and incubated on a VWR tube rotator (VWR, Cat# 10136-084) for 30 minutes, while protected from light with foil. The cells were then centrifuged for 5 minutes at 8,000 x g. The bacterial pellets were washed five times with 1% BSA in PBS. For mouse tissues, 200 μL of the Click-&-Go reaction cocktail was added per slide. The slides were covered with foil and incubated for 30 minutes at room temperature, also protected from light. Following incubation, the supernatant was discarded, and the tissue slide was washed three times with 1% BSA/PBS. Isolation of choline-utilizing microbes via FACS To isolate propargylcholine using bacteria, a BD Biosciences FACSMelody™ Cell Sorter (BD Biosciences, San Jose, CA) was used. Samples were filtered using a 5 mL round-bottom polystyrene test tube with a 35 µm cell strainer snap cap (Falcon,352235). The AF647-azide dye was excited using a 640 nm red laser, and fluorescence was captured with a 660 nm/20 nm filter. The gating strategy to focus on the choline- utilizing bacteria population included using the Side Scatter Area (SSC-A) and Forward Scatter Area (FSC-A) to distinguish microbes from debris. We then used the Side Scatter Width (SSC-W) and Side Scatter Height (SSC-H) followed by the Forward Scatter Width (FSC-W) and Forward Scatter Height (FSC-H) parameters, to distinguish single cells from doublets and aggregates. The SSC-A and AF647 parameters were employed to identify and gate the two populations of interest: the choline-utilizing gut microbe population (AF647+) and the non-choline-utilizing microbe population (AF647-). B. theta treated with palmitic acid-alkyne (PAA), referred to as B. thetaPAA 49 was used as a control to establish the positive gate for AF647-positive choline-utilizing microbes. A no-stain sample of B. theta and a vehicle (PBS) control ileum sample were used to set the negative gate for AF647 to identify non-choline-interacting microbes. The microbes were then sorted into two different tubes. Cryosection Mouse tissues were harvested after euthanasia and briefly rinsed with PBS to remove residual blood. The tissues were then embedded in optimal temperature cutting (OTC) compound (Tissue-Tek, 4583) within cryomolds and rapidly frozen using liquid nitrogen-cooled isopentane. Embedded samples were stored at -80°C until sectioning. For cryosectioning, frozen tissue blocks were equilibrated to -20°C in a cryostat chamber for 10 minutes before sectioning. Tissues were sectioned at a 10 µm thickness and collected on Superfrost Plus microscopy slides. The slides were fixed in 10% formalin for 10 minutes, rinsed in PBS, and processed for click staining as described above. Fluorescence microscopy The bacterial cell suspension was smeared onto glass slides and allowed to air dry for two minutes. We then added 10 µL of SlowFade Diamond Antifade Mountant with DAPI (Invitrogen™, S36967) and placed a glass coverslip on the sample. Host tissues were click stained as describes above, one drop of SlowFade Diamond Antifade Mountant with DAPI was added and a coverslip was placed on the tissues. The slides were imaged using a Zeiss LSM 710 Confocal or a Leica DM500 fluorescence microscope (Leica, Buffalo Grove, IL). Images were analyzed using Fiji Image J software. 50 DNA extraction The sorted bacterial pellet was transferred into a sterile 1.5 mL Eppendorf tube. The pellet was then resuspended in 475 µL TE buffer and 1.5 µL of ReadyLyse lysozyme and incubated at 37 °C for 1 hour. Then, 25 µL of 10% SDS and 5.4 µL of proteinase k (NEB) were added and incubated at 55°C for 10 minutes. The tube was then incubated on ice for 5 minutes to stop the reaction, spun down, and transferred into screw-top tubes prefilled with 0.1 mm beads, and 65 µL of 5M NaCl was added. The bead-filled tubes were vortexed vigorously for 30 seconds and incubated on ice for 3 minutes. Then 500 µL of phenol: chloroform: isoamyl-alcohol (25: 24: 1) (PCl), pH 8.0 was added, and the tubes were gently inverted 10 times. The samples were centrifuged at max speed for 10 minutes, and the aqueous phase was transferred into a new tube. The same volume taken from the aqueous phase was added to chloroform: isoamyl alcohol (24:1) into the tube and gently inverted 10 times. The samples were centrifuged at max speed for 10 minutes, then the aqueous phase was transferred into a new Eppendorf tube. 0.1 vol 3M sodium acetate, 1 vol ice-cold isopropanol, and 1 µL glycogen were added into the tube and incubated at -20°C overnight for DNA precipitation. The samples were centrifuged at 4°C for 30 minutes at max speed. The supernatant was discarded and washed 3 times with ice-cold 70% EtOH; the pellet was air-dried and resuspended in DNase-free water. Shotgun sequencing The bacterial DNA was extracted as described above. Shotgun metagenomic sequencing library were prepared using the Nextera XT DNA Library Prep Kit (Illumina) with the Nextera XT Index Kit v2 (Illumina) for sample indexing according to manufacturer’s protocol. Sequencing was performed on an Illumina NextSeq 1000 platform using a P2 100-cycle kit, generating unpaired 75 bp reads. Raw sequencing reads were quality filtered using Kneaddata v0.12.0 107 with default parameters to remove low-quality 51 reads and potential contaminants. Taxonomic classification was performed using MetaPhlAn v4.1.1,108 optimized for 75 bp reads, with the --ignore_eukaryotes and -- ignore_archaea options to exclude non-bacterial taxa from classification. Post- classification, taxa assigned as "Archaea," "Viruses," or "Eukaryota" were removed in R. Additionally, taxa classified as "Unclassified" or ambiguous were excluded. The relative abundance of remaining taxa was scaled to 100 at the Species rank, with excluded taxa removed prior to downstream taxonomic summary visualization. Visualizations were performed using custom plot functions developed in R Studio using ggplot2.109 52 CHAPTER 3 MECHANISTIC EXPLORATION OF THE INTERACTIONS BETWEEN DIETARY CHOLINE AND LIMOSILACTOBACILLUS REUTERI AND ITS RELATION TO HOST PHYSIOLOGY Introduction This chapter explores the findings related to Limosilactobacillus reuteri ATCC 23272’s utilization of dietary choline and its subsequent effects on host physiology. It is well established that the gut microbiome's ability to utilize dietary choline has significant implications for host health, particularly through trimethylamine (TMA) production. However, the focus on identifying choline-utilizing bacteria has traditionally been based on their ability to produce TMA. As a result, probiotic bacteria, such as L. reuteri, had previously not been recognized as choline-utilizing microbes, as they lack choline TMA-lyase, the enzyme primarily responsible for converting choline to TMA.24,54 In Chapter 2, we utilized the BOSSS workflow to identify L. reuteri as a novel choline- utilizing bacterium. This microbe is noteworthy because it is a well-studied component of the human gut microbiota and has been examined for its probiotic properties.110 L. reuteri is also found in various dietary probiotic supplements that many use to improve gut health and overall well-being. However, there is currently no understanding of how this supplement might influence the utilization and metabolism of dietary choline. Despite the wealth of studies on this organism, the specific metabolic pathways through which L. reuteri processes dietary choline and the broader implications for host health have not been explored. To address this gap in the literature, we conducted a series of experiments to investigate the metabolic interactions between dietary choline and L. reuteri. We hypothesize that the interaction between L. reuteri and diet-derived affects host physiology through the production of choline-derived metabolites. This chapter aims to address this hypothesis through the following objectives: 53 1. Characterize the effects of dietary choline-L. reuteri interactions on hepatic pathology and gene expression. 2. Identify L. reuteri-dependent choline-derived metabolites that may influence host physiological functions. Significance Although L. reuteri is a well-studied probiotic known for its positive health effects, such as alleviating infant colic111 and modulating immune responses,112 no studies have examined how dietary choline and L. reuteri interactions influence host health. In our research, we aim to characterize the mechanisms involved in the utilization of dietary choline by L. reuteri and assess its functional contributions to the host's physiological responses. By identifying differentially expressed genes due to these interactions, we may uncover novel therapeutic targets for modulating host health. In addition, understanding how these interactions modulate choline metabolism can help us determine the contexts in which L. reuteri supplementation benefits individuals with higher dietary choline intake needs. Furthermore, this research could lead to the discovery of postbiotics from L. reuteri, which are beneficial compounds made by bacteria that can positively affect our health.113 By identifying novel metabolites that L. reuteri makes from dietary choline, we can explore their potential health impacts and consider using them therapeutically as postbiotics. This approach may prove to be a more effective therapy derived from the microbiome since probiotic supplementation often faces challenges related to gut colonization.114 Results Choline utilization by L. reuteri in vitro 54 To verify the ability of L. reuteri to utilize choline, we performed an in vitro culture using de Man, Rogosa, and Sharpe (MRS) media supplemented with 60 mM of propargylcholine. The propargyl group facilitated the detection of propargylcholine utilization by L. reuteri using click chemistry to covalently link an azide-containing 55 fluorophore to any propargyl-containing metabolites. The L. reuteri cell pellet was incubated with an azide-containing detection reagent (AF647-azide) (Fig. 1A), allowing the detection of L. reuteri choline utilization via fluorescent microscopy, indicated by the red fluorescence (Fig. 1B). Using the gating strategy outlined in chapter 2, we found that 99.6% of the L. reuteri population were AF647+ (Fig. 1C), indicating propargylcholine utilization. This gating strategy is used henceforth in all flow cytometry to determine the percent of propargylcholine interactors. Additionally, we confirmed L. reuteri's utilization of choline through mass LC-MS. The detection of propargylcholine utilization by L. reuteri was facilitated by the propargyl group, which allowed us to observe the mass difference (24) between choline and propargylcholine. We cultured L. reuteri in MRS media with either 60 mM propargylcholine or 60 mM native choline. We isolated the bacterial cell pellet to extract Figure 3.1Choline utilization by L. reuteri in vitro. (A) Schematic of the approach to label L. reuteri choline utilization in vitro. L. reuteri was cultured in MRS media supplemented with 60 mM of propargyl-modified-choline (propargylcholine) (n=1). The bacterial cells were then collected and click-stained with AF647-azide. (B) A representative confocal fluorescent microscopy image of L. reuteri cultured in media supplemented with 60 mM of propargylcholine, then isolated and click-stained with an azide-conjugated AF647 detection reagent (red). DNA was stained using a mountant with DAPI (blue). Images were acquired at 100x magnification. Scale bar 20 μm. (C) Flow cytometry density plot of L. reuteri cultured with 60 mM of propargylcholine. The samples were then click-stained with AF647-azide. The x- axis shows the level of AF647 intensity, and the y-axis shows the side scatter (SSC). (D) L. reuteri was cultured in vitro in MRS media supplemented with either 60 mM of propargylcholine or 60 mM of native choline. The cultures were incubated at 37 °C under anaerobic conditions for 18 hours. The L. reuteri cell pellet was isolated, and the metabolome was extracted. Choline utilization was assessed by measuring choline levels in the cell pellet using LC-MS. (E) Extracted ion chromatograms from LC-MS analysis showing the detection of propargylcholine (m/z 128.107) and native choline (m/z 104.107) from L. reuteri cultured in vitro in MRS media supplemented with either 60 mM of propargylcholine or 60 mM of native choline, respectively. The x- axis represents retention time (min), and the y-axis represents ion relative intensity. Chromatograms are shown for L. reuteri cultured with 60 mM of propargylcholine (orange) and 60 mM of native choline (purple), and they peak at 1 minute. 56 L. reuteri’s metabolome (Fig. 1D). Ion chromatograms showed that propargylcholine was present in L. reuteri’s metabolome when cultured with propargylcholine and was absent in those cultured with native choline (Fig 1E). This indicates that exogenous propargylcholine was taken up by L. reuteri in culture. Dietary propargylcholine utilization by L. reuteri in a gnotobiotic mouse model To investigate how L. reuteri metabolizes dietary choline in vivo, we conducted an experiment in which germ-free mice were monocolonized with L. reuteri. The purity 57 of the monoclonization was confirmed by shotgun metagenomic sequencing of stool pellets from mice, which confirmed that the mice were only associated with our microbe of interest (Fig. 2D). To determine whether dietary choline interacts with L. reuteri in vivo, mice were administered 1% propargylcholine via drinking water for 2 days. 58 Afterward, we isolated L. reuteri from the ileal contents of the mice and “click” labeled them AF647-azide (red) (Fig. 2A). Using the previously described gating strategy (see Chapter 2) along with flow cytometry analysis showed that ~45.9% of the L. reuteri population utilized dietary propargylcholine in the monoassociated in vivo model (AF647+) while ~23.7% did not (AF647-). A portion of the L. reuteri population fell in the middle of the two gates, indicating lower AF647 intensity (Figure 2B). Microscopy analysis allowed us to visualize the utilization of dietary choline by L. reuteri in the presorted population, as indicated by the red fluorescence. We sorted the L. reuteri populations into AF647+, choline-utilizing, and AF647-, non-choline-utilizing groups. As expected, we observed positive fluorescence indicating propargyl choline utilization in the sorted AF647+ sample of L. reuteri, while the AF647- L. reuteri population showed no red fluorescence, indicating a lack of choline utilization (Figure 2C). This bimodal population of L. reuteri interactions may be due to limitations of the concentration of propargylcholine available to gut L. reuteri. Additionally, this may indicate differences in functional capacity by L. reuteri even though these microbes all Figure 3.2 L. reuteri utilizes dietary propargylcholine choline in a gnotobiotic mouse model. (A) Schematic of approach to monocolonized mice with L. reuteri and administer propargylcholine. Germ-free mice were monocolonized with L. reuteri via oral gavage and treated with 1% propargylcholine in drinking water for 2 days. (B) A flow cytometry density plot showing a pooled sample of (n=4) of L. reuteri isolated from the ileum of mice treated with propargylcholine and then click- stained with AF647. The x-axis shows the AF647 intensity, and the y-axis is the side scatter (SSC). The AF647 gates were set using the gating strategy described in chapter 2. (C) A representative fluorescence microscopy image of L. reuteri isolated from the cecum of mice treated with 1% propargylcholine then click stained with AF647-azide. The samples were then click-stained with AF647-azide (red) and counterstained with DAPI (blue nuclei). The images show (I) L. reuteri before it was sorted, (II) the sorted choline-utilizing L. reuteri population (AF647+), and (III) the sorted non-choline-utilizing L. reuteri population (AF647-). Images were captured at 64X magnification. Scale bar 20 μm (D) Relative abundance of microbes at the species level in the stool of mice mono-colonized with L. reuteri (n=3). Each bar represents a single sample. Genera comprising < 0.1% relative abundance were grouped as Other” 59 stemmed from the same commercial bacterial stock. Further investigations altering propargylcholine exposure and doing functional analysis between the AF647 positive and negative L. reuteri populations will provide further insight into this observation. Overall, the AF647 population confirms L. reuteri’s ability to utilize dietary choline in vivo. Dietary choline-dependent effects on hepatic physiology To investigate how L. reuteri and dietary choline affect host physiology, four groups of germ-free mice were assigned to different treatments. One group was fed a choline- sufficient diet and mono-colonized with L. reuteri via oral gavage (choline-LR). The second group was also fed a choline-sufficient diet but remained germ-free (choline- GF). The third group was fed a choline-deficient diet and mono-colonized with L. reuteri and (deficient-LR), while the fourth group was fed a choline-deficient diet and remained germ-free (deficient-GF). The choline-sufficient diet contained 1.575 g/kg of choline, and the choline-deficient diet contained no choline or L-methionine (Dyets, Inc.). The mice were administered L. reuteri via oral gavage at the beginning of the experiment and placed on their respective diet treatments for three weeks (Figure 3A). Post-treatment, a portion of the liver was harvested and processed for hematoxylin and eosin (H&E) staining. Brightfield microscopy images of the liver show that the mice in the two groups that were placed on a choline-sufficient diet exhibited normal liver histology. In contrast, the two groups that were placed on a choline-deficient diet demonstrated striking lipid accumulation (white circles) that has been previously documented with the consumption of a choline-deficient diet.115 This hepatic lipid accumulation occurred regardless of the L. reuteri colonization status of the mouse. (Figure 3B). These results suggest that diet is the primary factor driving the observed 60 hepatic phenotypes by H&E staining. Thus, to identify any L. reuteri-specific effect, comparing conditions within the diet treatments would be necessary. To investigate whether the interactions of L. reuteri with dietary choline drove any differences in host choline metabolism, we next measured choline levels in the host's cecal contents, liver, and serum using LC-MS. As expected, the cecal contents of the two groups of mice on a choline-deficient diet (deficient-LR and deficient-GF) had lower choline levels compared to mice on a choline-sufficient diet (choline-GF). Interestingly, we found that the interaction of dietary choline and L. reuteri in the cecum significantly decreased choline levels compared to the choline-GF, deficient-LR, and deficient-LR treatment groups. This result suggests that L. reuteri and dietary choline interactions may modulate choline levels available to the host in the cecum. In the liver, choline levels were not significantly different between the choline-LR when compared with the choline-GF treatment group. There was a significant difference in liver choline levels between choline-GF and both deficient-LR and deficient-GF treatment groups (Fig 3D), affirming that diet is the main driver of choline levels in the liver. The serum showed a significant difference in choline levels in the choline-LR treatment group when compared to the choline-GF, deficient-LR, and deficient-GF treatment groups (Fig 3E). These results indicate that interactions between L. reuteri and dietary choline influence host choline metabolism, as evidenced by the varying choline levels in the cecum and serum. These findings also demonstrate that dietary choline itself is the primary factor influencing choline metabolism. To investigate possible mechanisms behind these observations, we conducted RNA sequencing on the livers of the previously mentioned treatment groups. Principal component analysis (PCA) was performed on variance-stabilized transformed liver gene expression data, using the choline-LR group as the reference condition. The first 61 principal component (PC1) explains 67% of the total variance, while the second principal component (PC2) accounts for 18%. A distinct separation of the choline and deficient groups can be observed along PC2, which accounts for the majority of the variance within the gene expression data. This observation is congruent with findings 62 from the hepatic histology, indicating that diet is the primary factor influencing phenotypic differences (Fig. 3C). Thus, we observe that in order to understand the specific contributions of L. reuteri to the host's state, it is essential to compare expression changes within the same diet. L. reuteri and dietary choline-dependent effects hepatic differential gene expression To determine differential gene expression due to L. reuteri and dietary choline interactions, we focused on determining significant hepatic expression differences between differently colonized mice placed on a choline-sufficient diet (+choline) (Fig.4 A). We generated a heatmap of differentially expressed genes comparing the choline- GF (n=4) and choline-LR (n=3) conditions displaying the top differentially expressed genes (p-value <0.05). Variance-stabilizing transformation (VST) was applied to the Figure 3.3 Dietary choline-dependent effects on hepatic physiology. (A) Schematic of experimental design. Germ-free mice were kept germ-free or mono-colonized with L. reuteri via oral gavage and placed on a choline-sufficient diet (+choline) or a choline-deficient diet (-choline) for 3 weeks (n=4). Mice were euthanized, and samples were then analyzed. (B) Representative brightfield images of hematoxylin and eosin (H&E) stained liver sections from mice in the four treatment groups described above. Hematoxylin stains nuclei blue, and eosin stains cytoplasm and extracellular matrix pink. Fat accumulation appears as clear round spaces within hepatocytes (white circles), indicative of hepatic steatosis. Images were captured at 20X magnification. Scale bars are shown in each image. Scale bar = 100 μm. (C-E) The area under the curve (AUC) of choline was detected via LC-MS across different treatment groups in the (C) cecum, (D) liver, and (E) serum. The x-axis represents the treatment conditions, and the y-axis shows the integrated AUC from extracted ion chromatograms, reflecting relative abundance. Data are presented as mean +/- SEM. Statistical Significance was determined using one-way ANOVA followed by Tukey’s multiple comparison test *p < 0.05, **p<0.005, ***p<0.0005, ****p<0.00005. (GraphPad Prism, v10.4.1) (F) Principal Component Analysis (PCA) was performed on variance-stabilized transformed liver gene expression data, using the choline-LR group as the reference condition. Each point in the plot represents an individual mouse sample, with its position determined by the scores on the first two principal components (PC1 and PC2). PC1 explains 67% of the total variance, while PC2 accounts for 18%. 63 count data, followed by row-wise z-score normalization across samples to display scaled expression values for each gene. Hierarchical clustering revealed a separation between the choline-GF and choline-LR-treated mice, with choline-LR treated samples exhibiting up-regulation in lipid and fatty acid metabolism genes. Notably, genes such as stearoyl-CoA desaturase 1 (Scd1), acetyl-CoA carboxylase alpha (Acaca), and fatty acid synthase (Fasn) were significantly upregulated in response to L. reuteri mono- colonization, suggesting that L. reuteri plays a role in modulating host lipid metabolism. L. reuteri-dependent upregulation of lysophosphatidylcholine in the host Building on our understanding that dietary choline-L. reuteri interactions upregulate genes related to host lipid metabolism, we aimed to illuminate the functional changes in host metabolism driven by these interactions. To this end, we set out to identify L. reuteri-dependent choline-derived metabolites in the host that may contribute to altered host metabolism. To identify the metabolites that were specifically upregulated in the choline-LR group, we conducted an untargeted comparative metabolomics experiment, focusing on those metabolites that were elevated solely in the choline-LR treatment group when compared to the choline-GF, deficient-LR, and deficient-LR treatment groups. This experimental design allowed us to determine novel metabolites that were dependent on both the presence of choline in the diet and the presence of L. reuteri in the gut microbiome. This experimental design is significant for thinking about targeted microbiome therapeutics because it demonstrates that a knowledge of both dietary input and microbial interaction is necessary to have a defined metabolic effect on the host. 64 Figure 3.4 Significantly differentially expressed hepatic genes in response to L. reuteri and dietary choline interactions. Heatmap of the top differentially expressed genes in the liver between mice fed a choline-sufficient diet that are germ- free (choline-GF) and mice that were fed a choline-sufficient diet and were mono- colonized with L. reuteri (choline-LR) conditions in the liver. The differentially expressed genes identified on p-value < 0.05 from the DESeq2 comparison of choline-GF vs choline-LR. Variance-stabilizing transformation (VST) was applied to the count data, followed by row-wise z-score normalization. Samples are annotated by treatment condition (choline-LR: purple, choline-GF: blue). Rows represent the genes, and columns represent individual samples. The color scale represents the scaled expression values, with red indicating upregulation and blue indicating downregulation and yellow indicates no difference between groups. 65 From this analysis, we were able to determine that multiple choline-containing lipids were upregulated in response to dietary choline and L. reuteri colonization. For this, we quantified the area under the curve (AUC) for the metabolite masses detected in the cecal contents of the mice based on extracted ion chromatograms from LC-MS analysis. The results demonstrated that choline-LR treated mice had significantly elevated levels of several LPC species, including LPC (16:1) (Fig. 5A), LPC (18:2) (Fig. 5B), LPC (20:4) (Fig. 5C), and LPC (18:1) (Fig. 5D). LPC is a type of phospholipid that is involved in cellular signaling116, membrane dynamics87, and inflammation.117,118 These results show that L. reuteri dietary choline interactions lead to the upregulation of LPCs in the host, highlighting an involvement in lipid metabolism. L. reuteri produces a novel choline-derived metabolite which is also detected in the liver of choline fed L reuteri monocolonized mice To identify choline-derived metabolites produced exclusively by L. reuteri, we employed the mass spectrometry portion of the BOSSS workflow described in Chapter 1. We utilized the mass difference of the propargyl group (24) in propargylcholine to compare it with native choline, enabling us to find propargylcholine L. reuteri-derived metabolites. Identifying a mass unique to the propargylcholine state indicates a propargyl-derived metabolite. To achieve this, L. reuteri was cultured overnight in MRS media supplemented with either 60 mM of propargylcholine or 60 mM of native choline. Afterward, the L. reuteri cell pellets were isolated. The L. reuteri cell pellet metabolome was extracted and analyzed using a 30-minute detection method with LC-MS (Fig. 6A). The extracted ion chromatograms show the detection of a propargyl choline-derived metabolite produced by L. reuteri in vitro of m/z 338.305 at 11 minutes retention time, 66 shown in purple. No signal was detected in the L. reuteri sample treated with native Figure 3.5 L. reuteri-dependent upregulation of multiple species of lysophosphatidylcholine. The abundance (AUC) of lysophosphatidylcholine (LPC) species in the cecal contents of mice. (A) 16:1, (B) 18:2, (C) 20:4, and (D) 18:1 was detected via LC-MS in the cecum of mice. These mice were either monocolonized with L. reuteri or remained germ-free and were placed on a choline-sufficient diet (+choline) or a choline-deficient diet (-choline). The x-axis represents the treatment conditions, and the y-axis shows the integrated AUC from extracted ion chromatograms, reflecting abundance. Data are presented as mean +/- SEM. Statistical Significance was determined using one-way ANOVA followed by Tukey’s multiple comparison test *p < 0.05, **p<0.005, ***p<0.0005, ****p<0.00005. 67 choline, which is shown in blue (Fig. 6B). Because the metabolite found was only present in the propargylcholine-treated sample, this indicated that L. reuteri makes this metabolite from propargylcholine. Given the discovery of this propargylcholine-derived metabolite produced by L. reuteri, we next sought to identify if this metabolite was present in mice monocolonized with L. reuteri. To investigate this, we monocolonized germ-free female Swiss Webster mice with L. reuteri via oral gavage and administered either 1% propargylcholine or 1% native choline via their drinking water. After two days, the mice were sacrificed, and their livers were collected. The liver metabolome was then extracted and analyzed using a 50-minute detection method with LC-MS (Fig. 6C). The extracted ion chromatograms show the detection of a propargylcholine-derived metabolite m/z 338.305 at 17 minutes retention time, shown in purple and no signal was detected in the sample treated with native choline, which is shown in blue (Fig. 6D). While we did detect a metabolite with the same mass (m/z 338.305, rt=17 min) in the liver metabolome as was found in the in vitro L. reuteri cell experiment (m/z 338.305, rt=11 min), the two experiments were analyzed using different detection methods, with analysis times of 30 minutes and 50 minutes, respectively. This difference in detection method expectedly resulted in the m/z 338.305 metabolite appearing at different retention times (in L. reuteri, rt = 11 min; in the liver, rt = 17 minutes). To ensure that the metabolite we observed in the liver was the same as in the in vitro L. reuteri sample, we analyzed a pooled liver sample (n=2) using the 30-minute detection method to analyze the L. reuteri cell pellet. The extracted ion chromatograms show the detection of a propargylcholine-derived metabolite m/z 338.305 at 11 minutes retention time in 68 the liver, shown in purple (Fig. 6E). This demonstrates that the metabolite produced by L. reuteri in vitro is also detected in the liver of mice monocolonized with L. reuteri. Preliminary estimations of the chemical structure suggest that this molecule may have a lipid-containing choline structure, which indicates significant bioactive functions similar to other choline-containing lipids, such as lysophosphatidylcholine. 69 Discussion This chapter explored the impact of L. reuteri-dietary choline interactions on host physiology. Our study validated that L. reuteri can utilize propargylcholine when cultured in MRS media supplemented with this compound. This finding is particularly significant because it reveals that the probiotic L. reuteri, despite the absence of the cutC gene cluster associated with microbial choline metabolism,53 has the metabolic capabilities to utilize choline effectively. Figure 3.6 A metabolite derived from choline produced by L. reuteri in vitro is detected in the liver of L. reuteri monocolonized choline fed mice. (A) L. reuteri was cultured overnight in MRS media supplemented with 60 mM of propargylcholine. Afterward, bacterial cell pellets were isolated, and their metabolome was extracted. (B) Extracted ion chromatograms from LC-MS analysis show the detection of a propargyl choline-derived metabolite produced by L. reuteri in vitro at m/z 338.305, shown in purple, while an L. reuteri sample treated with native choline is shown in blue as a control. The x-axis displays retention time, and the y-axis indicates ion intensity. The samples were run using a 30- minute detection method using mass spectrometry. (C) Germ-free female Swiss Webster mice were mono-colonized with L. reuteri and then given 1% propargyl choline via their drinking water. After 2 days, the mice were sacrificed, and their liver was collected. The liver metabolome was extracted. (D) The ion chromatogram of the metabolome from L. reuteri monocolonized mice treated with propargylcholine (n=1) shows retention time on the x-axis and relative intensity on the y-axis. A metabolite detected in the liver sample of mice treated with propargylcholine is indicated in purple, while the control group, consisting of L. reuteri monocolonized mice treated with native choline, is represented in blue. The samples were run using a 50-minute detection method using mass spectrometry detection method (E). The Ion chromatogram of a pooled liver sample from L. reuteri monocolonized mice treated with propargylcholine (n=4) shows retention time on the x-axis and relative intensity on the y-axis. A metabolite detected in the liver sample of mice treated with propargylcholine is indicated in purple. The samples were run using a 30-minute mass spectrometry detection method. 70 We also examined how effectively L. reuteri can utilize dietary choline in vivo. To do this, we mono-colonized germ-free mice with L. reuteri and gave them 1% propargylcholine in their drinking water. We utilized fluorescent microscopy images to observe the utilization of dietary choline by L. reuteri. Additionally, we assessed the extent of choline utilization through flow cytometry, finding that L. reuteri does utilize propargylcholine in vivo. Interestingly, a portion of the L. reuteri population residing in the small intestine did not utilize choline. This observation raises important questions about the mechanisms involving dietary choline and L. reuteri interactions that lead to differing choline utilization within the L. reuteri population. A possible explanation for the variability in dietary choline utilization includes limited bioavailability of dietary choline due to rapid host absorption of dietary choline.119,120 These findings show that L. reuteri can directly metabolize propargylcholine in vitro and dietary propargylcholine in vivo, thus contributing to the host's dietary choline biochemical landscape. Liver histology revealed that diet was the main driver of the development of fatty liver disease. These results are consistent with previous findings linking choline deficiency with the development of fatty liver disease.44 Interestingly, the presence of L. reuteri did not exhibit protective effects against the development of fatty liver disease when placed on a choline-deficient diet. This contradicts previous studies suggesting that L. reuteri can improve fatty liver disease.121,122 In addition, principal component analysis revealed clustering by diet treatment, with PC1 accounting for 67% of the total variance. Mice treated with choline-sufficient diets formed a separate cluster from those treated with a choline-deficient diet. This separation indicates that diet is the main driver in hepatic gene differentiation. Furthermore, we observed that L. reuteri-dietary choline interactions decrease the levels 71 of choline detected in the cecum. This result might initially suggest that L. reuteri is sequestering choline, thus decreasing host choline bioavailability. However, we found that choline levels were upregulated in the serum of these mice, indicating that L. reuteri-dietary choline interactions actually led to increased circulatory choline. In addition, contrary to our finding that the choline-utilizing microbe L. reuteri increases circulatory choline levels in the serum, previous research has indicated that choline- utilizing bacteria encoding the cutC/cutD gene cluster compete with the host for this choline, thereby reducing choline bioavailability in the serum.123 These findings suggest that L. reuteri could potentially be used to increase circulatory choline levels to aid individuals with greater choline requirements, such as pregnant and lactating women.38 This highlights that our powerful methodology provides a broader understanding of the metabolism of dietary choline by the gut microbiome. This work could provide insights into gut microbiome choline metabolism and help tailor dietary guidelines to individual needs, potentially improving health outcomes on a more personalized level. Furtheremore, RNA sequencing data revealed 31 differentially expressed genes when comparing L. reuteri-treated mice with germ-free mice, both of which were on a choline-sufficient diet. Interestingly, Tmprss2, a gene that helps the SARS-CoV-2 virus fuse with host membranes, allowing it to enter and infect human cells, is downregulated in mice monocolonized with L. reuteri. This is significant because current research efforts are attempting to find molecules that show inhibitory activity against Tmprss2 to reduce viral infection.124 Our work indicates that L. reuteri could be leveraged to inhibit the expression of Tmprss2 in effort to reduce SARS-CoV-2 viral infection. One of the most significant findings in our study was that interactions between L. reuteri and dietary choline influence the host's lipid metabolism. Notably, genes involved in 72 hepatic lipid metabolism, such as stearoyl-CoA desaturase 1 (Scd1), acetyl-CoA carboxylase alpha (Acaca), and fatty acid synthase (Fasn), were upregulated in mice treated with choline. Scd1 is involved in the conversion of saturated fatty acids to monounsaturated fatty acids (MUFAs). For example, it can introduce a double bond to stearic acid (18:1) and palmitic acid (16:0) and convert them to oleic acid (18:1) and palmitoleic acid (16:1), respectively.125 Both of these MUFAs were observed in the LPCs species that were upregulated in the cecum of mice due to L. reuteri and dietary choline interactions. The Acaca gene encodes acetyl-CoA carboxylase alpha (ACCα), a key regulatory enzyme in fatty acid biosynthesis. ACCα catalyzes the carboxylation of acetyl-CoA to form malonyl-CoA.126 Malonyl-CoA is used by fatty acid synthase, encoded by the gene Fasn, to make long-chain fatty acids.127 Fatty acids are essential building blocks of lipids, which are crucial for various biological functions.128 For instance, fatty acids are the building blocks of lipid structures, including phosphatidylcholine, an important component of cell membranes that helps maintain proper cell structure and influences membrane fluidity and permeability.129 Fatty acids also act as signaling molecules, influencing various biological processes that help maintain overall health.130 These differential patterns indicate that the presence of L. reuteri may trigger host lipid transcriptional programs, thus affecting host physiological processes. To better understand the mechanisms behind the transcriptional responses resulting from interactions between L. reuteri and dietary choline, we aimed to investigate the metabolic consequences of these interactions. We performed untargeted LC-MS-based profiling of the cecal contents of the previously mentioned mice. Comparative analysis revealed that several species of LPC were significantly upregulated due to L. reuteri and dietary choline interactions. The elevation of LPC species in response to L. reuteri and 73 dietary choline suggests alternative choline transformation routes, which may carry different physiological consequences. Existing literature shows that microbial-derived LPC 79. However, our findings show that LPC is also present at lower levels in the germ-free treatment groups. This indicates that the increase in LPC caused by L. reuteri likely relies on a metabolic process derived from the host. This highlights how L. reuteri can influence host biological processes. Research has shown that elevated levels of LPCs can affect inflammation and lipid accumulation.131 To identify metabolites derived from L. reuteri derived from choline, we performed comparative metabolomics using MS1 high-resolution LC-MS on cultures of L. reuteri supplemented with either propargylcholine or native choline. We identified four choline-derived metabolites produced by L. reuteri in vitro. Surprisingly, these metabolites were also observed in the liver of mice monocolonized with L. reuteri, suggesting that these choline-dependent L. reuteri-derived metabolites could be involved in host physiological processes. These findings highlight the complexity of L. reuteri choline metabolism in the gut and underscore the need for unbiased methodologies to identify choline utilizing microbes beyond genomic predictions to assess microbial contributions to host nutrient processing. In summary, this work expands the landscape of known choline-utilizing bacteria and includes the probiotic L. reuteri as a choline-utilizing microbe. L. reuteri was shown to modulate host physiological processes and produce choline-derived metabolites detected in the host. This study sets the stage for future research to explore new ways previously uncharacterized choline-utilizing microbes utilize dietary choline, identify the dietary choline-derived metabolites they produce, and explore how these 74 interactions affect host health. In the long run, a deeper understanding of these pathways could help guide the development of microbiome-based strategies to fine-tune choline metabolism and promote better health outcomes. Materials and Methods All mouse experiments were performed according to a protocol approved by the Cornell University Institutional Animal Care and Use Committee (protocol no. 2010-0065). In vitro cultures of L. reuteri with propargyl choline A glycerol stock of L. reuteri ATCC 23272 was cultivated overnight in MRS broth media (Millipore, 69966) at 37°C under anaerobic conditions. Following this, L. reuteri OD600 was adjusted to an optical density 0.5. Subsequently, 20 mL of MRS media supplemented with 60 mM of propargyl-Cho was inoculated with 0.5 mL of the overnight culture of L. reuteri and incubated overnight at 37°C under anaerobic conditions. Isolation and fixation of L. reuteri from ex vivo cultures. After incubating with 60 mM MRS media, the L. reuteri culture was transferred to a 50 mL centrifuge tube (VWR, 89039-660) and centrifuged for 5 minutes at 8,000 g. The cell pellet was then resuspended in 1 mL of PBS and transferred to a new 1.5 mL microcentrifuge tube (VWR, 1615-5500), followed by another round of centrifugation for 5 minutes at 8,000 g. The bacterial pellets were washed twice with 1% BSA in PBS. The supernatants were discarded, and the bacterial cell pellet was resuspended in a 10% formalin solution for 10 minutes. The bacterial cells were subsequently washed with 1% BSA/PBS, and 0.1% Triton X-100 in PBS was added to permeabilize the cells for 10 minutes at room temperature. Finally, the bacterial cells were washed again with 1% BSA/PBS. 75 Gnotobiotic husbandry Experimental diets were sterilized by irradiation (50 kGy) and packed in small bags of 1kg to ensure sterility. Four-week-old germ-free Swiss Webster mice were purchased from Taconic Bioscience, aseptically transferred to sterile micro isolator cages, and housed in an ISO rack. Germ-free mice were put on the experimental diet the same day as L. reuteri colonization. The sterility of germ-free animals was assessed by incubating fecal pellets under aerobic and anaerobic conditions on BHIS plates. Preparation of L. reuteri for Mouse Colonization L. reuteri was cultured anaerobically in MRS media overnight at 37 °C, after which the culture was adjusted to an optical density of 0.5 at 600 nm (OD600). Bacterial pellets were then washed twice with pre-warmed sterile PBS. Four-week-old germ-free mice were administered 100 µL of L. reuteri suspended in PBS via oral gavage. To verify the colonization of L. reuteri, fecal pellets were collected and incubated in MRS media overnight at 37 °C after one week. Propargyl-choline uptake by L. reuteri in germ-free mice Female, 4-week-old, germ-free Swiss Webster mice were randomly assigned to one of four treatment groups. The groups included mice that were fed a choline-sufficient diet (Dyets, 519595) and colonized with L. reuteri (ATCC, 23272), mice that were fed a choline-sufficient diet and were maintained on a germ-free colonization status, mice that were fed a choline-deficient diet (Dyets, 518810) and colonized with L. reuteri, and mice that were fed a choline-deficient diet and maintained on a germ-free colonization status. Mice were housed four per cage in a climate-controlled environment with 12- hour light and dark cycles with ad libitum access to autoclaved water. After 3 weeks, 76 mice fasted for 3 hours and were euthanized via decapitation. Intestinal contents and tissues were collected, snap-frozen using liquid nitrogen, and stored at -80 °C until further processing. For propargylcholine-treated mice, mice were given 1% of propargylcholine in their drinking water and a choline-deficient diet for three days. The mice were then fasted for 3 hours and euthanized via decapitation. Intestinal contents and tissues were collected, snap-frozen using liquid nitrogen, and stored at -80 °C until further processing. Isolation and fixation of L. reuteri from intestinal content samples. The procedure was modified from the workflow demonstrated in Lee et al. 106. Intestinal contents were removed from -80°C and allowed to thaw at room temperature for 2-3 minutes. Then, 1 mL of sterile PBS was added directly into the sample tube. The samples were then vortexed until homogenized and sonicated for 10 seconds, using an alternating pattern of 1 second on and 2 seconds off at an amplitude of 1 (Qsonica Ultrasonic Processor, model Q700, with a water bath adaptor, model 431C2) to separate the microbiome from the debris. After sonication, the samples were allowed to settle at room temperature for 2 minutes and were centrifuged at 200 g for 30 seconds. The resulting supernatant was filtered into a 5 mL round-bottom polystyrene test tube using a 35 µm cell strainer snap cap (Falcon, 352235). The filtered supernatant was then transferred to a new 1.5 mL microcentrifuge tube (VWR, 1615-5500) and centrifuged for 5 minutes at 8,000 g. The bacterial pellets were washed twice with 1% BSA in PBS. After washing, the supernatants were discarded, and the bacterial cell pellet was collected. Fluorescence microscopy 77 The bacterial cell suspension was smeared onto glass slides and 10 µL of SlowFade Diamond Antifade Mountant with DAPI (Invitrogen™, S36967). Slides were imaged using a Zeiss LSM 710 Confocal or a Leica DM500 fluorescence microscope (Leica, Buffalo Grove, IL). Images were analyzed using Fiji Image J software. Fluorescence-activated cell sorting to identify propargyl choline utilizing bacteria. The general procedure was adapted from the BOSSS workflow demonstrated in Lee et al.106 To isolate propargylcholine using bacteria, a BD Bioscience FACSMelody™ Cell sorter (BD Biosciences, San Jose, CA) was used. Samples were filtered using a 5 mL round-bottom polystyrene test tube with a 35 µm cell strainer snap cap (Falcon,352235). The AF647-azide dye was excited using a 640 nm red laser, and fluorescence was captured with a 660 nm/20 nm filter. Liver histology Liver samples were harvested, stored in 10% formalin, and held at 4°C for 24 hours. They were placed in cassettes and then transferred to 70% ethanol. The cassettes were submitted to the Cornell Animal Health Diagnostic Center (AHDC) for sample processing and H&E staining. RNA sequencing and analysis Total liver RNA was extracted using TRIzol reagent (Invitrogen, Cat. No. 15596026) according to the manufacturer’s instructions. Isolated RNA samples were submitted to the Cornell Institute of Biotechnology Genomics Facility for RNA integrity quality control and sequencing. RNA libraries were prepared using the QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, Vienna, Austria) following the manufacturer’s protocol. 78 Sequencing was performed on the Illumina NextSeq500 platform, generating 75 bp single-end reads. Raw sequencing reads underwent initial quality assessment using FastQC (v0.12.1).132 Adapter trimming and removal of low-quality bases were performed using Trimmomatic (v0.39)133 with the parameters SLIDINGWINDOW:4:20 MINLEN:35. The Mus musculus reference genome (GCF_000001635.27, GRCm39)134 was indexed using STAR (v2.7.10b)135 with the options --sjdbOverhang 100 -- sjdbGTFtagExonParentTranscript Parent. Trimmed reads were then aligned to the indexed genome using STAR, generating BAM files sorted by coordinate (-- outSAMtype BAM SortedByCoordinate). Aligned reads were quantified using HTSeq (v2.0.2)136 with htseq-count and the options -s no -r pos -t exon -i pacid -f bam to produce count matrices for downstream differential gene expression analysis. Differential Expression Differential expression analysis was conducted using DESeq2137 in R Studio. A DESeqDataSet was created from the count matrix and associated sample metadata, specifying the experimental condition as the design formula (~ condition). The reference level for the condition variable was set to either "CGF", “CDGF”, “CLR”, or “CDLR” using relevel (). Differential expression analysis was carried out using the DESeq () function. Results were extracted using the results () function, and log-fold change shrinkage was applied using the apeglm method through lfcShrink(). Differentially expressed genes were ranked by p-value, and significant genes were filtered using an adjusted p-value threshold of < 0.05. 79 To annotate the results, Ensembl gene IDs were mapped to gene symbols using the biomaRt package,138 with annotations retrieved via the useEnsembl () function for Mus musculus.139 Metabolomics Cecal and Liver Samples Frozen cecal and liver samples were lyophilized overnight to remove water content. A dry weight-based normalization approach was used to normalize metabolite extraction across biological samples. The dry weights of all samples were recorded, and the sample with the lowest weight was designated as the reference sample. A fixed volume of 1500 μL of extraction solvent (methanol) was added to all samples, regardless of weight. Homogenization was performed to ensure complete solubilization of cecal contents. To account for differences in sample mass, a normalized volume of homogenate to extract was calculated based on the reference sample's dry weight using the formula: Normalized volume (μL) = (Reference sample dry weight×1500) / (Sample dry weight) Lipids were extracted overnight and transferred to the SpeedVac concentrator. After drying in the SpeedVac, samples were reconstituted in 100 μL of methanol, sonicated, centrifuged, and 80 μL of the supernatant was transferred to LC-MS vials for analysis. Serum Samples Serum samples were normalized by volume prior to extraction. A fixed 100 μL of each sample was aliquoted into microcentrifuge tubes, flash-frozen in liquid nitrogen, and lyophilized overnight. The dried serum metabolome was reconstituted in 100 μL of methanol, sonicated, and lipids extracted overnight. Samples were transferred to the SpeedVac concentrator to remove solvent. The dry metabolome was then reconstituted 80 in 50 μL of methanol, sonicated, centrifuged, and 30 μL of the supernatant was transferred to LC-MS vials for analysis. L. reuteri Cell Pellet Samples L. reuteri cultures were harvested, washed with PBS, and pelleted by centrifugation. To normalize sample input for metabolomic extraction, bacterial biomass was quantified by flow cytometry. Each sample's event rate (events/sec) was measured using a BD FACS Melody flow cytometer. The sample with the lowest event rate was designated as the reference sample. The normalized solvent volume to add to the cell pellets for extraction was calculated using the formula: Normalized extraction volume (μL) = (Reference sample event rate/Sample event rate) × Reference extraction volume. This approach ensured that an equivalent number of bacterial cells were extracted across all samples for downstream metabolomic analysis. Following normalization, lipids were extracted overnight in methanol. The samples were then dried using a SpeedVac concentrator. The dried metabolome was reconstituted in 1000 μL of methanol, sonicated, and centrifuged to remove particulates. Finally, 80 μL of the supernatant was transferred to LC-MS vials for analysis. Metabolomics Analysis Samples were run on a Thermo Scientific Vanquish Horizon UHPLC System coupled with a Thermo Scientific Q Exactive mass spectrometer for both MS1 and MS2 analyses. MS2 analysis of mouse samples was performed on a Thermo Scientific Vanquish Horizon UHPLC System coupled with a Thermo Scientific Q Exactive HF Orbitrap mass spectrometer. Ms1 analysis of mouse samples was performed on a Thermo Scientific UltiMate 3000 HPLC System coupled with a Q Exactive. 3 μL 81 metabolome extract was injected for MS1 analyses, and 8 μL extract was injected for tandem mass spectrometry analyses. For liquid chromatography, a Kinetex EVO C18 column (150 mm x 2.1 mm, 1.7 μm, Phenomenex, part number 00F-4726-AN) maintained at 40°C was used. Solvent A was 0.1% formic acid in water, and solvent B was 0.1% formic acid in acetonitrile. For the 32-min LC-MS method, the A/B gradient at a flow rate of 0.5 mL/min started at 10% B for 2 min, increased linearly to 100% B at 20 min, was held at 100% B for 8 min, decreased linearly to 10% B at 28.1 min and was held at 10% B until 32 min. For the 50-min LC-MS method, the A/B gradient at a flow rate of 0.3 mL/min started at 10% B for 2 min, increased linearly to 100% B at 32 min, was held at 100% B for 16 min, decreased linearly to 10% B at 48.1 min and was held at 10% B until 50 min. Mass spectrometer parameters were as follows for MS1 analyses: positive mode; HESI source; spray voltage of 3.5 kV; capillary temperature at 380°C; prober heater temperature at 400°C; sheath, auxiliary, and spare gases set to 60, 20, and 1, respectively; S-lens RF level at 50; resolution 140,000 at m/z 200; AGC target of 3e+06; and max ion injection time of 200 ms. The m/z scan range was 100 to 1000. Tandem mass spectrometry was carried out with an inclusion list of select features using a PRM method utilizing the same parameters as above with the following additions or adjustments: resolution 60,000 at m/z 200; AGC target of 5e+05; max ion injection time of 80 ms; isolation window of 0.7 m/z; stepped normalized collision energies at 25, 35 and 45. The Q Exactive was calibrated with Pierce calibration solutions from Thermo Fisher Scientific. Raw data files were converted to MZML files using the ProteoWizard MSConvert GUI.54 For untargeted metabolomics analysis of metabolites, peak detection and integration was performed on MZML files using MZmine (v.4.1.0). The aligned feature list exported from MZmine was imported into R, where statistical analyses were performed to identify choline-derived metabolites and metabolites enriched in L. reuteri-colonized choline- 82 sufficient mice. Peak area differences were validated by performing targeted metabolomics analysis and re-integrating peaks in Skyline. 83 CHAPTER 4 CONCLUSION AND OUTLOOK Conclusion This dissertation aimed to identify novel interactions between the gut microbiome and dietary choline. It focused on utilizing our BOSSS workflow, which employed propargylcholine to conduct an unbiased screening of choline-utilizing bacteria found in the gut microbiome. The findings presented here contribute to our understanding of microbiome-driven choline metabolism and its influence on host physiology. By utilizing the BOSSS workflow, which incorporates properties of click chemistry, fluorescent imaging, and flow cytometry, we discovered previously unknown members of the gut microbiome that can utilize dietary choline, including the probiotic L. reuteri. These findings highlight the significance of the BOSSS workflow with propargylcholine in uncovering a more intricate network of choline metabolism within the microbiome, adding insight to ongoing discussions about choline metabolism and health. In addition, through a combination of functional, genomic, and metabolomic analysis, this study characterized the effects of dietary choline-L. reuteri interactions on host physiology, such as the increase in choline levels in host circulation, differential expression of genes in the liver involved in lipid metabolism and the upregulation of LPCs in the cecum. These results underscore the significant role of choline utilization by the previously uncharacterized members of the gut microbiome in mediating physiological effects on the host. This work opens the door to future research focused on understanding the multifaceted effects that the gut microbiome's utilization of dietary choline on host physiology. For example, we have yet to explore how all the choline-utilizing microbes identified in this 84 study, aside from L. reuteri, metabolize dietary choline and the effects that this metabolism has on host health. In addition, the precise molecular mechanism by which L. reuteri drives the upregulation of genes involved in immune and lipid metabolism functions and leads to the upregulation of host LPCs is not understood. Outlook The findings of this dissertation pave the way for several exciting future directions. To begin, understanding the molecular mechanisms used by L. reuteri to elevate choline levels in the host's circulatory system is essential for clarifying L. reuteri's role in choline metabolism and absorption. This would determine if the supplementation of L. reuteri may assist those with increased choline dietary needs, such as pregnant and lactating women. This research may lead to tailored dietary suggestions for choline, determined by an individual's specific microbiome composition. In addition, future research should explore how the different LPC species, which were shown to be upregulated through the interactions between L. reuteri and dietary choline, impact physiological processes like immune responses and cell membrane dynamics. With the increasing interest in personalized medicine and nutrition, it's crucial to evaluate the implications of microbiome-based interventions within clinical settings. Regulating choline metabolism to influence the gut microbiome may create new possibilities in precision medicine, such as preventing metabolic diseases.140. In summary, this work uncovers a previously unexamined metabolism of dietary choline utilization within the gut microbiome. This has significant implications for future research and clinical practice. 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Mus musculus GRCm39. https://www.ensembl.org/Mus_musculus/Info/Index. 140. Hur, K.Y., and Lee, M.S. (2015). Gut Microbiota and Metabolic Disorders. Diabetes Metab J 39, 198-203. 10.4093/dmj.2015.39.3.198. https://www.ensembl.org/Mus_musculus/Info/Index BIOGRAPHICAL SKETCH ACKNOWLEDGMENTS LIST OF FIGURES LIST OF ABBREVIATIONS CHAPTER 1 Gut Microbial Metabolism Provides Functional Contributions to Host Biology Microbial Metabolites: Mediators of Host-Microbe Interactions Diet as a Modulator of Gut Microbiome Function Dietary choline: An Essential Nutrient with Known Interactions with the Gut Microbiome Preview of Chapters CHAPTER 2 Abstract Introduction Results Discussion Materials and Methods CHAPTER 3 Introduction Results Discussion Materials and Methods CHAPTER 4 Conclusion Outlook References