Variations in microbiota composition of laboratory mice influence Citrobacter rodentium infection via variable short-chain fatty acid production

Osbelt, Lisa; Thiemann, Sophie; Smit, Nathiana; Lesker, Till Robin; Schröter, Madita; Galvez, Eric; Schmidt‐Hohagen, Kerstin; Pils, Marina C.; Mühlen, Sabrina; Dersch, Petra; Hiller, Karsten; Schlüter, Dirk; Neumann‐Schaal, Meina; Strowig, Till

doi:10.1371/journal.ppat.1008448

Cited by 79 publications

(82 citation statements)

References 48 publications

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“…In the past, our group has attempted to introduce more variation into the mouse microbiota by using a variety of antibiotic treatments (17)(18)(19)(20). An alternative approach to maximize microbiota variation is to use mice from multiple sources (21,22). The differences between the microbiota of mice from vendors have been well documented and shown to influence susceptibility to a variety of diseases (23,24), including enteric infections (21,22,(25)(26)(27)(28)(29).…”

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confidence: 99%

The Initial Gut Microbiota and Response to Antibiotic Perturbation Influence Clostridioides difficile Clearance in Mice

Tomkovich

Stough

Bishop

et al. 2020

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The gut microbiota has a key role in determining susceptibility to Clostridioides difficile infections (CDIs). However, much of the mechanistic work examining CDIs in mouse models uses animals obtained from a single source. We treated mice from 6 sources (2 University of Michigan colonies and 4 commercial vendors) with clindamycin, followed by a C. difficile challenge, and then measured C. difficile colonization levels throughout the infection. The microbiota were profiled via 16S rRNA gene sequencing to examine the variation across sources and alterations due to clindamycin treatment and C. difficile challenge. While all mice were colonized 1 day postinfection, variation emerged from days 3 to 7 postinfection with animals from some sources colonized with C. difficile for longer and at higher levels. We identified bacteria that varied in relative abundance across sources and throughout the experiment. Some bacteria were consistently impacted by clindamycin treatment in all sources of mice, including Lachnospiraceae, Ruminococcaceae, and Enterobacteriaceae. To identify bacteria that were most important to colonization regardless of the source, we created logistic regression models that successfully classified mice based on whether they cleared C. difficile by 7 days postinfection using community composition data at baseline, post-clindamycin treatment, and 1 day postinfection. With these models, we identified 4 bacterial taxa that were predictive of whether C. difficile cleared. They varied across sources (Bacteroides) or were altered by clindamycin (Porphyromonadaceae) or both (Enterobacteriaceae and Enterococcus). Allowing for microbiota variation across sources better emulates human interindividual variation and can help identify bacterial drivers of phenotypic variation in the context of CDIs. IMPORTANCE Clostridioides difficile is a leading nosocomial infection. Although perturbation to the gut microbiota is an established risk, there is variation in who becomes asymptomatically colonized, develops an infection, or has adverse infection outcomes. Mouse models of C. difficile infection (CDI) are widely used to answer a variety of C. difficile pathogenesis questions. However, the interindividual variation between mice from the same breeding facility is less than what is observed in humans. Therefore, we challenged mice from 6 different breeding colonies with C. difficile. We found that the starting microbial community structures and C. difficile persistence varied by the source of mice. Interestingly, a subset of the bacteria that varied across sources were associated with how long C. difficile was able to colonize. By increasing the interindividual diversity of the starting communities, we were able to better model human diversity. This provided a more nuanced perspective of C. difficile pathogenesis.

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confidence: 99%

The Initial Gut Microbiota and Response to Antibiotic Perturbation Influence Clostridioides difficile Clearance in Mice

Tomkovich

Stough

Bishop

et al. 2020

mSphere

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“…Other studies have used mice from multiple sources to identify bacteria that either promote colonization resistance or increase susceptibility to enteric infections (22,23,(26)(27)(28)(29)(30). For example, against Salmonella infections, Enterobacteriaceae and segmented filamentous bacteria have emerged as protective (22,27).…”

Section: Discussionmentioning

confidence: 99%

“…For example, microbial metabolites, particularly secondary bile acids and butyrate production, have been implicated as important contributors to C. difficile resistance (33,44). Interestingly, butyrate has previously been shown to vary across mouse vendors and mediated resistance to Citrobacter rodentium infection, a model of enterohemorrhagic and enteropathogenic Escherichia coli infections (23). Evidence for immunological toning differences in IgA and Th17 cells across mice from different vendors have also been documented and (64,65) could influence the host response to CDI (66,67).…”

Section: Discussionmentioning

confidence: 99%

“…In the past, our group has attempted to introduce more variation into the mouse microbiota by using a variety of antibiotic treatments (18)(19)(20)(21). An alternative approach to maximize microbiota variation is to use mice from multiple sources (22,23). The differences between the microbiota of mice from vendors have been well documented and shown to influence susceptibility to a variety of diseases (24,25), including enteric infections (22,23,(26)(27)(28)(29)(30).…”

Section: Introductionmentioning

confidence: 99%

“…An alternative approach to maximize microbiota variation is to use mice from multiple sources (22,23). The differences between the microbiota of mice from vendors have been well documented and shown to influence susceptibility to a variety of diseases (24,25), including enteric infections (22,23,(26)(27)(28)(29)(30). Different research groups have also observed different CDI outcomes despite using similar murine models (13,18,21,(31)(32)(33).…”

Section: Introductionmentioning

confidence: 99%

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The initial gut microbiota and response to antibiotic perturbation influenceClostridioides difficilecolonization in mice

Tomkovich

Stough

Bishop

et al. 2020

Preprint

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The gut microbiota has a key role in determining susceptibility to Clostridioides difficile infections (CDIs). However, much of the mechanistic work examining CDIs in mouse models use animals obtained from a single source. We treated mice from 6 sources (2 University of Michigan colonies and 4 commercial vendors) with clindamycin, followed by a C. difficile challenge and then measured C. difficile colonization levels throughout the infection. The microbiota were profiled via 16S rRNA gene sequencing to examine the variation across sources and alterations due to clindamycin treatment and C. difficile challenge. While all mice were colonized 1-day post-infection, variation emerged from days 3-7 post-infection with animals from some sources colonized with C. difficile for longer and at higher levels. We identified bacteria that varied in relative abundance across sources and throughout the experiment. Some bacteria were consistently impacted by clindamycin treatment in all sources of mice including Lachnospiraceae, Ruminococcaceae, and Enterobacteriaceae. To identify bacteria that were most important to colonization regardless of the source, we created logistic regression models that successfully classified mice based on whether they cleared C. difficile by 7 days post-infection using community composition data at baseline, post-clindamycin, and 1-day post-infection. With these models, we identified 4 bacteria that were predictive of whether C. difficile cleared. They varied across sources (Bacteroides), were altered by clindamycin (Porphyromonadaceae), or both (Enterobacteriaceae and Enterococcus). Allowing for microbiota variation across sources better emulates human inter-individual variation and can help identify bacterial drivers of phenotypic variation in the context of CDIs.ImportanceClostridioides difficile is a leading nosocomial infection. Although perturbation to the gut microbiota is an established risk, there is variation in who becomes asymptomatically colonized, develops an infection, or has adverse infection outcomes. Mouse models of C. difficile infection (CDI) are widely used to answer a variety of C. difficile pathogenesis questions. However, the inter-individual variation between mice from the same breeding facility is less than what is observed in humans. Therefore, we challenged mice from 6 different breeding colonies with C. difficile. We found that the starting microbial community structures and C. difficile persistence varied by the source of mice. Interestingly, a subset of the bacteria that varied across sources were associated with how long C. difficile was able to colonize. By increasing the inter-individual diversity of the starting communities, we were able to better model human diversity. This provided a more nuanced perspective of C. difficile pathogenesis.

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Pectins that Structurally Differ in the Distribution of Methyl‐Esters Attenuate Citrobacter rodentium‐Induced Colitis

Beukema

Akkerman

Jermendi

et al. 2021

Molecular Nutrition Food Res

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Introduction: Pectins have anti-inflammatory properties on intestinal immunity through direct interactions on Toll-like receptors (TLRs) in the small intestine or via stimulating microbiota-dependent effects in the large intestine. Both the degree of methyl-esterification (DM) and the distribution of methyl-esters (degree of blockiness; DB) of pectins contribute to this influence on immunity, but whether and how the DB impacts immunity through microbiota-dependent effects in the large intestine is unknown. Therefore, this study tests pectins that structurally differ in DB in a mouse model with Citrobacter rodentium induced colitis and studies the impact on the intestinal microbiota composition and associated attenuation of inflammation. Methods and Results: Both low and high DB pectins induce a more rich and diverse microbiota composition. These pectins also lower the bacterial load of C. rodentium in cecal digesta. Through these effects, both low and high DB pectins attenuate C. rodentium induced colitis resulting in reduced intestinal damage, reduced numbers of Th1-cells, which are increased in case of C. rodentium induced colitis, and reduced levels of GATA3 + Tregs, which are related to tissue inflammation. Conclusion: Pectins prevent C. rodentium induced colonic inflammation by lowering the C. rodentium load in the caecum independently of the DB.

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Variations in microbiota composition of laboratory mice influence Citrobacter rodentium infection via variable short-chain fatty acid production

Cited by 79 publications

References 48 publications

The Initial Gut Microbiota and Response to Antibiotic Perturbation Influence Clostridioides difficile Clearance in Mice

The Initial Gut Microbiota and Response to Antibiotic Perturbation Influence Clostridioides difficile Clearance in Mice

The initial gut microbiota and response to antibiotic perturbation influenceClostridioides difficilecolonization in mice

Pectins that Structurally Differ in the Distribution of Methyl‐Esters Attenuate Citrobacter rodentium‐Induced Colitis

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