C Diff Molecular

After a veryyy long wait, I'm very proud to announce that @The_MRC has decided to fund our work on #Cdiff engulfasome!!! Thanks @barwinskasendra @AbbieKellySci for great work and collaborators @GillDouce1 @CJStewart7 and Mark Wilcox! pic.twitter.com/iZ17Liizwv— Paula Salgado (@pssalgado) July 26, 2021

We’d love to talk to you if you’ve taken #antibiotics or had #cdiff , to help guide our research - please get in touch! @LewthwaitePenny @LeedsMedHealth @LTHTAntibiotics @Mike_Fulton_ @BSACandJAC @CE4AMR @AmrPrecision

Study findings could offer a new approach to the toxin-based approach of other C. difficile vaccines in development.

Flagellar motility is important for the pathogenesis of many intestinal pathogens, allowing bacteria to move to their preferred ecological niche. Clostridioides difficile is currently the major cause for bacterial health care-associated intestinal infections in the western world. Most clinical strains produce peritrichous flagella and are motile in soft-agar. However, little knowledge exists on the C. difficile swimming behaviour and its regulation at the level of individual cells. We report here on the swimming strategy of C. difficile at the single cell level and its dependency on environmental parameters. A comprehensive analysis of motility parameters from several thousand bacteria was achieved with the aid of a recently developed bacterial tracking programme. C. difficile motility was found to be strongly dependent on the matrix elasticity of the medium. Long run phases of all four motile C. difficile clades were only observed in the presence of high molecular weight molecules such as polyvinylpyrrolidone (PVP) and mucin, which suggests an adaptation of the motility apparatus to the mucin-rich intestinal environment. Increasing mucin or PVP concentrations lead to longer and straighter runs with increased travelled distance per run and fewer turnarounds that result in a higher net displacement of the bacteria. The observed C. difficile swimming pattern under these conditions is characterised by bidirectional, alternating back and forth run phases, interrupted by a shor...

Background Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) that is widely identified worldwide. This study aimed to investigate the phenotypic characterization and molecular typing of Clostridium difficile isolates among patients with UC at an inflammatory bowel disease clinic in Iran. Methods In this cross-sectional study, conducted from April 2015 to December 2015, 85 UC patients were assessed for C.difficile infection (CDI). C. difficile isolates were characterized based on their toxin profile and antimicrobial resistance pattern. Multi-locus sequence typing analysis (MLST) and PCR ribotyping were performed to define the genetic relationships between different lineages of toxigenic strains. Results The prevalence of C. difficile isolates was 31.8% (27/85) in patients, of those 15 patients (17.6%) had CDI. Three different sequence types (STs) identified based on MLST among the toxigenic isolates, that is ST54 (33.3%), ST2 (53.3%), and ST37 (13.6%). C. difficile strains were divided into four different PCR-ribotypes (012, 014, 017 and IR1). The most common ribotype was 014 accounting for 48.3% (7/15) of all strains. The strains isolated during the first episode and recurrence of CDI usually belonged to PCR ribotype 014 (ST2). A high rate of CDI recurrence (14.1%, 12/85) experienced in UC patients. Colonization of the gastrointestinal tract with non-toxigenic C. difficile strains was frequent among patients with mild disease. All C. difficile isolates were susceptible to metronidazole, and vancomycin, 86 and 67% of isolates were resistant to clindamycin and erythromycin respectively. There was no correlation between the toxin type and antibiotic resistance (p > 0.05). Conclusion Overall CDI is rather prevalent in UC patients. All patients with CDI experienced moderate to severe disease and exposed to different antimicrobial and anti-inflammatory agents. Close monitoring and appropriate management including early detection and fast treatment of CDI will improve UC outcomes.

As the leading cause of worldwide hospital-acquired infection, Clostridioides difficile (C. difficile) infection has caused heavy economic and hospitalized burden, while its pathogenesis is not fully understood. Toxin B (TcdB) is one of the major virulent factors of C. difficile. Recently, CSPG4 and …

The children with IBD have a higher incidence of CDI than those without IBD. Severe disease conditions and use of broad-spectrum antibiotics or glucocorticoids may be associated with an increased incidence of CDI in children with IBD.

About 25% of IBD patients with CDI had a history of antibiotic use.

Fecal lithocholic acid and ursodeoxycholic acid are reduced in children with UC who have a history of CDI compared to children with UC without CDI. This is likely a result of decreased bacterial gene...

Clostridia comprise bacteria of environmental, biotechnological and medical interest and many commensals of the gut microbiota. Because of their strictly anaerobic lifestyle, oxygen is a major stress for Clostridia. However, recent data showed that these bacteria can cope with O2 better t …

The human microbiota is a complex microbial community living on and in our bodies. Its impact on a host's health is immense, affecting digestion ([ 1 ][1]), the immune system ([ 2 ][2]), behavior ([ 3 ][3]), metabolic diseases ([ 4 ][4]), and responses to drugs ([ 5 ][5]–[ 7 ][6]). Rapid advances in experimental and computational methods have moved the human microbiome field from identifying associations between microbiota composition and host health to unraveling the underlying molecular mechanisms ([ 8 ][7]–[ 10 ][8]). However, exactly how much the microbiota contributes to host health is a very difficult question to answer. By focusing on mechanistic and quantitative questions about the microbiome's contributions to host metabolism, I leverage my background in applied mathematics and systems biology to develop computational models describing host-microbiota interactions. Good models require good data from controlled experiments—a challenging proposition in complex host-microbiota systems. As a postdoc, I joined Andy Goodman's lab at Yale University and found myself in a perfect position to collect such data. By combining bacterial genetics with gnotobiotic mouse models, I learned how to modify the microbiome of germ-free, sterile mice. In the Goodman lab, we used these mice to study the contribution of microbiota to host metabolism of a number of pharmaceutical drugs. We found that this was also a good system to quantify host-microbiome interactions in vivo, because the compounds we used can be introduced into the system in a controlled way. We first focused on brivudine, an antiviral compound that can be converted into a potentially toxic metabolite, bromovinyluracil (BVU), by either a host or its microbiome ([ 11 ][9]). To identify bacteria capable of converting brivudine to BVU, we incubated individual bacterial species with the drug in vitro. One of the most potent brivudine metabolizers was Bacteroides thetaiotaomicron , a common gut bacterium with a genetic deletion library readily available. By incubating this library with the drug, we identified one bacterial mutant that had lost the capacity to convert brivudine to BVU. We then colonized germ-free mice with either the wild-type or mutant B. thetaiotaomicron , which provided us with a controllable host-microbiome system and two mouse groups that were identical, save for a single bacterial gene. When we administered brivudine to these two groups, the observed outcome was somewhat puzzling. Although drug levels in the intestine were much higher in mice colonized with the mutant bacterium, serum levels were comparable between the two mouse groups. The metabolite levels showed the opposite pattern: no difference (and very low levels) in the intestine but much higher metabolite levels in the sera of mice colonized with the wild-type bacterium (see the figure). These data could potentially be explained by bacterial conversion of the drug in the intestine and the rapid metabolite absorption into the serum. To test this explanation, we started with a simple kinetic model with two equations describing host drug metabolism in the liver and bacterial drug metabolism in the intestine. Once solved, this equation system showed that the difference between the amounts of metabolite absorbed into the sera of each of the two mouse groups was determined by the amount of BVU produced by microbes in the gut. This controlled experimental setup allowed us to quantify that the bacterial contribution to the toxic drug metabolite in vivo was about 70% ([ 12 ][10]) (see the figure). We expanded the model to describe drug metabolism processes in eight different tissues and in enterohepatic circulation (when the drug metabolized in the liver is secreted back into the small intestine via bile). We then demonstrated that our approach can be generalized to estimate the bacterial contribution to drug metabolism even if the metabolizing species remain unknown by using data from germ-free mice and mice harboring a complex microbial community. We also showed that microbial contribution to the drug metabolite far exceeds the host for sorivudine, an antiviral drug with different host and microbiome metabolism rates, and for clonazepam, an anxiolytic and anticonvulsant drug converted to multiple metabolites ([ 12 ][10]). ![Figure][11] Experimental and computational approaches that quantify host and microbial contributions to drug metabolism Oral drugs are administered to gnotobiotic mice that differ in a single microbial drug-metabolizing enzyme (GNMUT, mutant; GNWT, wild type); drug and drug metabolite kinetics are then quantified across tissues. A microbiome-host pharmacokinetic model developed from these measurements accurately predicts serum metabolite exposure and untangles host and microbiome contributions to drug metabolism. GRAPHIC: ADAPTED FROM M. ZIMMERMANN-KOGADEEVA BY N. CARY/ SCIENCE Quantifying the metabolic host-microbiome interactions is not the only purpose of our model. Having a robust model of host-microbiome interaction allows us to study, explain, and predict the system's behavior in different conditions. By analyzing how drug and metabolite profiles change when model parameters are varied, we found that the similarity of drug serum profiles between germ-free and colonized mice can be explained by the fast and microbiota-independent drug absorption from the small intestine. Our model further suggests that even for rapidly absorbed drugs, microbiome contributions to a host's metabolism can be substantial under certain conditions (e.g., a high microbiome to host ratio of drug metabolism or extensive enterohepatic circulation of the drug and its metabolites) ([ 13 ][12]). Such computational models enable us to investigate host-microbiota interactions in silico, guide experimental design, and help reduce the number of experiments needed to confirm model predictions. To systematically investigate microbial capacity to metabolize drugs, we next conducted a high-throughput in vitro screen. We found that microbiota contribution to drug metabolism might even be more widespread than we anticipated—two-thirds (176 out of 271) of the human-targeted drugs we examined were metabolized by at least one of the 76 tested bacteria ([ 14 ][13]). Although follow-up studies are required to test these microbiota-drug interactions in vivo, our findings emphasize that the microbiota should be considered when developing new drugs, stratifying patients, and choosing the most efficient treatment strategies. In the future, I believe that computational models combined with quantitative experimental data will allow us to measure host-microbiome interactions beyond drug metabolism and to better understand, predict, and control the effect of the microbiome on our health in everyday life. FINALIST Maria Zimmermann-Kogadeeva Maria Zimmermann-Kogadeeva received undergraduate degrees from Lomonosov Moscow State University in Russia and a PhD from ETH Zürich, Switzerland. After completing her postdoctoral fellowships at Yale University in the Goodman group and at European Molecular Biology Laboratory (EMBL) Heidelberg in the Bork group, Maria will start her laboratory in the Genome Biology Unit at EMBL Heidelberg in 2021. Her research combines computational modeling and multiomics data integration to investigate how microbes adapt to their surroundings and how metabolic adaptations of individual bacteria shape the functional outcome of microbial communities and their interactions with the host and the environment. [ www.sciencemag.org/content/373/6551/173.2 ][14] 1. [↵][15]1. H. J. Flint , Nutr. Rev. 70, S10 (2012). [OpenUrl][16][CrossRef][17][PubMed][18] 2. [↵][19]1. A. L. Kau, 2. P. P. Ahern, 3. N. W. Griffin, 4. A. L. Goodman, 5. J. I. Gordon , Nature 474, 327 (2011). [OpenUrl][20][CrossRef][21][PubMed][22][Web of Science][23] 3. [↵][24]1. T. R. Sampson, 2. S. K. Mazmanian , Cell Host Microbe 17, 565 (2015). [OpenUrl][25][CrossRef][26][PubMed][27] 4. [↵][28]1. J. Durack, 2. S. V. Lynch , J. Exp. Med. 216, 20 (2019). [OpenUrl][29][Abstract/FREE Full Text][30] 5. [↵][31]1. P. Spanogiannopoulos, 2. E. N. Bess, 3. R. N. Carmody, 4. P. J. Turnbaugh , Nat. Rev. Microbiol. 14, 273 (2016). [OpenUrl][32][CrossRef][33][PubMed][34] 6. 1. N. Koppel, 2. V. Maini Rekdal, 3. E. P. Balskus , Science 356, eaag2770 (2017). [OpenUrl][35][Abstract/FREE Full Text][36] 7. [↵][37]1. I. D. Wilson, 2. J. K. Nicholson , Transl. Res. 179, 204 (2017). [OpenUrl][38][CrossRef][39][PubMed][40] 8. [↵][41]1. T. S. B. Schmidt, 2. J. Raes, 3. P. Bork , Cell 172, 1198 (2018). [OpenUrl][42][PubMed][43] 9. 1. M. Alexander, 2. P. J. Turnbaugh , Immunity 53, 264 (2020). [OpenUrl][44] 10. [↵][45]1. C. Tropini, 2. K. A. Earle, 3. K. C. Huang, 4. J. L. Sonnenburg , Cell Host Microbe 21, 433 (2017). [OpenUrl][46][CrossRef][47][PubMed][48] 11. [↵][49]1. H. Machida et al. , Biochem. Pharmacol. 49, 763 (1995). [OpenUrl][50][CrossRef][51][PubMed][52] 12. [↵][53]1. M. Zimmermann, 2. M. Zimmermann-Kogadeeva, 3. R. Wegmann, 4. A. L. Goodman , Science 363, eaat9931 (2019). [OpenUrl][54][Abstract/FREE Full Text][55] 13. [↵][56]1. M. Zimmermann-Kogadeeva, 2. M. Zimmermann, 3. A. L. Goodman , Gut Microbes 11, 587 (2020). [OpenUrl][57] 14. [↵][58]1. M. Zimmermann, 2. M. Zimmermann-Kogadeeva, 3. R. Wegmann, 4. A. L. Goodman , Nature 570, 462 (2019). [OpenUrl][59][PubMed][43] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-7 [7]...

The influences of the microbiota on host physiology are so pervasive that the microbiota has been hypothesized to play a critical role in host evolution by shaping key host phenotypes ([ 1 ][1]). However, to contribute to host evolution, traits must be transmitted across generations. One way to assess whether some or all microbes are influenced by the genetic composition of the host, and therefore conserved across generations, is by measuring heritability. Host species–specific patterns in the composition of the microbiome—the genetic content of the microbiota—suggest there is some degree of heritability in the microbiota ([ 2 ][2]). However, studies evaluating variation in the microbiota and host genetics within a single host species have generally reported low heritability for a small proportion of microbial taxa ([ 3 ][3]). On page 181 of this issue, Grieneisen et al. ([ 4 ][4]) reject this common conclusion by demonstrating that most gut microbiota traits in wild baboons exhibit some degree of heritability. Grieneisen et al. analyzed fecal samples from 585 yellow baboons ( Papio cynocephalus , some of them admixed with anubis baboons, P. anubis ) from 10 free-ranging social groups sampled longitudinally across 14 years in Amboseli National Park, Kenya. They determined the microbial composition of each sample and defined 1134 microbiome traits (i.e., relative abundance and presence or absence of microbial taxa, and measures of overall microbiome composition). Then they used kinship data and environmental data, including rainfall, social interactions, and group-level diet, to calculate heritability (the proportion of the variation of a trait attributable to genetic variance as opposed to environmental factors) of each of these microbiome traits with a standard formula used for nonmicrobial traits in livestock and wild animals. The scale of this dataset is currently unmatched in any host-microbe system, which may explain why previous estimates of microbiome heritability have been so low. Most microbiome studies, including those of humans, analyze a small number of samples or target populations at a single point in time. When Grieneisen et al. subsampled their dataset to simulate smaller sample sizes, or sampling at a single time point, most signals of microbiome heritability were not detected. Without data from multiple time points and a large number of individuals to account for stochasticity as well as temporal variation in host environments and the microbiota, it is difficult to accurately estimate microbiome heritability. Therefore, microbiome heritability is likely higher than reported previously in most host species, including humans. Estimating heritability of the microbiota under natural conditions is essential to advancing knowledge of the extent to which the microbiota affects host ecology and evolution. However, measuring heritability of the microbiota is difficult because there are many environmental variables at play. As Grieneisen et al. demonstrate, nonhuman primates provide a strong but currently underutilized system for addressing this challenge. In addition to being genetically closely related to humans, the ecology of nonhuman primate populations is extensively studied and several populations, such as the Amboseli baboons, have decades of relevant data. Such long-term studies that follow known individuals across their lives ([ 5 ][5]) give primatologists an unmatched ability to collect detailed, longitudinal data describing environment, diet, and individual behavior, as well as noninvasive biological samples. Furthermore, a diversity of physiological and behavioral adaptations to a range of environments exists within the Primates order, allowing targeted testing of specific host-microbe interactions with different primate species and populations. Baboons represent an excellent general model for the human microbiota because previous research has demonstrated that humans share more microbiome traits (e.g., taxonomic and functional profiles) with baboons than chimpanzees ([ 6 ][6]). However, comparative data collected from a variety of primate taxa could reveal underlying mechanisms that maintain specific associations between host genetic traits and particular components of the primate gut microbiota. Despite reporting evidence of heritability in the majority of microbiome traits they assessed, Grieneisen et al. also show that a larger proportion of variation in microbiome data is attributable to environmental factors rather than host genetic factors, as has been shown consistently across studies in other systems ([ 3 ][3]). Furthermore, estimates of microbiome heritability varied between dry and wet seasons, and with diet and host age, as a result of changing environmental contributions to microbiome variation. These results highlight the plastic nature of the gut microbiota, which could allow it to play a role in facilitating rapid, local adaptation in hosts. However, the mechanisms by which these interactions occur remain unclear. The resolution of data for a single time point or individual precluded Grieneisen et al. from empirically identifying which environmental factors or host behaviors were driving temporal patterns in heritability. A key next step will be to improve host genomic resolution to identify the specific genomic regions and mechanisms through which associations between host genetics and the microbiota occur. Although some of these types of studies are being conducted for humans ([ 7 ][7]), studies of natural populations of other mammalian taxa would help identify generalizable principles that underlie host genetic-microbiota associations. For example, in hybrid zones of genetically and microbially divergent host species ([ 8 ][8], [ 9 ][9]), the study of paired microbiome and host genomic data for individuals with a range of admixed genotypes could help identify specific associations. Knowledge of the mechanisms shaping interactions between host genetics and gut bacterial communities will be critical for generating testable hypotheses for other body sites (e.g. skin, mouth, urogenital tract) and other microbial community members (e.g., microscopic eukaryotes, viruses). Similarly, improving resolution of data describing the microbiota will allow testing of the taxonomic specificity at which these interactions occur as well as the extent to which microbial taxonomy or functions are more strongly associated with host genetics. Technological advances are making it easier and more affordable to generate microbial whole-metagenome data. Together with the development of analytical tools to study the dual genomic composition of hosts and their microbiota in nonmodel organisms, these data will shift explorations of microbial influences on host evolution from correlation and theory to causation and mechanism. 1. [↵][10]1. K. R. Theis et al ., mSystems 1, e00028 (2016). [OpenUrl][11][CrossRef][12] 2. [↵][13]1. E. K. Mallott, 2. K. R. Amato , Nat. Rev. Microbiol., 1 (2021). 3. [↵][14]1. D. Rothschild et al ., Nature 555, 210 (2018). [OpenUrl][15][CrossRef][16][PubMed][17] 4. [↵][18]1. L. Grieneisen et al ., Science 373, 181 (2021). [OpenUrl][19][Abstract/FREE Full Text][20] 5. [↵][21]1. T. Clutton-Brock , in Long-Term Field Studies of Primates (Springer, 2012), pp. 437–449. 6. [↵][22]1. K. R. Amato et al ., Genome Biol. 20, e27879v27871 (2019). [OpenUrl][23] 7. [↵][24]1. A. Kurilshikov et al ., Nat. Genet. 53, 156 (2021). [OpenUrl][25] 8. [↵][26]1. L. Cortés-Ortiz et al ., Int. J. Primatol. 40, 114 (2019). [OpenUrl][27] 9. [↵][28]1. K. R. Amato et al ., Oecologia 180, 717 (2016). [OpenUrl][29][CrossRef][30] Acknowledgments: K.R.A. is a fellow of the Canadian Institute for Advanced Research “Humans and the Microbiome” program. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #xref-ref-1-1 "View reference 1 in text" [11]: {openurl}?query=rft.jtitle%253DmSystems%26rft.volume%253D1%26rft.spage%253De00028%26rft_id%253Dinfo%253Adoi%252F10.1128%252FmSystems.00028-16%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [12]: /lookup/external-ref?access_num=10.1128/mSystems.00028-16&link_type=DOI [13]: #xref-ref-2-1 "View reference 2 in text" [14]: #xref-ref-3-1 "View reference 3 in text" [15]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D555%26rft.spage%253D210%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature25973%26rft_id%253Dinfo%253Apmid%252F29489753%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [16]: /lookup/external-ref?access_num=10.1038/nature25973&link_type=DOI [17]: /lookup/external-ref?access_num=29489753&link_type=MED&atom=%2Fsci%2F373%2F6551%2F159.atom [18]: #xref-ref-4-1 "View reference 4 in text" [19]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DGrieneisen%26rft.auinit1%253DL.%26rft.volume%253D373%26rft.issue%253D6551%26rft.spage%253D181%26rft.epage%253D186%26rft.atitle%253DGut%2Bmicrobiome%2Bheritability%2Bis%2Bnearly%2Buniversal%2Bbut%2Benvironmentally%2Bcontingent%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aba5483%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1

The overall serum anti-toxin A and B antibody levels were similar at baseline between immunocompromised hosts and non-immunocompromised hosts.

Peggy Lillis Foundation is hosting an educational webinar with leading surgeon

Clostridioides difficile infection (CDI) is the leading cause of antibiotic-associated intestinal disease, resulting in severe diarrhea and fatal pseudomembranous colitis. TcdB, one of the essential virulence factors secreted by this bacterium, induces host cell apoptosis through a poorly understood mechanism. Here, we performed an RNAi screen customized to Caco-2 cells, a cell line model of the intestinal epithelium, to discover host factors involved in TcdB-induced apoptosis. We identified plakoglobin, also known as junction plakoglobin (JUP) or γ-catenin, a member of the catenin family, as a novel host factor, and a previously known cell death-related chromatin factor, high mobility group box 1 (HMGB1). Disruption of those host factors by RNAi and CRISPR resulted in resistance of cells to TcdB-mediated and mitochondria-dependent apoptosis. JUP was redistributed from adherens junctions to the mitochondria and colocalized with Bcl-XL after stimulation by TcdB, suggesting a role of JUP in cell death signaling through mitochondria. Treatment with glycyrrhizin, an HMGB1 inhibitor, resulted in significantly increased resistance to TcdB-induced epithelial damage in cultured cells and a mouse ligated colon loop model. These findings demonstrate the critical roles of JUP and HMGB1 in TcdB-induced epithelial cell apoptosis. ### Competing Interest Statement L.H. is a founder of Xiaomo Biotech Limited (Hong Kong SAR, China), which has commercialized the pro-siRNA technology. Other authors declare no competing interests.