Clostridium difficile Alters the Structure and Metabolism of Distinct Cecal Microbiomes during Initial Infection To Promote Sustained Colonization

Abstract

Clostridium difficile has become the most common single cause of hospital-acquired infection over the last decade in the United States. Colonization resistance to the nosocomial pathogen is primarily provided by the gut microbiota, which is also involved in clearing the infection as the community recovers from perturbation. As distinct antibiotics are associated with different risk levels for CDI, we utilized a mouse model of infection with 3 separate antibiotic pretreatment regimens to generate alternative gut microbiomes that each allowed for C. difficile colonization but varied in clearance rate. To assess community-level dynamics, we implemented an integrative multi-omics approach that revealed that infection significantly changed many aspects of the gut community. The degree to which the community changed was inversely correlated with clearance during the first 6 days of infection, suggesting that C. difficile differentially modifies the gut environment to promote persistence. This is the first time that metagenome-enabled metatranscriptomics have been employed to study the behavior of a host-associated microbiota in response to an infection. Our results allow for a previously unseen understanding of the ecology associated with C. difficile infection and provide the groundwork for identification of context-specific probiotic therapies.

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

“…3B). As a corollary to creating a smaller metabolic network than those using transcriptomes contextualized by RIPTiDe (by an average of 9.12%), fewer genes were distinctly essential in the model resulting from standard pFBA (5 genes) compared to any of the RIPTiDe-contextualized in vitro transcriptomes (8,15, and 20 genes respectively; Table S4). Interestingly, the contextualized models on average shared larger ratios of components (~7.07% reactions and~1.78% metabolites) with each other than with pFBA, supporting that RIPTiDe selects more biologically relevant patterns of metabolism than pFBA alone (Fig.…”

“…To address this problem, we utilized in vivo metatranscriptomic abundance data collected from the cecum of wildtype C57Bl6 mice. These data were chosen because the specific antibiotic pretreatment (intraperitoneal injection of clindamycin) resulted in a bacterial community composed of >90% E. coli [8] . To begin the analysis, we first made certain that the in vivo transcript abundance distribution reflected the same negative binomial type as those derived from in vitro transcriptomes mapped to the same E. coli K-12 MG1655 genome (Fig.…”

“…The data were then converted to a python dictionary of gene keys and associated numeric transcript abundances for use with RIPTiDe. In vivo untargeted metabolomic data were generated for a previous study [8] , and accessed from github.com/SchlossLab/Jenior_Metatranscriptomics_mSphere_2018/ . The current version of iJO1366 (GENRE for E. coli K-12 MG1655) was accessed from the BiGG Model database [55] on 09-26-2018.…”

“…RIPTiDe was able to accurately discern context-specific phenotypes including growth rates that closely match experimentally measured values under the same conditions, as well as gene essentiality predictions in metabolic pathways relevant to the corresponding media. The platform was subsequently tested utilizing in vivo metatranscriptomic sequence data from clindamycin-treated mouse cecal content where E. coli is the dominant member of the bacterial community [8] . When contrasted against results from the previous analysis, the in vivo RIPTiDe-contextualized model grew significantly slower than in aerobic rich media and more central metabolites were produced intracellularly when in competition with other bacterial species.…”

“…RIPTiDe reveals differential host-associated, metabolism utilizing in vivo metatranscriptomic data. Transcriptomic reads attributable to E. coli were extracted from a metatranscriptomic dataset sequenced from the cecum of mice in which E. coli is the most highly abundant community member[8] . (A) In vivo transcript abundance of reads recruited to the E. coli K-12 MG-1655 genome and metrics associated with the resulting context-specific model.…”