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Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile.

ABSTRACT: We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes, we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport, and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient uptake, we discovered 14 amino acids, diverse carbohydrates, and 10 metabolic genes as essential for C. difficile growth in the intestinal environment. Finally, we developed a PRIME model to uncover how EGRIN-inferred combinatorial gene regulation by transcription factors, such as CcpA and CodY, modulates essential metabolic processes to enable C. difficile growth relative to commensal colonization. The C. difficile interactive web portal provides access to these model resources to support collaborative systems-level studies of context-specific virulence mechanisms in C. difficile.

SUBMITTER: Arrieta-Ortiz ML

PROVIDER: S-EPMC8595754 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile.

Arrieta-Ortiz Mario L ML Immanuel Selva Rupa Christinal SRC Turkarslan Serdar S Wu Wei-Ju WJ Girinathan Brintha P BP Worley Jay N JN DiBenedetto Nicholas N Soutourina Olga O Peltier Johann J Dupuy Bruno B Bry Lynn L Baliga Nitin S NS

Cell host & microbe 20211011 11

We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes, we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport, and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient upta ...[more]

PMID: 34637780

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Project description:Clostridiodes difficile (C. difficile) was ranked an "urgent threat" by the Centers for Disease Control and Prevention (CDC) in 2019. C. difficile infection (CDI) is the most common healthcare-associated infection (HAI) in the United States of America as well as the leading cause of antibiotic-associated gastrointestinal disease. C. difficile is a gram-positive, rod-shaped, spore-forming, anaerobic bacterium that causes infection of the epithelial lining of the gut. CDI occurs most commonly after disruption of the human gut microflora following the prolonged use of broad-spectrum antibiotics. However, the recurrent nature of this disease has led to the hypothesis that biofilm formation may play a role in its pathogenesis. Biofilms are sessile communities of bacteria protected from extracellular stresses by a matrix of self-produced proteins, polysaccharides, and extracellular DNA. Biofilm regulation in C. difficile is still incompletely understood, and its role in disease recurrence has yet to be fully elucidated. However, many factors have been found to influence biofilm formation in C. difficile, including motility, adhesion, and hydrophobicity of the bacterial cells. Small changes in one of these systems can greatly influence biofilm formation. Therefore, the biofilm regulatory system would need to coordinate all these systems to create optimal biofilm-forming physiology under appropriate environmental conditions. The coordination of these systems is complex and multifactorial, and any analysis must take into consideration the influences of the stress response, quorum sensing (QS), and gene regulation by second messenger molecule cyclic diguanosine monophosphate (c-di-GMP). However, the differences in biofilm-forming ability between C. difficile strains such as 630 and the "hypervirulent" strain, R20291, make it difficult to assign a "one size fits all" mechanism to biofilm regulation in C. difficile. This review seeks to consolidate published data regarding the regulation of C. difficile biofilms in order to identify gaps in knowledge and propose directions for future study.

Project description:Clostridioides difficile is a gram-positive spore-forming pathogen that commonly causes diarrheal infections in the developed world. Although C. difficile is a genetically diverse species, certain ribotypes are overrepresented in human infections. It is unknown if metabolic adaptations are essential for the emergence of these epidemic ribotypes. Here, we tested carbon substrate utilization by 88 C. difficile isolates and looked for differences in growth between 22 ribotypes. By profiling clinical isolates, we assert that C. difficile is a generalist species capable of growing on a variety of carbon substrates. Further, C. difficile strains clustered by phylogenetic relationship and displayed ribotype-specific and clade-specific metabolic capabilities. Surprisingly, we observed that two emerging lineages, ribotypes 023 and 255, have divergent metabolic phenotypes. In addition, although C. difficile Clade 5 is the most evolutionary distant clade and often detected in animals, it displayed more robust growth on simple dietary sugars than Clades 1-4. Altogether, our results corroborate the generalist metabolic strategy of C. difficile and demonstrate lineage-specific metabolic capabilities. In addition, our approach can be adapted to the study of additional pathogens to ascertain their metabolic niches in the gut.ImportanceThe gut pathogen Clostridioides difficile utilizes a wide range of carbon sources. Microbial communities can be rationally designed to combat C. difficile by depleting its preferred nutrients in the gut. However, C. difficile is genetically diverse with hundreds of identified ribotypes and most of its metabolic studies were performed with lab-adapted strains. Here, we profiled carbon metabolism by a myriad of C. difficile clinical isolates. While the metabolic capabilities of these isolates clustered by their genetic lineage, we observed surprising metabolic divergence between two emerging lineages. We also found that the most genetically distant clade grew robustly on simple dietary sugars, posing intriguing questions about the adaptation of C. difficile to the human gut. Altogether, our results underscore the importance of considering the metabolic diversity of pathogens in the study of their evolution and the rational design of therapeutic interventions.

Project description:Clostridioides difficile (formerly Clostridium difficile) is a Gram-positive, anaerobe, spore-forming pathogen, which causes drug-induced diseases in hospitals worldwide. A detailed analysis of the proteome may provide new targets for drug development or therapeutic strategies to combat this pathogen. The application of metabolic labeling (ML) would allow for accurate quantification of significant differences in protein abundance, even in the case of very small changes. Additionally, it would be possible to perform more accurate studies of the membrane or surface proteomes, which usually require elaborated sample preparation. Such studies are therefore prone to higher standard deviations during the quantification. The implementation of ML strategies for C. difficile is complicated due to the lack in arginine and lysine auxotrophy as well as the Stickland dominated metabolism of this anaerobic pathogen. Hence, quantitative proteome analyses could only be carried out by label free or chemical labeling methods so far. In this paper, a ML approach for C. difficile is described. A cultivation procedure with 15N-labeled media for strain 630?erm was established achieving an incorporation rate higher than 97%. In a proof-of-principle experiment, the performance of the ML approach in C. difficile was tested. The proteome data of the cytosolic subproteome of C. difficile cells grown in complex medium as well as two minimal media in the late exponential and early stationary growth phase obtained via ML were compared with two label free relative quantification approaches (NSAF and LFQ). The numbers of identified proteins were comparable within the three approaches, whereas the number of quantified proteins were between 1,110 (ML) and 1,861 (LFQ) proteins. A hierarchical clustering showed clearly separated clusters for the different conditions and a small tree height with ML approach. Furthermore, it was shown that the quantification based on ML revealed significant altered proteins with small fold changes compared to the label free approaches. The quantification based on ML was accurate, reproducible, and even more sensitive compared to label free quantification strategies.

Dataset Information

Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile.

Publications

Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile.

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