Systems biology analysis of the Clostridioides difficile core-genome contextualizes microenvironmental evolutionary pressures leading to genotypic and phenotypic divergence

Adaptation mechanisms of Clostridioides difficile to auranofin and its impact on human gut microbiota

Auranofin (AF), a former rheumatoid polyarthritis treatment, gained renewed interest for its use as an antimicrobial. AF is an inhibitor of thioredoxin reductase (TrxB), a thiol and protein repair enzyme, with an antibacterial activity against several bacteria including C. difficile, an enteropathogen causing post-antibiotic diarrhea. Several studies demonstrated the effect of AF on C. difficile physiology, but the crucial questions of resistance mechanisms and impact on microbiota remain unaddressed. We explored potential resistance mechanisms by studying the impact of TrxB multiplicity and by generating and characterizing adaptive mutations. We showed that if mutants inactivated for trxB genes have a lower MIC of AF, the number of TrxBs naturally present in clinical strains does not impact the MIC. All stable mutations isolated after AF long-term exposure were in the anti-sigma factor of σB and strongly affect physiology. Finally, we showed that AF has less impact on human gut microbiota than vancomycin.

Emerging Clostridioides difficile ribotypes have divergent metabolic phenotypes

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.

IMPORTANCE

The 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.

Distinct increase in antimicrobial resistance genes among Vibrio parahaemolyticus in recent decades worldwide

Vibrio parahaemolyticus is a common pathogen, and has emerged with multiple antimicrobial resistance (AMR). However, few studies have conducted large-scale investigations of AMR and virulence trends of V. parahaemolyticus worldwide. This study longitudinally monitored antibiotic resistance genes (ARGs) and virulence factor genes (VFGs) trends of 1540 V. parahaemolyticus isolates isolated from 1951 to 2021. The number of ARGs in V. parahaemolyticus isolates distinctly increased over the years (P = 5.9e-10), while the number of VFGs decreased significantly (P < 2.2e-16). However, the number of VFGs of isolates isolated from humans has not changed significantly over the years (R = 0.013, P = 0.74), suggesting that the pathogenic risk to humans has not been reduced. Besides, mobile genetic elements are important contributors to ARGs in V. parahaemolyticus (R = 0.34, P < 2.2e-16), but have no promoting effect on VFGs (P = 0.50). The structural equation model illustrated that the human development index promoted the consumption of antibiotics, thereby indirectly promoting an increase in the AMR of the V. parahaemolyticus isolates. Finally, the random forest was performed to predict the ARG and VFG risks of global terrestrial V. parahaemolyticus isolates, and successfully map these threats with over 80% accuracy. This study aimed to evaluate the global risks posed by AMR and virulence, which helps to develop methods specifically targeting V. parahaemolyticus to mitigate these threats.

One size does not fit all - Trehalose metabolism by Clostridioides difficile is variable across the five phylogenetic lineages

Clostridioides difficile, the leading cause of antibiotic-associated diarrhoea worldwide, is a genetically diverse species which can metabolise a number of nutrient sources upon colonising a dysbiotic gut environment. Trehalose, a disaccharide sugar consisting of two glucose molecules bonded by an α 1,1-glycosidic bond, has been hypothesised to be involved in the emergence of C. difficile hypervirulence due to its increased utilisation by the RT027 and RT078 strains. Here, growth in trehalose as the sole carbon source was shown to be non-uniform across representative C. difficile strains, even though the genes for its metabolism were induced. Growth in trehalose reduced the expression of genes associated with toxin production and sporulation in the C. difficile R20291 (RT027) and M120 (RT078) strains in vitro, suggesting an inhibitory effect on virulence factors. Interestingly, the R20291 TreR transcriptional regulatory protein appeared to possess an activator function as its DNA-binding ability was increased in the presence of its effector, trehalose-6-phosphate. Using RNA-sequencing analysis, we report the identification of a putative trehalose metabolism pathway which is induced during growth in trehalose: this has not been previously described within the C. difficile species. These data demonstrate the metabolic diversity exhibited by C. difficile which warrants further investigation to elucidate the molecular basis of trehalose metabolism within this important gut pathogen.

Elucidating the CodY regulon in Staphylococcus aureus USA300 substrains TCH1516 and LAC

CodY is a conserved broad-acting transcription factor that regulates the expression of genes related to amino acid metabolism and virulence in Gram-positive bacteria. Here, we performed the first in vivo determination of CodY target genes using a novel CodY monoclonal antibody in methicillin-resistant Staphylococcus aureus (MRSA) USA300. Our results showed (i) the same 135 CodY promoter binding sites regulating the 165 target genes identified in two closely related virulent S. aureus USA300 TCH1516 and LAC strains; (ii) the differential binding intensity for the same target genes under the same conditions was due to sequence differences in the same CodY-binding site in the two strains; (iii) a CodY regulon comprising 72 target genes that are differentially regulated relative to a CodY deletion strain, representing genes that are mainly involved in amino acid transport and metabolism, inorganic ion transport and metabolism, transcription and translation, and virulence, all based on transcriptomic data; and (iv) CodY systematically regulated central metabolic flux to generate branched-chain amino acids (BCAAs) by mapping the CodY regulon onto a genome-scale metabolic model of S. aureus. Our study performed the first system-level analysis of CodY in two closely related USA300 TCH1516 and LAC strains, revealing new insights into the similarities and differences of CodY regulatory roles between the closely related strains. IMPORTANCE With the increasing availability of whole-genome sequences for many strains within the same pathogenic species, a comparative analysis of key regulators is needed to understand how the different strains uniquely coordinate metabolism and expression of virulence. To successfully infect the human host, Staphylococcus aureus USA300 relies on the transcription factor CodY to reorganize metabolism and express virulence factors. While CodY is a known key transcription factor, its target genes are not characterized on a genome-wide basis. We performed a comparative analysis to describe the transcriptional regulation of CodY between two dominant USA300 strains. This study motivates the characterization of common pathogenic strains and an evaluation of the possibility of developing specialized treatments for major strains circulating in the population.

Genome-scale metabolic network reconstructions of diverse Escherichia strains reveal strain-specific adaptations

Bottom-up approaches to systems biology rely on constructing a mechanistic basis for the biochemical and genetic processes that underlie cellular functions. Genome-scale network reconstructions of metabolism are built from all known metabolic reactions and metabolic genes in a target organism. A network reconstruction can be converted into a mathematical format and thus lend itself to mathematical analysis. Genome-scale models (GEMs) of metabolism enable a systems approach to characterize the pan and core metabolic capabilities of the Escherichia genus. In this work, GEMs were constructed for 222 representative strains of Escherichia across HC1100 levels spanning the known Escherichia phylogeny. The models were used to study Escherichia metabolic diversity and speciation on a large scale. The results show that unique strain-specific metabolic capabilities correspond to different species and nutrient niches. This work is a first step towards a curated reconstruction of pan-Escherichia metabolism. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power

SignificanceClostridioides difficile infections are the most common source of hospital-acquired infections and are responsible for an extensive burden on the health care system. Strains of the C. difficile species comprise diverse lineages and demonstrate genome variability, with advantageous trait acquisition driving the emergence of endemic lineages. Here, we present a systems biology analysis of C. difficile that evaluates strain-specific genotypes and phenotypes to investigate the overall diversity of the species. We develop a strain typing method based on similarity of accessory genomes to identify and contextualize genetic loci capable of discriminating between strain groups.

Mathematical models to study the biology of pathogens and the infectious diseases they cause

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

Antibacterial and Sporicidal Activity Evaluation of Theaflavin-3,3'-digallate

Theaflavin-3,3'-digallate (TFDG), a polyphenol derived from the leaves of Camellia sinensis, is known to have many health benefits. In this study, the antibacterial effect of TFDG against nine bacteria and the sporicidal activities on spore-forming Bacillus spp. have been investigated. Microplate assay, colony-forming unit, BacTiter-Glo^TM, and Live/Dead Assays showed that 250 µg/mL TFDG was able to inhibit bacterial growth up to 99.97%, while 625 µg/mL TFDG was able to inhibit up to 99.92% of the spores from germinating after a one-hour treatment. Binding analysis revealed the favorable binding affinity of two germination-associated proteins, GPR and Lgt (GerF), to TFDG, ranging from -7.6 to -10.3 kcal/mol. Semi-quantitative RT-PCR showed that TFDG treatment lowered the expression of gpr, ranging from 0.20 to 0.39 compared to the control in both Bacillus spp. The results suggest that TFDG not only inhibits the growth of vegetative cells but also prevents the germination of bacterial spores. This report indicates that TFDG is a promising broad-spectrum antibacterial and anti-spore agent against Gram-positive, Gram-negative, acid-fast bacteria, and endospores. The potential anti-germination mechanism has also been elucidated.

Computational approaches to understanding Clostridioides difficile metabolism and virulence

The progress of infection by Clostridioides difficile is strongly influenced by metabolic cues it encounters as it colonizes the gastrointestinal tract. Both colonization and regulation of virulence have a multi-factorial interaction between host, microbiome, and gene expression cascades. While these connections with metabolism have been understood for some time, many mechanisms of control have remained difficult to directly assay due to high metabolic variability among C. difficile isolates and difficult genetic systems. Computational systems offer a means to interrogate structure of complex or noisy datasets and generate useful, tractable hypotheses to be tested in the laboratory. Recently, in silico techniques have provided powerful insights into metabolic elements of C. difficile infection ranging from virulence regulation to interactions with the gut microbiota. In this review, we introduce and provide context to the methods of computational modeling that have been applied to C. difficile metabolism and virulence thus far. The techniques discussed here have laid the foundation for future multi-scale efforts aimed at understanding the complex interplay of metabolic activity between pathogen, host, and surrounding microbial community in the regulation of C. difficile pathogenesis.

Is the kinetome conserved?

Computational biologists have labored for decades to produce kinetic models to mechanistically explain complex metabolic phenomena. The estimation of numerical values for the large number of kinetic parameters required for constructing large-scale models has been a major challenge. This collection of kinetic constants has recently been termed the kinetome (Nilsson et al, 2017). In this Commentary, we discuss the recent advances in the field that suggest that the kinetome may be more conserved than expected. A conserved kinetome will accelerate the development of future kinetic models of integrated cellular functions and expand their scope and usability in many fields of biology and biomedicine.

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

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.

Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis

The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hypervirulent isolate (strain [str.] R20291) and a historic strain (str. 630), validating both with in vitro and in vivo data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the pentose phosphate pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in higher-order phenotypes like bacterial pathogenesis. IMPORTANCE Clostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors. In the past, genome-scale metabolic network reconstruction (GENRE) analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that contribute to downstream virulence phenotypes. With this in mind, we generated and extensively curated C. difficile GENREs for both a well-studied laboratory strain (str. 630) and a more recently characterized hypervirulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization data sets. Subsequent exploration of context-specific metabolism during both in vitro growth and infection revealed consistent patterns of metabolism which corresponded with experimentally measured increases in virulence factor expression. Our results support that differential C. difficile virulence is associated with distinct metabolic programs related to use of carbon sources and provide a platform for identification of novel therapeutic targets.

AMiGA: Software for Automated Analysis of Microbial Growth Assays

The analysis of microbial growth is one of the central methods in the field of microbiology. Microbial growth dynamics can be characterized by meaningful parameters, including carrying capacity, exponential growth rate, and growth lag. However, microbial assays with clinical isolates, fastidious organisms, or microbes under stress often produce atypical growth shapes that do not follow the classical microbial growth pattern. Here, we introduce the analysis of microbial growth assays (AMiGA) software, which streamlines the analysis of growth curves without any assumptions about their shapes. AMiGA can pool replicates of growth curves and infer summary statistics for biologically meaningful growth parameters. In addition, AMiGA can quantify death phases and characterize diauxic shifts. It can also statistically test for differential growth under distinct experimental conditions. Altogether, AMiGA streamlines the organization, analysis, and visualization of microbial growth assays. IMPORTANCE Our current understanding of microbial physiology relies on the simple method of measuring microbial populations' sizes over time and under different conditions. Many advances have increased the throughput of those assays and enabled the study of nonlab-adapted microbes under diverse conditions that widely affect their growth dynamics. Our software provides an all-in-one tool for estimating the growth parameters of microbial cultures and testing for differential growth in a high-throughput and user-friendly fashion without any underlying assumptions about how microbes respond to their growth conditions.