Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes.

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multilocus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.

Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism.

Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches identify some commonly affected pathways and metabolites but are not able to readily identify metabolic adaptations in response to cardiotoxicity. The paired data was integrated with a genome-scale metabolic network reconstruction of the heart to identify shifted metabolic functions, unique metabolic reactions, and changes in flux in metabolic reactions in response to these compounds. Using this approach, we confirm previously seen changes in the p53 pathway by doxorubicin and RNA synthesis by 5-fluorouracil, we find evidence for an increase in phospholipid metabolism in response to acetaminophen, and we see a shift in central carbon metabolism suggesting an increase in metabolic demand after treatment with doxorubicin and 5-fluorouracil.

Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes.

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multi-locus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.

Systems-ecology designed bacterial consortium protects from severe Clostridioides difficile infection.

Fecal Microbiota Transplant (FMT) is an emerging therapy that has had remarkable success in treatment and prevention of recurrent Clostridioides difficile infection (rCDI). FMT has recently been associated with adverse outcomes such as inadvertent transfer of antimicrobial resistance, necessitating development of more targeted bacteriotherapies. To address this challenge, we developed a novel systems biology pipeline to identify candidate probiotic strains that would be predicted to interrupt C. difficile pathogenesis. Utilizing metagenomic characterization of human FMT donor samples, we identified those metabolic pathways most associated with successful FMTs and reconstructed the metabolism of encoding species to simulate interactions with C. difficile . This analysis resulted in predictions of high levels of cross-feeding for amino acids in species most associated with FMT success. Guided by these in silico models, we assembled consortia of bacteria with increased amino acid cross-feeding which were then validated in vitro . We subsequently tested the consortia in a murine model of CDI, demonstrating total protection from severe CDI through decreased toxin levels, recovered gut microbiota, and increased intestinal eosinophils. These results support the novel framework that amino acid cross-feeding is likely a critical mechanism in the initial resolution of CDI by FMT. Importantly, we conclude that our predictive platform based on predicted and testable metabolic interactions between the microbiota and C. difficile led to a rationally designed biotherapeutic framework that may be extended to other enteric infections.

Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies.

Clostridioides difficile pathogenesis is mediated through its two toxin proteins, TcdA and TcdB, which induce intestinal epithelial cell death and inflammation. It is possible to alter C. difficile toxin production by changing various metabolite concentrations within the extracellular environment. However, it is unknown which intracellular metabolic pathways are involved and how they regulate toxin production. To investigate the response of intracellular metabolic pathways to diverse nutritional environments and toxin production states, we use previously published genome-scale metabolic models of C. difficile strains CD630 and CDR20291 (iCdG709 and iCdR703). We integrated publicly available transcriptomic data with the models using the RIPTiDe algorithm to create 16 unique contextualized C. difficile models representing a range of nutritional environments and toxin states. We used Random Forest with flux sampling and shadow pricing analyses to identify metabolic patterns correlated with toxin states and environment. Specifically, we found that arginine and ornithine uptake is particularly active in low toxin states. Additionally, uptake of arginine and ornithine is highly dependent on intracellular fatty acid and large polymer metabolite pools. We also applied the metabolic transformation algorithm (MTA) to identify model perturbations that shift metabolism from a high toxin state to a low toxin state. This analysis expands our understanding of toxin production in C. difficile and identifies metabolic dependencies that could be leveraged to mitigate disease severity.

S100B Inhibition Attenuates Intestinal Damage and Diarrhea Severity During Clostridioides difficile Infection by Modulating Inflammatory Response.

The involvement of the enteric nervous system, which is a source of S100B, in Clostridioides difficile (C. difficile) infection (CDI) is poorly understood although intestinal motility dysfunctions are known to occur following infection. Here, we investigated the role of S100B in CDI and examined the S100B signaling pathways activated in C. difficile toxin A (TcdA)- and B (TcdB)-induced enteric glial cell (EGC) inflammatory response. The expression of S100B was measured in colon tissues and fecal samples of patients with and without CDI, as well as in colon tissues from C. difficile-infected mice. To investigate the role of S100B signaling in IL-6 expression induced by TcdA and TcdB, rat EGCs were used. Increased S100B was found in colonic biopsies from patients with CDI and colon tissues from C. difficile-infected mice. Patients with CDI-promoted diarrhea exhibited higher levels of fecal S100B compared to non-CDI cases. Inhibition of S100B by pentamidine reduced the synthesis of IL-1ß, IL-18, IL-6, GMCSF, TNF-α, IL-17, IL-23, and IL-2 and downregulated a variety of NFκB-related genes, increased the transcription (SOCS2 and Bcl-2) of protective mediators, reduced neutrophil recruitment, and ameliorated intestinal damage and diarrhea severity in mice. In EGCs, TcdA and TcdB upregulated S100B-mediated IL-6 expression via activation of RAGE/PI3K/NFκB. Thus, CDI appears to upregulate colonic S100B signaling in EGCs, which in turn augment inflammatory response. Inhibition of S100B activity attenuates the intestinal injury and diarrhea caused by C. difficile toxins. Our findings provide new insight into the role of S100B in CDI pathogenesis and opens novel avenues for therapeutic interventions.

Multidimensional Clinical Surveillance of Pseudomonas aeruginosa Reveals Complex Relationships between Isolate Source, Morphology, and Antimicrobial Resistance.

Antimicrobial susceptibility in Pseudomonas aeruginosa is dependent on a complex combination of host and pathogen-specific factors. Through the profiling of 971 clinical P. aeruginosa isolates from 590 patients and collection of paired patient metadata, we show that antimicrobial resistance is associated with not only patient-centric factors (e.g., cystic fibrosis and antipseudomonal prescription history) but also microbe-specific phenotypes (e.g., mucoid colony morphology). Additionally, isolates from different sources (e.g., respiratory tract, urinary tract) displayed rates of antimicrobial resistance that were correlated with source-specific antimicrobial prescription strategies. Furthermore, isolates from the same patient often displayed a high degree of heterogeneity, highlighting a key challenge facing personalized treatment of infectious diseases. Our findings support novel relationships between isolate and patient-level data sets, providing a potential guide for future antimicrobial treatment strategies. IMPORTANCE P. aeruginosa is a leading cause of nosocomial infection and infection in patients with cystic fibrosis. While P. aeruginosa infection and treatment can be complicated by a variety of antimicrobial resistance and virulence mechanisms, pathogen virulence is rarely recorded in a clinical setting. In this study, we discovered novel relationships between antimicrobial resistance, virulence-linked morphologies, and isolate source in a large and variable collection of clinical P. aeruginosa isolates. Our work motivates the clinical surveillance of virulence-linked P. aeruginosa morphologies as well as the tracking of source-specific antimicrobial prescription and resistance patterns.

Untargeted Metabolomics Reveals Species-Specific Metabolite Production and Shared Nutrient Consumption by Pseudomonas aeruginosa and Staphylococcus aureus.

While bacterial metabolism is known to impact antibiotic efficacy and virulence, the metabolic capacities of individual microbes in cystic fibrosis lung infections are difficult to disentangle from sputum samples. Here, we show that untargeted metabolomic profiling of supernatants of multiple strains of Pseudomonas aeruginosa and Staphylococcus aureus grown in monoculture in synthetic cystic fibrosis media (SCFM) reveals distinct species-specific metabolic signatures despite intraspecies metabolic variability. We identify a set of 15 metabolites that were significantly consumed by both P. aeruginosa and S. aureus, suggesting that nutrient competition has the potential to impact community dynamics even in the absence of other pathogen-pathogen interactions. Finally, metabolites that were uniquely produced by one species or the other were identified. Specifically, the virulence factor precursor anthranilic acid, as well as the quinoline 2,4-quinolinediol (DHQ), were robustly produced across all tested strains of P. aeruginosa. Through the direct comparison of the extracellular metabolism of P. aeruginosa and S. aureus in a physiologically relevant environment, this work provides insight toward the potential for metabolic interactions in vivo and supports the development of species-specific diagnostic markers of infection. IMPORTANCE Interactions between P. aeruginosa and S. aureus can impact pathogenicity and antimicrobial efficacy. In this study, we aim to better understand the potential for metabolic interactions between P. aeruginosa and S. aureus in an environment resembling the cystic fibrosis lung. We find that S. aureus and P. aeruginosa consume many of the same nutrients, suggesting that metabolic competition may play an important role in community dynamics during coinfection. We further identify metabolites uniquely produced by either organism with the potential to be developed into species-specific biomarkers of infection in the cystic fibrosis lung.

Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model.

In diseased states, the heart can shift to use different carbon substrates, measured through changes in uptake of metabolites by imaging methods or blood metabolomics. However, it is not known whether these measured changes are a result of transcriptional changes or external factors. Here, we explore transcriptional changes in late-stage heart failure using publicly available data integrated with a model of heart metabolism. First, we present a heart-specific genome-scale metabolic network reconstruction (GENRE), iCardio. Next, we demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying tasks inferred from differential expression (TIDEs), which represent metabolic functions associated with changes in gene expression. We identify decreased gene expression for nitric oxide (NO) and N-acetylneuraminic acid (Neu5Ac) synthesis as common metabolic markers of heart failure. The methods presented here for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissues and diseases of interest.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.

Minimum Bactericidal Concentration of Ciprofloxacin to Pseudomonas aeruginosa Determined Rapidly Based on Pyocyanin Secretion.

Infections due to Pseudomonas aeruginosa (P. aeruginosa) often exhibit broad-spectrum resistance and persistence to common antibiotics. Persistence is especially problematic with immune-compromised subjects who are unable to eliminate the inhibited bacteria. Hence, antibiotics must be used at the appropriate minimum bactericidal concentration (MBC) rather than at minimum inhibitory concentration (MIC) levels. However, MBC determination by conventional methods requires a 24 h culture step in the antibiotic media to confirm inhibition, followed by a 24 h sub-culture step in antibiotic-free media to confirm the lack of bacterial growth. We show that electrochemical detection of pyocyanin (PYO), which is a redox-active bacterial metabolite secreted by P. aeruginosa, can be used to rapidly assess the critical ciprofloxacin level required for bactericidal deactivation of P. aeruginosa within just 2 hours in antibiotic-treated growth media. The detection sensitivity for PYO can be enhanced by using nanoporous gold that is modified with a self-assembled monolayer to lower interference from oxygen reduction, while maintaining a low charge transfer resistance level and preventing electrode fouling within biological sample matrices. In this manner, bactericidal efficacy of ciprofloxacin towards P. aeruginosa at the MBC level and bacterial persistence at the MIC level can be determined rapidly, as validated at later timepoints using bacterial subculture in antibiotic-free media.

A bivalent vaccine confers immunogenicity and protection against Shigella flexneri and enterotoxigenic Escherichia coli infections in mice.

Vaccine studies for Shigella flexneri and enterotoxigenic Escherichia coli have been impaired by the lack of optimal animal models. We used two murine models to show that a S. flexneri 2a bivalent vaccine (CVD 1208S-122) expressing enterotoxigenic Escherichia coli colonization factor antigen-I (CFA/I) and the binding subunits A2 and B of heat labile-enterotoxin (LTb) is immunogenic and protects against weight loss and diarrhea. These findings document the immunogenicity and pre-clinical efficacy effects of CVD 1208S-122 vaccine and suggest that further work can help elucidate relevant immune responses and ultimately its clinical efficacy in humans.

Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes.

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

Evaluating the efficacy of an algae-based treatment to mitigate elicitation of antibiotic resistance.

Antibiotics in the effluents of municipal wastewater treatment plants (WWTP) may create selective pressures to induce antibiotic resistance in bacteria downstream. This study evaluates ciprofloxacin (CIP) removal by a freshwater alga, Scenedesmus dimorphus, to assess the efficacy of algae-based tertiary treatment in reducing effluent-induced CIP resistance. Results show significant CIP removal in light-exposed samples without algae and experimental algae (EA) samples: 53% and 93%, respectively, over 144 h. A residual antibiotic potency assay reveals that untreated CIP is significantly more growth-inhibiting to a model bacterium (Escherichia coli) than the algae-treated and light-exposed samples during short exposures (6 h). Adaptive laboratory evolution (ALE), again using E. coli, reveals that treated samples exhibit reduced capacity to elicit CIP resistance during sustained exposures compared to untreated CIP. Finally, observed CIP resistance in the CIP-exposed ALE lineages is corroborated via genotype characterization, which reveals the presence of resistance-associated mutations in gyrase subunit A (gyrA) that are not present in ALE lineages exposed to algae treated or light-exposed samples. As such, algae-mediated tertiary treatment could be effective in suppressing CIP resistance in bacterial communities downstream from WWTP. In addition, ALE is useful for assessing the potential of wastewater-relevant samples to elicit antibiotic resistance downstream.

Outcomes of a Multidisciplinary Clinic in Evaluating Recurrent Clostridioides difficile Infection Patients for Fecal Microbiota Transplant: A Retrospective Cohort Analysis.

Fecal microbiota transplantation (FMT) has been shown to be an effective treatment for recurrent Clostridioides difficile infections (rCDIs). We assessed the benefits of a multidisciplinary C. difficile clinic for screening FMT eligibility in patients with rCDI. Patients seen at the University of Virginia Complicated C. difficile Clinic (CCDC) underwent comprehensive evaluation for possible FMT. Patients were eligible for FMT if there was history of greater than two episodes of rCDI. Patients were evaluated for the outcome after evaluation in the clinic. A total of 113 patients were evaluated: 77 were eligible for FMT, of which 25 patients did not undergo FMT. The rate of recurrence at three months and all-cause mortality were 4.5% and 7% for patients who received FMT and 16.7% and 12.5% for eligible patients who did not receive FMT. There were 36 patients who were not eligible for FMT, with two or fewer recurrences and a recurrence rate of 8.8% and all-cause mortality of 6%. One in three patients screened for FMT had a nutritional deficiency diagnosed, with zinc deficiency being most common (20%). Additional diagnoses, including inflammatory bowel disease, were made during the evaluation. FMT is a highly effective treatment for rCDI, most notably in patients with multiple recurrences. A systematic approach for evaluating patients with rCDI helps identify patients who benefit most from FMT and those who have other conditions.

A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools.

GEnome-scale Network REconstructions (GENREs) mathematically describe metabolic reactions of an organism or a specific cell type. GENREs can be used with a number of constraint-based reconstruction and analysis (COBRA) methods to make computational predictions on how a system changes in different environments. We created a simplified GENRE (referred to as iSIM) that captures central energy metabolism with nine metabolic reactions to illustrate the use of and promote the understanding of GENREs and constraint-based methods. We demonstrate the simulation of single and double gene deletions, flux variability analysis (FVA), and test a number of metabolic tasks with the GENRE. Code to perform these analyses is provided in Python, R, and MATLAB. Finally, with iSIM as a guide, we demonstrate how inaccuracies in GENREs can limit their use in the interrogation of energy metabolism.

Amixicile Reduces Severity of Cryptosporidiosis but Does Not Have In Vitro Activity against Cryptosporidium.

Cryptosporidium species cause significant morbidity in malnourished children. Nitazoxanide (NTZ) is the only approved treatment for cryptosporidiosis, but NTZ has diminished effectiveness during malnutrition. Here, we show that amixicile, a highly selective water-soluble derivative of NTZ diminishes Cryptosporidium infection severity in a malnourished mouse model despite a lack of direct anticryptosporidial activity. We suggest that amixicile, by tamping down anaerobes associated with intestinal inflammation, reverses weight loss and indirectly mitigates infection-associated pathology.

Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota.

The diversity and number of species present within microbial communities create the potential for a multitude of interspecies metabolic interactions. Here, we develop, apply, and experimentally test a framework for inferring metabolic mechanisms associated with interspecies interactions. We perform pairwise growth and metabolome profiling of co-cultures of strains from a model mouse microbiota. We then apply our framework to dissect emergent metabolic behaviors that occur in co-culture. Based on one of the inferences from this framework, we identify and interrogate an amino acid cross-feeding interaction and validate that the proposed interaction leads to a growth benefit in vitro. Our results reveal the type and extent of emergent metabolic behavior in microbial communities composed of gut microbes. We focus on growth-modulating interactions, but the framework can be applied to interspecies interactions that modulate any phenotype of interest within microbial communities.

A novel mouse model of Campylobacter jejuni enteropathy and diarrhea.

Campylobacter infections are among the leading bacterial causes of diarrhea and of 'environmental enteropathy' (EE) and growth failure worldwide. However, the lack of an inexpensive small animal model of enteric disease with Campylobacter has been a major limitation for understanding its pathogenesis, interventions or vaccine development. We describe a robust standard mouse model that can exhibit reproducible bloody diarrhea or growth failure, depending on the zinc or protein deficient diet and on antibiotic alteration of normal microbiota prior to infection. Zinc deficiency and the use of antibiotics create a niche for Campylobacter infection to establish by narrowing the metabolic flexibility of these mice for pathogen clearance and by promoting intestinal and systemic inflammation. Several biomarkers and intestinal pathology in this model also mimic those seen in human disease. This model provides a novel tool to test specific hypotheses regarding disease pathogenesis as well as vaccine development that is currently in progress.

Abundant production of exopolysaccharide by EAEC strains enhances the formation of bacterial biofilms in contaminated sprouts.

Enteroaggregative E. coli (EAEC) is associated with food-borne outbreaks of diarrhea and growth faltering among children in developing countries. A Shiga toxin-producing EAEC strain of serotype O104:H4 strain caused one of the largest outbreaks of a food-borne infection in Europe in 2011. The outbreak was traced to contaminated fenugreek sprouts, yet the mechanisms whereby such persistent contamination of sprouts could have occurred are not clear. We found that under ambient conditions of temperature and in minimal media, pathogenic Shiga toxin-producing EAEC O104:H4 227-11 and non-Shiga toxin-producing 042 strains both produce high levels of exopolysaccharide structures (EPS) that are released to the external milieu. The exopolysaccharide was identified as colanic acid (CA). Unexpectedly, Shiga-toxin producing EAEC strain 227-11 produced 3-6-fold higher levels of CA than the 042 strain, suggesting differential regulation of the CA in the two strains. The presence of CA was accompanied by the formation of large biofilm structures on the surface of sprouts. The wcaF-wza chromosomal locus was required for the synthesis of CA in EAEC 042. Deletion in the glycosyltransferase wcaE gene abolished the production of CA in 042, and resulted in diminished adherence to sprouts when co-cultured at ambient temperature. In conclusion, this work suggests that copious production of CA may contribute to persistence of EAEC in the environment and suggests a potential explanation for the large Shiga toxin-producing EAEC outbreak in 2011.