Biology subjects

Jenior, M. L.

Publications and source records attributed to Jenior, M. L..

12 recordsLinked to original sources

bioRxiv2023

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.

systems biology↗

bioRxiv2022

Reconstructor: A COBRApy compatible tool for automated genome-scale metabolic network reconstruction with parsimonious flux-based gap-filling

SummaryGenome-scale metabolic network reconstructions (GENREs) are valuable for understanding cellular metabolism in silico. Several tools exist for automatic GENRE generation. However, these tools frequently (1) do not readily integrate with some of the widely-used suites of packaged methods available for network analysis, (2) lack effective network curation tools, and (3) are not sufficiently user-friendly. Here, we present Reconstructor, a user-friendly COBRApy compatible tool with ModelSEED namespace compatibility and a pFBA-based gap-filling technique. We demonstrate how Reconstructor readily generates high-quality GENRES that are useful for further biological discovery. Availability and ImplementationThe Reconstructor package is freely available for download via pip in the command line (pip install reconstructor). Usage instructions and benchmarking data are available at http://github.com/emmamglass/reconstructor. ContactJason Papin: papin@virginia.edu

bioinformatics↗

bioRxiv2021

Identifying metabolic adaptations during cardiotoxicity using paired transcriptomics andmetabolomics data integrated with a model of heart metabolism

Improvements in the diagnosis and treatment of cancer has revealed the 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 (GENRE) 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 known mechanisms of doxorubicin-induced cardiotoxicity and provide hypotheses for metabolic adaptations in cardiotoxicity for 5-fluorouracil, doxorubicin, and acetaminophen.

bioengineering↗

bioRxiv2021

Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae

Infections due to carbapenem-resistant Enterobacteriaceae have recently emerged as one of the most urgent threats to hospitalized patients within the United States and Europe. By far the most common etiological agent of these infections is Klebsiella pneumoniae, frequently manifesting in hospital-acquired pneumonia with a mortality rate of [~]50% even with antimicrobial intervention. We performed transcriptomic analysis of data collected from in vitro characterization of both laboratory and clinical isolates revealed shifts in expression of multiple master metabolic regulators across isolate types. Metabolism has been previously shown to be an effective target for antibacterial therapy, and GENREs have provided a powerful means to accelerate identification of potential targets in silico. Combining these techniques with the transcriptome meta-analysis, we generated context-specific models of metabolism utilizing a well-curated GENRE of K. pneumoniae (iYL1228) to identify novel therapeutic targets. Functional metabolic analyses revealed that both composition and metabolic activity of clinical isolate-associated context-specific models significantly differs from laboratory isolate-associated models of the bacterium. Additionally, we identified increased consumption of L-valine in clinical isolate-specific growth simulations. Importantly, valine has been shown to augment macrophage phagocytosis, and this result could be indicative of an immunosuppressive strategy Klebsiella pneumoniae evolved for survival during infection. These findings warrant future studies for potential efficacy of valine transaminase inhibition as a target against K. pneumoniae infection. ImportanceIncidences of infection by Klebsiella pneumoniae have grown in frequency to become the leading agents of CRE infection among hospitalized patients in the United States and Europe. Transcriptomic meta-analysis of data collected from both laboratory and clinical isolates indicated significant shifts in expression of key transcription factors related to metabolism. Metabolic network reconstructions have previously proven effective for quickly identifying potential targets in silico, therefore we combined these approaches by integrating the transcriptomic data from each isolate type into a well-curated GENRE of K. pneumoniae to predict emergent metabolic patterns. Leveraging this systems-biology approach we found discordant patterns of active metabolism between clinical and laboratory isolates, with a striking difference in L-valine catabolism. Exogenous valine is known to increase macrophage phagocytosis, and our results may support immunomodulatory activity in K. pneumoniae evolved to avoid host clearance.

microbiology↗

bioRxiv2021

Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions

Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial community metabolism. The process of automatically generating GENREs includes identifying metabolic reactions supported by sufficient genomic evidence to generate a draft metabolic network. The draft GENRE is then gapfilled with additional reactions in order to recapitulate specific growth phenotypes as indicated with associated experimental data. Previous methods have implemented absolute mapping thresholds for the reactions automatically included in draft GENREs; however, there is growing evidence that integrating annotation evidence in a continuous form can improve model accuracy. There is a need for flexibility in the structure of GENREs to better account for uncertainty in biological data, unknown regulatory mechanisms, and context specificity associated with data inputs. To address this issue, we present a novel method that provides a framework for quantifying combined genomic, biochemical, and phenotypic evidence for each biochemical reaction during automated GENRE construction. Our method, Constraint-based Analysis Yielding reaction Usage across metabolic Networks (CANYUNs), generates accurate GENREs with a quantitative metric for the cumulative evidence for each reaction included in the network. The structure of a CANYUN GENRE allows for the simultaneous integration of three data inputs while maintaining all supporting evidence for biochemical reactions that may be active in an organism. CANYUNs is designed to maximize the utility of experimental and annotation datasets and to ultimately assist in the curation of the reference datasets used for the automatic reconstruction of metabolic networks. We validated CANYUNs by generating an E. coli K-12 model and compared it to the manually curated reconstruction iML1515. Finally, we demonstrated the use of CANYUNs to build a model by generating an E. coli Nissle CANYUN GENRE using novel phenotypic data that we collected. This method may address key challenges for the procedural construction of metabolic networks by leveraging uncertainty and redundancy in biological data.

systems biology↗

bioRxiv2021

Protection from lethal Clostridioides difficile infection via intraspecies competition for co-germinant

Clostridioides difficile, a Gram-positive, spore-forming bacterium, is the primary cause of infectious nosocomial diarrhea. Antibiotics are a major risk factor for C. difficile infection (CDI) as they disrupt the gut microbial community, enabling increased germination of spores and growth of vegetative C. difficile. To date the only single-species bacterial preparation that has demonstrated efficacy in reducing recurrent CDI in humans is non-toxigenic C. difficile. Using multiple infection models we determined that pre-colonization with a less virulent strain is sufficient to protect from challenge with a lethal strain of C. difficile, surprisingly even in the absence of adaptive immunity. Additionally, we showed that protection is dependent on high levels of colonization by the less virulent strain and that it is mediated by exclusion of the invading strain. Our results suggest that reduction of amino acids, specifically glycine following colonization by the first strain of C. difficile is sufficient to decrease germination of the second strain thereby limiting colonization by the lethal strain. ImportanceAntibiotic-associated colitis is often caused by infection with the bacterium Clostridioides difficile. In this study we found that reduction of the amino acid glycine by pre-colonization with a less virulent strain of C. difficile is sufficient to decrease germination of a second strain. This finding demonstrates that the axis of competition for nutrients can include multiple life stages. This work is important, as it is the first to identify a possible mechanism through which pre-colonization with C. difficile, a current clinical therapy, provides protection from reinfection. Furthermore, our work suggests that targeting nutrients utilized by all life stages could be an improved strategy for bacterial therapeutics that aim to restore colonization resistance in the gut.

microbiology↗

bioRxiv2020

Genome-scale metabolic reconstruction analysis of Clostridioides difficile identifies conserved patterns of virulence-related metabolic reprogramming

The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the US. 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 hyper-virulent isolate (str. R20291) and a historic strain (str. 630), validating both with in vitro and in vivo datasets. Growth simulations revealed significant correlations with measured carbon source usage (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 bacterial pathogenesis. ImportanceClostridioides difficile is a Gram-positive, sporulating anaerobe that has become the leading 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) as well as a more recently characterized hyper-virulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization datasets. 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 use of carbon sources and provides a platform for identification of novel therapeutic targets.

bioengineering↗

bioRxiv2019

Functional Anabolic Network Analysis of Human-associated Lactobacillus Strains

Members of the Lactobacillus genus are frequently utilized in the probiotic industry with many species conferring demonstrated health benefits; however, these effects are largely strain-dependent. We designed a method called PROTEAN (Probabilistic Reconstruction Of constituent Anabolic Networks) to computationally analyze the genomic annotations and predicted metabolic production capabilities of 144 strains across 16 species of Lactobacillus isolated from human intestinal, oral, and vaginal body sites. Using PROTEAN we conducted a genome-scale metabolic network comparison between strains, revealing that metabolic capabilities differ by isolation site. Notably, PROTEAN does not require a well-curated genome-scale metabolic network reconstruction to provide biological insights. We found that predicted metabolic capabilities of lactobacilli isolated from the vaginal microbiota cluster separately from intestinal and oral isolates, and we also uncovered an overlap in the predicted metabolic production capabilities of intestinal and oral isolates. Using machine learning, we determined the most informative metabolic products driving the difference between predicted metabolic capabilities of intestinal, oral, and vaginal isolates. Notably, intestinal and oral isolates were predicted to have a higher likelihood of producing D-alanine, D/L-serine, and L-proline, while the vaginal isolates were distinguished by a higher predicted likelihood of producing L-arginine, citrulline, and D/L-lactate. We found the distinguishing products to be consistent with published experimental literature. This study showcases a systematic technique, PROTEAN, for comparing the predicted functional metabolic output of microbes using genome-scale metabolic network analysis and computational modeling and provides unique insight into human-associated Lactobacillus biology.\n\nImportanceThe Lactobacillus genus has been shown to be important for human health. Lactobacilli have been isolated from human intestinal, oral, and vaginal sites. Members of the genus contribute significantly to the maintenance of vaginal health by providing colonization resistance to invading pathogens. A wide variety of clinical studies have indicated that Lactobacillus-based probiotics confer health benefits for several gut- and immune-associated diseases. Microbes interact with the human body in several ways, including the production of metabolites that influence physiology or other surrounding microbes. We have conducted a strain-level genome-scale metabolic network reconstruction analysis of human-associated Lactobacillus strains, revealing that predicted metabolic capabilities differ when comparing intestinal/oral isolate to vaginal isolates. The technique we present here allows for direct interpretation of discriminating features between the experimental groups.

systems biology↗

bioRxiv2019

Parsimonious transcript data integration improves context-specific predictions of bacterial metabolism in complex environments

The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cells investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities. Author SummaryTranscriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.

systems biology↗

bioRxiv2019

The gut microbiota is associated with clearance of Clostridium difficile infection independent of adaptive immunity

Clostridium (Clostridioides) difficile, a Gram-positive, anaerobic bacterium is the leading single cause of nosocomial infections in the United States. A major risk factor for C. difficile infection (CDI) is prior exposure to antibiotics as they increase susceptibility to CDI by altering the membership of the microbial community enabling colonization. The importance of the gut microbiota in providing protection from CDI is underscored by the reported 80-90% success rate of fecal microbial transplants in treating recurrent infection. Adaptive immunity, specifically humoral immunity, is also sufficient to protect from both acute and recurrent CDI. However, the role of the adaptive immune system in mediating clearance of C. difficile has yet to be resolved. Using murine models of CDI, we found that adaptive immunity is dispensable for clearance of C. difficile. However, Random Forest analysis using only 2 members of the resident bacterial community correctly identified animals that would go on to clear the infection with 66.7% accuracy. These findings indicate that the indigenous gut microbiota independent of adaptive immunity facilitates clearance of C. difficile from the murine gastrointestinal tract.\n\nImportanceC. difficile infection is a major cause of morbidity and mortality in hospitalized patients in the United States. Currently the role of the adaptive immune response in modulating levels of C. difficile colonization is unresolved. This work suggests that the indigenous gut microbiota is a main factor that promotes clearance of C. difficile from the GI tract. Our results show that clearance of C. difficile can occur without contributions from the adaptive immune response. This study also has implications for the design of preclinical studies testing the efficacy of vaccines on clearance of bacterial pathogens as inherent differences in the baseline community structure of animals may bias findings.

microbiology↗

bioRxiv2017

Clostridium difficile differentially alters the structure and metabolism of distinct cecal microbiomes to promote persistent colonization during infection

Susceptibility to Clostridium difficile infection is primarily associated with previous exposure to antibiotics, which compromise the structure and function of the gut bacterial community. Specific antibiotic classes correlate more strongly with recurrent or persistent C. difficile infection. As such, we utilized a mouse model of infection to explore the effect of distinct antibiotic classes on the impact that infection has on community-level transcription and metabolic signatures shortly following pathogen colonization and how those changes may associate with persistence of C. difficile. Untargeted metabolomic analysis revealed that C. difficile infection had significantly larger impacts on the metabolic environment across cefoperazone and streptomycin-pretreated mice, which become persistently colonized compared to clindamycin-pretreated mice where infection quickly became undetectable. Through metagenome-enabled metatranscriptomics we observed that transcripts for genes associated with carbon and energy acquisition were greatly reduced in infected animals, suggesting those niches were instead occupied by C. difficile. Furthermore, the largest changes in transcription were seen in the least abundant species indicating that C. difficile may \"attack the loser\" in gut environments where sustained infection occurs more readily. Overall, our results suggest that C. difficile is able to restructure the nutrient-niche landscape in the gut to promote persistent infection.

microbiology↗

bioRxiv2016

Clostridium difficile colonizes alternative nutrient niches during infection across distinct murine gut environments

Clostridium difficile is the largest single cause of hospital-acquired infection in the United States. A major risk factor for Clostridium difficile infection (CDI) is prior exposure to antibiotics, as they disrupt the gut bacterial community which protects from C. difficile colonization. Multiple antibiotic classes have been associated with CDI susceptibility; many leading to distinct community structures stemming from variation in bacterial targets of action. These community structures present separate metabolic challenges to C. difficile. Therefore we hypothesized that the pathogen adapts its physiology to the nutrients within different gut environments. Utilizing an in vivo CDI model, we demonstrated C. difficile highly colonized ceca of mice pretreated with any of three antibiotics from distinct classes. Levels of C. difficile spore formation and toxin activity varied between animals based on the antibiotic pretreatment. These physiologic processes in C. difficile are partially regulated by environmental nutrient concentrations. To investigate metabolic responses of the bacterium in vivo, we performed transcriptomic analysis of C. difficile from ceca of infected mice across pretreatments. This revealed heterogeneous expression in numerous catabolic pathways for diverse growth substrates. To assess which resources C. difficile exploited, we developed a genome-scale metabolic model with a transcriptome-enabled metabolite scoring algorithm integrating network architecture. This platform identified nutrients C. difficile used preferentially between pretreatments, which were validated through untargeted mass spectrometry of each microbiome. Our results supported the hypothesis that C. difficile inhabits alternative nutrient niches across cecal microbiomes with increased preference for nitrogen-containing carbon sources, particularly Stickland fermentation substrates and host-derived glycans.\n\nImportanceInfection by the bacterium Clostridium difficile causes an inflammatory diarrheal disease which can become life-threatening, and has grown to be the most prevalent nosocomial infection. Susceptibility to C. difficile infection is strongly associated with previous antibiotic treatment, which disrupts the gut microbiota and reduces its ability to prevent colonization. In this study we demonstrated that C. difficile altered pathogenesis between hosts pretreated with antibiotics from separate classes, and exploited different nutrient sources across these environments. Our metabolite score calculation also provides a platform to study nutrient requirements of pathogens during an infection. Our results suggest that C. difficile colonization resistance is mediated by multiple groups of bacteria competing for several subsets of nutrients and could explain why total reintroduction of competitors through fecal microbial transplant currently is the most effective treatment for recurrent CDI. This work could ultimately contribute to the identification of targeted, context-dependent measures that prevent or reduce C. difficile colonization including pre- and probiotic therapies.

microbiology↗