Biology subjects

Medlock, G. L.

Publications and source records attributed to Medlock, G. L..

10 recordsLinked to original sources

bioRxiv2022

A computational model of Pseudomonas syringae metabolism unveils the role of branched-chain amino acids in virulence expression at the early stages of Arabidopsis colonization

Leaf mesophyll-colonizing bacterial pathogens infect their plant hosts by adjusting their metabolism to the leaf mesophyll environment. Soon after the inoculation of naive, susceptible plants, the model bacterial pathogen Pseudomonas syringae pv. tomato DC3000 (PstDC3000) expresses virulence factors that suppress plant immunity, a requirement to produce robust infections. However, if plant immunity was elicited with Microbe-Associated Molecular-Patterns (MAMPs) prior to bacterial inoculation, PstDC3000 slows down virulence expression and only produces symptomless mild infections. To understand how bacterial metabolism adapts to these two contrasting conditions, we created iPst19, an in silico ensemble of genome-scale metabolic reconstructions. Constraining the in silico growth of iPst19 with in planta PstDC3000 gene expression data revealed that sugar catabolism is highly active in bacteria that have been inoculated in mock-treated plants. In contrast, branched-chain amino acids (BCAAs) catabolism is highly active in bacteria that have been inoculated in MAMP-pretreated plants. Bacterial growth and gene expression analysis showed that BCAAs suppress virulence gene expression without affecting bacterial growth in vitro. In planta, however, BCAAs suppress the expression of virulence genes at the early stages of the infection and significantly impair leaf colonization of the host plant Arabidopsis thaliana. While the overexpression of the conserved bacterial leucine-responsive transcriptional regulator Lrp induced the expression of virulence genes, its downregulation had the opposite effect, suggesting that BCAA-free Lrp induces virulence while BCAA-Lrp does not. Overall, our data provide mechanistic connections to understand how plant immunity impacts PstDC3000 metabolism and virulence, furthering our understanding of bacterial pathogenesis and plant disease.

microbiology↗

bioRxiv2020

Megasphaera in the stool microbiota is negatively associated with diarrheal cryptosporidiosis

BackgroundThe protozoan parasites in the Cryptosporidium genus cause both acute diarrheal disease and subclinical (i.e. non-diarrheal) disease. It is unclear if the microbiota can influence the manifestation of diarrhea during a Cryptosporidium infection. MethodsTo characterize the role of the gut microbiota in diarrheal cryptosporidiosis, the microbiome composition of both diarrheal and surveillance Cryptosporidium-positive fecal samples was evaluated using 16S rRNA gene sequencing. Additionally, the microbiome composition prior to infection was examined to test whether a preexisting microbiome profile could influence the Cryptosporidium infection phenotype. ResultsFecal microbiome composition was associated with diarrheal symptoms at two timepoints. Megasphaera was significantly less abundant in diarrheal samples when compared to subclinical samples at the time of Cryptosporidium detection (log2(fold change) = -4.3, p=10-10) and prior to infection (log2(fold change) = -2.0, p=10-4). Random forest classification also identified Megasphaera abundance in the pre- and post-exposure microbiota.as predictive of a subclinical infection. ConclusionsMicrobiome composition broadly, and specifically low Megasphaera abundance, was associated with diarrheal symptoms prior to and at the time of Cryptosporidium detection. This observation suggests that the gut microenvironment may play a role in determining the severity of a Cryptosporidium infection. SummaryMegasphaera abundance in the stool of Bangladeshi infants is associated with the development of diarrhea upon infection with the Cryptosporidium parasite.

microbiology↗

bioRxiv2019

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.

systems biology↗

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

Medusa: software to build and analyze ensembles of genome-scale metabolic network reconstructions

Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles, perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org/) and from github (https://github.com/gregmedlock/Medusa/), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest/.

systems biology↗

bioRxiv2018

Guiding the refinement of biochemical knowledgebases with ensembles of metabolic networks and semi-supervised learning

Mechanistic models are becoming common in biology and medicine. These models are often more generalizable than data-driven models because they explicitly represent biological knowledge, enabling simulation of scenarios that were not used to construct the model. While this generalizability has advantages, it also creates a dilemma: how should model curation efforts be focused to improve model performance? Here, we develop a machine learning-guided solution to this problem for genome-scale metabolic models. We generate an ensemble of candidate models consistent with experimental data, then perform in silico ensemble simulations for which improved predictiveness is desired. We apply unsupervised and supervised learning to the simulation output to identify structural variation in ensemble members that maximally influences variance in simulation outcomes across the ensemble. The resulting structural variants are high priority candidates for curation through targeted experimentation. We demonstrate this approach, called Automated Metabolic Model Ensemble-Driven Elimination of Uncertainty with Statistical learning (AMMEDEUS), by applying it to 29 bacterial species to identify curation targets that improve gene essentiality predictions. We then compile these curation targets from all 29 species to prioritize refinement of the entire biochemical database used to generate them. AMMEDEUS is a fully automated, scalable, and performance-driven recommendation system that complements human intuition during the curation of hypothesis-driven models and biochemical databases.\n\nSignificanceMechanistic computational models, such as metabolic and signaling networks, are becoming common in biology. These models contain a comprehensive representation of components and interactions for a given system, making them generalizable and often more predictive than simpler models. However, their size and connectivity make it difficult to identify which parts of a model need to be changed to improve performance further. Here, we develop a strategy to guide this process and apply it to metabolic models for a set of bacterial species. We use this strategy to identify model components that should be investigated, and demonstrate that it can improve predictive performance. This approach systematically aides the curation of metabolic models, and the databases used to construct them, without relying on the intuition of the curator.

systems biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

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 testing specific hypotheses regarding disease pathogenesis as well as vaccine development that is currently in progress.\n\nAuthor SummaryCampylobacter jejuni has been identified as one of the leading causes of enteropathy and diarrhea. In developing countries, these repeated enteric infections often result in growth deficits and cognitive impairment. There is a lack of small animal models of Campylobacter infection. This is a major hurdle in understanding the pathogenesis of Campylobacter infection in order to lead to therapeutic treatments and vaccines. We have developed a highly reproducible mouse model of Campylobacter infection that has clinical outcomes that match those of malnourished children. We hope that these insights into Campylobacter susceptibility will lead to the development of treatments against this major cause of diarrheal illness.

microbiology↗

bioRxiv2018

Metabolic mechanisms of interaction within a defined gut microbiota

Metabolic interactions among species are ubiquitous in nature, and the fitness costs and benefits they impose often reinforce and stabilize them over time. These interactions are of particular importance in the human gut, where they have functions ranging from enhancing digestion to preventing (or exacerbating) infections. The diversity and sheer number of species present lead to the potential for a multitude of metabolic interactions among species to occur. However, identifying the mechanism and consequences of metabolic interactions between even two species is incredibly challenging. Here, we develop, apply, and experimentally test a framework for identifying potential metabolic mechanisms associated with interspecies interactions. We perform pairwise growth and metabolome profiling of co-cultures of strains from the altered Schaedler flora (ASF), a defined murine microbiota. We then apply our novel framework, which we call the Constant Yield Expectation (ConYE) model, to dissect emergent metabolic behaviors that occur in co-culture. Using the ConYE model, we identify and interrogate an amino acid cross-feeding interaction that is likely to confer a growth benefit to one ASF strain (Clostridium sp. ASF356) in co-culture with another strain (Parabacteroides goldsteinii ASF519). We experimentally validate that the proposed interaction leads to a growth benefit for this strain via media supplementation experiments. Our results reveal the type and extent of emergent metabolic behavior in microbial communities and demonstrate how metabolomic data can be used to identify potential metabolic interactions between organisms such as gut microbes. Our in vitro characterization of the ASF strains and interactions between them also enhances our ability to interpret and design experiments that utilize ASF-colonized animals. We anticipate that this work will improve the tractability of studies utilizing mice colonized with the ASF. Here, we focus on growth-modulating interactions, but the framework we develop can be applied to generate specific hypotheses about mechanisms of interspecies interaction involved in any phenotype of interest within a microbial community.

microbiology↗

bioRxiv2017

Novel Co-Culture Plate Enables Growth Dynamic-Based Assessment Of Contact-Independent Microbial Interactions

Interactions between microbes are central to the dynamics of microbial communities. Understanding these interactions is essential for the characterization of communities, yet challenging to accomplish in practice. There are limited available tools for characterizing diffusion-mediated, contact-independent microbial interactions. A practical and widely implemented technique in such characterization involves the simultaneous co-culture of distinct bacterial species and subsequent analysis of relative abundance in the total population. However, distinguishing between species can be logistically challenging. In this paper, we present a low-cost, vertical membrane, co-culture plate to quantify contact-independent interactions between distinct bacterial populations in co-culture via real-time optical density measurements. These measurements can be used to facilitate the analysis of the interaction between microbes that are physically separated by a semipermeable membrane yet able to exchange diffusible molecules. We show that diffusion across the membrane occurs at a sufficient rate to enable effective interaction between physically separate cultures. Two bacterial species commonly found in the cystic fibrotic lung, Pseudomonas aeruginosa and Burkholderia cenocepacia, were co-cultured to demonstrate how this plate may be implemented to study microbial interactions. We have demonstrated that this novel co-culture device is able to reliably generate real-time measurements of optical density data that can be used to characterize interactions between microbial species.

microbiology↗