Biology subjects

Papin, J. A.

Publications and source records attributed to Papin, J. A..

34 records · Page 2Linked to original sources

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↗

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↗

bioRxiv2020

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

The heart is a metabolic omnivore, known to consume many different carbon substrates in order to maintain function. In diseased states, the hearts metabolism can shift between different carbon substrates; however, there is some disagreement in the field as to the metabolic shifts seen in end-stage heart failure and whether all heart failure converges to a common metabolic phenotype. Here, we present a new, validated cardiomyocyte-specific GEnome-scale metabolic Network REconstruction (GENRE), iCardio, and use the model to identify common shifts in metabolic functions across heart failure omics datasets. 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 NO and Neu5Ac synthesis as common metabolic markers of heart failure across datasets. Further, we highlight the differences in metabolic functions seen across studies, further highlighting the complexity of heart failure. The methods presented for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissue and diseases of interest.

bioengineering↗

bioRxiv2020

Histone deacetylase inhibition by gut microbe-generated short chain fatty acids entrains intestinal epithelial circadian rhythms

Background and aimsThe circadian clock orchestrates ~24-hour oscillations of gastrointestinal (GI) epithelial structure and function that drive diurnal rhythms in the composition, localization, and metabolism of gut microbiota. Here, we use experimental and computational approaches in enteroids to reveal reciprocal effects of microbial metabolites on intestinal epithelial timekeeping by an epigenetic mechanism. MethodsWe cultured 3D PER2::LUCIFERASE and Bmal1-ELuciferase jejunal enteroids in media supplemented with sterile supernatants from the altered Schaedler Flora (ASF), a defined murine microbiota. Circadian oscillations of bioluminescent PER2 and Bmal1 were measured in enteroids cultured in the presence or absence of individual ASF supernatants. Separately, we applied machine learning to ASF metabolic profiles to identify phase-shifting metabolites. ResultsFiltrates from 3 of 7 ASF species (ASF360 Lactobacillus intestinalis, ASF361 Ligilactobacillus murinus, ASF502 Clostridium spp.) induced minimal alterations in circadian rhythms, whereas 4 ASF species (ASF356 Clostridium spp., ASF492 Eubacterium plexicaudatum, ASF500 Pseudoflavonifactor spp., ASF519 Parabacteroides goldsteinii) induced profound, concentration-dependent phase delays. Random forest classification identified short chain fatty acids (SCFA: butyrate, propionate, acetate, and isovalerate) production as a discriminating feature of "shifters", i.e., ASF taxa whose metabolites induce phase delay. Experiments with SCFAs confirmed machine learning predictions, with a median phase delay of 6.2 hours. Pharmacological or botanical HDAC inhibitors generated similar phase delays. Further, mithramycin A, an inhibitor of HDAC inhibition, abrogated SCFA-induced phase delays by 20% (P<0.05). Key findings were reproducible in human Bmal1-luciferase enteroids. ConclusionsGut microbe-generated SCFAs entrain intestinal epithelial circadian rhythms, in part, by an HDACi-dependent mechanism, with critical implications for understanding microbial and circadian network regulation of intestinal epithelial homeostasis.

cell biology↗

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

Community standards to facilitate development and address challenges in metabolic modeling

Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remains challenging. As a result, the amount, quality, and format of the information contained within systems biology models are not consistent and therefore present challenges for widespread use and communication. Here, we focused on these standards, resources, and challenges in the field of metabolic modeling by conducting a community-wide survey. We used this feedback to (1) outline the major challenges that our field faces and to propose solutions and (2) identify a set of features that defines what a \"gold standard\" metabolic network reconstruction looks like concerning content, annotation, and simulation capabilities. We anticipate that this community-driven outline will help the long-term development of community-inspired resources as well as produce high-quality, accessible models. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure continued development of both practical, usable standards and reproducible, knowledge-rich models.

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

Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models.

Flux coupling identifies sets of reactions whose fluxes are \"coupled\" or correlated in genome-scale models. By identified sets of coupled reactions, modelers can 1.) reduce the dimensionality of genome-scale models, 2.) identify reactions that must be modulated together during metabolic engineering, and 3.) identify sets of important enzymes using high-throughput data. We present three computational tools to improve the efficiency, applicability, and biological interpretability of flux coupling analysis.\n\nThe first algorithm (cachedFCF) uses information from intermediate solutions to decrease the runtime of standard flux coupling methods by 10-100 fold. Importantly, cachedFCF makes no assumptions regarding the structure of the underlying model, allowing efficient flux coupling analysis of models with non-convex constraints.\n\nWe next developed a mathematical framework (FALCON) that incorporates enzyme activity as continuous variables in genome-scale models. Using data from gene expression and fitness assays, we verified that enzyme sets calculated directly from FALCON models are more functionally coherent than sets of enzymes collected from coupled reaction sets.\n\nFinally, we present a method (delete-and-couple) for expanding enzyme sets to allow redundancies and branches in the associated metabolic pathways. The expanded enzyme sets align with known biological pathways and retain functional coherence. The expanded enzyme sets allow pathway-level analyses of genome-scale metabolic models.\n\nTogether, our algorithms extend flux coupling techniques to enzymatic networks and models with transcriptional regulation and other non-convex constraints. By expanding the efficiency and flexibility of flux coupling, we believe this popular technique will find new applications in metabolic engineering, microbial pathogenesis, and other fields that leverage network modeling.

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

Leveraging the effects of chloroquine on resistant malaria parasites for combination therapies

Malaria is a major global health problem, with the Plasmodium falciparum protozoan parasite causing the most severe form of the disease. Prevalence of drug-resistant P. falciparum highlights the need to understand the biology of resistance and to identify novel combination therapies that are effective against resistant parasites. Resistance has compromised the therapeutic use of many antimalarial drugs, including chloroquine, and limited our ability to treat malaria across the world. Fortunately, chloroquine resistance comes at a fitness cost to the parasite; this can be leveraged in developing combination therapies or to reinstate use of chloroquine. To understand biological changes induced by chloroquine treatment, we compared transcriptomics data from chloroquine-resistant parasites in the presence or absence of the drug. Using both linear models and a genome-scale metabolic network reconstruction of the parasite to interpret the expression data, we identified targetable pathways in resistant parasites. This study identified an increased importance of lipid synthesis, glutathione production/cycling, isoprenoids biosynthesis, and folate metabolism in response to chloroquine. We identified potential drug targets for chloroquine combination therapies. Significantly, our analysis suggests that the combination of chloroquine and sulfadoxine-pyrimethamine or fosmidomycin may be more effective against chloroquine-resistant parasites than either drug alone; further studies will explore the use of these drugs as chloroquine resistance blockers. Additional metabolic weaknesses were found in glutathione generation and lipid synthesis during chloroquine treatment. These processes could be targeted with novel inhibitors to reduce parasite growth and reduce the burden of malaria infections. Thus, we identified metabolic weaknesses of chloroquine-resistant parasites and propose targeted chloroquine combination therapies.

microbiology↗

bioRxiv2018

Reconciling high-throughput gene essentiality data with metabolic network reconstructions

The identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.\n\nAuthor SummaryWith the rise of antibiotic resistance, there is a growing need to discover new therapeutic targets to treat bacterial infections. One attractive strategy is to target genes that are essential for growth and survival. Essential genes can be identified with transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification and analysis of essential genes. We performed a large-scale comparison of multiple gene essentiality screens of the microbial pathogen Pseudomonas aeruginosa. We implemented a computational model-driven approach to provide functional explanations for essentiality and reconcile differences between screens. The integration of computational modeling with high-throughput experimental screens may enable the identification of drug targets with high-confidence and provide greater understanding for the development of novel therapeutic strategies.

systems biology↗

bioRxiv2018

Network analysis reveals differential metabolic functionality in antibiotic-resistant Pseudomonas aeruginosa

Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To interrogate this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. We semi-automatically calculated growth dynamics (maximum growth density, growth rate, and time to mid-exponential phase) of over 2,800 growth curves. These data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. Drug-resistant lineages predominantly displayed decreased growth relative to the ancestral lineage; however, resistant lineages occasionally displayed enhanced growth on certain carbon sources, indicating that adaption to drug can provide a growth advantage in certain environments. A genome-scale metabolic network reconstruction (GENRE) of P. aeruginosa strain UCBPP-PA14 was paired with whole-genome sequencing data of one of the drug-evolved lineages to predict genes contributing to observed changes in metabolism. Finally, we experimentally validated in silico predictions to identify genes mutated in resistant P. aeruginosa affecting loss of catabolic function. Our results build upon previous mechanistic knowledge of drug-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens. Robust drug-driven changes in bacterial metabolism have the potential to be exploited to select against antibiotic-resistant populations in chronic infections.

systems biology↗

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↗

bioRxiv2017

Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance

BACKGROUNDMalaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms.\n\nRESULTSHere, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites including folate and polyamines, and others within the mitochondria. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistance parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood.\n\nCONCLUSIONUsing this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.

systems biology↗