1
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Kuper TJ, Islam MM, Peirce-Cottler SM, Papin JA, Ford RM. Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment. PLoS Comput Biol 2024; 20:e1012031. [PMID: 38669236 PMCID: PMC11051585 DOI: 10.1371/journal.pcbi.1012031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.
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Affiliation(s)
- Tracy J. Kuper
- Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Mohammad Mazharul Islam
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Shayn M. Peirce-Cottler
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Roseanne M Ford
- Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
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2
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Dougherty BV, Moore CJ, Rawls KD, Jenior ML, Chun B, Nagdas S, Saucerman JJ, Kolling GL, Wallqvist A, Papin JA. Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism. PLoS Comput Biol 2024; 20:e1011919. [PMID: 38422168 DOI: 10.1371/journal.pcbi.1011919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/12/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
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.
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Affiliation(s)
- Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Connor J Moore
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Matthew L Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Bryan Chun
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Sarbajeet Nagdas
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia Health System, Charlottesville, Virginia, United States of America
| | - Jeffrey J Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland, United States of America
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
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3
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Tubergen PJ, Medlock G, Moore A, Zhang X, Papin JA, Danna CH. A computational model of Pseudomonas syringae metabolism unveils a role for branched-chain amino acids in Arabidopsis leaf colonization. PLoS Comput Biol 2023; 19:e1011651. [PMID: 38150474 PMCID: PMC10775980 DOI: 10.1371/journal.pcbi.1011651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 01/09/2024] [Accepted: 11/02/2023] [Indexed: 12/29/2023] Open
Abstract
Bacterial pathogens adapt their metabolism to the plant environment to successfully colonize their hosts. In our efforts to uncover the metabolic pathways that contribute to the colonization of Arabidopsis thaliana leaves by Pseudomonas syringae pv tomato DC3000 (Pst DC3000), we created iPst19, an ensemble of 100 genome-scale network reconstructions of Pst DC3000 metabolism. We developed a novel approach for gene essentiality screens, leveraging the predictive power of iPst19 to identify core and ancillary condition-specific essential genes. Constraining the metabolic flux of iPst19 with Pst DC3000 gene expression data obtained from naïve-infected or pre-immunized-infected plants, revealed changes in bacterial metabolism imposed by plant immunity. Machine learning analysis revealed that among other amino acids, branched-chain amino acids (BCAAs) metabolism significantly contributed to the overall metabolic status of each gene-expression-contextualized iPst19 simulation. These predictions were tested and confirmed experimentally. Pst DC3000 growth and gene expression analysis showed that BCAAs suppress virulence gene expression in vitro without affecting bacterial growth. In planta, however, an excess of BCAAs suppress the expression of virulence genes at the early stages of infection and significantly impair the colonization of Arabidopsis leaves. Our findings suggesting that BCAAs catabolism is necessary to express virulence and colonize the host. Overall, this study provides valuable insights into how plant immunity impacts Pst DC3000 metabolism, and how bacterial metabolism impacts the expression of virulence.
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Affiliation(s)
- Philip J. Tubergen
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Greg Medlock
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Anni Moore
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Xiaomu Zhang
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Cristian H. Danna
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
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4
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Islam MM, Kolling GL, Glass EM, Goldberg JB, Papin JA. Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes. bioRxiv 2023:2023.10.08.561426. [PMID: 37873245 PMCID: PMC10592701 DOI: 10.1101/2023.10.08.561426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
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.
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Affiliation(s)
- Mohammad Mazharul Islam
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, 22903
| | - Glynis L. Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, 22903
| | - Emma M. Glass
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, 22903
| | | | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, 22903
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5
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Potter AD, Baiocco CM, Papin JA, Criss AK. Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution in Neisseria gonorrhoeae during neutrophil co-culture. mSystems 2023; 8:e0126522. [PMID: 37387581 PMCID: PMC10470122 DOI: 10.1128/msystems.01265-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/19/2023] [Indexed: 07/01/2023] Open
Abstract
The ability of bacterial pathogens to metabolically adapt to the environmental conditions of their hosts is critical to both colonization and invasive disease. Infection with Neisseria gonorrhoeae (the gonococcus, Gc) is characterized by the influx of neutrophils [polymorphonuclear leukocytes (PMNs)], which fail to clear the bacteria and make antimicrobial products that can exacerbate tissue damage. The inability of the human host to clear Gc infection is particularly concerning in light of the emergence of strains that are resistant to all clinically recommended antibiotics. Bacterial metabolism represents a promising target for the development of new therapeutics against Gc. Here, we generated a curated genome-scale metabolic network reconstruction (GENRE) of Gc strain FA1090. This GENRE links genetic information to metabolic phenotypes and predicts Gc biomass synthesis and energy consumption. We validated this model with published data and in new results reported here. Contextualization of this model using the transcriptional profile of Gc exposed to PMNs revealed substantial rearrangements of Gc central metabolism and induction of Gc nutrient acquisition strategies for alternate carbon source use. These features enhanced the growth of Gc in the presence of neutrophils. From these results, we conclude that the metabolic interplay between Gc and PMNs helps define infection outcomes. The use of transcriptional profiling and metabolic modeling to reveal new mechanisms by which Gc persists in the presence of PMNs uncovers unique aspects of metabolism in this fastidious bacterium, which could be targeted to block infection and thereby reduce the burden of gonorrhea in the human population. IMPORTANCE The World Health Organization designated Gc as a high-priority pathogen for research and development of new antimicrobials. Bacterial metabolism is a promising target for new antimicrobials, as metabolic enzymes are widely conserved among bacterial strains and are critical for nutrient acquisition and survival within the human host. Here we used genome-scale metabolic modeling to characterize the core metabolic pathways of this fastidious bacterium and to uncover the pathways used by Gc during culture with primary human immune cells. These analyses revealed that Gc relies on different metabolic pathways during co-culture with human neutrophils than in rich media. Conditionally essential genes emerging from these analyses were validated experimentally. These results show that metabolic adaptation in the context of innate immunity is important to Gc pathogenesis. Identifying the metabolic pathways used by Gc during infection can highlight new therapeutic targets for drug-resistant gonorrhea.
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Affiliation(s)
- Aimee D. Potter
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Christopher M. Baiocco
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Alison K. Criss
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
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6
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Jenior ML, Leslie JL, Kolling GL, Archbald-Pannone L, Powers DA, Petri WA, Papin JA. Systems-ecology designed bacterial consortium protects from severe Clostridioides difficile infection. bioRxiv 2023:2023.08.08.552483. [PMID: 37609255 PMCID: PMC10441344 DOI: 10.1101/2023.08.08.552483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
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.
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7
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Mac Gabhann F, Pitzer VE, Papin JA. The blossoming of methods and software in computational biology. PLoS Comput Biol 2023; 19:e1011390. [PMID: 37590323 PMCID: PMC10434930 DOI: 10.1371/journal.pcbi.1011390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023] Open
Affiliation(s)
- Feilim Mac Gabhann
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Virginia E. Pitzer
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
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8
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Moore CJ, Holstege CP, Papin JA. Metabolic modeling of sex-specific liver tissue suggests mechanism of differences in toxicological responses. PLoS Comput Biol 2023; 19:e1010927. [PMID: 37603574 PMCID: PMC10470949 DOI: 10.1371/journal.pcbi.1010927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 08/31/2023] [Accepted: 07/25/2023] [Indexed: 08/23/2023] Open
Abstract
Male subjects in animal and human studies are disproportionately used for toxicological testing. This discrepancy is evidenced in clinical medicine where females are more likely than males to experience liver-related adverse events in response to xenobiotics. While previous work has shown gene expression differences between the sexes, there is a lack of systems-level approaches to understand the direct clinical impact of these differences. Here, we integrate gene expression data with metabolic network models to characterize the impact of transcriptional changes of metabolic genes in the context of sex differences and drug treatment. We used Tasks Inferred from Differential Expression (TIDEs), a reaction-centric approach to analyzing differences in gene expression, to discover that several metabolic pathways exhibit sex differences including glycolysis, fatty acid metabolism, nucleotide metabolism, and xenobiotics metabolism. When TIDEs is used to compare expression differences in treated and untreated hepatocytes, we find several subsystems with differential expression overlap with the sex-altered pathways such as fatty acid metabolism, purine and pyrimidine metabolism, and xenobiotics metabolism. Finally, using sex-specific transcriptomic data, we create individual and averaged male and female liver models and find differences in the pentose phosphate pathway and other metabolic pathways. These results suggest potential sex differences in the contribution of the pentose phosphate pathway to oxidative stress, and we recommend further research into how these reactions respond to hepatotoxic pharmaceuticals.
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Affiliation(s)
- Connor J. Moore
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Christopher P. Holstege
- Department of Emergency Medicine, Division of Medical Toxicology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
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9
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Jenior ML, Glass EM, Papin JA. Reconstructor: a COBRApy compatible tool for automated genome-scale metabolic network reconstruction with parsimonious flux-based gap-filling. Bioinformatics 2023; 39:btad367. [PMID: 37279743 PMCID: PMC10275916 DOI: 10.1093/bioinformatics/btad367] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 05/22/2023] [Accepted: 06/02/2023] [Indexed: 06/08/2023] Open
Abstract
MOTIVATION Genome-scale metabolic network reconstructions (GENREs) are valuable for understanding cellular metabolism in silico. Several tools exist for automatic GENRE generation. However, these tools frequently (i) do not readily integrate with some of the widely-used suites of packaged methods available for network analysis, (ii) lack effective network curation tools, (iii) are not sufficiently user-friendly, and (iv) often produce low-quality draft reconstructions. RESULTS Here, we present Reconstructor, a user-friendly, COBRApy-compatible tool that produces high-quality draft reconstructions with reaction and metabolite naming conventions that are consistent with the ModelSEED biochemistry database and includes a gap-filling technique based on the principles of parsimony. Reconstructor can generate SBML GENREs from three input types: annotated protein .fasta sequences (Type 1 input), a BLASTp output (Type 2), or an existing SBML GENRE that can be further gap-filled (Type 3). While Reconstructor can be used to create GENREs of any species, we demonstrate the utility of Reconstructor with bacterial reconstructions. We demonstrate how Reconstructor readily generates high-quality GENRES that capture strain, species, and higher taxonomic differences in functional metabolism of bacteria and are useful for further biological discovery. AVAILABILITY AND IMPLEMENTATION The Reconstructor Python package is freely available for download. Complete installation and usage instructions and benchmarking data are available at http://github.com/emmamglass/reconstructor.
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Affiliation(s)
- Matthew L Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
| | - Emma M Glass
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
- Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, United States
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States
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10
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Powers DA, Jenior ML, Kolling GL, Papin JA. Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies. PLoS Comput Biol 2023; 19:e1011076. [PMID: 37099624 PMCID: PMC10166488 DOI: 10.1371/journal.pcbi.1011076] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/08/2023] [Accepted: 04/04/2023] [Indexed: 04/27/2023] Open
Abstract
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.
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Affiliation(s)
- Deborah A Powers
- Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
| | - Matthew L Jenior
- Biomedical Engineering, School of Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Glynis L Kolling
- Biomedical Engineering, School of Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A Papin
- Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
- Biomedical Engineering, School of Engineering, University of Virginia, Charlottesville, Virginia, United States of America
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11
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Lee CY, Dillard LR, Papin JA, Arnold KB. New perspectives into the vaginal microbiome with systems biology. Trends Microbiol 2023; 31:356-368. [PMID: 36272885 DOI: 10.1016/j.tim.2022.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/19/2022] [Accepted: 09/21/2022] [Indexed: 10/28/2022]
Abstract
The vaginal microbiome (VMB) is critical to female reproductive health; however, the mechanisms associated with optimal and non-optimal states remain poorly understood due to the complex community structure and dynamic nature. Quantitative systems biology techniques applied to the VMB have improved understanding of community composition and function using primarily statistical methods. In contrast, fewer mechanistic models that use a priori knowledge of VMB features to develop predictive models have been implemented despite their use for microbiomes at other sites, including the gastrointestinal tract. Here, we explore systems biology approaches that have been applied in the VMB, highlighting successful techniques and discussing new directions that hold promise for improving understanding of health and disease.
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Affiliation(s)
- Christina Y Lee
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Lillian R Dillard
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Kelly B Arnold
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
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12
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Dillard LR, Glass EM, Lewis AL, Thomas-White K, Papin JA. Metabolic Network Models of the Gardnerella Pangenome Identify Key Interactions with the Vaginal Environment. mSystems 2023; 8:e0068922. [PMID: 36511689 PMCID: PMC9948698 DOI: 10.1128/msystems.00689-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/13/2022] [Indexed: 12/15/2022] Open
Abstract
Gardnerella is the primary pathogenic bacterial genus present in the polymicrobial condition known as bacterial vaginosis (BV). Despite BV's high prevalence and associated chronic and acute women's health impacts, the Gardnerella pangenome is largely uncharacterized at both the genetic and functional metabolic levels. Here, we used genome-scale metabolic models to characterize in silico the Gardnerella pangenome metabolic content. We also assessed the metabolic functional capacity in a BV-positive cervicovaginal fluid context. The metabolic capacity varied widely across the pangenome, with 38.15% of all reactions being core to the genus, compared to 49.60% of reactions identified as being unique to a smaller subset of species. We identified 57 essential genes across the pangenome via in silico gene essentiality screens within two simulated vaginal metabolic environments. Four genes, gpsA, fas, suhB, and psd, were identified as core essential genes critical for the metabolic function of all analyzed bacterial species of the Gardnerella genus. Further understanding these core essential metabolic functions could inform novel therapeutic strategies to treat BV. Machine learning applied to simulated metabolic network flux distributions showed limited clustering based on the sample isolation source, which further supports the presence of extensive core metabolic functionality across this genus. These data represent the first metabolic modeling of the Gardnerella pangenome and illustrate strain-specific interactions with the vaginal metabolic environment across the pangenome. IMPORTANCE Bacterial vaginosis (BV) is the most common vaginal infection among reproductive-age women. Despite its prevalence and associated chronic and acute women's health impacts, the diverse bacteria involved in BV infection remain poorly characterized. Gardnerella is the genus of bacteria most commonly and most abundantly represented during BV. In this paper, we use metabolic models, which are a computational representation of the possible functional metabolism of an organism, to investigate metabolic conservation, gene essentiality, and pathway utilization across 110 Gardnerella strains. These models allow us to investigate in silico how strains may differ with respect to their metabolic interactions with the vaginal-host environment.
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Affiliation(s)
- Lillian R. Dillard
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
| | - Emma M. Glass
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Amanda L. Lewis
- Department of Obstetrics and Gynecology, University of California—San Diego, La Jolla, California, USA
| | | | - Jason A. Papin
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
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13
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Moore CJ, Holstege CP, Papin JA. Metabolic modeling of sex-specific tissue predicts mechanisms of differences in toxicological responses. bioRxiv 2023:2023.02.07.527430. [PMID: 36798158 PMCID: PMC9934589 DOI: 10.1101/2023.02.07.527430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Male subjects in animal and human studies are disproportionately used for toxicological testing. This discrepancy is evidenced in clinical medicine where females are more likely than males to experience liver-related adverse events in response to xenobiotics. While previous work has shown gene expression differences between the sexes, there is a lack of systems-level approaches to understand the direct clinical impact effect of these differences. Here, we integrate gene expression data with metabolic network models to characterize the impact of transcriptional changes of metabolic genes in the context of sex differences and drug treatment. We used Tasks Inferred from Differential Expression (TIDEs), a reaction-centric approach to analyzing differences in gene expression, to discover that androgen, ether lipid, glucocorticoid, tryptophan, and xenobiotic metabolism have more activity in the male liver, and serotonin, melatonin, pentose, glucuronate, and vitamin A metabolism have more activity in the female liver. When TIDEs is used to compare expression differences in treated and untreated hepatocytes, we see little response in those sex-altered subsystems, and the largest differences are in subsystems related to lipid metabolism. Finally, using sex-specific transcriptomic data, we create individual and averaged male and female liver models and find differences in the import of bile acids and salts. This result suggests that the sexually dimorphic behavior of the liver may be caused by differences in enterohepatic recirculation, and we suggest an investigation into sex-specific microbiome composition as an avenue of further research. Author Summary Male-bias in clinical testing of drugs has led to a disproportionate number of hepatotoxic events in women. Previous works use gene-by-gene differences in biological sex to explain this discrepancy, but there is little focus on the systematic interactions of these differences. To this end, we use a combination of gene expression data and metabolic modeling to compare metabolic activity between the male and female liver and treated and untreated hepatocytes. We find several subsystems with differential activity in each sex; however, when comparing these subsystems with those pathways altered by hepatotoxic agents, we find little overlap. To explore these differences on a reaction-by-reaction basis, we use the same sex-specific transcriptomic data to contextualize the previously published Human1 human cell metabolic model. In these models we find a difference in flux for the import of bile acids and salts, suggesting a potential difference in enterohepatic circulation. These findings can help guide future drug design, toxicological testing, and sex-specific research to better account for the entire human population.
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Affiliation(s)
- Connor J Moore
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Christopher P Holstege
- Department of Emergency Medicine, Division of Medical Toxicology, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA
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14
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Moutinho TJ, Powers DA, Hanson GF, Levy S, Baveja R, Hefner I, Mohamed M, Abdelghani A, Baker RL, Papin JA, Moore SR, Hourigan SK. Fecal sphingolipids predict parenteral nutrition-associated cholestasis in the neonatal intensive care unit. JPEN J Parenter Enteral Nutr 2022; 46:1903-1913. [PMID: 35285019 PMCID: PMC9468188 DOI: 10.1002/jpen.2374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/23/2022] [Accepted: 03/06/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Parenteral nutrition-associated cholestasis (PNAC) in the neonatal intensive care unit (NICU) causes significant morbidity and associated healthcare costs. Laboratory detection of PNAC currently relies on elevated serum conjugated bilirubin levels in the aftermath of impaired bile flow. Here, we sought to identify fecal biomarkers, which when integrated with clinical data, would better predict risk for developing PNAC. METHODS Using untargeted metabolomics in 200 serial stool samples from 60 infants, we applied statistical and machine learning approaches to identify clinical features and metabolic biomarkers with the greatest associative potential for risk of developing PNAC. Stools were collected prospectively from infants receiving PN with soybean oil-based lipid emulsion at a level IV NICU. RESULTS Low birth weight, extreme prematurity, longer duration of PN, and greater number of antibiotic courses were all risk factors for PNAC (P < 0.05). We identified 78 stool biomarkers with early predictive potential (P < 0.05). From these 78 biomarkers, we further identified 12 sphingomyelin lipids with high association for the development of PNAC in precholestasis stool samples when combined with birth anthropometry. CONCLUSION We demonstrate the potential for stool metabolomics to enhance early identification of PNAC risk. Earlier detection of high-risk infants would empower proactive mitigation with alterations to PN for at-risk infants and optimization of energy nutrition with PN for infants at lower risk.
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Affiliation(s)
- Thomas J. Moutinho
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Deborah A. Powers
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Gabriel F. Hanson
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Shira Levy
- Inova Children's HospitalFalls ChurchVirginiaUSA
| | - Rajiv Baveja
- Fairfax Neonatal AssociatesFalls ChurchVirginiaUSA
| | | | | | | | | | - Jason A. Papin
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Sean R. Moore
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of PediatricsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Suchitra K. Hourigan
- Inova Children's HospitalFalls ChurchVirginiaUSA,Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of PediatricsUniversity of VirginiaCharlottesvilleVirginiaUSA,Division of Pediatric GastroenterologyPediatric Specialists of VirginiaFairfaxVirginiaUSA,Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious DiseasesNational Institutes of HealthBethesdaMarylandUSA
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15
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Fawad JA, Luzader DH, Hanson GF, Moutinho TJ, McKinney CA, Mitchell PG, Brown-Steinke K, Kumar A, Park M, Lee S, Bolick DT, Medlock GL, Zhao JY, Rosselot AE, Chou CJ, Eshleman EM, Alenghat T, Hong CI, Papin JA, Moore SR. Histone Deacetylase Inhibition by Gut Microbe-Generated Short-Chain Fatty Acids Entrains Intestinal Epithelial Circadian Rhythms. Gastroenterology 2022; 163:1377-1390.e11. [PMID: 35934064 DOI: 10.1053/j.gastro.2022.07.051] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND & AIMS The circadian clock orchestrates ∼24-hour oscillations of gastrointestinal epithelial structure and function that drive diurnal rhythms in gut microbiota. Here, we use experimental and computational approaches in intestinal organoids to reveal reciprocal effects of gut microbial metabolites on epithelial timekeeping by an epigenetic mechanism. METHODS We cultured 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 the presence or absence of individual ASF supernatants. Separately, we applied machine learning to ASF metabolomics to identify phase-shifting metabolites. RESULTS Sterile filtrates from 3 of 7 ASF species (ASF360 Lactobacillus intestinalis, ASF361 Ligilactobacillus murinus, and ASF502 Clostridium species) induced minimal alterations in circadian rhythms, whereas filtrates from 4 ASF species (ASF356 Clostridium species, ASF492 Eubacterium plexicaudatum, ASF500 Pseudoflavonifactor species, and ASF519 Parabacteroides goldsteinii) induced profound, concentration-dependent phase shifts. Random forest classification identified short-chain fatty acid (SCFA) (butyrate, propionate, acetate, and isovalerate) production as a discriminating feature of ASF "shifters." Experiments with SCFAs confirmed machine learning predictions, with a median phase shift of 6.2 hours in murine enteroids. Pharmacologic or botanical histone deacetylase (HDAC) inhibitors yielded similar findings. Further, mithramycin A, an inhibitor of HDAC inhibition, reduced SCFA-induced phase shifts by 20% (P < .05) and conditional knockout of HDAC3 in enteroids abrogated butyrate effects on Per2 expression. Key findings were reproducible in human Bmal1-luciferase enteroids, colonoids, and Per2-luciferase Caco-2 cells. CONCLUSIONS Gut microbe-generated SCFAs entrain intestinal epithelial circadian rhythms by an HDACi-dependent mechanism, with critical implications for understanding microbial and circadian network regulation of intestinal epithelial homeostasis.
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Affiliation(s)
- Jibraan A Fawad
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Deborah H Luzader
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Gabriel F Hanson
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Thomas J Moutinho
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Craig A McKinney
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Paul G Mitchell
- University of Virginia School of Medicine, Charlottesville, Virginia
| | - Kathleen Brown-Steinke
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Ajay Kumar
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Miri Park
- Department of Pharmacology & Systems Physiology, University of Cincinnati, Cincinnati, Ohio
| | - Suengwon Lee
- Department of Pharmacology & Systems Physiology, University of Cincinnati, Cincinnati, Ohio
| | - David T Bolick
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Greg L Medlock
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Jesse Y Zhao
- University of Virginia School of Medicine, Charlottesville, Virginia
| | - Andrew E Rosselot
- Department of Pharmacology & Systems Physiology, University of Cincinnati, Cincinnati, Ohio
| | - C James Chou
- College of Pharmacy, Medical University of South Carolina, Charleston, South Carolina
| | - Emily M Eshleman
- Division of Immunobiology, Center for Inflammation and Tolerance, Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Theresa Alenghat
- Division of Immunobiology, Center for Inflammation and Tolerance, Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Christian I Hong
- Department of Pharmacology & Systems Physiology, University of Cincinnati, Cincinnati, Ohio
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Sean R Moore
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, University of Virginia, Charlottesville, Virginia.
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16
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Jenior ML, Dickenson ME, Papin JA. Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae. NPJ Syst Biol Appl 2022; 8:41. [PMID: 36307414 PMCID: PMC9616910 DOI: 10.1038/s41540-022-00252-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
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 previously from in vitro characterization of both laboratory and clinical isolates which 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 genome-scale metabolic network reconstructions (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 catabolism of L-valine in clinical isolate-specific growth simulations. These findings warrant future studies for potential efficacy of valine transaminase inhibition as a target against K. pneumoniae infection.
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Affiliation(s)
- Matthew L Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Mary E Dickenson
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA. .,Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, VA, USA. .,Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
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17
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Dillard LR, Wase N, Ramakrishnan G, Park JJ, Sherman NE, Carpenter R, Young M, Donlan AN, Petri W, Papin JA. Leveraging metabolic modeling to identify functional metabolic alterations associated with COVID-19 disease severity. Metabolomics 2022; 18:51. [PMID: 35819731 PMCID: PMC9273921 DOI: 10.1007/s11306-022-01904-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/01/2022] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Since the COVID-19 pandemic began in early 2020, SARS-CoV2 has claimed more than six million lives world-wide, with over 510 million cases to date. To reduce healthcare burden, we must investigate how to prevent non-acute disease from progressing to severe infection requiring hospitalization. METHODS To achieve this goal, we investigated metabolic signatures of both non-acute (out-patient) and severe (requiring hospitalization) COVID-19 samples by profiling the associated plasma metabolomes of 84 COVID-19 positive University of Virginia hospital patients. We utilized supervised and unsupervised machine learning and metabolic modeling approaches to identify key metabolic drivers that are predictive of COVID-19 disease severity. Using metabolic pathway enrichment analysis, we explored potential metabolic mechanisms that link these markers to disease progression. RESULTS Enriched metabolites associated with tryptophan in non-acute COVID-19 samples suggest mitigated innate immune system inflammatory response and immunopathology related lung damage prevention. Increased prevalence of histidine- and ketone-related metabolism in severe COVID-19 samples offers potential mechanistic insight to musculoskeletal degeneration-induced muscular weakness and host metabolism that has been hijacked by SARS-CoV2 infection to increase viral replication and invasion. CONCLUSIONS Our findings highlight the metabolic transition from an innate immune response coupled with inflammatory pathway inhibition in non-acute infection to rampant inflammation and associated metabolic systemic dysfunction in severe COVID-19.
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Affiliation(s)
- L R Dillard
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA
| | - N Wase
- School of Medicine Core Facilities, University of Virginia, Charlottesville, VA, 22908, USA
| | - G Ramakrishnan
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
| | - J J Park
- School of Medicine Core Facilities, University of Virginia, Charlottesville, VA, 22908, USA
| | - N E Sherman
- School of Medicine Core Facilities, University of Virginia, Charlottesville, VA, 22908, USA
| | - R Carpenter
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
| | - M Young
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
| | - A N Donlan
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
| | - W Petri
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia Health System, Charlottesville, VA, 22908, USA
| | - J A Papin
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA.
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA.
- Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA, 22908, USA.
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18
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Carey MA, Medlock GL, Stolarczyk M, Petri WA, Guler JL, Papin JA. Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models. PLoS Comput Biol 2022; 18:e1009870. [PMID: 35196325 PMCID: PMC8901074 DOI: 10.1371/journal.pcbi.1009870] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 03/07/2022] [Accepted: 01/27/2022] [Indexed: 01/01/2023] Open
Abstract
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 and discovered that phylogeny is not the sole predictor of metabolic similarity. 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.
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Affiliation(s)
- Maureen A. Carey
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- * E-mail: (MAC); (JP)
| | - Gregory L. Medlock
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Michał Stolarczyk
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - William A. Petri
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Jennifer L. Guler
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- * E-mail: (MAC); (JP)
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19
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Moutinho TJ, Neubert BC, Jenior ML, Papin JA. Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions. PLoS Comput Biol 2022; 18:e1009341. [PMID: 35130271 PMCID: PMC8853471 DOI: 10.1371/journal.pcbi.1009341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 02/17/2022] [Accepted: 01/19/2022] [Indexed: 01/26/2023] Open
Abstract
Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial 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 structuring of CANYUNs 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 construction 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 CANYUNs model 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.
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Affiliation(s)
- Thomas J. Moutinho
- University of Virginia, Department of Biomedical Engineering, Charlottesville, Virginia, United States of America
| | - Benjamin C. Neubert
- University of Virginia, Department of Biomedical Engineering, Charlottesville, Virginia, United States of America
| | - Matthew L. Jenior
- University of Virginia, Department of Biomedical Engineering, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- University of Virginia, Department of Biomedical Engineering, Charlottesville, Virginia, United States of America
- * E-mail:
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20
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Jenior ML, Papin JA. Computational approaches to understanding Clostridioides difficile metabolism and virulence. Curr Opin Microbiol 2022; 65:108-115. [PMID: 34839237 PMCID: PMC8792252 DOI: 10.1016/j.mib.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/06/2021] [Accepted: 11/08/2021] [Indexed: 02/03/2023]
Abstract
The progress of infection by Clostridioides difficile is strongly influenced by metabolic cues it encounters as it colonizes the gastrointestinal tract. Both colonization and regulation of virulence have a multi-factorial interaction between host, microbiome, and gene expression cascades. While these connections with metabolism have been understood for some time, many mechanisms of control have remained difficult to directly assay due to high metabolic variability among C. difficile isolates and difficult genetic systems. Computational systems offer a means to interrogate structure of complex or noisy datasets and generate useful, tractable hypotheses to be tested in the laboratory. Recently, in silico techniques have provided powerful insights into metabolic elements of C. difficile infection ranging from virulence regulation to interactions with the gut microbiota. In this review, we introduce and provide context to the methods of computational modeling that have been applied to C. difficile metabolism and virulence thus far. The techniques discussed here have laid the foundation for future multi-scale efforts aimed at understanding the complex interplay of metabolic activity between pathogen, host, and surrounding microbial community in the regulation of C. difficile pathogenesis.
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Affiliation(s)
- Matthew L Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA,denotes co-corresponding author
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA, Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, VA, USA, Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA,denotes co-corresponding author
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21
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Grimes KL, Dunphy LJ, Kolling GL, Papin JA, Colosi LM. Algae-mediated treatment offers apparent removal of a model antibiotic resistance gene. ALGAL RES 2021. [DOI: 10.1016/j.algal.2021.102540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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22
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Filho JQ, Junior FS, Lima TBR, Viana VAF, Burgoa JSV, Soares AM, Leite ÁM, Herron SA, Newland HL, Sarnaik KS, Hanson GF, Papin JA, Moore SR, Lima AAM. Perinatal Outcomes of Asynchronous Influenza Vaccination, Ceará, Brazil, 2013-2018. Emerg Infect Dis 2021; 27:2409-2420. [PMID: 34424181 PMCID: PMC8386782 DOI: 10.3201/eid2709.203791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
In Ceará, Brazil, seasonal influenza transmission begins before national annual vaccination campaigns commence. To assess the perinatal consequences of this misalignment, we tracked severe acute respiratory infection (SARI), influenza, and influenza immunizations during 2013-2018. Among 3,297 SARI cases, 145 (4.4%) occurred in pregnant women. Statewide vaccination coverage was >80%; however, national vaccination campaigns began during or after peak influenza season. Thirty to forty weeks after peak influenza season, birthweights decreased by 40 g, and rates of prematurity increased from 10.7% to 15.5%. We identified 61 children born to mothers with SARI during pregnancy; they weighed 10% less at birth and were more likely to be premature than 122 newborn controls. Mistiming of influenza vaccination campaigns adversely effects perinatal outcomes in Ceará. Because Ceará is the presumptive starting point for north-to-south seasonal influenza transmission in Brazil, earlier national immunization campaigns would provide greater protection for pregnant women and their fetuses in Ceará and beyond.
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23
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Carey MA, Dräger A, Beber ME, Papin JA, Yurkovich JT. Community standards to facilitate development and address challenges in metabolic modeling. Mol Syst Biol 2021; 16:e9235. [PMID: 32845080 PMCID: PMC8411906 DOI: 10.15252/msb.20199235] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remain 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 constraint-based metabolic modeling by conducting a community-wide survey. We used this feedback to (i) outline the major challenges that our field faces and to propose solutions and (ii) 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 within our field. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure the continued development of both practical, usable standards and reproducible, knowledge-rich models.
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Affiliation(s)
- Maureen A Carey
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Biomedical Informatics (IBMI), University of Tübingen, Tübingen, Germany.,Department of Computer Science, University of Tübingen, Tübingen, Germany.,German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Denmark
| | - Jason A Papin
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, VA, USA.,Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
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24
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Abstract
Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks for constructing mechanistic models of microbial community metabolism and discuss available algorithms for model analysis. We highlight essential decision points that greatly influence algorithm selection, as well as model analysis. Polymicrobial metabolic models can be utilized to gain insights into host-pathogen interactions, bacterial engineering, and many more translational applications.
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Affiliation(s)
- Lillian R. Dillard
- Department of Biochemistry and Molecular Genetics, University of VirginiaCharlottesvilleVA 22908USA
| | - Dawson D. Payne
- Department of Biomedical Engineering, University of VirginiaBox 800759, Health SystemCharlottesvilleVA 22908USA
| | - Jason A. Papin
- Department of Biochemistry and Molecular Genetics, University of VirginiaCharlottesvilleVA 22908USA
- Department of Biomedical Engineering, University of VirginiaBox 800759, Health SystemCharlottesvilleVA 22908USA
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25
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Affiliation(s)
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Feilim Mac Gabhann
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
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26
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Dougherty BV, Rawls KD, Kolling GL, Vinnakota KC, Wallqvist A, Papin JA. Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model. Cell Rep 2021; 34:108836. [PMID: 33691118 DOI: 10.1016/j.celrep.2021.108836] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/07/2021] [Accepted: 02/17/2021] [Indexed: 11/28/2022] Open
Abstract
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.
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Affiliation(s)
- Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA
| | - Kalyan C Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.
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27
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Rawls KD, Dougherty BV, Vinnakota KC, Pannala VR, Wallqvist A, Kolling GL, Papin JA. Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model. Toxicol Appl Pharmacol 2021; 412:115390. [PMID: 33387578 PMCID: PMC7859602 DOI: 10.1016/j.taap.2020.115390] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 11/02/2020] [Accepted: 12/26/2020] [Indexed: 12/12/2022]
Abstract
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.
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Affiliation(s)
- Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kalyan C Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, MD 20817, USA
| | - Venkat R Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, MD 20817, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.
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28
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29
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Abstract
In this issue of Cell Host & Microbe, publications from Bushman et al. (2020) and Knippel et al. (2020) outline elements of host epithelial damage or inflammation that Clostridioides difficile subverts, enabling continued growth. These mechanisms provide insight into how this important pathogen influences the gut environment to promote its metabolic strategy during infection.
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Affiliation(s)
- Matthew L Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
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30
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Liu Y, Moore JH, Kolling GL, McGrath JS, Papin JA, Swami NS. Minimum Bactericidal Concentration of Ciprofloxacin to Pseudomonas aeruginosa Determined Rapidly Based on Pyocyanin Secretion. Sens Actuators B Chem 2020; 312:127936. [PMID: 32606491 PMCID: PMC7326315 DOI: 10.1016/j.snb.2020.127936] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
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.
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Affiliation(s)
- Yi Liu
- Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia 22904, USA
| | - John H. Moore
- Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Glynis L. Kolling
- Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - John S. McGrath
- Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Jason A Papin
- Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - Nathan S. Swami
- Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia 22904, USA
- Chemistry, University of Virginia, Charlottesville, VA 22904, USA
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31
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Affiliation(s)
- Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail: (JAP); (DN)
| | - Feilim Mac Gabhann
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- * E-mail: (JAP); (DN)
| | - Anand Rampadarath
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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32
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Jenior ML, Moutinho TJ, Dougherty BV, Papin JA. Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. PLoS Comput Biol 2020; 16:e1007099. [PMID: 32298268 PMCID: PMC7188308 DOI: 10.1371/journal.pcbi.1007099] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 04/28/2020] [Accepted: 02/24/2020] [Indexed: 11/18/2022] Open
Abstract
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 cell’s 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. Transcriptomic 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.
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Affiliation(s)
- Matthew L. Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Thomas J. Moutinho
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Bonnie V. Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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33
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White JA, Gaver DP, Butera RJ, Choi B, Dunlop MJ, Grande-Allen KJ, Grosberg A, Hitchcock RW, Huang-Saad AY, Kotche M, Kyle AM, Lerner AL, Linehan JH, Linsenmeier RA, Miller MI, Papin JA, Setton L, Sgro A, Smith ML, Zaman M, Lee AP. Core Competencies for Undergraduates in Bioengineering and Biomedical Engineering: Findings, Consequences, and Recommendations. Ann Biomed Eng 2020; 48:905-912. [DOI: 10.1007/s10439-020-02468-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 01/23/2020] [Indexed: 11/24/2022]
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34
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Pannala VR, Vinnakota KC, Estes SK, Trenary I, OˈBrien TP, Printz RL, Papin JA, Reifman J, Oyama T, Shiota M, Young JD, Wallqvist A. Genome-Scale Model-Based Identification of Metabolite Indicators for Early Detection of Kidney Toxicity. Toxicol Sci 2020; 173:293-312. [PMID: 31722432 PMCID: PMC8000070 DOI: 10.1093/toxsci/kfz228] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Identifying early indicators of toxicant-induced organ damage is critical to provide effective treatment. To discover such indicators and the underlying mechanisms of toxicity, we used gentamicin as an exemplar kidney toxicant and performed systematic perturbation studies in Sprague Dawley rats. We obtained high-throughput data 7 and 13 h after administration of a single dose of gentamicin (0.5 g/kg) and identified global changes in genes in the liver and kidneys, metabolites in the plasma and urine, and absolute fluxes in central carbon metabolism. We used these measured changes in genes in the liver and kidney as constraints to a rat multitissue genome-scale metabolic network model to investigate the mechanism of gentamicin-induced kidney toxicity and identify metabolites associated with changes in tissue gene expression. Our experimental analysis revealed that gentamicin-induced metabolic perturbations could be detected as early as 7 h postexposure. Our integrated systems-level analyses suggest that changes in kidney gene expression drive most of the significant metabolite alterations in the urine. The analyses thus allowed us to identify several significantly enriched injury-specific pathways in the kidney underlying gentamicin-induced toxicity, as well as metabolites in these pathways that could serve as potential early indicators of kidney damage.
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Affiliation(s)
- Venkat R Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland 20817
| | - Kalyan C Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland 20817
| | - Shanea K Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Irina Trenary
- Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, Tennessee
| | - Tracy P OˈBrien
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Richard L Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Tatsuya Oyama
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland 20817
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jamey D Young
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee
- Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, Tennessee
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
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35
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Medlock GL, Papin JA. Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning. Cell Syst 2020; 10:109-119.e3. [PMID: 31926940 PMCID: PMC6975163 DOI: 10.1016/j.cels.2019.11.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 08/27/2019] [Accepted: 11/14/2019] [Indexed: 11/16/2022]
Abstract
Mechanistic models explicitly represent hypothesized biological knowledge. As such, they offer more generalizability than data-driven models. However, identifying model curation efforts that improve performance for mechanistic models is nontrivial. Here, we develop a solution to this problem for genome-scale metabolic models. We generate an ensemble of models, each equally consistent with experimental data, then perform simulations with them. We apply machine learning to the simulation output to identify model structure variation that maximally influences simulations. These variants are high-priority candidates for curation through removal, addition, or reannotation in the model. We apply this approach, automated metabolic model ensemble-driven elimination of uncertainty with statistical learning (AMMEDEUS), to 29 bacterial species to improve gene essentiality predictions. We explore targets for individual species and compile pan-species targets to improve the database used during model construction. AMMEDEUS is an automated and performance-driven recommendation system that complements intuition during curation of biochemical knowledgebases. Prioritizing curation of complex mechanistic models is challenging Development of curation guidance approach for genome-scale metabolic models Ensembles and machine learning are used to prioritize possible curation efforts Application to metabolic models for 29 bacterial species and a biochemical database
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Affiliation(s)
- Gregory L Medlock
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
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36
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Rawls KD, Blais EM, Dougherty BV, Vinnakota KC, Pannala VR, Wallqvist A, Kolling GL, Papin JA. Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes. Toxicol Sci 2019; 172:279-291. [PMID: 31501904 PMCID: PMC6876259 DOI: 10.1093/toxsci/kfz197] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
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.
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Affiliation(s)
- Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Edik M Blais
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Kalyan C Vinnakota
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland 20817
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Venkat R Pannala
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland 20817
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
- Department of Medicine, Division of Infectious Diseases and International Health
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
- Department of Medicine, Division of Infectious Diseases and International Health
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia 22908
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37
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Grimes KL, Dunphy LJ, Loudermilk EM, Melara AJ, Kolling GL, Papin JA, Colosi LM. Evaluating the efficacy of an algae-based treatment to mitigate elicitation of antibiotic resistance. Chemosphere 2019; 237:124421. [PMID: 31382196 DOI: 10.1016/j.chemosphere.2019.124421] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 07/18/2019] [Accepted: 07/19/2019] [Indexed: 06/10/2023]
Abstract
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.
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Affiliation(s)
- Kassandra L Grimes
- Department of Civil and Environmental Engineering, University of Virginia, P.O. Box 400742, Charlottesville, VA, 22904-4742, United States
| | - Laura J Dunphy
- Department of Biomedical Engineering, University of Virginia, P.O. Box 800759, Charlottesville, VA, 22908-0759, United States
| | - Erica M Loudermilk
- Department of Civil and Environmental Engineering, University of Virginia, P.O. Box 400742, Charlottesville, VA, 22904-4742, United States
| | - A Jasmin Melara
- Department of Civil and Environmental Engineering, University of Virginia, P.O. Box 400742, Charlottesville, VA, 22904-4742, United States
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, P.O. Box 800759, Charlottesville, VA, 22908-0759, United States; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22903, United States
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, P.O. Box 800759, Charlottesville, VA, 22908-0759, United States
| | - Lisa M Colosi
- Department of Civil and Environmental Engineering, University of Virginia, P.O. Box 400742, Charlottesville, VA, 22904-4742, United States.
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Liu Y, McGrath JS, Moore JH, Kolling GL, Papin JA, Swami NS. Electrofabricated biomaterial-based capacitor on nanoporous gold for enhanced redox amplification. Electrochim Acta 2019. [DOI: 10.1016/j.electacta.2019.06.127] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Affiliation(s)
- Jason A. Papin
- University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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Gonyar LA, Gelbach PE, McDuffie DG, Koeppel AF, Chen Q, Lee G, Temple LM, Stibitz S, Hewlett EL, Papin JA, Damron FH, Eby JC. In Vivo Gene Essentiality and Metabolism in Bordetella pertussis. mSphere 2019; 4:e00694-18. [PMID: 31118307 PMCID: PMC6531889 DOI: 10.1128/msphere.00694-18] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 05/10/2019] [Indexed: 12/30/2022] Open
Abstract
Bordetella pertussis is the causative agent of whooping cough, a serious respiratory illness affecting children and adults, associated with prolonged cough and potential mortality. Whooping cough has reemerged in recent years, emphasizing a need for increased knowledge of basic mechanisms of B. pertussis growth and pathogenicity. While previous studies have provided insight into in vitro gene essentiality of this organism, very little is known about in vivo gene essentiality, a critical gap in knowledge, since B. pertussis has no previously identified environmental reservoir and is isolated from human respiratory tract samples. We hypothesize that the metabolic capabilities of B. pertussis are especially tailored to the respiratory tract and that many of the genes involved in B. pertussis metabolism would be required to establish infection in vivo In this study, we generated a diverse library of transposon mutants and then used it to probe gene essentiality in vivo in a murine model of infection. Using the CON-ARTIST pipeline, 117 genes were identified as conditionally essential at 1 day postinfection, and 169 genes were identified as conditionally essential at 3 days postinfection. Most of the identified genes were associated with metabolism, and we utilized two existing genome-scale metabolic network reconstructions to probe the effects of individual essential genes on biomass synthesis. This analysis suggested a critical role for glucose metabolism and lipooligosaccharide biosynthesis in vivo This is the first genome-wide evaluation of in vivo gene essentiality in B. pertussis and provides tools for future exploration.IMPORTANCE Our study describes the first in vivo transposon sequencing (Tn-seq) analysis of B. pertussis and identifies genes predicted to be essential for in vivo growth in a murine model of intranasal infection, generating key resources for future investigations into B. pertussis pathogenesis and vaccine design.
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Affiliation(s)
- Laura A Gonyar
- Department of Pediatrics, University of Virginia, Charlottesville, Virginia, USA
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, USA
| | - Patrick E Gelbach
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Dennis G McDuffie
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Alexander F Koeppel
- Department of Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Qing Chen
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
| | - Gloria Lee
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
| | - Louise M Temple
- Department of Integrated Science and Technology, James Madison University, Harrisonburg, Virginia, USA
| | - Scott Stibitz
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
| | - Erik L Hewlett
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, USA
| | - Jason A Papin
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
| | - F Heath Damron
- Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, West Virginia, USA
- Vaccine Development Center, West Virginia University Health Sciences Center, Morgantown, West Virginia, USA
| | - Joshua C Eby
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, USA
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41
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Untaroiu AM, Carey MA, Guler JL, Papin JA. Leveraging the effects of chloroquine on resistant malaria parasites for combination therapies. BMC Bioinformatics 2019; 20:186. [PMID: 30987583 PMCID: PMC6466727 DOI: 10.1186/s12859-019-2756-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 03/19/2019] [Indexed: 11/10/2022] Open
Abstract
Background 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. Results 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. Conclusions We identified potential drug targets for chloroquine combination therapies. Significantly, our analysis predicts 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. Electronic supplementary material The online version of this article (10.1186/s12859-019-2756-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ana M Untaroiu
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.,Present address: Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Maureen A Carey
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.,Present address: Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Jennifer L Guler
- Department of Biology, University of Virginia, Charlottesville, VA, USA.
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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Blazier AS, Papin JA. Reconciling high-throughput gene essentiality data with metabolic network reconstructions. PLoS Comput Biol 2019; 15:e1006507. [PMID: 30973869 PMCID: PMC6478342 DOI: 10.1371/journal.pcbi.1006507] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 04/23/2019] [Accepted: 03/06/2019] [Indexed: 11/30/2022] Open
Abstract
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. With 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.
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Affiliation(s)
- Anna S. Blazier
- Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Medicine, Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, United States of America
- Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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Pannala VR, Vinnakota KC, Rawls KD, Estes SK, O'Brien TP, Printz RL, Papin JA, Reifman J, Shiota M, Young JD, Wallqvist A. Mechanistic identification of biofluid metabolite changes as markers of acetaminophen-induced liver toxicity in rats. Toxicol Appl Pharmacol 2019; 372:19-32. [PMID: 30974156 DOI: 10.1016/j.taap.2019.04.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/22/2019] [Accepted: 04/05/2019] [Indexed: 12/12/2022]
Abstract
Acetaminophen (APAP) is the most commonly used analgesic and antipyretic drug in the world. Yet, it poses a major risk of liver injury when taken in excess of the therapeutic dose. Current clinical markers do not detect the early onset of liver injury associated with excess APAP-information that is vital to reverse injury progression through available therapeutic interventions. Hence, several studies have used transcriptomics, proteomics, and metabolomics technologies, both independently and in combination, in an attempt to discover potential early markers of liver injury. However, the casual relationship between these observations and their relation to the APAP mechanism of liver toxicity are not clearly understood. Here, we used Sprague-Dawley rats orally gavaged with a single dose of 2 g/kg of APAP to collect tissue samples from the liver and kidney for transcriptomic analysis and plasma and urine samples for metabolomic analysis. We developed and used a multi-tissue, metabolism-based modeling approach to integrate these data, characterize the effect of excess APAP levels on liver metabolism, and identify a panel of plasma and urine metabolites that are associated with APAP-induced liver toxicity. Our analyses, which indicated that pathways involved in nucleotide-, lipid-, and amino acid-related metabolism in the liver were most strongly affected within 10 h following APAP treatment, identified a list of potential metabolites in these pathways that could serve as plausible markers of APAP-induced liver injury. Our approach identifies toxicant-induced changes in endogenous metabolism, is applicable to other toxicants based on transcriptomic data, and provides a mechanistic framework for interpreting metabolite alterations.
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Affiliation(s)
- Venkat R Pannala
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA; Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.
| | - Kalyan C Vinnakota
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA; Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
| | - Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Box 800759, Health System, Charlottesville, Virginia 22908, USA
| | - Shanea K Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Tracy P O'Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Richard L Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Box 800759, Health System, Charlottesville, Virginia 22908, USA
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jamey D Young
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN 37232, USA.
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.
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Dunphy LJ, Yen P, Papin JA. Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa. Cell Syst 2019; 8:3-14.e3. [PMID: 30611675 DOI: 10.1016/j.cels.2018.12.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 10/08/2018] [Accepted: 12/04/2018] [Indexed: 12/13/2022]
Abstract
Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To study this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. Our data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. A genome-scale metabolic network reconstruction of P. aeruginosa was paired with whole-genome sequencing data to predict genes contributing to observed changes in metabolism. We experimentally validated computational predictions to identify mutations in resistant P. aeruginosa affecting loss of catabolic function. Finally, we found a shared metabolic phenotype between lab-evolved P. aeruginosa and clinical isolates with similar mutational landscapes. Our results build upon previous knowledge of antibiotic-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens.
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Affiliation(s)
- Laura J Dunphy
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Phillip Yen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Medicine, Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
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Affiliation(s)
- Philip E. Bourne
- Data Science Institute, University of Virginia, Charlottesville, VA, United States of America
- * E-mail:
| | - Fran Lewitter
- Bioinformatics and Research Computing, Whitehead Institute, Cambridge, MA, United States of America
| | - Scott Markel
- Dassault Systèmes BIOVIA, San Diego, CA, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
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46
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Rawls KD, Dougherty BV, Blais EM, Stancliffe E, Kolling GL, Vinnakota K, Pannala VR, Wallqvist A, Papin JA. A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools. Comput Biol Med 2018; 105:64-71. [PMID: 30584952 DOI: 10.1016/j.compbiomed.2018.12.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/11/2018] [Accepted: 12/13/2018] [Indexed: 11/26/2022]
Abstract
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.
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Affiliation(s)
- Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Edik M Blais
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Ethan Stancliffe
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
| | - Kalyan Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA
| | - Venkat R Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
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47
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Medlock GL, Carey MA, McDuffie DG, Mundy MB, Giallourou N, Swann JR, Kolling GL, Papin JA. Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota. Cell Syst 2018; 7:245-257.e7. [PMID: 30195437 PMCID: PMC6166237 DOI: 10.1016/j.cels.2018.08.003] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 06/15/2018] [Accepted: 08/03/2018] [Indexed: 12/20/2022]
Abstract
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.
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Affiliation(s)
- Gregory L Medlock
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Maureen A Carey
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA
| | - Dennis G McDuffie
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Michael B Mundy
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Natasa Giallourou
- Department of Surgery and Cancer, Division of Integrative Systems Medicine and Digestive Diseases, Faculty of Medicine, Imperial College London, South Kensington, London, UK
| | - Jonathan R Swann
- Department of Surgery and Cancer, Division of Integrative Systems Medicine and Digestive Diseases, Faculty of Medicine, Imperial College London, South Kensington, London, UK
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
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Dunphy LJ, Papin JA. Biomedical applications of genome-scale metabolic network reconstructions of human pathogens. Curr Opin Biotechnol 2018; 51:70-79. [PMID: 29223465 PMCID: PMC5991985 DOI: 10.1016/j.copbio.2017.11.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/22/2017] [Accepted: 11/24/2017] [Indexed: 12/14/2022]
Abstract
The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.
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Affiliation(s)
- Laura J Dunphy
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA; Department of Medicine, Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22903, USA.
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Janes KA, Chandran PL, Ford RM, Lazzara MJ, Papin JA, Peirce SM, Saucerman JJ, Lauffenburger DA. An engineering design approach to systems biology. Integr Biol (Camb) 2018; 9:574-583. [PMID: 28590470 DOI: 10.1039/c7ib00014f] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Measuring and modeling the integrated behavior of biomolecular-cellular networks is central to systems biology. Over several decades, systems biology has been shaped by quantitative biologists, physicists, mathematicians, and engineers in different ways. However, the basic and applied versions of systems biology are not typically distinguished, which blurs the separate aspirations of the field and its potential for real-world impact. Here, we articulate an engineering approach to systems biology, which applies educational philosophy, engineering design, and predictive models to solve contemporary problems in an age of biomedical Big Data. A concerted effort to train systems bioengineers will provide a versatile workforce capable of tackling the diverse challenges faced by the biotechnological and pharmaceutical sectors in a modern, information-dense economy.
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Affiliation(s)
- Kevin A Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
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Affiliation(s)
- Maureen A. Carey
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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