Publications v2

@article{papin_celebrating_2024,

title = {Celebrating a body of work.},

author = {Jason A. Papin and Feilim Mac Gabhann and Virginia E. Pitzer},

doi = {10.1371/journal.pcbi.1012441},

issn = {1553-7358 1553-734X},

year  = {2024},

date = {2024-09-01},

journal = {PLoS computational biology},

volume = {20},

number = {9},

pages = {e1012441},

note = {Place: United States},

keywords = {*Computational Biology, Humans, Periodicals as Topic},

pubstate = {published},

tppubtype = {article}

}

@article{furtado_clostridioides_2024,

title = {Clostridioides difficile-mucus interactions encompass shifts in gene expression, metabolism, and biofilm formation.},

author = {Kathleen L. Furtado and Lucas Plott and Matthew Markovetz and Deborah Powers and Hao Wang and David B. Hill and Jason Papin and Nancy L. Allbritton and Rita Tamayo},

doi = {10.1128/msphere.00081-24},

issn = {2379-5042},

year  = {2024},

date = {2024-06-01},

journal = {mSphere},

volume = {9},

number = {6},

pages = {e0008124},

abstract = {In a healthy colon, the stratified mucus layer serves as a crucial innate immune barrier to protect the epithelium from microbes. Mucins are complex glycoproteins that serve as a nutrient source for resident microflora and can be exploited by pathogens. We aimed to understand how the intestinal pathogen, Clostridioides difficile, independently uses or manipulates mucus to its benefit, without contributions from members of the microbiota. Using a 2-D primary human intestinal epithelial cell model to generate physiologic mucus, we assessed C. difficile-mucus interactions through growth assays, RNA-Seq, biophysical characterization of mucus, and contextualized metabolic modeling. We found that host-derived mucus promotes C. difficile growth both in vitro and in an infection model. RNA-Seq revealed significant upregulation of genes related to central metabolism in response to mucus, including genes involved in sugar uptake, the Wood-Ljungdahl pathway, and the glycine cleavage system. In addition, we identified differential expression of genes related to sensing and transcriptional control. Analysis of mutants with deletions in highly upregulated genes reflected the complexity of C. difficile-mucus interactions, with potential interplay between sensing and growth. Mucus also stimulated biofilm formation in vitro, which may in turn alter the viscoelastic properties of mucus. Context-specific metabolic modeling confirmed differential metabolism and the predicted importance of enzymes related to serine and glycine catabolism with mucus. Subsequent growth experiments supported these findings, indicating mucus is an important source of serine. Our results better define responses of C. difficile to human gastrointestinal mucus and highlight flexibility in metabolism that may influence pathogenesis. IMPORTANCE: Clostridioides difficile results in upward of 250,000 infections and 12,000 deaths annually in the United States. Community-acquired infections continue to rise, and recurrent disease is common, emphasizing a vital need to understand C. difficile pathogenesis. C. difficile undoubtedly interacts with colonic mucus, but the extent to which the pathogen can independently respond to and take advantage of this niche has not been explored extensively. Moreover, the metabolic complexity of C. difficile remains poorly understood but likely impacts its capacity to grow and persist in the host. Here, we demonstrate that C. difficile uses native colonic mucus for growth, indicating C. difficile possesses mechanisms to exploit the mucosal niche. Furthermore, mucus induces metabolic shifts and biofilm formation in C. difficile, which has potential ramifications for intestinal colonization. Overall, our work is crucial to better understand the dynamics of C. difficile-mucus interactions in the context of the human gut.},

note = {Place: United States},

keywords = {*Biofilms/growth & development, *Clostridioides difficile/genetics/physiology/metabolism, *Gene Expression Regulation, *Mucus/microbiology/metabolism, Bacterial, biofilm, Clostridioides difficile, Clostridium difficile, Clostridium Infections/microbiology, Epithelial Cells/microbiology, Humans, Intestinal Mucosa/microbiology/metabolism, metabolic modeling, mucus, transcriptomics},

pubstate = {published},

tppubtype = {article}

}

@article{islam_model-driven_2024,

title = {Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes.},

author = {Mohammad Mazharul Islam and Glynis L. Kolling and Emma M. Glass and Joanna B. Goldberg and Jason A. Papin},

doi = {10.1099/mgen.0.001259},

issn = {2057-5858},

year  = {2024},

date = {2024-06-01},

journal = {Microbial genomics},

volume = {10},

number = {6},

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, multilocus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.},

note = {Place: England},

keywords = {*Genotype, *Metabolic Networks and Pathways/genetics, *Phenotype, *Pseudomonas aeruginosa/genetics/isolation & purification, *Pseudomonas Infections/microbiology, Bacterial, Genetic Variation, Genome, Humans, metabolic modelling, Multilocus Sequence Typing, phenotypic diversity, Pseudomonas aeruginosa, Whole Genome Sequencing/methods},

pubstate = {published},

tppubtype = {article}

}

@article{kuper_spatial_2024,

title = {Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment.},

author = {Tracy J. Kuper and Mohammad Mazharul Islam and Shayn M. Peirce-Cottler and Jason A. Papin and Roseanne M. Ford},

doi = {10.1371/journal.pcbi.1012031},

issn = {1553-7358 1553-734X},

year  = {2024},

date = {2024-04-01},

journal = {PLoS computational biology},

volume = {20},

number = {4},

pages = {e1012031},

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.},

note = {Place: United States},

keywords = {*Biofilms/growth & development, *Models, *Oxidative Stress/physiology, *Pseudomonas aeruginosa/genetics/metabolism/physiology, *Transcriptome/genetics, Biological, Computational Biology, Computer Simulation, Denitrification, Metabolic Networks and Pathways/genetics, Nitric Oxide/metabolism},

pubstate = {published},

tppubtype = {article}

}

@article{dougherty_identifying_2024,

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

author = {Bonnie V. Dougherty and Connor J. Moore and Kristopher D. Rawls and Matthew L. Jenior and Bryan Chun and Sarbajeet Nagdas and Jeffrey J. Saucerman and Glynis L. Kolling and Anders Wallqvist and Jason A. Papin},

doi = {10.1371/journal.pcbi.1011919},

issn = {1553-7358 1553-734X},

year  = {2024},

date = {2024-02-01},

journal = {PLoS computational biology},

volume = {20},

number = {2},

pages = {e1011919},

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.},

note = {Place: United States},

keywords = {*Acetaminophen, *Cardiotoxicity/metabolism, Doxorubicin/pharmacology, Fluorouracil/pharmacology, Gene Expression Profiling, Humans, Metabolomics},

pubstate = {published},

tppubtype = {article}

}

@article{tubergen_computational_2023,

title = {A computational model of Pseudomonas syringae metabolism unveils a role for branched-chain amino acids in Arabidopsis leaf colonization.},

author = {Philip J. Tubergen and Greg Medlock and Anni Moore and Xiaomu Zhang and Jason A. Papin and Cristian H. Danna},

doi = {10.1371/journal.pcbi.1011651},

issn = {1553-7358 1553-734X},

year  = {2023},

date = {2023-12-01},

journal = {PLoS computational biology},

volume = {19},

number = {12},

pages = {e1011651},

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.},

note = {Place: United States},

keywords = {*Arabidopsis/genetics/metabolism, Amino Acids, Branched-Chain/metabolism, Plant Diseases/genetics/microbiology, Plant Leaves/genetics, Pseudomonas syringae/genetics, Virulence/genetics},

pubstate = {published},

tppubtype = {article}

}

@article{moore_metabolic_2023,

title = {Metabolic modeling of sex-specific liver tissue suggests mechanism of differences in toxicological responses.},

author = {Connor J. Moore and Christopher P. Holstege and Jason A. Papin},

doi = {10.1371/journal.pcbi.1010927},

issn = {1553-7358 1553-734X},

year  = {2023},

date = {2023-08-01},

journal = {PLoS computational biology},

volume = {19},

number = {8},

pages = {e1010927},

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.},

note = {Place: United States},

keywords = {*Sexual Behavior, *Xenobiotics/toxicity, Animals, Fatty Acids, Female, Humans, Liver, Male, Sex Characteristics},

pubstate = {published},

tppubtype = {article}

}

@article{mac_gabhann_blossoming_2023,

title = {The blossoming of methods and software in computational biology.},

author = {Feilim Mac Gabhann and Virginia E. Pitzer and Jason A. Papin},

doi = {10.1371/journal.pcbi.1011390},

issn = {1553-7358 1553-734X},

year  = {2023},

date = {2023-08-01},

journal = {PLoS computational biology},

volume = {19},

number = {8},

pages = {e1011390},

note = {Place: United States},

keywords = {*Computational Biology, *Software},

pubstate = {published},

tppubtype = {article}

}

@article{potter_transcriptome-guided_2023,

title = {Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution in Neisseria gonorrhoeae during neutrophil co-culture.},

author = {Aimee D. Potter and Christopher M. Baiocco and Jason A. Papin and Alison K. Criss},

doi = {10.1128/msystems.01265-22},

issn = {2379-5077},

year  = {2023},

date = {2023-08-01},

journal = {mSystems},

volume = {8},

number = {4},

pages = {e0126522},

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.},

note = {Place: United States},

keywords = {*Gonorrhea/genetics, *Neisseria gonorrhoeae/genetics, carbon metabolism, Coculture Techniques, genome-scale metabolic network reconstruction, Humans, metabolic network analysis, Metabolic Networks and Pathways/genetics, metabolism, Neisseria gonorrhoeae, neutrophils, Transcriptome, transcriptomics},

pubstate = {published},

tppubtype = {article}

}

@article{jenior_reconstructor_2023,

title = {Reconstructor: a COBRApy compatible tool for automated genome-scale metabolic network reconstruction with parsimonious flux-based gap-filling.},

author = {Matthew L. Jenior and Emma M. Glass and Jason A. Papin},

doi = {10.1093/bioinformatics/btad367},

issn = {1367-4811 1367-4803},

year  = {2023},

date = {2023-06-01},

journal = {Bioinformatics (Oxford, England)},

volume = {39},

number = {6},

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.},

note = {Place: England},

keywords = {*Genome, *Software, Bacteria/metabolism, Databases, Factual, Metabolic Networks and Pathways},

pubstate = {published},

tppubtype = {article}

}

@article{powers_network_2023,

title = {Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies.},

author = {Deborah A. Powers and Matthew L. Jenior and Glynis L. Kolling and Jason A. Papin},

doi = {10.1371/journal.pcbi.1011076},

issn = {1553-7358 1553-734X},

year  = {2023},

date = {2023-04-01},

journal = {PLoS computational biology},

volume = {19},

number = {4},

pages = {e1011076},

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.},

note = {Place: United States},

keywords = {*Bacterial Toxins, *Clostridioides difficile, Bacterial Proteins/metabolism, Clostridioides/metabolism, Enterotoxins/metabolism},

pubstate = {published},

tppubtype = {article}

}

@article{lee_new_2023,

title = {New perspectives into the vaginal microbiome with systems biology.},

author = {Christina Y. Lee and Lillian R. Dillard and Jason A. Papin and Kelly B. Arnold},

doi = {10.1016/j.tim.2022.09.011},

issn = {1878-4380 0966-842X},

year  = {2023},

date = {2023-04-01},

journal = {Trends in microbiology},

volume = {31},

number = {4},

pages = {356–368},

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.},

note = {Place: England},

keywords = {*Microbiota, *Systems Biology, Female, Gastrointestinal Tract, Humans, Systems Biology, Vagina, vaginal microbiome, Women's Health},

pubstate = {published},

tppubtype = {article}

}

@article{dillard_metabolic_2023,

title = {Metabolic Network Models of the Gardnerella Pangenome Identify Key Interactions with the Vaginal Environment.},

author = {Lillian R. Dillard and Emma M. Glass and Amanda L. Lewis and Krystal Thomas-White and Jason A. Papin},

doi = {10.1128/msystems.00689-22},

issn = {2379-5077},

year  = {2023},

date = {2023-02-01},

journal = {mSystems},

volume = {8},

number = {1},

pages = {e0068922},

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.},

note = {Place: United States},

keywords = {*Vaginosis, Bacteria, bacterial vaginosis, Bacterial/genetics, Female, Gardnerella, Gardnerella vaginalis/genetics, Humans, metabolic modeling, Metabolic Networks and Pathways/genetics, Vagina/microbiology, women’s health},

pubstate = {published},

tppubtype = {article}

}

@article{fernandes_identifying_2023,

title = {Identifying metabolic shifts in Crohn's disease using' omics-driven contextualized computational metabolic network models.},

author = {Philip Fernandes and Yash Sharma and Fatima Zulqarnain and Brooklyn McGrew and Aman Shrivastava and Lubaina Ehsan and Dawson Payne and Lillian Dillard and Deborah Powers and Isabelle Aldridge and Jason Matthews and Subra Kugathasan and Facundo M. Fernández and David Gaul and Jason A. Papin and Sana Syed},

doi = {10.1038/s41598-022-26816-5},

issn = {2045-2322},

year  = {2023},

date = {2023-01-01},

journal = {Scientific reports},

volume = {13},

number = {1},

pages = {203},

abstract = {Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets.},

note = {Place: England},

keywords = {*Crohn Disease/metabolism, Biomarkers/metabolism, Gene Expression Profiling, Humans, Metabolic Networks and Pathways, Metabolomics},

pubstate = {published},

tppubtype = {article}

}

@article{kuchina_how_2022,

title = {How can systems approaches help us understand and treat infectious disease?},

author = {Anna Kuchina and Jason Yang and Bree Aldridge and Kevin A. Janes and Naeha Subramanian and Nevan J. Krogan and Mehdi Bouhaddou and Shirit Einav and Jason Papin and Ronald N. Germain},

doi = {10.1016/j.cels.2022.11.009},

issn = {2405-4720 2405-4712},

year  = {2022},

date = {2022-12-01},

journal = {Cell systems},

volume = {13},

number = {12},

pages = {945–949},

abstract = {Leading researchers at the intersection of infectious disease and systems biology speak about how systems approaches have influenced modern infectious disease research and what these tools can offer for the future of the field.},

note = {Place: United States},

keywords = {*Communicable Diseases/therapy, Humans, Systems Biology},

pubstate = {published},

tppubtype = {article}

}

@article{papin_ten_2022,

title = {Ten simple rules for launching an academic research career.},

author = {Jason A. Papin and Jessica Keim-Malpass and Sana Syed},

doi = {10.1371/journal.pcbi.1010689},

issn = {1553-7358 1553-734X},

year  = {2022},

date = {2022-12-01},

journal = {PLoS computational biology},

volume = {18},

number = {12},

pages = {e1010689},

note = {Place: United States},

keywords = {*Career Choice},

pubstate = {published},

tppubtype = {article}

}

@article{smith_enterococci_2022,

title = {Enterococci enhance Clostridioides difficile pathogenesis.},

author = {Alexander B. Smith and Matthew L. Jenior and Orlaith Keenan and Jessica L. Hart and Jonathan Specker and Arwa Abbas and Paula C. Rangel and Chao Di and Jamal Green and Katelyn A. Bustin and Jennifer A. Gaddy and Maribeth R. Nicholson and Clare Laut and Brendan J. Kelly and Megan L. Matthews and Daniel R. Evans and Daria Van Tyne and Emma E. Furth and Jason A. Papin and Frederic D. Bushman and Jessi Erlichman and Robert N. Baldassano and Michael A. Silverman and Gary M. Dunny and Boone M. Prentice and Eric P. Skaar and Joseph P. Zackular},

doi = {10.1038/s41586-022-05438-x},

issn = {1476-4687 0028-0836},

year  = {2022},

date = {2022-11-01},

journal = {Nature},

volume = {611},

number = {7937},

pages = {780–786},

abstract = {Enteric pathogens are exposed to a dynamic polymicrobial environment in the gastrointestinal tract(1). This microbial community has been shown to be important during infection, but there are few examples illustrating how microbial interactions can influence the virulence of invading pathogens(2). Here we show that expansion of a group of antibiotic-resistant, opportunistic pathogens in the gut-the enterococci-enhances the fitness and pathogenesis of Clostridioides difficile. Through a parallel process of nutrient restriction and cross-feeding, enterococci shape the metabolic environment in the gut and reprogramme C. difficile metabolism. Enterococci provide fermentable amino acids, including leucine and ornithine, which increase C. difficile fitness in the antibiotic-perturbed gut. Parallel depletion of arginine by enterococci through arginine catabolism provides a metabolic cue for C. difficile that facilitates increased virulence. We find evidence of microbial interaction between these two pathogenic organisms in multiple mouse models of infection and patients infected with C. difficile. These findings provide mechanistic insights into the role of pathogenic microbiota in the susceptibility to and the severity of C. difficile infection.},

note = {Place: England},

keywords = {*Clostridioides difficile/metabolism/pathogenicity/physiology, *Enterococcus/drug effects/metabolism/pathogenicity/physiology, *Microbial Interactions, Animal, Animals, Anti-Bacterial Agents/pharmacology, Arginine/deficiency/metabolism, Bacterial, Disease Models, Disease Susceptibility, Drug Resistance, Gastrointestinal Microbiome/drug effects, Humans, Intestines/drug effects/metabolism/microbiology, Leucine/metabolism, Mice, Ornithine/metabolism, Virulence},

pubstate = {published},

tppubtype = {article}

}

@article{fawad_histone_2022,

title = {Histone Deacetylase Inhibition by Gut Microbe-Generated Short-Chain Fatty Acids Entrains Intestinal Epithelial Circadian Rhythms.},

author = {Jibraan A. Fawad and Deborah H. Luzader and Gabriel F. Hanson and Thomas J. Jr Moutinho and Craig A. McKinney and Paul G. Mitchell and Kathleen Brown-Steinke and Ajay Kumar and Miri Park and Suengwon Lee and David T. Bolick and Greg L. Medlock and Jesse Y. Zhao and Andrew E. Rosselot and C. James Chou and Emily M. Eshleman and Theresa Alenghat and Christian I. Hong and Jason A. Papin and Sean R. Moore},

doi = {10.1053/j.gastro.2022.07.051},

issn = {1528-0012 0016-5085},

year  = {2022},

date = {2022-11-01},

journal = {Gastroenterology},

volume = {163},

number = {5},

pages = {1377–1390.e11},

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.},

note = {Place: United States},

keywords = {*Circadian Rhythm/physiology, *Gastrointestinal Microbiome/physiology, Animals, ARNTL Transcription Factors, Butyrates, Caco-2 Cells, Circadian Rhythm, Fatty Acids, Histone Deacetylase Inhibitors/pharmacology, Histone Deacetylases, Histone Deacetylation, Humans, Intestinal Organoids, Luciferases, Mice, Microbiome, Propionates, Short-Chain Fatty Acids, Sulforaphane, Volatile/metabolism},

pubstate = {published},

tppubtype = {article}

}

@article{moutinho_fecal_2022,

title = {Fecal sphingolipids predict parenteral nutrition-associated cholestasis in the neonatal intensive care unit.},

author = {Thomas J. Moutinho and Deborah A. Powers and Gabriel F. Hanson and Shira Levy and Rajiv Baveja and Isabel Hefner and Masouma Mohamed and Alaa Abdelghani and Robin L. Baker and Jason A. Papin and Sean R. Moore and Suchitra K. Hourigan},

doi = {10.1002/jpen.2374},

issn = {1941-2444 0148-6071},

year  = {2022},

date = {2022-11-01},

journal = {JPEN. Journal of parenteral and enteral nutrition},

volume = {46},

number = {8},

pages = {1903–1913},

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.},

note = {Place: United States},

keywords = {*Cholestasis/diagnosis/etiology/therapy, *Intensive Care Units, Biomarkers, early detection, Humans, infant, Metabolomics, Neonatal, neonatal intensive care unit, Newborn, parenteral nutrition-associated cholestasis, Parenteral Nutrition/adverse effects, Sphingolipids, sphingomyelin, stool},

pubstate = {published},

tppubtype = {article}

}

@article{jenior_genome-scale_2022,

title = {Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae.},

author = {Matthew L. Jenior and Mary E. Dickenson and Jason A. Papin},

doi = {10.1038/s41540-022-00252-7},

issn = {2056-7189},

year  = {2022},

date = {2022-10-01},

journal = {NPJ systems biology and applications},

volume = {8},

number = {1},

pages = {41},

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 textasciitilde50% 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.},

note = {Place: England},

keywords = {*Carbapenem-Resistant Enterobacteriaceae, *Klebsiella Infections/drug therapy/genetics/microbiology, Anti-Bacterial Agents/pharmacology/therapeutic use, Humans, Klebsiella pneumoniae/genetics, Valine/genetics/pharmacology},

pubstate = {published},

tppubtype = {article}

}

@article{cadwallader_advancing_2022,

title = {Advancing code sharing in the computational biology community.},

author = {Lauren Cadwallader and Feilim Mac Gabhann and Jason Papin and Virginia E. Pitzer},

doi = {10.1371/journal.pcbi.1010193},

issn = {1553-7358 1553-734X},

year  = {2022},

date = {2022-06-01},

journal = {PLoS computational biology},

volume = {18},

number = {6},

pages = {e1010193},

note = {Place: United States},

keywords = {*Computational Biology, *Software, Computer Simulation},

pubstate = {published},

tppubtype = {article}

}

@article{carey_comparative_2022,

title = {Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models.},

author = {Maureen A. Carey and Gregory L. Medlock and Michał Stolarczyk and William A. Jr Petri and Jennifer L. Guler and Jason A. Papin},

doi = {10.1371/journal.pcbi.1009870},

issn = {1553-7358 1553-734X},

year  = {2022},

date = {2022-02-01},

journal = {PLoS computational biology},

volume = {18},

number = {2},

pages = {e1009870},

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.},

note = {Place: United States},

keywords = {*Cryptosporidiosis/genetics, *Cryptosporidium/genetics, *Parasites/genetics, *Plasmodium/genetics, Animals, Eukaryota/genetics, Genome, Protozoan/genetics},

pubstate = {published},

tppubtype = {article}

}

@article{moutinho_quantifying_2022,

title = {Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions.},

author = {Thomas J. Jr Moutinho and Benjamin C. Neubert and Matthew L. Jenior and Jason A. Papin},

doi = {10.1371/journal.pcbi.1009341},

issn = {1553-7358 1553-734X},

year  = {2022},

date = {2022-02-01},

journal = {PLoS computational biology},

volume = {18},

number = {2},

pages = {e1009341},

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.},

note = {Place: United States},

keywords = {*Genomics, *Metabolic Networks and Pathways, *Phenotype, Bacterial, Biological, Escherichia coli/*genetics, Genes, Models},

pubstate = {published},

tppubtype = {article}

}

@article{jenior_computational_2022,

title = {Computational approaches to understanding Clostridioides difficile metabolism and virulence.},

author = {Matthew L. Jenior and Jason A. Papin},

doi = {10.1016/j.mib.2021.11.002},

issn = {1879-0364 1369-5274},

year  = {2022},

date = {2022-02-01},

journal = {Current opinion in microbiology},

volume = {65},

pages = {108–115},

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.},

note = {Place: England},

keywords = {*Clostridioides difficile/genetics, *Clostridium Infections, *Gastrointestinal Microbiome, Clostridioides, Humans, Virulence},

pubstate = {published},

tppubtype = {article}

}

@article{payne_updated_2021,

title = {An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism.},

author = {Dawson D. Payne and Alina Renz and Laura J. Dunphy and Taylor Lewis and Andreas Dräger and Jason A. Papin},

doi = {10.1038/s41540-021-00198-2},

issn = {2056-7189},

year  = {2021},

date = {2021-10-01},

journal = {NPJ systems biology and applications},

volume = {7},

number = {1},

pages = {37},

abstract = {Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.},

note = {Place: England},

keywords = {*Mucins/genetics/metabolism, *Pseudomonas aeruginosa/genetics/metabolism, Bacteria/metabolism, Metabolic Networks and Pathways/genetics},

pubstate = {published},

tppubtype = {article}

}

@article{jenior_novel_2021,

title = {Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis.},

author = {Matthew L. Jenior and Jhansi L. Leslie and Deborah A. Powers and Elizabeth M. Garrett and Kimberly A. Walker and Mary E. Dickenson and William A. Jr Petri and Rita Tamayo and Jason A. Papin},

doi = {10.1128/mSystems.00919-21},

issn = {2379-5077},

year  = {2021},

date = {2021-10-01},

journal = {mSystems},

volume = {6},

number = {5},

pages = {e0091921},

abstract = {The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hypervirulent isolate (strain [str.] R20291) and a historic strain (str. 630), validating both with in vitro and in vivo data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the pentose phosphate pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in higher-order phenotypes like bacterial pathogenesis. IMPORTANCE Clostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors. In the past, genome-scale metabolic network reconstruction (GENRE) analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that contribute to downstream virulence phenotypes. With this in mind, we generated and extensively curated C. difficile GENREs for both a well-studied laboratory strain (str. 630) and a more recently characterized hypervirulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization data sets. Subsequent exploration of context-specific metabolism during both in vitro growth and infection revealed consistent patterns of metabolism which corresponded with experimentally measured increases in virulence factor expression. Our results support that differential C. difficile virulence is associated with distinct metabolic programs related to use of carbon sources and provide a platform for identification of novel therapeutic targets.},

note = {Place: United States},

keywords = {Clostridioides difficile, metabolic modeling, transcriptomics, virulence factors},

pubstate = {published},

tppubtype = {article}

}

@article{carey_megasphaera_2021,

title = {Megasphaera in the Stool Microbiota Is Negatively Associated With Diarrheal Cryptosporidiosis.},

author = {Maureen A. Carey and Gregory L. Medlock and Masud Alam and Mamun Kabir and Md Jashim Uddin and Uma Nayak and Jason Papin and A. S. G. Faruque and Rashidul Haque and William A. Petri and Carol A. Gilchrist},

doi = {10.1093/cid/ciab207},

issn = {1537-6591 1058-4838},

year  = {2021},

date = {2021-09-01},

journal = {Clinical infectious diseases : an official publication of the Infectious Diseases Society of America},

volume = {73},

number = {6},

pages = {e1242–e1251},

abstract = {BACKGROUND: The protozoan parasites in the Cryptosporidium genus cause both acute diarrheal disease and subclinical (ie, nondiarrheal) disease. It is unclear if the microbiota can influence the manifestation of diarrhea during a Cryptosporidium infection. METHODS: To characterize the role of the gut microbiota in diarrheal cryptosporidiosis, the microbiome composition of both diarrheal and surveillance Cryptosporidium-positive fecal samples from 72 infants was evaluated using 16S ribosomal RNA gene sequencing. Additionally, the microbiome composition prior to infection was examined to test whether a preexisting microbiome profile could influence the Cryptosporidium infection phenotype. RESULTS: Fecal microbiome composition was associated with diarrheal symptoms at 2 timepoints. Megasphaera was significantly less abundant in diarrheal samples compared with subclinical samples at the time of Cryptosporidium detection (log2 [fold change] = -4.3; P = 10-10) and prior to infection (log2 [fold change] = -2.0; P = 10-4); this assigned sequence variant was detected in 8 children who had diarrhea and 30 children without diarrhea. Random forest classification also identified Megasphaera abundance in the pre- and postexposure microbiota as predictive of a subclinical infection. CONCLUSIONS: Microbiome composition broadly, and specifically low Megasphaera abundance, was associated with diarrheal symptoms prior to and at the time of Cryptosporidium detection. This observation suggests that the gut microenvironment may play a role in determining the severity of a Cryptosporidium infection. Clinical Trials Registration. NCT02764918.},

note = {Place: United States},

keywords = {*Cryptosporidiosis, *Cryptosporidium/genetics, *Microbiota, Bangladesh, Cryptosporidium, diarrhea, Feces, Humans, infant, Megasphaera, microbiome/microbiota, parasite},

pubstate = {published},

tppubtype = {article}

}

@article{dunphy_multidimensional_2021,

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

author = {Laura J. Dunphy and Glynis L. Kolling and Matthew L. Jenior and Joanne Carroll and April E. Attai and Farzad Farnoud and Amy J. Mathers and Molly A. Hughes and Jason A. Papin},

doi = {10.1128/mSphere.00393-21},

issn = {2379-5042},

year  = {2021},

date = {2021-08-01},

journal = {mSphere},

volume = {6},

number = {4},

pages = {e0039321},

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

note = {Place: United States},

keywords = {*Drug Resistance, Adolescent, Adult, Aged, Anti-Bacterial Agents/*pharmacology, antimicrobial resistance, Bacterial, Child, clinical risk factors, Cross Infection, Female, Humans, infant, infectious disease, Male, Microbial Sensitivity Tests, Middle Aged, Multiple, Phenotype, Preschool, Pseudomonas aeruginosa, Pseudomonas aeruginosa/*drug effects/genetics/isolation & purification/*physiology, Pseudomonas Infections/microbiology, virulence factors, Young Adult},

pubstate = {published},

tppubtype = {article}

}

@article{dunphy_untargeted_2021,

title = {Untargeted Metabolomics Reveals Species-Specific Metabolite Production and Shared Nutrient Consumption by Pseudomonas aeruginosa and Staphylococcus aureus.},

author = {Laura J. Dunphy and Kassandra L. Grimes and Nishikant Wase and Glynis L. Kolling and Jason A. Papin},

doi = {10.1128/mSystems.00480-21},

issn = {2379-5077},

year  = {2021},

date = {2021-06-01},

journal = {mSystems},

volume = {6},

number = {3},

pages = {e0048021},

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

note = {Place: United States},

keywords = {LC-MS, metabolism, Pseudomonas aeruginosa, Staphylococcus aureus},

pubstate = {published},

tppubtype = {article}

}

@article{dillard_mechanistic_2021,

title = {Mechanistic models of microbial community metabolism.},

author = {Lillian R. Dillard and Dawson D. Payne and Jason A. Papin},

doi = {10.1039/d0mo00154f},

issn = {2515-4184},

year  = {2021},

date = {2021-06-01},

journal = {Molecular omics},

volume = {17},

number = {3},

pages = {365–375},

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.},

note = {Place: England},

keywords = {Algorithms, Bacteria/classification/*metabolism, Bioengineering, Biological, Host-Pathogen Interactions, Humans, Microbial Interactions, Microbiota, Models},

pubstate = {published},

tppubtype = {article}

}

@article{cadwallader_collaborating_2021,

title = {Collaborating with our community to increase code sharing.},

author = {Lauren Cadwallader and Jason A. Papin and Feilim Mac Gabhann and Rebecca Kirk},

doi = {10.1371/journal.pcbi.1008867},

issn = {1553-7358 1553-734X},

year  = {2021},

date = {2021-03-01},

journal = {PLoS computational biology},

volume = {17},

number = {3},

pages = {e1008867},

note = {Place: United States},

keywords = {*Cooperative Behavior, *Information Dissemination, *Open Access Publishing, *Software, Computational Biology/*organization & administration, Humans},

pubstate = {published},

tppubtype = {article}

}

@article{dougherty_identifying_2021,

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

author = {Bonnie V. Dougherty and Kristopher D. Rawls and Glynis L. Kolling and Kalyan C. Vinnakota and Anders Wallqvist and Jason A. Papin},

doi = {10.1016/j.celrep.2021.108836},

issn = {2211-1247},

year  = {2021},

date = {2021-03-01},

journal = {Cell reports},

volume = {34},

number = {10},

pages = {108836},

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.},

note = {Place: United States},

keywords = {*Models, Biological, Databases, GENRE, heart failure, Heart Failure/*genetics/pathology, heart metabolism, Humans, metabolic network, Metabolic Networks and Pathways/*genetics, Metabolomics/methods, Myocardium/*metabolism, N-acetylneuraminic acid, N-Acetylneuraminic Acid/metabolism, nitric oxide, Nitric Oxide/metabolism, Protein, Severity of Illness Index, transcriptomics},

pubstate = {published},

tppubtype = {article}

}

@article{rawls_predicting_2021,

title = {Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.},

author = {Kristopher D. Rawls and Bonnie V. Dougherty and Kalyan C. Vinnakota and Venkat R. Pannala and Anders Wallqvist and Glynis L. Kolling and Jason A. Papin},

doi = {10.1016/j.taap.2020.115390},

issn = {1096-0333 0041-008X},

year  = {2021},

date = {2021-02-01},

journal = {Toxicology and applied pharmacology},

volume = {412},

pages = {115390},

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.},

note = {Place: United States},

keywords = {Acetaminophen/toxicity, Animals, Cells, Cultured, Databases, Energy Metabolism/*drug effects/genetics, Epithelial Cells/*drug effects/metabolism/pathology, Female, Gene Expression Profiling, Gene Regulatory Networks, Genetic, Gentamicins/toxicity, Kidney, Kidney Diseases/*chemically induced/genetics/metabolism/pathology, Kidney Metabolism, Kidney Tubules, metabolic modeling, Metabolome/*drug effects/genetics, Metabolomics, Nephrotoxicity, Polychlorinated Dibenzodioxins/toxicity, Proximal/*drug effects/metabolism/pathology, Rats, Sprague-Dawley, Transcriptome/*drug effects, transcriptomics, Trichloroethylene/toxicity},

pubstate = {published},

tppubtype = {article}

}

@article{filho_perinatal_2021,

title = {Perinatal Outcomes of Asynchronous Influenza Vaccination, Ceará, Brazil, 2013-2018.},

author = {José Q. Filho and Francisco S. Junior and Thaisy B. R. Lima and Vânia A. F. Viana and Jaqueline S. V. Burgoa and Alberto M. Soares and Álvaro M. Leite and Simone A. Herron and Hunter L. Newland and Kunaal S. Sarnaik and Gabriel F. Hanson and Jason A. Papin and Sean R. Moore and Aldo A. M. Lima},

doi = {10.3201/eid2709.203791},

issn = {1080-6059 1080-6040},

year  = {2021},

date = {2021-01-01},

journal = {Emerging infectious diseases},

volume = {27},

number = {9},

pages = {2409–2420},

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.},

note = {Place: United States},

keywords = {*Influenza, *Pregnancy Complications, Brazil, Brazil/epidemiology, Child, childbirth, Female, Human/epidemiology/prevention & control, Humans, infant, Infectious/epidemiology/prevention & control, influenza, influenza viruses, Newborn, Parturition, pregnancy, premature birth, respiratory infections, severe acute respiratory infection, severe flu syndrome, underweight, vaccination, vaccines, viruses},

pubstate = {published},

tppubtype = {article}

}

@article{jenior_clostridioides_2020,

title = {Clostridioides difficile: Sometimes It Pays To Be Difficult.},

author = {Matthew L. Jenior and Jason A. Papin},

doi = {10.1016/j.chom.2020.08.010},

issn = {1934-6069 1931-3128},

year  = {2020},

date = {2020-09-01},

journal = {Cell host & microbe},

volume = {28},

number = {3},

pages = {358–359},

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.},

note = {Place: United States},

keywords = {*Clostridioides difficile, *Clostridium Infections, Clostridiales, Heme, Humans, Oxidative Stress},

pubstate = {published},

tppubtype = {article}

}

@article{carey_community_2020,

title = {Community standards to facilitate development and address challenges in metabolic modeling.},

author = {Maureen A. Carey and Andreas Dräger and Moritz E. Beber and Jason A. Papin and James T. Yurkovich},

doi = {10.15252/msb.20199235},

issn = {1744-4292},

year  = {2020},

date = {2020-08-01},

journal = {Molecular systems biology},

volume = {16},

number = {8},

pages = {e9235},

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.},

note = {Place: Germany},

keywords = {Computer Simulation, Genetic, Humans, Metabolic Networks and Pathways, Models, Software, Systems Biology/*standards},

pubstate = {published},

tppubtype = {article}

}

@article{liu_minimum_2020,

title = {Minimum Bactericidal Concentration of Ciprofloxacin to Pseudomonas aeruginosa Determined Rapidly Based on Pyocyanin Secretion.},

author = {Yi Liu and John H. Moore and Glynis L. Kolling and John S. McGrath and Jason A. Papin and Nathan S. Swami},

doi = {10.1016/j.snb.2020.127936},

issn = {0925-4005},

year  = {2020},

date = {2020-06-01},

journal = {Sensors and actuators. B, Chemical},

volume = {312},

pages = {127936},

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.},

note = {Place: Switzerland},

keywords = {Antibiotics, Bacteria, microfluidics, nanoporous gold, persistence, pyocyanin},

pubstate = {published},

tppubtype = {article}

}

@article{papin_improving_2020,

title = {Improving reproducibility in computational biology research.},

author = {Jason A. Papin and Feilim Mac Gabhann and Herbert M. Sauro and David Nickerson and Anand Rampadarath},

doi = {10.1371/journal.pcbi.1007881},

issn = {1553-7358 1553-734X},

year  = {2020},

date = {2020-05-01},

journal = {PLoS computational biology},

volume = {16},

number = {5},

pages = {e1007881},

note = {Place: United States},

keywords = {Computational Biology/*standards, Reproducibility of Results, Research},

pubstate = {published},

tppubtype = {article}

}

@article{hourigan_gram-negative_2020,

title = {Gram-negative Microbiota Blooms in Premature Twins Discordant for Parenteral Nutrition-associated Cholestasis.},

author = {Suchitra K. Hourigan and Thomas J. Jr Moutinho and Andrew Berenz and Jason Papin and Pallabi Guha and Lois Bangiolo and Sandra Oliphant and Marina Provenzano and Raj Baveja and Robin Baker and Thierry Vilboux and Shira Levy and Varsha Deopujari and James P. Nataro and John E. Niederhuber and Sean R. Moore},

doi = {10.1097/MPG.0000000000002617},

issn = {1536-4801 0277-2116},

year  = {2020},

date = {2020-05-01},

journal = {Journal of pediatric gastroenterology and nutrition},

volume = {70},

number = {5},

pages = {640–644},

abstract = {Parenteral nutrition-associated cholestasis (PNAC) causes serious morbidity in the neonatal intensive care unit. Infection with gut-associated bacteria is associated with cholestasis, but the role of intestinal microbiota in PNAC is poorly understood. We examined the composition of stool microbiota from premature twins discordant for PNAC as a strategy to reduce confounding from variables associated with both microbiota and cholestasis. Eighty-four serial stool samples were included from 4 twin sets discordant for PNAC. Random Forests was utilized to determine genera most discriminatory in classifying samples from infants with and without PNAC. In infants with PNAC, we detected a significant increase in the relative abundance of Klebsiella, Veillonella, Enterobacter, and Enterococcus (P < 0.05). Bray-Curtis dissimilarities in infants with PNAC were significantly different (P < 0.05) from infants without PNAC. Our findings warrant further exploration in larger cohorts and experimental models of PNAC to determine if a microbiota signature predicts PNAC, as a basis for future interventions to mitigate liver injury.},

note = {Place: United States},

keywords = {*Cholestasis/etiology/therapy, *Microbiota, Gestational Age, Humans, infant, Newborn, Parenteral Nutrition/adverse effects, Premature},

pubstate = {published},

tppubtype = {article}

}

@article{medlock_medusa_2020,

title = {Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions.},

author = {Gregory L. Medlock and Thomas J. Moutinho and Jason A. Papin},

doi = {10.1371/journal.pcbi.1007847},

issn = {1553-7358 1553-734X},

year  = {2020},

date = {2020-04-01},

journal = {PLoS computational biology},

volume = {16},

number = {4},

pages = {e1007847},

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

note = {Place: United States},

keywords = {*Software, Computational Biology/*methods, Computer Simulation, Genome/*genetics, Machine Learning, Metabolic Networks and Pathways/*genetics},

pubstate = {published},

tppubtype = {article}

}

@article{jenior_transcriptome-guided_2020,

title = {Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments.},

author = {Matthew L. Jenior and Thomas J. Jr Moutinho and Bonnie V. Dougherty and Jason A. Papin},

doi = {10.1371/journal.pcbi.1007099},

issn = {1553-7358 1553-734X},

year  = {2020},

date = {2020-04-01},

journal = {PLoS computational biology},

volume = {16},

number = {4},

pages = {e1007099},

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.},

note = {Place: United States},

keywords = {*Metabolic Flux Analysis, *Metabolic Networks and Pathways, *Transcriptome, Algorithms, Animals, Bacterial, Biological, Cecum/microbiology, Computational Biology, Computer Simulation, Dysbiosis, Escherichia coli Proteins/*metabolism, Escherichia coli/*metabolism, Gastrointestinal Microbiome, Gene Expression Profiling, Genome, Inbred C57BL, Mice, Models},

pubstate = {published},

tppubtype = {article}

}

@article{lieven_publisher_2020,

title = {Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.},

author = {Christian Lieven and Moritz E. Beber and Brett G. Olivier and Frank T. Bergmann and Meric Ataman and Parizad Babaei and Jennifer A. Bartell and Lars M. Blank and Siddharth Chauhan and Kevin Correia and Christian Diener and Andreas Dräger and Birgitta E. Ebert and Janaka N. Edirisinghe and José P. Faria and Adam M. Feist and Georgios Fengos and Ronan M. T. Fleming and Beatriz García-Jiménez and Vassily Hatzimanikatis and Wout Helvoirt and Christopher S. Henry and Henning Hermjakob and Markus J. Herrgård and Ali Kaafarani and Hyun Uk Kim and Zachary King and Steffen Klamt and Edda Klipp and Jasper J. Koehorst and Matthias König and Meiyappan Lakshmanan and Dong-Yup Lee and Sang Yup Lee and Sunjae Lee and Nathan E. Lewis and Filipe Liu and Hongwu Ma and Daniel Machado and Radhakrishnan Mahadevan and Paulo Maia and Adil Mardinoglu and Gregory L. Medlock and Jonathan M. Monk and Jens Nielsen and Lars Keld Nielsen and Juan Nogales and Intawat Nookaew and Bernhard O. Palsson and Jason A. Papin and Kiran R. Patil and Mark Poolman and Nathan D. Price and Osbaldo Resendis-Antonio and Anne Richelle and Isabel Rocha and Benjamín J. Sánchez and Peter J. Schaap and Rahuman S. Malik Sheriff and Saeed Shoaie and Nikolaus Sonnenschein and Bas Teusink and Paulo Vilaça and Jon Olav Vik and Judith A. H. Wodke and Joana C. Xavier and Qianqian Yuan and Maksim Zakhartsev and Cheng Zhang},

doi = {10.1038/s41587-020-0477-4},

issn = {1546-1696 1087-0156},

year  = {2020},

date = {2020-04-01},

journal = {Nature biotechnology},

volume = {38},

number = {4},

pages = {504},

abstract = {An amendment to this paper has been published and can be accessed via a link at the top of the paper.},

note = {Place: United States},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{white_core_2020,

title = {Core Competencies for Undergraduates in Bioengineering and Biomedical Engineering: Findings, Consequences, and Recommendations.},

author = {John A. White and Donald P. Gaver and Robert J. Jr Butera and Bernard Choi and Mary J. Dunlop and K. Jane Grande-Allen and Anna Grosberg and Robert W. Hitchcock and Aileen Y. Huang-Saad and Miiri Kotche and Aaron M. Kyle and Amy L. Lerner and John H. Linehan and Robert A. Linsenmeier and Michael I. Miller and Jason A. Papin and Lori Setton and Allyson Sgro and Michael L. Smith and Muhammad Zaman and Abraham P. Lee},

doi = {10.1007/s10439-020-02468-2},

issn = {1573-9686 0090-6964},

year  = {2020},

date = {2020-03-01},

journal = {Annals of biomedical engineering},

volume = {48},

number = {3},

pages = {905–912},

abstract = {This paper provides a synopsis of discussions related to biomedical engineering core curricula that occurred at the Fourth BME Education Summit held at Case Western Reserve University in Cleveland, Ohio in May 2019. This summit was organized by the Council of Chairs of Bioengineering and Biomedical Engineering, and participants included over 300 faculty members from 100+ accredited undergraduate programs. This discussion focused on six key questions: QI: Is there a core curriculum, and if so, what are its components? QII: How does our purported core curriculum prepare students for careers, particularly in industry? QIII: How does design distinguish BME/BIOE graduates from other engineers? QIV: What is the state of engineering analysis and systems-level modeling in BME/BIOE curricula? QV: What is the role of data science in BME/BIOE undergraduate education? QVI: What core experimental skills are required for BME/BIOE undergrads? s. Indeed, BME/BIOI core curricula exists and has matured to emphasize interdisciplinary topics such as physiology, instrumentation, mechanics, computer programming, and mathematical modeling. Departments demonstrate their own identities by highlighting discipline-specific sub-specialties. In addition to technical competence, Industry partners most highly value our students' capacity for problem solving and communication. As such, BME/BIOE curricula includes open-ended projects that address unmet patient and clinician needs as primary methods to prepare graduates for careers in industry. Culminating senior design experiences distinguish BME/BIOE graduates through their development of client-centered engineering solutions to healthcare problems. Finally, the overall BME/BIOE curriculum is not stagnant-it is clear that data science will become an ever-important element of our students' training and that new methods to enhance student engagement will be of pedagogical importance as we embark on the next decade.},

note = {Place: United States},

keywords = {*Curriculum, Biomedical Engineering Curriculum, Biomedical Engineering Design, Biomedical Engineering Education, Biomedical Engineering Research, Biomedical Engineering/*education, Data Science, Humans, Students, Universities},

pubstate = {published},

tppubtype = {article}

}

@article{lieven_memote_2020,

title = {MEMOTE for standardized genome-scale metabolic model testing.},

author = {Christian Lieven and Moritz E. Beber and Brett G. Olivier and Frank T. Bergmann and Meric Ataman and Parizad Babaei and Jennifer A. Bartell and Lars M. Blank and Siddharth Chauhan and Kevin Correia and Christian Diener and Andreas Dräger and Birgitta E. Ebert and Janaka N. Edirisinghe and José P. Faria and Adam M. Feist and Georgios Fengos and Ronan M. T. Fleming and Beatriz García-Jiménez and Vassily Hatzimanikatis and Wout Helvoirt and Christopher S. Henry and Henning Hermjakob and Markus J. Herrgård and Ali Kaafarani and Hyun Uk Kim and Zachary King and Steffen Klamt and Edda Klipp and Jasper J. Koehorst and Matthias König and Meiyappan Lakshmanan and Dong-Yup Lee and Sang Yup Lee and Sunjae Lee and Nathan E. Lewis and Filipe Liu and Hongwu Ma and Daniel Machado and Radhakrishnan Mahadevan and Paulo Maia and Adil Mardinoglu and Gregory L. Medlock and Jonathan M. Monk and Jens Nielsen and Lars Keld Nielsen and Juan Nogales and Intawat Nookaew and Bernhard O. Palsson and Jason A. Papin and Kiran R. Patil and Mark Poolman and Nathan D. Price and Osbaldo Resendis-Antonio and Anne Richelle and Isabel Rocha and Benjamín J. Sánchez and Peter J. Schaap and Rahuman S. Malik Sheriff and Saeed Shoaie and Nikolaus Sonnenschein and Bas Teusink and Paulo Vilaça and Jon Olav Vik and Judith A. H. Wodke and Joana C. Xavier and Qianqian Yuan and Maksim Zakhartsev and Cheng Zhang},

doi = {10.1038/s41587-020-0446-y},

issn = {1546-1696 1087-0156},

year  = {2020},

date = {2020-03-01},

journal = {Nature biotechnology},

volume = {38},

number = {3},

pages = {272–276},

note = {Place: United States},

keywords = {*Metabolic Networks and Pathways, Biological, Computational Biology/*methods, Genome, Models, Molecular Sequence Annotation, Workflow},

pubstate = {published},

tppubtype = {article}

}

@article{pannala_genome-scale_2020,

title = {Genome-Scale Model-Based Identification of Metabolite Indicators for Early Detection of Kidney Toxicity.},

author = {Venkat R. Pannala and Kalyan C. Vinnakota and Shanea K. Estes and Irina Trenary and Tracy P. OˈBrien and Richard L. Printz and Jason A. Papin and Jaques Reifman and Tatsuya Oyama and Masakazu Shiota and Jamey D. Young and Anders Wallqvist},

doi = {10.1093/toxsci/kfz228},

issn = {1096-0929 1096-6080},

year  = {2020},

date = {2020-02-01},

journal = {Toxicological sciences : an official journal of the Society of Toxicology},

volume = {173},

number = {2},

pages = {293–312},

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.},

note = {Place: United States},

keywords = {*Gene Expression Profiling, aminoaciduria, Animals, Biomarkers, Biomarkers/blood/urine, genome-scale models, gentamicin, Gentamicins/*toxicity, Kidney/*drug effects/metabolism, Liver/drug effects/metabolism, Male, Metabolic Networks and Pathways/*drug effects/*genetics, metabolic pathways, Metabolome/*drug effects/*genetics, Rats, Sprague-Dawley, urine},

pubstate = {published},

tppubtype = {article}

}

@article{medlock_guiding_2020,

title = {Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning.},

author = {Gregory L. Medlock and Jason A. Papin},

doi = {10.1016/j.cels.2019.11.006},

issn = {2405-4720 2405-4712},

year  = {2020},

date = {2020-01-01},

journal = {Cell systems},

volume = {10},

number = {1},

pages = {109–119.e3},

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.},

note = {Place: United States},

keywords = {ensemble modeling, Humans, Machine Learning, Machine Learning/*standards, mechanistic models, metabolic modeling, Metabolic Networks and Pathways/*genetics, metabolism, model curation, Systems Biology},

pubstate = {published},

tppubtype = {article}

}