Biomedical applications of genome-scale metabolic network reconstructions of human pathogens

Dunphy, Laura J.; Papin, Jason A.

doi:10.1016/j.copbio.2017.11.014

Cited by 37 publications

(31 citation statements)

References 79 publications

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“…Genome-scale metabolic network reconstructions are becoming increasingly prevalent in the study of antibiotic resistance (25,28,61,62). Although the P. aeruginosa metabolic network reconstruction does not account for many genes associated with resistance (e.g.…”

Section: Discussionmentioning

confidence: 99%

Network analysis reveals differential metabolic functionality in antibiotic-resistantPseudomonas aeruginosa

Dunphy

Yen

Papin

2018

Preprint

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

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Section: Discussionmentioning

confidence: 99%

Network analysis reveals differential metabolic functionality in antibiotic-resistantPseudomonas aeruginosa

Dunphy

Yen

Papin

2018

Preprint

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“…The metabolic modeling community frequently utilizes COnstraint-Based Reconstruction and Analysis (COBRA) methods to build and compute computational models that represent an organism's metabolic phenotype. Investigating metabolic processes using mechanistic computational models has led to insights into many complex phenotypes and phenomena, including drug resistance (Dunphy and Papin 2018), aging (Nilsson et al 2017), and cancer (Bordbar et al 2014). The construction of genome-scale metabolic network reconstructions and models is a multi-step process that involves the reconstruction of a metabolic network, manual curation to incorporate known physiology, computation of metabolic phenotypes, and the distribution of the models and results (Box 1).…”

Section: Standardization In Metabolic Modeling: a Case Studymentioning

confidence: 99%

Community standards to facilitate development and address challenges in metabolic modeling

Carey

Draeger

Papin

et al. 2019

Preprint

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

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“…In particular, Flux Balance Analysis (FBA) [1] reduces the problem of determining the metabotype (the fluxes through all reactions in the system) to a tractable linear program under the assumptions of steadystate and optimality. Due to its scalability and the informative results it generates, FBA is widely used, for example to predict growth phenotypes and adaptation in specific environmental conditions [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Occhipinti

Hamadi²,

Kugler

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns.After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multiobjective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes.We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

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Biomedical applications of genome-scale metabolic network reconstructions of human pathogens

Cited by 37 publications

References 79 publications

Network analysis reveals differential metabolic functionality in antibiotic-resistantPseudomonas aeruginosa

Network analysis reveals differential metabolic functionality in antibiotic-resistantPseudomonas aeruginosa

Community standards to facilitate development and address challenges in metabolic modeling

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

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