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Article ; Online: Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation

Sanjeev Dahal / Alina Renz / Andreas Dräger / Laurence Yang

Communications Biology, Vol 6, Iss 1, Pp 1-

2023 Volume 10

Abstract: An updated genome-scale model of Pseudomonas aeruginosa explains the metabolic pathways leading to drug resistance and provides a computational platform to design experiments targeting P. aeruginosa metabolism. ...

Abstract	An updated genome-scale model of Pseudomonas aeruginosa explains the metabolic pathways leading to drug resistance and provides a computational platform to design experiments targeting P. aeruginosa metabolism.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth.

Colton J Lloyd / Jonathan Monk / Laurence Yang / Ali Ebrahim / Bernhard O Palsson

PLoS Computational Biology, Vol 17, Iss 6, p e

2021 Volume 1007817

Abstract: Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource ... ...

Abstract	Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource allocation models, such as genome-scale models of metabolism and gene expression (ME-models), have the ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we apply the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME-model mostly agree with the standard biomass objective function used in models of metabolism alone (M-models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of peroxyl scavenging acids in the proteins used to sustain aerobic growth; (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.
Keywords	Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A biochemically-interpretable machine learning classifier for microbial GWAS

Erol S. Kavvas / Laurence Yang / Jonathan M. Monk / David Heckmann / Bernhard O. Palsson

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 11

Abstract: Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux ... ...

Abstract	Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles.
Keywords	Science ; Q
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells.

James T Yurkovich / Laurence Yang / Bernhard O Palsson

PLoS Computational Biology, Vol 13, Iss 3, p e

2017 Volume 1005424

Abstract: Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the ... ...

Abstract	Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites (p < 0.05) in RBC metabolism using only measurements of these five biomarkers. The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. The ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.
Keywords	Biology (General) ; QH301-705.5
Subject code	500
Language	English
Publishing date	2017-03-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Restoration of fitness lost due to dysregulation of the pyruvate dehydrogenase complex is triggered by ribosomal binding site modifications

Amitesh Anand / Connor A. Olson / Anand V. Sastry / Arjun Patel / Richard Szubin / Laurence Yang / Adam M. Feist / Bernhard O. Palsson

Cell Reports, Vol 35, Iss 1, Pp 108961- (2021)

2021

Abstract: Summary: Pyruvate dehydrogenase complex (PDC) functions as the main determinant of the respiro-fermentative balance because it converts pyruvate to acetyl-coenzyme A (CoA), which then enters the TCA (tricarboxylic acid cycle). PDC is repressed by the ... ...

Abstract	Summary: Pyruvate dehydrogenase complex (PDC) functions as the main determinant of the respiro-fermentative balance because it converts pyruvate to acetyl-coenzyme A (CoA), which then enters the TCA (tricarboxylic acid cycle). PDC is repressed by the pyruvate dehydrogenase complex regulator (PdhR) in Escherichia coli. The deletion of the pdhR gene compromises fitness in aerobic environments. We evolve the E. coli pdhR deletion strain to examine its achievable growth rate and the underlying adaptive strategies. We find that (1) optimal proteome allocation to PDC is critical in achieving optimal growth rate; (2) expression of PDC in evolved strains is reduced through mutations in the Shine-Dalgarno sequence; (3) rewiring of the TCA flux and increased reactive oxygen species (ROS) defense occur in the evolved strains; and (4) the evolved strains adapt to an efficient biomass yield. Together, these results show how adaptation can find alternative regulatory mechanisms for a key cellular process if the primary regulatory mode fails.
Keywords	adaptive laboratory evolution ; system biology ; proteome allocation ; transcriptional regulatory network ; bioenergetics ; Biology (General) ; QH301-705.5
Subject code	570
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli.

Bin Du / Laurence Yang / Colton J Lloyd / Xin Fang / Bernhard O Palsson

PLoS Computational Biology, Vol 15, Iss 12, p e

2019 Volume 1007525

Abstract: Response to acid stress is critical for Escherichia coli to successfully complete its life-cycle by passing through the stomach to colonize the digestive tract. To develop a fundamental understanding of this response, we established a molecular ... ...

Abstract	Response to acid stress is critical for Escherichia coli to successfully complete its life-cycle by passing through the stomach to colonize the digestive tract. To develop a fundamental understanding of this response, we established a molecular mechanistic description of acid stress mitigation responses in E. coli and integrated them with a genome-scale model of its metabolism and macromolecular expression (ME-model). We considered three known mechanisms of acid stress mitigation: 1) change in membrane lipid fatty acid composition, 2) change in periplasmic protein stability over external pH and periplasmic chaperone protection mechanisms, and 3) change in the activities of membrane proteins. After integrating these mechanisms into an established ME-model, we could simulate their responses in the context of other cellular processes. We validated these simulations using RNA sequencing data obtained from five E. coli strains grown under external pH ranging from 5.5 to 7.0. We found: i) that for the differentially expressed genes accounted for in the ME-model, 80% of the upregulated genes were correctly predicted by the ME-model, and ii) that these genes are mainly involved in translation processes (45% of genes), membrane proteins and related processes (18% of genes), amino acid metabolism (12% of genes), and cofactor and prosthetic group biosynthesis (8% of genes). We also demonstrated several intervention strategies on acid tolerance that can be simulated by the ME-model. We thus established a quantitative framework that describes, on a genome-scale, the acid stress mitigation response of E. coli that has both scientific and practical uses.
Keywords	Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2019-12-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes

Nathan Mih / Jonathan M. Monk / Xin Fang / Edward Catoiu / David Heckmann / Laurence Yang / Bernhard O. Palsson

BMC Bioinformatics, Vol 21, Iss 1, Pp 1-

2020 Volume 23

Abstract: Abstract Background The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the ... ...

Abstract	Abstract Background The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. Results Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain’s sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain’s sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. Conclusion We thus demonstrate that structural systems ...
Keywords	Structural systems biology ; Oxidative stress ; Structural proteome ; Physicochemical properties ; Oxidative damage ; Metabolic model ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	500
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Visualizing metabolic network dynamics through time-series metabolomic data

Lea F. Buchweitz / James T. Yurkovich / Christoph Blessing / Veronika Kohler / Fabian Schwarzkopf / Zachary A. King / Laurence Yang / Freyr Jóhannsson / Ólafur E. Sigurjónsson / Óttar Rolfsson / Julian Heinrich / Andreas Dräger

BMC Bioinformatics, Vol 21, Iss 1, Pp 1-

2020 Volume 10

Abstract: Abstract Background New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of ... ...

Abstract	Abstract Background New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems—the human platelet and erythrocyte under cold storage for use in transfusion medicine. Results The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures. Conclusions The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user’s guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the ...
Keywords	Data visualization ; Metabolism ; Metabolomics ; Platelet ; Red blood cell ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: BOFdat

Jean-Christophe Lachance / Colton J Lloyd / Jonathan M Monk / Laurence Yang / Anand V Sastry / Yara Seif / Bernhard O Palsson / Sébastien Rodrigue / Adam M Feist / Zachary A King / Pierre-Étienne Jacques

PLoS Computational Biology, Vol 15, Iss 4, p e

Generating biomass objective functions for genome-scale metabolic models from experimental data.

2019 Volume 1006971

Abstract: Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass ... ...

Abstract	Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).
Keywords	Biology (General) ; QH301-705.5
Subject code	612 ; 004
Language	English
Publishing date	2019-04-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Escherichia coli transcriptome mostly consists of independently regulated modules

Anand V. Sastry / Ye Gao / Richard Szubin / Ying Hefner / Sibei Xu / Donghyuk Kim / Kumari Sonal Choudhary / Laurence Yang / Zachary A. King / Bernhard O. Palsson

Nature Communications, Vol 10, Iss 1, Pp 1-

2019 Volume 14

Abstract: Mechanistic insight into the regulation of transcriptional modules remains scarce. Here, the authors identify statistically independent gene sets by applying independent component analysis to a high-quality E. coli RNA-seq data compendium and find that ... ...

Abstract	Mechanistic insight into the regulation of transcriptional modules remains scarce. Here, the authors identify statistically independent gene sets by applying independent component analysis to a high-quality E. coli RNA-seq data compendium and find that most gene sets represent the effects of specific transcriptional regulators.
Keywords	Science ; Q
Language	English
Publishing date	2019-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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