A genome-scale protein interaction profile of Drosophila p53 uncovers additional nodes of the human p53 network

Abstract We have undertaken a large-scale genetic screen to identify genes with a seedling-lethal mutant phenotype. From screening ~38,000 insertional mutant lines, we identified >500 seedling-lethal mutants, completed cosegregation analysis of the insertion and the lethal phenotype for >200 mutants, molecularly characterized 54 mutants, and provided a detailed description for 22 of them. Most of the seedling-lethal mutants seem to affect chloroplast function because they display altered pigmentation and affect genes encoding proteins predicted to have chloroplast localization. Although a high level of functional redundancy in Arabidopsis might be expected because 65% of genes are members of gene families, we found that 41% of the essential genes found in this study are members of Arabidopsis gene families. In addition, we isolated several interesting classes of mutants and genes. We found three mutants in the recently discovered nonmevalonate isoprenoid biosynthetic pathway and mutants disrupting genes similar to Tic40 and tatC, which are likely to be involved in chloroplast protein translocation. Finally, we directly compared T-DNA and Ac/Ds transposon mutagenesis methods in Arabidopsis on a genome scale. In each population, we found only about one-third of the insertion mutations cosegregated with a mutant phenotype.

Download Full-text

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

Toxicology and Applied Pharmacology ◽

10.1016/j.taap.2020.115390 ◽

2021 ◽

Vol 412 ◽

pp. 115390

Author(s):

Kristopher D. Rawls ◽

Bonnie V. Dougherty ◽

Kalyan C. Vinnakota ◽

Venkat R. Pannala ◽

Anders Wallqvist ◽

...

Keyword(s):

Metabolic Model ◽

Renal Metabolism ◽

A Genome ◽

Compound Exposure ◽

Genome Scale

Download Full-text

Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

Journal of Bacteriology ◽

10.1128/jb.01583-07 ◽

2008 ◽

Vol 190 (8) ◽

pp. 2790-2803 ◽

Cited By ~ 175

Author(s):

Matthew A. Oberhardt ◽

Jacek Puchałka ◽

Kimberly E. Fryer ◽

Vítor A. P. Martins dos Santos ◽

Jason A. Papin

Keyword(s):

Pseudomonas Aeruginosa ◽

Metabolic Network ◽

Opportunistic Pathogen ◽

Scale Model ◽

Modeling Framework ◽

Major Life ◽

Metabolic Versatility ◽

A Genome ◽

Scale Properties ◽

Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

Download Full-text

Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Applied and Environmental Microbiology ◽

10.1128/aem.01795-12 ◽

2012 ◽

Vol 78 (24) ◽

pp. 8735-8742 ◽

Cited By ~ 12

Author(s):

Yilin Fang ◽

Michael J. Wilkins ◽

Steven B. Yabusaki ◽

Mary S. Lipton ◽

Philip E. Long

Keyword(s):

Proteomic Analysis ◽

In Silico ◽

Field Experiments ◽

Metabolic Model ◽

Geobacter Metallireducens ◽

Content Type ◽

Proteomic Data ◽

Subsurface Environment ◽

A Genome ◽

Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

Download Full-text

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Nature Communications ◽

10.1038/s41467-021-25158-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Omid Oftadeh ◽

Pierre Salvy ◽

Maria Masid ◽

Maxime Curvat ◽

Ljubisa Miskovic ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

Expression System ◽

Biomass Composition ◽

Industrial Biotechnology ◽

Overflow Metabolism ◽

Cellular Expression ◽

A Genome ◽

Wide Range ◽

Eukaryotic Organisms ◽

Genome Scale

AbstractEukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

Download Full-text

A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers

Metabolites ◽

10.3390/metabo11030168 ◽

2021 ◽

Vol 11 (3) ◽

pp. 168

Author(s):

John I. Hendry ◽

Hoang V. Dinh ◽

Debolina Sarkar ◽

Lin Wang ◽

Anindita Bandyopadhyay ◽

...

Keyword(s):

Electron Transport ◽

Pyruvate Decarboxylase ◽

Metabolic Model ◽

Cell Types ◽

Economic Feasibility ◽

Nitrogen Fixing ◽

Design Algorithm ◽

A Genome ◽

Anabaena Sp ◽

Genome Scale

Nitrogen fixing-cyanobacteria can significantly improve the economic feasibility of cyanobacterial production processes by eliminating the requirement for reduced nitrogen. Anabaena sp. ATCC 33047 is a marine, heterocyst forming, nitrogen fixing cyanobacteria with a very short doubling time of 3.8 h. We developed a comprehensive genome-scale metabolic (GSM) model, iAnC892, for this organism using annotations and content obtained from multiple databases. iAnC892 describes both the vegetative and heterocyst cell types found in the filaments of Anabaena sp. ATCC 33047. iAnC892 includes 953 unique reactions and accounts for the annotation of 892 genes. Comparison of iAnC892 reaction content with the GSM of Anabaena sp. PCC 7120 revealed that there are 109 reactions including uptake hydrogenase, pyruvate decarboxylase, and pyruvate-formate lyase unique to iAnC892. iAnC892 enabled the analysis of energy production pathways in the heterocyst by allowing the cell specific deactivation of light dependent electron transport chain and glucose-6-phosphate metabolizing pathways. The analysis revealed the importance of light dependent electron transport in generating ATP and NADPH at the required ratio for optimal N2 fixation. When used alongside the strain design algorithm, OptForce, iAnC892 recapitulated several of the experimentally successful genetic intervention strategies that over produced valerolactam and caprolactam precursors.

Download Full-text

A genome‐scale metabolic network reconstruction of tomato ( Solanum lycopersicum L.) and its application to photorespiratory metabolism

The Plant Journal ◽

10.1111/tpj.13075 ◽

2016 ◽

Vol 85 (2) ◽

pp. 289-304 ◽

Cited By ~ 32

Author(s):

Huili Yuan ◽

C.Y. Maurice Cheung ◽

Mark G. Poolman ◽

Peter A. J. Hilbers ◽

Natal A. W. Riel

Keyword(s):

Metabolic Network ◽

Solanum Lycopersicum ◽

Network Reconstruction ◽

Metabolic Network Reconstruction ◽

Solanum Lycopersicum L ◽

A Genome ◽

Genome Scale

Download Full-text

Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

Microbiology ◽

10.1099/mic.0.076091-0 ◽

2014 ◽

Vol 160 (6) ◽

pp. 1252-1266 ◽

Cited By ~ 28

Author(s):

Hassan B. Hartman ◽

David A. Fell ◽

Sergio Rossell ◽

Peter Ruhdal Jensen ◽

Martin J. Woodward ◽

...

Keyword(s):

Salmonella Enterica ◽

Energy Demand ◽

Drug Targets ◽

Minimal Medium ◽

Model Organism ◽

Metabolic Model ◽

Scale Model ◽

Glucose Minimal Medium ◽

A Genome ◽

Genome Scale

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

Download Full-text