scholarly journals Pan-genome-scale network reconstruction: a framework to increase the quantity and quality of metabolic network reconstructions throughout the tree of life

2018 ◽  
Author(s):  
Kevin Correia ◽  
Radhakrishnan Mahadevan
Keyword(s):  
Metabolic Networks ◽  
Single Species ◽  
Network Reconstruction ◽  
Tree Of Life ◽  
Pan Genome ◽  
Yeast Fungi ◽  
A Genome ◽  
Scale Network ◽  
Genome Scale

ABSTRACTA genome-scale network reconstruction (GENRE) represents the knowledgebase of an organism and can be used in a variety of applications. The drop in genome sequencing costs has led to an increase in sequenced genomes, but the number of curated GENRE’ s has not kept pace. This gap hinders our ability to study physiology across the tree of life. Furthermore, our analysis of yeast GENRE’ s has found they contain significant commission and omission errors, especially in central metabolism. To address these quantity and quality issues for GENRE’ s, we propose open and transparent curation of the pan-genome, pan-reactome, pan-metabolome, and pan-phenome for taxons by research communities, rather than for a single species. We outline our approach with a Fungi pan-GENRE by integrating AYbRAH, our ortholog database, and AYbRAHAM, our new fungal reaction database. This pan-GENRE was used to compile 33 yeast/fungi GENRE’ s in the Dikarya subkingdom, spanning 600 million years. The fungal pan-GENRE contains 1547 orthologs, 2726 reactions, 2226 metabolites, and 10 compartments. The strain GENRE’ s have a wider genomic and metabolic than previous yeast and fungi GENRE’ s. Metabolic simulations show the amino acid yields from glucose differs between yeast lineages, indicating metabolic networks have evolved in yeasts. Curating ortholog and reaction databases for a taxon can be used to increase the quantity and quality of strain GENRE’ s. This pan-GENRE framework provides the ability to scale high-quality GENRE’ s to more branches in the tree of life.

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

2016 ◽  
Vol 85 (2) ◽  
pp. 289-304 ◽  
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

scholarly journals Genome-scale reconstructions to assess metabolic phylogeny and organism clustering

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0240953
Author(s):  
Christian Schulz ◽  
Eivind Almaas
Keyword(s):  
Phylogenetic Trees ◽  
Metabolic Networks ◽  
Sulfur Metabolism ◽  
Phylogenetic Analyses ◽  
Tree Of Life ◽  
Significant Heterogeneity ◽  
Metabolic Reaction ◽  
High Quality ◽  
Conserved Genes ◽  
Genome Scale

Approaches for systematizing information of relatedness between organisms is important in biology. Phylogenetic analyses based on sets of highly conserved genes are currently the basis for the Tree of Life. Genome-scale metabolic reconstructions contain high-quality information regarding the metabolic capability of an organism and are typically restricted to metabolically active enzyme-encoding genes. While there are many tools available to generate draft reconstructions, expert-level knowledge is still required to generate and manually curate high-quality genome-scale metabolic models and to fill gaps in their reaction networks. Here, we use the tool AutoKEGGRec to construct 975 genome-scale metabolic draft reconstructions encoded in the KEGG database without further curation. The organisms are selected across all three domains, and their metabolic networks serve as basis for generating phylogenetic trees. We find that using all reactions encoded, these metabolism-based comparisons give rise to a phylogenetic tree with close similarity to the Tree of Life. While this tree is quite robust to reasonable levels of noise in the metabolic reaction content of an organism, we find a significant heterogeneity in how much noise an organism may tolerate before it is incorrectly placed in the tree. Furthermore, by using the protein sequences for particular metabolic functions and pathway sets, such as central carbon-, nitrogen-, and sulfur-metabolism, as basis for the organism comparisons, we generate highly specific phylogenetic trees. We believe the generation of phylogenetic trees based on metabolic reaction content, in particular when focused on specific functions and pathways, could aid the identification of functionally important metabolic enzymes and be of value for genome-scale metabolic modellers and enzyme-engineers.

scholarly journals Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

2021 ◽  
Author(s):  
Christopher E. Lawson ◽  
Aniela B. Mundinger ◽  
Hanna Koch ◽  
Tyler B. Jacobson ◽  
Coty A. Weathersby ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Scale Model ◽  
Nitrifying Bacteria ◽  
Metabolomic Analysis ◽  
A Genome ◽  
Nitrite Oxidizing Bacteria ◽  
Genome Scale ◽  
Nitrospira Moscoviensis

AbstractNitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

IN SILICO GENOME-SCALE RECONSTRUCTION AND ANALYSIS OF THE SHEWANELLA LOIHICA PV-4 METABOLIC NETWORK

2018 ◽  
Vol 26 (03) ◽  
pp. 373-397
Author(s):  
ZIXIANG XU ◽  
JING GUO ◽  
YUNXIA YUE ◽  
JING MENG ◽  
XIAO SUN
Keyword(s):  
Metabolic Network ◽  
Network Model ◽  
Microbial Fuel Cells ◽  
Metabolic Networks ◽  
Acid Metabolism ◽  
Electricity Production ◽  
Nucleic Acid Metabolism ◽  
Exchange Reactions ◽  
A Genome ◽  
Genome Scale

Microbial Fuel Cells (MFCs) are devices that generate electricity directly from organic compounds with microbes (electricigens) serving as anodic catalysts. As a novel environment-friendly energy source, MFCs have extensive practical value. Since the biological features and metabolic mechanism of electricigens have a great effect on the electricity production of MFCs, it is a big deal to screen strains with high electricity productivity for improving the power output of MFC. Reconstructions and simulations of metabolic networks are of significant help in studying the metabolism of microorganisms so as to guide gene engineering and metabolic engineering to improve their power-generating efficiency. Herein, we reconstructed a genome-scale constraint-based metabolic network model of Shewanella loihica PV-4, an important electricigen, based on its genomic functional annotations, reaction databases and published metabolic network models of seven microorganisms. The resulting network model iGX790 consists of 902 reactions (including 71 exchange reactions), 798 metabolites and 790 genes, covering the main pathways such as carbon metabolism, energy metabolism, amino acid metabolism, nucleic acid metabolism and lipid metabolism. Using the model, we simulated the growth rate, the maximal synthetic rate of ATP, the flux variability analysis of metabolic network, gene deletion and so on to examine the metabolism of S. loihica PV-4.

scholarly journals An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice

2013 ◽  
Vol 10 (2) ◽  
pp. 94-102 ◽  
Author(s):  
Lili Liu ◽  
Qian Mei ◽  
Zhenning Yu ◽  
Tianhao Sun ◽  
Zijun Zhang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Metabolic Model ◽  
Network Reconstruction ◽  
Grand Challenge ◽  
Genome Wide Association Studies ◽  
Protein Protein Interactions ◽  
A Genome ◽  
Regulatory Information ◽  
Multi Level ◽  
Genome Scale

Summary Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs) approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

FCDECOMP: Decomposition of metabolic networks based on flux coupling relations

2014 ◽  
Vol 12 (05) ◽  
pp. 1450028 ◽  
Author(s):  
Abolfazl Rezvan ◽  
Sayed-Amir Marashi ◽  
Changiz Eslahchi
Keyword(s):  
Metabolic Networks ◽  
System Level ◽  
Computational Framework ◽  
Flux Coupling ◽  
Metabolic Functions ◽  
Original Genome ◽  
A Cell ◽  
Scale Network ◽  
Genome Scale

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

scholarly journals GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

2020 ◽  
Vol 14 (7) ◽  
pp. e0007871
Author(s):  
Khushboo Borah ◽  
Jacque-Lucca Kearney ◽  
Ruma Banerjee ◽  
Pankaj Vats ◽  
Huihai Wu ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Mycobacterium Leprae ◽  
Network Reconstruction ◽  
Human Pathogen ◽  
Metabolic Network Reconstruction ◽  
A Genome ◽  
Genome Scale

scholarly journals Functional Anabolic Network Analysis of Human-associated Lactobacillus Strains

2019 ◽  
Author(s):  
Thomas J. Moutinho ◽  
Benjamin C. Neubert ◽  
Matthew L. Jenior ◽  
Maureen A. Carey ◽  
Gregory L. Medlock ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Health Benefits ◽  
Network Reconstruction ◽  
Metabolic Network Reconstruction ◽  
Metabolic Network Analysis ◽  
A Genome ◽  
Direct Interpretation ◽  
The Difference ◽  
Genome Scale

AbstractMembers of the Lactobacillus genus are frequently utilized in the probiotic industry with many species conferring demonstrated health benefits; however, these effects are largely strain-dependent. We designed a method called PROTEAN (Probabilistic Reconstruction Of constituent Anabolic Networks) to computationally analyze the genomic annotations and predicted metabolic production capabilities of 144 strains across 16 species of Lactobacillus isolated from human intestinal, oral, and vaginal body sites. Using PROTEAN we conducted a genome-scale metabolic network comparison between strains, revealing that metabolic capabilities differ by isolation site. Notably, PROTEAN does not require a well-curated genome-scale metabolic network reconstruction to provide biological insights. We found that predicted metabolic capabilities of lactobacilli isolated from the vaginal microbiota cluster separately from intestinal and oral isolates, and we also uncovered an overlap in the predicted metabolic production capabilities of intestinal and oral isolates. Using machine learning, we determined the most informative metabolic products driving the difference between predicted metabolic capabilities of intestinal, oral, and vaginal isolates. Notably, intestinal and oral isolates were predicted to have a higher likelihood of producing D-alanine, D/L-serine, and L-proline, while the vaginal isolates were distinguished by a higher predicted likelihood of producing L-arginine, citrulline, and D/L-lactate. We found the distinguishing products to be consistent with published experimental literature. This study showcases a systematic technique, PROTEAN, for comparing the predicted functional metabolic output of microbes using genome-scale metabolic network analysis and computational modeling and provides unique insight into human-associated Lactobacillus biology.ImportanceThe Lactobacillus genus has been shown to be important for human health. Lactobacilli have been isolated from human intestinal, oral, and vaginal sites. Members of the genus contribute significantly to the maintenance of vaginal health by providing colonization resistance to invading pathogens. A wide variety of clinical studies have indicated that Lactobacillus-based probiotics confer health benefits for several gut- and immune-associated diseases. Microbes interact with the human body in several ways, including the production of metabolites that influence physiology or other surrounding microbes. We have conducted a strain-level genome-scale metabolic network reconstruction analysis of human-associated Lactobacillus strains, revealing that predicted metabolic capabilities differ when comparing intestinal/oral isolate to vaginal isolates. The technique we present here allows for direct interpretation of discriminating features between the experimental groups.

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