scholarly journals Pan-cancer analysis of the metabolic reaction network

2016 ◽  
Author(s):  
F. Gatto ◽  
J. Nielsen
Keyword(s):  
Metabolic Networks ◽  
Expression Patterns ◽  
Reaction Network ◽  
Metabolic Reprogramming ◽  
Metabolic Reaction ◽  
Primary Tumors ◽  
Normal Tissues ◽  
Normal Human ◽  
Family Gene ◽  
Genome Scale

ABSTRACTMetabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other nor from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumors based on the probability of expression for 3,765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4,721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.

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 Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes

2018 ◽  
Vol 20 (4) ◽  
pp. 1590-1603 ◽  
Author(s):  
Gaoyang Li ◽  
Huansheng Cao ◽  
Ying Xu
Keyword(s):  
Metabolic Networks ◽  
Reaction Network ◽  
Clustering Coefficient ◽  
Integrated Analysis ◽  
Node Degree ◽  
Power Law Distribution ◽  
Metabolic Fluxes ◽  
Reaction Cycles ◽  
Functional Analyses ◽  
Genome Scale

Abstract We present here an integrated analysis of structures and functions of genome-scale metabolic networks of 17 microorganisms. Our structural analyses of these networks revealed that the node degree of each network, represented as a (simplified) reaction network, follows a power-law distribution, and the clustering coefficient of each network has a positive correlation with the corresponding node degree. Together, these properties imply that each network has exactly one large and densely connected subnetwork or core. Further analyses revealed that each network consists of three functionally distinct subnetworks: (i) a core, consisting of a large number of directed reaction cycles of enzymes for interconversions among intermediate metabolites; (ii) a catabolic module, with a largely layered structure consisting of mostly catabolic enzymes; (iii) an anabolic module with a similar structure consisting of virtually all anabolic genes; and (iv) the three subnetworks cover on average ∼56, ∼31 and ∼13% of a network’s nodes across the 17 networks, respectively. Functional analyses suggest: (1) cellular metabolic fluxes generally go from the catabolic module to the core for substantial interconversions, then the flux directions to anabolic module appear to be determined by input nutrient levels as well as a set of precursors needed for macromolecule syntheses; and (2) enzymes in each subnetwork have characteristic ranges of kinetic parameters, suggesting optimized metabolic and regulatory relationships among the three subnetworks.

scholarly journals Phenotypic innovation through recombination in genome-scale metabolic networks

2016 ◽  
Vol 283 (1839) ◽  
pp. 20161536 ◽  
Author(s):  
Sayed-Rzgar Hosseini ◽  
Olivier C. Martin ◽  
Andreas Wagner
Keyword(s):  
Metabolic Networks ◽  
Chemical Elements ◽  
Carbon Sources ◽  
Computational Method ◽  
Metabolic Reaction ◽  
Flux Balance ◽  
Metabolic Systems ◽  
Balance Analysis ◽  
Genome Scale ◽  
Different Sources

Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a well-understood genotype–phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experimentally validated computational method that can predict metabolic phenotypes from metabolic genotypes. We show that recombination is much more likely to create novel metabolic abilities than random changes in chemical reactions of a metabolic network. We also find that phenotypic innovation is more likely when recombination occurs between parents that are genetically closely related, phenotypically highly diverse, and viable on few rather than many carbon sources. Survival on a new carbon source preferentially involves reactions that are superessential, that is, essential in many metabolic networks. We validate our observations with data from 61 reconstructed prokaryotic metabolic networks. Our systematic and quantitative analysis of metabolic systems helps understand how recombination creates innovation.

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

2020 ◽  
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

AbstractApproaches 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 Optimization of a modeling platform to predict oncogenes from genome‐scale metabolic networks of non‐small‐cell lung cancers

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
You‐Tyun Wang ◽  
Min‐Ru Lin ◽  
Wei‐Chen Chen ◽  
Wu‐Hsiung Wu ◽  
Feng‐Sheng Wang
Keyword(s):  
Metabolic Networks ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancers ◽  
Genome Scale

scholarly journals Identification and Roles of miR-29b-1-3p and miR29a-3p-Regulated and Non-Regulated lncRNAs in Endocrine-Sensitive and Resistant Breast Cancer Cells

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3530
Author(s):  
Penn Muluhngwi ◽  
Carolyn M. Klinge
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Expression Patterns ◽  
Endocrine Resistance ◽  
Estrogen Receptor Α ◽  
Resistant Cell ◽  
Differentially Expressed ◽  
Primary Tumors ◽  
Non Coding Rnas ◽  
Mcf 7

Despite improvements in the treatment of endocrine-resistant metastatic disease using combination therapies in patients with estrogen receptor α (ERα) primary tumors, the mechanisms underlying endocrine resistance remain to be elucidated. Non-coding RNAs (ncRNAs), including microRNAs (miRNA) and long non-coding RNAs (lncRNA), are targets and regulators of cell signaling pathways and their exosomal transport may contribute to metastasis. Previous studies have shown that a low expression of miR-29a-3p and miR-29b-3p is associated with lower overall breast cancer survival before 150 mos. Transient, modest overexpression of miR-29b1-3p or miR-29a-3p inhibited MCF-7 tamoxifen-sensitive and LCC9 tamoxifen-resistant cell proliferation. Here, we identify miR-29b-1/a-regulated and non-regulated differentially expressed lncRNAs in MCF-7 and LCC9 cells using next-generation RNA seq. More lncRNAs were miR-29b-1/a-regulated in LCC9 cells than in MCF-7 cells, including DANCR, GAS5, DSCAM-AS1, SNHG5, and CRND. We examined the roles of miR-29-regulated and differentially expressed lncRNAs in endocrine-resistant breast cancer, including putative and proven targets and expression patterns in survival analysis using the KM Plotter and TCGA databases. This study provides new insights into lncRNAs in endocrine-resistant breast cancer.

scholarly journals A Metabolic Choreography of Maize Plants Treated with a Humic Substance-Based Biostimulant under Normal and Starved Conditions

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 403
Author(s):  
Kgalaletso Othibeng ◽  
Lerato Nephali ◽  
Anza-Tshilidzi Ramabulana ◽  
Paul Steenkamp ◽  
Daniel Petras ◽  
...  
Keyword(s):  
Humic Substance ◽  
Metabolic Networks ◽  
Molecular Mechanisms ◽  
Plant Cell Wall ◽  
Growth Promotion ◽  
Redox Homeostasis ◽  
Metabolic Reprogramming ◽  
Sustainable Food ◽  
Crop Development ◽  
Maize Plants

Humic substance (HS)-based biostimulants show potentials as sustainable strategies for improved crop development and stress resilience. However, cellular and molecular mechanisms governing the agronomically observed effects of HS on plants remain enigmatic. Here, we report a global metabolic reprogramming of maize leaves induced by a humic biostimulant under normal and nutrient starvation conditions. This reconfiguration of the maize metabolism spanned chemical constellations, as revealed by molecular networking approaches. Plant growth and development under normal conditions were characterized by key differential metabolic changes such as increased levels of amino acids, oxylipins and the tricarboxylic acid (TCA) intermediate, isocitric acid. Furthermore, under starvation, the humic biostimulant significantly impacted pathways that are involved in stress-alleviating mechanisms such as redox homeostasis, strengthening of the plant cell wall, osmoregulation, energy production and membrane remodelling. Thus, this study reveals that the humic biostimulant induces a remodelling of inter-compartmental metabolic networks in maize, subsequently readjusting the plant physiology towards growth promotion and stress alleviation. Such insights contribute to ongoing efforts in elucidating modes of action of biostimulants, generating fundamental scientific knowledge that is necessary for development of the biostimulant industry, for sustainable food security.

scholarly journals Identifying Personalized Metabolic Signatures in Breast Cancer

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 20
Author(s):  
Priyanka Baloni ◽  
Wikum Dinalankara ◽  
John C. Earls ◽  
Theo A. Knijnenburg ◽  
Donald Geman ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Estrogen Metabolism ◽  
Acid Oxidation ◽  
A Genome ◽  
Context Specific

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

2021 ◽  
Author(s):  
Ecehan Abdik ◽  
Tunahan Cakir
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Network ◽  
Mouse Models ◽  
Mus Musculus ◽  
Metabolic Networks ◽  
Systematic Investigation ◽  
Omics Data ◽  
Metabolic Alterations ◽  
Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

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