LimeMap: a comprehensive map of lipid mediator metabolic pathways.
Ontology highlight
ABSTRACT:
SUBMITTER: Nishi A
PROVIDER: S-EPMC7840682 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Sterol regulatory-element binding proteins (SREBPs) are classical regulators of cellular lipid metabolism in the kidney and other tissues. SREBPs are currently recognized as versatile transcription factors involved in a myriad of cellular processes. Meanwhile, SREBPs have been recognized to mediate lipotoxicity, contributing to the progression of kidney diseases. SREBP1 has been shown to bind to the promoter region of TGF?, a major pro-fibrotic signaling mechanism in the kidney. Conversely, TGF? activates SREBP1 transcriptional activity suggesting a positive feedback loop of SREBP1 in TGF? signaling. Public ChIP-seq data revealed numerous non-lipid transcriptional targets of SREBPs that plausibly play roles in progressive kidney disease and fibrosis. This review provides new insights into SREBP as a mediator of kidney fibrosis via lipid-independent pathways.
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Project description:All three cytochrome P450 1 (CYP1) monooxygenases are believed to participate in lipid mediator biosynthesis and/or their local inactivation; however, distinct metabolic steps are unknown. We used multiple-reaction monitoring and liquid chromatography-UV coupled with tandem mass spectrometry-based lipid-mediator metabololipidomics to identify and quantify three lipid-mediator metabolomes in basal peritoneal and zymosan-stimulated inflammatory exudates, comparing Cyp1a1/1a2/1b1(?/?) C57BL/6J-background triple-knockout mice with C57BL/6J wild-type mice. Significant differences between untreated triple-knockout and wild-type mice were not found for peritoneal cell number or type or for basal CYP1 activities involving 11 identified metabolic steps. Following zymosan-initiated inflammation, 18 lipid mediators were identified, including members of the eicosanoids and specialized proresolving mediators (i.e., resolvins and protectins). Compared with wild-type mice, Cyp1 triple-knockout mice exhibited increased neutrophil recruitment in zymosan-treated peritoneal exudates. Zymosan stimulation was associated with eight statistically significantly altered metabolic steps: increased arachidonic acid-derived leukotriene B? (LTB?) and decreased 5S-hydroxyeicosatetraenoic acid; decreased docosahexaenoic acid-derived neuroprotectin D1/protectin D1, 17S-hydroxydocosahexaenoic acid, and 14S-hydroxydocosahexaenoic acid; and decreased eicosapentaenoic acid-derived 18R-hydroxyeicosapentaenoic acid (HEPE), 15S-HEPE, and 12S-HEPE. In neutrophils analyzed ex vivo, elevated LTB? levels were shown to parallel increased neutrophil numbers, and 20-hydroxy-LTB? formation was found to be deficient in Cyp1 triple-knockout mice. Together, these results demonstrate novel contributions of CYP1 enzymes to the local metabolite profile of lipid mediators that regulate neutrophilic inflammation.
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Project description:Prediction of functional potential of bacteria can only be ascertained by the accurate annotation of its metabolic pathways. Homology based methods decipher metabolic gene content but ignore the fact that homologs of same protein can function in different pathways. Therefore, mere presence of all constituent genes in an organism is not sufficient to indicate a pathway. Contextual occurrence of genes belonging to a pathway on the bacterial genome can hence be exploited for an accurate estimation of functional potential of a bacterium. In this communication, we present a novel annotation resource to accurately identify pathway presence by using gene context. Our tool FLIM-MAP (Functionally Important Modules in bacterial Metabolic Pathways) predicts biologically relevant functional units called 'GCMs' (Gene Context based Modules) from a given metabolic reaction network. We benchmark the accuracy of our tool on amino acids and carbohydrate metabolism pathways.
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Project description:BACKGROUND:Common metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body's metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors. METHODS:We analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group. RESULTS:In contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing ?-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension. CONCLUSIONS:Our results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.
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Project description:MOTIVATION:Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. RESULTS:In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In these simulations, the iMT1026 model has shown a better performance than the previous existing models.
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