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Identification of metabolic pathways underlying FGF1 and CHIR99021-mediated cardioprotection.

ABSTRACT: Acute myocardial infarction is a leading cause of death worldwide. We have previously identified two cardioprotective molecules - FGF1 and CHIR99021- that confer cardioprotection in mouse and pig models of acute myocardial infarction. Here, we aimed to determine if improved myocardial metabolism contributes to this cardioprotection. Nanofibers loaded with FGF1 and CHIR99021 were intramyocardially injected to ischemic myocardium of adult mice immediately following surgically induced myocardial infarction. Animals were euthanized 3 and 7 days later. Our data suggested that FGF1/CHIR99021 nanofibers enhanced the heart's capacity to utilize glycolysis as an energy source and reduced the accumulation of branched-chain amino acids in ischemic myocardium. The impact of FGF1/CHIR99021 on metabolism was more obvious in the first three days post myocardial infarction. Taken together, these findings suggest that FGF1/CHIR99021 protects the heart against ischemic injury via improving myocardial metabolism which may be exploited for treatment of acute myocardial infarction in humans.

SUBMITTER: Xu B

PROVIDER: S-EPMC9189130 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Identification of metabolic pathways underlying FGF1 and CHIR99021-mediated cardioprotection.

Xu Bing B Li Fan F Zhang Wenjing W Su Yajuan Y Tang Ling L Li Pengsheng P Joshi Jyotsna J Yang Aaron A Li Dong D Wang Zhao Z Wang Shu S Xie Jingwei J Gu Haiwei H Zhu Wuqiang W

iScience 20220523 6

Acute myocardial infarction is a leading cause of death worldwide. We have previously identified two cardioprotective molecules - FGF1 and CHIR99021- that confer cardioprotection in mouse and pig models of acute myocardial infarction. Here, we aimed to determine if improved myocardial metabolism contributes to this cardioprotection. Nanofibers loaded with FGF1 and CHIR99021 were intramyocardially injected to ischemic myocardium of adult mice immediately following surgically induced myocardial in ...[more]

PMID: 35707727

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Project description:BackgroundCommon 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.MethodsWe 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.ResultsIn 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 β-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.ConclusionsOur 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.

Project description:Guar (Cyamopsis tetragonoloba (L.) Taub.) is an annual legume crop native to India and Pakistan. Seeds of the plant serve as a source of galactomannan polysaccharide (guar gum) used in the food industry as a stabilizer (E412) and as a gelling agent in oil and gas fracturing fluids. There were several attempts to introduce this crop to countries of more northern latitudes. However, guar is a plant of a short photoperiod, therefore, its introduction, for example, to Russia is complicated by a long day length during the growing season. Breeding of new guar varieties insensitive to photoperiod slowed down due to the lack of information on functional molecular markers, which, in turn, requires information on guar genome. Modern breeding strategies, e.g., genomic predictions, benefit from integration of multi-omics approaches such as transcriptome, proteome and metabolome assays. Here we present an attempt to use transcriptome-metabolome integration to understand the genetic determination of flowering time variation among guar plants that differ in their photoperiod sensitivity. This study was performed on nine early- and six delayed-flowering guar varieties with the goal to find a connection between 63 metabolites and 1,067 differentially expressed transcripts using Shiny GAM approach. For the key biomarker of flowering in guar myo-inositol we also evaluated the KEGG biochemical pathway maps available for Arabidopsis thaliana. We found that the phosphatidylinositol signaling pathway is initiated in guar plants that are ready for flowering through the activation of the phospholipase C (PLC) gene, resulting in an exponential increase in the amount of myo-inositol in its free form observed on GC-MS chromatograms. The signaling pathway is performed by suppression of myo-inositol phosphate kinases (phosphorylation) and alternative overexpression of phosphatases (dephosphorylation). Our study suggests that metabolome and transcriptome information taken together, provide valuable information about biomarkers that can be used as a tool for marker-assisted breeding, metabolomics and functional genomics of this important legume crop.

Dataset Information

Identification of metabolic pathways underlying FGF1 and CHIR99021-mediated cardioprotection.

Publications

Identification of metabolic pathways underlying FGF1 and CHIR99021-mediated cardioprotection.

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