ArticlePLoS genetics2008
Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.
Article in PLoS genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 143 papers, 1 of them a synthesis that pooled it.
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Who cites it
143 citing papers in PubMed, 1 synthesis or guideline pooled it, 213 citations in OpenAlex.
- Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis.PloS one · 2016Pooled it
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- Integration of metabolomic and transcriptomic analyses reveals novel regulatory functions of the ChREBP transcription factor in energy metabolism.bioRxiv : the preprint server for biology · 2024Article
- Optical imaging reveals chemotherapy-induced metabolic reprogramming of residual disease and recurrence.Science advances · 2024Article
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- Mining cholesterol genes from thousands of mouse livers identifies aldolase C as a regulator of cholesterol biosynthesis.Journal of lipid research · 2024Article
- A mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constants in characterizing a biochemical network.Scientific reports · 2024Article
- Circulating metabolic profile in idiopathic pulmonary fibrosis: data from the IPF-PRO Registry.Respiratory research · 2024Observational
- Catabolism of fats and branched-chain amino acids in children with Type 1 diabetes: Association with glycaemic control and total daily insulin dose.Endocrinology, diabetes & metabolism · 2023Article
- Metabolic factors associated with incident fracture among older adults with type 2 diabetes mellitus: a nested case-control study.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2023Article
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- Analysis of a genetic region affecting mouse body weight.Physiological genomics · 2023Article
- Branched-chain α-keto acids and glutamate/glutamine: Biomarkers of insulin resistance in childhood obesity.Endocrinology, diabetes & metabolism · 2023Article
- ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network.Frontiers in molecular biosciences · 2023Article
83 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin(ob/ob) and the diabetes-susceptible BTBR leptin(ob/ob) mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.