Biology subjects

Barupal, D. K.

Publications and source records attributed to Barupal, D. K..

3 recordsLinked to original sources

bioRxiv2019

A comprehensive plasma metabolomics dataset for a cohort of mouse knockouts within the international mouse phenotyping consortium

Mouse knockouts allow studying gene functions. Often, multiple phenotypes are impacted when a gene is inactivated. The International Mouse Phenotyping Consortium (IPMC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples of 30 mouse gene knockouts and corresponding wildtype mice from IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes.\n\nData SetThe dataset is available at the MetabolomicsWorkbench repository (accession ID: ST001154)\n\nData Set Licenselicense under which the data set is made available (CC0).

bioinformatics↗

bioRxiv2019

Sets of Co-regulated Serum Lipids are Associated with Alzheimer Disease Pathophysiology

INTRODUCTIONAltered regulation of lipid metabolism in Alzheimer disease (AD) can be characterized using lipidomic profiling. METHOD349 serum lipids were measured in 806 participants enrolled in the Alzheimer Disease Neuroimaging Initiative Phase 1 (ADNI1) cohort and analysed using lipid regression models and lipid set enrichment statistics. RESULTSAD diagnosis was associated with 7 of 28 lipid sets of which four also correlated with cognitive decline, including polyunsaturated fatty acids. CSF amyloid beta A{beta}1-42 correlated with glucosylceramides, lysophosphatidyl cholines and unsaturated triacylglycerides; CSF total tau and brain atrophy correlated with monounsaturated sphingomyelins and ceramides, in addition to EPA-containing lipids. DISCUSSIONLipid desaturation, elongation and acyl chain remodeling are dysregulated across the spectrum of AD pathogenesis. Monounsaturated lipids were important in early stages of AD, while polyunsaturated lipid metabolism was associated with later stages of AD. SIGNFICANCEBoth metabolic genes and co-morbidity with metabolic diseases indicate that lipid metabolism is critical in the etiology of Alzheimers disease (AD). For 800 subjects, we found that sets of blood lipids were associated with current AD-biomarkers and with AD clinical symptoms. Our study highlights the role of disturbed acyl chain lipid remodelling in several lipid classes. Our work has significant implications on finding a cure for AD. Depending on subject age, human blood lipids may have different effects on AD development. Remodelling of acyl chains needs to be studied in relation to genetic variants and environmental factors. Specifically, the impact of dietary supplements and drugs on lipid remodelling must be investigated.

neuroscience↗

bioRxiv2019

Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining

Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p < 0.05) between ERneg and ERpos breast tumors. In the nine public transcriptomics datasets, on average 23% of all genes were significantly different (raw p < 0.05). Specifically, up to 60% of the metabolic genes were significantly different (meta-analysis raw p < 0.05) across the transcriptomics datasets. Reactome pathway analysis of all omics showed that energy metabolism, and biosynthesis of nucleotides, amino acids, and lipids were associated with ERneg status. Text mining revealed that several significant metabolic genes and enzymes have been rarely reported to date, including PFKP, GART, PLOD1, ASS1, NUDT12, FAR1, PDE7A, FAHD1, ITPK1, SORD, HACD3, CDS2 and PDSS1. Metabolic processes associated with ERneg tumors were identified by multi-omics integration analysis of metabolomics, proteomics and transcriptomics data. Overall results suggested that TCA anaplerosis, proline biosynthesis, synthesis of complex lipids and mechanisms for recycling substrates were activated in ERneg tumors. Under-reported genes were revealed by text mining which may serve as novel candidates for drug targets in cancer therapies. The workflow presented here can also be used for other tumor types.

bioinformatics↗