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Banskota, S.

Publications and source records attributed to Banskota, S..

2 recordsLinked to original sources

Pyruvate dehydrogenase kinase expression profile is a biomarker for cancer sensitivity to dichloroacetate-mediated growth inhibition.

BackgroundCancer cells favour glycolysis and lactate production over mitochondrial metabolism despite the presence of oxygen (the Warburg effect). Increased pyruvate dehydrogenase kinase (PDK) activity contributes to this glycolytic phenotype. Dichloroacetate (DCA) is a PDK inhibitor with anti-cancer potential that inhibits all four isoforms of PDK but with differing potencies, thus expression of different isoforms may determine sensitivity to DCA. MethodsThe association of sensitivity to growth inhibition by DCA, on-target effects of DCA and expression of all four isoforms of PDKs in a range of epithelial cancer cell lines was investigated in vitro and in vivo. ResultsDCA inhibited growth of cancer cells in vivo and in vitro, reduced pyruvate dehydrogenase phosphorylation and reduced lactate production. The magnitude of the effect of DCA on growth was variable and correlated with the PDK expression profiles of the cells, with low expression of PDK3 (highest Ki for DCA) conferring the highest sensitivity towards DCA. PDK2 siRNA-knockdown inhibited growth to a similar extent to DCA, whilst PDK3 knockdown significantly increased sensitivity to DCA. ConclusionThe PDK expression profile is a potential biomarker for sensitivity to DCA, and should be considered when translating PDK inhibitors into clinical use.

cancer biology↗

Multi-tissue integrative analysis of personal epigenomes

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of personal epigenomes, for [~]25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

genomics↗