Biology subjects

Khusnutdinova, E.

Publications and source records attributed to Khusnutdinova, E..

3 recordsLinked to original sources

bioRxiv2020

Structural variability, expression profile and pharmacogenetics properties of TMPRSS2 gene as a potential target for COVID-19 therapy

The human serine protease TMPRSS2 gene is involved in the priming of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins being one of the possible targets for COVID-19 therapy. TMPRSS2 gene is possibly co-expressed with SARS-CoV-2 cell receptor genes ACE2 and BSG, but only TMPRSS2 demonstrates tissue-specific expression in alveolar cells according to single cell RNA sequencing data. Our analysis of the structural variability of the TMPRSS2 gene based on genome-wide data of 76 human populations demonstrates that functionally significant missense mutation in exon 6/7 in TMPRSS2 gene, was found in many human populations in relatively high frequency, featuring region-specific distribution patterns. The frequency of the missense mutation encoded by the rs12329760, which previously was found to be associated with prostate cancer, is ranged between 10% and 63% being significantly higher in populations of Asian origin compared to European populations. In addition to SNPs, two copy numbers variants (CNV) were detected in the TMPRSS2 gene. Number of microRNAs have been predicted to regulate TMPRSS2 and BSG expression levels, but none of them is enriched in lung or respiratory tract cells. Several well studied drugs can downregulate the expression of TMPRSS2 in human cells, including Acetaminophen (Paracetamol) and Curcumin. Thus TMPRSS2 interaction with the SARS-CoV-2, its structural variability, gene-gene interactions, and expression regulation profiles, and pharmacogenomics properties characterize this gene as a potential target for COVID-19 therapy.

genomics↗

bioRxiv2020

Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations

We report a meta-analysis of breast, prostate, ovarian, and endometrial cancer genome-wide association data (effective sample size: 237,483 cases/317,006 controls). This identified 465 independent lead variants (P<5x10-8) across 192 genomic regions. Four lead variants were >1Mb from previously identified risk loci for the four cancers and an additional 23 lead variant-cancer associations were novel for one of the cancers. Bayesian models supported pleiotropic effects involving at least two cancers at 222/465 lead variants in 118/192 regions. Gene-level association analysis identified 13 shared susceptibility genes (P<2.6x10-6) in 13 regions not previously implicated in any of the four cancers and not uncovered by our variant-level meta-analysis. Several lead variants had opposite effects across cancers, including a cluster of such variants in the TP53 pathway. Fifty-four lead variants were associated with blood cell traits and suggested genetic overlaps with clonal hematopoiesis. Our study highlights the remarkable pervasiveness of pleiotropy across hormone-related cancers, further illuminating their shared genetic and mechanistic origins at variant- and gene-level resolution.

genetics↗

bioRxiv2020

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

Familial, genome-wide association (GWAS), and sequencing studies and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWAS) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through novel application of a pleiotropy-guided conditional/conjunction false discovery rate approach for the first time in the setting of a TWAS. This identified 14 new candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 new candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were > 1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. Overlaying candidate causal risk variants identified by GWAS fine mapping onto expression prediction models for genes at known loci suggested that the association for 55% of these genes was driven by the underlying GWAS signal. SignificanceThe 22 new genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

genetics↗