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Biology subjects

Yao, C. Q.

Publications and source records attributed to Yao, C. Q..

4 recordsLinked to original sources

The Landscape of Prostate Tumour Methylation

Prostate cancer is characterized by profound clinical and molecular heterogeneity. While its genomic heterogeneity is well-characterized, its epigenomic heterogeneity remains less understood. We therefore created a compendium of 3,001 multi-ancestry prostate methylomes spanning normal tissue through localized disease of all grades to poly-metastatic disease. A subset of 884 samples had multi-omic DNA and/or RNA characterization. We identify four epigenomic subtypes that risk-stratify patients and reflect distinct evolutionary trajectories. We demonstrate extensive regulatory interplay between DNA copy number and methylation, with transcriptional consequences that vary across genes and disease stages. We define epigenetic dysregulation signatures for 15 important clinico-molecular features, creating predictive models for each. For example, we identify specific epigenetic features that predict patient outcome and are synergistic with clinical prognostic features. These results define a complex interplay between tumour genetics and epigenetics that converges to modify gene-expression programs and clinical presentation, in part through modulation of epigenetic aging.

cancer biology↗

Identifying TCDD-resistance genes via murine and rat comparative genomics and transcriptomics

The aryl hydrocarbon receptor (AHR) mediates many of the toxic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). However, the AHR alone is insufficient to explain the widely different outcomes among organisms. Attempts to identify unknown factor(s) have been confounded by genetic variability of model organisms. Here, we evaluated three transgenic mouse lines, each expressing a different rat AHR isoform (rWT, DEL, and INS), as well as C57BL/6 and DBA/2 mice. We supplement these with whole-genome sequencing and transcriptomic analyses of the corresponding rat models: Long-Evans (L-E) and Han/Wistar (H/W) rats. These integrated multi-species genomic and transcriptomic data were used to identify genes associated with TCDD-response phenotypes.\n\nWe identified several genes that show consistent transcriptional changes in both transgenic mice and rats. Hepatic Pxdc1 was significantly repressed by TCDD in C57BL/6, rWT mice, and in L-E rat. Three genes demonstrated different AHRE-1 (full) motif occurrences within their promoter regions: Cxxc5 had fewer occurrences in H/W, as compared with L-E; Sugp1 and Hgfac (in either L-E or H/W respectively). These genes also showed different patterns of mRNA abundance across strains.\n\nThe AHR isoform explains much of the transcriptional variability: up to 50% of genes with altered mRNA abundance following TCDD exposure are associated with a single AHR isoform (30% and 10% unique to DEL and rWT respectively following 500 g/kg TCDD). Genomic and transcriptomic evidence allowed identification of genes potentially involved in phenotypic outcomes: Pxdc1 had differential mRNA abundance by phenotype; Cxxc5 had altered AHR binding sites and differential mRNA abundance.\n\nAuthor SummaryEnvironmental contaminants such as dioxins cause many toxic responses, anything from chloracne (common in humans) to death. These toxic responses are mostly regulated by the Ahr, a ligand-activated transcription factor with roles in drug metabolism and immune responses, however other contributing factors remain unclear. Studies are complicated by the underlying genetic heterogeneity of model organisms. Our team evaluated a number of mouse and rat models, including two strains of mouse, two strains of rat and three transgenic mouse lines which differ only at the Ahr locus, that present widely different sensitivities to the most potent dioxin: 2,3,7,8 tetrachlorodibenzo-p-dioxin (TCDD). We identified a number of changes to gene expression that were associated with different toxic responses. We then contrasted these findings with results from whole-genome sequencing of the H/W and L-E rats and found some key genes, such as Cxxc5 and Mafb, which might contribute to TCDD toxicity. These transcriptomic and genomic datasets will provide a valuable resource for future studies into the mechanisms of dioxin toxicities.

genomics↗

Accurate Reference-Free Somatic Variant-Calling by Integrating Genomic, Sequencing and Population Data

The detection of somatic single nucleotide variants (SNVs) is critical in both research and clinical applications. Studies of human cancer typically use matched normal (reference) samples from a distant tissue to increase SNV prediction accuracy. This process both doubles sequencing costs and poses challenges when reference samples are not readily available, such as for many cell-lines. To address these challenges, we created S22S: an approach for the prediction of somatic mutations without need for matched reference tissue. S22S takes underlying sequence data, augments them with genomic background context and population frequency information, and classifies SNVs as somatic or non-somatic. We validated S22S using primary tumor/normal pairs from four tumor types, spanning two different sequencing technologies. S22S robustly identifies somatic SNVs, with the area under the precision recall curve reaching 0.97 in kidney clear cell carcinoma, comparable to the best tumor/normal analysis pipelines. S22S is freely available at http://labs.oicr.on.ca/Boutros-lab/software/s22s.

bioinformatics↗

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗