Prabakaran, S. | bioRxiv Science

bioRxiv2018

Loss Of Kat2a Enhances Transcriptional Noise And Depletes Acute Myeloid Leukemia Stem-Like Cells

Acute Myeloid Leukemia (AML) is an aggressive hematological malignancy with abnormal progenitor self-renewal and defective myelo-monocytic differentiation. Its pathogenesis comprises subversion of transcriptional regulation, through mutation and by hijacking normal chromatin regulation. Kat2a is a histone acetyltransferase central to promoter activity that we recently associated with stability of pluripotency networks, and identified as a genetic vulnerability in AML. Through combined chromatin profiling and single-cell transcriptomics, we demonstrate that Kat2a contributes to leukemia propagation through homogeneity of transcriptional programs and preservation of leukemia stem-like cells. Kat2a loss reduces transcriptional bursting frequency in a subset of gene promoters, generating enhanced variability of transcript levels but minimal effects on mean gene expression. Destabilization of target programs shifts cellular equilibrium out of self-renewal towards differentiation. We propose that control of transcriptional variability is central to leukemia stem-like cell propagation, and establish a paradigm exploitable in different tumors and at distinct stages of cancer evolution.

cancer biology↗

bioRxiv2017

Identification And Prioritisation Of Variants In The Short Open-Reading Frame Regions Of The Human Genome

As whole-genome sequencing technologies improve and accurate maps of the entire genome are assembled, short open-reading frames (sORFs) are garnering interest as functionally important regions that were previously overlooked. However, there is a paucity of tools available to investigate variants in sORF regions of the genome. Here we investigate the performance of commonly used tools for variant calling and variant prioritisation in these regions, and present a framework for optimising these processes. First, the performance of four widely used germline variant calling algorithms is systematically compared. Haplotype Caller is found to perform best across the whole genome, but FreeBayes is shown to produce the most accurate variant set in sORF regions. An accurate set of variants is found by taking the intersection of called variants. The potential deleteriousness of each variant is then predicted using a pathogenicity scoring algorithm developed here, called sORF-c. This algorithm uses supervised machine-learning to predict the pathogenicity of each variant, based on a holistic range of functional, conservation-based and region-based scores defined for each variant. By training on a dataset of over 130,000 variants, sORF-c outperforms other comparable pathogenicity scoring algorithms on a test set of variants in sORF regions of the human genome.\n\nList of Abbreviations

genetics↗