Biology subjects

Deshwar, A.

Publications and source records attributed to Deshwar, A..

4 recordsLinked to original sources

bioRxiv2023

An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics

Accurately modeling and predicting RNA biology has been a long-standing challenge, bearing significant clinical ramifications for variant interpretation and the formulation of tailored therapeutics. We describe a foundation model for RNA biology, "BigRNA", which was trained on thousands of genome-matched datasets to predict tissue-specific RNA expression, splicing, microRNA sites, and RNA binding protein specificity from DNA sequence. Unlike approaches that are restricted to missense variants, BigRNA can identify pathogenic non-coding variant effects across diverse mechanisms, including polyadenylation, exon skipping and intron retention. BigRNA accurately predicted the effects of steric blocking oligonucleotides (SBOs) on increasing the expression of 4 out of 4 genes, and on splicing for 18 out of 18 exons across 14 genes, including those involved in Wilson disease and spinal muscular atrophy. We anticipate that BigRNA and foundation models like it will have widespread applications in the field of personalized RNA therapeutics.

bioinformatics↗

bioRxiv2019

ATP7B Variant c.1934T>G p.Met645Arg Causes Wilson Disease by Promoting Exon 6 Skipping

Wilson Disease is a recessive genetic disorder caused by pathogenic loss-of-function variants in the ATP7B gene. It is characterized by disrupted copper homeostasis resulting in liver disease and/or neurological abnormalities. The variant NM_000053.3:c.1934T>G (Met645Arg) has been reported as compound heterozygous and is highly prevalent among Wilson Disease patients of Spanish descent. Accordingly, it is classified as pathogenic by leading molecular diagnostic centers. However, functional studies suggest that the amino acid change does not alter protein function, leading one ClinVar submitter to question its pathogenicity. Here we used a minigene system and gene-edited HepG2 cells to demonstrate that c.1934T>G causes approximately 70% skipping of exon 6. Exon 6 skipping results in frameshift and stop gain, which is expected to cause loss of ATP7B function. The elucidation of the mechanistic effect for this variant resolves any doubt about its pathogenicity and enables the development of genetic medicines for restoring correct splicing.

genetics↗

bioRxiv2018

TrackSig: reconstructing evolutionary trajectories of mutation signature exposure

We present a new method, TrackSig, to estimate the evolutionary trajectories of signatures of different somatic mutational processes from DNA sequencing data from a single, bulk tumour sample. TrackSig uses probability distributions over mutation types, called mutational signatures, to represent different mutational processes and detects the changes in the signature activity using an optimal segmentation algorithm that groups somatic mutations based on their estimated cancer cellular fraction (CCF) and their mutation type (e.g. CAG->CTG). We use two different simulation frameworks to assess both TrackSigs reconstruction accuracy and its robustness to violations of its assumptions, as well as to compare it to a baseline approach. We find 2-4% median error in reconstructing the signature activities on simulations with varying difficulty with one to three subclones at an average depth of 30x. The size and the direction of the activity change is consistent in 83% and 95% of cases respectively. There were an average of 0.02 missed and 0.12 false positive subclones per sample. In our simulations, grouping mutations by mutation type (TrackSig), rather than by clustering CCF (baseline strategy), performs better at estimating signature activities and at identifying subclonal populations in the complex scenarios like branching, CNA gain, violation of infinite site assumption, and the inclusion of neutrally evolving mutations. TrackSig is open source software, freely available at https://github.com/morrislab/TrackSig.

bioinformatics↗

bioRxiv2017

The evolutionary history of 2,658 cancers

Cancer develops through a process of somatic evolution. Here, we use whole-genome sequencing of 2,778 tumour samples from 2,658 donors to reconstruct the life history, evolution of mutational processes, and driver mutation sequences of 39 cancer types. The early phases of oncogenesis are driven by point mutations in a small set of driver genes, often including biallelic inactivation of tumour suppressors. Early oncogenesis is also characterised by specific copy number gains, such as trisomy 7 in glioblastoma or isochromosome 17q in medulloblastoma. By contrast, increased genomic instability, a nearly four-fold diversification of driver genes, and an acceleration of point mutation processes are features of later stages. Copy-number alterations often occur in mitotic crises leading to simultaneous gains of multiple chromosomal segments. Timing analysis suggests that driver mutations often precede diagnosis by many years, and in some cases decades, providing a window of opportunity for early cancer detection.

cancer biology↗