Biology subjects

Grinev, V. V.

Publications and source records attributed to Grinev, V. V..

3 recordsLinked to original sources

bioRxiv2021

Epigenetic regulator genes direct lineage switching in MLL-AF4 leukaemia

The fusion gene MLL-AF4 defines a high-risk subtype of pro-B acute lymphoblastic leukaemia. However, relapse can be associated with a switch from acute lymphoblastic to acute myeloid leukaemia. Here we show that these myeloid relapses share oncogene fusion breakpoints with their matched lymphoid presentations and can originate in either early, multipotent progenitors or committed B-cell precursors. Lineage switching is linked to substantial changes in chromatin accessibility and rewiring of transcriptional programmes indicating that the execution and maintenance of lymphoid lineage differentiation is impaired. We show that this subversion is recurrently associated with the dysregulation of repressive chromatin modifiers, notably the nucleosome remodelling and deacetylation complex, NuRD. In addition to mutations, we show differential expression or alternative splicing of NuRD members and other genes is able to reprogram the B lymphoid into a myeloid gene regulatory network. Lineage switching in MLL-AF4 leukaemia is therefore driven and maintained by defunct epigenetic regulation. Statement of SignificanceWe demonstrate diverse cellular origins of lineage switched relapse within MLL-AF4 pro-B acute leukaemia. Irrespective of the developmental origin of relapse, dysregulation of NuRD and/or other epigenetic machinery underpins fundamental lineage reprogramming with profound implications for the increasing use of epitope directed therapies in this high-risk leukaemia.

cancer biology↗

bioRxiv2021

Splice sites obey the power-law during splicing in leukemia cells

Alternative splicing is an essential characteristic of living cells that usually infers a various exon-exon junction governed by different splice sites. The traditional classification based on the mode of use designates splice site to one of the two groups, constitutive or alternative. Here, we considered another criterion and reorganized splice sites into "unisplice" and "multisplice" groups according to the number of undertaken splicing events. This approach provided us with a new insight in the organization and functionality of leukemia cells. We determined features associated with uni- and multisplice sites and found that combinatorics of these sites follows strict rules of the power-law in the t(8;21)-positive leukemia cells. We also found that system splicing characteristics of the transcriptome of leukemia cells remained persistent after drastic changes in the transcript composition caused by knockdown of the RUNX1-RUNX1T1 oncogene. In this work, we show for the first time that leukemia cells possess a sub-set of unisplice sites with a hidden multisplice potential. These findings reveal a new side in organization and functioning of the leukemic cells and open up new perspectives in the study of the t(8;21)-positive leukemia.

bioinformatics↗

bioRxiv2021

ORFhunteR: an accurate approach for the automatic identification and annotation of open reading frames in human mRNA molecules

MotivationModern methods of whole transcriptome sequencing accurately recover nucleotide sequences of RNA molecules present in cells and allow for determining their quantitative abundances. The coding potential of such molecules can be estimated using open reading frames (ORF) finding algorithms, implemented in a number of software packages. However, these algorithms show somewhat limited accuracy, are intended for single-molecule analysis and do not allow selecting proper ORFs in the case of long mRNAs containing multiple ORF candidates. ResultsWe developed a computational approach, corresponding machine learning model and a package, dedicated to automatic identification of the ORFs in large sets of human mRNA molecules. It is based on vectorization of nucleotide sequences into features, followed by classification using a random forest. The predictive model was validated on sets of human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. The developed methods and pre-trained classification model were implemented in a powerful ORFhunteR computational tool that performs an automatic identification of true ORFs among large set of human mRNA molecules. Availability and implementationThe developed open-source R package ORFhunteR is available for the community at GitHub repository (https://github.com/rfctbio-bsu/ORFhunteR), from Bioconductor (https://bioconductor.org/packages/devel/bioc/html/ORFhunteR.html) and as a web application (http://orfhunter.bsu.by).

bioinformatics↗