Biology subjects

Yurovsky, A.

Publications and source records attributed to Yurovsky, A..

3 recordsLinked to original sources

bioRxiv2025

Improving Differential Expression and Survival Analyses with Sample Specific Compartment Deconvolution.

MotivationStudies on bulk RNA-seq of tumor biopsies can yield incorrect results because varying proportions of non-tumor tissues in the samples obscure the true signal and impact the accuracy of survival and differential expression analyses. Single-cell sequencing avoids these problems, but is still too expensive in clinical settings. Other deconvolution algorithms extract tissue-specific gene expression profiles from bulk sequencing, but cannot do this on a per-sample basis. ResultsWe introduce SSCD - sample specific compartment deconvolution. SSCD extends non-negative matrix factorization with per-sample, per-gene constraint optimization. On simulated data, SSCD shows improvements in accuracy over existing methods. Using several real cancer datasets, we show that SSCD refines Differential Expression and survival analyses. AvailabilityCode and data are available at https://github.com/ayurovsky/SSCD.

cancer biology↗

bioRxiv2023

Gene recoding by synonymous mutations creates promiscuous intragenic transcription initiation in mycobacteria

Each genome encodes some codons more frequently than their synonyms (codon usage bias), but codons are also arranged more frequently into specific pairs (codon pair bias). Recoding viral genomes and yeast or bacterial genes with non-optimal codon pairs has been shown to decrease gene expression. Gene expression is thus importantly regulated not only by the use of particular codons but by their proper juxtaposition. We therefore hypothesized that non-optimal codon pairing could likewise attenuate Mtb genes. We explored the role of codon pair bias by recoding Mtb genes (rpoB, mmpL3, ndh) and assessing their expression in the closely related and tractable model organism M. smegmatis. To our surprise, recoding caused the expression of multiple smaller protein isoforms from all three genes. We confirmed that these smaller proteins were not due to protein degradation, but instead issued from new transcription initiation sites positioned within the open reading frame. New transcripts gave rise to intragenic translation initiation sites, which in turn led to the expression of smaller proteins. We next identified the nucleotide changes associated with these new sites of transcription and translation. Our results demonstrated that apparently benign, synonymous changes can drastically alter gene expression in mycobacteria. More generally, our work expands our understanding of the codon-level parameters that control translation and transcription initiation. IMPORTANCEMycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, one of the deadliest infectious diseases worldwide. Previous studies have established that synonymous recoding to introduce rare codon pairings can attenuate viral pathogens. We hypothesized that non-optimal codon pairing could be an effective strategy for attenuating gene expression to create a live vaccine for Mtb. We instead discovered that these synonymous changes enabled the transcription of functional mRNA that initiated in the middle of the open reading frame and from which many smaller protein products were expressed. To our knowledge, this is the first report that synonymous recoding of a gene in any organism can create or induce intragenic transcription start sites.

molecular biology↗

bioRxiv2022

A Statistical Detector for Ribosomal Frameshifts and Dual Encodings based on Ribosome Profiling

During protein synthesis, the ribosome shifts along the messenger RNA (mRNA) by exactly three nucleotides for each amino acid added to the protein being translated. However, in special cases, the sequence of the mRNA somehow induces the ribosome to shift forward by either two or four nucleotides. This shifts the "reading frame" in which the mRNA is translated, and gives rise to an otherwise unexpected protein. Such "programmed frameshifts" are well-known in viruses, including coronavirus, and a few cases of programmed frameshifting are also known in cellular genes. However, there is no good way, either experimental or informatic, to identify novel cases of programmed frameshifting. Thus it is possible that substantial numbers of cellular proteins generated by programmed frameshifting in human and other organisms remain unknown. Here, we build on prior work observing that data from ribosome profiling can be analyzed for anomalies in mRNA reading frame periodicity to identify putative programmed frameshifts. We develop a statistical framework to identify all likely (even for very low frameshifting rates) frameshift positions in a genome. We also develop a frameshift simulator for ribosome profiling data to verify our algorithm. We show high sensitivity of prediction on the simulated data, retrieving 97.4% of the simulated frameshifts. Furthermore, our method found all three of the known yeast genes with programmed frameshifts. We list several hundred yeast genes that may contain +1 or -1 frameshifts. Our results suggest there could be a large number of un-annotated alternative proteins in the yeast genome generated by programmed frameshifting. This motivates further study and parallel investigations in the human genome. Frameshift Detector algorithms and instructions can be accessed in Github: https://github.com/ayurovsky/Frame-Shift-Detector.

bioinformatics↗