Biology subjects

Pushel, I.

Publications and source records attributed to Pushel, I..

5 recordsLinked to original sources

bioRxiv2025

A comprehensive bulk and single-cell transcriptional atlas of pediatric leukemias

Advancements in understanding the molecular factors driving pediatric leukemias have led to an ever-increasing volume and diversity of data being generated. Recent studies are moving beyond DNA-based profiling to incorporate transcriptional data, enhancing the characterization of these cancers and informing clinical decisions. However, many existing datasets focus on specific leukemia subtypes, limiting the extraction of broader clinically relevant insights. Here, we present a comprehensive dataset of bulk and single-cell transcriptional data from 69 pediatric leukemia patients, encompassing eight leukemias. This is one of the most diverse pediatric leukemia datasets published to date. Through comparative analysis and explainable machine learning, we demonstrate the utility of these datasets in improving pediatric leukemia characterization and supporting the integration of transcriptional data into clinical testing.

cancer biology↗

bioRxiv2025

Integrated multi-omic analysis reveals novel subtype-specific regulatory interactions in pediatric B-cell acute lymphoblastic leukemia

Molecular subtyping of pediatric B-cell acute lymphoblastic leukemia (B-ALL) has improved patient outcomes through stratification and selection of targeted therapies. Despite extensive genomic and transcriptomic profiling of this cancer, few studies to date have characterized the proteomic landscape, although proteins are the direct targets of many therapeutic agents. In this study, we demonstrate the utility of multi-omic integration of global transcriptomic, proteomic, and phosphoproteomic profiles of samples from patients diagnosed with either of two B-ALL subtypes - Ph- like (BCR::ABL1-like) and ETV6::RUNX1. Through individual and multi-omic analysis, we recapitulate known transcriptomic findings and identify novel subtype-specific proteomic and phosphoproteomic biomarkers. Our findings suggest a previously undescribed role for calcium-dependent signaling processes in Ph-like B-ALL, which has the potential to serve as a novel avenue for targeted treatments. By integrating multiple omics modalities, we identify not only features of interest but also begin to unravel the regulatory interactions driving subtype-specific mechanisms of leukemogenesis. This integrated analytic approach paves the way for enhanced precision medicine for precise subtyping and treatment selection for pediatric leukemia patients. Mass spectrometry data generated in this study have been deposited in MassIVE under accession MSV000097955.

cancer biology↗

bioRxiv2024

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

bioinformatics↗

bioRxiv2021

Specification of axial identity by Hoxa2 distinguishes between a phenotypic and molecular ground state in mouse cranial neural crest cells

Hox genes play a key role in head formation by specifying the axial identity of neural crest cells (NCCs) migrating into embryonic pharyngeal arches. In the absence of Hoxa2, NCC derivatives of the second pharyngeal arch (PA2) undergo a homeotic transformation and duplicate structures formed by first arch (PA1) NCCs. Current models postulate that PA1 represents a NCC ground state and loss of Hoxa2 causes a reversion of PA2 NCCs to the PA1 ground state. We use bulk and single-cell RNAseq to investigate the molecular mechanisms driving this phenotypic transformation in the mouse. In Hoxa2-/- mutants, PA2 NCCs generally maintain expression of the PA2 transcriptional signature and fail to strongly upregulate a PA1 transcriptional signature. Our analyses identify putative HOXA2 targets and suggest that subsets of NCCs may respond to HOXA2 activity in distinct manners. This separation of phenotypic and molecular states has significant implications for understanding craniofacial development.

developmental biology↗

bioRxiv2016

Hoxa1 and TALE proteins display cross-regulatory interactions and form a combinatorial binding code on Hoxa1 targets

Hoxa1 has diverse functional roles in differentiation and development. We have identified and characterized properties of regions bound by Hoxa1 on a genome-wide basis in differentiating mouse ES cells. Hoxa1 bound regions are enriched for clusters of consensus binding motifs for Hox, Pbx and Meis and many display co-occupancy of Pbx and Meis. Pbx and Meis are members of the TALE family and genome-wide analysis of multiple TALE members (Pbx, Meis, TGIF, Prep1 and Prep2) show that nearly all Hoxa1 targets display occupancy of one or more TALE members. The combinatorial binding patterns of TALE proteins defines distinct classes of Hoxa1 targets and indicates a role as cofactors in modulating the specificity of Hox proteins. We also discovered extensive auto- and cross-regulatory interactions among the Hoxa1 and TALE genes. This study provides new insight into a regulatory network involving combinatorial interactions between Hoxa1 and TALE proteins.

developmental biology↗