Biology subjects

Bozsaky, E.

Publications and source records attributed to Bozsaky, E..

3 recordsLinked to original sources

bioRxiv2022

Cross-species analysis identifies conserved transcriptional mechanisms of neutrophil maturation

Neutrophils are evolutionarily conserved innate defense cells implicated in diverse pathological processes. Zebrafish models have contributed substantially to our understanding of neutrophil functions, but similarities to human neutrophil maturation have not been characterized limiting applicability to study human disease. We generated transgenic zebrafish strains to distinguish neutrophil maturation grades in vivo and established a high-resolution transcriptional profile of neutrophil maturation. We linked gene expression at each stage to characteristic transcription factors, including C/ebp{beta}, important for late neutrophil maturation. Cross-species comparison of zebrafish, mouse, and human confirmed high molecular similarity in immature stages and discriminated zebrafish-specific from pan-species gene signatures. Applying pan-species neutrophil maturation signatures in RNA-seq data from neuroblastoma patients revealed an association of metastatic tumor cell infiltration in the bone marrow with an increase in mature neutrophils. Our detailed neutrophil maturation atlas provides a valuable resource for studying neutrophil function at different stages across species in health and disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/518146v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@d46deaorg.highwire.dtl.DTLVardef@7c2647org.highwire.dtl.DTLVardef@b535eeorg.highwire.dtl.DTLVardef@e7dc38_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Neuroblastoma-associated chromosomal aberrations drive cell identity loss in human neural crest via disruption of developmental regulators

Early childhood tumours arise from transformed embryonic cells, which often carry large copy number alterations (CNA). However, it remains unclear how CNAs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ female human embryonic stem cell (hESC) differentiation and single-cell transcriptome and epigenome analysis to assess the effects of chromosome 17q/1q gains, which are prevalent in the embryonal tumour neuroblastoma (NB). We show that CNAs impair the specification of trunk neural crest (NC) cells and their sympathoadrenal derivatives, the putative cells-of-origin of NB. This effect is exacerbated upon overexpression of MYCN, whose amplification co-occurs with CNAs in NB. Moreover, CNAs potentiate the pro-tumourigenic effects of MYCN and mutant NC cells resemble NB cells in tumours. These changes correlate with a stepwise aberration of developmental transcription factor networks. Together, our results sketch a mechanistic framework for the CNA-driven initiation of embryonal tumours.

cancer biology↗

bioRxiv2018

Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data

Diagnosis and risk stratification of cancer and many other diseases require the detection of genomic breakpoints as a prerequisite of calling copy number alterations (CNA). This, however, is still challenging and requires time-consuming manual curation. As deep-learning methods outperformed classical state-of-the-art algorithms in various domains and have also been successfully applied to life science problems including medicine and biology, we here propose Deep SNP, a novel Deep Neural Network to learn from genomic data. Specifically, we used a manually curated dataset from 12 genomic single nucleotide polymorphism array (SNPa) profiles as truth-set and aimed at predicting the presence or absence of genomic breakpoints, an indicator of structural chromosomal variations, in windows of 40,000 probes. We compare our results with well-known neural network models as well as Rawcopy though this tool is designed to predict breakpoints and in addition genomic segments with high sensitivity. We show, that Deep SNP is capable of successfully predicting the presence or absence of a breakpoint in large genomic windows and outperforms state-of-the-art neural network models. Qualitative examples suggest that integration of a localization unit may enable breakpoint detection and prediction of genomic segments, even if the breakpoint coordinates were not provided for network training. These results warrant further evaluation of DeepSNP for breakpoint localization and subsequent calling of genomic segments.

genomics↗