Biology subjects

Haas, B.

Publications and source records attributed to Haas, B..

7 recordsLinked to original sources

bioRxiv2026

A long-read RNA sequencing and polysome profiling framework reveals transposable element-driven transcript diversity and translational rewiring in glioblastoma

BackgroundTransposable elements (TEs) account for over half of the human genome and are often derepressed in cancer. TEs can add cryptic splice sites, undergo exonization, and generate gene-TE fusion transcripts, but the combined effects of TEs on RNA processing and translation in glioblastoma stem cells (GSCs) remains incompletely elucidated. ResultsWe combined long-read RNA sequencing with polysome profiling in four patient-derived GSCs and two neural stem cell (NSC) controls to resolve TE-associated transcript diversity and its relationship to ribosomal engagement. Across GSCs, we identified 13,421 alternative splicing (AS) events, 3,077 of which contained TEs within 150 bp of splice junctions. AS sites proximal to TEs were associated with increased isoform switching compared to non-TE-associated AS sites (odds ratio 2.9 - 4.3). Moreover, AS isoforms generated from TE-proximal sites were more likely to exhibit altered ribosomal association (odds ratio 2.54). Directional shifts were observed, with shorter isoforms associating with monosome fractions and longer isoforms with polysome fractions. To enable systematic detection of gene - TE chimeric transcripts, we developed FuTER (Fusion TE Reporter), a long-read-based framework for identifying TE-associated fusions. Application to GSC datasets identified 78 GSC enriched fusion transcripts, several supported by breakpoint-spanning reads in polysome fractions, consistent with ribosome association. ConclusionsOur data suggest that TEs correlate with abnormal splicing activity and altered ribosome engagement in glioblastoma stem cells. By integrating long-read sequencing with polysome profiling and fusion detection, we establish a framework for analysis of TE-induced transcript diversity and its effects on cancer evolution and plasticity.

cancer biology↗

bioRxiv2025

Complex HPV-human DNA structures revealed by large-scale DNA analyses in an HPV-cancer derived cell line

Most human papillomavirus (HPV)-associated cancers harbor viral DNA integrated into the human genome as extrachromosomal circles, intrachromosomal segments, or both. Distinguishing intrachromosomal from identical-sequence extrachromosomal DNA (ecDNA) by sequencing alone is challenging, and the architecture of large-scale HPV-human DNA structures remains incompletely understood. To address this, we applied complementary genomic tools, spanning single-nucleotide to megabase resolution, to the HPV16-positive oropharyngeal cancer-cell line UM-SCC-47. These revealed that an initial integration event formed a 23 kb extrachromosomal heterocatemer circle comprising 7.5 kb of HPV16 DNA and 16 kb of the human TP63 gene. Subsequent genomic rearrangements generated heterocatemer tandem arrays extending to 0.6 megabases, plus additional large-scale rearrangements involving the HPV-TP63 structures, as revealed by long-read DNA sequencing and optical genome mapping. Fluorescent in situ Hybridization (FISH) showed that the heterocatemers were intrachromosomally localized at chromosome 3 at the TP63 locus in 100% of the cells. Long-read RNA sequencing further showed that these intrachromosomal templates produced spliced, polyadenylated transcripts. A subset of cells also harbored HPV16 ecDNA derived from the intrachromosomal HPV-TP63 DNAs. These findings define previously unrecognized higher-order architecture of integrated HPV DNA and highlight the power of FISH for distinguishing intrachromosomal from extrachromosomal DNA structures. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/680684v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@f3d584org.highwire.dtl.DTLVardef@470811org.highwire.dtl.DTLVardef@162c88corg.highwire.dtl.DTLVardef@5c80dd_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

TD2: finding protein coding regions in transcripts

The transcriptome encompasses all RNA transcripts in eukaryotic cells, orchestrating gene expression and regulating cellular function, development, and adaptation. Identifying open reading frames (ORFs) in transcripts is a critical step in transcriptome analysis. We introduce TD2, a new tool for ab initio annotation of protein-coding ORFs in transcripts. We find TD2 to be sensitive and precise when compared to other state-of-the-art tools in reference transcripts and transcriptome assemblies from a diverse array of eukaryotes. TD2 is available at https://github.com/Markusjsommer/TD2. The project is open-source, developed in Python with PyTorch, and is freely available to all academic, government, and commercial users under the MIT license.

genomics↗

bioRxiv2023

Gene expression shifts in Emperor penguin adaptation to the extreme Antarctic environment

Gene expression can accelerate ecological divergence by rapidly tweaking the response of an organism to novel environments, with more divergent environments exerting stronger selection and supposedly, requiring faster adaptive responses. Organisms adapted to extreme environments provide ideal systems to test this hypothesis, particularly when compared to related species with milder ecological niches. The Emperor penguin (Aptenodytes forsteri) is the only warm-blooded vertebrate breeding in the harsh Antarctic winter, in stark contrast with the less cold-adapted sister species, the King penguin (A. patagonicus). Assembling the first de novo transcriptomes and analysing multi-tissue (brain, kidney, liver, muscle, skin) RNAseq data from natural populations of both species, we quantified the shifts in tissue-enhanced genes, co-expression gene networks, and differentially expressed genes characterising Emperor penguin adaptation to extreme Antarctic ecology. Our analyses revealed the crucial role played by muscle and liver in temperature homeostasis, fasting and whole-body energy metabolism (glucose/insulin regulation, lipid metabolism, fatty acid beta-oxidation, and blood coagulation). Repatterning at the regulatory level appears as more important in the brain of the Emperor penguin, showing the lowest signature of differential gene expression but the largest co-expression gene network shift. Nevertheless, over-expressed genes related to mTOR signalling in the brain and the liver support their central role in cold and fasting responses. Besides contributing to understanding the genetics underlying complex traits, like body energy reservoir management, our results provide a first insight into the role of gene expression in adaptation to one of the most extreme environmental conditions endured by an endotherm.

evolutionary biology↗

bioRxiv2021

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector

MotivationGene fusions play a key role as driver oncogenes in tumors, and their reliable discovery and detection are important for cancer research, diagnostics, prognostics and guiding personalized therapy. While discovering gene fusions from genome sequencing can be laborious and costly, the resulting "fusion transcripts" can be recovered from RNA-seq data of tumor and normal samples. However, alleged and putative fusion transcripts can also arise from multiple sources other than chromosomal rearrangements, including cis- or trans-splicing events, experimental artifacts during RNA-seq or computational errors of transcriptome reconstruction methods. Understanding how to discern, interpret, categorize, and verify predicted fusion transcripts is essential for consideration in clinical settings and prioritization for further research. SummaryHere, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA-seq and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes, and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies detected between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico, and helps identify and characterize numerous understudied fusions in tumor and normal tissues samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and helps with transparent explanation and interpretation of machine learning predictions and their experimental sources. HighlightsO_LIFusionInspector software for supervised analysis of candidate fusion transcripts C_LIO_LIClustering of recurrent fusion transcripts resolves biologically relevant fusions C_LIO_LIIdentification of distinguishing characteristics of known and novel fusion transcripts in tumor and normal tissues C_LI

bioinformatics↗

bioRxiv2019

Opposing immune and genetic forces shape oncogenic programs in synovial sarcoma

Synovial sarcoma is an aggressive mesenchymal neoplasm, driven by the SS18-SSX fusion, and characterized by immunogenic antigens expression and exceptionally low T cell infiltration levels. To study the cancer-immune interplay in this disease, we profiled 16,872 cells from 12 human synovial sarcoma tumors using single-cell RNA-sequencing (scRNA-Seq). Synovial sarcoma manifests antitumor immunity, high cellular plasticity and a core oncogenic program, which is predictive of low immune levels and poor clinical outcomes. Using genetic and pharmacological perturbations, we demonstrate that the program is controlled by the SS18-SSX driver and repressed by cytokines secreted by macrophages and T cells in the tumor microenvironment. Network modeling predicted that SS18-SSX promotes the program through HDAC1 and CDK6. Indeed, the combination of HDAC and CDK4/6 inhibitors represses the program, induces immunogenic cell states, and selectively targets synovial sarcoma cells. Our study demonstrates that immune evasion, cellular plasticity, and cell cycle are co-regulated and can be co-targeted in synovial sarcoma and potentially in other malignancies.

cancer biology↗

bioRxiv2017

STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq

MotivationFusion genes created by genomic rearrangements can be potent drivers of tumorigenesis. However, accurate identification of functionally fusion genes from genomic sequencing requires whole genome sequencing, since exonic sequencing alone is often insufficient. Transcriptome sequencing provides a direct, highly effective alternative for capturing molecular evidence of expressed fusions in the precision medicine pipeline, but current methods tend to be inefficient or insufficiently accurate, lacking in sensitivity or predicting large numbers of false positives. Here, we describe STAR-Fusion, a method that is both fast and accurate in identifying fusion transcripts from RNA-Seq data.\n\nResultsWe benchmarked STAR-Fusions fusion detection accuracy using both simulated and genuine Illumina paired-end RNA-Seq data, and show that it has superior performance compared to popular alternative fusion detection methods.\n\nAvailability and implementationSTAR-Fusion is implemented in Perl, freely available as open source software at http://star-fusion.github.io, and supported on Linux.\n\nContactbhaas@broadinstitute.org

bioinformatics↗