Biology subjects

Dewey, C. N.

Publications and source records attributed to Dewey, C. N..

6 recordsLinked to original sources

bioRxiv2026

ALS-associated exitron splicing produces UBQLN2 isoforms with distinct properties

X-linked amyotrophic lateral sclerosis (ALS) implicated Ubiquilin 2 (UBQLN2) is expressed from a single exon. Here, we show that human UBQLN2 mRNA is alternatively spliced by virtue of a cryptic exonic intron (exitron) spanning the 5' untranslated region and the 5' end of UBQLN2 coding sequence. Splice-out of this exitron generates a spliced UBQLN2 (UBQLN2-Sp) transcript that is translated from codon M243 to produce an N-terminally truncated UBQLN2 isoform (UBQLN2-M243) with reduced stability, diminished proteasome targeting, and altered aggregation behavior following the introduction of ALS mutations. The RNA-binding proteins SRSF1 and PTBP1 control UBQLN2 splicing through binding to splice donor-proximal motifs, while TDP-43 was implicated as an indirect splicing repressor. UBQLN2 splicing was elevated and inversely correlated with UBQLN2 gene expression in the medial motor cortex of male ALS patients. These findings suggest that alternative splicing regulates UBQLN2 gene dosage and function, which may impact UBQLN2-ALS proteinopathy.

molecular biology↗

bioRxiv2022

Novel populations of CD4+ T cells associated with vaccine efficacy

Memory T cells underpin vaccine-induced immunity but are not yet fully understood. To distinguish features of memory cells that confer protective immunity, we used single cell transcriptome analysis to compare antigen-specific CD4+ T cells recalled to lungs of mice that received a protective or nonprotective subunit vaccine followed by challenge with a fungal pathogen. We unexpectedly found populations specific to protection that expressed a strong type I interferon response signature, whose distinctive transcriptional signature appeared unconventionally dependent on IFN-{gamma} receptor. We also detected a unique population enriched in protection that highly expressed the gene for the natural killer cell marker NKG7. Lastly, we detected differences in TCR gene use and in Th1- and Th17-skewed responses after protective and nonprotective vaccine, respectively, reflecting heterogeneous Ifng- and Il17a-expressing populations. Our findings highlight key features of transcriptionally diverse and distinctive antigen-specific T cells associated with protective vaccine-induced immunity.

immunology↗

bioRxiv2019

Cell type specific gene expression profiling reveals a role for the complement component C3A in neutrophil migration to tissue damage

Following acute injury, leukocytes rapidly infiltrate into tissues. For efficient recruitment, leukocytes must sense and respond to signals from both from the damaged tissue and from one another. However, the cell type specific transcriptional changes that influence leukocyte recruitment and wound healing have not been well characterized. In this study, we performed a large-scale translating ribosome affinity purification (TRAP) and RNA sequencing screen in larval zebrafish to identify genes differentially expressed by neutrophils, macrophages, and epithelial cells in the context of wounding. We identified the complement pathway and c3a.1, homologous to the C3A component of human complement, as significantly increased in neutrophils in response to a wound. We report that c3a.1-/- zebrafish larvae have impaired neutrophil responses to both tail wounds and localized bacterial infections, as well as increased susceptibility to infection due to a neutrophil-intrinsic function of C3A. We further show that C3A enhances migration of human primary neutrophils to IL-8 and that c3a.1-/- larvae have impaired neutrophil migration in vivo, and a decrease in neutrophil directed migration speed early after wounding. Together, our findings suggest a role for C3A in mediating efficient neutrophil migration to damaged tissues and support the power of TRAP to identify cell-specific changes in gene expression associated with wound-associated inflammation.

immunology↗

bioRxiv2019

PRAM: a novel pooling approach for discovering intergenic transcripts from large-scale RNA sequencing experiments

Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by joint examination of large collections of RNA-seq datasets has emerged as one such analysis. Current methods for transcript discovery rely on a 2-Step approach where the first step encompasses building transcripts from individual datasets, followed by the second step that merges predicted transcripts across datasets. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a novel 1-Step approach named Pooling RNA-seq and Assembling Models (PRAM) that builds transcript models from pooled RNA-seq datasets. We demonstrate in a computational benchmark that 1-Step outperforms 2-Step approaches in predicting overall transcript structures and individual splice junctions, while performing competitively in detecting exonic nucleotides. Applying PRAM to 30 human ENCODE RNA-seq datasets identified unannotated transcripts with epigenetic and RAMPAGE signatures similar to those of recently annotated transcripts. In a case study, we discovered and experimentally validated new transcripts through the application of PRAM to mouse hematopoietic RNA-seq datasets. Notably, we uncovered new transcripts that share a differential expression pattern with a neighboring gene Pik3cg implicated in human hematopoietic phenotypes, and we provided evidence for the conservation of this relationship in human. PRAM is implemented as an R/Bioconductor package and is available at https://bioconductor.org/packages/pram.

bioinformatics↗

bioRxiv2019

Hierarchical cell type classification using mass, heterogeneous RNA-seq data from human primary cells

Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellOs comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellOs linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellOs models across the ontology. HighlightO_LIWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell Ontology C_LIO_LICellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read Archive C_LIO_LICellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training set C_LIO_LICellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell Ontology C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/634097v2_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@208aeeorg.highwire.dtl.DTLVardef@814bd8org.highwire.dtl.DTLVardef@9a7c39org.highwire.dtl.DTLVardef@5195fb_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2016

MetaSRA: normalized sample-specific metadata for the Sequence Read Archive

MotivationThe NCBIs Sequence Read Archive (SRA) promises great biological insight if one could analyze the data in the aggregate; however, the data remain largely underutilized, in part, due to the poor structure of the metadata associated with each sample. The rules governing submissions to the SRA do not dictate a standardized set of terms that should be used to describe the biological samples from which the sequencing data are derived. As a result, the metadata include many synonyms, spelling variants, and references to outside sources of information. Furthermore, manual annotation of the data remains intractable due to the large number of samples in the archive. For these reasons, it has been difficult to perform large-scale analyses that study the relationships between biomolecular processes and phenotype across diverse diseases, tissues, and cell types present in the SRA.\n\nResultsWe present MetaSRA, a database of normalized SRA sample-specific metadata following a schema inspired by the metadata organization of the ENCODE project. This schema involves mapping samples to terms in biomedical ontologies, labeling each sample with a sample-type category, and extracting real-valued properties. We automated these tasks via a novel computational pipeline.\n\nAvailabilityThe MetaSRA database is available at http://deweylab.biostat.wisc.edu/metasra. Software implementing our computational pipeline is available at https://github.com/deweylab/metasra-pipeline.\n\nContactcdewey@biostat.wisc.edu

bioinformatics↗