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Cribbs, A. P.

Publications and source records attributed to Cribbs, A. P..

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Hypoxia induces transcriptional and translational downregulation of the type I interferon (IFN) pathway in multiple cancer cell types

Hypoxia is a common phenomenon in solid tumours and is considered a hallmark of cancer. Increasing evidence shows that hypoxia promotes local immune suppression. Type I IFN is involved in supporting cytotoxic T lymphocytes by stimulating the maturation of dendritic cells (DCs) and enhancing their capacity to process and present antigens. However, there is little information about the relationship between hypoxia and the type I interferon (IFN) pathway, which comprises the sensing of double-stranded RNA and DNA (dsRNA/dsDNA), followed by IFN/{beta} secretion and transcription activation of IFN-stimulated genes (ISGs). The aims of this study were to determine both the effect and mechanisms of hypoxia on the I IFN pathway in breast cancer.\n\nThere was a downregulation of the type I IFN pathway expression at mRNA and protein level in cancer cell lines under hypoxia in vitro and in vivo in xenografts. This pathway was suppressed at each level of signalling, from the dsRNA sensors (RIG-I, MDA5), the adaptor (MAVS), transcription factors (IRF3, IRF7, STAT1) and several ISGs (RIG-I, IRF7, STAT1, ADAR-p150). There was also lower IFN secretion under hypoxic conditions. HIF1 and HIF2 regulation of gene expression did not explain most of the effects. However, ATAC-Seq data revealed that in hypoxia peaks with STAT1 and IRF3 motifs had decreased accessibility.\n\nThus hypoxia leads to an overall 50% downregulation of the type I IFN pathway due to repressed transcription and lower chromatin accessibility in a HIF1/2-independent manner, which could contribute to immunosuppression in hypoxic tumours.

cancer biology

tRNAnalysis: A flexible pre-processing and next-generation sequencing data analysis pipeline for transfer RNA

Many tools have been developed to analyse small RNA sequencing data, however it remains a challenging task to accurately process reads aligning to small RNA due to their short-read length. Most pipelines have been developed with miRNA analysis in mind and there are currently very few workflows focused on the analysis of transfer RNAs. Moreover, these workflows suffer from being low throughput, difficult to install and lack sufficient visualisation to make the output interpretable. To address these issues, we have built a comprehensive and customisable small RNA-seq data analysis pipeline, with emphasis on the analysis of tRNAs. The pipeline takes as an input a fastq file of small RNA sequencing reads and performs successive steps of mapping and alignment to transposable elements, gene transcripts, miRNAs, snRNAs, rRNA and tRNAs. Subsequent steps are then performed to generate summary statistics on reads of tRNA origin, which are then visualised in a html report. Unlike other low-throughput analysis tools currently available, our high-throughput method allows for the simultaneous analysis of multiple samples and scales with the number of input files. tRNAnalysis is command line runnable and is implemented predominantly using Python and R. The source code is available at https://github.com/Acribbs/tRNAnalysis.

bioinformatics