Biology subjects

Taylor, D.

Publications and source records attributed to Taylor, D..

4 recordsLinked to original sources

bioRxiv2018

Characterization of novel inhibition of indoleamine 2,3-dioxygenase by targeting its apo form.

Indoleamine-2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the rate-limiting step in the kynurenine pathway of tryptophan (TRP) metabolism. As an inflammation-induced immunoregulatory enzyme, pharmacological inhibition of IDO1 activity is currently being pursued as a potential therapeutic tool for the treatment of cancer and other disease states. As such, a detailed understanding of the mechanism of action of established and novel IDO1 inhibitors remains of great interest. Comparison of a newly-developed IDO1 inhibitor (GSK5628) to the existing best-in-class compound, epacadostat (Incyte), allows us to report on a unique inhibition mechanism for IDO1. Here, we demonstrate that GSK5628 inhibits IDO1 by competing with heme for binding to a heme-free conformation of the enzyme (apo-IDO1) while epacadostat coordinates its binding with the iron atom of the IDO1 heme cofactor. Comparison of these two compounds in cellular systems reveals a long-lasting inhibitory effect of GSK5628, undescribed for other known IDO1 inhibitors. Detailed characterization of this apo-binding mechanism for IDO1 inhibition may help design superior inhibitors or may confer a unique competitive advantage over other IDO1 inhibitors vis-a-vis specificity and pharmacokinetic parameters.

biochemistry↗

bioRxiv2017

Aberrant splicing in B-cell acute lymphoblastic leukemia

Aberrant splicing is a hallmark of leukemias with mutations in splicing factor (SF)-encoding genes. Here we investigated its prevalence in pediatric B-cell acute lymphoblastic leukemias (B-ALL), where SFs are not mutated. By comparing them to normal pro-B cells, we found thousands of aberrant local splice variations (LSVs) per sample, with 279 LSVs in 241 genes present in every comparison. These genes were enriched in RNA processing pathways and encoded ~100 SFs, e.g. hnRNPA1. hnRNPA1 3UTR was pervasively misspliced, yielding the transcript subject to nonsense-mediated decay. Thus, we knocked it down in B-lymphoblastoid cells, identified 213 hnRNPA1-dependent splicing events, and defined the hnRNPA1 splicing signature in pediatric leukemias. One of its elements was DICER1, a known tumor suppressor gene; its LSVs were consistent with reduced translation of DICER1 mRNA. Additionally, we searched for LSVs in other leukemia and lymphoma drivers and discovered 81 LSVs in 41 genes. 77 LSVs were confirmed using two large independent B-ALL RNA-seq datasets. In fact, the twenty most common B-ALL drivers showed higher prevalence of aberrant splicing than of somatic mutations. Thus, post-transcriptional deregulation of SF can drive widespread changes in B-ALL splicing and likely contribute to disease pathogenesis.

cancer biology↗

bioRxiv2017

GSA-Genie: a web application for gene set analysis

Gene set analysis is often used to interpret results from upstream analysis through predefined gene sets that are linked to biological features such as cell cycle or tumorgenesis. Gene sets have been defined in the literature via various criteria and are archived by numerous databases. We compiled over 2.3 million gene sets from 17 sources, and made them accessible through a web application, GSA-Genie. Selected gene sets can be analyzed online using one of 16 statistical methods. These methods can be grouped into two strategies: test of gene set over-representation within a gene list, or comparison of a gene-level statistics between gene set and background. GSA-Genie operates on a Shiny web server, hosted in a cloud instance within Amazon Web Services. GSA-Genie offers a broad selection of gene sets and statistical methods comparing to existing tools. GSA-Genie is freely available at http://gsagenie.awsomics.org.

bioinformatics↗

bioRxiv2017

RNA-Seq 2G: Online Analysis Of Differential Gene Expression With Comprehensive Options Of Statistical Methods

RNA-seq has become the most prevalent technology for measuring genome-wide gene expression, but the best practices for processing and analysing RNA-seq data are still an open question. Many statistical methods have been developed to identify genes differentially expressed between sample groups from RNA-seq data. These methods differ by their data distribution assumptions, choice of statistical test, and computational resource requirements. Over 25 methods of differential expression detection were validated and made available through a user-friendly web portal, RNA-seq 2G. All methods are suitable for analysing differential gene expression between two groups of samples. They commonly use a read count matrix derived from RNA-seq data as input and statistically compare groups for each gene. The web portal uses a Shiny app front-end and is hosted by a cloud-based server provided by Amazon Web Service. The comparison of methods showed that the data distribution assumption is the major determinant of differences between methods. Most methods are more likely to find that longer genes are differentially expressed, which substantially impacts downstream gene set-level analysis. Combining results from multiple methods can potentially diminish this bias. RNA-seq 2G makes the analysis of RNA-seq data more accessible and efficient, and is freely available at http://rnaseq2g.awsomics.org.

bioinformatics↗