Biology subjects

Sheridan, C.

Publications and source records attributed to Sheridan, C..

3 recordsLinked to original sources

bioRxiv2023

Palmitoyl transferase ZDHHC20 promotes pancreatic cancer metastasis

Metastasis is one of the defining features of pancreatic ductal adenocarcinoma (PDAC) that contributes to poor prognosis. In this study, the palmitoyl transferase ZDHHC20 was identified in an in vivo shRNA screen as critical for metastatic outgrowth, with no effect on proliferation and migration in vitro, or primary PDAC growth in mice. This phenotype is abrogated in immunocompromised animals, and in animals with depleted natural killer (NK) cells, indicating that ZDHHC20 affects the interaction of tumour cells and the innate immune system. Using a chemical genetics platform for ZDHHC20-specific substrate profiling, a number of novel substrates of this enzyme were identified. These results describe a role for palmitoylation in enabling distant metastasis that could not have been detected using in vitro screening approaches and identify potential effectors through which ZDHHC20 promotes metastasis of PDAC.

cancer biology↗

bioRxiv2022

Using Machine Learning to identify microRNA biomarkers for predisposition to Juvenile Onset Huntington's Disease

BackgroundHuntingtons disease (HD) is an autosomal dominant disease which is triggered by a large expansion of CAG nucleotides in the HTT gene. While the CAG expansion linearly correlates with the age of disease onset in HD, twin-studies and cohorts of Juvenile Onset HD (JOHD) patients have shown other factors influence the progression of HD. Thus, it would be of interest to identify molecular biomarkers which indicate predisposition to the development of HD, and as microRNAs (miRNAs) circulate in bio-fluids they would be particularly useful biomarkers. We explored a large HD miRNA-mRNA expression dataset (GSE65776) to establish appropriate questions that could be addressed using Machine Learning (ML). We sought sets of features (mRNAs or miRNAs) to predict HD or WT samples from aged or young mouse cortex samples, and we asked if a set of features could predict predisposition to HD or WT genotypes by training models on aged samples and testing the models on young samples. Several models were created using ADAboost, ExtraTrees, GaussianNB and Random Forest, and the best performing models were further analysed using AUC curves and PCA plots. Finally, genes used to train our miRNA-based predisposition model were mined from HD patient bio-fluid samples. ResultsOur testing accuracies were between 66-100% and AUC scores were between 31-100%. We generated several excellent models with testing accuracies >80% and AUC scores >90%. We also identified homologues of mmu-miR-154-5p, mmu-miR-181a-5p, mmu-miR-212-3p, mmu-miR-378b, mmu-miR-382-5p and mmu-miR-770-5p from our miRNA-based predisposition model to be circulating in HD patient blood samples at p.values of <0.05. ConclusionsWe generated several age-based models which could differentiate between HD and WT samples, including an aged mRNA-based model with a 100% AUC score, an aged miRNA-based model with a 92% AUC score and an aged miRNA-based model with a 96% AUC score. We also identified several miRNAs used to train our miRNA-based predisposition model which were detectable in HD patient blood samples, which suggests they could be potential candidates for use as non-invasive biomarkers for HD research.

bioinformatics↗

bioRxiv2020

The SEQC2 Epigenomics Quality Control (EpiQC) Study: Comprehensive Characterization of Epigenetic Methods, Reproducibility, and Quantification

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and a global resource for epigenetics research from the FDAs Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus was on 5-methylcytosine modifications. Each sample was processed in two or more technical replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), one enzymatic deamination method (EMseq), targeted methylation sequencing (Illumina Methyl Capture EPIC), and single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, BWAMeth, and GemBS), we found overall high concordance between assays (R=0.87-R0.93), differences in efficency of read mapping and CpG capture and coverage, and platform performance. The data provided herein can guide continued used of these reference materials in epigenomics assays, as well as provide best practices for epigenomics research and experimental design in future studies.

genomics↗