Biology subjects

Barry, S. C.

Publications and source records attributed to Barry, S. C..

4 recordsLinked to original sources

bioRxiv2021

Progesterone receptor-A isoform interaction with RUNX transcription factors controls chromatin remodelling at promoters during ovulation

Progesterone receptor (PGR) plays diverse roles in reproductive tissues and thus coordinates mammalian fertility. In the ovary, acutely induced PGR is the key determinant of ovulation through transcriptional control of a unique set of genes that culminates in follicle rupture. However, the molecular mechanisms for PGRs specialised function in ovulation is poorly understood. To address this, we assembled a detailed genomic profile of PGR action through combined ATAC-seq, RNA-seq and ChIP-seq analysis in wildtype and isoform-specific PGR null mice. We demonstrated the unique action of PGR-A isoform in the ovary through a transcriptional complex involving physical interaction with RUNX and JUN/FOS transcription factors. The assembly of this unique complex directs targeting of PGR binding to proximal promoter regions and enables chromatin accessibility, leading to ovulatory gene induction. This PGR signalling mechanism is specific to ovulation and provides potential targets for infertility treatments as well as new contraceptives that block ovulation.

molecular biology↗

bioRxiv2020

Seeing the forest through the trees: Identifying functional interactions from Hi-C

Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data however is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that identify significant functional interactions. We classify three groups of approaches; structurally-associated domain discovery methods e.g. topologically-associated domains and compartments, detection of statistically significant interactions via background models, and the use of epigenomic data integration to identify functional interactions. Careful use of these three approaches is crucial to successfully identifying functional interactions within the genome.

bioinformatics↗

bioRxiv2020

3DFAACTS-SNP: Using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of Type-1 Diabetes (T1D) risk

BackgroundGenome-wide association and fine-mapping studies have enabled the discovery of single nucleotide polymorphisms (SNPs) and other variants that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the SNPs lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. MethodsAutoimmunity results from a failure of immune tolerance, suggesting that regulatory T cells (Treg) are likely a significant point of impact for this genetic risk, as Treg are critical for immune tolerance. Focusing on T1D as a model of defective function of Treg in autoimmunity, we designed a SNPs filtering workflow called 3 Dimensional Functional Annotation of Accessible Cell Type Specific SNPs (3DFAACTS-SNP) that utilises overlapping profiles of Treg-specific epigenomic data (ATAC-seq, Hi-C and FOXP3-ChIP) to identify regulatory elements potentially driving the effect of variants associated with T1D, and the gene(s) that they control. ResultsUsing 3DFAACTS-SNP we identified 36 SNPs with plausible Treg-specific mechanisms of action contributing to T1D from 1,228 T1D fine-mapped variants, identifying 119 novel interacting regions resulting in the identification of 51 candidate target genes. We further demonstrated the utility of the workflow by applying it to three other fine-mapped/meta-analysed SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of any genetic variation using all common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune research. ConclusionsWe demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function and illustrate the power of using cell type specific multi-omics datasets to determine disease mechanisms. The 3DFAACTS-SNP workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

bioinformatics↗

bioRxiv2017

Influence of fecal collection conditions and 16S rRNA gene sequencing protocols at two centers on human gut microbiota analysis

BackgroundTo optimise fecal sampling and analysis yielding reproducible microbiome data, and gain further insight into sources of its variation, we compared different collection conditions and 16S rRNA gene sequencing protocols in two centers. Fecal samples were collected on three sequential days from six healthy adults and placed in commercial collection tubes (OMNIgeneGut OMR-200) at room temperature or in sterile 5 ml screw-top tubes in a home fridge or home freezer for 6-24 h, before transfer at 4{degrees}C to the laboratory and storage at - 80{degrees}C within 24 hours. Replicate samples were shipped on dry ice to centers in Australia and the USA for DNA extraction and sequencing of the V4 region of the 16S rRNA gene, using different PCR protocols. Sequences were analysed with the QIIME pipeline and Greengenes database at the Australian center and with an in-house pipeline and SILVA database at the USA center.\n\nResultsVariation in gut microbiome composition and diversity was dominated by differences between individuals. Minor differences in the abundance of taxa were found between collection-processing methods and day of collection. Larger differences were evident between the two centers, including in the relative abundances of genus Akkermansia, in phylum Verrucomicrobiales, and Bifidobacteria in Actinobacteria.\n\nConclusionsCollection with storage and transport at 4{degrees}C within 24 h is adequate for 16S rRNA analysis of the gut microbiome. However, variation between sequencing centers suggests that cohort samples should be sequenced by the same method in one center. Differences in handling, shipping and methods of PCR gene amplification and sequence analysis in different centers introduce variation in ways that are not fully understood. These findings are particularly relevant as microbiome studies shift towards larger population-based and multicenter studies.

bioinformatics↗