Biology subjects

Mason, J. C.

Publications and source records attributed to Mason, J. C..

4 recordsLinked to original sources

bioRxiv2023

Hemin-driven chromatin remodelling by atherosclerotic risk gene SMARCA4 switches human blood-derived macrophages from leukocyte disposal to erythrocyte disposal

BackgroundPutative genetic risk loci for atherosclerotic vascular disease include SMARCA4, a chromatin remodeling gene important for gene activation. Its causal role in atherosclerosis has been uncertain. Intraplaque hemorrhage (IPH) is a late event in atherosclerosis that is linked to plaque destabilisation and increased inflammation. IPH is countered by Mhem macrophages, which are directed by hemin-mediated induction of Heme Oxygenase 1 (HMOX1) via Activating Transcription Factor 1 (ATF1). Atf1 deficiency in vivo impairs hematoma clearance, promoting inflammation and oxidative stress. Like its homologue cyclic-adenosine monophosphate response element binding protein 1 (CREB1), ATF1 is normally cyclic-AMP activated. HypothesisHemin-directed chromatin remodelling by SMARCA4 regulates specificity of ATF1 gene-binding, thereby switching between leukocyte disposal and erythrocyte disposal, contributing to its role in atherosclerosis. ResultsWe here show that SMARCA4 is genetically independent of the adjacent LDLR locus (p<0.05). In human blood-derived macrophages, hemin triggered histone acetylation (H3K9Ac) and SMARCA4 recruitment in advance of p-ATF1 recruitment at the HMOX1 enhancer. si-RNA-mediated SMARCA4-knockdown suppressed p-ATF1 binding to HMOX1 but increased its binding to cyclic-AMP responsive genes FOS and NR4A2, with corresponding changes in mRNA levels. This functionally correlated with SMARCA4-knockdown switching hemin to mimic prostacyclin (PGI2), for induced genes and phagocytic disposal of leukocytes rather than erythrocytes. ConclusionsThese data establish SMARCA4 as an independent atherosclerosis risk gene and reveal a novel mechanism in which it switches between disposal of leukocytes or erythrocytes, with important clinical implications for atherosclerotic inflammation and intraplaque hemorrhage including treatment by histone deacetylase inhibitors.

pathology↗

bioRxiv2020

OpenStats: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput Phenotypic Data

Reproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.

bioinformatics↗

bioRxiv2019

High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation. Specifically, high-throughput phenotyping of 530 knockout mouse lines identified 140 monogenic \"hits\" (>25%), most of which had never hitherto been implicated in immunology. Furthermore, they were conspicuously enriched in genes for which humans show poor tolerance to loss-of-function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with one another and with specific physiologic traits. By limiting the freedom of individual immune parameters, such linkages impose genetically regulated \"immunological structures\", whose integrity was found to be associated with immunocompetence. Hence, our findings provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.

immunology↗

bioRxiv2019

Soft Windowing Application to Improve Analysis of High-throughput Phenotyping Data

MotivationHigh-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximises analytic power while minimising noise from unspecified environmental factors.\n\nResultsHere we introduce \"soft windowing\", a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant p-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft windowed and non-windowed approaches, respectively, from a set of 2,082 mutant mouse lines. Our method is generalisable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.\n\nAvailability and ImplementationThe method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin.

bioinformatics↗