Female gene networks are expressed in myofibroblast-like smooth muscle cells in vulnerable atherosclerotic plaques

Women presenting with coronary artery disease (CAD) more often present with fibrous atherosclerotic plaques, which are currently understudied. Phenotypically modulated smooth muscle cells (SMCs) contribute to atherosclerosis in women. How these phenotypically modulated SMCs shape female versus male plaques is unknown. Here, we show sex-stratified gene regulatory networks (GRNs) from human carotid atherosclerotic tissue. Prioritization of these networks identified two main SMC GRNs in late-stage atherosclerosis. Single-cell RNA-sequencing mapped these GRNs to two SMC phenotypes: a phenotypically modulated myofibroblast-like SMC network and a contractile SMC network. The myofibroblast-like GRN was mostly expressed in plaques that were vulnerable in females. Finally, mice orthologs of the female myofibroblast-like genes showed retained expression in advanced plaques from female mice but were downregulated in male mice during atherosclerosis progression. Female atherosclerosis is driven by GRNs that promote a fibrous vulnerable plaque rich in myofibroblast-like SMCs

Female Gene Networks Are Expressed in Myofibroblast-Like Smooth Muscle Cells in Vulnerable Atherosclerotic Plaques

BACKGROUND: Women presenting with coronary artery disease more often present with fibrous atherosclerotic plaques, which are currently understudied. Phenotypically modulated smooth muscle cells (SMCs) contribute to atherosclerosis in women. How these phenotypically modulated SMCs shape female versus male plaques is unknown. METHODS: Gene regulatory networks were created using RNAseq gene expression data from human carotid atherosclerotic plaques. The networks were prioritized based on sex bias, relevance for smooth muscle biology, and coronary artery disease genetic enrichment. Network expression was linked to histologically determined plaque phenotypes. In addition, their expression in plaque cell types was studied at single-cell resolution using single-cell RNAseq. Finally, their relevance for disease progression was studied in female and male Apoe -/- mice fed a Western diet for 18 and 30 weeks. RESULTS: Here, we identify multiple sex-stratified gene regulatory networks from human carotid atherosclerotic plaques. Prioritization of the female networks identified 2 main SMC gene regulatory networks in late-stage atherosclerosis. Single-cell RNA sequencing mapped these female networks to 2 SMC phenotypes: a phenotypically modulated myofibroblast-like SMC network and a contractile SMC network. The myofibroblast-like network was mostly expressed in plaques that were vulnerable in women. Finally, the mice ortholog of key driver gene MFGE8 (milk fat globule EGF and factor V/VIII domain containing) showed retained expression in advanced plaques from female mice but was downregulated in male mice during atherosclerosis progression. CONCLUSIONS: Female atherosclerosis is characterized by gene regulatory networks that are active in fibrous vulnerable plaques rich in myofibroblast-like SMCs

Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants

The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR-Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD. 2022, The Author(s).T. Kessler is supported by the Corona-Foundation (Junior Research Group Translational Cardiovascular Genomics) and the German Research Foundation (DFG) as part of the Sonderforschungsbereich SFB 1123 (B02). T.J. was supported by a Medical Research Council DTP studentship (MR/S502443/1). J.D. is a British Heart Foundation Professor, European Research Council Senior Investigator, and National Institute for Health and Care Research (NIHR) Senior Investigator. J.C.H. acknowledges personal funding from the British Heart Foundation (FS/14/55/30806) and is a member of the Oxford BHF Centre of Research Excellence (RE/13/1/30181). R.C. has received funding from the British Heart Foundation and British Heart Foundation Centre of Research Excellence. O.G. has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). P.S.d.V. was supported by American Heart Association grant number 18CDA34110116 and National Heart, Lung, and Blood Institute grant R01HL146860. The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I and HHSN268201700005I), R01HL087641, R01HL059367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. We thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by grant UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. The Trøndelag Health Study (The HUNT Study) is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology), Trøndelag County Council, Central Norway Regional Health Authority and the Norwegian Institute of Public Health. The K.G. Jebsen Center for Genetic Epidemiology is financed by Stiftelsen Kristian Gerhard Jebsen; Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology; and Central Norway Regional Health Authority. Whole genome sequencing for the HUNT study was funded by HL109946. The GerMIFs gratefully acknowledge the support of the Bavarian State Ministry of Health and Care, furthermore founded this work within its framework of DigiMed Bayern (grant DMB-1805-0001), the German Federal Ministry of Education and Research (BMBF) within the framework of ERA-NET on Cardiovascular Disease (Druggable-MI-genes, 01KL1802), within the scheme of target validation (BlockCAD, 16GW0198K), within the framework of the e:Med research and funding concept (AbCD-Net, 01ZX1706C), the British Heart Foundation (BHF)/German Centre of Cardiovascular Research (DZHK)-collaboration (VIAgenomics) and the German Research Foundation (DFG) as part of the Sonderforschungsbereich SFB 1123 (B02), the Sonderforschungsbereich SFB TRR 267 (B05), and EXC2167 (PMI). This work was supported by the British Heart Foundation (BHF) under grant RG/14/5/30893 (P.D.) and forms part of the research themes contributing to the translational research portfolios of the Barts Biomedical Research Centre funded by the UK National Institute for Health Research (NIHR). I.S. is supported by a Precision Health Scholars Award from the University of Michigan Medical School. This work was supported by the European Commission (HEALTH-F2–2013-601456) and the TriPartite Immunometabolism Consortium (TrIC)-NovoNordisk Foundation (NNF15CC0018486), VIAgenomics (SP/19/2/344612), the British Heart Foundation, a Wellcome Trust core award (203141/Z/16/Z to M.F. and H.W.) and the NIHR Oxford Biomedical Research Centre. M.F. and H.W. are members of the Oxford BHF Centre of Research Excellence (RE/13/1/30181). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. C.P.N. and T.R.W. received funding from the British Heart Foundation (SP/16/4/32697). C.J.W. is funded by NIH grant R35-HL135824. B.N.W. is supported by the National Science Foundation Graduate Research Program (DGE, 1256260). This research was supported by BHF (SP/13/2/30111) and conducted using the UK Biobank Resource (application 9922). O.M. was funded by the Swedish Heart and Lung Foundation, the Swedish Research Council, the European Research Council ERC-AdG-2019-885003 and Lund University Infrastructure grant ‘Malmö population-based cohorts’ (STYR 2019/2046). T.R.W. is funded by the British Heart Foundation. I.K., S. Koyama, and K. Ito are funded by the Japan Agency for Medical Research and Development, AMED, under grants JP16ek0109070h0003, JP18kk0205008h0003, JP18kk0205001s0703, JP20km0405209 and JP20ek0109487. The Biobank Japan is supported by AMED under grant JP20km0605001. J.L.M.B. acknowledges research support from NIH R01HL125863, American Heart Association (A14SFRN20840000), the Swedish Research Council (2018-02529) and Heart Lung Foundation (20170265) and the Foundation Leducq (PlaqueOmics: New Roles of Smooth Muscle and Other Matrix Producing Cells in Atherosclerotic Plaque Stability and Rupture, 18CVD02. A.V.K. has been funded by grant 1K08HG010155 from the National Human Genome Research Institute. K.G.A. has received support from the American Heart Association Institute for Precision Cardiovascular Medicine (17IFUNP3384001), a KL2/Catalyst Medical Research Investigator Training (CMeRIT) award from the Harvard Catalyst (KL2 TR002542) and the NIH (1K08HL153937). A.S.B. has been supported by funding from the National Health and Medical Research Council (NHMRC) of Australia (APP2002375). D.S.A. has received support from a training grant from the NIH (T32HL007604). N.P.B., M.C.C., J.F. and D.-K.J. have been funded by the National Institute of Diabetes and Digestive and Kidney Diseases (2UM1DK105554). EPIC-CVD was funded by the European Research Council (268834) and the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). The coordinating center was supported by core funding from the UK Medical Research Council (G0800270; MR/L003120/1), British Heart Foundation (SP/09/002, RG/13/13/30194, RG/18/13/33946) and NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute.Scopu

Plio-Pleistocene diversification and biogeographic barriers in southern Australia reflected in the phylogeography of a widespread and common lizard species

Palaeoclimatic events and biogeographical processes since the mid-Tertiary have played an important role in shaping the evolution and distribution of Australian fauna. However, their impacts on fauna in southern and arid zone regions of Australia are not well understood. Here we investigate the phylogeography of an Australian scincid lizard, Tiliqua rugosa, across southern Australia using mitochondrial DNA (mtDNA) and 11 nuclear DNA markers (nuDNA), including nine anonymous nuclear loci. Phylogenetic analyses revealed three major mtDNA lineages within T. rugosa, geographically localised north and south of the Murray River in southern Australia, and west of the Nullarbor Plain. Molecular variance and population analyses of both mtDNA and nuDNA haplotypes revealed significant variation among the three populations, although potential introgression of nuDNA markers was also detected for the Northern and Southern population. Coalescent times for major mtDNA lineages coincide with an aridification phase, which commenced after the early Pliocene and increased in intensity during the Late Pliocene-Pleistocene. Species distribution modelling and a phylogeographic diffusion model suggest that the range of T. rugosa may have contracted during the Last Glacial Maximum and the locations of optimal habitat appear to coincide with the geographic origin of several distinct mtDNA lineages. Overall, our analyses suggest that Plio-Pleistocene climatic changes and biogeographic barriers associated with the Nullarbor Plain and Murray River have played a key role in shaping the present-day distribution of genetic diversity in T. rugosa and many additional ground-dwelling animals distributed across southern Australia.Mina Hojat Ansari, Steven J.B. Cooper, Michael P. Schwarz, Mehregan Ebrahimi, Gaynor Dolman, Leah Reinberger, Kathleen M. Saint, Stephen C. Donnellan, C. Michael Bull, Michael G. Gardne

Mining for Lexons: Applying Unsupervised Learning Methods to Create Ontology Bases

Ontologies in current computer science parlance are computer based resources that represent agreed domain semantics. This paper first introduces ontologies in general and subsequently, in particular, shortly outlines the DOGMA ontology engineering approach that separates "atomic" conceptual relations from "predicative" domain rules. In the main part of the paper, we describe and experimentally evaluate work in progress on a potential method to automatically derive the atomic conceptual relations mentioned above from a corpus of English medical texts. Preliminary outcomes are presented based on the clustering of nouns and compound nouns according to co-occurrence frequencies in the subject-verbobject syntactic context