Speculation on optimal numbers of examined lymph node for early-stage epithelial ovarian cancer from the perspective of stage migration

IntroductionIn early-stage epithelial ovarian cancer (EOC), how to perform lymphadenectomy to avoid stage migration and achieve reliable targeted excision has not been explored in depth. This study comprehensively considered the stage migration and survival to determine appropriate numbers of examined lymph node (ELN) for early-stage EOC and high-grade serous ovarian cancer (HGSOC).MethodsFrom the Surveillance, Epidemiology, and End Results database, we obtained 10372 EOC cases with stage T1M0 and ELN ≥ 2, including 2849 HGSOC cases. Generalized linear models with multivariable adjustment were used to analyze associations between ELN numbers and lymph node stage migration, survival and positive lymph node (PLN). LOESS regression characterized dynamic trends of above associations followed by Chow test to determine structural breakpoints of ELN numbers. Survival curves were plotted using Kaplan-Meier method.ResultsMore ELNs were associated with more node-positive diseases, more PLNs and better prognosis. ELN structural breakpoints were different in subgroups of early-stage EOC, which for node stage migration or PLN were more than those for improving outcomes. The meaning of ELN structural breakpoint varied with its location and the morphology of LOESS curve. To avoid stage migration, the optimal ELN for early-stage EOC was 29 and the minimal ELN for HGSOC was 24. For better survival, appropriate ELN number were 13 and 8 respectively. More ELNs explained better prognosis only at a certain range.DiscussionNeither too many nor too few numbers of ELN were ideal for early-stage EOC and HGSOC. Excision with appropriate numbers of lymph node draining the affected ovary may be more reasonable than traditional sentinel lymph node resection and systematic lymphadenectomy

De novo mutations in histone modifying genes in congenital heart disease

Congenital heart disease (CHD) is the most frequent birth defect, affecting 0.8% of live births1. Many cases occur sporadically and impair reproductive fitness, suggesting a role for de novo mutations. By analysis of exome sequencing of parent-offspring trios, we compared the incidence of de novo mutations in 362 severe CHD cases and 264 controls. CHD cases showed a significant excess of protein-altering de novo mutations in genes expressed in the developing heart, with an odds ratio of 7.5 for damaging mutations. Similar odds ratios were seen across major classes of severe CHD. We found a marked excess of de novo mutations in genes involved in production, removal or reading of H3K4 methylation (H3K4me), or ubiquitination of H2BK120, which is required for H3K4 methylation2–4. There were also two de novo mutations in SMAD2; SMAD2 signaling in the embryonic left-right organizer induces demethylation of H3K27me5. H3K4me and H3K27me mark `poised' promoters and enhancers that regulate expression of key developmental genes6. These findings implicate de novo point mutations in several hundred genes that collectively contribute to ~10% of severe CHD

Histone deacetylase 9 promotes endothelial to mesenchymal transition and an unfavorable atherosclerotic plaque phenotype

Endothelial-mesenchymal transition (EndMT) is associated with various cardiovascular diseases and in particular with atherosclerosis and plaque instability. However, the molecular pathways that govern EndMT are poorly defined. Specifically, the role of epigenetic factors and histone deacetylases (HDACs) in controlling EndMT and the atherosclerotic plaque phenotype remains unclear. Here, we identified histone deacetylation, specifically that mediated by HDAC9 (a class IIa HDAC), as playing an important role in both EndMT and atherosclerosis. Using in vitro models, we found class IIa HDAC inhibition sustained the expression of endothelial proteins and mitigated the increase in mesenchymal proteins, effectively blocking EndMT. Similarly, ex vivo genetic knockout of Hdac9 in endothelial cells prevented EndMT and preserved a more endothelial-like phenotype. In vivo, atherosclerosis-prone mice with endothelial-specific Hdac9 knockout showed reduced EndMT and significantly reduced plaque area. Furthermore, these mice displayed a more favorable plaque phenotype, with reduced plaque lipid content and increased fibrous cap thickness. Together, these findings indicate that HDAC9 contributes to vascular pathology by promoting EndMT. Our study provides evidence for a pathological link among EndMT, HDAC9, and atherosclerosis and suggests that targeting of HDAC9 may be beneficial for plaque stabilization or slowing the progression of atherosclerotic disease

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

Embedded Self-Distillation in Compact Multi-Branch Ensemble Network for Remote Sensing Scene Classification

Remote sensing (RS) image scene classification task faces many challenges due to the interference from different characteristics of different geographical elements. To solve this problem, we propose a multi-branch ensemble network to enhance the feature representation ability by fusing features in final output logits and intermediate feature maps. However, simply adding branches will increase the complexity of models and decline the inference efficiency. On this issue, we embed self-distillation (SD) method to transfer knowledge from ensemble network to main-branch in it. Through optimizing with SD, main-branch will have close performance as ensemble network. During inference, we can cut other branches to simplify the whole model. In this paper, we first design compact multi-branch ensemble network, which can be trained in an end-to-end manner. Then, we insert SD method on output logits and feature maps. Compared to previous methods, our proposed architecture (ESD-MBENet) performs strongly on classification accuracy with compact design. Extensive experiments are applied on three benchmark RS datasets AID, NWPU-RESISC45 and UC-Merced with three classic baseline models, VGG16, ResNet50 and DenseNet121. Results prove that our proposed ESD-MBENet can achieve better accuracy than previous state-of-the-art (SOTA) complex models. Moreover, abundant visualization analysis make our method more convincing and interpretable.Comment: 14 pages,9 figure

Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation

Obesity is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes (T2D). Excessive accumulation of fat in the abdomen further increases T2D risk. Abdominal obesity is measured by calculating the ratio of waist-to-hip circumference adjusted for the body-mass index (WHRadjBMI), a trait with a significant genetic inheritance. Genetic loci associated with WHRadjBMI identified in genome-wide association studies are predicted to act through adipose tissues, but many of the exact molecular mechanisms underlying fat distribution and its consequences for T2D risk are poorly understood. Further, mechanisms that uncouple the genetic inheritance of abdominal obesity from T2D risk have not yet been described. Here we utilize multi-omic data to predict mechanisms of action at loci associated with discordant effects on abdominal obesity and T2D risk. We find six genetic signals in five loci associated with protection from T2D but also with increased abdominal obesity. We predict the tissues of action at these discordant loci and the likely effector Genes (eGenes) at three discordant loci, from which we predict significant involvement of adipose biology. We then evaluate the relationship between adipose gene expression of eGenes with adipogenesis, obesity, and diabetic physiological phenotypes. By integrating these analyses with prior literature, we propose models that resolve the discordant associations at two of the five loci. While experimental validation is required to validate predictions, these hypotheses provide potential mechanisms underlying T2D risk stratification within abdominal obesity

A Matrix Metalloproteinase-1 Polymorphism, MMP1–1607 (1G>2G), Is Associated with Increased Cancer Risk: A Meta-Analysis Including 21,327 Patients

Although the matrix metalloproteinase-1 (MMP1) polymorphism MMP1–1607 (1G>2G) has been associated with susceptibility to various cancers, these findings are controversial. Therefore, we conducted this meta-analysis to explore the association between MMP1–1607 (1G>2G) and cancer risk. A systematic search of literature through PubMed, Embase, ISI Web of Knowledge, and Google Scholar yielded 77 articles with 21,327 cancer patients and 23,245 controls. The association between the MMP1–1607 (1G>2G) polymorphism and cancer risks was detected in an allele model (2G vs. 1G, overall risk [OR]: 1.174, 95% confidence interval [CI]: 1.107–1.244), a dominant model (2G2G/1G2G vs. 1G1G OR, OR: 1.192, 95% CI: 1.090–1.303), and a recessive model (2G2G vs. 1G2G/1G1G, OR: 1.231, 95% CI: 1.141–1.329). In subgroup analysis, these associations were detected in both Asians and Caucasians. After stratification by cancer types, associations were found in lung, colorectal, nervous system, renal, bladder, and nasopharyngeal cancers. This meta-analysis revealed that MMP1–1607 (1G>2G) polymorphism was significantly associated with elevated risk of cancers