Evolutionary Origin and Methylation Status of Human Intronic Cpg Islands That Are Not Present in Mouse
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Proteomic Profile of Human Spermatozoa in Healthy And
Cao et al. Reproductive Biology and Endocrinology (2018) 16:16 https://doi.org/10.1186/s12958-018-0334-1 REVIEW Open Access Proteomic profile of human spermatozoa in healthy and asthenozoospermic individuals Xiaodan Cao, Yun Cui, Xiaoxia Zhang, Jiangtao Lou, Jun Zhou, Huafeng Bei and Renxiong Wei* Abstract Asthenozoospermia is considered as a common cause of male infertility and characterized by reduced sperm motility. However, the molecular mechanism that impairs sperm motility remains unknown in most cases. In the present review, we briefly reviewed the proteome of spermatozoa and seminal plasma in asthenozoospermia and considered post-translational modifications in spermatozoa of asthenozoospermia. The reduction of sperm motility in asthenozoospermic patients had been attributed to factors, for instance, energy metabolism dysfunction or structural defects in the sperm-tail protein components and the differential proteins potentially involved in sperm motility such as COX6B, ODF, TUBB2B were described. Comparative proteomic analysis open a window to discover the potential pathogenic mechanisms of asthenozoospermia and the biomarkers with clinical significance. Keywords: Proteome, Spermatozoa, Sperm motility, Asthenozoospermia, Infertility Background fertilization failure [4] and it has become clear that iden- Infertility is defined as the lack of ability to achieve a tifying the precise proteins and the pathways involved in clinical pregnancy after one year or more of unprotected sperm motility is needed [5]. and well-timed intercourse with the same partner [1]. It is estimated that around 15% of couples of reproductive age present with infertility, and about half of the infertil- Application of proteomic techniques in male ity is associated with male partner [2, 3]. -
A Curated Gene List for Reporting Results of Newborn Genomic Sequencing
Original Research Article © American College of Medical Genetics and Genomics © American College of Medical Genetics and Genomics ORIGINAL RESEARCH ARTICLE A curated gene list for reporting results of newborn genomic sequencing Ozge Ceyhan-Birsoy, PhD1,2,3, Kalotina Machini, PhD1,2,3, Matthew S. Lebo, PhD1,2,3, Tim W. Yu, MD3,4,5, Pankaj B. Agrawal, MD, MMSC3,4,6, Richard B. Parad, MD, MPH3,7, Ingrid A. Holm, MD, MPH3,4, Amy McGuire, PhD8, Robert C. Green, MD, MPH3,9,10, Alan H. Beggs, PhD3,4, Heidi L. Rehm, PhD1,2,3,10; for the BabySeq Project Purpose: Genomic sequencing (GS) for newborns may enable detec- of newborn GS (nGS), and used our curated list for the first 15 new- tion of conditions for which early knowledge can improve health out- borns sequenced in this project. comes. One of the major challenges hindering its broader application Results: Here, we present our curated list for 1,514 gene–disease is the time it takes to assess the clinical relevance of detected variants associations. Overall, 954 genes met our criteria for return in nGS. and the genes they impact so that disease risk is reported appropri- This reference list eliminated manual assessment for 41% of rare vari- ately. ants identified in 15 newborns. Methods: To facilitate rapid interpretation of GS results in new- Conclusion: Our list provides a resource that can assist in guiding borns, we curated a catalog of genes with putative pediatric relevance the interpretive scope of clinical GS for newborns and potentially for their validity based on the ClinGen clinical validity classification other populations. -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
An All-To-All Approach to the Identification of Sequence-Specific Readers for Epigenetic DNA Modifications on Cytosine
bioRxiv preprint doi: https://doi.org/10.1101/638700; this version posted May 16, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. An All-to-All Approach to the Identification of Sequence-Specific Readers for Epigenetic DNA Modifications on Cytosine Guang Song1,6, Guohua Wang2,6, Ximei Luo2,3,6, Ying Cheng4, Qifeng Song1, Jun Wan3, Cedric Moore1, Hongjun Song5, Peng Jin4, Jiang Qian3,7,*, Heng Zhu1,7,8,* 1Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA 2School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China 3Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA 4Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA 5Department of Neuroscience and Mahoney Institute for Neurosciences, University of Pennsylvania, Philadelphia, PA 19104, USA 6These authors contributed equally 7Senior author 8Lead Contact *Correspondence: [email protected] (H.Z.), [email protected] (J.Q.). 1 bioRxiv preprint doi: https://doi.org/10.1101/638700; this version posted May 16, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. SUMMARY Epigenetic modifications of DNA in mammals play important roles in many biological processes. Identification of readers of these epigenetic marks is a critical step towards understanding the underlying molecular mechanisms. Here, we report the invention and application of an all-to-all approach, dubbed Digital Affinity Profiling via Proximity Ligation (DAPPL), to simultaneously profile human TF-DNA interactions using mixtures of random DNA libraries carrying four different epigenetic modifications (i.e., 5-methylcytosine, 5- hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine). -
In This Table Protein Name, Uniprot Code, Gene Name P-Value
Supplementary Table S1: In this table protein name, uniprot code, gene name p-value and Fold change (FC) for each comparison are shown, for 299 of the 301 significantly regulated proteins found in both comparisons (p-value<0.01, fold change (FC) >+/-0.37) ALS versus control and FTLD-U versus control. Two uncharacterized proteins have been excluded from this list Protein name Uniprot Gene name p value FC FTLD-U p value FC ALS FTLD-U ALS Cytochrome b-c1 complex P14927 UQCRB 1.534E-03 -1.591E+00 6.005E-04 -1.639E+00 subunit 7 NADH dehydrogenase O95182 NDUFA7 4.127E-04 -9.471E-01 3.467E-05 -1.643E+00 [ubiquinone] 1 alpha subcomplex subunit 7 NADH dehydrogenase O43678 NDUFA2 3.230E-04 -9.145E-01 2.113E-04 -1.450E+00 [ubiquinone] 1 alpha subcomplex subunit 2 NADH dehydrogenase O43920 NDUFS5 1.769E-04 -8.829E-01 3.235E-05 -1.007E+00 [ubiquinone] iron-sulfur protein 5 ARF GTPase-activating A0A0C4DGN6 GIT1 1.306E-03 -8.810E-01 1.115E-03 -7.228E-01 protein GIT1 Methylglutaconyl-CoA Q13825 AUH 6.097E-04 -7.666E-01 5.619E-06 -1.178E+00 hydratase, mitochondrial ADP/ATP translocase 1 P12235 SLC25A4 6.068E-03 -6.095E-01 3.595E-04 -1.011E+00 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 Protein kinase C and casein Q9BY11 PACSIN1 3.837E-03 -5.863E-01 3.680E-06 -1.824E+00 kinase substrate in neurons protein 1 Tubulin polymerization- O94811 TPPP 6.466E-03 -5.755E-01 6.943E-06 -1.169E+00 promoting protein MIC C9JRZ6 CHCHD3 2.912E-02 -6.187E-01 2.195E-03 -9.781E-01 Mitochondrial 2- -
Definition of the Landscape of Promoter DNA Hypomethylation in Liver Cancer
Published OnlineFirst July 11, 2011; DOI: 10.1158/0008-5472.CAN-10-3823 Cancer Therapeutics, Targets, and Chemical Biology Research Definition of the Landscape of Promoter DNA Hypomethylation in Liver Cancer Barbara Stefanska1, Jian Huang4, Bishnu Bhattacharyya1, Matthew Suderman1,2, Michael Hallett3, Ze-Guang Han4, and Moshe Szyf1,2 Abstract We use hepatic cellular carcinoma (HCC), one of the most common human cancers, as a model to delineate the landscape of promoter hypomethylation in cancer. Using a combination of methylated DNA immunopre- cipitation and hybridization with comprehensive promoter arrays, we have identified approximately 3,700 promoters that are hypomethylated in tumor samples. The hypomethylated promoters appeared in clusters across the genome suggesting that a high-level organization underlies the epigenomic changes in cancer. In normal liver, most hypomethylated promoters showed an intermediate level of methylation and expression, however, high-CpG dense promoters showed the most profound increase in gene expression. The demethylated genes are mainly involved in cell growth, cell adhesion and communication, signal transduction, mobility, and invasion; functions that are essential for cancer progression and metastasis. The DNA methylation inhibitor, 5- aza-20-deoxycytidine, activated several of the genes that are demethylated and induced in tumors, supporting a causal role for demethylation in activation of these genes. Previous studies suggested that MBD2 was involved in demethylation of specific human breast and prostate cancer genes. Whereas MBD2 depletion in normal liver cells had little or no effect, we found that its depletion in human HCC and adenocarcinoma cells resulted in suppression of cell growth, anchorage-independent growth and invasiveness as well as an increase in promoter methylation and silencing of several of the genes that are hypomethylated in tumors. -
Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo -
Bypass Budget Proposal for Fiscal Year 2020 �
Open Science, � Big Data, and You � Working Together to Treat and Prevent Alzheimer’s Disease and Related Dementias NIH BYPASS BUDGET PROPOSAL FOR FISCAL YEAR 2020 � National Institutes of Health CONTENTS OPEN SCIENCE, BIG DATA, AND YOU: WORKING TOGETHER TO TREAT AND PREVENT ALZHEIMER’S DISEASE AND RELATED DEMENTIAS ....................................................................... 1 INTRODUCTION .............................................................................................................................. 3 A Sense of Urgency ..................................................................................................................... 4 The Nation’s Commitment Becomes Law ................................................................................... 5 Increasing and Targeting the Investment in Research ............................................................... 6 Budgeting in FY 2020 to Fight Dementia .................................................................................... 7 Together, We Make the Difference .......................................................................................... 11 FISCAL YEAR 2020 PROFESSIONAL JUDGMENT BUDGET: ALZHEIMER’S DISEASE AND RELATED DEMENTIAS ................................................................................................................................... 12 THE ROAD TO TREATMENT AND PREVENTION OF ALZHEIMER’S DISEASE AND RELATED DEMENTIAS .................................................................................................................................. -
Germline Chd8 Haploinsufficiency Alters Brain Development in Mouse
Lawrence Berkeley National Laboratory Recent Work Title Germline Chd8 haploinsufficiency alters brain development in mouse. Permalink https://escholarship.org/uc/item/0ck9n78k Journal Nature neuroscience, 20(8) ISSN 1097-6256 Authors Gompers, Andrea L Su-Feher, Linda Ellegood, Jacob et al. Publication Date 2017-08-01 DOI 10.1038/nn.4592 Peer reviewed eScholarship.org Powered by the California Digital Library University of California ART ic LE s Germline Chd8 haploinsufficiency alters brain development in mouse Andrea L Gompers1,2,10, Linda Su-Feher1,2,10 , Jacob Ellegood3,10, Nycole A Copping1,4,10, M Asrafuzzaman Riyadh5,10, Tyler W Stradleigh1,2, Michael C Pride1,4, Melanie D Schaffler1,4, A Ayanna Wade1,2 , Rinaldo Catta-Preta1,2 , Iva Zdilar1,2, Shreya Louis1,2 , Gaurav Kaushik5, Brandon J Mannion6, Ingrid Plajzer-Frick6, Veena Afzal6, Axel Visel6–8 , Len A Pennacchio6,7, Diane E Dickel6, Jason P Lerch3,9, Jacqueline N Crawley1,4, Konstantinos S Zarbalis5, Jill L Silverman1,4 & Alex S Nord1,2 The chromatin remodeling gene CHD8 represents a central node in neurodevelopmental gene networks implicated in autism. We examined the impact of germline heterozygous frameshift Chd8 mutation on neurodevelopment in mice. Chd8+/del5 mice displayed normal social interactions with no repetitive behaviors but exhibited cognitive impairment correlated with increased regional brain volume, validating that phenotypes of Chd8+/del5 mice overlap pathology reported in humans with CHD8 mutations. We applied network analysis to characterize neurodevelopmental gene expression, revealing widespread transcriptional changes in Chd8+/del5 mice across pathways disrupted in neurodevelopmental disorders, including neurogenesis, synaptic processes and neuroimmune signaling. We identified a co-expression module with peak expression in early brain development featuring dysregulation of RNA processing, chromatin remodeling and cell-cycle genes enriched for promoter binding by Chd8, and we validated increased neuronal proliferation and developmental splicing perturbation in Chd8+/del5 mice. -
Variation in Protein Coding Genes Identifies Information Flow
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Epigenetic Changes in Human Model KMT2A Leukemias Highlight Early Events During Leukemogenesis
Epigenetic changes in human model KMT2A leukemias highlight early events during leukemogenesis Thomas Milan1, Magalie Celton1, Karine Lagacé1, Élodie Roques1, Safia Safa-Tahar-Henni1, Eva Bresson2,3,4, Anne Bergeron2,3,4, Josée Hebert5,6, Soheil Meshinchi7, Sonia Cellot8, Frédéric Barabé2,3,4, 1,6 Brian T Wilhelm Author contributions: BTW and TM designed the study, analyzed data and drafted the manuscript. TM and KL performed ChIP-seq and ATAC-seq, analysed the data and generated figures. MC performed Methyl-seq experiments, analysed data, generated figures and drafted the manuscript. SS-T-H assisted with the ATAC-seq analysis and generation of figures. KL, ER, EB, and AB helped with molecular biology work, cloning and cell culture. EB, AB and FB generated the model on mice from CD34+ cells. SM provided expression and clinical data for patient samples and JH and SC collected, characterized, and banked the local patient samples used in this study. Affiliations: 1Laboratory for High Throughput Biology, Institute for Research in Immunology and Cancer, Montréal, QC, Canada 2Centre de recherche en infectiologie du CHUL, Centre de recherche du CHU de Québec – Université Laval, Québec City, QC, Canada, 3CHU de Québec – Université Laval – Hôpital Enfant-Jésus; Québec City, QC, Canada; 4Department of Medicine, Université Laval, Quebec City, QC, Canada 5Division of Hematology-Oncology and Leukemia Cell Bank of Quebec, Maisonneuve-Rosemont Hospital, Montréal, QC, Canada, 6Department of Medicine, Université de Montréal, Montréal, QC, Canada, 7Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA 8Department of pediatrics, division of Hematology, Ste-Justine Hospital, Montréal, QC, Canada Running head: Early epigenetic changes in KM3-AML *Correspondence to: Brian T Wilhelm Institute for Research in Immunology and Cancer Université de Montréal P.O.