Transcriptional Network Analysis in Frontal Cortex in Lewy Body Diseases with Focus on Dementia with Lewy Bodies
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Resolving Transcriptional States and Predicting Lineages in the Annelid Capitella Teleta Using 1 Single-Cell Rnaseq 2 3 Abhinav
bioRxiv preprint doi: https://doi.org/10.1101/2020.10.16.342709; this version posted October 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 1 Resolving transcriptional states and predicting lineages in the annelid Capitella teleta using 2 single-cell RNAseq 3 4 Abhinav Sur1 and Néva P. Meyer2* 5 6 1Unit on Cell Specification and Differentiation, National Institute of Child Health and Human 7 Development (NICHD), Bethesda, Maryland, USA, 20814 8 9 2Department of Biology, Clark University, 950 Main Street, Worcester, Massachusetts, USA, 10 01610. 11 12 13 [email protected] 14 [email protected] 15 16 *Corresponding author 17 18 19 20 21 Keywords: neurogenesis, single-cell RNAseq, annelid, cell type, differentiation trajectory, 22 pseudotime, RNA velocity, gene regulatory network. 23 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.16.342709; this version posted October 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 24 Abstract 25 Evolution and diversification of cell types has contributed to animal evolution. However, gene 26 regulatory mechanisms underlying cell fate acquisition during development remains largely 27 uncharacterized in spiralians. Here we use a whole-organism, single-cell transcriptomic approach 28 to map larval cell types in the annelid Capitella teleta at 24- and 48-hours post gastrulation 29 (stages 4 and 5). -
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International Journal of Molecular Sciences Article Phenotypic Subtyping and Re-Analysis of Existing Methylation Data from Autistic Probands in Simplex Families Reveal ASD Subtype-Associated Differentially Methylated Genes and Biological Functions Elizabeth C. Lee y and Valerie W. Hu * Department of Biochemistry and Molecular Medicine, The George Washington University, School of Medicine and Health Sciences, Washington, DC 20037, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-202-994-8431 Current address: W. Harry Feinstone Department of Molecular Microbiology and Immunology, y Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. Received: 25 August 2020; Accepted: 17 September 2020; Published: 19 September 2020 Abstract: Autism spectrum disorder (ASD) describes a group of neurodevelopmental disorders with core deficits in social communication and manifestation of restricted, repetitive, and stereotyped behaviors. Despite the core symptomatology, ASD is extremely heterogeneous with respect to the severity of symptoms and behaviors. This heterogeneity presents an inherent challenge to all large-scale genome-wide omics analyses. In the present study, we address this heterogeneity by stratifying ASD probands from simplex families according to the severity of behavioral scores on the Autism Diagnostic Interview-Revised diagnostic instrument, followed by re-analysis of existing DNA methylation data from individuals in three ASD subphenotypes in comparison to that of their respective unaffected siblings. We demonstrate that subphenotyping of cases enables the identification of over 1.6 times the number of statistically significant differentially methylated regions (DMR) and DMR-associated genes (DAGs) between cases and controls, compared to that identified when all cases are combined. Our analyses also reveal ASD-related neurological functions and comorbidities that are enriched among DAGs in each phenotypic subgroup but not in the combined case group. -
ZNF354C Is a Transcriptional Repressor That Inhibits Endothelial Angiogenic Sprouting James A
www.nature.com/scientificreports OPEN ZNF354C is a transcriptional repressor that inhibits endothelial angiogenic sprouting James A. Oo1,3, Barnabas Irmer1, Stefan Günther2, Timothy Warwick1, Katalin Pálf1, Judit Izquierdo Ponce1, Tom Teichmann1, Beatrice Pfüger‑Müller1,3, Ralf Gilsbach1,3, Ralf P. Brandes1,3 & Matthias S. Leisegang1,3* Zinc fnger proteins (ZNF) are a large group of transcription factors with diverse functions. We recently discovered that endothelial cells harbour a specifc mechanism to limit the action of ZNF354C, whose function in endothelial cells is unknown. Given that ZNF354C has so far only been studied in bone and tumour, its function was determined in endothelial cells. ZNF354C is expressed in vascular cells and localises to the nucleus and cytoplasm. Overexpression of ZNF354C in human endothelial cells results in a marked inhibition of endothelial sprouting. RNA‑sequencing of human microvascular endothelial cells with and without overexpression of ZNF354C revealed that the protein is a potent transcriptional repressor. ZNF354C contains an active KRAB domain which mediates this suppression as shown by mutagenesis analysis. ZNF354C interacts with dsDNA, TRIM28 and histones, as observed by proximity ligation and immunoprecipitation. Moreover, chromatin immunoprecipitation revealed that the ZNF binds to specifc endothelial‑relevant target‑gene promoters. ZNF354C suppresses these genes as shown by CRISPR/Cas knockout and RNAi. Inhibition of endothelial sprouting by ZNF354C is dependent on the amino acids DV and MLE of the KRAB domain. These results demonstrate that ZNF354C is a repressive transcription factor which acts through a KRAB domain to inhibit endothelial angiogenic sprouting. Te vascular system is controlled by numerous signaling pathways and growth factors which all contribute to the regulation of gene expression. -
Trastuzumab Modulates the Protein Cargo of Extracellular Vesicles Released by ERBB2+ Breast Can‐ Cer Cells
Supplementary Material: Trastuzumab Modulates the Protein Cargo of Extracellular Vesicles Released by ERBB2+ Breast Can‐ cer Cells Silvia Marconi, Sara Santamaria, Martina Bartolucci, Sara Stigliani, Cinzia Aiello, Maria Cristina Gagliani, Grazia Bellese, Andrea Petretto, Katia Cortese and Patrizio Castagnola Table S1. Antibodies used in the study. Antibody Catalog number Manufacturer Anti‐ALIX sc‐271975 Santa Cruz1 Anti‐CD9 PA5‐85955 Thermofisher Scientific2 Anti‐CD63 sc‐15363 Santa Cruz Anti‐ErbB2 (9G6) sc‐08 Santa Cruz Anti‐GAPDH 14C10 Cell signaling3 Anti‐HSP90 sc‐13119 Santa Cruz 1 Dallas, TX, USA; 2 Waltham, MA, USA; 3 Danvers, MA, USA. Table S2. Differentially regulated proteins by trastuzumab Tz treatment in extracellular vesicles EVs purified from SKBR‐ 3 cells with a statistically significant p‐value resulted from Studentʹs T‐test. The gene symbols coding for proteins downregulated by Tz (and hence upregulated in IgG treated cells) are highlighted in red while proteins upregulated by Tz are highlighted in blue. Official Gene Symbol 1 Gene product (Protein name) ACVR1B Activin A receptor type 1B ANO1 Anoctamin 1 ARFGEF2 ADP Ribosylation Factor Guanine Nucleotide Exchange Factor 2 BTN2A1 Butyrophilin subfamily 2 member A1 CIAPIN1 Cytokine Induced Apoptosis Inhibitor 1 CIT Citron Rho‐Interacting Serine/Threonine Kinase CPPED1 Calcineurin Like Phosphoesterase Domain Containing 1 DNAH7 Dynein Axonemal Heavy Chain 7 EIF3F Eukaryotic translation initiation factor 3 subunit F ESD Esterase D ESYT2 Extended Synaptotagmin 2 F2RL1 F2R Like Trypsin Receptor 1 RIPOR3 RIPOR Family Member 3 FZD6 Frizzled‐6 GAN Gigaxonin GTPBP2 GTP‐binding protein 2 GUCD1 Guanylyl Cyclase Domain Containing 1 HNRNPM Heterogeneous nuclear ribonucleoprotein M LMAN2 Lectin, Mannose Binding 2 LRRC8A Volume‐regulated anion channel subunit LRRC8A NOTCH4 Notch Receptor 4 NT5C2 5ʹ‐Nucleotidase, Cytosolic II PCID2 PCI domain‐containing 2 PDCD5 Programmed cell death 5 PHLDB3 Pleckstrin homology like domain family B member 3 PLAA Phospholipase A2 activating protein Membranes 2021, 11, 199. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
Multi-Targeted Mechanisms Underlying the Endothelial Protective Effects of the Diabetic-Safe Sweetener Erythritol
Multi-Targeted Mechanisms Underlying the Endothelial Protective Effects of the Diabetic-Safe Sweetener Erythritol Danie¨lle M. P. H. J. Boesten1*., Alvin Berger2.¤, Peter de Cock3, Hua Dong4, Bruce D. Hammock4, Gertjan J. M. den Hartog1, Aalt Bast1 1 Department of Toxicology, Maastricht University, Maastricht, The Netherlands, 2 Global Food Research, Cargill, Wayzata, Minnesota, United States of America, 3 Cargill RandD Center Europe, Vilvoorde, Belgium, 4 Department of Entomology and UCD Comprehensive Cancer Center, University of California Davis, Davis, California, United States of America Abstract Diabetes is characterized by hyperglycemia and development of vascular pathology. Endothelial cell dysfunction is a starting point for pathogenesis of vascular complications in diabetes. We previously showed the polyol erythritol to be a hydroxyl radical scavenger preventing endothelial cell dysfunction onset in diabetic rats. To unravel mechanisms, other than scavenging of radicals, by which erythritol mediates this protective effect, we evaluated effects of erythritol in endothelial cells exposed to normal (7 mM) and high glucose (30 mM) or diabetic stressors (e.g. SIN-1) using targeted and transcriptomic approaches. This study demonstrates that erythritol (i.e. under non-diabetic conditions) has minimal effects on endothelial cells. However, under hyperglycemic conditions erythritol protected endothelial cells against cell death induced by diabetic stressors (i.e. high glucose and peroxynitrite). Also a number of harmful effects caused by high glucose, e.g. increased nitric oxide release, are reversed. Additionally, total transcriptome analysis indicated that biological processes which are differentially regulated due to high glucose are corrected by erythritol. We conclude that erythritol protects endothelial cells during high glucose conditions via effects on multiple targets. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Oas1b-Dependent Immune Transcriptional Profiles of West Nile
MULTIPARENTAL POPULATIONS Oas1b-dependent Immune Transcriptional Profiles of West Nile Virus Infection in the Collaborative Cross Richard Green,*,† Courtney Wilkins,*,† Sunil Thomas,*,† Aimee Sekine,*,† Duncan M. Hendrick,*,† Kathleen Voss,*,† Renee C. Ireton,*,† Michael Mooney,‡,§ Jennifer T. Go,*,† Gabrielle Choonoo,‡,§ Sophia Jeng,** Fernando Pardo-Manuel de Villena,††,‡‡ Martin T. Ferris,†† Shannon McWeeney,‡,§,** and Michael Gale Jr.*,†,1 *Department of Immunology and †Center for Innate Immunity and Immune Disease (CIIID), University of Washington, § Seattle, Washington 98109, ‡OHSU Knight Cancer Institute, Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, and **Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, Oregon 97239, ††Department of Genetics and ‡‡Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27514 ABSTRACT The oligoadenylate-synthetase (Oas) gene locus provides innate immune resistance to virus KEYWORDS infection. In mouse models, variation in the Oas1b gene influences host susceptibility to flavivirus infection. Oas However, the impact of Oas variation on overall innate immune programming and global gene expression flavivirus among tissues and in different genetic backgrounds has not been defined. We examined how Oas1b acts viral infection in spleen and brain tissue to limit West Nile virus (WNV) susceptibility and disease across a range of innate immunity genetic backgrounds. The laboratory founder strains of the mouse Collaborative Cross (CC) (A/J, C57BL/6J, multiparental 129S1/SvImJ, NOD/ShiLtJ, and NZO/HlLtJ) all encode a truncated, defective Oas1b, whereas the three populations wild-derived inbred founder strains (CAST/EiJ, PWK/PhJ, and WSB/EiJ) encode a full-length OAS1B pro- Multi-parent tein. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
Exploring the Relationship Between Gut Microbiota and Major Depressive Disorders
E3S Web of Conferences 271, 03055 (2021) https://doi.org/10.1051/e3sconf/202127103055 ICEPE 2021 Exploring the Relationship between Gut Microbiota and Major Depressive Disorders Catherine Tian1 1Shanghai American School, Shanghai, China Abstract. Major Depressive Disorder (MDD) is a psychiatric disorder accompanied with a high rate of suicide, morbidity and mortality. With the symptom of an increasing or decreasing appetite, there is a possibility that MDD may have certain connections with gut microbiota, the colonies of microbes which reside in the human digestive system. In recent years, more and more studies started to demonstrate the links between MDD and gut microbiota from animal disease models and human metabolism studies. However, this relationship is still largely understudied, but it is very innovative since functional dissection of this relationship would furnish a new train of thought for more effective treatment of MDD. In this study, by using multiple genetic analytic tools including Allen Brain Atlas, genetic function analytical tools, and MicrobiomeAnalyst, I explored the genes that shows both expression in the brain and the digestive system to affirm that there is a connection between gut microbiota and the MDD. My approach finally identified 7 MDD genes likely to be associated with gut microbiota, implicating 3 molecular pathways: (1) Wnt Signaling, (2) citric acid cycle in the aerobic respiration, and (3) extracellular exosome signaling. These findings may shed light on new directions to understand the mechanism of MDD, potentially facilitating the development of probiotics for better psychiatric disorder treatment. 1 Introduction 1.1 Major Depressive Disorder Major Depressive Disorder (MDD) is a mood disorder that will affect the mood, behavior and other physical parts.