AXL Confers Cell Migration and Invasion by Hijacking a PEAK1-Regulated Focal Adhesion Protein Network
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SRC Antibody - N-Terminal Region (ARP32476 P050) Data Sheet
SRC antibody - N-terminal region (ARP32476_P050) Data Sheet Product Number ARP32476_P050 Product Name SRC antibody - N-terminal region (ARP32476_P050) Size 50ug Gene Symbol SRC Alias Symbols ASV; SRC1; c-SRC; p60-Src Nucleotide Accession# NM_005417 Protein Size (# AA) 536 amino acids Molecular Weight 60kDa Product Format Lyophilized powder NCBI Gene Id 6714 Host Rabbit Clonality Polyclonal Official Gene Full Name V-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) Gene Family SH2D This is a rabbit polyclonal antibody against SRC. It was validated on Western Blot by Aviva Systems Biology. At Aviva Systems Biology we manufacture rabbit polyclonal antibodies on a large scale (200-1000 Description products/month) of high throughput manner. Our antibodies are peptide based and protein family oriented. We usually provide antibodies covering each member of a whole protein family of your interest. We also use our best efforts to provide you antibodies recognize various epitopes of a target protein. For availability of antibody needed for your experiment, please inquire (). Peptide Sequence Synthetic peptide located within the following region: QTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGG This gene is highly similar to the v-src gene of Rous sarcoma virus. This proto-oncogene may play a role in the Description of Target regulation of embryonic development and cell growth. SRC protein is a tyrosine-protein kinase whose activity can be inhibited by phosphorylation by c-SRC kinase. Mutations in this gene could be involved in the -
PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Bioinformatics Identi Cation of Prognostic Factors Associated With
Bioinformatics Identication of Prognostic Factors Associated with Breast Cancer Ying Wei Sichuan University https://orcid.org/0000-0001-8178-4705 Shipeng Zhang College of Pharmacy, North Sichuan Medical College Li Xiao West China School of Basic Medical Sciences and Forensic Medicine Jing Zou West China School of Basic Medical Sciences and Forensic Medicine Yingqing Fu West China School of Basic Medical Sciences and Forensic Medicine Yi Ye West China School of Basic Medical Sciences and Forensic Medicine Linchuan Liao ( [email protected] ) West China School of Basic Medical Sciences and Forensic Medicine https://orcid.org/0000-0003-3700-8471 Research Keywords: Breast cancer, Differentially expressed genes, miRNAs, Transcription factors, Bioinformatic analysis Posted Date: December 2nd, 2020 DOI: https://doi.org/10.21203/rs.3.rs-117477/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/23 Abstract Background: Breast cancer (BRCA) remains one of the most common forms of cancer and is the most prominent driver of cancer-related death among women. The mechanistic basis for BRCA, however, remains incompletely understood. In particular, the relationships between driver mutations and signaling pathways in BRCA are poorly characterized, making it dicult to identify reliable clinical biomarkers that can be employed in diagnostic, therapeutic, or prognostic contexts. Methods: First, we downloaded publically available BRCA datasets (GSE45827, GSE42568, and GSE61304) from the Gene Expression Omnibus (GEO) database. We then compared gene expression proles between tumor and control tissues in these datasets using Venn diagrams and the GEO2R analytical tool. We further explore the functional relevance of BRCA-associated differentially expressed genes (DEGs) via functional and pathway enrichment analyses using the DAVID tool, and we then constructed a protein-protein interaction network incorporating DEGs of interest using the Search Tool for the Retrieval of Interacting Genes (STRING) database. -
Interplay Between Coding and Exonic Splicing Regulatory Sequences
Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Research Interplay between coding and exonic splicing regulatory sequences Nicolas Fontrodona,1,4 Fabien Aubé,1,4 Jean-Baptiste Claude,1 Hélène Polvèche,1,5 Sébastien Lemaire,1 Léon-Charles Tranchevent,2 Laurent Modolo,3 Franck Mortreux,1 Cyril F. Bourgeois,1 and Didier Auboeuf1 1Université Lyon, ENS de Lyon, Université Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, F-69007, Lyon, France; 2Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health (LIH), L-1445 Strassen, Luxembourg; 3LBMC Biocomputing Center, CNRS UMR 5239, INSERM U1210, F-69007, Lyon, France The inclusion of exons during the splicing process depends on the binding of splicing factors to short low-complexity reg- ulatory sequences. The relationship between exonic splicing regulatory sequences and coding sequences is still poorly un- derstood. We demonstrate that exons that are coregulated by any given splicing factor share a similar nucleotide composition bias and preferentially code for amino acids with similar physicochemical properties because of the nonran- domness of the genetic code. Indeed, amino acids sharing similar physicochemical properties correspond to codons that have the same nucleotide composition bias. In particular, we uncover that the TRA2A and TRA2B splicing factors that bind to adenine-rich motifs promote the inclusion of adenine-rich exons coding preferentially for hydrophilic amino acids that correspond to adenine-rich codons. SRSF2 that binds guanine/cytosine-rich motifs promotes the inclusion of GC-rich exons coding preferentially for small amino acids, whereas SRSF3 that binds cytosine-rich motifs promotes the inclusion of exons coding preferentially for uncharged amino acids, like serine and threonine that can be phosphorylated. -
Mechanical Forces Induce an Asthma Gene Signature in Healthy Airway Epithelial Cells Ayşe Kılıç1,10, Asher Ameli1,2,10, Jin-Ah Park3,10, Alvin T
www.nature.com/scientificreports OPEN Mechanical forces induce an asthma gene signature in healthy airway epithelial cells Ayşe Kılıç1,10, Asher Ameli1,2,10, Jin-Ah Park3,10, Alvin T. Kho4, Kelan Tantisira1, Marc Santolini 1,5, Feixiong Cheng6,7,8, Jennifer A. Mitchel3, Maureen McGill3, Michael J. O’Sullivan3, Margherita De Marzio1,3, Amitabh Sharma1, Scott H. Randell9, Jefrey M. Drazen3, Jefrey J. Fredberg3 & Scott T. Weiss1,3* Bronchospasm compresses the bronchial epithelium, and this compressive stress has been implicated in asthma pathogenesis. However, the molecular mechanisms by which this compressive stress alters pathways relevant to disease are not well understood. Using air-liquid interface cultures of primary human bronchial epithelial cells derived from non-asthmatic donors and asthmatic donors, we applied a compressive stress and then used a network approach to map resulting changes in the molecular interactome. In cells from non-asthmatic donors, compression by itself was sufcient to induce infammatory, late repair, and fbrotic pathways. Remarkably, this molecular profle of non-asthmatic cells after compression recapitulated the profle of asthmatic cells before compression. Together, these results show that even in the absence of any infammatory stimulus, mechanical compression alone is sufcient to induce an asthma-like molecular signature. Bronchial epithelial cells (BECs) form a physical barrier that protects pulmonary airways from inhaled irritants and invading pathogens1,2. Moreover, environmental stimuli such as allergens, pollutants and viruses can induce constriction of the airways3 and thereby expose the bronchial epithelium to compressive mechanical stress. In BECs, this compressive stress induces structural, biophysical, as well as molecular changes4,5, that interact with nearby mesenchyme6 to cause epithelial layer unjamming1, shedding of soluble factors, production of matrix proteins, and activation matrix modifying enzymes, which then act to coordinate infammatory and remodeling processes4,7–10. -
Regulation of Cdc42 and Its Effectors in Epithelial Morphogenesis Franck Pichaud1,2,*, Rhian F
© 2019. Published by The Company of Biologists Ltd | Journal of Cell Science (2019) 132, jcs217869. doi:10.1242/jcs.217869 REVIEW SUBJECT COLLECTION: ADHESION Regulation of Cdc42 and its effectors in epithelial morphogenesis Franck Pichaud1,2,*, Rhian F. Walther1 and Francisca Nunes de Almeida1 ABSTRACT An overview of Cdc42 Cdc42 – a member of the small Rho GTPase family – regulates cell Cdc42 was discovered in yeast and belongs to a large family of small – polarity across organisms from yeast to humans. It is an essential (20 30 kDa) GTP-binding proteins (Adams et al., 1990; Johnson regulator of polarized morphogenesis in epithelial cells, through and Pringle, 1990). It is part of the Ras-homologous Rho subfamily coordination of apical membrane morphogenesis, lumen formation and of GTPases, of which there are 20 members in humans, including junction maturation. In parallel, work in yeast and Caenorhabditis elegans the RhoA and Rac GTPases, (Hall, 2012). Rho, Rac and Cdc42 has provided important clues as to how this molecular switch can homologues are found in all eukaryotes, except for plants, which do generate and regulate polarity through localized activation or inhibition, not have a clear homologue for Cdc42. Together, the function of and cytoskeleton regulation. Recent studies have revealed how Rho GTPases influences most, if not all, cellular processes. important and complex these regulations can be during epithelial In the early 1990s, seminal work from Alan Hall and his morphogenesis. This complexity is mirrored by the fact that Cdc42 can collaborators identified Rho, Rac and Cdc42 as main regulators of exert its function through many effector proteins. -
Transcriptome Analyses of Rhesus Monkey Pre-Implantation Embryos Reveal A
Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Transcriptome analyses of rhesus monkey pre-implantation embryos reveal a reduced capacity for DNA double strand break (DSB) repair in primate oocytes and early embryos Xinyi Wang 1,3,4,5*, Denghui Liu 2,4*, Dajian He 1,3,4,5, Shengbao Suo 2,4, Xian Xia 2,4, Xiechao He1,3,6, Jing-Dong J. Han2#, Ping Zheng1,3,6# Running title: reduced DNA DSB repair in monkey early embryos Affiliations: 1 State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China 2 Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China 3 Yunnan Key Laboratory of Animal Reproduction, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China 4 University of Chinese Academy of Sciences, Beijing, China 5 Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China 6 Primate Research Center, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China * Xinyi Wang and Denghui Liu contributed equally to this work 1 Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press # Correspondence: Jing-Dong J. Han, Email: [email protected]; Ping Zheng, Email: [email protected] Key words: rhesus monkey, pre-implantation embryo, DNA damage 2 Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press ABSTRACT Pre-implantation embryogenesis encompasses several critical events including genome reprogramming, zygotic genome activation (ZGA) and cell fate commitment. -
Functional Screening of Alzheimer Risk Loci Identifies PTK2B
OPEN Molecular Psychiatry (2017) 22, 874–883 www.nature.com/mp ORIGINAL ARTICLE Functional screening of Alzheimer risk loci identifies PTK2B as an in vivo modulator and early marker of Tau pathology P Dourlen1,2,3, FJ Fernandez-Gomez4,5,6, C Dupont1,2,3, B Grenier-Boley1,2,3, C Bellenguez1,2,3, H Obriot4,5,6, R Caillierez4,5,6, Y Sottejeau1,2,3, J Chapuis1,2,3, A Bretteville1,2,3, F Abdelfettah1,2,3, C Delay1,2,3, N Malmanche1,2,3, H Soininen7, M Hiltunen7,8, M-C Galas4,5,6, P Amouyel1,2,3,9, N Sergeant4,5,6, L Buée4,5,6, J-C Lambert1,2,3,11 and B Dermaut1,2,3,10,11 A recent genome-wide association meta-analysis for Alzheimer’s disease (AD) identified 19 risk loci (in addition to APOE) in which the functional genes are unknown. Using Drosophila, we screened 296 constructs targeting orthologs of 54 candidate risk genes within these loci for their ability to modify Tau neurotoxicity by quantifying the size of 46000 eyes. Besides Drosophila Amph (ortholog of BIN1), which we previously implicated in Tau pathology, we identified p130CAS (CASS4), Eph (EPHA1), Fak (PTK2B) and Rab3-GEF (MADD) as Tau toxicity modulators. Of these, the focal adhesion kinase Fak behaved as a strong Tau toxicity suppressor in both the eye and an independent focal adhesion-related wing blister assay. Accordingly, the human Tau and PTK2B proteins biochemically interacted in vitro and PTK2B co-localized with hyperphosphorylated and oligomeric Tau in progressive pathological stages in the brains of AD patients and transgenic Tau mice. -
Integrative Analyses Identify Potential Key Genes and Pathways in Keshan
medRxiv preprint doi: https://doi.org/10.1101/2021.03.12.21253491; this version posted March 15, 2021. 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. Integrative analyses identify potential key genes and pathways in Keshan disease using whole-exome sequencing Jichang Huang1#, Chenqing Zheng2#, Rong Luo1#, Mingjiang Liu3, Qingquan Gu4, Jinshu Li5, Xiushan Wu6, Zhenglin Yang3, Xia Shen2*, Xiaoping Li3* 1 Institute of Geriatric Cardiovascular Disease, Chengdu Medical College, Chengdu, People’s Republic of China 2 State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China 3 Department of Cardiology, Hospital of the University of Electronic Science and Technology of China and Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China 4 Shenzhen RealOmics (Biotech) Co., Ltd., Shenzhen, China 5 Institute of Endemic Disease, Center for Disease Control and Prevention of Sichuan Province, Chengdu, Sichuan, China 6 The Center of Heart Development, College of Life Sciences, Hunan Norma University, Changsha, China #, These authors contributed equally to this work. *, Authors for correspondence. 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/10.1101/2021.03.12.21253491; this version posted March 15, 2021. 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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Análise Integrativa De Perfis Transcricionais De Pacientes Com
UNIVERSIDADE DE SÃO PAULO FACULDADE DE MEDICINA DE RIBEIRÃO PRETO PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Ribeirão Preto – 2012 ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Tese apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo para obtenção do título de Doutor em Ciências. Área de Concentração: Genética Orientador: Prof. Dr. Eduardo Antonio Donadi Co-orientador: Prof. Dr. Geraldo A. S. Passos Ribeirão Preto – 2012 AUTORIZO A REPRODUÇÃO E DIVULGAÇÃO TOTAL OU PARCIAL DESTE TRABALHO, POR QUALQUER MEIO CONVENCIONAL OU ELETRÔNICO, PARA FINS DE ESTUDO E PESQUISA, DESDE QUE CITADA A FONTE. FICHA CATALOGRÁFICA Evangelista, Adriane Feijó Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. Ribeirão Preto, 2012 192p. Tese de Doutorado apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo. Área de Concentração: Genética. Orientador: Donadi, Eduardo Antonio Co-orientador: Passos, Geraldo A. 1. Expressão gênica – microarrays 2. Análise bioinformática por module maps 3. Diabetes mellitus tipo 1 4. Diabetes mellitus tipo 2 5. Diabetes mellitus gestacional FOLHA DE APROVAÇÃO ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. -
NICU Gene List Generator.Xlsx
Neonatal Crisis Sequencing Panel Gene List Genes: A2ML1 - B3GLCT A2ML1 ADAMTS9 ALG1 ARHGEF15 AAAS ADAMTSL2 ALG11 ARHGEF9 AARS1 ADAR ALG12 ARID1A AARS2 ADARB1 ALG13 ARID1B ABAT ADCY6 ALG14 ARID2 ABCA12 ADD3 ALG2 ARL13B ABCA3 ADGRG1 ALG3 ARL6 ABCA4 ADGRV1 ALG6 ARMC9 ABCB11 ADK ALG8 ARPC1B ABCB4 ADNP ALG9 ARSA ABCC6 ADPRS ALK ARSL ABCC8 ADSL ALMS1 ARX ABCC9 AEBP1 ALOX12B ASAH1 ABCD1 AFF3 ALOXE3 ASCC1 ABCD3 AFF4 ALPK3 ASH1L ABCD4 AFG3L2 ALPL ASL ABHD5 AGA ALS2 ASNS ACAD8 AGK ALX3 ASPA ACAD9 AGL ALX4 ASPM ACADM AGPS AMELX ASS1 ACADS AGRN AMER1 ASXL1 ACADSB AGT AMH ASXL3 ACADVL AGTPBP1 AMHR2 ATAD1 ACAN AGTR1 AMN ATL1 ACAT1 AGXT AMPD2 ATM ACE AHCY AMT ATP1A1 ACO2 AHDC1 ANK1 ATP1A2 ACOX1 AHI1 ANK2 ATP1A3 ACP5 AIFM1 ANKH ATP2A1 ACSF3 AIMP1 ANKLE2 ATP5F1A ACTA1 AIMP2 ANKRD11 ATP5F1D ACTA2 AIRE ANKRD26 ATP5F1E ACTB AKAP9 ANTXR2 ATP6V0A2 ACTC1 AKR1D1 AP1S2 ATP6V1B1 ACTG1 AKT2 AP2S1 ATP7A ACTG2 AKT3 AP3B1 ATP8A2 ACTL6B ALAS2 AP3B2 ATP8B1 ACTN1 ALB AP4B1 ATPAF2 ACTN2 ALDH18A1 AP4M1 ATR ACTN4 ALDH1A3 AP4S1 ATRX ACVR1 ALDH3A2 APC AUH ACVRL1 ALDH4A1 APTX AVPR2 ACY1 ALDH5A1 AR B3GALNT2 ADA ALDH6A1 ARFGEF2 B3GALT6 ADAMTS13 ALDH7A1 ARG1 B3GAT3 ADAMTS2 ALDOB ARHGAP31 B3GLCT Updated: 03/15/2021; v.3.6 1 Neonatal Crisis Sequencing Panel Gene List Genes: B4GALT1 - COL11A2 B4GALT1 C1QBP CD3G CHKB B4GALT7 C3 CD40LG CHMP1A B4GAT1 CA2 CD59 CHRNA1 B9D1 CA5A CD70 CHRNB1 B9D2 CACNA1A CD96 CHRND BAAT CACNA1C CDAN1 CHRNE BBIP1 CACNA1D CDC42 CHRNG BBS1 CACNA1E CDH1 CHST14 BBS10 CACNA1F CDH2 CHST3 BBS12 CACNA1G CDK10 CHUK BBS2 CACNA2D2 CDK13 CILK1 BBS4 CACNB2 CDK5RAP2