Understanding Chronic Kidney Disease: Genetic and Epigenetic Approaches
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Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis 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 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
THE ROLE of HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 in CARDIAC CONTRACTILITY and HYPERTROPHIC RESPONSE by © COPYRIGHT by KYOUNG H
THE ROLE OF HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 IN CARDIAC CONTRACTILITY AND HYPERTROPHIC RESPONSE By KYOUNG HAN KIM A THESIS SUBMITTED IN CONFORMITY WITH THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE DEPARTMENT OF PHYSIOLOGY UNIVERSITY OF TORONTO © COPYRIGHT BY KYOUNG HAN KIM (2011) THE ROLE OF HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 IN CARDIAC CONTRACTILITY AND HYPERTROPHIC RESPONSE KYOUNG HAN KIM DOCTOR OF PHILOSOPHY GRADUATE DEPARTMENT OF PHYSIOLOGY UNIVERSITY OF TORONTO 2011 ABSTRACT Irx5 is a homeodomain transcription factor that negatively regulates cardiac fast transient + outward K currents (Ito,f) via the KV4.2 gene and is thereby a major determinant of the transmural repolarization gradient. While Ito,f is invariably reduced in heart disease and changes in Ito,f can modulate both cardiac contractility and hypertrophy, less is known about a functional role of Irx5, and its relationship with Ito,f, in the normal and diseased heart. Here I show that Irx5 plays crucial roles in the regulation of cardiac contractility and proper adaptive hypertrophy. Specifically, Irx5-deficient (Irx5-/-) hearts had reduced cardiac contractility and lacked the normal regional difference in excitation-contraction with decreased action potential duration, Ca2+ transients and myocyte shortening in sub-endocardial, but not sub-epicardial, myocytes. In addition, Irx5-/- mice showed less cardiac hypertrophy, but increased interstitial fibrosis and greater contractility impairment following pressure overload. A defect in hypertrophic responses in Irx5-/- myocardium was confirmed in cultured neonatal mouse ventricular myocytes, exposed to norepinephrine while being restored with Irx5 replacement. Interestingly, studies using mice ii -/- virtually lacking Ito,f (i.e. KV4.2-deficient) showed that reduced contractility in Irx5 mice was completely restored by loss of KV4.2, whereas hypertrophic responses to pressure-overload in hearts remained impaired when both Irx5 and Ito,f were absent. -
Funktionelle in Vitro Und in Vivo Charakterisierung Des Putativen Tumorsuppressorgens SFRP1 Im Humanen Mammakarzinom
Funktionelle in vitro und in vivo Charakterisierung des putativen Tumorsuppressorgens SFRP1 im humanen Mammakarzinom Von der Fakult¨at fur¨ Mathematik, Informatik und Naturwissenschaften der RWTH Aachen University zur Erlangung des akademischen Grades einer Doktorin der Naturwissenschaften genehmigte Dissertation vorgelegt von Diplom-Biologin Laura Huth (geb. Franken) aus Julich¨ Berichter: Universit¨atsprofessor Dr. rer. nat. Edgar Dahl Universit¨atsprofessor Dr. rer. nat. Ralph Panstruga Tag der mundlichen¨ Prufung:¨ 6. August 2014 Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verfugbar.¨ Zusammenfassung Krebserkrankungen stellen weltweit eine der h¨aufigsten Todesursachen dar. Aus diesem Grund ist die Aufkl¨arung der zugrunde liegenden Mechanismen und Ur- sachen ein essentielles Ziel der molekularen Onkologie. Die Tumorforschung der letzten Jahre hat gezeigt, dass die Entstehung solider Karzinome ein Mehrstufen- Prozess ist, bei dem neben Onkogenen auch Tumorsuppresorgene eine entschei- dende Rolle spielen. Viele der heute bekannten Gene des WNT-Signalweges wur- den bereits als Onkogene oder Tumorsuppressorgene charakterisiert. Eine Dere- gulation des WNT-Signalweges wird daher mit der Entstehung und Progression vieler humaner Tumorentit¨aten wie beispielsweise auch dem Mammakarzinom, der weltweit h¨aufigsten Krebserkrankung der Frau, assoziiert. SFRP1, ein nega- tiver Regulator der WNT-Signalkaskade, wird in Brusttumoren haupts¨achlich durch den epigenetischen Mechanismus der Promotorhypermethylierung -
DNA Methylation Regulates Discrimination of Enhancers From
Sharifi-Zarchi et al. BMC Genomics (2017) 18:964 DOI 10.1186/s12864-017-4353-7 RESEARCHARTICLE Open Access DNA methylation regulates discrimination of enhancers from promoters through a H3K4me1-H3K4me3 seesaw mechanism Ali Sharifi-Zarchi1,2,3,4†, Daniela Gerovska5†, Kenjiro Adachi6, Mehdi Totonchi3, Hamid Pezeshk7,8, Ryan J. Taft9, Hans R. Schöler6,10, Hamidreza Chitsaz2, Mehdi Sadeghi8,11, Hossein Baharvand3,12* and Marcos J. Araúzo-Bravo5,13,14* Abstract Background: DNA methylation at promoters is largely correlated with inhibition of gene expression. However, the role of DNA methylation at enhancers is not fully understood, although a crosstalk with chromatin marks is expected. Actually, there exist contradictory reports about positive and negative correlations between DNA methylation and H3K4me1, a chromatin hallmark of enhancers. Results: We investigated the relationship between DNA methylation and active chromatin marks through genome- wide correlations, and found anti-correlation between H3K4me1 and H3K4me3 enrichment at low and intermediate DNA methylation loci. We hypothesized “seesaw” dynamics between H3K4me1 and H3K4me3 in the low and intermediate DNA methylation range, in which DNA methylation discriminates between enhancers and promoters, marked by H3K4me1 and H3K4me3, respectively. Low methylated regions are H3K4me3 enriched, while those with intermediate DNA methylation levels are progressively H3K4me1 enriched. Additionally, the enrichment of H3K27ac, distinguishing active from primed enhancers, follows a plateau in the lower range of the intermediate DNA methylation level, corresponding to active enhancers, and decreases linearly in the higher range of the intermediate DNA methylation. Thus, the decrease of the DNA methylation switches smoothly the state of the enhancers from a primed to an active state. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
CST9L (NM 080610) Human Tagged ORF Clone – RC206646L4
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RC206646L4 CST9L (NM_080610) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: CST9L (NM_080610) Human Tagged ORF Clone Tag: mGFP Symbol: CST9L Synonyms: bA218C14.1; CTES7B Vector: pLenti-C-mGFP-P2A-Puro (PS100093) E. coli Selection: Chloramphenicol (34 ug/mL) Cell Selection: Puromycin ORF Nucleotide The ORF insert of this clone is exactly the same as(RC206646). Sequence: Restriction Sites: SgfI-MluI Cloning Scheme: ACCN: NM_080610 ORF Size: 441 bp This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 CST9L (NM_080610) Human Tagged ORF Clone – RC206646L4 OTI Disclaimer: The molecular sequence of this clone aligns with the gene accession number as a point of reference only. However, individual transcript sequences of the same gene can differ through naturally occurring variations (e.g. polymorphisms), each with its own valid existence. This clone is substantially in agreement with the reference, but a complete review of all prevailing variants is recommended prior to use. More info OTI Annotation: This clone was engineered to express the complete ORF with an expression tag. Expression varies depending on the nature of the gene. RefSeq: NM_080610.1 RefSeq Size: 982 bp RefSeq ORF: 444 bp Locus ID: 128821 UniProt ID: Q9H4G1, A0A140VJH1 Protein Families: Secreted Protein, Transmembrane MW: 17.3 kDa Gene Summary: The cystatin superfamily encompasses proteins that contain multiple cystatin-like sequences. -
Myopia in African Americans Is Significantly Linked to Chromosome 7P15.2-14.2
Genetics Myopia in African Americans Is Significantly Linked to Chromosome 7p15.2-14.2 Claire L. Simpson,1,2,* Anthony M. Musolf,2,* Roberto Y. Cordero,1 Jennifer B. Cordero,1 Laura Portas,2 Federico Murgia,2 Deyana D. Lewis,2 Candace D. Middlebrooks,2 Elise B. Ciner,3 Joan E. Bailey-Wilson,1,† and Dwight Stambolian4,† 1Department of Genetics, Genomics and Informatics and Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States 2Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, United States 3The Pennsylvania College of Optometry at Salus University, Elkins Park, Pennsylvania, United States 4Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, United States Correspondence: Joan E. PURPOSE. The purpose of this study was to perform genetic linkage analysis and associ- Bailey-Wilson, NIH/NHGRI, 333 ation analysis on exome genotyping from highly aggregated African American families Cassell Drive, Suite 1200, Baltimore, with nonpathogenic myopia. African Americans are a particularly understudied popula- MD 21131, USA; tion with respect to myopia. [email protected]. METHODS. One hundred six African American families from the Philadelphia area with a CLS and AMM contributed equally to family history of myopia were genotyped using an Illumina ExomePlus array and merged this work and should be considered co-first authors. with previous microsatellite data. Myopia was initially measured in mean spherical equiv- JEB-W and DS contributed equally alent (MSE) and converted to a binary phenotype where individuals were identified as to this work and should be affected, unaffected, or unknown. -
Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected]
University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School July 2017 Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected] Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the Pathology Commons Scholar Commons Citation Mohamed, Mai, "Role of Amylase in Ovarian Cancer" (2017). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/6907 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Role of Amylase in Ovarian Cancer by Mai Mohamed A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Pathology and Cell Biology Morsani College of Medicine University of South Florida Major Professor: Patricia Kruk, Ph.D. Paula C. Bickford, Ph.D. Meera Nanjundan, Ph.D. Marzenna Wiranowska, Ph.D. Lauri Wright, Ph.D. Date of Approval: June 29, 2017 Keywords: ovarian cancer, amylase, computational analyses, glycocalyx, cellular invasion Copyright © 2017, Mai Mohamed Dedication This dissertation is dedicated to my parents, Ahmed and Fatma, who have always stressed the importance of education, and, throughout my education, have been my strongest source of encouragement and support. They always believed in me and I am eternally grateful to them. I would also like to thank my brothers, Mohamed and Hussien, and my sister, Mariam. I would also like to thank my husband, Ahmed. -
Integrative Analysis of Single-Cell Genomics Data by Coupled Nonnegative Matrix Factorizations
Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations Zhana Durena,b,1, Xi Chena,b,1, Mahdi Zamanighomia,b,c,1, Wanwen Zenga,b,d, Ansuman T. Satpathyc, Howard Y. Changc, Yong Wange,f, and Wing Hung Wonga,b,c,2 aDepartment of Statistics, Stanford University, Stanford, CA 94305; bDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305; cCenter for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305; dMinistry of Education Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, Department of Automation, Tsinghua University, 100084 Beijing, China; eAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, 100080 Beijing, China; and fCenter for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223 Kunming, China Contributed by Wing Hung Wong, June 14, 2018 (sent for review April 4, 2018; reviewed by Andrew D. Smith and Nancy R. Zhang) When different types of functional genomics data are generated on Approach single cells from different samples of cells from the same hetero- Coupled NMF. We first introduce our approach in general terms. geneous population, the clustering of cells in the different samples Let O be a p1 by n1 matrix representing data on p1 features for n1 should be coupled. We formulate this “coupled clustering” problem units in the first sample; then a “soft” clustering of the units in as an optimization problem and propose the method of coupled this sample can be obtained from a nonnegative factorization nonnegative matrix factorizations (coupled NMF) for its solution. O = W1H1 as follows: The ith column of W1 gives the mean The method is illustrated by the integrative analysis of single-cell vector for the ith cluster of units, while the jth column of H1 gives RNA-sequencing (RNA-seq) and single-cell ATAC-sequencing (ATAC- the assignment weights of the jth unit to the different clusters. -
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 -
Supplementary Material
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry Page 1 / 45 SUPPLEMENTARY MATERIAL Appendix A1: Neuropsychological protocol. Appendix A2: Description of the four cases at the transitional stage. Table A1: Clinical status and center proportion in each batch. Table A2: Complete output from EdgeR. Table A3: List of the putative target genes. Table A4: Complete output from DIANA-miRPath v.3. Table A5: Comparison of studies investigating miRNAs from brain samples. Figure A1: Stratified nested cross-validation. Figure A2: Expression heatmap of miRNA signature. Figure A3: Bootstrapped ROC AUC scores. Figure A4: ROC AUC scores with 100 different fold splits. Figure A5: Presymptomatic subjects probability scores. Figure A6: Heatmap of the level of enrichment in KEGG pathways. Kmetzsch V, et al. J Neurol Neurosurg Psychiatry 2021; 92:485–493. doi: 10.1136/jnnp-2020-324647 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry Appendix A1. Neuropsychological protocol The PREV-DEMALS cognitive evaluation included standardized neuropsychological tests to investigate all cognitive domains, and in particular frontal lobe functions. The scores were provided previously (Bertrand et al., 2018). Briefly, global cognitive efficiency was evaluated by means of Mini-Mental State Examination (MMSE) and Mattis Dementia Rating Scale (MDRS). Frontal executive functions were assessed with Frontal Assessment Battery (FAB), forward and backward digit spans, Trail Making Test part A and B (TMT-A and TMT-B), Wisconsin Card Sorting Test (WCST), and Symbol-Digit Modalities test. -
Human CST9L / Testatin Protein (Fc Tag)
Human CST9L / Testatin Protein (Fc Tag) Catalog Number: 13243-H02H General Information SDS-PAGE: Gene Name Synonym: bA218C14.1; CTES7B; PRO3543; UNQ1835 Protein Construction: A DNA sequence encoding the human CST9L (Q9H4G1) (Met 1-His 147) was fused with the Fc region of human IgG1 at the C-terminus. Source: Human Expression Host: Human Cells QC Testing Purity: > 92 % as determined by SDS-PAGE Endotoxin: Protein Description < 1.0 EU per μg of the protein as determined by the LAL method Testatin is a member of the Cystatin family. Cystatins comprise genes that Stability: all show expression patterns that are strikingly restricted to reproductive tissue. Cystatins are a family of cysteine protease inhibitors with homology Samples are stable for up to twelve months from date of receipt at -70 ℃ to chicken cystatin. There are typically about 115 amino acids in this family. They are largely acidic, contain four conserved cysteine residues known to Predicted N terminal: Trp 29 form two disulfide bonds, may be glycosylated and/or phosphorylated, with Molecular Mass: similarity to fetuins, kininogens, stefins, histidine-rich glycoproteins and cystatin-related proteins. Testatin shows homology to family 2 cystatins, a The recombinant human CST9L/Fc chimera is a disulfide-linked group of broadly expressed small secretory proteins that are inhibitors of homodimeric protein. The reduced monomer consists of 360 amino acids cysteine proteases in vitro but whose in vivo functions are unclear. It is and has a calculated molecular mass of 41.3 kDa. In SDS-PAGE under expressed in germ cells and somatic cells in reproductive tissues. Testatin reducing conditions, the apparent molecular mass of rhCST9L/Fc is considered a strong candidate for involvement in early testis monomer is approximately 48 kDa.