Modeling the Human Segmentation Clock with Pluripotent Stem Cells 2 3 Mitsuhiro Matsuda1,10, Yoshihiro Yamanaka2,10, Maya Uemura2,3, Mitsujiro Osawa4, 4 Megumu K
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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
Regulation of Cardiac Progenitors by Combination of Mesp1 and ETS
REGULATION OF CARDIAC PROGENITORS BY COMBINATION OF MESP1 AND ETS TRANSCRIPTION FACTORS A Dissertation by KUO-CHAN WENG Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Chair of Committee, Fen Wang Co-Chair of Committee, Robert J. Schwartz Committee Members, James F. Martin Jiang Chang Head of Department, Fen Wang May 2014 Major Subject: Medical Science Copyright 2014 Kuo-Chan Weng ABSTRACT Heart disease remains the leading cause of death worldwide. By understanding the regulating networks during cardiac development we can exploit those networks to manipulate adult cells into cardiac progenitors and provide an alternative for repairing diseased hearts. Mesp1 is considered to have critical roles during cardiac development but the molecular mechanisms need to be further studied. The roles of ETS transcription factors have been primarily limited to hematopoietic differentiation and cancer progression. The ETS transcription factors are known to have proliferating roles and were hypothesized to also be involved in cardiac differentiation and may potentially be used for cell reprogramming. The first part of this study characterizes the expression pattern of Mesp1 protein in early mouse embryo from E6.5 to E9.5 and provides a full expression profile in differentiating embryoid bodies in vitro from the undifferentiated stage to Day10. Our work showed Mesp1 expresses in the posterior region of E6.5 embryo then starts migrating through the primitive streak toward anterior mesoderm and endoderm in E7.5. A Mesp1 linage tracing ES cell line was established, and it allowed us to trace the Mesp1 derived cell population. -
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. -
Detailed Review Paper on Retinoid Pathway Signalling
1 1 Detailed Review Paper on Retinoid Pathway Signalling 2 December 2020 3 2 4 Foreword 5 1. Project 4.97 to develop a Detailed Review Paper (DRP) on the Retinoid System 6 was added to the Test Guidelines Programme work plan in 2015. The project was 7 originally proposed by Sweden and the European Commission later joined the project as 8 a co-lead. In 2019, the OECD Secretariat was added to coordinate input from expert 9 consultants. The initial objectives of the project were to: 10 draft a review of the biology of retinoid signalling pathway, 11 describe retinoid-mediated effects on various organ systems, 12 identify relevant retinoid in vitro and ex vivo assays that measure mechanistic 13 effects of chemicals for development, and 14 Identify in vivo endpoints that could be added to existing test guidelines to 15 identify chemical effects on retinoid pathway signalling. 16 2. This DRP is intended to expand the recommendations for the retinoid pathway 17 included in the OECD Detailed Review Paper on the State of the Science on Novel In 18 vitro and In vivo Screening and Testing Methods and Endpoints for Evaluating 19 Endocrine Disruptors (DRP No 178). The retinoid signalling pathway was one of seven 20 endocrine pathways considered to be susceptible to environmental endocrine disruption 21 and for which relevant endpoints could be measured in new or existing OECD Test 22 Guidelines for evaluating endocrine disruption. Due to the complexity of retinoid 23 signalling across multiple organ systems, this effort was foreseen as a multi-step process. -
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 -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
Watsonjn2018.Pdf (1.780Mb)
UNIVERSITY OF CENTRAL OKLAHOMA Edmond, Oklahoma Department of Biology Investigating Differential Gene Expression in vivo of Cardiac Birth Defects in an Avian Model of Maternal Phenylketonuria A THESIS SUBMITTED TO THE GRADUATE FACULTY In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN BIOLOGY By Jamie N. Watson Edmond, OK June 5, 2018 J. Watson/Dr. Nikki Seagraves ii J. Watson/Dr. Nikki Seagraves Acknowledgements It is difficult to articulate the amount of gratitude I have for the support and encouragement I have received throughout my master’s thesis. Many people have added value and support to my life during this time. I am thankful for the education, experience, and friendships I have gained at the University of Central Oklahoma. First, I would like to thank Dr. Nikki Seagraves for her mentorship and friendship. I lucked out when I met her. I have enjoyed working on this project and I am very thankful for her support. I would like thank Thomas Crane for his support and patience throughout my master’s degree. I would like to thank Dr. Shannon Conley for her continued mentorship and support. I would like to thank Liz Bullen and Dr. Eric Howard for their training and help on this project. I would like to thank Kristy Meyer for her friendship and help throughout graduate school. I would like to thank my committee members Dr. Robert Brennan and Dr. Lilian Chooback for their advisement on this project. Also, I would like to thank the biology faculty and staff. I would like to thank the Seagraves lab members: Jailene Canales, Kayley Pate, Mckayla Muse, Grace Thetford, Kody Harvey, Jordan Guffey, and Kayle Patatanian for their hard work and support. -
Supplementary Figure S4
18DCIS 18IDC Supplementary FigureS4 22DCIS 22IDC C D B A E (0.77) (0.78) 16DCIS 14DCIS 28DCIS 16IDC 28IDC (0.43) (0.49) 0 ADAMTS12 (p.E1469K) 14IDC ERBB2, LASP1,CDK12( CCNE1 ( NUTM2B SDHC,FCGR2B,PBX1,TPR( CD1D, B4GALT3, BCL9, FLG,NUP21OL,TPM3,TDRD10,RIT1,LMNA,PRCC,NTRK1 0 ADAMTS16 (p.E67K) (0.67) (0.89) (0.54) 0 ARHGEF38 (p.P179Hfs*29) 0 ATG9B (p.P823S) (0.68) (1.0) ARID5B, CCDC6 CCNE1, TSHZ3,CEP89 CREB3L2,TRIM24 BRAF, EGFR (7p11); 0 ABRACL (p.R35H) 0 CATSPER1 (p.P152H) 0 ADAMTS18 (p.Y799C) 19q12 0 CCDC88C (p.X1371_splice) (0) 0 ADRA1A (p.P327L) (10q22.3) 0 CCNF (p.D637N) −4 −2 −4 −2 0 AKAP4 (p.G454A) 0 CDYL (p.Y353Lfs*5) −4 −2 Log2 Ratio Log2 Ratio −4 −2 Log2 Ratio Log2 Ratio 0 2 4 0 2 4 0 ARID2 (p.R1068H) 0 COL27A1 (p.G646E) 0 2 4 0 2 4 2 EDRF1 (p.E521K) 0 ARPP21 (p.P791L) ) 0 DDX11 (p.E78K) 2 GPR101, p.A174V 0 ARPP21 (p.P791T) 0 DMGDH (p.W606C) 5 ANP32B, p.G237S 16IDC (Ploidy:2.01) 16DCIS (Ploidy:2.02) 14IDC (Ploidy:2.01) 14DCIS (Ploidy:2.9) -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 Log Ratio Log Ratio Log Ratio Log Ratio 12DCIS 0 ASPM (p.S222T) Log Ratio Log Ratio 0 FMN2 (p.G941A) 20 1 2 3 2 0 1 2 3 2 ERBB3 (p.D297Y) 2 0 1 2 3 20 1 2 3 0 ATRX (p.L1276I) 20 1 2 3 2 0 1 2 3 0 GALNT18 (p.F92L) 2 MAPK4, p.H147Y 0 GALNTL6 (p.E236K) 5 C11orf1, p.Y53C (10q21.2); 0 ATRX (p.R1401W) PIK3CA, p.H1047R 28IDC (Ploidy:2.0) 28DCIS (Ploidy:2.0) 22IDC (Ploidy:3.7) 22DCIS (Ploidy:4.1) 18IDC (Ploidy:3.9) 18DCIS (Ploidy:2.3) 17q12 0 HCFC1 (p.S2025C) 2 LCMT1 (p.S34A) 0 ATXN7L2 (p.X453_splice) SPEN, p.P677Lfs*13 CBFB 1 2 3 4 5 6 7 8 9 10 11 -
Understanding Chronic Kidney Disease: Genetic and Epigenetic Approaches
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches Yi-An Ko Ko University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Systems Biology Commons Recommended Citation Ko, Yi-An Ko, "Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches" (2017). Publicly Accessible Penn Dissertations. 2404. https://repository.upenn.edu/edissertations/2404 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2404 For more information, please contact [email protected]. Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches Abstract The work described in this dissertation aimed to better understand the genetic and epigenetic factors influencing chronic kidney disease (CKD) development. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with chronic kidney disease. However, these studies have not effectively identified target genes for the CKD variants. Most of the identified variants are localized to non-coding genomic regions, and how they associate with CKD development is not well-understood. As GWAS studies only explain a small fraction of heritability, we hypothesized that epigenetic changes could explain part of this missing heritability. To identify potential gene targets of the genetic variants, we performed expression quantitative loci (eQTL) analysis, using genotyping arrays and RNA sequencing from human kidney samples. To identify the target genes of CKD-associated SNPs, we integrated the GWAS-identified SNPs with the eQTL results using a Bayesian colocalization method, coloc. This resulted in a short list of target genes, including PGAP3 and CASP9, two genes that have been shown to present with kidney phenotypes in knockout mice. -
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 -
Key Genes Regulating Skeletal Muscle Development and Growth in Farm Animals
animals Review Key Genes Regulating Skeletal Muscle Development and Growth in Farm Animals Mohammadreza Mohammadabadi 1 , Farhad Bordbar 1,* , Just Jensen 2 , Min Du 3 and Wei Guo 4 1 Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman 77951, Iran; [email protected] 2 Center for Quantitative Genetics and Genomics, Aarhus University, 8210 Aarhus, Denmark; [email protected] 3 Washington Center for Muscle Biology, Department of Animal Sciences, Washington State University, Pullman, WA 99163, USA; [email protected] 4 Muscle Biology and Animal Biologics, Animal and Dairy Science, University of Wisconsin-Madison, Madison, WI 53558, USA; [email protected] * Correspondence: [email protected] Simple Summary: Skeletal muscle mass is an important economic trait, and muscle development and growth is a crucial factor to supply enough meat for human consumption. Thus, understanding (candidate) genes regulating skeletal muscle development is crucial for understanding molecular genetic regulation of muscle growth and can be benefit the meat industry toward the goal of in- creasing meat yields. During the past years, significant progress has been made for understanding these mechanisms, and thus, we decided to write a comprehensive review covering regulators and (candidate) genes crucial for muscle development and growth in farm animals. Detection of these genes and factors increases our understanding of muscle growth and development and is a great help for breeders to satisfy demands for meat production on a global scale. Citation: Mohammadabadi, M.; Abstract: Farm-animal species play crucial roles in satisfying demands for meat on a global scale, Bordbar, F.; Jensen, J.; Du, M.; Guo, W. -
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.