Effects of Per2 Overexpression on Growth Inhibition and Metastasis
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Longitudinal Study of Leukocyte DNA Methylation and Biomarkers for Cancer Risk in Older Adults
bioRxiv preprint doi: https://doi.org/10.1101/597666; this version posted April 3, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 1 Longitudinal Study of Leukocyte DNA Methylation and 2 Biomarkers for Cancer Risk in Older Adults 3 Alexandra H. Bartlett1, Jane W Liang1, Jose Vladimir Sandoval-Sierra1, Jay H 4 Fowke 1, Eleanor M Simonsick2, Karen C Johnson1, Khyobeni Mozhui1* 5 1Department of Preventive Medicine, University of Tennessee Health Science 6 Center, Memphis, Tennessee, USA 7 2Intramural Research Program, National Institute on Aging, Baltimore Maryland, 8 USA 9 AHB: [email protected]; JWL: [email protected]; JVSS: 10 [email protected]; JHF: [email protected]; EMS: [email protected]; 11 KCJ: [email protected]; KM: [email protected] 12 *Corresponding author: Khyobeni Mozhui 13 14 15 16 17 18 1 bioRxiv preprint doi: https://doi.org/10.1101/597666; this version posted April 3, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 19 Abstract 20 Background: Changes in DNA methylation over the course of life may provide 21 an indicator of risk for cancer. We explored longitudinal changes in CpG 22 methylation from blood leukocytes, and likelihood of a future cancer diagnosis. -
Longitudinal Study of Leukocyte DNA Methylation and Biomarkers for Cancer Risk in Older Adults Alexandra H
Bartlett et al. Biomarker Research (2019) 7:10 https://doi.org/10.1186/s40364-019-0161-3 RESEARCH Open Access Longitudinal study of leukocyte DNA methylation and biomarkers for cancer risk in older adults Alexandra H. Bartlett1, Jane W. Liang1, Jose Vladimir Sandoval-Sierra1, Jay H. Fowke1, Eleanor M. Simonsick2, Karen C. Johnson1 and Khyobeni Mozhui1* Abstract Background: Changes in DNA methylation over the course of life may provide an indicator of risk for cancer. We explored longitudinal changes in CpG methylation from blood leukocytes, and likelihood of future cancer diagnosis. Methods: Peripheral blood samples were obtained at baseline and at follow-up visit from 20 participants in the Health, Aging and Body Composition prospective cohort study. Genome-wide CpG methylation was assayed using the Illumina Infinium Human MethylationEPIC (HM850K) microarray. Results: Global patterns in DNA methylation from CpG-based analyses showed extensive changes in cell composition over time in participants who developed cancer. By visit year 6, the proportion of CD8+ T-cells decreased (p-value = 0. 02), while granulocytes cell levels increased (p-value = 0.04) among participants diagnosed with cancer compared to those who remained cancer-free (cancer-free vs. cancer-present: 0.03 ± 0.02 vs. 0.003 ± 0.005 for CD8+ T-cells; 0.52 ± 0. 14 vs. 0.66 ± 0.09 for granulocytes). Epigenome-wide analysis identified three CpGs with suggestive p-values ≤10− 5 for differential methylation between cancer-free and cancer-present groups, including a CpG located in MTA3, agene linked with metastasis. At a lenient statistical threshold (p-value ≤3×10− 5), the top 10 cancer-associated CpGs included a site near RPTOR that is involved in the mTOR pathway, and the candidate tumor suppressor genes REC8, KCNQ1,andZSWIM5. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
Contribution of Enhancer-Driven and Master-Regulator Genes to Autoimmune Disease Revealed Using Functionally Informed SNP-To-Gene Linking Strategies Kushal K
bioRxiv preprint doi: https://doi.org/10.1101/2020.09.02.279059; this version posted March 31, 2021. 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. Contribution of enhancer-driven and master-regulator genes to autoimmune disease revealed using functionally informed SNP-to-gene linking strategies Kushal K. Dey1, Steven Gazal1, Bryce van de Geijn 1,3, Samuel Sungil Kim 1,4, Joseph Nasser5, Jesse M. Engreitz5, Alkes L. Price 1,2,5 1 Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA 2 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 3 Genentech, South San Francisco, CA 4 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 5 Broad Institute of MIT and Harvard, Cambridge, MA, USA Abstract Gene regulation is known to play a fundamental role in human disease, but mechanisms of regulation vary greatly across genes. Here, we explore the con- tributions to disease of two types of genes: genes whose regulation is driven by enhancer regions as opposed to promoter regions (Enhancer-driven) and genes that regulate many other genes in trans (Master-regulator). We link these genes to SNPs using a comprehensive set of SNP-to-gene (S2G) strategies and apply stratified LD score regression to the resulting SNP annotations to draw three main conclusions about 11 autoimmune diseases and blood cell traits (average Ncase=13K across 6 autoimmune diseases, average N=443K across 5 blood cell traits). -
Supplementary File 2A Revised
Supplementary file 2A. Differentially expressed genes in aldosteronomas compared to all other samples, ranked according to statistical significance. Missing values were not allowed in aldosteronomas, but to a maximum of five in the other samples. Acc UGCluster Name Symbol log Fold Change P - Value Adj. P-Value B R99527 Hs.8162 Hypothetical protein MGC39372 MGC39372 2,17 6,3E-09 5,1E-05 10,2 AA398335 Hs.10414 Kelch domain containing 8A KLHDC8A 2,26 1,2E-08 5,1E-05 9,56 AA441933 Hs.519075 Leiomodin 1 (smooth muscle) LMOD1 2,33 1,3E-08 5,1E-05 9,54 AA630120 Hs.78781 Vascular endothelial growth factor B VEGFB 1,24 1,1E-07 2,9E-04 7,59 R07846 Data not found 3,71 1,2E-07 2,9E-04 7,49 W92795 Hs.434386 Hypothetical protein LOC201229 LOC201229 1,55 2,0E-07 4,0E-04 7,03 AA454564 Hs.323396 Family with sequence similarity 54, member B FAM54B 1,25 3,0E-07 5,2E-04 6,65 AA775249 Hs.513633 G protein-coupled receptor 56 GPR56 -1,63 4,3E-07 6,4E-04 6,33 AA012822 Hs.713814 Oxysterol bining protein OSBP 1,35 5,3E-07 7,1E-04 6,14 R45592 Hs.655271 Regulating synaptic membrane exocytosis 2 RIMS2 2,51 5,9E-07 7,1E-04 6,04 AA282936 Hs.240 M-phase phosphoprotein 1 MPHOSPH -1,40 8,1E-07 8,9E-04 5,74 N34945 Hs.234898 Acetyl-Coenzyme A carboxylase beta ACACB 0,87 9,7E-07 9,8E-04 5,58 R07322 Hs.464137 Acyl-Coenzyme A oxidase 1, palmitoyl ACOX1 0,82 1,3E-06 1,2E-03 5,35 R77144 Hs.488835 Transmembrane protein 120A TMEM120A 1,55 1,7E-06 1,4E-03 5,07 H68542 Hs.420009 Transcribed locus 1,07 1,7E-06 1,4E-03 5,06 AA410184 Hs.696454 PBX/knotted 1 homeobox 2 PKNOX2 1,78 2,0E-06 -
Novel Insights Into the Thaumarchaeota in the Deepest Oceans: Their Metabolism and Potential Adaptation Mechanisms
Zhong et al. Microbiome (2020) 8:78 https://doi.org/10.1186/s40168-020-00849-2 RESEARCH Open Access Novel insights into the Thaumarchaeota in the deepest oceans: their metabolism and potential adaptation mechanisms Haohui Zhong1,2, Laura Lehtovirta-Morley3, Jiwen Liu1,2, Yanfen Zheng1, Heyu Lin1, Delei Song1, Jonathan D. Todd3, Jiwei Tian4 and Xiao-Hua Zhang1,2,5* Abstract Background: Marine Group I (MGI) Thaumarchaeota, which play key roles in the global biogeochemical cycling of nitrogen and carbon (ammonia oxidizers), thrive in the aphotic deep sea with massive populations. Recent studies have revealed that MGI Thaumarchaeota were present in the deepest part of oceans—the hadal zone (depth > 6000 m, consisting almost entirely of trenches), with the predominant phylotype being distinct from that in the “shallower” deep sea. However, little is known about the metabolism and distribution of these ammonia oxidizers in the hadal water. Results: In this study, metagenomic data were obtained from 0–10,500 m deep seawater samples from the Mariana Trench. The distribution patterns of Thaumarchaeota derived from metagenomics and 16S rRNA gene sequencing were in line with that reported in previous studies: abundance of Thaumarchaeota peaked in bathypelagic zone (depth 1000–4000 m) and the predominant clade shifted in the hadal zone. Several metagenome-assembled thaumarchaeotal genomes were recovered, including a near-complete one representing the dominant hadal phylotype of MGI. Using comparative genomics, we predict that unexpected genes involved in bioenergetics, including two distinct ATP synthase genes (predicted to be coupled with H+ and Na+ respectively), and genes horizontally transferred from other extremophiles, such as those encoding putative di-myo-inositol-phosphate (DIP) synthases, might significantly contribute to the success of this hadal clade under the extreme condition. -
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 -
Nature Genetics | © 2018 Nature America Inc., Part of Springer Nature
ARTICLES https://doi.org/10.1038/s41588-018-0102-3 Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity John B. Harley1,2,3,4,5,9*, Xiaoting Chen1,9, Mario Pujato1,9, Daniel Miller1, Avery Maddox1, Carmy Forney1, Albert F. Magnusen1, Arthur Lynch1, Kashish Chetal6, Masashi Yukawa7, Artem Barski 4,7,8, Nathan Salomonis4,6, Kenneth M. Kaufman1,2,4,5, Leah C. Kottyan 1,4* and Matthew T. Weirauch 1,3,4,6* Explaining the genetics of many diseases is challenging because most associations localize to incompletely characterized regu- latory regions. Using new computational methods, we show that transcription factors (TFs) occupy multiple loci associated with individual complex genetic disorders. Application to 213 phenotypes and 1,544 TF binding datasets identified 2,264 rela- tionships between hundreds of TFs and 94 phenotypes, including androgen receptor in prostate cancer and GATA3 in breast cancer. Strikingly, nearly half of systemic lupus erythematosus risk loci are occupied by the Epstein–Barr virus EBNA2 pro- tein and many coclustering human TFs, showing gene–environment interaction. Similar EBNA2-anchored associations exist in multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, type 1 diabetes, juvenile idiopathic arthritis and celiac disease. Instances of allele-dependent DNA binding and downstream effects on gene expression at plausibly causal variants support genetic mechanisms dependent on EBNA2. Our results nominate mechanisms that operate across risk loci within dis- ease phenotypes, suggesting new models for disease origins. he mechanisms generating genetic associations have proven Results difficult to elucidate for most diseases because the vast major- Intersection of disease risk loci with TF-DNA binding interac- Tity of the pertinent variants are presumed to be components tions. -
Transcription Factors Recognize DNA Shape Without Nucleotide Recognition
bioRxiv preprint doi: https://doi.org/10.1101/143677; this version posted May 29, 2017. 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. Transcription+factors+recognize+DNA+shape+without+nucleotide+recognition++ Md.$Abul$Hassan$Samee1,$Benoit$G.$Bruneau1,2,$Katherine$S.$Pollard$1,3$ 1:$Gladstone$Institutes,$1650$Owens$St.,$San$Francisco,$CA$94158$ 2:$$Department$of$Pediatrics$and$Cardiovascular$Research$Institute,$University$of$California,$ San$Francisco,$CA$94158$ 3:$Division$of$Bioinformatics,$Institute$for$Human$Genetics,$and$Institute$for$Computational$ Health$Sciences,$University$of$California,$San$Francisco,$CA$94158$ $ + bioRxiv preprint doi: https://doi.org/10.1101/143677; this version posted May 29, 2017. 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+ We$hypothesized$that$transcription$factors$(TFs)$recognize$DNA$shape$without$nucleotide$ sequence$recognition.$Motivating$an$independent$role$for$shape,$many$TF$binding$sites$lack$ a$sequenceZmotif,$DNA$shape$adds$specificity$to$sequenceZmotifs,$and$different$sequences$ can$ encode$ similar$ shapes.$ We$ therefore$ asked$ if$ binding$ sites$ of$ a$ TF$ are$ enriched$ for$ specific$patterns$of$DNA$shapeZfeatures,$e.g.,$helical$twist.$We$developed$ShapeMF,$which$ discovers$ these$ shapeZmotifs$ de%novo%without$ taking$ sequence$ information$ into$ account.$ -
Target Gene Gene Description Validation Diana Miranda
Supplemental Table S1. Mmu-miR-183-5p in silico predicted targets. TARGET GENE GENE DESCRIPTION VALIDATION DIANA MIRANDA MIRBRIDGE PICTAR PITA RNA22 TARGETSCAN TOTAL_HIT AP3M1 adaptor-related protein complex 3, mu 1 subunit V V V V V V V 7 BTG1 B-cell translocation gene 1, anti-proliferative V V V V V V V 7 CLCN3 chloride channel, voltage-sensitive 3 V V V V V V V 7 CTDSPL CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase-like V V V V V V V 7 DUSP10 dual specificity phosphatase 10 V V V V V V V 7 MAP3K4 mitogen-activated protein kinase kinase kinase 4 V V V V V V V 7 PDCD4 programmed cell death 4 (neoplastic transformation inhibitor) V V V V V V V 7 PPP2R5C protein phosphatase 2, regulatory subunit B', gamma V V V V V V V 7 PTPN4 protein tyrosine phosphatase, non-receptor type 4 (megakaryocyte) V V V V V V V 7 EZR ezrin V V V V V V 6 FOXO1 forkhead box O1 V V V V V V 6 ANKRD13C ankyrin repeat domain 13C V V V V V V 6 ARHGAP6 Rho GTPase activating protein 6 V V V V V V 6 BACH2 BTB and CNC homology 1, basic leucine zipper transcription factor 2 V V V V V V 6 BNIP3L BCL2/adenovirus E1B 19kDa interacting protein 3-like V V V V V V 6 BRMS1L breast cancer metastasis-suppressor 1-like V V V V V V 6 CDK5R1 cyclin-dependent kinase 5, regulatory subunit 1 (p35) V V V V V V 6 CTDSP1 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase 1 V V V V V V 6 DCX doublecortin V V V V V V 6 ENAH enabled homolog (Drosophila) V V V V V V 6 EPHA4 EPH receptor A4 V V V V V V 6 FOXP1 forkhead box P1 V -
Snail/PRMT5/Nurd Complex Contributes to DNA Hypermethylation in Cervical Cancer by TET1 Inhibition
Cell Death & Differentiation (2021) 28:2818–2836 https://doi.org/10.1038/s41418-021-00786-z ARTICLE Snail/PRMT5/NuRD complex contributes to DNA hypermethylation in cervical cancer by TET1 inhibition 1,2 2 1 3 4 4 1 Jie Gao ● Ruiqiong Liu ● Dandan Feng ● Wei Huang ● Miaomiao Huo ● Jingyao Zhang ● Shuai Leng ● 1 3 3 3 3 4 1,4 Yang Yang ● Tianshu Yang ● Xin Yin ● Xu Teng ● Hefen Yu ● Baowen Yuan ● Yan Wang Received: 15 September 2020 / Revised: 8 April 2021 / Accepted: 15 April 2021 / Published online: 5 May 2021 © The Author(s) 2021. This article is published with open access Abstract The biological function of PRMT5 remains poorly understood in cervical cancer metastasis. Here, we report that PRMT5 physically associates with the transcription factor Snail and the NuRD(MTA1) complex to form a transcriptional-repressive complex that catalyzes the symmetrical histone dimethylation and deacetylation. This study shows that the Snail/PRMT5/ NuRD(MTA1) complex targets genes, such as TET1 and E-cadherin, which are critical for epithelial-mesenchymal transition (EMT). This complex also affects the conversion of 5mC to 5hmC. This study demonstrates that the Snail/ PRMT5/NuRD(MTA1) complex promotes the invasion and metastasis of cervical cancer in vitro and in vivo. This study 1234567890();,: 1234567890();,: also shows that PRMT5 expression is upregulated in cervical cancer and various human cancers, and the PRMT5 inhibitor EPZ015666 suppresses EMT and the invasion potential of cervical cancer cells by disinhibiting the expression of TET1 and increasing 5hmC, suggesting that PRMT5 is a potential target for cancer therapy. Introduction These authors contributed equally: Jie Gao, Ruiqiong Liu, Protein arginine methyltransferase 5 (PRMT5) is a type II Dandan Feng protein arginine methyltransferase (PRMT) that has been Edited by R Johnstone reported to catalyze the symmetrical dimethylation of argi- nine [1]. -
Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm
Hindawi Complexity Volume 2017, Article ID 9194801, 8 pages https://doi.org/10.1155/2017/9194801 Research Article Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm Jincai Yang,1 Huichao Gu,1 Xingpeng Jiang,1 Qingyang Huang,2 Xiaohua Hu,1 and Xianjun Shen1 1 School of Computer Science, Central China Normal University, Wuhan 430079, China 2School of Life Science, Central China Normal University, Wuhan 430079, China Correspondence should be addressed to Jincai Yang; [email protected] Received 31 March 2017; Revised 26 May 2017; Accepted 8 June 2017; Published 7 August 2017 Academic Editor: Fang-Xiang Wu Copyright © 2017 Jincai Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the past 20 years, much progress has been made on the genetic analysis of osteoporosis. A number of genes and SNPs associated with osteoporosis have been found through GWAS method. In this paper, we intend to identify the suspected risky SNPs of osteoporosis with computational methods based on the known osteoporosis GWAS-associated SNPs. The process includes two steps. Firstly, we decided whether the genes associated with the suspected risky SNPs are associated with osteoporosis by using random walk algorithm on the PPI network of osteoporosis GWAS-associated genes and the genes associated with the suspected risky SNPs. In order to solve the overfitting problem in ID3 decision tree algorithm, we then classified the SNPs with positive results based on their features of position and function through a simplified classification decision tree which was constructed by ID3 decision tree algorithm with PEP (Pessimistic-Error Pruning).