DOCSLIB.ORG

[1] Employs Elastic-Net Penalized Regression Method [2] to Estimate Cis-Eqtl Effect Sizes � from Equation (1) in the Main Text

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
[1] Employs Elastic-Net Penalized Regression Method [2] to Estimate Cis-Eqtl Effect Sizes � from Equation (1) in the Main Text Supplemental Texts Text S1. Details of PrediXcan’s approach of estimating cis-eQTL effect sizes PrediXcan TWAS method [1] employs Elastic-Net penalized regression method [2] to estimate cis-eQTL effect sizes � from Equation (1) in the main text. Basically, the Elastic-Net method assumes a combined LASSO (�!) [3] and Ridge (�") [4] penalty and estimate � by the following equation " 1 " �# = ������(,�� − ��, + �(�||�||! + (1 − �)||�||")) , � " 2 where ‖∙‖! denotes �! norm, ‖∙‖" denotes �" norm. Particularly, � is taken as 0.5 by PrediXcan method [1] and penalty parameter � can be tuned by a 5-fold cross validation. Text S2. Details of TIGAR’s approach of estimating cis-eQTL effect sizes TIGAR8 provides a more flexible approach to nonparametrically estimate cis-eQTL effect sizes � from Equation (1) in the main text by a Bayesian DPR method [5]. The DPR method " assumes a normal prior distribution �(0, �%) for cis-eQTL effect sizes and a Dirichlet process prior � [6] for effect-size variance �� as follows: " " w'~N(0, σ(), σ(~D, D~DP(IG(a, b), ξ). That is, the prior distribution � of effect-size variance deviates from a Dirichlet Process (DP) with an inverse gamma (IG) distribution and concentration parameter ξ. As proposed by previous studies, variational Bayesian algorithm [7, 8] is implemented to efficiently obtain posterior estimates �#. Text S3. Details of VC-TWAS approach with summary-level GWAS data and P-value calculation VC-TWAS with summary-level GWAS data Since summary-level GWAS data are generally generated by meta-analysis based on the following single variant (SNP) regression model: " � = �.��+ + �, e, ~ �(0, �. ) . Here, the phenotype is assumed to be adjusted for other confounding covariates with mean 0, and the genotype vectors (�.�, j=1,…,m) are also assumed to be centered with mean 0. Without loss of generality, we assume GWAS summary statistics include the single variant effect size 01 estimate �O+ and corresponding standard error �#/ for the � SNP, sample size �, and a reference LD covariance matrix � of all test SNPs. Following the derivation provided by [9], given the marginal SNP effect size �+ estimate # R �∙�� 4 �/ = # , the denominator �.��.� can be approximated by using the ��ℎ diagonal element of the �.��.� 4 reference LD covariance matrix, � ≈ �′�/(� − 1), with �.��.� = (� − 1)Σ+,+. Thus, the numerator of the score statistic for the �01 SNP as shown in the main text (Equation (6)) can be estimated by 4 R �∙�� = (� − 1)�/Σ+,+ . In addition, based on the estimate for the marginal SNP effect size variance, the " phenotype variance �7 can be estimated by # =& # @ & �4� (� �.�)< (9:!)>(� �.�)? " �" = ≈ .� % .� ' = Σ �Y"(� − 1) + Σ �O . 7 (9:!) (9:!) +,+ / +,+ + Since this estimate might vary with respect to the summary GWAS data of different SNPs, we Y" " " take the median of Σ+,+�/ (� − 1) + Σ+,+�O+ across all the SNPs as �Z7 as suggested by the previous study [9]. Then the � statistic used by VC-TWAS using only GWAS summary-level data can be approximated by A �4 � " " ∙� � = \ �+ ^ _ . �Z" +B! 7 P-value calculation for VC-TWAS Under the null hypothesis, the � statistic used by VC-TWAS follows a mixture of chi- A " square distribution ∑+B! �+ �+,! [10, 11], where (�!, ⋯ , �A) are nonzero eigenvalues of �, :� � = ���, � = �4��, � = �:� − �:��i�4�:��j �′�:� " where � is the � × � genotype matrix, � is the matrix of covariate data, � = �Z7 � for continuous traits with identify matrix I, � = ����[�#�(� − �#�), … , �#�(� − �#�)] for dichotomous traits. If phenotype � is centered and adjusted for other covariates as assumed when using (9:!)� summary-level GWAS data, then � can be simplified and approximated by � ≈ & with <F( reference LD covariance matrix � [12]. The p-value by VC-TWAS can then be conveniently obtained from several approximation and exact methods like the Davies exact method [13], which can be done by using both individual-level and summary-level GWAS data. Text S4. Details of ROS/MAP data In our applications of studying Alzheimer’s dementia (AD), we used transcriptome and individual-level GWAS data generated for samples from the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP) [14-17] cohorts. ROS recruits nuns, priests, and brothers across the United States. MAP recruits participants living in private homes, subsidized housings, and retirement facilities across the greater Chicago metropolitan area. ROS/MAP data can be requested at www.radc.rush.edu. Both studies employ harmonized data collection methods performed by the same staff for annual testing during life and for structured autopsy and collection of genomic data from blood and brain biospecimens. Harmonized data collection facilitates joint analyses of the studies’ data. Details of the studies are described elsewhere. We used microarray genotype data generated for 2,093 European-decent subjects from ROS/MAP [14-17], which are further imputed to the 1000 Genome Project Phase 3 [18]. Post- mortem brain samples (gray matter of the dorsolateral prefrontal cortex) from ~30% these ROS/MAP participants with assayed genotype data are also profiled for transcriptomic data by next-generation RNA-sequencing [19], which are used as reference data to train GReX prediction models in our application studies. Using ROS/MAP data, we conducted TWAS for clinical diagnosis of late on-site Alzheimer’s dementia (LOAD) as well as pathology indices of AD quantified with �-antibody and PHFtau specific immunostains. Quantitative pathology phenotypes �-amyloid load and PHFtau tangle density were studied. An additional phenotype of the summary measure of the burden of AD pathology (a combination of neuritic and diffuse plaques and neurofibrillary tangles based on modified Bielschowsky silver stain) [14, 15, 17] was also studied. The tangle density quantifies the average PHFtau tangle density within two or more 20µm sections from eight brain regions –– hippocampus, entorhinal cortex, midfrontal cortex, inferior temporal, angular gyrus, calcarine cortex, anterior cingulate cortex, and superior frontal cortex. The �-amyloid load quantifies the average percent area of cortex occupied by �-amyloid protein in adjacent sections from the same eight brain regions. These two are based on immunohistochemistry. The global measure of AD pathology is based on counts of neuritic and diffuse plaques and neurofibrillary tangles (15 counts) on 6µm sections stained with modified Bielschowsky [14, 15, 17]. Supplementary Figures (A) pcausal 0.001 pcausal 0.01 100 100 75 75 50 50 Power Power 25 25 0 0 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 Gene expression heritability Gene expression heritability pcausal 0.1 pcausal 0.2 100 100 75 75 50 50 Power Power 25 25 0 0 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 Gene expression heritability Gene expression heritability BurdenTWAS_DPR BurdenTWAS_PrediXcan VCTWAS_filtered_DPR Method BurdenTWAS_filtered_DPR VCTWAS_DPR VCTWAS_PrediXcan (B) pcausal 0.001 pcausal 0.01 100 100 75 75 50 50 Power Power 25 25 0 0 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 Gene expression heritability Gene expression heritability pcausal 0.1 pcausal 0.2 100 100 75 75 50 50 Power Power 25 25 0 0 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 Gene expression heritability Gene expression heritability Figure 1. TWASFig powerS1. TWAS comparison power comparison for VC for- TWASVC-TWAS and and Burden Burden--TWASTWAS with with phenotypes phenotypes simulated from Model I (A) and Model II (B). Various types of SNP weights were considered, including those simulated fromderived Model from I (A) PrediXcan and Model method, II (B). DPR Various method, types and of filteredSNP weights DPR weights were. considered,In Model I, the including those derived from PrediXcan method, DPR method, and filtered DPR weights." Using combinations of causal probability and phenotype heritability arei�GHIJHK, ℎLj = ((0.001,0.2), DPR weights resulted(0.01,0. 3in), higher (0.1,0.4 TWAS), (0.2,0 power.5)). In Modelthan usingII, the combinationsPrediXcan weights of causal across probability all scenariosand phenotype with . TWAS using filtered" DPR weights had comparable performance as using p!"#$"% ≥ 0.heritability01 are i�GHIJHK, ℎLj = ((0.001,0.1), (0.01,0.1), (0.1,0.15), (0.2,0.15)). complete DPR weights across all scenarios. For phenotypes simulated from Model I (panel A), Burden-TWAS had either comparable power with VC-TWAS when p!"#$"% = (0.001, 0.01) or slightly higher power p!"#$"% = (0.1, 0.2). For phenotypes simulated from Model II (panel B), VC- TWAS had higher power than Burden-TWAS under all scenarios. VC-TWAS with DPR weights resulted in the highest power. A) Model I B) Model II 100 100 75 75 50 50 Power Power 25 25 0 0 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 Gene expression heritability Gene expression heritability CoMM VCTWAS_DPR VCTWASSS CoMMSS VCTWAS_DPR_f VCTWASSS_f Fig S2. TWAS power comparison for VC-TWAS and CoMM with phenotypes simulated from Model I (A) and Model II (B) using individual-level and summary-level data under the scenarios with �GHIJHK = 0.2. Complete DPR Weights Filtered DPR Weights 30000 20000 Number of SNPs 10000 Median:6632 Median:2872 0 Complete DPR Weights Filtered DPR Weights Fig S3. Box plot of the number of test SNPs considered by VC-TWAS of all genome-wide genes in the application studies of AD, with complete DPR weights and filtered DPR weights derived from the ROS/MAP training data. Fig S4. Q-Q plots for VC-TWAS and Burden-TWAS with DPR weights, filtered DPR weights, and PrediXcan weights under null hypothesis, where quantitative gene expression traits were " generated with �GHIJHK = 0.2 and ℎM = 0.1. A) ZNF234 TRAPPC6A values) TOMM40 log10(p 0123456 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 chromosome B) HSPBAP1 values) log10(p 0123456 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 chromosome Fig S5.
Load more

Recommended publications
  • 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.
    [Show full text]
  • In Silico Structural and Functional Characterization of the RSUME Splice Variants
    In Silico Structural and Functional Characterization of the RSUME Splice Variants Juan Gerez1., Mariana Fuertes1., Lucas Tedesco1, Susana Silberstein1,2, Gustavo Sevlever3, Marcelo Paez-Pereda4, Florian Holsboer4, Adria´n G. Turjanski5, Eduardo Arzt1,2,4* 1 Instituto de Investigacio´n en Biomedicina de Buenos Aires (IBioBA)-CONICET- Partner Institute of the Max Planck Society, Buenos Aires, Argentina, 2 Departamento de Fisiologı´a y Biologı´a Molecular, FCEN, Universidad de Buenos Aires, Buenos Aires, Argentina, 3 Departamento de Neuropatologı´a y Biologı´a Molecular, FLENI, Buenos Aires, Argentina, 4 Max Planck Institute of Psychiatry, Munich, Germany, 5 Laboratorio de Bioinforma´tica Estructural, Departamento de Quı´mica Biolo´gica, INQUIMAE-CONICET, FCEN, Universidad de Buenos Aires, Buenos Aires, Argentina Abstract RSUME (RWD-containing SUMO Enhancer) is a small protein that increases SUMO conjugation to proteins. To date, four splice variants that codify three RSUME isoforms have been described, which differ in their C-terminal end. Comparing the structure of the RSUME isoforms we found that, in addition to the previously described RWD domain in the N-terminal, all these RSUME variants also contain an intermediate domain. Only the longest RSUME isoform presents a C-terminal domain that is absent in the others. Given these differences, we used the shortest and longest RSUME variants for comparative studies. We found that the C-terminal domain is dispensable for the SUMO-conjugation enhancer properties of RSUME. We also demonstrate that these two RSUME variants are equally induced by hypoxia. The NF-kB signaling pathway is inhibited and the HIF-1 pathway is increased more efficiently by the longest RSUME, by means of a greater physical interaction of RSUME267 with the target proteins.
    [Show full text]
  • Supplementary Table S2
    1-high in cerebrotropic Gene P-value patients Definition BCHE 2.00E-04 1 Butyrylcholinesterase PLCB2 2.00E-04 -1 Phospholipase C, beta 2 SF3B1 2.00E-04 -1 Splicing factor 3b, subunit 1 BCHE 0.00022 1 Butyrylcholinesterase ZNF721 0.00028 -1 Zinc finger protein 721 GNAI1 0.00044 1 Guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 1 GNAI1 0.00049 1 Guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 1 PDE1B 0.00069 -1 Phosphodiesterase 1B, calmodulin-dependent MCOLN2 0.00085 -1 Mucolipin 2 PGCP 0.00116 1 Plasma glutamate carboxypeptidase TMX4 0.00116 1 Thioredoxin-related transmembrane protein 4 C10orf11 0.00142 1 Chromosome 10 open reading frame 11 TRIM14 0.00156 -1 Tripartite motif-containing 14 APOBEC3D 0.00173 -1 Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3D ANXA6 0.00185 -1 Annexin A6 NOS3 0.00209 -1 Nitric oxide synthase 3 SELI 0.00209 -1 Selenoprotein I NYNRIN 0.0023 -1 NYN domain and retroviral integrase containing ANKFY1 0.00253 -1 Ankyrin repeat and FYVE domain containing 1 APOBEC3F 0.00278 -1 Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3F EBI2 0.00278 -1 Epstein-Barr virus induced gene 2 ETHE1 0.00278 1 Ethylmalonic encephalopathy 1 PDE7A 0.00278 -1 Phosphodiesterase 7A HLA-DOA 0.00305 -1 Major histocompatibility complex, class II, DO alpha SOX13 0.00305 1 SRY (sex determining region Y)-box 13 ABHD2 3.34E-03 1 Abhydrolase domain containing 2 MOCS2 0.00334 1 Molybdenum cofactor synthesis 2 TTLL6 0.00365 -1 Tubulin tyrosine ligase-like family, member 6 SHANK3 0.00394 -1 SH3 and multiple ankyrin repeat domains 3 ADCY4 0.004 -1 Adenylate cyclase 4 CD3D 0.004 -1 CD3d molecule, delta (CD3-TCR complex) (CD3D), transcript variant 1, mRNA.
    [Show full text]
  • Meta-Analysis of Maternal and Fetal Transcriptomic Data Elucidates the Role of Adaptive and Innate Immunity in Preterm Birth
    UCSF UC San Francisco Previously Published Works Title Meta-Analysis of Maternal and Fetal Transcriptomic Data Elucidates the Role of Adaptive and Innate Immunity in Preterm Birth. Permalink https://escholarship.org/uc/item/96v8r4d9 Journal Frontiers in immunology, 9(MAY) ISSN 1664-3224 Authors Vora, Bianca Wang, Aolin Kosti, Idit et al. Publication Date 2018 DOI 10.3389/fimmu.2018.00993 Peer reviewed eScholarship.org Powered by the California Digital Library University of California SYSTEMATIC REVIEW published: 09 May 2018 doi: 10.3389/fimmu.2018.00993 Meta-Analysis of Maternal and Fetal Transcriptomic Data Elucidates the Role of Adaptive and Innate Immunity in Preterm Birth Bianca Vora1,2, Aolin Wang1,3, Idit Kosti1,4, Hongtai Huang1,3, Ishan Paranjpe1, Tracey J. Woodruff3, Tippi MacKenzie4,5,6 and Marina Sirota1,4* 1 Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, United States, 2 Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, United States, 3 Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA, United States, 4 Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States, 5 Center for Maternal-Fetal Precision Medicine, University of California San Francisco, San Francisco, CA, United States, 6 Department of Surgery, University of California Edited by: San Francisco, San Francisco, CA, United States Nandor Gabor Than, Hungarian Academy of Sciences (MTA), Hungary Preterm birth (PTB) is the leading cause of newborn deaths around the world. Reviewed by: Spontaneous preterm birth (sPTB) accounts for two-thirds of all PTBs; however, there Kang Chen, remains an unmet need of detecting and preventing sPTB.
    [Show full text]
  • TRANSCRIPTOME ANALYSIS in MAMMALIAN CELL CULTURE: APPLICATIONS in PROCESS DEVELOPMENT and CHARACTERIZATION Anne Kantardjieff We
    TRANSCRIPTOME ANALYSIS IN MAMMALIAN CELL CULTURE: APPLICATIONS IN PROCESS DEVELOPMENT AND CHARACTERIZATION A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Anne Kantardjieff IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Wei-Shou Hu August, 2009 © Anne Kantardjieff, August 2009 ACKNOWLEDGMENTS First and foremost, I would like to thank my advisor, Prof. Wei-Shou Hu. He is a consummate teacher, who always puts the best interests of his students first. I am eternally grateful for all the opportunities he has given me and all that I have learned from him. I can only hope to prove as inspriring to others as he has been to me. I would like to thank my thesis committee members, Prof. Kevin Dorfman, Prof. Scott Fahrenkrug, and Prof. Friedrich Srienc, for taking the time to serve on my committee. It goes without saying that what makes the Hu lab a wonderful place to work are the people. I consider myself lucky to have joined what could only be described as a family. Thank you to all the Hu group members, past and present: Jongchan Lee, Wei Lian, Mugdha Gadgil, Sarika Mehra, Marcela de Leon Gatti, Ziomara Gerdtzen, Patrick Hossler, Katie Wlaschin, Gargi Seth, Fernando Ulloa, Joon Chong Yee, C.M. Cameron, David Umulis, Karthik Jayapal, Salim Charaniya, Marlene Castro, Nitya Jacob, Bhanu Mulukutla, Siguang Sui, Kartik Subramanian, Cornelia Bengea, Huong Le, Anushree Chatterjee, Jason Owens, Shikha Sharma, Kathryn Johnson, Eyal Epstein, ze Germans, Kirsten Keefe, Kim Coffee, Katherine Mattews and Jessica Raines-Jones.
    [Show full text]
  • CSF3 Is a Potential Drug Target for the Treatment of COVID-19
    fphys-11-605792 January 18, 2021 Time: 17:38 # 1 ORIGINAL RESEARCH published: 22 January 2021 doi: 10.3389/fphys.2020.605792 CSF3 Is a Potential Drug Target for the Treatment of COVID-19 Chao Fang1,2†, Jie Mei1†, Huixiang Tian3, Yu-Ligh Liou1, Dingchao Rong4, Wei Zhang1*, Qianjin Liao2* and Nayiyuan Wu1,2* 1 Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Xiangya Hospital, Institute of Clinical Pharmacology, Central South University, Changsha, China, 2 Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, 3 Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China, 4 Department of Spine Surgery, Xiangya Hospital, Central South University, Changsha, China Coronavirus Disease 2019 (COVID-19) is an acute respiratory infectious disease that appeared at the end of 2019. As of July 2020, the cumulative number of infections and deaths have exceeded 15 million and 630,000, respectively. And new cases are increasing. There are still many difficulties surrounding research on the mechanism and development of therapeutic vaccines. It is urgent to explore the pathogenic mechanism of viruses to help prevent and treat COVID-19. In our study, we downloaded two datasets related to COVID-19 (GSE150819 and GSE147507). By analyzing the high- throughput expression matrix of uninfected human bronchial organoids and infected Edited by: James J. Cai, human bronchial organoids in the GSE150819, 456 differentially expressed genes Texas A&M University, United States (DEGs) were identified, which were mainly enriched in the cytokine–cytokine receptor Reviewed by: interaction pathway and so on. We also constructed the protein–protein interaction Joyce Chen, (PPI) network of DEGs to identify the hub genes.
    [Show full text]
  • Severity of Innate Immune-Mediated Colitis Is Controlled by the Cytokine Deficiency-Induced Colitis Susceptibility-1 (Cdcs1) Locus
    Severity of innate immune-mediated colitis is controlled by the cytokine deficiency-induced colitis susceptibility-1 (Cdcs1) locus Joerg Ermanna,b,c, Wendy S. Garretta,c,d, Juhi Kuchrooa, Khadija Rouridaa, Jonathan N. Glickmane,f, Andre Bleichg, and Laurie H. Glimchera,b,c,h,1 aDepartment of Immunology and Infectious Diseases, Harvard School of Public Health, cHarvard Medical School, dThe Dana-Farber Cancer Institute, and bDivision of Rheumatology, Immunology and Allergy, and eDepartment of Pathology, Brigham and Women’s Hospital, Boston, MA 02115; fCaris Diagnostics, Newton, MA 02464; gInstitute for Laboratory Animal Science, Hannover Medical School, 30625 Hannover, Germany; and hThe Ragon Institute of Massachusetts General Hospital/Massachusetts Institute of Technology/Harvard University, Charlestown, MA 02129 Contributed by Laurie H. Glimcher, March 15, 2011 (sent for review March 08, 2011) Genetic modifier loci influence the phenotypic expression of many within this region and the cell type through which they exert their Mendelian traits; insight into disease pathogenesis gained from effects are still unknown. − − − − their identification in animal disease models may impact the We recently described that T-bet / .Rag2 / mice on a BALB/c treatment of human multigenic disorders. We previously described background in our colony develop spontaneous UC-like disease an innate immune-driven model of spontaneous ulcerative colitis in (TRUC). This disease is driven by colitogenic intestinal micro- T-bet−/−.Rag2−/− double-deficient mice that resembles human ulcer- biota, mediated by TNF, and results in spontaneous develop- ative colitis. On a BALB/c background, this disease is highly pene- ment of colorectal cancer (11–13). We report here that colitis − − − − trant and results in the development of colorectal cancer.
    [Show full text]
  • Download Special Issue
    BioMed Research International Novel Bioinformatics Approaches for Analysis of High-Throughput Biological Data Guest Editors: Julia Tzu-Ya Weng, Li-Ching Wu, Wen-Chi Chang, Tzu-Hao Chang, Tatsuya Akutsu, and Tzong-Yi Lee Novel Bioinformatics Approaches for Analysis of High-Throughput Biological Data BioMed Research International Novel Bioinformatics Approaches for Analysis of High-Throughput Biological Data Guest Editors: Julia Tzu-Ya Weng, Li-Ching Wu, Wen-Chi Chang, Tzu-Hao Chang, Tatsuya Akutsu, and Tzong-Yi Lee Copyright © 2014 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in “BioMed Research International.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Contents Novel Bioinformatics Approaches for Analysis of High-Throughput Biological Data,JuliaTzu-YaWeng, Li-Ching Wu, Wen-Chi Chang, Tzu-Hao Chang, Tatsuya Akutsu, and Tzong-Yi Lee Volume2014,ArticleID814092,3pages Evolution of Network Biomarkers from Early to Late Stage Bladder Cancer Samples,Yung-HaoWong, Cheng-Wei Li, and Bor-Sen Chen Volume 2014, Article ID 159078, 23 pages MicroRNA Expression Profiling Altered by Variant Dosage of Radiation Exposure,Kuei-FangLee, Yi-Cheng Chen, Paul Wei-Che Hsu, Ingrid Y. Liu, and Lawrence Shih-Hsin Wu Volume2014,ArticleID456323,10pages EXIA2: Web Server of Accurate and Rapid Protein Catalytic Residue Prediction, Chih-Hao Lu, Chin-Sheng
    [Show full text]
  • Genomic Approach in Idiopathic Intellectual Disability Maria De Fátima E Costa Torres
    ESTUDOS DE 8 01 PDPGM 2 CICLO Genomic approach in idiopathic intellectual disability Maria de Fátima e Costa Torres D Autor. Maria de Fátima e Costa Torres D.ICBAS 2018 Genomic approach in idiopathic intellectual disability Genomic approach in idiopathic intellectual disability Maria de Fátima e Costa Torres SEDE ADMINISTRATIVA INSTITUTO DE CIÊNCIAS BIOMÉDICAS ABEL SALAZAR FACULDADE DE MEDICINA MARIA DE FÁTIMA E COSTA TORRES GENOMIC APPROACH IN IDIOPATHIC INTELLECTUAL DISABILITY Tese de Candidatura ao grau de Doutor em Patologia e Genética Molecular, submetida ao Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto Orientadora – Doutora Patrícia Espinheira de Sá Maciel Categoria – Professora Associada Afiliação – Escola de Medicina e Ciências da Saúde da Universidade do Minho Coorientadora – Doutora Maria da Purificação Valenzuela Sampaio Tavares Categoria – Professora Catedrática Afiliação – Faculdade de Medicina Dentária da Universidade do Porto Coorientadora – Doutora Filipa Abreu Gomes de Carvalho Categoria – Professora Auxiliar com Agregação Afiliação – Faculdade de Medicina da Universidade do Porto DECLARAÇÃO Dissertação/Tese Identificação do autor Nome completo _Maria de Fátima e Costa Torres_ N.º de identificação civil _07718822 N.º de estudante __ 198600524___ Email institucional [email protected] OU: [email protected] _ Email alternativo [email protected] _ Tlf/Tlm _918197020_ Ciclo de estudos (Mestrado/Doutoramento) _Patologia e Genética Molecular__ Faculdade/Instituto _Instituto de Ciências
    [Show full text]
  • Whole Genome Sequence Analyses of Brain Imaging Measures in the Framingham Study
    ARTICLE OPEN ACCESS Whole genome sequence analyses of brain imaging measures in the Framingham Study Chlo´e Sarnowski, PhD, Claudia L. Satizabal, PhD, Charles DeCarli, MD, Achilleas N. Pitsillides, PhD, Correspondence L. Adrienne Cupples, PhD, Ramachandran S. Vasan, MD, James G. Wilson, MD, Joshua C. Bis, PhD, Dr. Sarnowski Myriam Fornage, PhD, Alexa S. Beiser, PhD, Anita L. DeStefano, PhD, Jos´ee Dupuis, PhD, [email protected] and Sudha Seshadri, MD, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, On behalf of the TOPMed Neurocognitive Working Group Neurology® 2018;90:e188-e196. doi:10.1212/WNL.0000000000004820 Abstract Objective We sought to identify rare variants influencing brain imaging phenotypes in the Framingham Heart Study by performing whole genome sequence association analyses within the Trans- Omics for Precision Medicine Program. Methods We performed association analyses of cerebral and hippocampal volumes and white matter hyperintensity (WMH) in up to 2,180 individuals by testing the association of rank-normalized residuals from mixed-effect linear regression models adjusted for sex, age, and total intracranial volume with individual variants while accounting for familial relatedness. We conducted gene- based tests for rare variants using (1) a sliding-window approach, (2) a selection of functional exonic variants, or (3) all variants. Results We detected new loci in 1p21 for cerebral volume (minor allele frequency [MAF] 0.005, p = − − 10 8) and in 16q23 for hippocampal volume (MAF 0.05, p = 2.7 × 10 8). Previously identified − associations in 12q24 for hippocampal volume (rs7294919, p = 4.4 × 10 4) and in 17q25 for − WMH (rs7214628, p = 2.0 × 10 3) were confirmed.
    [Show full text]
  • Renoprotective Effect of Combined Inhibition of Angiotensin-Converting Enzyme and Histone Deacetylase
    BASIC RESEARCH www.jasn.org Renoprotective Effect of Combined Inhibition of Angiotensin-Converting Enzyme and Histone Deacetylase † ‡ Yifei Zhong,* Edward Y. Chen, § Ruijie Liu,*¶ Peter Y. Chuang,* Sandeep K. Mallipattu,* ‡ ‡ † | ‡ Christopher M. Tan, § Neil R. Clark, § Yueyi Deng, Paul E. Klotman, Avi Ma’ayan, § and ‡ John Cijiang He* ¶ *Department of Medicine, Mount Sinai School of Medicine, New York, New York; †Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; ‡Department of Pharmacology and Systems Therapeutics and §Systems Biology Center New York, Mount Sinai School of Medicine, New York, New York; |Baylor College of Medicine, Houston, Texas; and ¶Renal Section, James J. Peters Veterans Affairs Medical Center, New York, New York ABSTRACT The Connectivity Map database contains microarray signatures of gene expression derived from approximately 6000 experiments that examined the effects of approximately 1300 single drugs on several human cancer cell lines. We used these data to prioritize pairs of drugs expected to reverse the changes in gene expression observed in the kidneys of a mouse model of HIV-associated nephropathy (Tg26 mice). We predicted that the combination of an angiotensin-converting enzyme (ACE) inhibitor and a histone deacetylase inhibitor would maximally reverse the disease-associated expression of genes in the kidneys of these mice. Testing the combination of these inhibitors in Tg26 mice revealed an additive renoprotective effect, as suggested by reduction of proteinuria, improvement of renal function, and attenuation of kidney injury. Furthermore, we observed the predicted treatment-associated changes in the expression of selected genes and pathway components. In summary, these data suggest that the combination of an ACE inhibitor and a histone deacetylase inhibitor could have therapeutic potential for various kidney diseases.
    [Show full text]
  • Setd1 Histone 3 Lysine 4 Methyltransferase Complex Components in Epigenetic Regulation
    SETD1 HISTONE 3 LYSINE 4 METHYLTRANSFERASE COMPLEX COMPONENTS IN EPIGENETIC REGULATION Patricia A. Pick-Franke Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Master of Science in the Department of Biochemistry and Molecular Biology Indiana University December 2010 Accepted by the Faculty of Indiana University, in partial fulfillment of the requirements for the degree of Master of Science. _____________________________________ David Skalnik, Ph.D., Chair _____________________________________ Kristin Chun, Ph.D. Master’s Thesis Committee _____________________________________ Simon Rhodes, Ph.D. ii DEDICATION This thesis is dedicated to my sons, Zachary and Zephaniah who give me great joy, hope and continuous inspiration. I can only hope that I successfully set a good example demonstrating that one can truly accomplish anything, if you never give up and reach for your dreams. iii ACKNOWLEDGEMENTS I would like to thank my committee members Dr. Skalnik, Dr. Chun and Dr. Rhodes for allowing me to complete this dissertation. They have been incredibly generous with their flexibility. I must make a special thank you to Jeanette McClintock, who willingly gave her expertise in statistical analysis with the Cfp1 microarray data along with encouragement, support and guidance to complete this work. I would like to thank Courtney Tate for her ceaseless willingness to share ideas, and her methods and materials, and Erika Dolbrota for her generous instruction as well as the name of a good doctor. I would also like to acknowledge the superb mentorship of Dr. Jeon Heong Lee, PhD and the contagious passion and excitement for the life of science of Dr.
    [Show full text]