Prognosis and Modulation Mechanisms of COMMD6 in Human Tumours Based on Expression Profiling and Comprehensive Bioinformatics An
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Old Data and Friends Improve with Age: Advancements with the Updated Tools of Genenetwork
bioRxiv preprint doi: https://doi.org/10.1101/2021.05.24.445383; this version posted May 25, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 4.0 International license. Old data and friends improve with age: Advancements with the updated tools of GeneNetwork Alisha Chunduri1, David G. Ashbrook2 1Department of Biotechnology, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India 2Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract Understanding gene-by-environment interactions is important across biology, particularly behaviour. Families of isogenic strains are excellently placed, as the same genome can be tested in multiple environments. The BXD’s recent expansion to 140 strains makes them the largest family of murine isogenic genomes, and therefore give great power to detect QTL. Indefinite reproducible genometypes can be leveraged; old data can be reanalysed with emerging tools to produce novel biological insights. To highlight the importance of reanalyses, we obtained drug- and behavioural-phenotypes from Philip et al. 2010, and reanalysed their data with new genotypes from sequencing, and new models (GEMMA and R/qtl2). We discover QTL on chromosomes 3, 5, 9, 11, and 14, not found in the original study. We narrowed down the candidate genes based on their ability to alter gene expression and/or protein function, using cis-eQTL analysis, and variants predicted to be deleterious. Co-expression analysis (‘gene friends’) and human PheWAS were used to further narrow candidates. -
Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
Nuclear and Mitochondrial Genome Defects in Autisms
UC Irvine UC Irvine Previously Published Works Title Nuclear and mitochondrial genome defects in autisms. Permalink https://escholarship.org/uc/item/8vq3278q Journal Annals of the New York Academy of Sciences, 1151(1) ISSN 0077-8923 Authors Smith, Moyra Spence, M Anne Flodman, Pamela Publication Date 2009 DOI 10.1111/j.1749-6632.2008.03571.x License https://creativecommons.org/licenses/by/4.0/ 4.0 Peer reviewed eScholarship.org Powered by the California Digital Library University of California THE YEAR IN HUMAN AND MEDICAL GENETICS 2009 Nuclear and Mitochondrial Genome Defects in Autisms Moyra Smith, M. Anne Spence, and Pamela Flodman Department of Pediatrics, University of California, Irvine, California In this review we will evaluate evidence that altered gene dosage and structure im- pacts neurodevelopment and neural connectivity through deleterious effects on synap- tic structure and function, and evidence that the latter are key contributors to the risk for autism. We will review information on alterations of structure of mitochondrial DNA and abnormal mitochondrial function in autism and indications that interactions of the nuclear and mitochondrial genomes may play a role in autism pathogenesis. In a final section we will present data derived using Affymetrixtm SNP 6.0 microar- ray analysis of DNA of a number of subjects and parents recruited to our autism spectrum disorders project. We include data on two sets of monozygotic twins. Col- lectively these data provide additional evidence of nuclear and mitochondrial genome imbalance in autism and evidence of specific candidate genes in autism. We present data on dosage changes in genes that map on the X chromosomes and the Y chro- mosome. -
ACE2 Interaction Networks in COVID-19: a Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors
Journal of Clinical Medicine Article ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors Zofia Wicik 1,2 , Ceren Eyileten 2, Daniel Jakubik 2,Sérgio N. Simões 3, David C. Martins Jr. 1, Rodrigo Pavão 1, Jolanta M. Siller-Matula 2,4,* and Marek Postula 2 1 Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Santo Andre 09606-045, Brazil; zofi[email protected] (Z.W.); [email protected] (D.C.M.J.); [email protected] (R.P.) 2 Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Center for Preclinical Research and Technology CEPT, 02-091 Warsaw, Poland; [email protected] (C.E.); [email protected] (D.J.); [email protected] (M.P.) 3 Federal Institute of Education, Science and Technology of Espírito Santo, Serra, Espírito Santo 29056-264, Brazil; [email protected] 4 Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, 1090 Vienna, Austria * Correspondence: [email protected]; Tel.: +43-1-40400-46140; Fax: +43-1-40400-42160 Received: 9 October 2020; Accepted: 17 November 2020; Published: 21 November 2020 Abstract: Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD. Methods: Using the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks that could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
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. -
Sarkar Et Al. SUPPLEMENTAL INFORMATION
Sarkar et al. SUPPLEMENTAL INFORMATION Figure S1 A 10 20 30 40 50 MADKRKLQGE IDRCLKKVSE GVEQFEDIWQ KLHNAANANQ KEKYEADLKK 60 70 80 90 100 EIKKLQRLRD QIKTWVASNE IKDKRQLIEN RKLIETQMER FKVVERETKT Predicted 110 120 130 140 150 coiled-coil KAYSKEGLGL AQKVDPAQKE KEEVGQWLTN TIDTLNMQVD QFESEVESLS domain 160 170 180 190 200 (1-241) VQTRKKKGDK DKQDRIEGLK RHIEKHRYHV RMLETILRML DNDSILVDAI 210 220 230 240 250 RKIKDDVEYY VDSSQDPDFE ENEFLYDDLD LEDIPQALVA TSPPSHSHME 260 270 280 290 300 DEIFNQSSST PTSTTSSSPI PPSPANCTTE NSEDDKKRGR ST* DSEVSQSP * 310 320 330 340 350 AKNGSKPVHS NQHPQSPAVP PTYPSGPPPT TSALSSTPGN NGASTPAAPT 360 370 380 390 400 SALGPKASPA PSHNSGTPAP YAQAVAPPNA SGPSNAQPRP PSAQPSGGSG 410 420 430 440 450 Intrinsically GGSGGSSSNS NSGTGGGAGK QNGATSYSSV VADSPAEVTL SSSGGSSASS disordered region 460 470 480 490 500 (242-605) QALGPTSGPH NPAPSTSKES STAAPSGAGN VASGSGNNSG GPSLLVPLPV 510 520 530 540 550 NPPSSPTPSF SEAKAAGTLL NGPPQFSTTP EIKAPEPLSS LKSMAERAAI 560 570 580 590 600 SSGIEDPVPT LHLTDRDIIL SSTSAPPTSS QPPLQLSEVN IPLSLGVCPL 610 620 630 640 650 GPVSLTKEQL YQQAMEEAAW HHMPHPSDSE RIRQYLPRNP CPTPPYHHQM 660 670 680 690 700 NAR/CS PPPHSDTVEF YQRLSTETLF FIFYYLEGTK AQYLAAKALK KQSWRFHTKY 710 720 730 740 750 NOT box (654-751) MMWFQRHEEP KTITDEFEQG TYIYFDYEKW GQRKKEGFTF EYRYLEDRDLQ B 2 Supplementary Figure 1: A. Sequence of the mouse CNOT3 protein (UNIPROT Q8K0V4). Vertical blue lines delineate the domains of the protein that are shown in Figure 1B. The green-shaded box indicates the the NOT box region, which is required for the interaction between CNOT3 and Aurora B (Figure 1C). Aurora B consensus phosphorylation sites are underlined. The red box and text indicate the putative nuclear localization sequence. * indicates residues 292 and 294, which were mutated in this study. B. Phosphorylation sites detected on the CNOT3 protein by in vivo proteomic discovery mass pectrometry. -
Transcriptomic Signature and Metabolic Programming of Bovine Classical and Nonclassical Monocytes Indicate Distinct Functional Specializations
bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362731; this version posted November 1, 2020. 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. Transcriptomic signature and metabolic programming of bovine classical and nonclassical monocytes indicate distinct functional specializations Stephanie C. Talker1,2, G. Tuba Barut1,2, Reto Rufener3, Lilly von Münchow4, Artur Summerfield1,2 1Institute of Virology and Immunology, Bern and Mittelhäusern, Switzerland 2Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland 3Institute of Parasitology, Vetsuisse Faculty, University of Bern, Bern, Switzerland 4 Bucher Biotec AG, Basel, Switzerland *Correspondence: Corresponding Author [email protected] Keywords: monocyte subsets, transcriptome, metabolism, cattle Abstract Similar to human monocytes, bovine monocytes can be split into CD14+CD16- classical and CD14-CD16+ nonclassical monocytes (cM and ncM, respectively). Here, we present an in-depth analysis of their steady-state transcriptomes, highlighting pronounced functional specializations. Gene transcription indicates that pro-inflammatory and antibacterial processes are associated with cM, while ncM appear to be specialized in regulatory/anti-inflammatory functions and tissue repair, as well as antiviral responses and T-cell immunomodulation. In support of these functional differences, we found that oxidative phosphorylation prevails in ncM, whereas cM are clearly biased towards aerobic glycolysis. Furthermore, bovine monocyte subsets differed in their responsiveness to TLR ligands, supporting an antiviral role of ncM. Taken together, these data clearly indicate a variety of subset-specific functions in cM and ncM that are likely to be transferable to monocyte subsets of other species, including humans. -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig. -
Sarkar Et Al. SUPPLEMENTAL INFORMATION
Sarkar et al. SUPPLEMENTAL INFORMATION Figure S1 A 10 20 30 40 50 MADKRKLQGE IDRCLKKVSE GVEQFEDIWQ KLHNAANANQ KEKYEADLKK 60 70 80 90 100 EIKKLQRLRD QIKTWVASNE IKDKRQLIEN RKLIETQMER FKVVERETKT Predicted 110 120 130 140 150 coiled-coil KAYSKEGLGL AQKVDPAQKE KEEVGQWLTN TIDTLNMQVD QFESEVESLS domain 160 170 180 190 200 (1-241) VQTRKKKGDK DKQDRIEGLK RHIEKHRYHV RMLETILRML DNDSILVDAI 210 220 230 240 250 RKIKDDVEYY VDSSQDPDFE ENEFLYDDLD LEDIPQALVA TSPPSHSHME 260 270 280 290 300 DEIFNQSSST PTSTTSSSPI PPSPANCTTE NSEDDKKRGR ST* DSEVSQSP * 310 320 330 340 350 AKNGSKPVHS NQHPQSPAVP PTYPSGPPPT TSALSSTPGN NGASTPAAPT 360 370 380 390 400 SALGPKASPA PSHNSGTPAP YAQAVAPPNA SGPSNAQPRP PSAQPSGGSG 410 420 430 440 450 Intrinsically GGSGGSSSNS NSGTGGGAGK QNGATSYSSV VADSPAEVTL SSSGGSSASS disordered region 460 470 480 490 500 (242-605) QALGPTSGPH NPAPSTSKES STAAPSGAGN VASGSGNNSG GPSLLVPLPV 510 520 530 540 550 NPPSSPTPSF SEAKAAGTLL NGPPQFSTTP EIKAPEPLSS LKSMAERAAI 560 570 580 590 600 SSGIEDPVPT LHLTDRDIIL SSTSAPPTSS QPPLQLSEVN IPLSLGVCPL 610 620 630 640 650 GPVSLTKEQL YQQAMEEAAW HHMPHPSDSE RIRQYLPRNP CPTPPYHHQM 660 670 680 690 700 NAR/CS PPPHSDTVEF YQRLSTETLF FIFYYLEGTK AQYLAAKALK KQSWRFHTKY 710 720 730 740 750 NOT box (654-751) MMWFQRHEEP KTITDEFEQG TYIYFDYEKW GQRKKEGFTF EYRYLEDRDLQ B 2 Supplementary Figure 1: A. Sequence of the mouse CNOT3 protein (UNIPROT Q8K0V4). Vertical blue lines delineate the domains of the protein that are shown in Figure 1B. The green-shaded box indicates the the NOT box region, which is required for the interaction between CNOT3 and Aurora B (Figure 1C). Aurora B consensus phosphorylation sites are underlined. The red box and red text indicate the putative nuclear localization sequence. * indicates residues 292 and 294, which were mutated in this study. B. Phosphorylation sites detected on the CNOT3 protein by in vivo proteomic discovery mass pectrometry. -
Noelia Díaz Blanco
Effects of environmental factors on the gonadal transcriptome of European sea bass (Dicentrarchus labrax), juvenile growth and sex ratios Noelia Díaz Blanco Ph.D. thesis 2014 Submitted in partial fulfillment of the requirements for the Ph.D. degree from the Universitat Pompeu Fabra (UPF). This work has been carried out at the Group of Biology of Reproduction (GBR), at the Department of Renewable Marine Resources of the Institute of Marine Sciences (ICM-CSIC). Thesis supervisor: Dr. Francesc Piferrer Professor d’Investigació Institut de Ciències del Mar (ICM-CSIC) i ii A mis padres A Xavi iii iv Acknowledgements This thesis has been made possible by the support of many people who in one way or another, many times unknowingly, gave me the strength to overcome this "long and winding road". First of all, I would like to thank my supervisor, Dr. Francesc Piferrer, for his patience, guidance and wise advice throughout all this Ph.D. experience. But above all, for the trust he placed on me almost seven years ago when he offered me the opportunity to be part of his team. Thanks also for teaching me how to question always everything, for sharing with me your enthusiasm for science and for giving me the opportunity of learning from you by participating in many projects, collaborations and scientific meetings. I am also thankful to my colleagues (former and present Group of Biology of Reproduction members) for your support and encouragement throughout this journey. To the “exGBRs”, thanks for helping me with my first steps into this world. Working as an undergrad with you Dr. -
Structural Variant Calling by Assembly in Whole Human Genomes: Applications in Hypoplastic Left Heart Syndrome by Matthew Kendzi
STRUCTURAL VARIANT CALLING BY ASSEMBLY IN WHOLE HUMAN GENOMES: APPLICATIONS IN HYPOPLASTIC LEFT HEART SYNDROME BY MATTHEW KENDZIOR THESIS Submitted in partial FulFillment oF tHe requirements for the degree of Master of Science in BioinFormatics witH a concentration in Crop Sciences in the Graduate College of the University oF Illinois at Urbana-Champaign, 2019 Urbana, Illinois Master’s Committee: ProFessor MattHew Hudson, CHair ResearcH Assistant ProFessor Liudmila Mainzer ProFessor SaurabH SinHa ABSTRACT Variant discovery in medical researcH typically involves alignment oF sHort sequencing reads to the human reference genome. SNPs and small indels (variants less than 50 nucleotides) are the most common types oF variants detected From alignments. Structural variation can be more diFFicult to detect From short-read alignments, and thus many software applications aimed at detecting structural variants From short read alignments have been developed. However, these almost all detect the presence of variation in a sample using expected mate-pair distances From read data, making them unable to determine the precise sequence of the variant genome at the speciFied locus. Also, reads from a structural variant allele migHt not even map to the reference, and will thus be lost during variant discovery From read alignment. A variant calling by assembly approacH was used witH tHe soFtware Cortex-var for variant discovery in Hypoplastic Left Heart Syndrome (HLHS). THis method circumvents many of the limitations oF variants called From a reFerence alignment: unmapped reads will be included in a sample’s assembly, and variants up to thousands of nucleotides can be detected, with the Full sample variant allele sequence predicted.