DOCSLIB.ORG

Genetic Analyses of Epigenetic Predictors That Estimate Aging, 2 Metabolic Traits, and Lifespan 3 Khyobeni Mozhui 1, 2*, Ake T

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Genetic Analyses of Epigenetic Predictors That Estimate Aging, 2 Metabolic Traits, and Lifespan 3 Khyobeni Mozhui 1, 2*, Ake T bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449634; this version posted June 24, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 1 Genetic Analyses of Epigenetic Predictors that Estimate Aging, 2 Metabolic Traits, and Lifespan 3 Khyobeni Mozhui 1, 2*, Ake T. Lu3, Caesar Z Li 3, Amin Haghani 4, Jose Vladimir Sandoval-Sierra 1, 4 Robert W. Williams 2, Steve Horvath3, 4 5 6 1 Department of Preventive Medicine, University of Tennessee Health Science Center, College of 7 Medicine, Memphis, Tennessee, USA 8 2 Department of Genetics, Genomics and Informatics, University of Tennessee Health Science 9 Center, College of Medicine, Memphis, Tennessee, USA 10 3 Department of Human Genetics, David Geffen School of Medicine, University of California Los 11 Angeles, Los Angeles, CA, USA 12 4 Department of Biostatistics, Fielding School of Public Health, University of California Los 13 Angeles, Los Angeles, CA, USA 14 15 *Correspondence: 16 Khyobeni Mozhui 17 [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449634; this version posted June 24, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 18 Abstract 19 DNA methylation (DNAm) clocks are accurate molecular biomarkers of aging. However, the 20 clock mechanisms remain unclear. Here, we used a pan-mammalian microarray to assay DNAm 21 in liver from 339 predominantly female mice belonging to the BXD family. We computed 22 epigenetic clocks and maximum lifespan predictor (predicted-maxLS), and examined 23 associations with DNAm entropy, diet, weight, metabolic traits, and genetic variation. The 24 epigenetic age acceleration (EAA) derived from the clocks, and predicted-maxLS were 25 correlated with lifespan of the BXD strains. Quantitative trait locus (QTL) analyses uncovered 26 significant QTLs on chromosome (Chr) 11 that encompasses the Erbb2/Her2 oncogenic region, 27 and on Chr19 that contains a cytochrome P450 cluster. Both loci harbor candidate genes 28 associated with EAA in humans (STXBP4, NKX2-3, CUTC). Transcriptome and proteome analyses 29 revealed enrichment in oxidation-reduction, metabolic, and mitotic genes. Our results highlight 30 loci that are concordant in human and mouse, and demonstrate intimate links between 31 metabolism, body weight, and epigenetic aging. 32 33 34 Keywords: epigenetic clock, lifespan, aging, DNA methylation, QTL, weight, diet 2 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449634; this version posted June 24, 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. 35 Introduction 36 Epigenetic clocks are widely used molecular biomarkers of aging1. These biological clocks are 37 based on the methylation status across an ensemble of “clock CpGs” that are collectively used 38 to derive a DNA methylation (DNAm) based estimate of age (DNAmAge). This estimate tracks 39 closely, but not perfectly, with an individual’s chronological age. How much the DNAmAge 40 deviates from the known chronological age is a measure of the rate of biological aging. Denoted 41 as epigenetic age acceleration (EAA), a more accelerated measure (positive EAA) suggests an 42 older biological age. While DNAmAge predicts age, its age-adjusted counterpart, EAA, is 43 associated with health, fitness, exposure to stressors, body mass index (BMI), and even life 44 expectancy2-6. 45 DNAm clocks were initially reported for humans7,8. Since then, the age estimator has been 46 extended to model organisms9-11, and different variants of human clocks have also been 47 developed. Some clocks are tissue specific, others are pan-tissue, and others perform well at 48 predicting health and life expectancy5,8,12-14. 49 A new microarray platform was recently developed to profile CpGs that have high conservation 50 across mammalian clades. This pan-mammalian DNAm array (HorvathMammalMethylChip40) 51 provides a common platform to measure DNAm, and has been used to build universal 52 epigenetic clocks that can estimate age across a variety of tissues and mammalian species15,16. 53 Another remarkable development with this array is the novel lifespan predictor that can 54 estimate the maximum lifespan of over 190 mammals at high accuracy17. 55 Here, we examine these novel clocks, lifespan predictor, and methylome entropy in a cohort of 56 mice belonging to the BXD family that were maintained on either normal chow or high-fat diet 57 (HFD)18,19. The BXDs are a well-established mouse genetic reference panel that were first 58 created as a family of recombinant inbred (RI) strains by crossing two inbred progenitors: 59 C57BL/6J (B6) and DBA/2J (D2). The family has been expanded to ~150 fully sequenced progeny 60 strains20,21. Members of the BXD family vary greatly in their metabolic profiles, aging rates, and 61 natural life expectancy18,19,22-24. The genetic variation, and the availability of accompanying 62 deep -omic data make the BXDs a unique experimental population for dissecting the genetic 63 modulators of epigenetic aging. Previously, we explored the aging methylome in a small 64 number of BXD cases and found that HFD and higher body weight were associated with higher 65 age-dependent changes in methylation25. In the present work, our goals were to (1) test the 66 accuracy of the DNAm measures in predicting age, lifespan, and association with diet and 67 metabolic characteristics, and (2) apply quantitative trait locus (QTL) mapping and gene 68 expression analyses to uncover loci and genes that contribute to these DNAm biomarkers. 69 Our results are consistent with a faster clock for cases on HFD, and with higher body weight. 70 Both the DNAmAge and lifespan predictors were correlated with the genotype-dependent life 71 expectancy of female BXDs. We report QTLs on chromosomes (Chrs) 11 and 19. A strong 72 candidate gene in the chromosome (Chr) 11 interval (referred to as Eaaq11) is Stxbp4, a gene 73 that has been consistently associated with EAA by human genome-wide association studies 74 (GWAS)26-28. The Chr19 QTL (Eaaq19) also harbors strong contenders including Cyp26a1, Myof, 75 Cutc, and Nkx2–3, and the conserved genes in humans have been associated with longevity and 3 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449634; this version posted June 24, 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. 76 EAA28-30. Eaaq19 may also have an effect on body weight change with age. We performed gene 77 expression analyses to clarify the physiology associated with the DNAm traits, and this, perhaps 78 unsurprisingly, highlighted metabolic networks as strong expression correlates of epigenetic 79 aging. 80 Results 81 Description of samples 82 The present study uses liver DNAm data from 339 predominantly female mice (18 males only) 83 belonging to 45 isogenic members of the BXD family, including F1 hybrids, and both parental 84 strains. Age ranged from 5.6 to 33.4 months. Mice were all weaned onto a normal chow 85 (control diet; CD) and a balanced subset of cases were then randomly assigned to the HFD (see 86 Roy et al for details 18). Tissues were collected at approximately six months intervals (see 87 Williams et al. 19). Individual-level data of cases used in this study are in Data S1. 88 Correlation with chronological age 89 For biological age prediction, three different types of mouse DNAm clocks were computed, 90 each as a pair: liver-specific, and pan-tissue (Table 1). These are: (1) a general DNAm clock 91 (referred to simply as DNAmAge): clock trained without pre-selecting for any specific CpG 92 subsets; (2) developmental clock (dev.DNAmAge): built from CpGs that change during 93 development; and (3) interventional clock (int.DNAmAge): built from CpGs that change in 94 response to aging related interventions such as caloric restriction, HFD, or dwarfing alleles9,11,25. 95 These clocks were trained either in an independent mouse dataset that did not include the 96 BXDs and were therefore unbiased to BXD characteristics (unbiased mouse clocks), or trained in 97 a subset of the BXD CD mice and used to estimate age in the full BXD cohort (BXD-biased Table 1. Epigenetic predictors and correlation with chronological age and entropy Training DNAmAge r with age r with entropy Clock type Tissue set1 name (n=339) (n=339) Standard unbiased DNAmAge pan 0.89 0.50 Standard unbiased DNAmAge liver 0.92 0.51 Developmental unbiased dev.DNAmAge pan 0.87 0.46 Developmental unbiased dev.DNAmAge liver 0.91 0.45 Interventional unbiased int.DNAmAge pan 0.85 0.38 Interventional unbiased int.DNAmAge liver 0.86 0.49 Standard BXD DNAmAge pan 0.93 0.55 Standard BXD DNAmAge liver 0.97 0.54 Developmental BXD dev.DNAmAge pan 0.95 0.51 Developmental BXD dev.DNAmAge liver 0.96 0.51 Interventional BXD int.DNAmAge pan 0.89 0.43 Interventional BXD int.DNAmAge liver 0.94 0.53 Universal unbiased univ.DNAmAge pan 0.92 0.59 Predicted- LiFespan unbiased pan -0.07 (ns) -0.13 (p=0.02) maxLS 1 Unbiased denotes that the clocks were trained in a completely independent dataset.
Load more

Recommended publications
  • Alterations of Genetic Variants and Transcriptomic Features of Response to Tamoxifen in the Breast Cancer Cell Line
    Alterations of Genetic Variants and Transcriptomic Features of Response to Tamoxifen in the Breast Cancer Cell Line Mahnaz Nezamivand-Chegini Shiraz University Hamed Kharrati-Koopaee Shiraz University https://orcid.org/0000-0003-2345-6919 seyed taghi Heydari ( [email protected] ) Shiraz University of Medical Sciences https://orcid.org/0000-0001-7711-1137 Hasan Giahi Shiraz University Ali Dehshahri Shiraz University of Medical Sciences Mehdi Dianatpour Shiraz University of Medical Sciences Kamran Bagheri Lankarani Shiraz University of Medical Sciences Research Keywords: Tamoxifen, breast cancer, genetic variants, RNA-seq. Posted Date: August 17th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-783422/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/33 Abstract Background Breast cancer is one of the most important causes of mortality in the world, and Tamoxifen therapy is known as a medication strategy for estrogen receptor-positive breast cancer. In current study, two hypotheses of Tamoxifen consumption in breast cancer cell line (MCF7) were investigated. First, the effect of Tamoxifen on genes expression prole at transcriptome level was evaluated between the control and treated samples. Second, due to the fact that Tamoxifen is known as a mutagenic factor, there may be an association between the alterations of genetic variants and Tamoxifen treatment, which can impact on the drug response. Methods In current study, the whole-transcriptome (RNA-seq) dataset of four investigations (19 samples) were derived from European Bioinformatics Institute (EBI). At transcriptome level, the effect of Tamoxifen was investigated on gene expression prole between control and treatment samples.
    [Show full text]
  • Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts
    Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing
    [Show full text]
  • MOCHI Enables Discovery of Heterogeneous Interactome Modules in 3D Nucleome
    Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press MOCHI enables discovery of heterogeneous interactome modules in 3D nucleome Dechao Tian1,# , Ruochi Zhang1,# , Yang Zhang1, Xiaopeng Zhu1, and Jian Ma1,* 1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA #These two authors contributed equally *Correspondence: [email protected] Contact To whom correspondence should be addressed: Jian Ma School of Computer Science Carnegie Mellon University 7705 Gates-Hillman Complex 5000 Forbes Avenue Pittsburgh, PA 15213 Phone: +1 (412) 268-2776 Email: [email protected] 1 Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract The composition of the cell nucleus is highly heterogeneous, with different constituents forming complex interactomes. However, the global patterns of these interwoven heterogeneous interactomes remain poorly understood. Here we focus on two different interactomes, chromatin interaction network and gene regulatory network, as a proof-of-principle, to identify heterogeneous interactome modules (HIMs), each of which represents a cluster of gene loci that are in spatial contact more frequently than expected and that are regulated by the same group of transcription factors. HIM integrates transcription factor binding and 3D genome structure to reflect “transcriptional niche” in the nucleus. We develop a new algorithm MOCHI to facilitate the discovery of HIMs based on network motif clustering in heterogeneous interactomes. By applying MOCHI to five different cell types, we found that HIMs have strong spatial preference within the nucleus and exhibit distinct functional properties. Through integrative analysis, this work demonstrates the utility of MOCHI to identify HIMs, which may provide new perspectives on the interplay between transcriptional regulation and 3D genome organization.
    [Show full text]
  • Mouse Cutc Conditional Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Cutc Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Cutc conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Cutc gene (NCBI Reference Sequence: NM_001113562 ; Ensembl: ENSMUSG00000025193 ) is located on Mouse chromosome 19. 9 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 9 (Transcript: ENSMUST00000112047). Exon 5~6 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Cutc gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP24-63D21 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 5 starts from about 49.14% of the coding region. The knockout of Exon 5~6 will result in frameshift of the gene. The size of intron 4 for 5'-loxP site insertion: 629 bp, and the size of intron 6 for 3'-loxP site insertion: 1953 bp. The size of effective cKO region: ~2818 bp. The cKO region does not have any other known gene. Page 1 of 7 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 3 4 5 6 7 9 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Cutc Homology arm cKO region loxP site Page 2 of 7 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats.
    [Show full text]
  • Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Fall 2010 Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Renuka Nayak University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Computational Biology Commons, and the Genomics Commons Recommended Citation Nayak, Renuka, "Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress" (2010). Publicly Accessible Penn Dissertations. 1559. https://repository.upenn.edu/edissertations/1559 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1559 For more information, please contact [email protected]. Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Abstract Genes interact in networks to orchestrate cellular processes. Here, we used coexpression networks based on natural variation in gene expression to study the functions and interactions of human genes. We asked how these networks change in response to stress. First, we studied human coexpression networks at baseline. We constructed networks by identifying correlations in expression levels of 8.9 million gene pairs in immortalized B cells from 295 individuals comprising three independent samples. The resulting networks allowed us to infer interactions between biological processes. We used the network to predict the functions of poorly-characterized human genes, and provided some experimental support. Examining genes implicated in disease, we found that IFIH1, a diabetes susceptibility gene, interacts with YES1, which affects glucose transport. Genes predisposing to the same diseases are clustered non-randomly in the network, suggesting that the network may be used to identify candidate genes that influence disease susceptibility.
    [Show full text]
  • ABSTRACTS 2013 Prise.Harvard.Edu Layout and Design: Ved Topkar ‘16 • Jennifer Tu ‘16 -4- TABLE of CONTENTS
    -2- ABSTRACTS 2013 prise.harvard.edu LAYOUT AND DESIGN: Ved Topkar ‘16 • Jennifer Tu ‘16 -4- TABLE OF CONTENTS Letter from the Director 7 Letter from the Editors 7 Abstracts BLISS 8 PRIMO 13 PRISE Astronomy and Astrophysics 17 Chemistry and Biochemistry 19 Computer Science 23 Earth and Planetary Sciences 25 Engineering and Bioengineering 26 Mathematics and Statistics 30 Microbiology and Immunology 34 Molecular and Cellular Biology 37 Neuroscience and Psychology 45 Organismic and Evolutionary Biology 53 Physics and Biophysics 54 Stem Cell and Regenerative Biology 57 SHARP 62 Acknowledgments 65 -5- -6- Letter from the Director Harvard’s Summer Undergraduate Research Village 2013 has been an outstanding collaboration of College scholars across the full spectrum of academic disciplines conducting formative and substantive research projects with Harvard faculty in the Faculty of Arts and Sciences, virtually all the other Schools in the Harvard universe, the teaching hospitals, and affiliated research enterprises across the Boston area. Along with the Program for Research in Science and Engineering (PRISE, now in its 8th year), the Behavioral Laboratory in the Social Sciences (BLISS, year 3), and the Program for Research in Markets and Organizations (PRIMO, co-hosted by the Harvard Business School, year 3), we have added the pilot SHARP: the Summer Humanities and Arts Research Program. I continue to be gratified and amazed by the consistently high degrees of enthusiasm, inclusivity, creativity, and energy in the Research Village residents of Mather House—the mind-boggling, diverse calendar of fellow-initiated activities is a testimony of the great effort and works in building community among these talented young investigators.
    [Show full text]
  • Guo, Updegrove Et Al., Supplemental Information 1
    Guo, Updegrove et al., Supplemental Information 1 Supplemental Information MicL, a new σE-dependent sRNA, combats envelope stress by repressing synthesis of Lpp, the major outer membrane lipoprotein 1,4 2,4 1,5 3 Monica S. Guo, Taylor B. Updegrove, Emily B. Gogol, Svetlana A. Shabalina, Carol A. Gross,1,6 and Gisela Storz2,6 1Department of Microbiology and Immunology, University of California, San Francisco, CA 94158, USA; 2Cell Biology and Metabolism Program, Eunice Kennedy Shriver National Institutes of Health, Bethesda, MD 20892, USA; 3National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA 4These authors contributed equally to this work. 5Current affiliation: Genentech 6Corresponding authors Email [email protected] Email [email protected] Guo, Updegrove et al., Supplemental Information 2 Supplemental Figures Guo, Updegrove et al., Supplemental Information 3 Supplemental Figure S1. Re-annotation of cutC region. (A) Transcription of the yecM-cutC region as observed in mRNA-seq. cutC appears to be co-transcribed with yecM in all conditions tested. (B) Sequence of micL-cutC genomic region. MicL (chromosome positions 1956771 to 1956462) and MicL-S (chromosome positions 1956544 to 1956462) terminate within the poly(T) stretch of the rho-independent terminator of cutC (stem indicated by arrows). The predicted -10 and -35 σE promoter elements of micL are underlined and the site of cleavage to generate MicL-S is indicated by the vertical arrow. A previous study reported a σE-dependent promoter for cutC at position 1957010 (Dartigalongue et al., 2001). We find no evidence for this site in our tiling array, deep sequencing and Northern data upon σE overexpression.
    [Show full text]
  • Statistical Methods and Analysis to Identify Disease-Related Variants
    Statistical Methods and Analysis to Identify Disease-Related Variants from Genetics Studies by Sai Chen A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Bioinformatics) in the University of Michigan 2017 Doctoral Committee: Professor Goncalo Abecasis, Chair Assisstant Professor Hyun Min Kang Assisstant Professor Jefferey M. Kidd Associate Professor Jun Li Professor Kerby Shedden © Sai Chen 2017 [email protected] ORCID: 0000-0003-3106-5643 To my love Bing Dedication ii Acknowledgements I would like to express my greatest gratitude to Dr. Goncalo Abecasis for his guidance in my research. When I meet difficulties in my research, his suggestions always opened a brand new view, and finally helped me solve the problem in a precise and simple way. He patiently gave me time and flexibility to try and to learn from such a broad range, and thanks to him, I finally found my true motivation and interests. I am so grateful to have such an understanding and brilliant mentor during my PhD. At the same time, I would like to thank Dr Hyun Min Kang. He guided me all the way through the GECCO project, and is always willing to teach me any new coding and analysis skills. I would like to thank my committee Dr Jun Li, Dr Jefferey Kidd and Dr Kerby Shedden. The rotations in their groups were my first step in Bioinformatics. I would like to thank Kerby Shedden for valuable suggestions on the statistical components in my thesis. Apart from my committee members, I would like to thank all my collaborators.
    [Show full text]
  • Network Mining Approach to Cancer Biomarker Discovery
    NETWORK MINING APPROACH TO CANCER BIOMARKER DISCOVERY THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Praneeth Uppalapati, B.E. Graduate Program in Computer Science and Engineering The Ohio State University 2010 Thesis Committee: Dr. Kun Huang, Advisor Dr. Raghu Machiraju Copyright by Praneeth Uppalapati 2010 ABSTRACT With the rapid development of high throughput gene expression profiling technology, molecule profiling has become a powerful tool to characterize disease subtypes and discover gene signatures. Most existing gene signature discovery methods apply statistical methods to select genes whose expression values can differentiate different subject groups. However, a drawback of these approaches is that the selected genes are not functionally related and hence cannot reveal biological mechanism behind the difference in the patient groups. Gene co-expression network analysis can be used to mine functionally related sets of genes that can be marked as potential biomarkers through survival analysis. We present an efficient heuristic algorithm EigenCut that exploits the properties of gene co- expression networks to mine functionally related and dense modules of genes. We apply this method to brain tumor (Glioblastoma Multiforme) study to obtain functionally related clusters. If functional groups of genes with predictive power on patient prognosis can be identified, insights on the mechanisms related to metastasis in GBM can be obtained and better therapeutical plan can be developed. We predicted potential biomarkers by dividing the patients into two groups based on their expression profiles over the genes in the clusters and comparing their survival outcome through survival analysis.
    [Show full text]
  • Download the Supplementary Figures and Tables
    Supplementary Figures and Tables The EFI Web Resource for Genomic Enzymology Web Tools: Leveraging Protein, Genome, and Metagenome Databases to Discover Novel Enzymes and Metabolic Pathways Rémi Zallot1, Nils O. Oberg1, and John A. Gerlt*,1, 2, 3 1Institute for Genomic Biology, 2Department of Biochemistry, and 3Department of Chemistry, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, Illinois 61801, United States. Corresponding author: John A. Gerlt ([email protected]). Phone: +1-217-244-7414 180 160 140 TrEMBL 120 SwissProt 100 80 Millions 60 40 20 0 1996 1999 2002 2005 2008 2011 2014 2017 Figure S1. The growth of the UniProtKB database. The web tools use the UniProtKB database for sequences used to generate SSNs and bioinformatic data included as node attributes in the SSNs. UniProtKB is the aggregate of the UniProtKB/TrEMBL database that contains computationally annotated entries [156,077,686 in Release 2019_04 (08-May-2019)] and the UniProtKB/SwissProt database that contains manually curated entries (560,118 in Release 2019_04). The decrease in 2015 is the result of archiving sequences from redundant proteomes in UniParc to manage the growth of the database. A B C Figure S2. The home pages for the web tools. Panel A, EFI-EST for generating sequence similarity networks (SSNs), accessible at https://efi.igb.illinois.edu/efi-est/. Panel B, EFI-GNT for collecting and analyzing genome context for bacterial, archaeal, and fungal proteins in SSNs, accessible at https://efi.igb.illinois.edu/efi-gnt/. Panel C, EFI-CGFP for mapping metagenome abundance to SSNs clusters, accessible at https://efi.igb.illinois.edu/efi-cgfp/.
    [Show full text]
  • FARE2015 WINNERS Sorted by Study Section
    FARE2015 WINNERS Sorted By Study Section Biochemistry - Proteins Andrezza Campos-Chagas Visiting Fellow NIAID Sicpin, the first immunomodulatory salivary protein described in black flies significantly reduced T and B cell proliferation and directly binds to soluble CD4 receptor Hematophagy is key to blood feeding arthropods reproductive success and an important link in pathogen transmission cycles. Salivary gland homogenates from blackflies have been shown to contain immunomodulatory activity on murine splenocytes. However, the molecule(s) responsible for this salivary activity remains elusive thus far. Here, we report the first immunosuppressive protein from blackfly salivary glands. Sicpin (Simulium cell proliferation inhibitor) was produced in E. coli and purified using size exclusion and ion exchange chromatography. Purified Sicpin was LPS-decontaminated and its sequence verified by N-terminal sequencing and LC-MS/MS analysis. Sicpin inhibited cell proliferation in a dose-response manner independently of the mitogen utilized (ConA, LPS, CD3/CD28 and Pokeweed). LPS or ConA stimulated cells had a significant lower proliferation rates (P<0.001) in the presence of Sicpin (IC50=0.5uM) with 10uM completely abrogating cell proliferation. Flow cytometry analysis showed that Sicpin inhibited proliferation of CD19+ B-cells and CD4+/CD8+ T-cells. Sicpin did not induce apoptosis or necrosis in mitogen-induced proliferative responses by murine splenocytes as determined by flow cytometry. Sicpin also inhibits antigen-specific cell proliferation without inducing apoptosis in resting or mitogen-induced splenocytes. We demonstrate that the production IFN-alpha, IL4, IL5, IL6 and IL10 by splenocytes stimulated by ConA or LPS was dose-dependently reduced by Sicpin. Reduction of cytokines in presence of Sicpin could lead to a retardation of B and T cell activity.
    [Show full text]
  • Genome Sequencing and Analysis of the First Spontaneous Nanosilver 4 Resistant Bacterium Proteus Mirabilis Strain SCDR1
    bioRxiv preprint doi: https://doi.org/10.1101/089961; this version posted November 27, 2016. 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. 1 Running Title: Nanosilver resistant Proteus mirabilis 2 3 Genome sequencing and analysis of the first spontaneous Nanosilver 4 resistant bacterium Proteus mirabilis strain SCDR1 5 6 Amr T. M. Saeb 1*, Khalid A. Al-Rubeaan1, Mohamed Abouelhoda 2, 3, Manojkumar Selvaraju 3, 4, and 7 Hamsa T. Tayeb 2, 3 8 9 1. Genetics and Biotechnology Department, Strategic Center for Diabetes Research, College of 10 medicine, King Saud University, KSA. 11 2. Genetics Department, King Faisal Specialist Hospital and Research Center, Riyadh, KSA. 12 3. Saudi Human Genome Project, King Abdulaziz City for Science and Technology (KACST), 13 Riyadh, KSA 14 4. Integrated Gulf Biosystems, Riyadh, KSA. 15 5. Department of Pathology & Laboratory Medicine, King Faisal Specialist Hospital and Research 16 Center, Riyadh, KSA. 17 6. Department of Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, KSA 18 19 20 21 22 23 24 *Corresponding Author: 25 Amr T. M. Saeb, Ph.D. 26 Genetics and Biotechnology Department, 27 Strategic Center for Diabetes Research, 28 College of medicine, King Saud University, KSA. 29 Tel: +966-566263979 30 Fax: +966-11-4725682 31 Email: [email protected] 32 bioRxiv preprint doi: https://doi.org/10.1101/089961; this version posted November 27, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.
    [Show full text]