Characterization of Intron Loss Events in Mammals
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Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Aberrant Modulation of Ribosomal Protein S6 Phosphorylation Confers Acquired Resistance to MAPK Pathway Inhibitors in BRAF-Mutant Melanoma
www.nature.com/aps ARTICLE Aberrant modulation of ribosomal protein S6 phosphorylation confers acquired resistance to MAPK pathway inhibitors in BRAF-mutant melanoma Ming-zhao Gao1,2, Hong-bin Wang1,2, Xiang-ling Chen1,2, Wen-ting Cao1,LiFu1, Yun Li1, Hai-tian Quan1,2, Cheng-ying Xie1,2 and Li-guang Lou1,2 BRAF and MEK inhibitors have shown remarkable clinical efficacy in BRAF-mutant melanoma; however, most patients develop resistance, which limits the clinical benefit of these agents. In this study, we found that the human melanoma cell clones, A375-DR and A375-TR, with acquired resistance to BRAF inhibitor dabrafenib and MEK inhibitor trametinib, were cross resistant to other MAPK pathway inhibitors. In these resistant cells, phosphorylation of ribosomal protein S6 (rpS6) but not phosphorylation of ERK or p90 ribosomal S6 kinase (RSK) were unable to be inhibited by MAPK pathway inhibitors. Notably, knockdown of rpS6 in these cells effectively downregulated G1 phase-related proteins, including RB, cyclin D1, and CDK6, induced cell cycle arrest, and inhibited proliferation, suggesting that aberrant modulation of rpS6 phosphorylation contributed to the acquired resistance. Interestingly, RSK inhibitor had little effect on rpS6 phosphorylation and cell proliferation in resistant cells, whereas P70S6K inhibitor showed stronger inhibitory effects on rpS6 phosphorylation and cell proliferation in resistant cells than in parental cells. Thus regulation of rpS6 phosphorylation, which is predominantly mediated by BRAF/MEK/ERK/RSK signaling in parental cells, was switched to mTOR/ P70S6K signaling in resistant cells. Furthermore, mTOR inhibitors alone overcame acquired resistance and rescued the sensitivity of the resistant cells when combined with BRAF/MEK inhibitors. -
Allele-Specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish
Allele-specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish by Ailu Chen A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama August 1, 2015 Keywords: catfish, interspecific hybrids, allele-specific expression, ribosomal protein Copyright 2015 by Ailu Chen Approved by Zhanjiang Liu, Chair, Professor, School of Fisheries, Aquaculture and Aquatic Sciences Nannan Liu, Professor, Entomology and Plant Pathology Eric Peatman, Associate Professor, School of Fisheries, Aquaculture and Aquatic Sciences Aaron M. Rashotte, Associate Professor, Biological Sciences Abstract Interspecific hybridization results in a vast reservoir of allelic variations, which may potentially contribute to phenotypical enhancement in the hybrids. Whether the allelic variations are related to the downstream phenotypic differences of interspecific hybrid is still an open question. The recently developed genome-wide allele-specific approaches that harness high- throughput sequencing technology allow direct quantification of allelic variations and gene expression patterns. In this work, I investigated allele-specific expression (ASE) pattern using RNA-Seq datasets generated from interspecific catfish hybrids. The objective of the study is to determine the ASE genes and pathways in which they are involved. Specifically, my study investigated ASE-SNPs, ASE-genes, parent-of-origins of ASE allele and how ASE would possibly contribute to heterosis. My data showed that ASE was operating in the interspecific catfish system. Of the 66,251 and 177,841 SNPs identified from the datasets of the liver and gill, 5,420 (8.2%) and 13,390 (7.5%) SNPs were identified as significant ASE-SNPs, respectively. -
A Network Propagation Approach to Prioritize Long Tail Genes in Cancer
bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429983; this version posted February 8, 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. A Network Propagation Approach to Prioritize Long Tail Genes in Cancer Hussein Mohsen1,*, Vignesh Gunasekharan2, Tao Qing2, Sahand Negahban3, Zoltan Szallasi4, Lajos Pusztai2,*, Mark B. Gerstein1,5,6,3,* 1 Computational Biology & Bioinformatics Program, Yale University, New Haven, CT 06511, USA 2 Breast Medical Oncology, Yale School of Medicine, New Haven, CT 06511, USA 3 Department of Statistics & Data Science, Yale University, New Haven, CT 06511, USA 4 Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA 5 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA 6 Department of Computer Science, Yale University, New Haven, CT 06511, USA * Corresponding author Abstract Introduction. The diversity of genomic alterations in cancer pose challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implication in cancer development. The study of these genes often requires integrative approaches with multiple types of biological data. Network propagation methods have demonstrated high efficacy in uncovering genomic patterns underlying cancer using biological interaction networks. Yet, the majority of these analyses have focused their assessment on detecting known cancer genes or identifying altered subnetworks. -
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. -
Multivariate Meta-Analysis of Differential Principal Components Underlying Human Primed and Naive-Like Pluripotent States
bioRxiv preprint doi: https://doi.org/10.1101/2020.10.20.347666; this version posted October 21, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. October 20, 2020 To: bioRxiv Multivariate Meta-Analysis of Differential Principal Components underlying Human Primed and Naive-like Pluripotent States Kory R. Johnson1*, Barbara S. Mallon2, Yang C. Fann1, and Kevin G. Chen2*, 1Intramural IT and Bioinformatics Program, 2NIH Stem Cell Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892, USA Keywords: human pluripotent stem cells; naive pluripotency, meta-analysis, principal component analysis, t-SNE, consensus clustering *Correspondence to: Dr. Kory R. Johnson ([email protected]) Dr. Kevin G. Chen ([email protected]) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.20.347666; this version posted October 21, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. ABSTRACT The ground or naive pluripotent state of human pluripotent stem cells (hPSCs), which was initially established in mouse embryonic stem cells (mESCs), is an emerging and tentative concept. To verify this important concept in hPSCs, we performed a multivariate meta-analysis of major hPSC datasets via the combined analytic powers of percentile normalization, principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and SC3 consensus clustering. -
SLFN11 Promotes CDT1 Degradation by CUL4 in Response to Replicative DNA Damage, While Its Absence Leads to Synthetic Lethality with ATR/CHK1 Inhibitors
SLFN11 promotes CDT1 degradation by CUL4 in response to replicative DNA damage, while its absence leads to synthetic lethality with ATR/CHK1 inhibitors Ukhyun Joa,1, Yasuhisa Muraia, Sirisha Chakkab, Lu Chenb, Ken Chengb, Junko Muraic, Liton Kumar Sahaa, Lisa M. Miller Jenkinsd, and Yves Pommiera,1 aDevelopmental Therapeutics Branch, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20814; bNational Center for Advancing Translational Sciences, Functional Genomics Laboratory, NIH, Rockville, MD 20850; cInstitute for Advanced Biosciences, Keio University, 997-0052 Yamagata, Japan; and dLaboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892 Edited by Richard D. Kolodner, Ludwig Institute for Cancer Research, La Jolla, CA, and approved December 8, 2020 (received for review July 29, 2020) Schlafen-11 (SLFN11) inactivation in ∼50% of cancer cells confers condensation related to deposition of H3K27me3 in the gene broad chemoresistance. To identify therapeutic targets and under- body of SLFN11 by EZH2, a histone methyltransferase (11). lying molecular mechanisms for overcoming chemoresistance, we Targeting epigenetic regulators is therefore an attractive com- performed an unbiased genome-wide RNAi screen in SLFN11-WT bination strategy to overcome chemoresistance of SLFN11- and -knockout (KO) cells. We found that inactivation of Ataxia deficient cancers (10, 25, 26). An alternative approach is to at- Telangiectasia- and Rad3-related (ATR), CHK1, BRCA2, and RPA1 tack SLFN11-negative cancer cells by targeting the essential SLFN11 overcome chemoresistance to camptothecin (CPT) in -KO pathways that cells use to overcome replicative damage and cells. Accordingly, we validate that clinical inhibitors of ATR replication stress. -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies
LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES by Rick Matthew Jordan B.S. Biology, University of Pittsburgh, 1996 M.S. Molecular Biology/Biotechnology, East Carolina University, 2001 M.S. Biomedical Informatics, University of Pittsburgh, 2005 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2016 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Rick Matthew Jordan It was defended on December 2, 2015 and approved by Shyam Visweswaran, M.D., Ph.D., Associate Professor Rebecca Jacobson, M.D., M.S., Professor Songjian Lu, Ph.D., Assistant Professor Dissertation Advisor: Vanathi Gopalakrishnan, Ph.D., Associate Professor ii Copyright © by Rick Matthew Jordan 2016 iii LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES Rick Matthew Jordan, M.S. University of Pittsburgh, 2016 Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature- mining evidence with pathway analysis evidence is an open problem in biomedical informatics research. -
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Spectrum Name of Protein Count of Peptides Ratio (POL2RA/IgG control) POLR2A_228kdBand POLR2A DNA-directed RNA polymerase II subunit RPB1 197 NOT IN CONTROL IP POLR2A_228kdBand POLR2B DNA-directed RNA polymerase II subunit RPB2 146 NOT IN CONTROL IP POLR2A_228kdBand RPAP2 Isoform 1 of RNA polymerase II-associated protein 2 24 NOT IN CONTROL IP POLR2A_228kdBand POLR2G DNA-directed RNA polymerase II subunit RPB7 23 NOT IN CONTROL IP POLR2A_228kdBand POLR2H DNA-directed RNA polymerases I, II, and III subunit RPABC3 19 NOT IN CONTROL IP POLR2A_228kdBand POLR2C DNA-directed RNA polymerase II subunit RPB3 17 NOT IN CONTROL IP POLR2A_228kdBand POLR2J RPB11a protein 7 NOT IN CONTROL IP POLR2A_228kdBand POLR2E DNA-directed RNA polymerases I, II, and III subunit RPABC1 8 NOT IN CONTROL IP POLR2A_228kdBand POLR2I DNA-directed RNA polymerase II subunit RPB9 9 NOT IN CONTROL IP POLR2A_228kdBand ALMS1 ALMS1 3 NOT IN CONTROL IP POLR2A_228kdBand POLR2D DNA-directed RNA polymerase II subunit RPB4 6 NOT IN CONTROL IP POLR2A_228kdBand GRINL1A;Gcom1 Isoform 12 of Protein GRINL1A 6 NOT IN CONTROL IP POLR2A_228kdBand RECQL5 Isoform Beta of ATP-dependent DNA helicase Q5 3 NOT IN CONTROL IP POLR2A_228kdBand POLR2L DNA-directed RNA polymerases I, II, and III subunit RPABC5 5 NOT IN CONTROL IP POLR2A_228kdBand KRT6A Keratin, type II cytoskeletal 6A 3 NOT IN CONTROL IP POLR2A_228kdBand POLR2K DNA-directed RNA polymerases I, II, and III subunit RPABC4 2 NOT IN CONTROL IP POLR2A_228kdBand RFC4 Replication factor C subunit 4 1 NOT IN CONTROL IP POLR2A_228kdBand RFC2 -
Mtorc1 Controls Mitochondrial Activity and Biogenesis Through 4E-BP-Dependent Translational Regulation
Cell Metabolism Article mTORC1 Controls Mitochondrial Activity and Biogenesis through 4E-BP-Dependent Translational Regulation Masahiro Morita,1,2 Simon-Pierre Gravel,1,2 Vale´ rie Che´ nard,1,2 Kristina Sikstro¨ m,3 Liang Zheng,4 Tommy Alain,1,2 Valentina Gandin,5,7 Daina Avizonis,2 Meztli Arguello,1,2 Chadi Zakaria,1,2 Shannon McLaughlan,5,7 Yann Nouet,1,2 Arnim Pause,1,2 Michael Pollak,5,6,7 Eyal Gottlieb,4 Ola Larsson,3 Julie St-Pierre,1,2,* Ivan Topisirovic,5,7,* and Nahum Sonenberg1,2,* 1Department of Biochemistry 2Goodman Cancer Research Centre McGill University, Montreal, QC H3A 1A3, Canada 3Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 171 76, Sweden 4Cancer Research UK, The Beatson Institute for Cancer Research, Switchback Road, Glasgow G61 1BD, Scotland, UK 5Lady Davis Institute for Medical Research 6Cancer Prevention Center, Sir Mortimer B. Davis-Jewish General Hospital McGill University, Montreal, QC H3T 1E2, Canada 7Department of Oncology, McGill University, Montreal, QC H2W 1S6, Canada *Correspondence: [email protected] (J.S.-P.), [email protected] (I.T.), [email protected] (N.S.) http://dx.doi.org/10.1016/j.cmet.2013.10.001 SUMMARY ATP under physiological conditions in mammals and play a crit- ical role in overall energy balance (Vander Heiden et al., 2009). mRNA translation is thought to be the most energy- The mechanistic/mammalian target of rapamycin (mTOR) is a consuming process in the cell. Translation and serine/threonine kinase that has been implicated in a variety of energy metabolism are dysregulated in a variety of physiological processes and pathological states (Zoncu et al., diseases including cancer, diabetes, and heart 2011). -
The Use of Phosphoproteomic Data to Identify Altered Kinases and Signaling Pathways in Cancer
The use of phosphoproteomic data to identify altered kinases and signaling pathways in cancer By Sara Renee Savage Thesis Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in Biomedical Informatics August 10, 2018 Nashville, Tennessee Approved: Bing Zhang, Ph.D. Carlos Lopez, Ph.D. Qi Liu, Ph.D. ACKNOWLEDGEMENTS The work presented in this thesis would not have been possible without the funding provided by the NLM training grant (T15-LM007450) and the support of the Biomedical Informatics department at Vanderbilt. I am particularly indebted to Rischelle Jenkins, who helped me solve all administrative issues. Furthermore, this work is the result of a collaboration between all members of the Zhang lab and the larger CPTAC consortium. I would like to thank the other CPTAC centers for processing the data, and Chen Huang and Suhas Vasaikar in the Zhang lab for analyzing the colon cancer copy number and proteomic data, respectively. All members of the Zhang lab have been extremely helpful in answering any questions I had and offering suggestions on my work. Finally, I would like to acknowledge my mentor, Bing Zhang. I am extremely grateful for his guidance and for giving me the opportunity to work on these projects. ii TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ................................................................................................ ii LIST OF TABLES............................................................................................................