Genomic Analysis of Allele-Specific Expression in the Mouse Liver
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
Bioinformatics Identi Cation of Prognostic Factors Associated With
Bioinformatics Identication of Prognostic Factors Associated with Breast Cancer Ying Wei Sichuan University https://orcid.org/0000-0001-8178-4705 Shipeng Zhang College of Pharmacy, North Sichuan Medical College Li Xiao West China School of Basic Medical Sciences and Forensic Medicine Jing Zou West China School of Basic Medical Sciences and Forensic Medicine Yingqing Fu West China School of Basic Medical Sciences and Forensic Medicine Yi Ye West China School of Basic Medical Sciences and Forensic Medicine Linchuan Liao ( [email protected] ) West China School of Basic Medical Sciences and Forensic Medicine https://orcid.org/0000-0003-3700-8471 Research Keywords: Breast cancer, Differentially expressed genes, miRNAs, Transcription factors, Bioinformatic analysis Posted Date: December 2nd, 2020 DOI: https://doi.org/10.21203/rs.3.rs-117477/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/23 Abstract Background: Breast cancer (BRCA) remains one of the most common forms of cancer and is the most prominent driver of cancer-related death among women. The mechanistic basis for BRCA, however, remains incompletely understood. In particular, the relationships between driver mutations and signaling pathways in BRCA are poorly characterized, making it dicult to identify reliable clinical biomarkers that can be employed in diagnostic, therapeutic, or prognostic contexts. Methods: First, we downloaded publically available BRCA datasets (GSE45827, GSE42568, and GSE61304) from the Gene Expression Omnibus (GEO) database. We then compared gene expression proles between tumor and control tissues in these datasets using Venn diagrams and the GEO2R analytical tool. We further explore the functional relevance of BRCA-associated differentially expressed genes (DEGs) via functional and pathway enrichment analyses using the DAVID tool, and we then constructed a protein-protein interaction network incorporating DEGs of interest using the Search Tool for the Retrieval of Interacting Genes (STRING) database. -
View of HER2: Human Epidermal Growth Factor Receptor 2; TNBC: Triple-Negative Breast Resistance to Systemic Therapy in Patients with Breast Cancer
Wen et al. Cancer Cell Int (2018) 18:128 https://doi.org/10.1186/s12935-018-0625-9 Cancer Cell International PRIMARY RESEARCH Open Access Sulbactam‑enhanced cytotoxicity of doxorubicin in breast cancer cells Shao‑hsuan Wen1†, Shey‑chiang Su2†, Bo‑huang Liou3, Cheng‑hao Lin1 and Kuan‑rong Lee1* Abstract Background: Multidrug resistance (MDR) is a major obstacle in breast cancer treatment. The predominant mecha‑ nism underlying MDR is an increase in the activity of adenosine triphosphate (ATP)-dependent drug efux trans‑ porters. Sulbactam, a β-lactamase inhibitor, is generally combined with β-lactam antibiotics for treating bacterial infections. However, sulbactam alone can be used to treat Acinetobacter baumannii infections because it inhibits the expression of ATP-binding cassette (ABC) transporter proteins. This is the frst study to report the efects of sulbactam on mammalian cells. Methods: We used the breast cancer cell lines as a model system to determine whether sulbactam afects cancer cells. The cell viabilities in the present of doxorubicin with or without sulbactam were measured by MTT assay. Protein identities and the changes in protein expression levels in the cells after sulbactam and doxorubicin treatment were determined using LC–MS/MS. Real-time reverse transcription polymerase chain reaction (real-time RT-PCR) was used to analyze the change in mRNA expression levels of ABC transporters after treatment of doxorubicin with or without sulbactam. The efux of doxorubicin was measures by the doxorubicin efux assay. Results: MTT assay revealed that sulbactam enhanced the cytotoxicity of doxorubicin in breast cancer cells. The results of proteomics showed that ABC transporter proteins and proteins associated with the process of transcription and initiation of translation were reduced. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
AN INVESTIGATION of the ROLE of PAK6 in TUMORIGENESIS By
AN INVESTIGATION OF THE ROLE OF PAK6 IN TUMORIGENESIS by JoAnn Roberts A Thesis Submitted to the Faculty of The Charles E. Schmidt College of Medicine In Partial Fulfillment of the Requirements for the Degree of Master of Science Florida Atlantic University Boca Raton, Florida August 2012 ACKNOWLEDGMENTS This material is based upon work supported by the National Science Foundation under Grant No. DGE: 0638662. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. I would like to thank and acknowledge my thesis advisor, Dr. Michael Lu, for his support and guidance throughout the writing of this thesis and design of experiments in this manuscript. I would also like to thank my colleagues for assistance in various trouble-shooting circumstances. Last, but certainly not least, I would like to thank my family and friends for their support in the pursuit of my graduate studies. iii ABSTRACT Author: JoAnn Roberts Title: An Investigation of the Role of PAK6 in Tumorigenesis Institution: Florida Atlantic University Thesis Advisor: Dr. Michael Lu Degree: Master of Science Year: 2012 The function and role of PAK6, a serine/threonine kinase, in cancer progression has not yet been clearly identified. Several studies reveal that PAK6 may participate in key changes contributing to cancer progression such as cell survival, cell motility, and invasiveness. Based on the membrane localization of PAK6 in prostate and breast cancer cells, we speculated that PAK6 plays a role in cancer progression cells by localizing on the membrane and modifying proteins linked to motility and proliferation. -
Crystal Structure of Human Gadd45 Reveals an Active Dimer
Protein Cell 2011, 2(10): 814–826 Protein & Cell DOI 10.1007/s13238-011-1090-6 RESEARCH ARTICLE Crystal structure of human Gadd45 reveals an active dimer 1,2,3* 1* 4 4 1 1 3 Wenzheng Zhang , Sheng Fu , Xuefeng Liu , Xuelian Zhao✉ , Wenchi Zhang✉ , Wei Peng , Congying Wu , Yuanyuan Li3, Xuemei Li1, Mark Bartlam1,2, Zong-Hao Zeng1 , Qimin Zhan4 , Zihe Rao1,2,3 1 National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China 2 Tianjin Key Laboratory of Protein Science, College of Life Sciences, Nankai University, Tianjin 300071, China 3 Laboratory of Structural Biology, Tsinghua University, Beijing 100084, China 4 State Key Laboratory of Molecular Oncology, Cancer Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China ✉ Correspondence: [email protected] (Z.-H. Zeng), [email protected] (Q. Zhan) Received August 23, 2011 Accepted September 19, 2011 ABSTRACT INTRODUCTION The human Gadd45 protein family plays critical roles in The human Gadd45 family of growth arrest and DNA DNA repair, negative growth control, genomic stability, damage-inducible proteins has three members, Gadd45α, cell cycle checkpoints and apoptosis. Here we report the -β, and -γ, grouped together on the basis of their amino acid crystal structure of human Gadd45, revealing a unique sequences and functional similarities (Vairapandi et al., 1996; dimer formed via a bundle of four parallel helices, Takekawa and Saito, 1998; Nakayama et al., 1999). The involving the most conserved residues among the genes encoding the α, β and γ isoforms of Gadd45 are Gadd45 isoforms. -
Broad Poster Vivek
A novel computational method for finding regions with copy number abnormalities in cancer cells Vivek, Manuel Garber, and Mike Zody Broad Institute of MIT and Harvard, Cambridge, MA, USA Introduction Results Cancer can result from the over expression of oncogenes, genes which control and regulate cell growth. Sometimes oncogenes increase in 1 2 3 activity due to a specific genetic mutation called a translocation (Fig 1). SMAD4 – a gene known to be deleted in pancreatic COX10 – a gene deleted in cytochrome c oxidase AK001392 – a hereditary prostate cancer protein This translocation allows the oncogene to remain as active as its paired carcinoma deficiency, known to be related to cell proliferation gene. Amplification of this mutation can occur, thereby creating the proper conditions for uncontrolled cell growth; consequently, each Results from Analysis Program Results from Analysis Program Results from Analysis Program component of the translocation will amplify in similar quantities. In this mutation, the chromosomal region containing the oncogene displaces to Region 1 Region 2 R2 Region 1 Region 2 R2 Region 1 Region 2 R2 a region on another chromosome containing a gene that is expressed Chr18:47044749-47311978 Chr17:13930739-14654741 0.499070821478475 Chr17:13930739-14654741 Chr18:26861790-27072166 0.47355172850856 Chr17:12542326-13930738 Chr8:1789292-1801984 0.406208680312004 frequently. Actual region containing gene Actual region containing gene Actual region containing gene chr18: 45,842,214 - 48,514,513 chr17: 13,966,862 - 14,068,461 chr17: 12,542,326 - 13,930,738 Fig 1. Two chromosomal regions (abcdef and ghijk) are translocating to create two new regions (abckl and ghijedf). -
Broad and Thematic Remodeling of the Surface Glycoproteome on Isogenic
bioRxiv preprint doi: https://doi.org/10.1101/808139; this version posted October 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Broad and thematic remodeling of the surface glycoproteome on isogenic cells transformed with driving proliferative oncogenes Kevin K. Leung1,5, Gary M. Wilson2,5, Lisa L. Kirkemo1, Nicholas M. Riley2,4, Joshua J. Coon2,3, James A. Wells1* 1Department of Pharmaceutical Chemistry, UCSF, San Francisco, CA, USA Departments of Chemistry2 and Biomolecular Chemistry3, University of Wisconsin- Madison, Madison, WI, 53706, USA 4Present address Department of Chemistry, Stanford University, Stanford, CA, 94305, USA 5These authors contributed equally *To whom correspondence should be addressed bioRxiv preprint doi: https://doi.org/10.1101/808139; this version posted October 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract: The cell surface proteome, the surfaceome, is the interface for engaging the extracellular space in normal and cancer cells. Here We apply quantitative proteomics of N-linked glycoproteins to reveal how a collection of some 700 surface proteins is dramatically remodeled in an isogenic breast epithelial cell line stably expressing any of six of the most prominent proliferative oncogenes, including the receptor tyrosine kinases, EGFR and HER2, and downstream signaling partners such as KRAS, BRAF, MEK and AKT. -
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. -
Supplementary Materials
1 Supplementary Materials: Supplemental Figure 1. Gene expression profiles of kidneys in the Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice. (A) A heat map of microarray data show the genes that significantly changed up to 2 fold compared between Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice (N=4 mice per group; p<0.05). Data show in log2 (sample/wild-type). 2 Supplemental Figure 2. Sting signaling is essential for immuno-phenotypes of the Fcgr2b-/-lupus mice. (A-C) Flow cytometry analysis of splenocytes isolated from wild-type, Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice at the age of 6-7 months (N= 13-14 per group). Data shown in the percentage of (A) CD4+ ICOS+ cells, (B) B220+ I-Ab+ cells and (C) CD138+ cells. Data show as mean ± SEM (*p < 0.05, **p<0.01 and ***p<0.001). 3 Supplemental Figure 3. Phenotypes of Sting activated dendritic cells. (A) Representative of western blot analysis from immunoprecipitation with Sting of Fcgr2b-/- mice (N= 4). The band was shown in STING protein of activated BMDC with DMXAA at 0, 3 and 6 hr. and phosphorylation of STING at Ser357. (B) Mass spectra of phosphorylation of STING at Ser357 of activated BMDC from Fcgr2b-/- mice after stimulated with DMXAA for 3 hour and followed by immunoprecipitation with STING. (C) Sting-activated BMDC were co-cultured with LYN inhibitor PP2 and analyzed by flow cytometry, which showed the mean fluorescence intensity (MFI) of IAb expressing DC (N = 3 mice per group). 4 Supplemental Table 1. Lists of up and down of regulated proteins Accession No. -
PIK3C2G (NM 004570) Human Mutant ORF Clone Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RC402758 PIK3C2G (NM_004570) Human Mutant ORF Clone Product data: Product Type: Mutant ORF Clones Product Name: PIK3C2G (NM_004570) Human Mutant ORF Clone Mutation Description: P146L Affected Codon#: 146 Affected NT#: 437 Nucleotide Mutation: PIK3C2G Mutant (P146L), Myc-DDK-tagged ORF clone of Homo sapiens phosphoinositide-3- kinase, class 2, gamma polypeptide (PIK3C2G) as transfection-ready DNA Effect: Dibees, ype 2, ssoiion wih Symbol: PIK3C2G Synonyms: PI3K-C2-gamma; PI3K-C2GAMMA Vector: pCMV6-Entry (PS100001) Tag: Myc-DDK ACCN: NM_004570 ORF Size: 4335 bp ORF Nucleotide >RC402758 representing NM_004570 Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGGCATATTCTTGGCAAACGGATCCAAATCCTAATGAATCACACGAAAAGCAGTATGAACACCAAGAAT TTCTCTTTGTAAATCAACCCCATTCTTCTAGCCAAGTCAGTCTGGGTTTTGATCAGATAGTAGATGAGAT CAGTGGCAAAATTCCACACTACGAGAGTGAAATTGATGAAAACACCTTTTTTGTGCCCACTGCACCAAAA TGGGACTCAACAGGGCATTCATTAAATGAAGCACACCAAATATCCTTGAATGAATTCACTTCTAAAAGCC GTGAACTCTCCTGGCATCAAGTTAGCAAAGCACCAGCAATTGGTTTTAGTCCTTCTGTGTTACCAAAACC TCAAAATACGAATAAAGAATGCTCCTGGGGAAGCCCCATAGGAAAACATCATGGTGCTGATGATTCCAGA TTCAGTATTTTAGCTCTATCATTCACAAGTTTGGATAAAATTAATCTAGAGAAAGAATTAGAAAATGAAA ATCATAACTACCATATAGGATTTGAAAGTAGCATTCCTCCAACAAATTCATCCTTCTCAAGTGACTTCAT GCCGAAAGAAGAGAATAAAAGGAGTGGACATGTGAACATTGTGGAACCATCTTTGATGCTTTTGAAAGGC -
Methylation Status of Vault RNA 2-1 Promoter Is a Predictor of Glycemic Response To
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) BMJ Open Diab Res Care SUPPLEMEMTARY DATA Methylation Status of Vault RNA 2-1 Promoter Is a Predictor of Glycemic Response to Glucagon-like Peptide-1 Analogue Therapy in Type 2 Diabetes Mellitus Chia-Hung Lin, Yun-Shien Lee, Yu-Yao Huang, Chi-Neu Tsai List of supplemental Tables Supplemental Table S1 Demographic variables of 10 participants (training group) applied for DNA methylation chip at baselines Supplement Table S2 Demographic variables of participants (validation group) enrolled in this study Supplemental Table S3 Primers used for bisulfite sequencing primers (BSP), pyrosequencing reaction and centromeric CTCF polymorphism Supplemental Table S4 Differentially methylated regions (DMRs) in human genome and association with glycemic response of GLP-1 treatment in training group List of supplemental Figures Supplement Figure S1: A gender difference was identified in Infinium® MethylationEPIC BeadChip analysis in the training group of this study through volcano and heatmap plot. Supplemental Fig. S2: The volcano plots of the differentially methylation comparing responsive and non-responsive groups before and after GLP-1 analogue treatment. Supplemental Fig. S3: Serum level of TGF-1 as a predictor of a glycemic response to GLP-1 analogue treatment and the correlation of VTRNA2-1 RNA expression versus its promoter methylation status. Supplement Figure S4: The association between change in A1C and (A) VTRNA 2-1 methylation level, (B) TGF-beta1 levels, and (C) mRNA levels of VTRNA 2-1 Lin C-H, et al.