RNA Splicing in the Transition from B Cells
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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
Genome-Wide Analysis of 5-Hmc in the Peripheral Blood of Systemic Lupus Erythematosus Patients Using an Hmedip-Chip
INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE 35: 1467-1479, 2015 Genome-wide analysis of 5-hmC in the peripheral blood of systemic lupus erythematosus patients using an hMeDIP-chip WEIGUO SUI1*, QIUPEI TAN1*, MING YANG1, QIANG YAN1, HUA LIN1, MINGLIN OU1, WEN XUE1, JIEJING CHEN1, TONGXIANG ZOU1, HUANYUN JING1, LI GUO1, CUIHUI CAO1, YUFENG SUN1, ZHENZHEN CUI1 and YONG DAI2 1Guangxi Key Laboratory of Metabolic Diseases Research, Central Laboratory of Guilin 181st Hospital, Guilin, Guangxi 541002; 2Clinical Medical Research Center, the Second Clinical Medical College of Jinan University (Shenzhen People's Hospital), Shenzhen, Guangdong 518020, P.R. China Received July 9, 2014; Accepted February 27, 2015 DOI: 10.3892/ijmm.2015.2149 Abstract. Systemic lupus erythematosus (SLE) is a chronic, Introduction potentially fatal systemic autoimmune disease characterized by the production of autoantibodies against a wide range Systemic lupus erythematosus (SLE) is a typical systemic auto- of self-antigens. To investigate the role of the 5-hmC DNA immune disease, involving diffuse connective tissues (1) and modification with regard to the onset of SLE, we compared is characterized by immune inflammation. SLE has a complex the levels 5-hmC between SLE patients and normal controls. pathogenesis (2), involving genetic, immunologic and envi- Whole blood was obtained from patients, and genomic DNA ronmental factors. Thus, it may result in damage to multiple was extracted. Using the hMeDIP-chip analysis and valida- tissues and organs, especially the kidneys (3). SLE arises from tion by quantitative RT-PCR (RT-qPCR), we identified the a combination of heritable and environmental influences. differentially hydroxymethylated regions that are associated Epigenetics, the study of changes in gene expression with SLE. -
IP6K1 Upregulates the Formation of Processing Bodies by Promoting Proteome Remodeling on the Mrna Cap
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.13.199828; this version posted July 13, 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. IP6K1 upregulates the formation of processing bodies by promoting proteome remodeling on the mRNA cap Akruti Shah1,2 and Rashna Bhandari1* 1Laboratory of Cell Signalling, Centre for DNA Fingerprinting and Diagnostics (CDFD), Inner Ring Road, Uppal, Hyderabad 500039, India. 2Graduate studies, Manipal Academy of Higher Education, Manipal 576104, India. *Correspondence to Rashna Bhandari; Email: [email protected] Running title: IP6K1 promotes mRNA turnover to induce P-bodies ORCID IDs Akruti Shah - 0000-0001-9557-4952 Rashna Bhandari - 0000-0003-3101-0204 This PDF file includes: Main Text Figures 1 to 6 Keywords mRNA decay/mRNA metabolism/P-bodies/translation suppression 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.13.199828; this version posted July 13, 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. Abstract Inositol hexakisphosphate kinases (IP6Ks) are ubiquitously expressed small molecule kinases that catalyze the conversion of the inositol phosphate IP6 to 5-IP7. IP6Ks have been reported to influence cellular functions by protein-protein interactions independent of their enzymatic activity. -
Sanjay Kumar Gupta
The human CCHC-type Zinc Finger Nucleic Acid Binding Protein (CNBP) binds to the G-rich elements in target mRNA coding sequences and promotes translation Das humane CCHC-Typ-Zinkfinger-Nukleinsäure-Binde-Protein (CNBP) bindet an G-reiche Elemente in der kodierenden Sequenz seiner Ziel-mRNAs und fördert deren Translation Doctoral thesis for a doctoral degree at the Graduate School of Life Sciences, Julius-Maximilians-Universität WürzBurg, Section: Biomedicine suBmitted By Sanjay Kumar Gupta from Varanasi, India WürzBurg, 2016 1 Submitted on: …………………………………………………………..…….. Office stamp Members of the Promotionskomitee: Chairperson: Prof. Dr. Alexander Buchberger Primary Supervisor: Dr. Stefan Juranek Supervisor (Second): Prof. Dr. Utz Fischer Supervisor (Third): Dr. Markus Landthaler Date of Public Defence: …………………………………………….………… Date of Receipt of Certificates: ………………………………………………. 2 Summary The genetic information encoded with in the genes are transcribed and translated to give rise to the functional proteins, which are building block of a cell. At first, it was thought that the regulation of gene expression particularly occurs at the level of transcription By various transcription factors. Recent discoveries have shown the vital role of gene regulation at the level of RNA also known as post-transcriptional gene regulation (PTGR). Apart from non-coding RNAs e.g. micro RNAs, various RNA Binding proteins (RBPs) play essential role in PTGR. RBPs have been implicated in different stages of mRNA life cycle ranging from splicing, processing, transport, localization and decay. In last 20 years studies have shown the presence of hundreds of RBPs across eukaryotic systems many of which are widely conserved. Given the rising numBer of RBPs and their link to human diseases it is quite evident that RBPs have major role in cellular processes and their regulation. -
The Mineralocorticoid Receptor Leads to Increased Expression of EGFR
www.nature.com/scientificreports OPEN The mineralocorticoid receptor leads to increased expression of EGFR and T‑type calcium channels that support HL‑1 cell hypertrophy Katharina Stroedecke1,2, Sandra Meinel1,2, Fritz Markwardt1, Udo Kloeckner1, Nicole Straetz1, Katja Quarch1, Barbara Schreier1, Michael Kopf1, Michael Gekle1 & Claudia Grossmann1* The EGF receptor (EGFR) has been extensively studied in tumor biology and recently a role in cardiovascular pathophysiology was suggested. The mineralocorticoid receptor (MR) is an important efector of the renin–angiotensin–aldosterone‑system and elicits pathophysiological efects in the cardiovascular system; however, the underlying molecular mechanisms are unclear. Our aim was to investigate the importance of EGFR for MR‑mediated cardiovascular pathophysiology because MR is known to induce EGFR expression. We identifed a SNP within the EGFR promoter that modulates MR‑induced EGFR expression. In RNA‑sequencing and qPCR experiments in heart tissue of EGFR KO and WT mice, changes in EGFR abundance led to diferential expression of cardiac ion channels, especially of the T‑type calcium channel CACNA1H. Accordingly, CACNA1H expression was increased in WT mice after in vivo MR activation by aldosterone but not in respective EGFR KO mice. Aldosterone‑ and EGF‑responsiveness of CACNA1H expression was confrmed in HL‑1 cells by Western blot and by measuring peak current density of T‑type calcium channels. Aldosterone‑induced CACNA1H protein expression could be abrogated by the EGFR inhibitor AG1478. Furthermore, inhibition of T‑type calcium channels with mibefradil or ML218 reduced diameter, volume and BNP levels in HL‑1 cells. In conclusion the MR regulates EGFR and CACNA1H expression, which has an efect on HL‑1 cell diameter, and the extent of this regulation seems to depend on the SNP‑216 (G/T) genotype. -
DNA Methylation Regulates Discrimination of Enhancers From
Sharifi-Zarchi et al. BMC Genomics (2017) 18:964 DOI 10.1186/s12864-017-4353-7 RESEARCHARTICLE Open Access DNA methylation regulates discrimination of enhancers from promoters through a H3K4me1-H3K4me3 seesaw mechanism Ali Sharifi-Zarchi1,2,3,4†, Daniela Gerovska5†, Kenjiro Adachi6, Mehdi Totonchi3, Hamid Pezeshk7,8, Ryan J. Taft9, Hans R. Schöler6,10, Hamidreza Chitsaz2, Mehdi Sadeghi8,11, Hossein Baharvand3,12* and Marcos J. Araúzo-Bravo5,13,14* Abstract Background: DNA methylation at promoters is largely correlated with inhibition of gene expression. However, the role of DNA methylation at enhancers is not fully understood, although a crosstalk with chromatin marks is expected. Actually, there exist contradictory reports about positive and negative correlations between DNA methylation and H3K4me1, a chromatin hallmark of enhancers. Results: We investigated the relationship between DNA methylation and active chromatin marks through genome- wide correlations, and found anti-correlation between H3K4me1 and H3K4me3 enrichment at low and intermediate DNA methylation loci. We hypothesized “seesaw” dynamics between H3K4me1 and H3K4me3 in the low and intermediate DNA methylation range, in which DNA methylation discriminates between enhancers and promoters, marked by H3K4me1 and H3K4me3, respectively. Low methylated regions are H3K4me3 enriched, while those with intermediate DNA methylation levels are progressively H3K4me1 enriched. Additionally, the enrichment of H3K27ac, distinguishing active from primed enhancers, follows a plateau in the lower range of the intermediate DNA methylation level, corresponding to active enhancers, and decreases linearly in the higher range of the intermediate DNA methylation. Thus, the decrease of the DNA methylation switches smoothly the state of the enhancers from a primed to an active state. -
Differential Analysis of Gene Expression and Alternative Splicing
i bioRxiv preprint doi: https://doi.org/10.1101/2020.08.23.234237; this version posted August 24, 2020. The copyright holder for this preprint i (which was not certified by peer review)“main” is the author/funder, — 2020/8/23 who has — granted 9:38 — bioRxiv page a1 license — #1 to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. i i EmpiReS: Differential Analysis of Gene Expression and Alternative Splicing Gergely Csaba∗, Evi Berchtold*,y, Armin Hadziahmetovic, Markus Gruber, Constantin Ammar and Ralf Zimmer Department of Informatics, Ludwig-Maximilians-Universitat¨ Munchen,¨ 80333, Munich, Germany ABSTRACT to translation. Different transcripts of the same gene are often expressed at the same time, possibly at different abundance, While absolute quantification is challenging in high- yielding a defined mixture of isoforms. Changes to this throughput measurements, changes of features composition are further referred to as differential alternative between conditions can often be determined with splicing (DAS). Various diseases can be linked to DAS high precision. Therefore, analysis of fold changes events (3). Most hallmarks of cancer like tumor progression is the standard method, but often, a doubly and immune escape (4, 5) can be attributed to changes in differential analysis of changes of changes is the transcript composition (6). DAS plays a prominent role required. Differential alternative splicing is an in various types of muscular dystrophy (MD) (7), splicing example of a doubly differential analysis, i.e. fold intervention by targeted exon removal is a therapeutic option changes between conditions for different isoforms in Duchenne MD (8). -
PDF Output of CLIC (Clustering by Inferred Co-Expression)
PDF Output of CLIC (clustering by inferred co-expression) Dataset: Num of genes in input gene set: 7 Total number of genes: 16493 CLIC PDF output has three sections: 1) Overview of Co-Expression Modules (CEMs) Heatmap shows pairwise correlations between all genes in the input query gene set. Red lines shows the partition of input genes into CEMs, ordered by CEM strength. Each row shows one gene, and the brightness of squares indicates its correlations with other genes. Gene symbols are shown at left side and on the top of the heatmap. 2) Details of each CEM and its expansion CEM+ Top panel shows the posterior selection probability (dataset weights) for top GEO series datasets. Bottom panel shows the CEM genes (blue rows) as well as expanded CEM+ genes (green rows). Each column is one GEO series dataset, sorted by their posterior probability of being selected. The brightness of squares indicates the gene's correlations with CEM genes in the corresponding dataset. CEM+ includes genes that co-express with CEM genes in high-weight datasets, measured by LLR score. 3) Details of each GEO series dataset and its expression profile: Top panel shows the detailed information (e.g. title, summary) for the GEO series dataset. Bottom panel shows the background distribution and the expression profile for CEM genes in this dataset. Overview of Co-Expression Modules (CEMs) with Dataset Weighting Scale of average Pearson correlations Num of Genes in Query Geneset: 7. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Ctr9 Rtf1 Paf1 Cdc73 Leo1 Wdr61 Pex2 Ctr9 Rtf1 -
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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
Epigenome-Wide Exploratory Study of Monozygotic Twins Suggests Differentially Methylated Regions to Associate with Hand Grip Strength
Biogerontology (2019) 20:627–647 https://doi.org/10.1007/s10522-019-09818-1 (0123456789().,-volV)( 0123456789().,-volV) RESEARCH ARTICLE Epigenome-wide exploratory study of monozygotic twins suggests differentially methylated regions to associate with hand grip strength Mette Soerensen . Weilong Li . Birgit Debrabant . Marianne Nygaard . Jonas Mengel-From . Morten Frost . Kaare Christensen . Lene Christiansen . Qihua Tan Received: 15 April 2019 / Accepted: 24 June 2019 / Published online: 28 June 2019 Ó The Author(s) 2019 Abstract Hand grip strength is a measure of mus- significant CpG sites or pathways were found, how- cular strength and is used to study age-related loss of ever two of the suggestive top CpG sites were mapped physical capacity. In order to explore the biological to the COL6A1 and CACNA1B genes, known to be mechanisms that influence hand grip strength varia- related to muscular dysfunction. By investigating tion, an epigenome-wide association study (EWAS) of genomic regions using the comb-p algorithm, several hand grip strength in 672 middle-aged and elderly differentially methylated regions in regulatory monozygotic twins (age 55–90 years) was performed, domains were identified as significantly associated to using both individual and twin pair level analyses, the hand grip strength, and pathway analyses of these latter controlling the influence of genetic variation. regions revealed significant pathways related to the Moreover, as measurements of hand grip strength immune system, autoimmune disorders, including performed over 8 years were available in the elderly diabetes type 1 and viral myocarditis, as well as twins (age 73–90 at intake), a longitudinal EWAS was negative regulation of cell differentiation.