A Heroin Addiction Severity-Associated Intronic Single
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SRC Antibody - N-Terminal Region (ARP32476 P050) Data Sheet
SRC antibody - N-terminal region (ARP32476_P050) Data Sheet Product Number ARP32476_P050 Product Name SRC antibody - N-terminal region (ARP32476_P050) Size 50ug Gene Symbol SRC Alias Symbols ASV; SRC1; c-SRC; p60-Src Nucleotide Accession# NM_005417 Protein Size (# AA) 536 amino acids Molecular Weight 60kDa Product Format Lyophilized powder NCBI Gene Id 6714 Host Rabbit Clonality Polyclonal Official Gene Full Name V-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) Gene Family SH2D This is a rabbit polyclonal antibody against SRC. It was validated on Western Blot by Aviva Systems Biology. At Aviva Systems Biology we manufacture rabbit polyclonal antibodies on a large scale (200-1000 Description products/month) of high throughput manner. Our antibodies are peptide based and protein family oriented. We usually provide antibodies covering each member of a whole protein family of your interest. We also use our best efforts to provide you antibodies recognize various epitopes of a target protein. For availability of antibody needed for your experiment, please inquire (). Peptide Sequence Synthetic peptide located within the following region: QTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGG This gene is highly similar to the v-src gene of Rous sarcoma virus. This proto-oncogene may play a role in the Description of Target regulation of embryonic development and cell growth. SRC protein is a tyrosine-protein kinase whose activity can be inhibited by phosphorylation by c-SRC kinase. Mutations in this gene could be involved in the -
Analysis of the Stability of 70 Housekeeping Genes During Ips Reprogramming Yulia Panina1,2*, Arno Germond1 & Tomonobu M
www.nature.com/scientificreports OPEN Analysis of the stability of 70 housekeeping genes during iPS reprogramming Yulia Panina1,2*, Arno Germond1 & Tomonobu M. Watanabe1 Studies on induced pluripotent stem (iPS) cells highly rely on the investigation of their gene expression which requires normalization by housekeeping genes. Whether the housekeeping genes are stable during the iPS reprogramming, a transition of cell state known to be associated with profound changes, has been overlooked. In this study we analyzed the expression patterns of the most comprehensive list to date of housekeeping genes during iPS reprogramming of a mouse neural stem cell line N31. Our results show that housekeeping genes’ expression fuctuates signifcantly during the iPS reprogramming. Clustering analysis shows that ribosomal genes’ expression is rising, while the expression of cell-specifc genes, such as vimentin (Vim) or elastin (Eln), is decreasing. To ensure the robustness of the obtained data, we performed a correlative analysis of the genes. Overall, all 70 genes analyzed changed the expression more than two-fold during the reprogramming. The scale of this analysis, that takes into account 70 previously known and newly suggested genes, allowed us to choose the most stable of all genes. We highlight the fact of fuctuation of housekeeping genes during iPS reprogramming, and propose that, to ensure robustness of qPCR experiments in iPS cells, housekeeping genes should be used together in combination, and with a prior testing in a specifc line used in each study. We suggest that the longest splice variants of Rpl13a, Rplp1 and Rps18 can be used as a starting point for such initial testing as the most stable candidates. -
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. -
Systematic Description of the Expression and Prognostic Value of M6A Regulators in Human Ovarian Cancer
Systematic Description of the Expression and Prognostic Value of M6A Regulators in Human Ovarian Cancer Yuwei Chen Fujian Medical University Fujian Cancer Hospital Yang Sun ( [email protected] ) Fujian Cancer Hospital https://orcid.org/0000-0003-4224-8832 Xinbei Chen Fujian Medical University Fujian Cancer Hospital Siming Li Fujian Medical University Fujian Cancer Hospital Hongmei Xia Fujian Cancer Hospital Research Keywords: m6A regulators, m6A, ovarian cancer, Prognostic values, bioinformatics analysis. Posted Date: December 22nd, 2020 DOI: https://doi.org/10.21203/rs.3.rs-131682/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Systematic description of the expression and prognostic value of m6A regulators in human ovarian cancer 1 Yuwei Chen1, Yang Sun1*, Xinbei Chen1, Siming Li1, Hongmei Xia1 2 * Correspondence: 3 Yang Sun 4 [email protected] 5 Total words:5253 6 Total Figures and Tables: 11 Figures and 3 Tables 7 Keywords: m6A regulators, m6A, ovarian cancer, Prognostic values, 8 bioinformatics analysis. 9 Abstract 10 Background: N6-methyladenine (m6A) methylation, known as a kind of RNA 11 methylation regulator, has become a hotspot for research in the life sciences in recent 12 years. The existing studies revealed that m6A regulators helped to regulate the 13 progression of several malignant tumors. However, the expression mode and 14 prognostic value of m6A regulators in ovarian cancer have not been fully elucidated. 15 Methods: ONCOMINE, GEPIA, Human protein atlas, Kaplan-meier, UALCAN, 16 cBioPortal, GeneMANIA, DAVID, Sring, and Metascape were utilized in this study. 17 Results: In this study, through a comprehensive use of multiple database systems, 18 m6A regulators-induced mRNA and protein expression levels and their prognostic 19 value in the epithelial ovarian cancer of various histological types were analyzed. -
Clinical Application of Chromosomal Microarray Analysis for Fetuses With
Xu et al. Molecular Cytogenetics (2020) 13:38 https://doi.org/10.1186/s13039-020-00502-5 RESEARCH Open Access Clinical application of chromosomal microarray analysis for fetuses with craniofacial malformations Chenyang Xu1†, Yanbao Xiang1†, Xueqin Xu1, Lili Zhou1, Huanzheng Li1, Xueqin Dong1 and Shaohua Tang1,2* Abstract Background: The potential correlations between chromosomal abnormalities and craniofacial malformations (CFMs) remain a challenge in prenatal diagnosis. This study aimed to evaluate 118 fetuses with CFMs by applying chromosomal microarray analysis (CMA) and G-banded chromosome analysis. Results: Of the 118 cases in this study, 39.8% were isolated CFMs (47/118) whereas 60.2% were non-isolated CFMs (71/118). The detection rate of chromosomal abnormalities in non-isolated CFM fetuses was significantly higher than that in isolated CFM fetuses (26/71 vs. 7/47, p = 0.01). Compared to the 16 fetuses (16/104; 15.4%) with pathogenic chromosomal abnormalities detected by karyotype analysis, CMA identified a total of 33 fetuses (33/118; 28.0%) with clinically significant findings. These 33 fetuses included cases with aneuploidy abnormalities (14/118; 11.9%), microdeletion/microduplication syndromes (9/118; 7.6%), and other pathogenic copy number variations (CNVs) only (10/118; 8.5%).We further explored the CNV/phenotype correlation and found a series of clear or suspected dosage-sensitive CFM genes including TBX1, MAPK1, PCYT1A, DLG1, LHX1, SHH, SF3B4, FOXC1, ZIC2, CREBBP, SNRPB, and CSNK2A1. Conclusion: These findings enrich our understanding of the potential causative CNVs and genes in CFMs. Identification of the genetic basis of CFMs contributes to our understanding of their pathogenesis and allows detailed genetic counselling. -
Supplementary Materials
Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented. -
Bioinformatics-Based Screening of Key Genes for Transformation of Liver
Jiang et al. J Transl Med (2020) 18:40 https://doi.org/10.1186/s12967-020-02229-8 Journal of Translational Medicine RESEARCH Open Access Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma Chen Hao Jiang1,2, Xin Yuan1,2, Jiang Fen Li1,2, Yu Fang Xie1,2, An Zhi Zhang1,2, Xue Li Wang1,2, Lan Yang1,2, Chun Xia Liu1,2, Wei Hua Liang1,2, Li Juan Pang1,2, Hong Zou1,2, Xiao Bin Cui1,2, Xi Hua Shen1,2, Yan Qi1,2, Jin Fang Jiang1,2, Wen Yi Gu4, Feng Li1,2,3 and Jian Ming Hu1,2* Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver tumour, and is closely related to liver cirrhosis. Previous studies have focussed on the pathogenesis of liver cirrhosis developing into HCC, but the molecular mechanism remains unclear. The aims of the present study were to identify key genes related to the transformation of cirrhosis into HCC, and explore the associated molecular mechanisms. Methods: GSE89377, GSE17548, GSE63898 and GSE54236 mRNA microarray datasets from Gene Expression Omni- bus (GEO) were analysed to obtain diferentially expressed genes (DEGs) between HCC and liver cirrhosis tissues, and network analysis of protein–protein interactions (PPIs) was carried out. String and Cytoscape were used to analyse modules and identify hub genes, Kaplan–Meier Plotter and Oncomine databases were used to explore relationships between hub genes and disease occurrence, development and prognosis of HCC, and the molecular mechanism of the main hub gene was probed using Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis. -
A Novel Hypoxic Long Noncoding RNA KB-1980E6.3 Maintains Breast Cancer Stem Cell Stemness Via Interacting with IGF2BP1 to Facilitate C-Myc Mrna Stability
Oncogene (2021) 40:1609–1627 https://doi.org/10.1038/s41388-020-01638-9 ARTICLE A novel hypoxic long noncoding RNA KB-1980E6.3 maintains breast cancer stem cell stemness via interacting with IGF2BP1 to facilitate c-Myc mRNA stability 1 2 3 4 1 1 1 1 1 Pengpeng Zhu ● Fang He ● Yixuan Hou ● Gang Tu ● Qiao Li ● Ting Jin ● Huan Zeng ● Yilu Qin ● Xueying Wan ● 1 1 5 1 Yina Qiao ● Yuxiang Qiu ● Yong Teng ● Manran Liu Received: 15 June 2020 / Revised: 13 November 2020 / Accepted: 18 December 2020 / Published online: 19 January 2021 © The Author(s), under exclusive licence to Springer Nature Limited 2021. This article is published with open access Abstract The hostile hypoxic microenvironment takes primary responsibility for the rapid expansion of breast cancer tumors. However, the underlying mechanism is not fully understood. Here, using RNA sequencing (RNA-seq) analysis, we identified a hypoxia-induced long noncoding RNA (lncRNA) KB-1980E6.3, which is aberrantly upregulated in clinical breast cancer tissues and closely correlated with poor prognosis of breast cancer patients. The enhanced lncRNA KB- 1980E6.3 facilitates breast cancer stem cells (BCSCs) self-renewal and tumorigenesis under hypoxic microenvironment both 1234567890();,: 1234567890();,: in vitro and in vivo. Mechanistically, lncRNA KB-1980E6.3 recruited insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) to form a lncRNA KB-1980E6.3/IGF2BP1/c-Myc signaling axis that retained the stability of c-Myc mRNA through increasing binding of IGF2BP1 with m6A-modified c-Myc coding region instability determinant (CRD) mRNA. In conclusion, we confirm that lncRNA KB-1980E6.3 maintains the stemness of BCSCs through lncRNA KB-1980E6.3/ IGF2BP1/c-Myc axis and suggest that disrupting this axis might provide a new therapeutic target for refractory hypoxic tumors. -
Identification of the RNA Recognition Element of the RBPMS Family of RNA-Binding Proteins and Their Transcriptome-Wide Mrna Targets
Downloaded from rnajournal.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press Identification of the RNA recognition element of the RBPMS family of RNA-binding proteins and their transcriptome-wide mRNA targets THALIA A. FARAZI,1,5 CARL S. LEONHARDT,1,5 NEELANJAN MUKHERJEE,2 ALEKSANDRA MIHAILOVIC,1 SONG LI,3 KLAAS E.A. MAX,1 CINDY MEYER,1 MASASHI YAMAJI,1 PAVOL CEKAN,1 NICHOLAS C. JACOBS,2 STEFANIE GERSTBERGER,1 CLAUDIA BOGNANNI,1 ERIK LARSSON,4 UWE OHLER,2 and THOMAS TUSCHL1,6 1Laboratory of RNA Molecular Biology, Howard Hughes Medical Institute, The Rockefeller University, New York, New York 10065, USA 2Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany 3Biology Department, Duke University, Durham, North Carolina 27708, USA 4Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden ABSTRACT Recent studies implicated the RNA-binding protein with multiple splicing (RBPMS) family of proteins in oocyte, retinal ganglion cell, heart, and gastrointestinal smooth muscle development. These RNA-binding proteins contain a single RNA recognition motif (RRM), and their targets and molecular function have not yet been identified. We defined transcriptome-wide RNA targets using photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) in HEK293 cells, revealing exonic mature and intronic pre-mRNA binding sites, in agreement with the nuclear and cytoplasmic localization of the proteins. Computational and biochemical approaches defined the RNA recognition element (RRE) as a tandem CAC trinucleotide motif separated by a variable spacer region. Similar to other mRNA-binding proteins, RBPMS family of proteins relocalized to cytoplasmic stress granules under oxidative stress conditions suggestive of a support function for mRNA localization in large and/or multinucleated cells where it is preferentially expressed. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Supplementary Table 9. Functional Annotation Clustering Results for the Union (GS3) of the Top Genes from the SNP-Level and Gene-Based Analyses (See ST4)
Supplementary Table 9. Functional Annotation Clustering Results for the union (GS3) of the top genes from the SNP-level and Gene-based analyses (see ST4) Column Header Key Annotation Cluster Name of cluster, sorted by descending Enrichment score Enrichment Score EASE enrichment score for functional annotation cluster Category Pathway Database Term Pathway name/Identifier Count Number of genes in the submitted list in the specified term % Percentage of identified genes in the submitted list associated with the specified term PValue Significance level associated with the EASE enrichment score for the term Genes List of genes present in the term List Total Number of genes from the submitted list present in the category Pop Hits Number of genes involved in the specified term (category-specific) Pop Total Number of genes in the human genome background (category-specific) Fold Enrichment Ratio of the proportion of count to list total and population hits to population total Bonferroni Bonferroni adjustment of p-value Benjamini Benjamini adjustment of p-value FDR False Discovery Rate of p-value (percent form) Annotation Cluster 1 Enrichment Score: 3.8978262119731335 Category Term Count % PValue Genes List Total Pop Hits Pop Total Fold Enrichment Bonferroni Benjamini FDR GOTERM_CC_DIRECT GO:0005886~plasma membrane 383 24.33290978 5.74E-05 SLC9A9, XRCC5, HRAS, CHMP3, ATP1B2, EFNA1, OSMR, SLC9A3, EFNA3, UTRN, SYT6, ZNRF2, APP, AT1425 4121 18224 1.18857065 0.038655922 0.038655922 0.086284383 UP_KEYWORDS Membrane 626 39.77128335 1.53E-04 SLC9A9, HRAS, -
Figure S1. Androgen Signaling Upregulates an Intronic
Figure S1. Androgen signaling upregulates an intronic polyadenylated EWSR1 isoform A) Gene expression correlation of EWSR1 and AR in 550 prostate cancer patients from the PRAD data set. B) EWSR1 polyadenylation site (PAS) information from PolyA_db v3.2. There are seven PAS for this gene and they are numbered starting from the 5’ end of the gene. C) Diagram of PAS locations at EWSR1 numbered according to table in A. D) Normalized read count for PAS #2, the PAS for ntEWS, from 3’ sequencing data across various cell types. E) RNA levels of PSA in VCaP, LNCaP, and LNCaP-AR cells treated with DMSO or 10nM R1881 for 24 hours. Expression is normalized to 18S and relative to the DMSO condition. The mean ± SEM for three replicates is shown. F) Immuno blot of ntEWS in PC3 cells overexpressing HA-ntEWS. G) Immunoblot of ntEWS in VCaP cells overexpressing vector alone or shRNAs targeting the 3’ UTR of ntEWS. Tubulin is used as a loading control for immunoblots. Figure S2. AR binding to Intron 5 of EWSR1 directly regulates ntEWS expression A) Gene tracks for AR binding in patient tumor and matched adjacent normal tissue at known AR enhancers. Order of tracks is consistent with Figure 2a. Figure S3. ntEWS promotes phenotypes related to oncogenesis A) Immunoblot of 3xHA tagged EWS isoforms expressed in PC3 cells. Tubulin is used as a loading control. B) MTT proliferation assay of PC3 isoform-expressing lines. Figure S4. The ntEWS alternative last exon encodes an alpha helical domain important for function A) IUPRED prediction of disorder of ntEWS (bottom) and EWS(1-355aa) (top).