RNA Binding Proteins Involved in Regulation of Protein Synthesis to Initiate Biogenesis of Secondary Tumor in Hepatocellular Carcinoma in Mice
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Topological Scoring of Protein Interaction Networks
bioRxiv preprint doi: https://doi.org/10.1101/438408; this version posted October 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Topological Scoring of Protein Interaction Networks Mihaela E. Sardiu1, Joshua M. Gilmore1,2, Brad D. Groppe1,3, Arnob Dutta1,4, Laurence Florens1, and Michael P. Washburn1,5‡ 1Stowers Institute for Medical Research, Kansas City, MO 64110 U.S.A. 2Current Address: Boehringer Ingelheim Vetmedica, St. Joseph, MO 64506 U.S.A. 3Current Address: Thermo Fisher Scientific, Waltham, MA 02451, U.S.A. 4Current Address: Department of Cell and Molecular Biology, University of Rhode Island, 287 CBLS, 120 Flagg Road, Kingston, RI 02881. 5 Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, Kansas 66160, USA ‡To whom correspondence should be addressed: Michael Washburn, Ph.D. Stowers Institute for Medical Research 1000 E. 50th St. Kansas City, MO 64110 Phone: 816-926-4457 E-mail: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/438408; this version posted October 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic analyses of affinity purifications. -
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. -
The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid
The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid Leukemia Cells Supported Only by Exogenous Cytokines Is Associated with a Patient Subset with Adverse Outcome Annette K. Brenner, Elise Aasebø, Maria Hernandez-Valladares, Frode Selheim, Frode Berven, Ida-Sofie Grønningsæter, Sushma Bartaula-Brevik and Øystein Bruserud Supplementary Material S2 of S31 Table S1. Detailed information about the 68 AML patients included in the study. # of blasts Viability Proliferation Cytokine Viable cells Change in ID Gender Age Etiology FAB Cytogenetics Mutations CD34 Colonies (109/L) (%) 48 h (cpm) secretion (106) 5 weeks phenotype 1 M 42 de novo 241 M2 normal Flt3 pos 31.0 3848 low 0.24 7 yes 2 M 82 MF 12.4 M2 t(9;22) wt pos 81.6 74,686 low 1.43 969 yes 3 F 49 CML/relapse 149 M2 complex n.d. pos 26.2 3472 low 0.08 n.d. no 4 M 33 de novo 62.0 M2 normal wt pos 67.5 6206 low 0.08 6.5 no 5 M 71 relapse 91.0 M4 normal NPM1 pos 63.5 21,331 low 0.17 n.d. yes 6 M 83 de novo 109 M1 n.d. wt pos 19.1 8764 low 1.65 693 no 7 F 77 MDS 26.4 M1 normal wt pos 89.4 53,799 high 3.43 2746 no 8 M 46 de novo 26.9 M1 normal NPM1 n.d. n.d. 3472 low 1.56 n.d. no 9 M 68 MF 50.8 M4 normal D835 pos 69.4 1640 low 0.08 n.d. -
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. -
Genes with 5' Terminal Oligopyrimidine Tracts Preferentially Escape Global Suppression of Translation by the SARS-Cov-2 NSP1 Protein
Downloaded from rnajournal.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Genes with 5′ terminal oligopyrimidine tracts preferentially escape global suppression of translation by the SARS-CoV-2 Nsp1 protein Shilpa Raoa, Ian Hoskinsa, Tori Tonna, P. Daniela Garciaa, Hakan Ozadama, Elif Sarinay Cenika, Can Cenika,1 a Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA 1Corresponding author: [email protected] Key words: SARS-CoV-2, Nsp1, MeTAFlow, translation, ribosome profiling, RNA-Seq, 5′ TOP, Ribo-Seq, gene expression 1 Downloaded from rnajournal.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract Viruses rely on the host translation machinery to synthesize their own proteins. Consequently, they have evolved varied mechanisms to co-opt host translation for their survival. SARS-CoV-2 relies on a non-structural protein, Nsp1, for shutting down host translation. However, it is currently unknown how viral proteins and host factors critical for viral replication can escape a global shutdown of host translation. Here, using a novel FACS-based assay called MeTAFlow, we report a dose-dependent reduction in both nascent protein synthesis and mRNA abundance in cells expressing Nsp1. We perform RNA-Seq and matched ribosome profiling experiments to identify gene-specific changes both at the mRNA expression and translation level. We discover that a functionally-coherent subset of human genes are preferentially translated in the context of Nsp1 expression. These genes include the translation machinery components, RNA binding proteins, and others important for viral pathogenicity. Importantly, we uncovered a remarkable enrichment of 5′ terminal oligo-pyrimidine (TOP) tracts among preferentially translated genes. -
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. -
Ncomms2091.Pdf
ARTICLE Received 18 Apr 2012 | Accepted 24 Aug 2012 | Published 25 Sep 2012 DOI: 10.1038/ncomms2091 Insights into the biomedical effects of carboxylated single-wall carbon nanotubes on telomerase and telomeres Yong Chen1,2, Konggang Qu1, Chuanqi Zhao1, Li Wu1, Jinsong Ren1, Jiasi Wang1 & Xiaogang Qu1 Both human telomeric G-rich and C-rich DNA have been considered as specific drug targets for cancer therapy. However, due to i-motif structure instability and lack of specific binding agents, it remains unclear whether stabilization of telomeric i-motif can inhibit telomerase activity. Single-walled carbon nanotubes (SWNTs) have been reported as the first ligand that can selectively stabilize human telomeric i-motif DNA. Here we report that SWNTs can inhibit telomerase activity through stabilization of i-motif structure. The persistence of i-motif and the concomitant G-quadruplex eventually leads to telomere uncapping and displaces telomere- binding proteins from telomere. The dysfunctional telomere triggers DNA damage response and elicits upregulation of p16 and p21 proteins. This is the first example that SWNTs can inhibit telomerase activity and interfere with the telomere functions in cancer cells. These results provide new insights into understanding the biomedical effects of SWNTs and the biological importance of i-motif DNA. 1 Division of Biological Inorganic Chemistry, State Key Laboratory of Rare Earth Resource Utilization, Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Graduate School of the Chinese Academy of Sciences, Chinese Academy of Sciences, Changchun, Jilin 130022, China. 2 College of Life Science, Jilin University, Changchun, Jilin 130012, China. Correspondence and requests for materials should be addressed to X.Q. -
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, -
Nuclear PTEN Safeguards Pre-Mrna Splicing to Link Golgi Apparatus for Its Tumor Suppressive Role
ARTICLE DOI: 10.1038/s41467-018-04760-1 OPEN Nuclear PTEN safeguards pre-mRNA splicing to link Golgi apparatus for its tumor suppressive role Shao-Ming Shen1, Yan Ji2, Cheng Zhang1, Shuang-Shu Dong2, Shuo Yang1, Zhong Xiong1, Meng-Kai Ge1, Yun Yu1, Li Xia1, Meng Guo1, Jin-Ke Cheng3, Jun-Ling Liu1,3, Jian-Xiu Yu1,3 & Guo-Qiang Chen1 Dysregulation of pre-mRNA alternative splicing (AS) is closely associated with cancers. However, the relationships between the AS and classic oncogenes/tumor suppressors are 1234567890():,; largely unknown. Here we show that the deletion of tumor suppressor PTEN alters pre-mRNA splicing in a phosphatase-independent manner, and identify 262 PTEN-regulated AS events in 293T cells by RNA sequencing, which are associated with significant worse outcome of cancer patients. Based on these findings, we report that nuclear PTEN interacts with the splicing machinery, spliceosome, to regulate its assembly and pre-mRNA splicing. We also identify a new exon 2b in GOLGA2 transcript and the exon exclusion contributes to PTEN knockdown-induced tumorigenesis by promoting dramatic Golgi extension and secretion, and PTEN depletion significantly sensitizes cancer cells to secretion inhibitors brefeldin A and golgicide A. Our results suggest that Golgi secretion inhibitors alone or in combination with PI3K/Akt kinase inhibitors may be therapeutically useful for PTEN-deficient cancers. 1 Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China. 2 Institute of Health Sciences, Shanghai Institutes for Biological Sciences of Chinese Academy of Sciences and SJTU-SM, Shanghai 200025, China.