DOCSLIB.ORG

Genome-Wide Quantification of New Biological and Pathological Functions of Genes and Pathways ∗ ∗ ∗ Xu Chi , Maureen A

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Genome-Wide Quantification of New Biological and Pathological Functions of Genes and Pathways ∗ ∗ ∗ Xu Chi , Maureen A Briefings in Bioinformatics, 00(00), 2019, 1–16 doi: 10.1093/bib/bbz093 Advance Access Publication Date: Problem solving protocol Downloaded from https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbz093/5593802 by University of Pittsburgh user on 31 October 2019 Universal concept signature analysis: genome-wide quantification of new biological and pathological functions of genes and pathways ∗ ∗ ∗ Xu Chi , Maureen A. Sartor , Sanghoon Lee , Meenakshi Anurag, Snehal Patil, Pelle Hall, Matthew Wexler and Xiao-Song Wang Corresponding author: Xiao-Song Wang, Departments of Pathology and Biomedical Informatics, UPMC Hillman Cancer Center, 5117 Centre Avenue, Pittsburgh, PA 15232. Tel.: +1-412-623-1587; Fax: 412-623-1010; E-mail: [email protected] ∗ These authors contributed equally to this work. Abstract Identifying new gene functions and pathways underlying diseases and biological processes are major challenges in genomics research. Particularly, most methods for interpreting the pathways characteristic of an experimental gene list defined by genomic data are limited by their dependence on assessing the overlapping genes or their interactome topology, which cannot account for the variety of functional relations. This is particularly problematic for pathway discovery from single-cell genomics with low gene coverage or interpreting complex pathway changes such as during change of cell states. Here, we exploited the comprehensive sets of molecular concepts that combine ontologies, pathways, interactions and domains to help inform the functional relations. We first developed a universal concept signature (uniConSig) analysis for genome-wide quantification of new gene functions underlying biological or pathological processes based on the signature molecular concepts computed from known functional gene lists. We then further developed a novel concept signature enrichment analysis (CSEA) for deep functional assessment of the pathways enriched in an experimental gene list. This method is grounded on the framework of shared concept signatures between gene sets at multiple functional levels, thus overcoming the limitations of the current methods. Through meta-analysis of transcriptomic data sets of cancer cell line models and single hematopoietic stem cells, we demonstrate the broad applications of CSEA on pathway discovery from Xu Chi is a post-doctoral researcher in the Department of Pathology, School of Medicine, at the University of Pittsburgh. He has been working in the field of bioinformatics and genomics. Current address: CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China. Maureen A. Sartor is an associate professor in the Department of Computational Medicine & Bioinformatics, Medical School, at the University of Michigan. She has been working in the field of bioinformatics and biostatistics. Sanghoon Lee is a graduate student in the Department of Biomedical Informatics at the University of Pittsburgh. His research area is bioinformatics and machine learning. Meenakshi Anurag is an assistant professor in the Department of Medicine at the Baylor College of Medicine. Her research specialties are cancer multiomics analysis and next-gen sequencing (NGS) data analysis. Snehal Patil is an application programmer in the Department of Computational Medicine & Bioinformatics at the University of Michigan. She is an expert in information science. Pelle Hall is a researcher in the Department in Computational Medicine & Bioinformatics at the University of Michigan. His research area is bioinformatics and statistics. Matthew Wexler is a graduate student in the Department of Pathology, School of Medicine at the University of Pittsburgh. He has been researching in the field of cancer genomics. Xiaosong Wang is an associate professor in the Department of Pathology and Biomedical Informatics, School of Medicine, at the University of Pittsburgh. He has been researching in the field of cancer genomics, bioinformatics, and Molecular Cancer Biology. Submitted: 11 March 2019; Received (in revised form): 23 May 2019 © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected] 1 2 Chi et al. gene expression and single-cell transcriptomic data sets for genetic perturbations and change of cell states, which complements the current modalities. The R modules for uniConSig analysis and CSEA are available through https://github. com/wangxlab/uniConSig. Key words: quantification of genome function; disease gene discovery; causal pathway discovery Downloaded from https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbz093/5593802 by University of Pittsburgh user on 31 October 2019 Background cancer by assessing their associations with the cancer gene The identification of causal genes and pathways underlying dis- signature concepts (i.e. oncogenic pathways, interactions with eases (such as cancer) based on proteomic, genomic, transcrip- key oncogenic proteins, characteristic protein domains, specific tomic, or single-cell profiling/sequencing data sets is a daunting gene ontologies). This algorithm provides a unique quantitative task, yet critical to translational research. The first challenge estimation of new gene functions underlying cancer, which have is that genomics data can provide molecular evidence for the been successfully applied by our group to identify new cancer pathological factors contributing to certain diseases; however, it genes from genomics data sets [6–10]. Other algorithms may is often challenging for biologists to interpret the possible func- utilize network analysis, text mining, or similarity profiling to tions of an overwhelming number of aberrant genes cataloged by rank candidate genes [11–24], which generally fail to account for genomics, and prioritize those genes based on existing human the variety of ways in which genes can be related to each other. knowledge of their biological functions. This often happens in It bears notable that all gene set-based algorithms including cancer genomics studies where hundreds of candidate patho- ConSig require merging of a wide array of molecular concept logical genetic alterations are identified, making it exceedingly databases thanks to their outgrowth in recent years [25–33]. difficult to pinpoint the key causal genes and prioritize them Such data fusion, however, carries the inherent challenge of data for experimental validation. This calls for innovative algorithms redundancy because different data sources follow different rules to compute novel gene–disease or gene–function associations for the nomenclature of concepts and different categories of con- based on genome knowledge databases. Here, we define ‘gene cepts possess levels of inherent redundancy. Hence, the ability set’ as a group of genes that are functionally related, such as a of the gene set-based algorithms to identify the redundancy and set of genes that function in certain diseases (disease gene set) sieve only the effective or unique signature concepts for further or pathways (pathway gene set). Thus, this type of associations calculation is critical. Here, we developed a powerful algorithm can be generalized as ‘gene to gene set associations’. called universal ConSig (uniConSig) analysis, which has sub- Another major challenge is to interpret the pathways stantially improved performance for genome-wide quantifica- characteristic of a list of aberrant genes, such as differentially tion of gene functions based on redundant molecular concept expressed or mutated genes. Here, we define a nominal gene list databases (Figure 1A). Based on this algorithm, we further devel- of interest cataloged by genomics data as an experimental gene oped a ConSig enrichment analysis (CSEA) for the quantification list. Pathway gene set analysis, originally derived from analyzing of precise functional associations between molecular concepts gene expression data, has progressively been applied to genetic (Figure 1B) by deep interpreting their shared signature concepts data. The current gene set analysis methods have been outlined rather than assessing shared gene numbers, which cannot be in a recent review [1]. While pathway tools are vastly available carried out by any of the previous methods. The uniConSig and for gene expression data of continuous variables, only handful of CSEA algorithms directly measure the functional interconnect- tools are available for genetic data of nominal variables (Table 1). edness of genes and gene sets, which will have wide applications A majority of these tools rely on overrepresentation analysis in genomics studies, such as discovering new gene functions (ORA) that performs statistical test to assess the statistical underlying certain disease or identifying the pathways under- overrepresentation of experimental gene list compared to the lying experimentally defined gene lists. pathway gene sets and are thus severely limited by their dependence on the genes included in the experimental gene list and the pathways. To illustrate, suppose two gene sets A Materials and methods and B do not share any common genes. Even if gene sets A and B each consist entirely of genes involved in DNA repair, they Compiling the molecular concept database and training will not be found to have functional relationship by current gene lists approaches. While recent studies has attempted to resolve this To generate a comprehensive and
Load more

Recommended publications
  • Regulation and Dysregulation of Chromosome Structure in Cancer
    Regulation and Dysregulation of Chromosome Structure in Cancer The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Hnisz, Denes et al. “Regulation and Dysregulation of Chromosome Structure in Cancer.” Annual Review of Cancer Biology 2, 1 (March 2018): 21–40 © 2018 Annual Reviews As Published https://doi.org/10.1146/annurev-cancerbio-030617-050134 Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/117286 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ Regulation and dysregulation of chromosome structure in cancer Denes Hnisz1*, Jurian Schuijers1, Charles H. Li1,2, Richard A. Young1,2* 1 Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA 02142, USA 2 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA * Corresponding authors Corresponding Authors: Denes Hnisz Whitehead Institute for Biomedical Research 455 Main Street Cambridge, MA 02142 Tel: (617) 258-7181 Fax: (617) 258-0376 [email protected] Richard A. Young Whitehead Institute for Biomedical Research 455 Main Street Cambridge, MA 02142 Tel: (617) 258-5218 Fax: (617) 258-0376 [email protected] 1 Summary Cancer arises from genetic alterations that produce dysregulated gene expression programs. Normal gene regulation occurs in the context of chromosome loop structures called insulated neighborhoods, and recent studies have shown that these structures are altered and can contribute to oncogene dysregulation in various cancer cells. We review here the types of genetic and epigenetic alterations that influence neighborhood structures and contribute to gene dysregulation in cancer, present models for insulated neighborhoods associated with the most prominent human oncogenes, and discuss how such models may lead to further advances in cancer diagnosis and therapy.
    [Show full text]
  • DNA Replication Stress Response Involving PLK1, CDC6, POLQ
    DNA replication stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients C. Allera-Moreau, I. Rouquette, B. Lepage, N. Oumouhou, M. Walschaerts, E. Leconte, V. Schilling, K. Gordien, L. Brouchet, Mb Delisle, et al. To cite this version: C. Allera-Moreau, I. Rouquette, B. Lepage, N. Oumouhou, M. Walschaerts, et al.. DNA replica- tion stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients. Oncogenesis, Nature Publishing Group: Open Access Journals - Option C, 2012, 1, pp.e30. 10.1038/oncsis.2012.29. hal-00817701 HAL Id: hal-00817701 https://hal.archives-ouvertes.fr/hal-00817701 Submitted on 9 Jun 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives| 4.0 International License Citation: Oncogenesis (2012) 1, e30; doi:10.1038/oncsis.2012.29 & 2012 Macmillan Publishers Limited All rights reserved 2157-9024/12 www.nature.com/oncsis ORIGINAL ARTICLE DNA replication stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients C Allera-Moreau1,2,7, I Rouquette2,7, B Lepage3, N Oumouhou3, M Walschaerts4, E Leconte5, V Schilling1, K Gordien2, L Brouchet2, MB Delisle1,2, J Mazieres1,2, JS Hoffmann1, P Pasero6 and C Cazaux1 Lung cancer is the leading cause of cancer deaths worldwide.
    [Show full text]
  • Supplemental Information
    Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig.
    [Show full text]
  • Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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 The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes.
    [Show full text]
  • Supplementary Table S1. Correlation Between the Mutant P53-Interacting Partners and PTTG3P, PTTG1 and PTTG2, Based on Data from Starbase V3.0 Database
    Supplementary Table S1. Correlation between the mutant p53-interacting partners and PTTG3P, PTTG1 and PTTG2, based on data from StarBase v3.0 database. PTTG3P PTTG1 PTTG2 Gene ID Coefficient-R p-value Coefficient-R p-value Coefficient-R p-value NF-YA ENSG00000001167 −0.077 8.59e-2 −0.210 2.09e-6 −0.122 6.23e-3 NF-YB ENSG00000120837 0.176 7.12e-5 0.227 2.82e-7 0.094 3.59e-2 NF-YC ENSG00000066136 0.124 5.45e-3 0.124 5.40e-3 0.051 2.51e-1 Sp1 ENSG00000185591 −0.014 7.50e-1 −0.201 5.82e-6 −0.072 1.07e-1 Ets-1 ENSG00000134954 −0.096 3.14e-2 −0.257 4.83e-9 0.034 4.46e-1 VDR ENSG00000111424 −0.091 4.10e-2 −0.216 1.03e-6 0.014 7.48e-1 SREBP-2 ENSG00000198911 −0.064 1.53e-1 −0.147 9.27e-4 −0.073 1.01e-1 TopBP1 ENSG00000163781 0.067 1.36e-1 0.051 2.57e-1 −0.020 6.57e-1 Pin1 ENSG00000127445 0.250 1.40e-8 0.571 9.56e-45 0.187 2.52e-5 MRE11 ENSG00000020922 0.063 1.56e-1 −0.007 8.81e-1 −0.024 5.93e-1 PML ENSG00000140464 0.072 1.05e-1 0.217 9.36e-7 0.166 1.85e-4 p63 ENSG00000073282 −0.120 7.04e-3 −0.283 1.08e-10 −0.198 7.71e-6 p73 ENSG00000078900 0.104 2.03e-2 0.258 4.67e-9 0.097 3.02e-2 Supplementary Table S2.
    [Show full text]
  • Critical Function of Siah2 in Tumorigenesis
    AIMS Molecular Science, 4(4): 415-423 DOI: 10.3934/molsci.2017.4.415 Received 31 July 2017, Accepted 21 September 2017, Published 11 October 2017 http://www.aimspress.com/journal/Molecular Review Critical function of Siah2 in tumorigenesis Kazunobu Baba, Tadaaki Miyazaki* Department of Probiotics Immunology, Institute for Genetic Medicine, Hokkaido University, North 15 West 7, Kita-ku, Sapporo City, Hokkaido, Japan * Correspondence: Email: [email protected]; Tel: 81-011-706-8095. Abstract: The seven in absentia homolog (Siah) family proteins are components of E3 RING zinc finger ubiquitin ligase complexes that catalyze the ubiquitination of proteins. Siah proteins target their substrates for proteasomal degradation. Evidence is growing that Siah proteins are implicated in the progression of various cancer cells and play a critical role in angiogenesis and tumorigenesis, particularly through Ras, p53, estrogen, and hypoxia inducible factor (HIF)-mediated signaling pathways in response to DNA damage or hypoxia. Keywords: Siah2; Nrf2; ROS metabolism; Hypoxia 1. Introduction The seven in absentia (Sina) gene was initially identified to be critical for photoreceptor cell development in Drosophila [1]. As its mammalian orthologs, murine and human homologs of the Sina gene have also been identified. In Xenopus and mice, Sina homologs (Siahs) were isolated as three Siah proteins, Siah1a, Siah1b, and Siah2, which are encoded by different genes [2]. In contrast, the human Siah proteins consist of two homologs, Siah1 and Siah2, which are encoded by the SIAH1 and SIAH2 genes, respectively [3]. Siah1 and Siah2 have the high sequence similarity of their N-terminal RING finger domain, central cysteine-rich domain, and C-terminal substrate binding domain (SBD); however, Siah1 and Siah2 apparently have distinct but overlapping functions as proteins of a tumor suppressor gene and a proto-oncogene, respectively (Figure 1) [4].
    [Show full text]
  • Statistical and Bioinformatic Analysis of Hemimethylation Patterns in Non-Small Cell Lung Cancer
    Statistical and Bioinformatic Analysis of Hemimethylation Patterns in Non-Small Cell Lung Cancer Shuying Sun ( [email protected] ) Texas State University San Marcos https://orcid.org/0000-0003-3974-6996 Austin Zane Texas A&M University College Station Carolyn Fulton Schreiner University Jasmine Philipoom Case Western Reserve University Research article Keywords: Methylation, Hemimethylation, Lung Cancer, Bioinformatics, Epigenetics Posted Date: October 12th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-17794/v2 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published on March 12th, 2021. See the published version at https://doi.org/10.1186/s12885-021-07990-7. Page 1/29 Abstract Background: DNA methylation is an epigenetic event involving the addition of a methyl-group to a cytosine-guanine base pair (i.e., CpG site). It is associated with different cancers. Our research focuses on studying non- small cell lung cancer hemimethylation, which refers to methylation occurring on only one of the two DNA strands. Many studies often assume that methylation occurs on both DNA strands at a CpG site. However, recent publications show the existence of hemimethylation and its signicant impact. Therefore, it is important to identify cancer hemimethylation patterns. Methods: In this paper, we use the Wilcoxon signed rank test to identify hemimethylated CpG sites based on publicly available non-small cell lung cancer methylation sequencing data. We then identify two types of hemimethylated CpG clusters, regular and polarity clusters, and genes with large numbers of hemimethylated sites.
    [Show full text]
  • 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
    [Show full text]
  • Siah - a Promising Anti-Cancer Target
    Author Manuscript Published OnlineFirst on March 1, 2013; DOI: 10.1158/0008-5472.CAN-12-4348 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Siah - a promising anti-cancer target Christina SF Wong1 and Andreas Möller1 1 Tumour Microenvironment Laboratory, Queensland Institute of Medical Research, 300 Herston Road, Herston, Queensland 4006, Australia. Corresponding Author: Andreas Möller ([email protected]) Running title: Siah and Cancer Keywords: Siah1, Siah2, E3 Ubiquitin ligases, Cancer Potential conflict of interest: The authors declare no conflict of interest. Word count: Abstract: 100 words; Text: 2590 words; Number of Figures: 1; Number of Tables: 1 1 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2013 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 1, 2013; DOI: 10.1158/0008-5472.CAN-12-4348 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Abstract: Siah ubiquitin ligases play important roles in a number of signaling pathways involved in the progression and spread of cancer in cell-based models but their role in tumor progression remains controversial. Siah proteins have been described to be both oncogenic as well as tumor-suppressive in a variety of patient cohort studies and animal cancer models. This review collates the current knowledge of Siah in cancer progression and identifies potential methods of translation of these findings into the clinic. Furthermore, key experiments needed to close the gaps in our understanding of the role Siah proteins play in tumor progression are suggested. 2 Downloaded from cancerres.aacrjournals.org on September 29, 2021.
    [Show full text]
  • Disruption of Chtf18 Causes Defective Meiotic Recombination in Male Mice
    Disruption of Chtf18 Causes Defective Meiotic Recombination in Male Mice Karen M. Berkowitz1,2*, Aislinn R. Sowash1,2, Lydia R. Koenig1,2, Dawnette Urcuyo1,2, Fahmida Khan1,2, Fang Yang3, P. Jeremy Wang3, Thomas A. Jongens4, Klaus H. Kaestner4 1 Department of Obstetrics and Gynecology, Drexel University College of Medicine, Philadelphia, Pennsylvania, United States of America, 2 Department of Biochemistry and Molecular Biology, Drexel University College of Medicine, Philadelphia, Pennsylvania, United States of America, 3 Department of Animal Biology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, 4 Department of Genetics, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America Abstract CHTF18 (chromosome transmission fidelity factor 18) is an evolutionarily conserved subunit of the Replication Factor C-like complex, CTF18-RLC. CHTF18 is necessary for the faithful passage of chromosomes from one daughter cell to the next during mitosis in yeast, and it is crucial for germline development in the fruitfly. Previously, we showed that mouse Chtf18 is expressed throughout the germline, suggesting a role for CHTF18 in mammalian gametogenesis. To determine the role of CHTF18 in mammalian germ cell development, we derived mice carrying null and conditional mutations in the Chtf18 gene. Chtf18-null males exhibit 5-fold decreased sperm concentrations compared to wild-type controls, resulting in subfertility. Loss of Chtf18 results in impaired spermatogenesis; spermatogenic cells display abnormal morphology, and the stereotypical arrangement of cells within seminiferous tubules is perturbed. Meiotic recombination is defective and homologous chromosomes separate prematurely during prophase I. Repair of DNA double-strand breaks is delayed and incomplete; both RAD51 and cH2AX persist in prophase I.
    [Show full text]
  • SIAH2-Mediated and Organ-Specific Restriction of HO-1 Expression by A
    www.nature.com/scientificreports OPEN SIAH2-mediated and organ-specifc restriction of HO-1 expression by a dual mechanism Shashipavan Chillappagari1*, Ratnal Belapurkar1, Andreas Möller2, Nicole Molenda3, Michael Kracht4, Susanne Rohrbach3 & M. Lienhard Schmitz1* The intracellular levels of the cytoprotective enzyme heme oxygenase-1 (HO-1) are tightly controlled. Here, we reveal a novel mechanism preventing the exaggerated expression of HO-1. The analysis of mice with a knock-out in the ubiquitin E3 ligase seven in absentia homolog 2 (SIAH2) showed elevated HO-1 protein levels in specifc organs such as heart, kidney and skeletal muscle. Increased HO-1 protein amounts were also seen in human cells deleted for the SIAH2 gene. The higher HO-1 levels are not only due to an increased protein stability but also to elevated expression of the HO-1 encoding HMOX1 gene, which depends on the transcription factor nuclear factor E2-related factor 2 (NRF2), a known SIAH2 target. Dependent on its RING (really interesting new gene) domain, expression of SIAH2 mediates proteasome-dependent degradation of its interaction partner HO-1. Additionally SIAH2-defcient cells are also characterized by reduced expression levels of glutathione peroxidase 4 (GPX4), rendering the knock-out cells more sensitive to ferroptosis. Ubiquitin E3 ligases regulate the activity and turnover of many target proteins, thus controlling key features such as metabolism, stress signaling and cell cycle progression1. Te RING family of ubiquitin E3 ligases comprises the SIAH family. Te human genome encodes SIAH1 and the homologous SIAH2 protein as well as SIAH3, which lacks a functional RING domain and is only expressed in a limited subset of cancer cell lines2.
    [Show full text]
  • Cryptic Splicing Events in the Iron Transporter ABCB7 and Other Key Target Genes in SF3B1-Mutant Myelodysplastic Syndromes
    OPEN Leukemia (2016) 30, 2322–2331 www.nature.com/leu ORIGINAL ARTICLE Cryptic splicing events in the iron transporter ABCB7 and other key target genes in SF3B1-mutant myelodysplastic syndromes H Dolatshad1,2,8, A Pellagatti1,2,8, FG Liberante3, M Llorian4, E Repapi5, V Steeples1,2, S Roy1,2, L Scifo1,2, RN Armstrong1,2, J Shaw1,2, BH Yip1,2, S Killick6,RKušec7, S Taylor5, KI Mills3, KI Savage3, CWJ Smith4 and J Boultwood1,2 The splicing factor SF3B1 is the most frequently mutated gene in myelodysplastic syndromes (MDS), and is strongly associated with the presence of ring sideroblasts (RS). We have performed a systematic analysis of cryptic splicing abnormalities from RNA sequencing data on hematopoietic stem cells (HSCs) of SF3B1-mutant MDS cases with RS. Aberrant splicing events in many downstream target genes were identified and cryptic 3′ splice site usage was a frequent event in SF3B1-mutant MDS. The iron transporter ABCB7 is a well-recognized candidate gene showing marked downregulation in MDS with RS. Our analysis unveiled aberrant ABCB7 splicing, due to usage of an alternative 3′ splice site in MDS patient samples, giving rise to a premature termination codon in the ABCB7 mRNA. Treatment of cultured SF3B1-mutant MDS erythroblasts and a CRISPR/Cas9-generated SF3B1-mutant cell line with the nonsense-mediated decay (NMD) inhibitor cycloheximide showed that the aberrantly spliced ABCB7 transcript is targeted by NMD. We describe cryptic splicing events in the HSCs of SF3B1-mutant MDS, and our data support a model in which NMD-induced downregulation of the iron exporter ABCB7 mRNA transcript resulting from aberrant splicing caused by mutant SF3B1 underlies the increased mitochondrial iron accumulation found in MDS patients with RS.
    [Show full text]