DOCSLIB.ORG

Decoding Pathogenesis Factors Involved in the Progression of ATLL Or HAM/TSP After Infection by HTLV-1 Through a Systems Virology Study

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Decoding Pathogenesis Factors Involved in the Progression of ATLL Or HAM/TSP After Infection by HTLV-1 Through a Systems Virology Study Decoding pathogenesis factors involved in the progression of ATLL or HAM/TSP after infection by HTLV-1 through a systems virology study Mohadeseh Zarei Ghobadi University of Isfahan Rahman Emamzadeh ( [email protected] ) University of Isfahan Majid Teymoori-Rad Tehran University of Medical Sciences Sayed-Hamidreza Mozhgani Alborz University of Medical Sciences Research Article Keywords: HTLV-1, ACs, ATLL, HAM/TSP, differentially co-expressed modules, pathogenesis Posted Date: July 16th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-705476/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/21 Abstract Background Human T-cell Leukemia Virus type-1 (HTLV-1) is a retrovirus that causes two diseases including Adult T- cell Leukemia/Lymphoma (ATLL cancer) and HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP, a neurodegenerative disease) after a long latency period as an asymptomatic carrier (AC). There are no obvious explanations about how AC carriers develop each of the mentioned diseases. Finding the discriminative molecular factors and pathways may clarify the destiny of the infection. Methods To shed light on the involved molecular players and activated pathways in each state, differentially co- expressed modules (DiffCoEx) algorithm was employed to identify the highly correlated genes which were co-expressed differently between normal and ACs, ACs and ATLL, as well as ACs and HAM/TSP samples. Through differential pathway analysis, the dysregulated pathways and the specic disease- genes-pathways were gured out. Moreover, the common genes between the member of DiffCoEx and differentially expressed genes were found and the specic genes in ATLL and HAM/TSP were considered as possible biomarkers. Results The dysregulated genes in the ATLL were mostly enriched in immune and cancer-related pathways while the ones in the HAM/TSP were enriched in immune, inammation, and neurological pathways. The differential pathway analysis claried the differences between the gene players in the common activated pathways. Eventually, the nal analysis revealed the involvement of specic dysregulated genes including KIRREL2, RAB36, and KANK1 in HAM/TSP as well as LTB4R2, HCN4, FZD9, GRIK5, CREB3L4, TACR2, FRMD1, LHB, FGF3, TEAD3, GRIN2D, GNRH2, PRLH, GPR156, and CRHR2 in ATLL. Conclusion The identied potential prognostic biomarkers and therapeutic targets are proposed as the most important platers in developing ATLL or HAM/TSP. Moreover, the proposed signaling network claries the differences between the functional players in the activated pathways in ACs, ATLL, and HAM/TSP. 1. Introduction Human T-cell Leukemia Virus type-1 (HTLV-1) belongs to the family Retroviridae and subfamily Orthoretrovirinae [1]. Adult T-cell Leukemia/Lymphoma (ATLL cancer) and Tropical Spastic Page 2/21 Paraparesis/HTLV-1 Associated Myelopathy (TSP/HAM) are two major diseases that may be developed after HTLV-1 infection and elapsing an asymptomatic carrier (AC) state. Although HTLV-1 is not a widespread virus all over the world, it is an endemic pathogen in sub-Saharan Africa, East north of Iran, the Caribbean region, Japan, and South America [2]. Approximately, 90% of the infected HTLV-1 are asymptomatic carriers (ACs) [3] with the capability of the silent transmission of the virus through blood contact, sexual intercourse, breastfeeding, etc [4]. Three common receptors on various cells including neuropilin, glucose transporter 1 (GLUT-1), and heperan sulfate proteoglycan are the main receptors for HTLV-1 [5]. The HTLV-1 proviral DNA is found in the immune cell types comprising dendritic cells, monocytes, CD8 + T-cells, B cells, and in higher extent in CD4 + T-cells [6, 7]. Two oncogenic proteins named as the HTLV-1 basic leucine zipper protein (HBZ) and the transactivator protein (Tax) are frequently expressed by the HTLV-1 genome. However, the tax gene expresses in only around 40% of ATLL patients [8]. Tax has a critical function in the viral pathogenesis of HAM/TSP patients through promoting the proliferation of infected cells by activating NFκB and AP-1 pathways, avoiding apoptosis, and activating cytotoxic T lymphocyte (CTL) response [3, 9]. On the other hand, HBZ prohibits NF-κB pathway, promotes tumor progression, and boosts T cell proliferation and lymphoma [10, 11]. One of the benecial approaches to nd the patterns of co-regulated genes is gene co-expression analysis [12]. Two common approaches are utilized to determine the mechanistic diversity between two conditions including identifying differential gene expression and differential gene co-expression. Through co-expression analysis, the highly correlated genes (a module) can be identied. Moreover, the differential co-expressed analysis determines the specic co-regulated genes in each condition. DiffCoEx is a sensitive and ecient method to nd the gene co-expression differences grouped in various modules between multiple conditions [13]. The major advantage of this method is identifying signicant differential co-expressed gene groups even in the attendance of the within-group correlation across two conditions. Since the pathogenesis mechanism of HTLV-1 as a virus-caused cancer and virus-caused neurologic disease have not been yet fully ascertained, the identication of differential co-regulated genes that are implicated in ACs, ATLL, and HAM/TSP can determine new functional players in each condition. To this end, we integrated various gene expression datasets and then found the differential co-expressed genes between every two conditions employing DiffCoEx approach. The outcomes disclosed the gene players and activated pathways for the development of each disease after infection. Figure 1 indicates the workow of the suggested procedure to nd the most important genes and pathways implicated in each condition. 2. Methods 2.1 Data collection, merging, and preprocessing Page 3/21 To nd the relevant datasets correspond to our study purpose, Gene Expression Omnibus (GEO) repository database was searched. A total of four microarray datasets including GSE29312, GSE29332, GSE33615, and GSE55851 were selected to be involved in our analysis. In total, these datasets contain 58 normal samples, 43 ACs samples, 62 ATLL samples, and 20 HAM/TSP samples. Since datasets belong to different platforms, removeBatchEffect function in the R limma package was utilized to remove the batch effect across different datasets [14]. The expression data of samples related to each condition were merged, individually. A total of 15565 common genes were used for further analysis. The merged data were quantile-normalized and log2-transformed. Moreover, to validate the identied modules, the ACs and HAM/TSP samples from GSE38537 and ATLL samples from GSE43017 were used. The more details related to each dataset is explained in Table 1. Table 1 Characteristics of datasets involved in the analysis and validation. Datasets for main analyses Dataset Platform Number of Samples GSE29312 Illumina HumanHT-12 V3.0 expression beadchip Normal: 9 ( GPL6947) ACs: 20 HAM/TSP: 10 GSE29332 Illumina HumanWG-6 v3.0 expression beadchip Normal: 8 ( GPL6884) ACs: 17 HAM/TSP: 10 GSE55851 Agilent-026652 Whole Human Genome Microarray 4x44K v2 Normal: 3 ( GPL10332) ACs: 6 ATLL: 12 GSE33615 Agilent-014850 Whole Human Genome Microarray 4x44K Normal: 21 G4112F ATLL: 52 ( GPL4133) Datasets for validation GSE38537 Agilent-014850 Whole Human Genome Microarray 4x44K ACs: 4 G4112F HAM/TSP: 4 ( GPL6480) GSE43017 Affymetrix Human Genome U133 Plus 2.0 Array ATLL: 7 ( GPL570) 2.2 Determination of differential co-expressed modules Page 4/21 To nd the differential co-expressed modules among two conditions, the DiffCoEx algorithm was employed [13]. DiffCoEx principally uses Weighted Gene Co-expression Analysis (WGCNA) [12] and contains ve steps as follows: i) An adjacency matrix is constructed for each condition in which their elements are gene pair-wise correlations. We used Pearson coecient to measure correlations. ii) A matrix of adjacency difference (dij) is determined by calculating the signed squared correlation coecients. The signicant co-expression changes between two genes are specied via higher dij values. The soft threshold parameter β is determined so that the network follows a scale-free topology [15]. Then, dij values reach the power of β. iii) The topological overlap dissimilarity matrix (dissTOM) is constructed to detect the shared genes with common neighbors in the graph which is constituted based on the adjacency matrix containing the differential correlation network. The topological overlap between co- expressed genes in the resulted network belonging to a module in only one condition contains all the genes within that module. iv) The hierarchical clustering is performed with ashClust [16]. Then, dynamicTreeCut function is used to extract gene groups (modules) from the obtained dendrogram and mergeCloseModules function is employed to merge close modules. Each module is specied with a color. v) The statistical signicance of differential co-expression is evaluated using the dispersion statistic to quantify the correlation alteration between two conditions. In this study, we identied differential co- expressed modules between normal and ACs, ACs and ATLL, ACs and HAM/TSP. Moreover, through this approach, module-to-module co-expression changes were examined through evaluating the signicance of the correlation alterations among the genes from each possible module pair, employing a similar
Load more

Recommended publications
  • High-Throughput Discovery of Novel Developmental Phenotypes
    High-throughput discovery of novel developmental phenotypes The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Dickinson, M. E., A. M. Flenniken, X. Ji, L. Teboul, M. D. Wong, J. K. White, T. F. Meehan, et al. 2016. “High-throughput discovery of novel developmental phenotypes.” Nature 537 (7621): 508-514. doi:10.1038/nature19356. http://dx.doi.org/10.1038/nature19356. Published Version doi:10.1038/nature19356 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:32071918 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nature. Manuscript Author Author manuscript; Manuscript Author available in PMC 2017 March 14. Published in final edited form as: Nature. 2016 September 22; 537(7621): 508–514. doi:10.1038/nature19356. High-throughput discovery of novel developmental phenotypes A full list of authors and affiliations appears at the end of the article. Abstract Approximately one third of all mammalian genes are essential for life. Phenotypes resulting from mouse knockouts of these genes have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5000 knockout mouse lines, we have identified 410 Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms #Corresponding author: [email protected].
    [Show full text]
  • Noninvasive Sleep Monitoring in Large-Scale Screening of Knock-Out Mice
    bioRxiv preprint doi: https://doi.org/10.1101/517680; this version posted January 11, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Noninvasive sleep monitoring in large-scale screening of knock-out mice reveals novel sleep-related genes Shreyas S. Joshi1*, Mansi Sethi1*, Martin Striz1, Neil Cole2, James M. Denegre2, Jennifer Ryan2, Michael E. Lhamon3, Anuj Agarwal3, Steve Murray2, Robert E. Braun2, David W. Fardo4, Vivek Kumar2, Kevin D. Donohue3,5, Sridhar Sunderam6, Elissa J. Chesler2, Karen L. Svenson2, Bruce F. O'Hara1,3 1Dept. of Biology, University of Kentucky, Lexington, KY 40506, USA, 2The Jackson Laboratory, Bar Harbor, ME 04609, USA, 3Signal solutions, LLC, Lexington, KY 40503, USA, 4Dept. of Biostatistics, University of Kentucky, Lexington, KY 40536, USA, 5Dept. of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA. 6Dept. of Biomedical Engineering, University of Kentucky, Lexington, KY 40506, USA. *These authors contributed equally Address for correspondence and proofs: Shreyas S. Joshi, Ph.D. Dept. of Biology University of Kentucky 675 Rose Street 101 Morgan Building Lexington, KY 40506 U.S.A. Phone: (859) 257-2805 FAX: (859) 257-1717 Email: [email protected] Running title: Sleep changes in knockout mice bioRxiv preprint doi: https://doi.org/10.1101/517680; this version posted January 11, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
    [Show full text]
  • Characterizing Genomic Duplication in Autism Spectrum Disorder by Edward James Higginbotham a Thesis Submitted in Conformity
    Characterizing Genomic Duplication in Autism Spectrum Disorder by Edward James Higginbotham A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Molecular Genetics University of Toronto © Copyright by Edward James Higginbotham 2020 i Abstract Characterizing Genomic Duplication in Autism Spectrum Disorder Edward James Higginbotham Master of Science Graduate Department of Molecular Genetics University of Toronto 2020 Duplication, the gain of additional copies of genomic material relative to its ancestral diploid state is yet to achieve full appreciation for its role in human traits and disease. Challenges include accurately genotyping, annotating, and characterizing the properties of duplications, and resolving duplication mechanisms. Whole genome sequencing, in principle, should enable accurate detection of duplications in a single experiment. This thesis makes use of the technology to catalogue disease relevant duplications in the genomes of 2,739 individuals with Autism Spectrum Disorder (ASD) who enrolled in the Autism Speaks MSSNG Project. Fine-mapping the breakpoint junctions of 259 ASD-relevant duplications identified 34 (13.1%) variants with complex genomic structures as well as tandem (193/259, 74.5%) and NAHR- mediated (6/259, 2.3%) duplications. As whole genome sequencing-based studies expand in scale and reach, a continued focus on generating high-quality, standardized duplication data will be prerequisite to addressing their associated biological mechanisms. ii Acknowledgements I thank Dr. Stephen Scherer for his leadership par excellence, his generosity, and for giving me a chance. I am grateful for his investment and the opportunities afforded me, from which I have learned and benefited. I would next thank Drs.
    [Show full text]
  • Associations Between Human Disease Genes and Overlapping Gene Groups and Multiple Amino Acid Runs
    Associations between human disease genes and overlapping gene groups and multiple amino acid runs Samuel Karlin*†, Chingfer Chen*, Andrew J. Gentles*, and Michael Cleary‡ Departments of *Mathematics and ‡Pathology, Stanford University, Stanford, CA 94305 Contributed by Samuel Karlin, October 30, 2002 Overlapping gene groups (OGGs) arise when exons of one gene are Chr 22. Such rearrangements may be mediated by recombination contained within the introns of another. Typically, the two over- events based on region-specific low copy repeats. The DGS lapping genes are encoded on opposite DNA strands. OGGs are region of 22q11.2 is particularly rich with segmental duplications, often associated with specific disease phenotypes. In this report, which can induce deletions, translocations, and genomic insta- we identify genes with OGG architecture and genes encoding bility (4). There are several anomalous sequence features asso- multiple long amino acid runs and examine their relations to ciated with OGGs, including Alu sequences intersecting exons, diseases. OGGs appear to be susceptible to genomic rearrange- pseudogenes occupying introns, and single-exon (intronless) ments as happens commonly with the loci of the DiGeorge syn- genes that often result from a processed multiexon gene. drome on human chromosome 22. We also examine the degree of At least 28 genes in Chr 21 are related to diseases, as conservation of OGGs between human and mouse. Our analyses characterized in the GeneCards database (5), as are 64 genes in suggest that (i) a high proportion of genes in OGG regions are Chr 22. Specific disorders that have been mapped to genes on disease-associated, (ii) genomic rearrangements are likely to occur Chr 21 and that involve OGG structures include: amyotrophic within OGGs, possibly as a consequence of anomalous sequence lateral sclerosis (ALS, Lou Gehrig’s disease), linked to the features prevalent in these regions, and (iii) multiple amino acid GRIK1 ionotrophic kainate 1 glutamate receptor gene at 21q22 runs are also frequently associated with pathologies.
    [Show full text]
  • Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
    Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ......................................................
    [Show full text]
  • 393LN V 393P 344SQ V 393P Probe Set Entrez Gene
    393LN v 393P 344SQ v 393P Entrez fold fold probe set Gene Gene Symbol Gene cluster Gene Title p-value change p-value change chemokine (C-C motif) ligand 21b /// chemokine (C-C motif) ligand 21a /// chemokine (C-C motif) ligand 21c 1419426_s_at 18829 /// Ccl21b /// Ccl2 1 - up 393 LN only (leucine) 0.0047 9.199837 0.45212 6.847887 nuclear factor of activated T-cells, cytoplasmic, calcineurin- 1447085_s_at 18018 Nfatc1 1 - up 393 LN only dependent 1 0.009048 12.065 0.13718 4.81 RIKEN cDNA 1453647_at 78668 9530059J11Rik1 - up 393 LN only 9530059J11 gene 0.002208 5.482897 0.27642 3.45171 transient receptor potential cation channel, subfamily 1457164_at 277328 Trpa1 1 - up 393 LN only A, member 1 0.000111 9.180344 0.01771 3.048114 regulating synaptic membrane 1422809_at 116838 Rims2 1 - up 393 LN only exocytosis 2 0.001891 8.560424 0.13159 2.980501 glial cell line derived neurotrophic factor family receptor alpha 1433716_x_at 14586 Gfra2 1 - up 393 LN only 2 0.006868 30.88736 0.01066 2.811211 1446936_at --- --- 1 - up 393 LN only --- 0.007695 6.373955 0.11733 2.480287 zinc finger protein 1438742_at 320683 Zfp629 1 - up 393 LN only 629 0.002644 5.231855 0.38124 2.377016 phospholipase A2, 1426019_at 18786 Plaa 1 - up 393 LN only activating protein 0.008657 6.2364 0.12336 2.262117 1445314_at 14009 Etv1 1 - up 393 LN only ets variant gene 1 0.007224 3.643646 0.36434 2.01989 ciliary rootlet coiled- 1427338_at 230872 Crocc 1 - up 393 LN only coil, rootletin 0.002482 7.783242 0.49977 1.794171 expressed sequence 1436585_at 99463 BB182297 1 - up 393
    [Show full text]
  • Transcriptional Profile of Human Anti-Inflamatory Macrophages Under Homeostatic, Activating and Pathological Conditions
    UNIVERSIDAD COMPLUTENSE DE MADRID FACULTAD DE CIENCIAS QUÍMICAS Departamento de Bioquímica y Biología Molecular I TESIS DOCTORAL Transcriptional profile of human anti-inflamatory macrophages under homeostatic, activating and pathological conditions Perfil transcripcional de macrófagos antiinflamatorios humanos en condiciones de homeostasis, activación y patológicas MEMORIA PARA OPTAR AL GRADO DE DOCTOR PRESENTADA POR Víctor Delgado Cuevas Directores María Marta Escribese Alonso Ángel Luís Corbí López Madrid, 2017 © Víctor Delgado Cuevas, 2016 Universidad Complutense de Madrid Facultad de Ciencias Químicas Dpto. de Bioquímica y Biología Molecular I TRANSCRIPTIONAL PROFILE OF HUMAN ANTI-INFLAMMATORY MACROPHAGES UNDER HOMEOSTATIC, ACTIVATING AND PATHOLOGICAL CONDITIONS Perfil transcripcional de macrófagos antiinflamatorios humanos en condiciones de homeostasis, activación y patológicas. Víctor Delgado Cuevas Tesis Doctoral Madrid 2016 Universidad Complutense de Madrid Facultad de Ciencias Químicas Dpto. de Bioquímica y Biología Molecular I TRANSCRIPTIONAL PROFILE OF HUMAN ANTI-INFLAMMATORY MACROPHAGES UNDER HOMEOSTATIC, ACTIVATING AND PATHOLOGICAL CONDITIONS Perfil transcripcional de macrófagos antiinflamatorios humanos en condiciones de homeostasis, activación y patológicas. Este trabajo ha sido realizado por Víctor Delgado Cuevas para optar al grado de Doctor en el Centro de Investigaciones Biológicas de Madrid (CSIC), bajo la dirección de la Dra. María Marta Escribese Alonso y el Dr. Ángel Luís Corbí López Fdo. Dra. María Marta Escribese
    [Show full text]
  • Proteome Analyses of Host Membranes Modified by Intracellular Salmonella Enterica Typhimurium
    Proteome analyses of host membranes modified by intracellular Salmonella enterica Typhimurium D i s s e r t a t i o n zur Erlangung des Grades “Doctor rerum naturalium” (Dr. rer. nat.) des Fachbereichs Biologie/Chemie an der Universität Osnabrück vorgelegt von Stephanie Vorwerk aus Bielefeld Osnabrück, März 2016 Hauptberichterstatter: Prof. Dr. Michael Hensel 2. Berichterstatter: Prof. Dr. Hubert Hilbi Weitere Mitglieder der Prüfungskommission: Prof. Dr. Karlheinz Altendorf Dr. Heiko Harten „Zwei Dinge sind zu unserer Arbeit nötig: Unermüdliche Ausdauer und die Bereitschaft, etwas, in das man viel Zeit und Arbeit gesteckt hat, wieder wegzuwerfen.“ Albert Einstein Für meine Eltern I Table of Content I INTRODUCTION............................................................................................................. 1 I.1 Home inside the host cell – the endosomal system ..................................... 1 I.1.1 Rab GTPases as master regulators ............................................................ 3 I.1.2 Regulation of the endocytic pathway........................................................... 5 I.1.3 Phagocytosis .............................................................................................. 8 I.2 Lifestyles of intracellular pathogens .............................................................. 9 I.2.1 Escape from the phagosome – cytosolic lifestyle .......................................11 I.2.2 Modification of the phagosome ─ vacuolar lifestyle ....................................13 I.3 Salmonella
    [Show full text]
  • Genomic Segmental Duplications on the Basis of the T(9;22) Rearrangement in Chronic Myeloid Leukemia
    Oncogene (2010) 29, 2509–2516 & 2010 Macmillan Publishers Limited All rights reserved 0950-9232/10 $32.00 www.nature.com/onc ORIGINAL ARTICLE Genomic segmental duplications on the basis of the t(9;22) rearrangement in chronic myeloid leukemia F Albano1,3, L Anelli1,3, A Zagaria1,3, N Coccaro1, P D’Addabbo2, V Liso1, M Rocchi2,4 and G Specchia1,4 1Department of Hematology, University of Bari, Bari, Italy and 2Department of Genetics and Microbiology, University of Bari, Bari, Italy A crucial role of segmental duplications (SDs) of the (SDs) in triggering constitutional and also tumor human genome has been shown in chromosomal re- chromosomal abnormalities (Sharp et al., 2006; Gibcus arrangements associated with several genomic disorders. et al., 2007; Darai-Ramqvist et al., 2008; Gu et al., 2008; Limited knowledge is yet available on the molecular Mefford and Eichler, 2009). Several rearrangements processes resulting in chromosomal rearrangements in have been described so far to explain the occurrence of tumors. The t(9;22)(q34;q11) rearrangement causing the genomic disorders: recurrent, sharing a common size 50BCR/30ABL gene formation has been detected in more and showing clustering of breakpoints inside the SDs, than 90% of cases with chronic myeloid leukemia (CML). and nonrecurrent rearrangements, involving regions of In 10–18% of patients with CML, genomic deletions different sizes and showing breakpoints scattering in were detected on der(9) chromosome next to translocation large regions (Gu et al., 2008). Most of the recurrent breakpoints. The molecular mechanism triggering the rearrangements result from a nonallelic homologous t(9;22) and deletions on der(9) is still speculative.
    [Show full text]
  • Investigation of Selected Genomic Deletions and Duplications in A
    D’Angelo et al. Molecular Cytogenetics 2014, 7:75 http://www.molecularcytogenetics.org/content/7/1/75 RESEARCH Open Access Investigation of selected genomic deletions and duplications in a cohort of 338 patients presenting with syndromic obesity by multiplex ligation-dependent probe amplification using synthetic probes Carla S D’Angelo1*, Monica C Varela1, Cláudia IE de Castro1, Chong A Kim2, Débora R Bertola2, Charles M Lourenço3, Ana Beatriz A Perez4 and Celia P Koiffmann1 Abstract Background: Certain rare syndromes with developmental delay or intellectual disability caused by genomic copy number variants (CNVs), either deletions or duplications, are associated with higher rates of obesity. Current strategies to diagnose these syndromes typically rely on phenotype-driven investigation. However, the strong phenotypic overlap between syndromic forms of obesity poses challenges to accurate diagnosis, and many different individual cytogenetic and molecular approaches may be required. Multiplex ligation-dependent probe amplification (MLPA) enables the simultaneous analysis of multiple targeted loci in a single test, and serves as an important screening tool for large cohorts of patients in whom deletions and duplications involving specific loci are suspected. Our aim was to design a synthetic probe set for MLPA analysis to investigate in a cohort of 338 patients with syndromic obesity deletions and duplications in genomic regions that can cause this phenotype. Results: We identified 18 patients harboring copy number imbalances; 18 deletions and 5 duplications. The alterations in ten patients were delineated by chromosomal microarrays, and in the remaining cases by additional MLPA probes incorporated into commercial kits. Nine patients showed deletions in regions of known microdeletion syndromes with obesity as a clinical feature: in 2q37 (4 cases), 9q34 (1 case) and 17p11.2 (4 cases).
    [Show full text]
  • A Simple Competing Endogenous RNA Network Identifies Novel Mrna, Mirna, and Lncrna Markers in Human Cholangiocarcinoma
    Hindawi BioMed Research International Volume 2019, Article ID 3526407, 13 pages https://doi.org/10.1155/2019/3526407 Research Article A Simple Competing Endogenous RNA Network Identifies Novel mRNA, miRNA, and lncRNA Markers in Human Cholangiocarcinoma Cheng Zhang and Chunlin Ge Department of Pancreatic and Biliary Surgery, e First Hospital of China Medical University, Shenyang, Liaoning , China Correspondence should be addressed to Chunlin Ge; [email protected] Received 10 December 2018; Revised 22 February 2019; Accepted 23 February 2019; Published 24 March 2019 Academic Editor: Udayan Apte Copyright © 2019 Cheng Zhang and Chunlin Ge. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Cholangiocarcinoma (CCA) is the second most common malignant primary liver tumor and has shown an alarming increase in incidence over the last two decades. However, the mechanisms behind tumorigenesis and progression remain insufcient. Te present study aimed to uncover the underlying regulatory mechanism on CCA and fnd novel biomarkers for the disease prognosis. Method. Te RNA-sequencing (RNA-seq) datasets of lncRNAs, miRNAs, and mRNAs in CCA as well as relevant clinical information were obtained from the Cancer Genome Atlas (TCGA) database. Afer pretreatment, diferentially expressed RNAs (DERNAs) were identifed and further interrogated for their correlations with clinical information. Prognostic RNAs were selected using univariate Cox regression. Ten, a ceRNA network was constructed based on these RNAs. Results. We identifed a total of fve prognostic DEmiRNAs, 63 DElncRNAs, and 90 DEmRNAs between CCA and matched normal tissues.
    [Show full text]
  • Inhibition of Mir-1247 on Cell Proliferation and Invasion in Bladder Cancer Through Its Downstream Target of RAB36
    J Biosci Vol. 43, No. 2, June 2018, pp. 365–373 Ó Indian Academy of Sciences DOI: 10.1007/s12038-018-9755-4 Inhibition of miR-1247 on cell proliferation and invasion in bladder cancer through its downstream target of RAB36 YUDI ZHU*, SHAOSI LIANG,HUAFENG PAN,ZHONGLIANG CHENG and XIN RUI Department of Urology, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China *Corresponding author (Email, [email protected]) MS received 21 November 2017; accepted 4 April 2018; published online 21 April 2018 Recently, microRNA-1247 (miR-1247) has been reported to function as tumour suppressor in several cancer types, including pancreatic cancer, hepatocellular cancer and lung cancer. However, the biological function of miR-1247 in bladder cancer and the underlying mechanisms have remained largely uncovered. In this study, the expression of miR-1247 was significantly downregulated, while RAB36 protein was remarkably upregulated in bladder cancer tissues and cell lines compared with that in paired adjacent normal tissues or normal cell line (SU-HUC-1). The function of miR-1247 and RAB36 in the cell viability, proliferation and invasion of bladder cancer cells (T24 and J82) was assessed by CCK-8, colony formation and Transwell assay, respectively. Gain of function studies showed that upregulation of miR-1247 significantly inhibited cell proliferation and invasion capacity of bladder cancer cells. Consistently, downregulation of RAB36 mimicked the suppressive effects of miR-1247 overexpression in bladder cancer cells. Importantly, miR-1247 was confirmed to target the 30untranslated region (UTR) of RAB36 and downregulated its expression using luciferase reporter assay and Western blot assays. In conclusion, these results provide the first clues regarding the role of miR-1247 might be a potential therapeutic agent and diagnostic marker of bladder cancer by inhibiting RAB36 expression.
    [Show full text]