Genome Scan Study of Prostate Cancer in Arabs: Identification of Three

Total Page:16

File Type:pdf, Size:1020Kb

Genome Scan Study of Prostate Cancer in Arabs: Identification of Three Shan et al. Journal of Translational Medicine 2013, 11:121 http://www.translational-medicine.com/content/11/1/121 RESEARCH Open Access Genome scan study of prostate cancer in Arabs: identification of three genomic regions with multiple prostate cancer susceptibility loci in Tunisians Jingxuan Shan1, Khalid Al-Rumaihi2, Danny Rabah3, Issam Al-Bozom4, Dhanya Kizhakayil1, Karim Farhat3, Sami Al-Said2, Hala Kfoury3, Shoba P Dsouza1, Jillian Rowe5, Hanif G Khalak5, Shahzad Jafri5, Idil I Aigha1 and Lotfi Chouchane1* Abstract Background: Large databases focused on genetic susceptibility to prostate cancer have been accumulated from population studies of different ancestries, including Europeans and African-Americans. Arab populations, however, have been only rarely studied. Methods: Using Affymetrix Genome-Wide Human SNP Array 6, we conducted a genome-wide association study (GWAS) in which 534,781 single nucleotide polymorphisms (SNPs) were genotyped in 221 Tunisians (90 prostate cancer patients and 131 age-matched healthy controls). TaqMan® SNP Genotyping Assays on 11 prostate cancer associated SNPs were performed in a distinct cohort of 337 individuals from Arab ancestry living in Qatar and Saudi Arabia (155 prostate cancer patients and 182 age-matched controls). In-silico expression quantitative trait locus (eQTL) analysis along with mRNA quantification of nearby genes was performed to identify loci potentially cis-regulated by the identified SNPs. Results: Three chromosomal regions, encompassing 14 SNPs, are significantly associated with prostate cancer risk in the Tunisian population (P =1×10-4 to P =1×10-5). In addition to SNPs located on chromosome 17q21, previously found associated with prostate cancer in Western populations, two novel chromosomal regions are revealed on chromosome 9p24 and 22q13. eQTL analysis and mRNA quantification indicate that the prostate cancer associated SNPs of chromosome 17 could enhance the expression of STAT5B gene. Conclusion: Our findings, identifying novel GWAS prostate cancer susceptibility loci, indicate that prostate cancer genetic risk factors could be ethnic specific. Keywords: Prostate cancer, GWAS, Arab population Introduction males over 65 years old [3]. In Kuwait, the incidence Prostate cancer (PCa) is the most common malignancy in of prostate cancer rose to 12.3/100,000 men/year in western countries and the second cause of cancer-related 2004 [4]. In 2003, prostate cancer was ranked as the death in Europe and the United States [1]. With lifestyle fourth most diagnosed cancer in Tunisia [5]. In Lebanon, changes, the incidence of the disease has been increasing the age-adjusted standardized incidence was 21.5/100,000 in the Arab populations [2]. From 1991–2006, prostate men/year in 1998 [6]. In Arab populations, the incidence cancer was ranked first among cancers in Qatari of prostate cancer correlates with low prostate volume and testosterone. Despite the low levels of testosterone, * Correspondence: [email protected] the aggressive forms of prostate cancer are found 1Laboratory of Genetic Medicine and Immunology, Weill Cornell Medical frequently in Arab patients, which indicate an increased College in Qatar, Qatar Foundation, Doha, Qatar Full list of author information is available at the end of the article sensitivity of Arab men to this steroid [7]. © 2013 Shan et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Shan et al. Journal of Translational Medicine 2013, 11:121 Page 2 of 8 http://www.translational-medicine.com/content/11/1/121 Prostate cancer is generally considered to be a complex advanced group (T3 − T4N0 M0 and T1 − T4N0–1M1/ disease with several genes underlying onset and severity, T1− T4N1M0–1). Histopathological grade was recorded as and minor susceptibility genes may play a larger role in the Gleason score and was classified into two groups: the prostate cancer risk. Recently, genome-wide association low-grade group (Gleason score < 7) and the high-grade studies (GWAS) have identified dozens of common group (Gleason score⩾7). A detailed description of the variants that confer susceptibility to prostate cancer across clinical-pathological characteristics of this cohort is various ethnicities from European, Asian and African summarized in Table 1. ancestry [8,9]. However, little is known about the prostate The control group consisted of 131 healthy male subjects cancer genetic susceptibility in populations of Arab ancestry. having no evidence of any personal or family history of The genetic susceptibility to prostate cancer is variable cancer. Their PSA levels were within the normal limit among different populations [10]. The difference may (<4 ng/ml) and showed no signs of prostate hyperplasia or highlight the impact of genetic background on disease risk PCa by digital rectal examination. Those who had other between populations. For instance, several SNPs located known malignancies were also excluded. in 8q24 have been implied to a different population There were 155 unrelated PCa patients and 182 susceptibility to prostate cancer [11,12]. These variants, age-matched controls from Arab ancestry living in Qatar located in c-Myc gene, reduced the risk of prostate cancer and Saudi Arabia included in the replication genotyping in Caucasian but not in African Americans, thus protective study. mechanisms that might reduce c-Myc expression in Caucasians do not exist in African Americans. Identification of the genetic variants in different populations may help DNA and RNA extraction us to better understand the genetic and molecular Genomic DNA was extracted from peripheral blood W mechanism of prostate cancer. samples using QIAamp DNA blood Maxi Kit according With the aim to identify genetic variants associated with to the manufacturer’s protocol (Qiagen, Valencia, CA). prostate cancer in Arab populations, we first conducted Thirty-six RNA samples were extracted from archived a GWAS with DNA samples from a Tunisian cohort. formalin-fixed paraffin-embedded non-malignant prostate ™ We further extended the study to evaluate potential tissues using RecoverAll Total Nucleic Acid Isolation associations of 11 SNPs, identified by GWAS, in a cohort Kit (Ambion, Grand Island, NY). from Arab ancestry living in Qatar and Saudi Arabia. Functional significance of the identified prostate cancer associated SNPs was assessed by in-silico analysis and Table 1 Description of the subjects for GWAS study mRNA quantification of certain adjacent genes. Case number 90 Age (mean ± SD) 73.7 ± 8.4a Materials and methods PSA (mean ± SD) 225.5 ± 368.9 Ethics statement Characteristic Number (%) The Institutional Review Boards of Weill Cornell Medical Tumor Stage College in Qatar, the Hamad Medical Corporation, T1-T2 17 (18.9) and King Khalid University Hospital approved the study protocols. All subjects signed informed consent documents T3-T4 44 (48.9) for participation in the study. Data missing 29 (32.2) Lymph node Subjects Negative 52 (57.8) A total of 221 unrelated Tunisian men consisting of 90 Positive 7 (7.8) PCa patients and 131 male age-matched controls were Data missing 31 (34.4) selected from the same population living in the middle coast of Tunisia. PCa patients were recruited from the Metastasis two departments of Urology of Monastir and Sousse Negative 42 (46.7) Hospital, Tunisia, in whom the diagnosis has been Positive 18 (20.0) confirmed histologically. The serum prostate-specific Data missing 25 (33.3) antigen (PSA) values were measured in all the cases before Gleason Score treatment. Clinical characteristics including Gleason grade, GS ≥ 7 44 (48.9) TNM stage, age at diagnosis, and family history were obtained from medical records. The pathological stage at GS < 7 19 (21.1) the time of diagnosis was classified according to TNM Data missing 27 (30.0) system into the localized group (T1− T2N0M0) and the a Age (mean ± SD) for controls: 59.7 ± 12.6. Shan et al. Journal of Translational Medicine 2013, 11:121 Page 3 of 8 http://www.translational-medicine.com/content/11/1/121 Genotyping levels between genotypes were compared using a linear Genome-wide scanning was applied with Affymetrix regression model. To avoid false positive associations Genome-Wide Human SNP Array 6.0 following the due to multiple tests, we set a significance threshold − manufacturer’s protocol. After genotype calling using of P <1.0×10 3 and also assessed significance using the Birdsuite [13], the total SNPs on Affymetrix arrays 10,000-fold permutations (Perm). were subjected to quality control. Nineteen samples were excluded due to low call rate (<95%). SNPs were Gene expression quantification W excluded with low minor allele frequency (<5%), low call Using GoTaq 2-Step RT-qPCR System, total RNA frequency (<95%) and replication error. The final SNP was prepared from 36 non-malignant prostate tissues set included 534,781 SNPs for genome-wide association and reverse transcribed and the mRNA of genes adjacent analysis. to the GWAS prostate cancer SNPs were quantified. Δ The replication study was performed with the Expression values were calculated as 2- Ct using GAPDH W TaqMan SNP genotyping assays on a 7500 Real-Time gene as reference. Primer sequences are listed in the PCR System (Applied Biosystems, Grand Island, NY), Additional file 2 Table S1. The difference in gene expres- with no template as negative controls. The PCR thermal sion between prostate tumors carrying GWAS prostate cycling was as follows: initial denaturing at 95°C for cancer risk alleles and that without was calculated using T 10 min, 40 cycles of 92°C for 15 s and 60°C for 1 min. test with Welch’s correction. Genotype call success rate for cases and for controls was 94.8% and 98.2% respectively. Randomly selected 47 Results samples were verified genotype reproducibility with a Genome Scan analysis of DNA from 90 Tunisian patients coincidence rate 100%.
Recommended publications
  • PARSANA-DISSERTATION-2020.Pdf
    DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks.
    [Show full text]
  • Seq2pathway Vignette
    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
    [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]
  • CDH12 Cadherin 12, Type 2 N-Cadherin 2 RPL5 Ribosomal
    5 6 6 5 . 4 2 1 1 1 2 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 A A A A A A A A A A A A A A A A A A A A C C C C C C C C C C C C C C C C C C C C R R R R R R R R R R R R R R R R R R R R B , B B B B B B B B B B B B B B B B B B B , 9 , , , , 4 , , 3 0 , , , , , , , , 6 2 , , 5 , 0 8 6 4 , 7 5 7 0 2 8 9 1 3 3 3 1 1 7 5 0 4 1 4 0 7 1 0 2 0 6 7 8 0 2 5 7 8 0 3 8 5 4 9 0 1 0 8 8 3 5 6 7 4 7 9 5 2 1 1 8 2 2 1 7 9 6 2 1 7 1 1 0 4 5 3 5 8 9 1 0 0 4 2 5 0 8 1 4 1 6 9 0 0 6 3 6 9 1 0 9 0 3 8 1 3 5 6 3 6 0 4 2 6 1 0 1 2 1 9 9 7 9 5 7 1 5 8 9 8 8 2 1 9 9 1 1 1 9 6 9 8 9 7 8 4 5 8 8 6 4 8 1 1 2 8 6 2 7 9 8 3 5 4 3 2 1 7 9 5 3 1 3 2 1 2 9 5 1 1 1 1 1 1 5 9 5 3 2 6 3 4 1 3 1 1 4 1 4 1 7 1 3 4 3 2 7 6 4 2 7 2 1 2 1 5 1 6 3 5 6 1 3 6 4 7 1 6 5 1 1 4 1 6 1 7 6 4 7 e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m
    [Show full text]
  • KDELR3 Dnaxpab a C-Terminal Tetrapeptide Signal, Usually Lys-Asp-Glu-Leu (KDEL) in Animal Cells, and His-Asp-Glu-Leu (HDEL) in S
    KDELR3 DNAxPab a C-terminal tetrapeptide signal, usually lys-asp-glu-leu (KDEL) in animal cells, and his-asp-glu-leu (HDEL) in S. Catalog Number: H00011015-W01P cerevisiae. This process is mediated by a receptor that recognizes, and binds the tetrapeptide-containing Regulation Status: For research use only (RUO) protein, and returns it to the ER. In yeast, the sorting receptor encoded by a single gene, ERD2, is a Product Description: Rabbit polyclonal antibody raised seven-transmembrane protein. Unlike yeast, several against a full-length human KDELR3 DNA using DNAx™ human homologs of the ERD2 gene, constituting the Immune technology. KDEL receptor gene family, have been described. KDELR3 was the third member of the family to be Immunogen: Full-length human DNA identified, and it encodes a protein highly homologous to KDELR1 and KDELR2 proteins. Two transcript variants Sequence: of KDELR3 that arise by alternative splicing, and encode MNVFRILGDLSHLLAMILLLGKIWRSKCCKGISGKSQIL different isoforms of KDELR3 receptor, have been FALVFTTRYLDLFTNFISIYNTVMKVVFLLCAYVTVYMIY described. [provided by RefSeq] GKFRKTFDSENDTFRLEFLLVPVIGLSFLENYSFTLLEIL WTFSIYLESVAILPQLFMISKTGEAETITTHYLFFLGLYR ALYLANWIRRYQTENFYDQIAVVSGVVQTIFYCDFFYL YVTKVLKGKKLSLPMPI Host: Rabbit Reactivity: Human Applications: Flow Cyt-Tr, IF-Ex, IF-Tr, WB-Tr (See our web site product page for detailed applications information) Protocols: See our web site at http://www.abnova.com/support/protocols.asp or product page for detailed protocols Purification: Protein A Storage Buffer: In 1x PBS, pH 7.4 Storage Instruction: Store at -20°C or lower. Aliquot to avoid repeated freezing and thawing. Entrez GeneID: 11015 Gene Symbol: KDELR3 Gene Alias: ERD2L3 Gene Summary: Retention of resident soluble proteins in the lumen of the endoplasmic reticulum (ER) is achieved in both yeast and animal cells by their continual retrieval from the cis-Golgi, or a pre-Golgi compartment.
    [Show full text]
  • Identification of Biomarkers in Macrophages of Atherosclerosis By
    Huang et al. Lipids in Health and Disease (2019) 18:107 https://doi.org/10.1186/s12944-019-1056-x RESEARCH Open Access Identification of biomarkers in macrophages of atherosclerosis by microarray analysis He-ming Huang1, Xin Jiang1, Meng-lei Hao2, Meng-jie Shan3, Yong Qiu4, Gai-feng Hu5, Quan Wang5, Zi-qing Yu6, Ling-bing Meng7*† and Yun-yun Zou8*† Abstract Background: Atherosclerotic cardiovascular disease (ASCVD) refers to a series of diseases caused by atherosclerosis (AS). It is one of the most important causes of death worldwide. According to the inflammatory response theory, macrophages play a critical role in AS. However, the potential targets associated with macrophages in the development of AS are still obscure. This study aimed to use bioinformatics tools for screening and identifying molecular targets in AS macrophages. Methods: Two expression profiling datasets (GSE7074 and GSE9874) were obtained from the Gene Expression Omnibus dataset, and differentially expressed genes (DEGs) between non-AS macrophages and AS macrophages were identified. Functional annotation of the DEGs was performed by analyzing the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. STRING and Cytoscape were employed for constructing a protein–protein interaction network and analyzing hub genes. Results: A total of 98 DEGs were distinguished between non-AS macrophages and AS macrophages. The functional variations in DEGs were mainly enriched in response to hypoxia, respiratory gaseous exchange, protein binding, and intracellular, ciliary tip, early endosome membrane, and Lys63-specific deubiquitinase activities. Three genes were identified as hub genes, including KDELR3, CD55,andDYNC2H1. Conclusion: Hub genes and DEGs identified by using microarray techniques can be used as diagnostic and therapeutic biomarkers for AS.
    [Show full text]
  • Downloaded the “Top Edge” Version
    bioRxiv preprint doi: https://doi.org/10.1101/855338; this version posted December 6, 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 4.0 International license. 1 Drosophila models of pathogenic copy-number variant genes show global and 2 non-neuronal defects during development 3 Short title: Non-neuronal defects of fly homologs of CNV genes 4 Tanzeen Yusuff1,4, Matthew Jensen1,4, Sneha Yennawar1,4, Lucilla Pizzo1, Siddharth 5 Karthikeyan1, Dagny J. Gould1, Avik Sarker1, Yurika Matsui1,2, Janani Iyer1, Zhi-Chun Lai1,2, 6 and Santhosh Girirajan1,3* 7 8 1. Department of Biochemistry and Molecular Biology, Pennsylvania State University, 9 University Park, PA 16802 10 2. Department of Biology, Pennsylvania State University, University Park, PA 16802 11 3. Department of Anthropology, Pennsylvania State University, University Park, PA 16802 12 4 contributed equally to work 13 14 *Correspondence: 15 Santhosh Girirajan, MBBS, PhD 16 205A Life Sciences Building 17 Pennsylvania State University 18 University Park, PA 16802 19 E-mail: [email protected] 20 Phone: 814-865-0674 21 1 bioRxiv preprint doi: https://doi.org/10.1101/855338; this version posted December 6, 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 4.0 International license. 22 ABSTRACT 23 While rare pathogenic copy-number variants (CNVs) are associated with both neuronal and non- 24 neuronal phenotypes, functional studies evaluating these regions have focused on the molecular 25 basis of neuronal defects.
    [Show full text]
  • Examining Differences of Ubiquitination on KDEL Receptor Isoform Expression Zachary Dixon Mentored by Dr
    Examining differences of ubiquitination on KDEL receptor isoform expression Zachary Dixon Mentored by Dr. Emily Wires and Dr. Brandon Harvey Introduction Materials and Methods (cont.) Results (cont.) The disruption of vital cellular processes, commonly supported by Following the identification of putative PTMs (Figure 2), KDELrs were probed Flag Pull-Down Flow-Through the transport of proteins emanant from the endoplasmic reticulum for ubiquitination. SY5Y cells were plated in a 24–well plate, and KDELr plasmids (ER), is a primary factor in disease pathogenesis. Key proteins in this were transfected into cells with no additional treatment, vehicle, or a proteasomal R1 R2 R1 R2 R1 R2 R1 R1 R1 R1 R2 R2 R2 R2 R1 transport are KDEL receptors (KDELrs). Post-translation, nascent inhibitor (MG132). Cells were then lysed and collected for immunoprecipitation R2 MANF proteins are folded by ER chaperones, a subgroup of ER-resident (IP). IP experiments were performed with magnetic beads incubated with FLAG- MANF proteins (ERPs), and moved them to the ER membrane (Trychta et antibodies, followed by PBS-T washes. Samples were centrifuged, incubated with al., 2018). They may also undergo post-translational modifications cell culture media, and eluted. Samples were then run on a 4–12% Bis-Tris gel and 51 kDa (PTMs). Post-secretion from the ER, they are trafficked through the proteins were transferred onto polyvinylidene difluoride (PVDF) membranes. For IgG Golgi, and to their final destination. However, ERPs are unique in this analysis of KDELr isoform relative abundance, PVDF membranes were probed 39 kDa regard; ERPs return from the Golgi to the ER upon interaction with a with IR secondary antibodies and scanned with an infrared scanner.
    [Show full text]
  • Co-Evolution of B7H6 and Nkp30, Identification of a New B7 Family Member, B7H7, and of B7's Historical Relationship with the MHC
    Immunogenetics (2012) 64:571–590 DOI 10.1007/s00251-012-0616-2 ORIGINAL PAPER Evolution of the B7 family: co-evolution of B7H6 and NKp30, identification of a new B7 family member, B7H7, and of B7's historical relationship with the MHC Martin F. Flajnik & Tereza Tlapakova & Michael F. Criscitiello & Vladimir Krylov & Yuko Ohta Received: 19 January 2012 /Accepted: 20 March 2012 /Published online: 11 April 2012 # Springer-Verlag 2012 Abstract The B7 family of genes is essential in the regula- Furthermore, we identified a Xenopus-specific B7 homolog tion of the adaptive immune system. Most B7 family mem- (B7HXen) and revealed its close linkage to B2M, which we bers contain both variable (V)- and constant (C)-type have demonstrated previously to have been originally domains of the immunoglobulin superfamily (IgSF). encoded in the MHC. Thus, our study provides further proof Through in silico screening of the Xenopus genome and that the B7 precursor was included in the proto MHC. subsequent phylogenetic analysis, we found novel genes Additionally, the comparative analysis revealed a new B7 belonging to the B7 family, one of which is the recently family member, B7H7, which was previously designated in discovered B7H6. Humans and rats have a single B7H6 the literature as an unknown gene, HHLA2. gene; however, many B7H6 genes were detected in a single large cluster in the Xenopus genome. The B7H6 expression Keywords B7 family. MHC . Evolution . Natural killer patterns also varied in a species-specific manner. Human receptors . Genetic linkage . Xenopus B7H6 binds to the activating natural killer receptor, NKp30. While the NKp30 gene is single-copy and maps to the MHC in most vertebrates, many Xenopus NKp30 genes were Introduction found in a cluster on a separate chromosome that does not harbor the MHC.
    [Show full text]
  • Prenatal Detection of TAR Syndrome in a Fetus with Compound Inheritance of an RBM8A SNP and a 334‑Kb Deletion: a Case Report
    MOLECULAR MEDICINE REPORTS 9: 163-165, 2014 Prenatal detection of TAR syndrome in a fetus with compound inheritance of an RBM8A SNP and a 334‑kb deletion: A case report IOANNIS PAPOULIDIS1, EIRINI OIKONOMIDOU1, SANDRO ORRU2, ELISAVET SIOMOU1, MARIA KONTODIOU1, MAKARIOS ELEFTHERIADES3, VASILIOS BACOULAS4, JUAN C. CIGUDOSA5, JAVIER SUELA5, LORETTA THOMAIDIS6 and EMMANOUIL MANOLAKOS1,2 1Laboratory of Genetics, Eurogenetica S.A., Thessaloniki 55133, Greece; 2Department of Medical Genetics, University of Cagliari, Binaghi Hospital, Cagliari I‑09126, Italy; 3Embryocare, Fetal Medicine Unit, Athens 11522; 4Fetal Medicine Centre, Athens 10674, Greece; 5NIMGenetics, Madrid 28049, Spain; 6Department of Pediatrics, Aglaia Kyriakou Children's Hospital, University of Athens, Athens 11527, Greece Received May 30, 2013; Accepted October 16, 2013 DOI: 10.3892/mmr.2013.1788 Abstract. Thrombocytopenia-absent radius syndrome identified the presence of a minimally deleted 200‑kb region (TAR) is a rare genetic disorder that is characterized by the at chromosome band 1q21.1 in patients with TAR, but it is not absence of the radius bone in each forearm and a markedly sufficient to cause the phenotype (3,4). A study identified two reduced platelet count that results in life-threatening bleeding rare single nucleotide polymorphisms (SNPs) in the regulatory episodes (thrombocytopenia). Tar syndrome has been associ- region of the RBM8A gene that are involved in TAR syndrome ated with a deletion of a segment of 1q21.1 cytoband. The through the reduction of the expression of the RBM8A-encoded 1q21.1 deletion syndrome phenotype includes Tar and other Y14 protein (4). The first allele (rs139428292 G>A), which is features such as mental retardation, autism and microcephaly.
    [Show full text]
  • Genome-Wide Transcriptional Analysis of Carboplatin Response in Chemosensitive and Chemoresistant Ovarian Cancer Cells
    1605 Genome-wide transcriptional analysis of carboplatin response in chemosensitive and chemoresistant ovarian cancer cells David Peters,1,2 John Freund,2 and Robert L. Ochs2 variety of disease processes, including cancer. Patterns of global gene expression can reveal the molecular pathways 1 Department of Pharmacology and Therapeutics, University of relevant to the disease process and identify potential new Liverpool, United Kingdom and 2Precision Therapeutics, Inc., Pittsburgh, Pennsylvania therapeutic targets. The use of this technology for the molecular classification of cancer was recently shown with the identification of an expression profile that was Abstract predictive of patient outcome for B-cell lymphoma (1). We have recently described an ex vivo chemoresponse In addition, this study showed that histologically similar assay for determining chemosensitivity in primary cultures tumors can be differentiated based on their gene expression of human tumors. In this study, we have extended these profiles. Ultimately, these unique patterns of gene expres- experiments in an effort to correlate chemoresponse data sion may be used as guidelines to direct different modes of with gene expression patterns at the level of transcription. therapy. Primary cultures of cells derived from ovarian carcinomas Although it is widely recognized that patients with the of individual patients (n = 6) were characterized using same histologic stage and grade of cancer respond to the ChemoFx assay and classified as either carboplatin therapies differently, few clinical tests can predict indi- sensitive (n = 3) or resistant (n = 3). Three representa- vidual patient responses. The next great challenge will be tive cultures of cells from each individual tumor were then to use the power of post-genomic technology, including subjected to Affymetrix gene chip analysis (n = 18) using microarray analyses, to correlate gene expression patterns U95A human gene chip arrays.
    [Show full text]
  • Download Special Issue
    BioMed Research International Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 Guest Editors: Tao Huang, Lei Chen, Jiangning Song, Mingyue Zheng, Jialiang Yang, and Zhenguo Zhang Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 BioMed Research International Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 GuestEditors:TaoHuang,LeiChen,JiangningSong, Mingyue Zheng, Jialiang Yang, and Zhenguo Zhang Copyright © 2016 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in “BioMed Research International.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Contents Integrated Analysis of Multiscale Large-Scale Biological Data for Investigating Human Disease 2016 Tao Huang, Lei Chen, Jiangning Song, Mingyue Zheng, Jialiang Yang, and Zhenguo Zhang Volume 2016, Article ID 6585069, 2 pages New Trends of Digital Data Storage in DNA Pavani Yashodha De Silva and Gamage Upeksha Ganegoda Volume 2016, Article ID 8072463, 14 pages Analyzing the miRNA-Gene Networks to Mine the Important miRNAs under Skin of Human and Mouse Jianghong Wu, Husile Gong, Yongsheng Bai, and Wenguang Zhang Volume 2016, Article ID 5469371, 9 pages Differential Regulatory Analysis Based on Coexpression Network in Cancer Research Junyi
    [Show full text]