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Pulmonary Arteriole Gene Expression Signature in Idiopathic Pulmonary Fibrosis

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Pulmonary Arteriole Gene Expression Signature in Idiopathic Pulmonary Fibrosis Nina M. Patel1, Steven M. Kawut2, Selim M. Arcasoy1, Sanja Jelic1, David J. Lederer1, Alain C. Borczuk1 Division of Pulmonary, Allergy & Critical Care Medicine, Columbia University, College of Physicians and Surgeons1 Department of Medicine and the Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA2 Online Data Supplement Methods: RNA Isolation, Microarray processing The RNAqueous-Micro Kit (Ambion) was utilized to isolate RNA. The Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA) was used to assess the quantity, quality and purity of the extracted RNA. The modified Eberwine procedure was used to amplify and biotinylate RNA for a minimum goal of 15 g RNA for hybridization. Target cRNA was hybridized to Affymetrix Hu133 2.0 Plus oligonucleotide arrays and processed at the Herbert Irving Comprehensive Cancer Center. Microarray Analysis Quality assessment was performed using Bioconductor. BRB array tools software v3.7.2. (National Cancer Institute, Dr. Richard Simon and Dr. Amy Lam) was used for normalization, filtering and initial quality control assessment of the genechip data. The Robust Multi-Array Average (RMA) algorithm was used to quantify the expression values for each probe set. Unsupervised clustering was performed using BRB array tools. Supervised analyses of differential gene expression between class assignments was performed using univariate analyses with a p < 0.001. The univariate analyses were performed utilizing 10,000 random permutations, with a global p < 0.01. The false discovery rate for all comparisons was set at a q < 10%. Supervised clustering of genes and scatterplot analysis between class assignments was also performed using BRB array tools software. Gene lists generated by the supervised analyses were further categorized into biological pathways using Ingenuity Pathways Analysis (IPA, Ingenuity Systems, www.ingenuity.com). The IPA Functional Analysis identified the biological functions and/or diseases that were most significant to the data set. Molecules from the dataset that were associated with biological functions and/or diseases in Ingenuity’s Knowledge Base were considered for the analysis. Two-tailed Fisher’s exact test was used to calculate a p- value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone. A data set containing gene identifiers and corresponding expression values was uploaded into the IPA application. Each identifier was mapped to its corresponding object in Ingenuity's Knowledge Base. Molecules, whose expression was significantly differentially regulated, called Network Eligible molecules, were overlaid onto a global molecular network developed from information contained in Ingenuity’s Knowledge Base. Networks of Network Eligible Molecules were then algorithmically generated based on their connectivity. Immunohistochemical staining (IHC) Paraffinized tissue blocks of IPF and normal lung were sectioned at thickness of < 5 m. Immunohistochemistry was performed on 10 IPF cases and 5 normal donor lungs using antibodies against SFRP2 (Sigma, monoclonal Rabbit anti-human, 1:75 dilution), SMOC2 (Lifespan Biosciences, polyclonal Rabbit anti human, 1:50 dilution) and STAT1 (Sigma rabbit anti human, monoclonal, 1:50 dilution). Antigen retrieval was performed using DAKO pH 6 (SFRP2 and SMOC2) or pH 9 (STAT1) retrieval buffer. Primary antibodies were incubated at room temperature for 1 hour, and developed using the DAKO Envision Dual Link kit (Dako, Denmark) with DAB substrate and hematoxylin counterstain. Examination for statistical significance was performed using the Fisher’s Exact Test (SPSS, v16.0). Supplemental Table E1: Gene Names Expression Values Endothelial Genes adrenomedullin 1532 ± 943 thrombomodulin 1110 ± 575 endothelin receptor type A 3623 ± 1236 SMAD family member 6 744 ± 368 Epithelial Genes keratin 15 72 ± 176 mucin 4, cell surface associated 77 ± 258 keratin 7 189 ± 190 surfactant protein A1B 39 ± 114 Values are mean normalized expression values rounded to the nearest integer ± standard deviation Supplemental Table E2: Univariate analysis comparing gene expression in NPH-IPF vs. PH-IPF arterioles Gene symbol Probe set Description Parametric FDR Fold- p-value change CCR1 205099_s_at chemokine (C-C motif) receptor 1 0.001158 0.9913072 0.4500061 PTMS 218045_x_at parathymosin 0.0011904 0.9913072 1.7440695 NPL 240440_at N-acetylneuraminate pyruvate lyase 0.0016084 0.9913072 0.5080298 (dihydrodipicolinate synthase) NA 241838_at NA 0.0022076 0.9913072 0.5771265 NA 238156_at NA 0.0029903 0.9913072 0.388007 PDGFRA 203131_at platelet-derived growth factor receptor, 0.0031531 0.9913072 0.6256235 alpha polypeptide FLT1 222033_s_at fms-related tyrosine kinase 1 (vascular 0.0033705 0.9913072 0.5934558 endothelial growth factor/vascular permeability factor receptor) TEF 225840_at thyrotrophic embryonic factor 0.0033854 0.9913072 2.3365277 NA 239808_at NA 0.0036163 0.9913072 0.395203 NA 1568983_a_at NA 0.0040027 0.9913072 0.5144699 NA 235172_at NA 0.0040874 0.9913072 0.3932263 NA 240690_at NA 0.0041938 0.9913072 0.5613011 NLGN4X 221933_at neuroligin 4, X-linked 0.0043912 0.9913072 0.4889389 ELTD1 219134_at EGF, latrophilin and seven transmembrane 0.0045867 0.9913072 0.4041308 domain containing 1 NA 239862_at NA 0.0045982 0.9913072 0.595371 B2M 232311_at beta-2-microglobulin 0.0046597 0.9913072 0.438037 ND6 1553575_at NADH dehydrogenase, subunit 6 (complex 0.0046924 0.9913072 0.5143606 I) NA 1564077_at NA 0.0048054 0.9913072 0.4258312 SPTBN1 226765_at spectrin, beta, non-erythrocytic 1 0.0048557 0.9913072 0.4490491 ZNF827 226764_at zinc finger protein 827 0.0048903 0.9913072 1.8218937 NEURL1B 225355_at neuralized homolog 1B (Drosophila) 0.0050316 0.9913072 1.49516 CDC14A 210742_at CDC14 cell division cycle 14 homolog A (S. 0.0053101 0.9913072 0.314476 cerevisiae) POM121L9P 222253_s_at POM121 membrane glycoprotein-like 9 (rat) 0.0053544 0.9913072 0.4984525 pseudogene 6-Sep 212413_at septin 6 0.0057778 0.9913072 0.5310485 NA 235274_at NA 0.0057811 0.9913072 0.5455783 NA 236592_at NA 0.0059805 0.9913072 0.5788008 PNKP 218961_s_at polynucleotide kinase 3@#$%&- 0.0062114 0.9913072 1.6995573 phosphatase THSD7A 214920_at thrombospondin, type I, domain containing 0.0062364 0.9913072 0.3822614 7A CPM 235019_at carboxypeptidase M 0.0066804 0.9913072 0.3709829 KCTD11 235857_at potassium channel tetramerisation domain 0.0067233 0.9913072 2.0062071 containing 11 KRR1 232441_at KRR1, small subunit (SSU) processome 0.00704 0.9913072 0.5056537 component, homolog (yeast) PITPNC1 219155_at phosphatidylinositol transfer protein, 0.0073711 0.9913072 0.5961627 cytoplasmic 1 NA 213705_at NA 0.007495 0.9913072 0.4530666 LOC100132430 214182_at hypothetical LOC100132430 0.0078264 0.9913072 0.5352828 HP1BP3 229204_at heterochromatin protein 1, binding protein 3 0.00794 0.9913072 0.58305 TNFRSF10A 231775_at tumor necrosis factor receptor superfamily, 0.0080608 0.9913072 0.5099776 member 10a NA 231484_at NA 0.0082447 0.9913072 0.4695896 PRMT1 206445_s_at protein arginine methyltransferase 1 0.0085897 0.9913072 1.7887033 CNN3 228297_at calponin 3, acidic 0.0088275 0.9913072 0.455391 STAT3 243213_at signal transducer and activator of 0.0088678 0.9913072 0.4965778 transcription 3 (acute-phase response factor) FAM86B1 65585_at family with sequence similarity 86, member 0.0091584 0.9913072 1.85688 B1 SH3KBP1 235692_at SH3-domain kinase binding protein 1 0.0093252 0.9913072 0.591637 NA 239907_at NA 0.0093355 0.9913072 0.3655385 HGF 209960_at hepatocyte growth factor (hepapoietin A; 0.0093767 0.9913072 0.4720159 scatter factor) NA 242397_at NA 0.0095407 0.9913072 0.377448 CATSPER2 1561405_s_at cation channel, sperm associated 2 0.0098016 0.9913072 1.6568168 ERO1L 222646_s_at ERO1-like (S. cerevisiae) 0.0099056 0.9913072 0.6118214 IDH3G 202471_s_at isocitrate dehydrogenase 3 (NAD+) gamma 0.0103207 0.9913072 1.5550527 NA 226365_at NA 0.0105412 0.9913072 0.6073307 GPRIN3 1556698_a_at GPRIN family member 3 0.0105564 0.9913072 0.6549646 NA 1566342_at NA 0.0108587 0.9913072 0.4650578 PPP2R5C 1557718_at protein phosphatase 2, regulatory subunit 0.0110155 0.9913072 0.5251526 B@#$%&, gamma isoform FYB 211794_at FYN binding protein (FYB-120/130) 0.0112537 0.9913072 0.5614993 TWIST1 213943_at twist homolog 1 (Drosophila) 0.0116602 0.9913072 0.5350974 OBFC2A 233085_s_at oligonucleotide/oligosaccharide-binding fold 0.0118959 0.9913072 0.4534448 containing 2A NA 232835_at NA 0.0119891 0.9913072 0.6310771 DNAJC13 1560020_at DnaJ (Hsp40) homolog, subfamily C, 0.0123426 0.9913072 1.5664626 member 13 SYK 244023_at spleen tyrosine kinase 0.0124291 0.9913072 0.3928391 SYNPO2L 243313_at synaptopodin 2-like 0.0124683 0.9913072 1.6573898 STEAP1 205542_at six transmembrane epithelial antigen of the 0.0125049 0.9913072 0.4392555 prostate 1 ACOX2 205364_at acyl-Coenzyme A oxidase 2, branched chain 0.0127468 0.9913072 1.5880573 CTSC 225646_at cathepsin C 0.0130762 0.9913072 0.4501248 DCP2 244777_at DCP2 decapping enzyme homolog (S. 0.0130986 0.9913072 0.6262102 cerevisiae) FBXL3 242829_x_at F-box and leucine-rich repeat protein 3 0.0132872 0.9913072 0.3470831 NA 236921_at NA 0.0134885 0.9913072 0.5898935 ZC3H12D 1559263_s_at zinc finger CCCH-type containing 12D 0.0135231 0.9913072 0.6001638 DLC1 220512_at deleted in liver cancer 1 0.013525 0.9913072 0.5404732 HIVEP3 235122_at human immunodeficiency virus type I 0.0136699
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    Baskı Mart, 2017 Bu yayının telif hakları Düzen Laboratuvarlar Grubu’na aittir. Bu yayının tümü ya da bir bölümü Düzen Laboratuvarlar Grubu’nun yazılı izni olmadan kopya edilemez. Bu yayın Düzen Laboratuvarlar Grubu tarafından tanıtım ve bilgilendirme amacıyla hazırlanmış olup hazırlanma ve basım esnasında metin ya da grafiklerde oluşabilecek her türlü hata ve eksikliklerden Düzen Laboratuvarlar Grubu sorumlu tutulamaz. Kaynak göstermek ve Düzen Laboratuvarlar Grubu’ndan yazılı izin almak suretiyle bu yayında alıntı yapılabilir. Düzen Laboratuvarlar Grubu Tunus Cad. No. 95 Kavaklıdere Çankaya 06680 Ankara www.duzen.com.tr VİZYONUMUZ Hasta haklarına saygılı, bilgilendirmeyi esas alan, testleri en doğru, izlenebilir ve tekrarlanabilir yöntemlerle çalışmak ve en az hatayı esas kabul edip, iç ve dış kalite kontrolleri ile bu kavramın gerçekleştiğini göstermektedir. MİSYONUMUZ Test sonuçları üzerinde laboratuvarmızın sorumluluğu, testin klinik laboratuvarcılık standartları ve iyi laboratuvar uygulamaları sınırları içinde, tüm kontoller yapılarak çalışılması ile sınırlıdır. Test sonuçları klinik bulgular ve diğer tüm yardımcı veriler dikkate alınarak değerlendirilmektedir. AKREDİTASYON Laboratuvarımız 2004 yılında Türk Akreditasyon Kurumu (TÜRKAK) tarafından TS EN IS IEC 17025 kapsamında akredite edilmiş, 2011 yılından itibaren ise ISO15189 kapsamında akreditasyona hak kazanmıştır. Hasta kayıt, numune alma, raporlama, kurumsal hizmetler ve tüm işletim sistemi akreditasyon kapsamındadır. GÜVENİRLİLİK Laboratuvarımız CLSI programlarına üyedir
  • Differential Patterns of Allelic Loss in Estrogen Receptor-Positive Infiltrating Lobular and Ductal Breast Cancer

    Differential Patterns of Allelic Loss in Estrogen Receptor-Positive Infiltrating Lobular and Ductal Breast Cancer

    GENES, CHROMOSOMES & CANCER 47:1049–1066 (2008) Differential Patterns of Allelic Loss in Estrogen Receptor-Positive Infiltrating Lobular and Ductal Breast Cancer L. W. M. Loo,1 C. Ton,1,2 Y.-W. Wang,2 D. I. Grove,2 H. Bouzek,1 N. Vartanian,1 M.-G. Lin,1 X. Yuan,1 T. L. Lawton,3 J. R. Daling,2 K. E. Malone,2 C. I. Li,2 L. Hsu,2 and P.L. Porter1,2,3* 1Division of Human Biology,Fred Hutchinson Cancer Research Center,Seattle,WA 2Division of Public Health Sciences,Fred Hutchinson Cancer Research Center,Seattle,WA 3Departmentof Pathology,Universityof Washington,Seattle,WA The two main histological types of infiltrating breast cancer, lobular (ILC) and the more common ductal (IDC) carcinoma are morphologically and clinically distinct. To assess the molecular alterations associated with these breast cancer subtypes, we conducted a whole-genome study of 166 archival estrogen receptor (ER)-positive tumors (89 IDC and 77 ILC) using the Affy- metrix GeneChip® Mapping 10K Array to identify sites of loss of heterozygosity (LOH) that either distinguished, or were shared by, the two phenotypes. We found single nucleotide polymorphisms (SNPs) of high-frequency LOH (>50%) common to both ILC and IDC tumors predominately in 11q, 16q, and 17p. Overall, IDC had a slightly higher frequency of LOH events across the genome than ILC (fractional allelic loss 5 0.186 and 0.156). By comparing the average frequency of LOH by chro- mosomal arm, we found IDC tumors with significantly (P < 0.05) higher frequency of LOH on 3p, 5q, 8p, 9p, 20p, and 20q than ILC tumors.
  • Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights Into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

    Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights Into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

    animals Article Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle Masoumeh Naserkheil 1 , Abolfazl Bahrami 1 , Deukhwan Lee 2,* and Hossein Mehrban 3 1 Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; [email protected] (M.N.); [email protected] (A.B.) 2 Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si, Gyeonggi-do 17579, Korea 3 Department of Animal Science, Shahrekord University, Shahrekord 88186-34141, Iran; [email protected] * Correspondence: [email protected]; Tel.: +82-31-670-5091 Received: 25 August 2020; Accepted: 6 October 2020; Published: 9 October 2020 Simple Summary: Hanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest.