SUPPLEMENTARY DATA Principal Investigators and Acknowledgment

Total Page:16

File Type:pdf, Size:1020Kb

SUPPLEMENTARY DATA Principal Investigators and Acknowledgment SUPPLEMENTARY DATA Principal Investigators and Acknowledgment Hispanic Community Health Study/Study of Latinos (HSHC/SOL) Robert Kaplan, Sylvia Wassertheil-Smoller (Bronx); Martha L. Daviglus, Aida L. Giachello (Chicago); Neil Schneiderman, David Lee, Leopoldo Raij, John Ryan (Miami); Greg Talavera, John Elder, Matthew Allison, Michael Criqui (San Deigo). The authors thank the staff and participants of HCHS/SOL for their important contributions. A complete list of staff and investigators has been provided by Sorlie P., et al. in Ann Epidemiol. 2010 ;20:642-649 and is also available on the study website http://www.cscc.unc.edu/hchs/. The baseline examination of the Hispanic Community Health Study/Study of Latinos was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/Centers/Offices contributed to the HCHS/SOL first funding period through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke, and the NIH Office of Dietary Supplements. The Genetic Analysis Center at the University of Washington was supported by NHLBI and NIDCR contracts (HHSN268201300005C AM03 and MOD03). Genotyping efforts were supported by NHLBI HSN 26220/20054C, NCATS CTSI grant UL1TR000123, and NIDDK Diabetes Research Center (DRC) grant DK063491. Starr County Health Study (SCHS) We thank the field staff in Starr County for their collection of these data and are grateful to the study participants who gave of their time and contributed to this study. This work was supported in part by grants DK073541, DK020595, AI085014, DK085501, and HL102830 from the National Institutes of Health and funds from the University of Texas Health Science Center at Houston. These studies (protocol SPH-02-042) were approved by the University of Texas Health Science Center at Houston’s Committee for the Protection of Human Subjects and carried out in a manner consistent with the Declaration of Helsinki. The studies were explained to all participants and written informed consent obtained. Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is funded through a federal contract from the National Institutes of Health (NIH) to The Johns Hopkins University, contract number HHSN268200782096C. Boston Puerto Rican Health Study (BPRHS) Katherine Tucker. The Boston Puerto Rican Health Study was funded by National Heart, Lung, and Blood Institute grant P50-HL105185 and National Institute on Aging grant P01-AG023394. BioMe Biobank Judy H Cho. We thank the participants in the BioMe Biobank for their invaluable contribution to biomedical research. Funding for the BioMe Biobank program is supported by the Andrea and Charles Bronfman Philanthropies. ©2019 American Diabetes Association. Published online at http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc19-0168/-/DC1 SUPPLEMENTARY DATA Supplementary Table S1. Characteristics of study samples without diabetes HCHS/SOL SCHS BPRHS BioMe-OMNIBioMe-MEGA No. of participants, n All 9636 395 832 1867 1683 men 4040 89 245 640 564 women 5596 306 587 1227 1119 Age, years 43.8 (13.8) 48.13 (8.68) 56.0 (NA) 50.2 (14.7) 51.3 (14.8) HbA1c, % 5.47 (0.36) 5.45 (0.35) 6.09 (NA) 5.55 (0.41) 5.56 (0.42) Hispanic background, n Central American 1097 0 0 76 50 Cuban 1750 0 0 25 17 Dominican 904 0 0 244 278 Mexican 3558 395 0 35 42 Puerto Rican 1533 0 831 488 404 South American 765 0 0 152 204 Data are mean (SD) or sample size (n). ©2019 American Diabetes Association. Published online at http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc19-0168/-/DC1 SUPPLEMENTARY DATA Supplementary Table S2. Associations of 18 newly identified SNPs with HbA1c levels (%) in the HCHS/SOL (P<1×10-5) and in replication studies Allele HCHS/SOL Replication studies* Combined P SNP Nearest locus Chr Position Effect Other EAF Beta (SE) P Beta (SE) P (SOL+ replication)† rs113282222b CEP104 1 3718591 TGGAG T 0.173 -0.031 (0.006) 9.98E-07 -0.006 (0.012) 0.595 5.94E-06 rs199584848b TM1EM206 1 212544641 A AAG 0.024 0.080 (0.015) 1.74E-07 0.006 (0.102) 0.953 2.27E-07 rs113119484a CADM2 3 85463419 T G 0.013 -0.102 (0.021) 8.86E-07 0.016 (0.034) 0.642 7.42E-05 rs10513537b IQCJ−SCHIP1 3 158904859 G A 0.209 0.031 (0.006) 3.83E-07 0.003 (0.012) 0.778 3.07E-06 rs74638476b ETV5 3 185735512 T C 0.026 0.079 (0.016) 8.03E-07 -0.005 (0.029) 0.872 2.32E-05 rs73774950a NUDT12 5 102957617 C T 0.024 -0.089 (0.016) 3.09E-08 0.016 (0.024) 0.513 2.19E-05 rs11752643a HLA−DQA2 6 32669373 T C 0.017 0.096 (0.018) 1.76E-07 0.012 (0.035) 0.737 1.77E-06 rs6949071b ETV1 7 13655049 C G 0.459 -0.025 (0.005) 4.50E-07 0.005 (0.010) 0.646 1.70E-05 rs6992753b UNC5D 8 35926402 A G 0.016 0.111 (0.020) 3.48E-08 0.017 (0.030) 0.581 9.51E-07 rs116781682b FAM188A 10 16218532 T C 0.012 -0.118 (0.023) 2.11E-07 -0.050 (0.035) 0.156 2.82E-07 rs334b HBB 11 5248232 A T 0.013 -0.305 (0.023) 2.72E-40 -0.127 (0.044) 0.004 2.94E-39 rs73393695a FBXO21 12 117644934 A G 0.051 -0.056 (0.011) 6.38E-07 -0.001 (0.020) 0.975 1.21E-05 rs145546625b HBM 16 220583 T C 0.066 0.059 (0.010) 8.20E-09 0.044 (0.023) 0.050 1.42E-09 rs144212976b RAB11FIP4 17 29821839 G A 0.020 0.087 (0.017) 5.51E-07 -0.013 (0.037) 0.719 1.24E-05 rs2748424b TMC6 17 76124865 G C 0.179 0.051 (0.006) 1.98E-15 0.032 (0.012) 0.008 1.49E-16 rs74575505b SIGLEC12 19 51942103 G A 0.128 0.036 (0.007) 7.27E-07 -0.007 (0.014) 0.622 2.67E-05 rs56222990b PDGFB 22 39652947 G C 0.069 0.045 (0.010) 6.34E-06 0.012 (0.017) 0.482 1.36E-04 rs1050828a G6PD X 153764217 T C 0.020 -0.346 (0.014) 9.59E-132 -0.292 (0.099) 0.003 1.34E-133 Chr, chromosome; Position: GRCh37 reference; EAF: effect allele frequency; Beta: effect size (%) for each effect allele of SNP on HbA1c. *Results from 4 replication studies (see Supplemental Table 3) were combined by fixed effect meta-analysis. †Results from SOL and replication studies were combined using by fixed effect meta-analysis. a genotyped in HCHS/SOL b imputed in HCHS/SOL. All imputation quality in HCHS/SOL >0.9 SNPs in bold had P≤0.05 in combined replication studies ©2019 American Diabetes Association. Published online at http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc19-0168/-/DC1 SUPPLEMENTARY DATA Supplementary Table S3. Associations of 18 SNPs with HbA1c levels (%) in replication studies Allele SCHS BPRHS BioMe-OMNI BioMe-MEGA SNP Gene Chr Position Effect Other EAF β (SE) P EAF β (SE) P EAF β (SE) P EAF β (SE) P rs113282222 CEP104 1 3718591 TGGAG T 0.161 -0.041 (0.033) 0.218 0.202 0.016 (0.058) 0.785 0.188 0.037 (0.017) 0.031 0.194 -0.047 (0.018) 0.011 rs199584848 TMEM206 1 212544641 A AAG 0.012 0.179 (0.127) 0.159 0.020 -0.305 (0.171) 0.074 NA NA NA NA NA NA rs113119484 CADM2 3 85463419 T G 0.019 -0.057 (0.091) 0.533 0.016 0.168 (0.175) 0.336 0.017 0.011 (0.051) 0.829 0.017 0.034 (0.056) 0.546 rs10513537 IQCJ−SCHIP1 3 158904859 G A 0.267 -0.017 (0.026) 0.522 0.155 0.043 (0.056) 0.445 0.157 0.006 (0.018) 0.725 0.143 0.007 (0.022) 0.739 rs74638476 ETV5 3 185735512 T C 0.003 -0.043 (0.233) 0.854 0.031 -0.011 (0.164) 0.948 0.029 0.003 (0.042) 0.953 0.032 -0.010 (0.041) 0.812 rs73774950 NUDT12 5 102957617 C T 0.003 -0.095 (0.240) 0.691 0.030 0.176 (0.113) 0.119 0.045 -0.014 (0.032) 0.667 0.036 0.043 (0.039) 0.269 rs11752643 HLA−DQA2 6 32669373 T C 0.015 0.066 (0.138) 0.630 NA NA NA 0.021 -0.022 (0.047) 0.645 0.017 0.049 (0.056) 0.377 rs6949071 ETV1 7 13655049 C G 0.420 0.027 (0.026) 0.288 0.489 0.057 (0.038) 0.138 0.492 -0.017 (0.015) 0.275 0.495 0.010 (0.016) 0.538 rs6992753 UNC5D 8 35926402 A G 0.005 0.023 (0.175) 0.897 0.027 0.018 (0.172) 0.915 0.027 -0.011 (0.041) 0.780 0.025 0.056 (0.049) 0.249 rs116781682 FAM188A 10 16218532 T C NA NA NA 0.021 -0.559 (0.165) 0.001 0.019 -0.056 (0.051) 0.274 0.020 0.006 (0.052) 0.915 rs334 HBB 11 5248232 A T 0.005 0.005 (0.182) 0.978 0.020 -1.190 (0.168) 0.000 0.011 0.039 (0.067) 0.562 0.013 -0.138 (0.064) 0.032 rs73393695 FBXO21 12 117644934 A G 0.028 -0.049 (0.092) 0.591 0.076 -0.101 (0.069) 0.144 0.066 -0.022 (0.029) 0.445 0.058 0.057 (0.033) 0.085 rs145546625 HBM 16 220583 T C 0.073 0.079 (0.046) 0.086 0.020 -0.062 (0.208) 0.766 0.027 0.023 (0.041) 0.568 0.041 0.043 (0.035) 0.225 rs144212976 RAB11FIP4 17 29821839 G A 0.032 0.021 (0.069) 0.758 0.011 -0.094 (0.218) 0.666 0.012 -0.049 (0.063) 0.438 0.014 0.000 (0.062) 0.995 rs2748424 TMC6 17 76124865 G C 0.195 0.072 (0.035) 0.038 NA NA NA 0.228 0.026 (0.017) 0.128 0.250 0.027 (0.019) 0.154 rs74575505 SIGLEC12 19 51942103 G A 0.109 -0.012 (0.038) 0.762 0.116 -0.062 (0.060) 0.302 0.115 -0.021 (0.022) 0.320 0.111 0.021 (0.024) 0.380 rs56222990 PDGFB 22 39652947 G C 0.039 0.092 (0.078) 0.234 0.106 0.051 (0.065) 0.437 0.084 0.051 (0.026) 0.051 0.093 -0.041 (0.026) 0.113 rs1050828 G6PD X 153764217 T C NA NA NA 0.026 -0.292 (0.098) 0.003 NA NA NA NA NA NA A1: effect allele; A2: reference allele; EAF: effect allele frequency; Beta: Effect size (%) for each effect allele of SNP on HbA1c.
Recommended publications
  • The Genetics of Nontraditional Glycemic Biomarkers of Type 2 Diabetes
    THE GENETICS OF NONTRADITIONAL GLYCEMIC BIOMARKERS OF TYPE 2 DIABETES by Stephanie J. Loomis, MPH A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland May, 2018 © 2018 Stephanie J. Loomis All rights reserved Abstract Type 2 diabetes is a major public health problem that affects over 10% of the US adult population. It is associated with substantially increased risks of mortality and serious clinical outcomes such as heart disease, stroke, kidney disease and retinopathy. Diabetes is defined by hyperglycemia, or elevated glucose concentrations in the blood, which are commonly measured by fasting glucose and hemoglobin A1c (HbA1c), but these have limitations. As a result, nontraditional glycemic biomarkers, fructosamine, glycated albumin and 1,5-anhydroglucitol (1,5-AG) are gaining interest. While it is established that genetics play a role in type 2 diabetes, fasting glucose, and HbA1c, the genetics of fructosamine, glycated albumin, and 1,5-AG have not been well explored. This dissertation sought to determine the amount of variation in each biomarker due to genetics through heritability estimation, and to determine the specific genetic variants associated with each biomarker though genome wide association study (GWAS) analysis, multivariate phenotype analysis, and exome sequencing analysis. Heritability estimates showed a substantial portion of fructosamine, glycated albumin and 1,5-AG variation was due to genetics, which is likely comprised of both common and rare variants. GWAS identified common variants associated with fructosamine and glycated albumin including a known diabetes variant and a likely nonglycemic variant. Exome sequencing did not identify variants associated with fructosamine and glycated albumin, but multivariate phenotype analysis identified a potentially interesting region in a gene that alters bilirubin levels that may affect fructosamine in a nonglycemic manner.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
    medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened.
    [Show full text]
  • Novel TMC8 Splice Site Mutation in Epidermodysplasia Verruciformis and Review of HPV Infections in Patients with the Disease
    Novel TMC8 splice site mutation in epidermodysplasia verruciformis and review of HPV infections in patients with the disease E Imahorn1, Z Yüksel2, I Spoerri1, G Gürel3, C Imhof4, ZN Saraçoğlu5, AE Koku Aksu6, PL Rady7, SK Tyring7, W Kempf8, PH Itin1,9, B Burger1 1) Department of Biomedicine, University Hospital Basel and University of Basel, Basel, Switzerland 2) Medical Genetics Department, Eskişehir Osmangazi University, Eskişehir, Turkey 3) Dermatology Department, Bozok University, Yozgat, Turkey 4) Stadtpraxis Brig, Brig, Switzerland 5) Dermatology Department, Eskişehir Osmangazi University, Eskişehir, Turkey 6) Dermatology Clinics, Istanbul Education and Research Hospital, Istanbul, Turkey 7) Department of Dermatology, McGovern Medical School at UTHealth, Houston, TX, USA 8) Kempf und Pfaltz Histologische Diagnostik, Zurich, Switzerland 9) Department of Dermatology, University Hospital Basel, Basel, Switzerland Contact: Bettina Burger Hebelstrasse 20 4031 Basel +41 61 328 69 03 [email protected] Funding sources: none Conflict of interest: none Keywords: Epidermodysplasia verruciformis, human papilloma virus, splice site mutation, TMC6, TMC8, genetic skin disease, genodermatosis Abstract Background: Epidermodysplasia verruciformis (EV) is a genodermatosis leading to infections with cutaneous HPV, persistent plane warts and a high rate of non-melanoma skin cancer (NMSC). Biallelic loss-of-function mutations in TMC6 and TMC8 are known to be causative. Objective: The aim of this study was to report EV-causing mutations in four patients with EV and to give an overview of all described EV patients. Patients and methods: We investigated four patients with classical features of EV from two families. All patients were affected by plane warts with typical EV histology since early childhood and β-HPVs were detected on their skin.
    [Show full text]
  • Supp Table 6.Pdf
    Supplementary Table 6. Processes associated to the 2037 SCL candidate target genes ID Symbol Entrez Gene Name Process NM_178114 AMIGO2 adhesion molecule with Ig-like domain 2 adhesion NM_033474 ARVCF armadillo repeat gene deletes in velocardiofacial syndrome adhesion NM_027060 BTBD9 BTB (POZ) domain containing 9 adhesion NM_001039149 CD226 CD226 molecule adhesion NM_010581 CD47 CD47 molecule adhesion NM_023370 CDH23 cadherin-like 23 adhesion NM_207298 CERCAM cerebral endothelial cell adhesion molecule adhesion NM_021719 CLDN15 claudin 15 adhesion NM_009902 CLDN3 claudin 3 adhesion NM_008779 CNTN3 contactin 3 (plasmacytoma associated) adhesion NM_015734 COL5A1 collagen, type V, alpha 1 adhesion NM_007803 CTTN cortactin adhesion NM_009142 CX3CL1 chemokine (C-X3-C motif) ligand 1 adhesion NM_031174 DSCAM Down syndrome cell adhesion molecule adhesion NM_145158 EMILIN2 elastin microfibril interfacer 2 adhesion NM_001081286 FAT1 FAT tumor suppressor homolog 1 (Drosophila) adhesion NM_001080814 FAT3 FAT tumor suppressor homolog 3 (Drosophila) adhesion NM_153795 FERMT3 fermitin family homolog 3 (Drosophila) adhesion NM_010494 ICAM2 intercellular adhesion molecule 2 adhesion NM_023892 ICAM4 (includes EG:3386) intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)adhesion NM_001001979 MEGF10 multiple EGF-like-domains 10 adhesion NM_172522 MEGF11 multiple EGF-like-domains 11 adhesion NM_010739 MUC13 mucin 13, cell surface associated adhesion NM_013610 NINJ1 ninjurin 1 adhesion NM_016718 NINJ2 ninjurin 2 adhesion NM_172932 NLGN3 neuroligin
    [Show full text]
  • Tissue-Specific Alteration of Metabolic Pathways Influences Glycemic Regulation
    bioRxiv preprint doi: https://doi.org/10.1101/790618; this version posted October 3, 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. Tissue-Specific Alteration of Metabolic Pathways Influences Glycemic Regulation 1,2 3 4 5,6,7 Natasha H. J. Ng *, Sara M. Willems *, Juan Fernandez , Rebecca S. Fine , Eleanor Wheeler8,3, Jennifer Wessel9,10, Hidetoshi Kitajima4, Gaelle Marenne8, Jana K. Rundle1, Xueling Sim11,12, Hanieh Yeghootkar13, Nicola L. Beer1, Anne Raimondo1, Andrei I. Tarasov1, Soren K. Thomsen1,14, Martijn van de Bunt4,1, Shuai Wang15, Sai Chen12, Yuning Chen15, Yii-Der Ida Chen16, Hugoline G. de Haan17, Niels Grarup18, Ruifang Li-Gao17, Tibor V. Varga19, Jennifer L Asimit8,20, Shuang Feng21, Rona J. Strawbridge22,23, Erica L. Kleinbrink24, Tarunveer S. Ahluwalia25,26, Ping An27, Emil V. Appel18, Dan E Arking28, Juha Auvinen29,30, Lawrence F. Bielak31, Nathan A. Bihlmeyer28, Jette Bork-Jensen18, Jennifer A. Brody32,33, Archie Campbell34, Audrey Y Chu35, Gail Davies36,37, Ayse Demirkan38, James S. Floyd32,33, Franco Giulianini35, Xiuqing Guo16, Stefan Gustafsson39, Benoit Hastoy1, Anne U. Jackson12, Johanna Jakobsdottir40, Marjo-Riitta Jarvelin41,29,42, Richard A. Jensen32,33, Stavroula Kanoni43, Sirkka Keinanen- Kiukaanniemi44,45, Jin Li46, Man Li47,48, Kurt Lohman49, Yingchang Lu50,51, Jian'an Luan3, Alisa K. Manning52,53, Jonathan Marten54, Carola Marzi55,56, Karina Meidtner57,56, Dennis O. Mook- Kanamori17,58, Taulant Muka59,38, Giorgio Pistis60,61, Bram Prins8, Kenneth M.
    [Show full text]
  • Supplementary Table 1
    Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7
    [Show full text]
  • The Effect of Phototherapy on Cancer Predisposition Genes of Diabetic and Normal Human Skin Fibroblasts
    Hindawi BioMed Research International Volume 2017, Article ID 7604861, 9 pages https://doi.org/10.1155/2017/7604861 Research Article The Effect of Phototherapy on Cancer Predisposition Genes of Diabetic and Normal Human Skin Fibroblasts Pongsathorn Chotikasemsri,1 Boonsin Tangtrakulwanich,2 and Surasak Sangkhathat3 1 Biomedical Engineering Institute, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand 2Department of Orthopedic Surgery and Physical Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand 3Department of Surgery, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand Correspondence should be addressed to Pongsathorn Chotikasemsri; [email protected] Received 3 October 2016; Accepted 19 February 2017; Published 12 March 2017 Academic Editor: Tokuya Omi Copyright © 2017 Pongsathorn Chotikasemsri et al. This 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. The purpose of this study was to investigate whether LED light at different wavelengths affects the expression profile of 143cancer predisposition genes in both diabetic and normal human fibroblasts. In this study, both diabetic and normal fibroblast cell lines were 2 cultured and irradiated with red (635 nm), green (520 nm), and blue (465 nm) LED light for 10 minutes at 0.67 J/cm each. After that, mRNA from all cell lines was extracted for microarray analysis. We found that green light activates EPHB2, KIT, ANTXR2, ESCO2, MSR1, EXT1, TSC1, KIT, NF1, BUB1B, FANCD2, EPCAM, FANCD2, NF, DIS3L2, and RET in normal fibroblast cells, while blue and red light can upregulate RUNX1, PDGFRA, EHBP1, GPC3, AXIN2, KDR, GLMN, MSMB, EPHB2, MSR1, KIT, FANCD2, BMPR1A, BUB1B, PDE11A, and RET.
    [Show full text]
  • The Kinesin Spindle Protein Inhibitor Filanesib Enhances the Activity of Pomalidomide and Dexamethasone in Multiple Myeloma
    Plasma Cell Disorders SUPPLEMENTARY APPENDIX The kinesin spindle protein inhibitor filanesib enhances the activity of pomalidomide and dexamethasone in multiple myeloma Susana Hernández-García, 1 Laura San-Segundo, 1 Lorena González-Méndez, 1 Luis A. Corchete, 1 Irena Misiewicz- Krzeminska, 1,2 Montserrat Martín-Sánchez, 1 Ana-Alicia López-Iglesias, 1 Esperanza Macarena Algarín, 1 Pedro Mogollón, 1 Andrea Díaz-Tejedor, 1 Teresa Paíno, 1 Brian Tunquist, 3 María-Victoria Mateos, 1 Norma C Gutiérrez, 1 Elena Díaz- Rodriguez, 1 Mercedes Garayoa 1* and Enrique M Ocio 1* 1Centro Investigación del Cáncer-IBMCC (CSIC-USAL) and Hospital Universitario-IBSAL, Salamanca, Spain; 2National Medicines Insti - tute, Warsaw, Poland and 3Array BioPharma, Boulder, Colorado, USA *MG and EMO contributed equally to this work ©2017 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2017.168666 Received: March 13, 2017. Accepted: August 29, 2017. Pre-published: August 31, 2017. Correspondence: [email protected] MATERIAL AND METHODS Reagents and drugs. Filanesib (F) was provided by Array BioPharma Inc. (Boulder, CO, USA). Thalidomide (T), lenalidomide (L) and pomalidomide (P) were purchased from Selleckchem (Houston, TX, USA), dexamethasone (D) from Sigma-Aldrich (St Louis, MO, USA) and bortezomib from LC Laboratories (Woburn, MA, USA). Generic chemicals were acquired from Sigma Chemical Co., Roche Biochemicals (Mannheim, Germany), Merck & Co., Inc. (Darmstadt, Germany). MM cell lines, patient samples and cultures. Origin, authentication and in vitro growth conditions of human MM cell lines have already been characterized (17, 18). The study of drug activity in the presence of IL-6, IGF-1 or in co-culture with primary bone marrow mesenchymal stromal cells (BMSCs) or the human mesenchymal stromal cell line (hMSC–TERT) was performed as described previously (19, 20).
    [Show full text]
  • Supplemental Table 1A. Differential Gene Expression Profile of Adehcd40l and Adehnull Treated Cells Vs Untreated Cells
    Supplemental Table 1a. Differential Gene Expression Profile of AdEHCD40L and AdEHNull treated cells vs Untreated Cells Fold change Regulation Fold change Regulation ([AdEHCD40L] vs ([AdEHCD40L] ([AdEHNull] vs ([AdEHNull] vs Probe Set ID [Untreated]) vs [Untreated]) [Untreated]) [Untreated]) Gene Symbol Gene Title RefSeq Transcript ID NM_001039468 /// NM_001039469 /// NM_004954 /// 203942_s_at 2.02 down 1.00 down MARK2 MAP/microtubule affinity-regulating kinase 2 NM_017490 217985_s_at 2.09 down 1.00 down BAZ1A fibroblastbromodomain growth adjacent factor receptorto zinc finger 2 (bacteria-expressed domain, 1A kinase, keratinocyte NM_013448 /// NM_182648 growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer 203638_s_at 2.10 down 1.01 down FGFR2 syndrome, Jackson-Weiss syndrome) NM_000141 /// NM_022970 1570445_a_at 2.07 down 1.01 down LOC643201 hypothetical protein LOC643201 XM_001716444 /// XM_001717933 /// XM_932161 231763_at 3.05 down 1.02 down POLR3A polymerase (RNA) III (DNA directed) polypeptide A, 155kDa NM_007055 1555368_x_at 2.08 down 1.04 down ZNF479 zinc finger protein 479 NM_033273 /// XM_001714591 /// XM_001719979 241627_x_at 2.15 down 1.05 down FLJ10357 hypothetical protein FLJ10357 NM_018071 223208_at 2.17 down 1.06 down KCTD10 potassium channel tetramerisation domain containing 10 NM_031954 219923_at 2.09 down 1.07 down TRIM45 tripartite motif-containing 45 NM_025188 242772_x_at 2.03 down 1.07 down Transcribed locus 233019_at 2.19 down 1.08 down CNOT7 CCR4-NOT transcription complex, subunit 7 NM_013354
    [Show full text]
  • PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday 31 Advances and References in Genomic Technology Room 210 #196–#204 2
    American Society of Human Genetics 63rd Annual Meeting October 22–26, 2013 Boston PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday 31 Advances and References in Genomic Technology Room 210 #196–#204 2. Plenary Abstract Presentations Hall B2 #1–#6 32 Genetic Testing for Neurodevelopmental Disease: Wednesday Genotype: Phenotype Challenges Room 205 #205–#213 2:00pm–4:15pm: Concurrent Platform Session A (10–18): 33 Gene Regulation—At a Multitude of 10. Which Comes First: The Sequence or Levels Room 253 #214–#222 the Biology? Hall B2 #7–#15 34 Cardiovascular Genetics: Exome 11 The Shifting Landscape of Genetic Sequencing and Animal Models Room 258 #223–#231 Testing: Approaches and Success 35 Genomic Medicine: Counseling, Stories Grand Education and Health Services Westin Hotel, Ballroom East #16–#24 Grand 12 Methods in Statistical Genetics Grand Ballroom AB #232–#240 Ballroom West #25–#33 36 Biochemical and Clinical 13 Genetic Variation in Gene Expression Room 210 #34–#42 Consequences of Mitochondrial 14 Cancer Epidemiology: New Loci and Dysfunction Westin Hotel, Methods Room 205 #43–#51 Grand 15 Psychiatric Disease: GWAS to Genes Room 253 #52–#60 Ballroom CDE #241–#249 16 Expanding Knowledge of Mendelian Disorders: Genes, Phenotypes & Friday Treatment Room 258 #61–#69 8:00am–10:15am:oncurrent Platform Session D (45–53): 17 Structural;shCopy Number Variation 45 Mo' Data, Mo' Problems? Hall B2 #250–#258 and Disease Westin Hotel, 46 Cancer Genomics Grand Grand Ballroom East #259–#267 Ballroom AB #70–#78 47 Demography In and Out of
    [Show full text]