DOCSLIB.ORG

Mrna Expression in Human Leiomyoma and Eker Rats As Measured by Microarray Analysis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mrna Expression in Human Leiomyoma and Eker Rats As Measured by Microarray Analysis Table 3S: mRNA Expression in Human Leiomyoma and Eker Rats as Measured by Microarray Analysis Human_avg Rat_avg_ PENG_ Entrez. Human_ log2_ log2_ RAPAMYCIN Gene.Symbol Gene.ID Gene Description avg_tstat Human_FDR foldChange Rat_avg_tstat Rat_FDR foldChange _DN A1BG 1 alpha-1-B glycoprotein 4.982 9.52E-05 0.68 -0.8346 0.4639 -0.38 A1CF 29974 APOBEC1 complementation factor -0.08024 0.9541 -0.02 0.9141 0.421 0.10 A2BP1 54715 ataxin 2-binding protein 1 2.811 0.01093 0.65 0.07114 0.954 -0.01 A2LD1 87769 AIG2-like domain 1 -0.3033 0.8056 -0.09 -3.365 0.005704 -0.42 A2M 2 alpha-2-macroglobulin -0.8113 0.4691 -0.03 6.02 0 1.75 A4GALT 53947 alpha 1,4-galactosyltransferase 0.4383 0.7128 0.11 6.304 0 2.30 AACS 65985 acetoacetyl-CoA synthetase 0.3595 0.7664 0.03 3.534 0.00388 0.38 AADAC 13 arylacetamide deacetylase (esterase) 0.569 0.6216 0.16 0.005588 0.9968 0.00 AADAT 51166 aminoadipate aminotransferase -0.9577 0.3876 -0.11 0.8123 0.4752 0.24 AAK1 22848 AP2 associated kinase 1 -1.261 0.2505 -0.25 0.8232 0.4689 0.12 AAMP 14 angio-associated, migratory cell protein 0.873 0.4351 0.07 1.656 0.1476 0.06 AANAT 15 arylalkylamine N-acetyltransferase -0.3998 0.7394 -0.08 0.8486 0.456 0.18 AARS 16 alanyl-tRNA synthetase 5.517 0 0.34 8.616 0 0.69 AARS2 57505 alanyl-tRNA synthetase 2, mitochondrial (putative) 1.701 0.1158 0.35 0.5011 0.6622 0.07 AARSD1 80755 alanyl-tRNA synthetase domain containing 1 4.403 9.52E-05 0.52 1.279 0.2609 0.13 AASDH 132949 aminoadipate-semialdehyde dehydrogenase -0.8921 0.4247 -0.12 -2.564 0.02993 -0.32 AASDHPPT 60496 aminoadipate-semialdehyde dehydrogenase-phosphopantetheinyl transferase0.2518 0.8408 0.08 3.597 0.003338 0.28 AASS 10157 aminoadipate-semialdehyde synthase -3.39 0.003216 -0.55 1.478 0.1958 0.52 AATF 26574 apoptosis antagonizing transcription factor 0.8235 0.4623 0.06 8.382 0 0.93 Included AATK 9625 apoptosis-associated tyrosine kinase -0.8873 0.4275 -0.13 -6.418 0 -1.58 ABAT 18 4-aminobutyrate aminotransferase 0.5607 0.6267 0.12 -0.3706 0.7503 -0.13 ABCA1 19 ATP-binding cassette, sub-family A (ABC1), member 1 -0.6104 0.5937 -0.11 1.53 0.1801 0.22 ABCA2 20 ATP-binding cassette, sub-family A (ABC1), member 2 -1.804 0.09535 -0.27 -2.631 0.02632 -0.47 ABCA3 21 ATP-binding cassette, sub-family A (ABC1), member 3 -0.3752 0.7564 -0.05 -4.502 0.000436 -0.46 ABCA4 24 ATP-binding cassette, sub-family A (ABC1), member 4 -0.2564 0.838 -0.05 1.367 0.2307 0.22 ABCA5 23461 ATP-binding cassette, sub-family A (ABC1), member 5 -4.678 9.52E-05 -1.09 3.514 0.004054 0.43 ABCA7 10347 ATP-binding cassette, sub-family A (ABC1), member 7 1.14 0.298 0.14 0.8051 0.479 0.10 ABCA8 10351 ATP-binding cassette, sub-family A (ABC1), member 8 -3.973 0.0007184 -1.18 3.25 0.007467 0.55 ABCB1 5243 ATP-binding cassette, sub-family B (MDR/TAP), member 1 -5.984 0 -1.01 -5.841 6.85E-06 -1.35 ABCB10 23456 ATP-binding cassette, sub-family B (MDR/TAP), member 10 -2.965 0.007852 -0.29 -0.9542 0.4016 -0.09 ABCB4 5244 ATP-binding cassette, sub-family B (MDR/TAP), member 4 0.1858 0.8863 0.07 1.235 0.2782 0.18 ABCB6 10058 ATP-binding cassette, sub-family B (MDR/TAP), member 6 0.8904 0.4256 0.09 2.201 0.05888 0.28 ABCB8 11194 ATP-binding cassette, sub-family B (MDR/TAP), member 8 4.035 0.0006339 0.73 3.181 0.008732 0.36 ABCB9 23457 ATP-binding cassette, sub-family B (MDR/TAP), member 9 -0.1758 0.8936 -0.04 -0.981 0.3885 -0.30 ABCC1 4363 ATP-binding cassette, sub-family C (CFTR/MRP), member 1 5.638 0 0.49 -6.31 0 -0.57 ABCC10 89845 ATP-binding cassette, sub-family C (CFTR/MRP), member 10 2.394 0.03038 0.19 1.123 0.3237 0.39 ABCC3 8714 ATP-binding cassette, sub-family C (CFTR/MRP), member 3 -0.3085 0.8016 -0.08 -0.1991 0.8694 -0.07 ABCC4 10257 ATP-binding cassette, sub-family C (CFTR/MRP), member 4 -2.529 0.02261 -0.53 6.379 0 0.65 ABCC5 10057 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 0.03625 0.9789 -0.01 2.052 0.07627 0.22 ABCC6 368 ATP-binding cassette, sub-family C (CFTR/MRP), member 6 2.404 0.02985 0.74 2.685 0.02366 0.45 ABCC8 6833 ATP-binding cassette, sub-family C (CFTR/MRP), member 8 -0.1046 0.9383 -0.05 2.875 0.01644 0.45 ABCC9 10060 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 -1.644 0.1295 -0.30 -2.779 0.01994 -0.54 ABCD1 215 ATP-binding cassette, sub-family D (ALD), member 1 1.138 0.299 0.17 -0.3935 0.7344 -0.04 ABCD2 225 ATP-binding cassette, sub-family D (ALD), member 2 -1.362 0.2127 -0.38 -1.074 0.3457 -0.28 ABCD3 5825 ATP-binding cassette, sub-family D (ALD), member 3 -1.018 0.3564 -0.15 -5.504 6.85E-06 -0.43 ABCD4 5826 ATP-binding cassette, sub-family D (ALD), member 4 -0.7356 0.514 -0.10 -0.2818 0.8115 -0.03 ABCE1 6059 ATP-binding cassette, sub-family E (OABP), member 1 -0.3688 0.7609 -0.03 4.014 0.001222 0.35 ABCF1 23 ATP-binding cassette, sub-family F (GCN20), member 1 2.918 0.008662 0.20 2.983 0.01325 0.27 ABCF2 10061 ATP-binding cassette, sub-family F (GCN20), member 2 1.582 0.1456 0.11 2.575 0.02933 0.18 ABCF3 55324 ATP-binding cassette, sub-family F (GCN20), member 3 2.814 0.0108 0.24 1.732 0.1309 0.17 ABCG1 9619 ATP-binding cassette, sub-family G (WHITE), member 1 -0.224 0.8586 -0.03 -0.9732 0.3923 -0.15 ABCG2 9429 ATP-binding cassette, sub-family G (WHITE), member 2 0.4723 0.6884 0.06 -1.93 0.09408 -0.34 ABCG4 64137 ATP-binding cassette, sub-family G (WHITE), member 4 -0.1777 0.892 -0.03 -11.64 0 -2.60 ABCG5 64240 ATP-binding cassette, sub-family G (WHITE), member 5 1.276 0.2453 0.42 -3.022 0.01227 -0.45 ABCG8 64241 ATP-binding cassette, sub-family G (WHITE), member 8 -0.2412 0.8465 -0.06 3.563 0.003572 0.84 ABHD1 84696 abhydrolase domain containing 1 0.46 0.6969 0.09 2.73 0.02184 0.59 ABHD10 55347 abhydrolase domain containing 10 -2.478 0.02512 -0.26 0.4264 0.7122 0.05 ABHD12 26090 abhydrolase domain containing 12 3.436 0.002909 0.43 3.259 0.007324 0.34 ABHD13 84945 abhydrolase domain containing 13 -1.39 0.2037 -0.23 -0.205 0.8658 -0.01 ABHD14A 25864 abhydrolase domain containing 14A -1.247 0.256 -0.16 -0.5459 0.6337 -0.06 ABHD14B 84836 abhydrolase domain containing 14B -0.7191 0.5249 -0.07 -2.248 0.0542 -0.23 ABHD2 11057 abhydrolase domain containing 2 -0.7381 0.5124 -0.17 0.9778 0.3901 0.09 ABHD3 171586 abhydrolase domain containing 3 -1.835 0.08985 -0.28 -0.5878 0.6083 -0.09 ABHD4 63874 abhydrolase domain containing 4 0.4368 0.7136 0.03 1.962 0.089 0.16 ABHD5 51099 abhydrolase domain containing 5 2.608 0.0188 0.43 11 0 1.53 ABHD6 57406 abhydrolase domain containing 6 -0.1666 0.9001 -0.05 0.8816 0.438 0.09 ABHD7 253152 abhydrolase domain containing 7 -0.8988 0.4211 -0.32 0.189 0.8756 0.07 ABHD8 79575 abhydrolase domain containing 8 3.158 0.005216 0.89 1.675 0.1434 0.22 ABI1 10006 abl-interactor 1 -1.691 0.1185 -0.17 -0.7531 0.5071 -0.13 ABI2 10152 abl interactor 2 0.9557 0.3885 0.11 -4.786 0.000166 -0.32 ABI3 51225 ABI family, member 3 -1.82 0.09243 -0.18 -4.406 0.000531 -0.59 ABL1 25 c-abl oncogene 1, receptor tyrosine kinase -0.5251 0.6527 -0.05 1.066 0.349 0.08 ABLIM1 3983 actin binding LIM protein 1 -4.974 9.52E-05 -1.12 0.8549 0.4527 0.24 ABLIM3 22885 actin binding LIM protein family, member 3 -6.626 0 -1.23 -2.95 0.01413 -0.57 ABP1 26 amiloride binding protein 1 (amine oxidase (copper-containing)) -0.481 0.6828 -0.13 -7.627 0 -1.34 ABR 29 active BCR-related gene 0.8718 0.4358 0.19 3.863 0.001779 0.40 ABRA 137735 actin-binding Rho activating protein -1.947 0.07276 -0.77 1.205 0.2895 0.39 ABT1 29777 activator of basal transcription 1 0.4599 0.6969 0.05 0.8536 0.4532 0.07 ABTB1 80325 ankyrin repeat and BTB (POZ) domain containing 1 0.2797 0.8226 0.09 -5.235 5.04E-05 -0.54 ABTB2 25841 ankyrin repeat and BTB (POZ) domain containing 2 1.765 0.1034 0.23 -3.186 0.008637 -0.62 ACAA2 10449 acetyl-Coenzyme A acyltransferase 2 -0.3678 0.7618 -0.04 0.9857 0.3862 0.38 ACACA 31 acetyl-Coenzyme A carboxylase alpha 1.852 0.08728 0.33 3.269 0.007183 0.40 ACACB 32 acetyl-Coenzyme A carboxylase beta -2.842 0.01028 -0.35 -2.048 0.07685 -0.83 ACAD9 28976 acyl-Coenzyme A dehydrogenase family, member 9 0.5064 0.6652 0.02 4.044 0.001164 0.35 ACADL 33 acyl-Coenzyme A dehydrogenase, long chain -1.811 0.09446 -0.56 -2.586 0.02873 -0.28 ACADM 34 acyl-Coenzyme A dehydrogenase, C-4 to C-12 straight chain -2.3 0.03734 -0.27 -10.56 0 -1.11 ACADS 35 acyl-Coenzyme A dehydrogenase, C-2 to C-3 short chain 0.7099 0.53 0.08 1.286 0.2585 0.14 ACADSB 36 acyl-Coenzyme A dehydrogenase, short/branched chain -1.718 0.1124 -0.18 -1.392 0.2223 -0.17 ACADVL 37 acyl-Coenzyme A dehydrogenase, very long chain 0.7889 0.4822 0.09 -0.8884 0.4345 -0.08 ACAP1 9744 ArfGAP with coiled-coil, ankyrin repeat and PH domains 1 -0.162 0.9034 -0.05 -3.82 0.00196 -1.44 ACAP2 23527 ArfGAP with coiled-coil, ankyrin repeat and PH domains 2 -1.739 0.1082 -0.22 0.9837 0.3872 0.23 ACAP3 116983 ArfGAP with coiled-coil, ankyrin repeat and PH domains 3 1.466 0.1799 0.19 4.124 0.000979 0.59 ACAT1 38 acetyl-Coenzyme A acetyltransferase 1 -1.449 0.1847 -0.16 1.261 0.2675 0.08 ACAT2 39 acetyl-Coenzyme A acetyltransferase 2 0.1155 0.9319 0.01 6.599 0 1.20 Included ACBD3 64746 acyl-Coenzyme A binding domain containing 3 -0.4356 0.7144 -0.15 0.6797 0.5501 0.03 ACBD4 79777 acyl-Coenzyme A binding domain containing 4 -0.6467 0.5689 -0.05 -3.184 0.0087 -0.50 ACBD6 84320 acyl-Coenzyme A binding domain containing 6 4.788 9.52E-05 0.34 4.853 0.000149 0.47 ACCN2 41 amiloride-sensitive cation channel 2, neuronal 0.8026 0.4748 0.17 0.9873 0.3856 0.20 ACCN3 9311 amiloride-sensitive cation channel 3 4.067 0.0005673 0.78 2.226 0.05614 0.54 ACCN4 55515 amiloride-sensitive cation channel 4, pituitary -0.2082 0.8697 -0.05 0.5559 0.6276 0.14 ACCN5 51802 amiloride-sensitive cation channel 5, intestinal 0.739 0.5121 0.23 -0.95 0.4032 -0.32 ACD 65057 adrenocortical dysplasia homolog (mouse) 1.987 0.0675 0.21 1.909 0.09751 0.15 ACE 1636 angiotensin I converting enzyme (peptidyl-dipeptidase A) 1 -1.64 0.1307 -0.29 -5.614 6.85E-06 -1.20 ACE2 59272 angiotensin I converting enzyme (peptidyl-dipeptidase A) 2 -2.09 0.05571 -0.56 1.507 0.1869 0.46 ACIN1 22985 apoptotic chromatin condensation inducer 1 0.2527 0.8402 0.02 -3.297 0.006829 -0.37 ACLY 47 ATP citrate lyase 0.1996 0.8767 0.03 4.455 0.000484 0.43 ACMSD 130013 aminocarboxymuconate semialdehyde decarboxylase -0.6321 0.579 -0.21 0.4311 0.709 0.10 ACN9 57001 ACN9 homolog (S.
Load more

Recommended publications
  • Supplemental Table S1
    Entrez Gene Symbol Gene Name Affymetrix EST Glomchip SAGE Stanford Literature HPA confirmed Gene ID Profiling profiling Profiling Profiling array profiling confirmed 1 2 A2M alpha-2-macroglobulin 0 0 0 1 0 2 10347 ABCA7 ATP-binding cassette, sub-family A (ABC1), member 7 1 0 0 0 0 3 10350 ABCA9 ATP-binding cassette, sub-family A (ABC1), member 9 1 0 0 0 0 4 10057 ABCC5 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 1 0 0 0 0 5 10060 ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 1 0 0 0 0 6 79575 ABHD8 abhydrolase domain containing 8 1 0 0 0 0 7 51225 ABI3 ABI gene family, member 3 1 0 1 0 0 8 29 ABR active BCR-related gene 1 0 0 0 0 9 25841 ABTB2 ankyrin repeat and BTB (POZ) domain containing 2 1 0 1 0 0 10 30 ACAA1 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiol 0 1 0 0 0 11 43 ACHE acetylcholinesterase (Yt blood group) 1 0 0 0 0 12 58 ACTA1 actin, alpha 1, skeletal muscle 0 1 0 0 0 13 60 ACTB actin, beta 01000 1 14 71 ACTG1 actin, gamma 1 0 1 0 0 0 15 81 ACTN4 actinin, alpha 4 0 0 1 1 1 10700177 16 10096 ACTR3 ARP3 actin-related protein 3 homolog (yeast) 0 1 0 0 0 17 94 ACVRL1 activin A receptor type II-like 1 1 0 1 0 0 18 8038 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 1 0 0 0 0 19 8751 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 1 0 0 0 0 20 8728 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 1 0 0 0 0 21 81792 ADAMTS12 ADAM metallopeptidase with thrombospondin type 1 motif, 12 1 0 0 0 0 22 9507 ADAMTS4 ADAM metallopeptidase with thrombospondin type 1
    [Show full text]
  • Aquaporin Channels in the Heart—Physiology and Pathophysiology
    International Journal of Molecular Sciences Review Aquaporin Channels in the Heart—Physiology and Pathophysiology Arie O. Verkerk 1,2,* , Elisabeth M. Lodder 2 and Ronald Wilders 1 1 Department of Medical Biology, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; [email protected] 2 Department of Experimental Cardiology, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; [email protected] * Correspondence: [email protected]; Tel.: +31-20-5664670 Received: 29 March 2019; Accepted: 23 April 2019; Published: 25 April 2019 Abstract: Mammalian aquaporins (AQPs) are transmembrane channels expressed in a large variety of cells and tissues throughout the body. They are known as water channels, but they also facilitate the transport of small solutes, gasses, and monovalent cations. To date, 13 different AQPs, encoded by the genes AQP0–AQP12, have been identified in mammals, which regulate various important biological functions in kidney, brain, lung, digestive system, eye, and skin. Consequently, dysfunction of AQPs is involved in a wide variety of disorders. AQPs are also present in the heart, even with a specific distribution pattern in cardiomyocytes, but whether their presence is essential for proper (electro)physiological cardiac function has not intensively been studied. This review summarizes recent findings and highlights the involvement of AQPs in normal and pathological cardiac function. We conclude that AQPs are at least implicated in proper cardiac water homeostasis and energy balance as well as heart failure and arsenic cardiotoxicity. However, this review also demonstrates that many effects of cardiac AQPs, especially on excitation-contraction coupling processes, are virtually unexplored.
    [Show full text]
  • RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases
    G C A T T A C G G C A T genes Review RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases Amber Willbanks, Shaun Wood and Jason X. Cheng * Department of Pathology, Hematopathology Section, University of Chicago, Chicago, IL 60637, USA; [email protected] (A.W.); [email protected] (S.W.) * Correspondence: [email protected] Abstract: Chromatin structure plays an essential role in eukaryotic gene expression and cell identity. Traditionally, DNA and histone modifications have been the focus of chromatin regulation; however, recent molecular and imaging studies have revealed an intimate connection between RNA epigenetics and chromatin structure. Accumulating evidence suggests that RNA serves as the interplay between chromatin and the transcription and splicing machineries within the cell. Additionally, epigenetic modifications of nascent RNAs fine-tune these interactions to regulate gene expression at the co- and post-transcriptional levels in normal cell development and human diseases. This review will provide an overview of recent advances in the emerging field of RNA epigenetics, specifically the role of RNA modifications and RNA modifying proteins in chromatin remodeling, transcription activation and RNA processing, as well as translational implications in human diseases. Keywords: 5’ cap (5’ cap); 7-methylguanosine (m7G); R-loops; N6-methyladenosine (m6A); RNA editing; A-to-I; C-to-U; 2’-O-methylation (Nm); 5-methylcytosine (m5C); NOL1/NOP2/sun domain Citation: Willbanks, A.; Wood, S.; (NSUN); MYC Cheng, J.X. RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases. Genes 2021, 12, 627.
    [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]
  • Steroid-Dependent Regulation of the Oviduct: a Cross-Species Transcriptomal Analysis
    University of Kentucky UKnowledge Theses and Dissertations--Animal and Food Sciences Animal and Food Sciences 2015 Steroid-dependent regulation of the oviduct: A cross-species transcriptomal analysis Katheryn L. Cerny University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Cerny, Katheryn L., "Steroid-dependent regulation of the oviduct: A cross-species transcriptomal analysis" (2015). Theses and Dissertations--Animal and Food Sciences. 49. https://uknowledge.uky.edu/animalsci_etds/49 This Doctoral Dissertation is brought to you for free and open access by the Animal and Food Sciences at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Animal and Food Sciences by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known.
    [Show full text]
  • 1 Metabolic Dysfunction Is Restricted to the Sciatic Nerve in Experimental
    Page 1 of 255 Diabetes Metabolic dysfunction is restricted to the sciatic nerve in experimental diabetic neuropathy Oliver J. Freeman1,2, Richard D. Unwin2,3, Andrew W. Dowsey2,3, Paul Begley2,3, Sumia Ali1, Katherine A. Hollywood2,3, Nitin Rustogi2,3, Rasmus S. Petersen1, Warwick B. Dunn2,3†, Garth J.S. Cooper2,3,4,5* & Natalie J. Gardiner1* 1 Faculty of Life Sciences, University of Manchester, UK 2 Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK 3 Centre for Endocrinology and Diabetes, Institute of Human Development, Faculty of Medical and Human Sciences, University of Manchester, UK 4 School of Biological Sciences, University of Auckland, New Zealand 5 Department of Pharmacology, Medical Sciences Division, University of Oxford, UK † Present address: School of Biosciences, University of Birmingham, UK *Joint corresponding authors: Natalie J. Gardiner and Garth J.S. Cooper Email: [email protected]; [email protected] Address: University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, United Kingdom Telephone: +44 161 275 5768; +44 161 701 0240 Word count: 4,490 Number of tables: 1, Number of figures: 6 Running title: Metabolic dysfunction in diabetic neuropathy 1 Diabetes Publish Ahead of Print, published online October 15, 2015 Diabetes Page 2 of 255 Abstract High glucose levels in the peripheral nervous system (PNS) have been implicated in the pathogenesis of diabetic neuropathy (DN). However our understanding of the molecular mechanisms which cause the marked distal pathology is incomplete. Here we performed a comprehensive, system-wide analysis of the PNS of a rodent model of DN.
    [Show full text]
  • Patient-Based Cross-Platform Comparison of Oligonucleotide Microarray Expression Profiles
    Laboratory Investigation (2005) 85, 1024–1039 & 2005 USCAP, Inc All rights reserved 0023-6837/05 $30.00 www.laboratoryinvestigation.org Patient-based cross-platform comparison of oligonucleotide microarray expression profiles Joerg Schlingemann1,*, Negusse Habtemichael2,*, Carina Ittrich3, Grischa Toedt1, Heidi Kramer1, Markus Hambek4, Rainald Knecht4, Peter Lichter1, Roland Stauber2 and Meinhard Hahn1 1Division of Molecular Genetics, Deutsches Krebsforschungszentrum, Heidelberg, Germany; 2Chemotherapeutisches Forschungsinstitut Georg-Speyer-Haus, Frankfurt am Main, Germany; 3Central Unit Biostatistics, Deutsches Krebsforschungszentrum, Heidelberg, Germany and 4Department of Otorhinolaryngology, Universita¨tsklinik, Johann-Wolfgang-Goethe-Universita¨t Frankfurt, Frankfurt, Germany The comparison of gene expression measurements obtained with different technical approaches is of substantial interest in order to clarify whether interplatform differences may conceal biologically significant information. To address this concern, we analyzed gene expression in a set of head and neck squamous cell carcinoma patients, using both spotted oligonucleotide microarrays made from a large collection of 70-mer probes and commercial arrays produced by in situ synthesis of sets of multiple 25-mer oligonucleotides per gene. Expression measurements were compared for 4425 genes represented on both platforms, which revealed strong correlations between the corresponding data sets. Of note, a global tendency towards smaller absolute ratios was observed when
    [Show full text]
  • Research Article Characterization of the Equine Skeletal Muscle
    McGivney et al. BMC Genomics 2010, 11:398 http://www.biomedcentral.com/1471-2164/11/398 RESEARCH ARTICLE Open Access CharacterizationResearch article of the equine skeletal muscle transcriptome identifies novel functional responses to exercise training Beatrice A McGivney1, Paul A McGettigan1, John A Browne1, Alexander CO Evans1,3, Rita G Fonseca2, Brendan J Loftus3, Amanda Lohan3, David E MacHugh1,3, Barbara A Murphy1, Lisa M Katz2 and Emmeline W Hill*1 Abstract Background: Digital gene expression profiling was used to characterize the assembly of genes expressed in equine skeletal muscle and to identify the subset of genes that were differentially expressed following a ten-month period of exercise training. The study cohort comprised seven Thoroughbred racehorses from a single training yard. Skeletal muscle biopsies were collected at rest from the gluteus medius at two time points: T1 - untrained, (9 ± 0.5 months old) and T2 - trained (20 ± 0.7 months old). Results: The most abundant mRNA transcripts in the muscle transcriptome were those involved in muscle contraction, aerobic respiration and mitochondrial function. A previously unreported over-representation of genes related to RNA processing, the stress response and proteolysis was observed. Following training 92 tags were differentially expressed of which 74 were annotated. Sixteen genes showed increased expression, including the mitochondrial genes ACADVL, MRPS21 and SLC25A29 encoded by the nuclear genome. Among the 58 genes with decreased expression, MSTN, a negative regulator of muscle growth, had the greatest decrease. Functional analysis of all expressed genes using FatiScan revealed an asymmetric distribution of 482 Gene Ontology (GO) groups and 18 KEGG pathways.
    [Show full text]
  • Cellular and Molecular Signatures in the Disease Tissue of Early
    Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of
    [Show full text]
  • APOBEC1 Complementation Factor (A1CF) and RBM47 Interact in Tissue-Specific Regulation of C to U RNA Editing in Mouse Intestine and Liver
    Downloaded from rnajournal.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press APOBEC1 complementation factor (A1CF) and RBM47 interact in tissue-specific regulation of C to U RNA editing in mouse intestine and liver Valerie Blanc1, Yan Xie1, Susan Kennedy1, Jesse D. Riordan2, Deborah C. Rubin1, Blair B. Madison1, Jason C. Mills1, Joseph H. Nadeau2, Nicholas O. Davidson1,3 1Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63105, 2Pacific Northwest Research Institute, Seattle, WA 98122 3To whom communication should be addressed: Email: [email protected] Running title: A1CF and RBM47 interactively regulate C to U RNA editing Keywords: APOBEC1; A1CF; RBM47; ApoB; Intestine; Liver Downloaded from rnajournal.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press ABSTRACT (246 words) Mammalian C to U RNA is mediated by APOBEC1, the catalytic deaminase, together with RNA binding cofactors (including A1CF and RBM47) whose relative physiological requirements are unresolved. Although A1CF complements APOBEC1 for in-vitro RNA editing, A1cf–/– mice exhibited no change in apolipoproteinB (apoB) RNA editing, while Rbm47 mutant mice exhibited impaired intestinal RNA editing of apoB as well as other targets. Here we examined the role of A1CF and RBM47 in adult mouse liver and intestine, following deletion of either one or both gene products and also following forced (liver or intestinal) transgenic A1CF expression. There were minimal changes in hepatic and intestinal apoB RNA editing in A1cf–/– mice and no changes in either liver- or intestine-specific A1CF transgenic mice. Rbm47 liver-specific knockout (Rbm47LKO) mice demonstrated reduced editing in a subset (11 of 20) RNA targets, including apoB.
    [Show full text]
  • Gene and Pathway-Based Second-Wave Analysis of Genome-Wide Association Studies
    European Journal of Human Genetics (2010) 18, 111–117 & 2010 Macmillan Publishers Limited All rights reserved 1018-4813/10 $32.00 www.nature.com/ejhg ARTICLE Gene and pathway-based second-wave analysis of genome-wide association studies Gang Peng1, Li Luo2, Hoicheong Siu1, Yun Zhu1, Pengfei Hu1, Shengjun Hong1, Jinying Zhao3, Xiaodong Zhou4, John D Reveille4, Li Jin1, Christopher I Amos5 and Momiao Xiong*,2 Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression
    [Show full text]
  • The Caenorhabditis Elegans Excretory System: a Model for Tubulogenesis, Cell Fate Specification, and Plasticity
    | WORMBOOK CELL AND ORGANELLE BIOLOGY The Caenorhabditis elegans Excretory System: A Model for Tubulogenesis, Cell Fate Specification, and Plasticity Meera V. Sundaram*,1 and Matthew Buechner† *Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 and †Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66045 ABSTRACT The excretory system of the nematode Caenorhabditis elegans is a superb model of tubular organogenesis involving a minimum of cells. The system consists of just three unicellular tubes (canal, duct, and pore), a secretory gland, and two associated neurons. Just as in more complex organs, cells of the excretory system must first adopt specific identities and then coordinate diverse processes to form tubes of appropriate topology, shape, connectivity, and physiological function. The unicellular topology of excretory tubes, their varied and sometimes complex shapes, and the dynamic reprogramming of cell identity and remodeling of tube connec- tivity that occur during larval development are particularly fascinating features of this organ. The physiological roles of the excretory system in osmoregulation and other aspects of the animal’s life cycle are only beginning to be explored. The cellular mechanisms and molecular pathways used to build and shape excretory tubes appear similar to those used in both unicellular and multicellular tubes in more complex organs, such as the vertebrate vascular system and kidney, making this simple organ system a
    [Show full text]