Delineating the Cardio-Myogenic Hierarchy During Mouse Embryonic Development
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A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer
Henry Ford Health System Henry Ford Health System Scholarly Commons Urology Articles Urology 12-11-2019 A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer. Osama Hamzeh Abedalrhman Alkhateeb Zhuoran Zheng Srinath Kandalam Crystal Leung See next page for additional authors Follow this and additional works at: https://scholarlycommons.henryford.com/urology_articles Recommended Citation Hamzeh O, Alkhateeb A, Zheng JZ, Kandalam S, Leung C, Atikukke G, Cavallo-Medved D, Palanisamy N, and Rueda L. A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer. Diagnostics (Basel) 2019; 9(4). This Article is brought to you for free and open access by the Urology at Henry Ford Health System Scholarly Commons. It has been accepted for inclusion in Urology Articles by an authorized administrator of Henry Ford Health System Scholarly Commons. Authors Osama Hamzeh, Abedalrhman Alkhateeb, Zhuoran Zheng, Srinath Kandalam, Crystal Leung, Govindaraja Atikukke, Dora Cavallo-Medved, Nallasivam Palanisamy, and Luis Rueda This article is available at Henry Ford Health System Scholarly Commons: https://scholarlycommons.henryford.com/ urology_articles/341 diagnostics Article A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer Osama Hamzeh 1,†, Abedalrhman Alkhateeb 1,*,† , Julia Zhuoran Zheng 1, Srinath Kandalam 2, Crystal Leung 3, Govindaraja Atikukke 4, Dora Cavallo-Medved 2, Nallasivam Palanisamy 5,* and Luis Rueda -
Mapping Autosomal Recessive Intellectual Disability: Combined Microarray and Exome
bioRxiv preprint doi: https://doi.org/10.1101/092346; this version posted March 15, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Mapping Autosomal Recessive Intellectual Disability: Combined Microarray and Exome Sequencing Identifies 26 Novel Candidate Genes in 192 Consanguineous Families Ricardo Harripaul, MSc1,2, Nasim Vasli, PhD1, Anna Mikhailov, BSc1, Muhammad Arshad Rafiq, PhD 1,3, Kirti Mittal, PhD 1, Christian Windpassinger, PhD4, Taimoor I. Sheikh, MPhil1,2, Abdul Noor, PhD5,6, Huda Mahmood, BSc1, Samantha Downey1,7, Maneesha Johnson1,7, Kayla Vleuten1,7, Lauren Bell1,7, Muhammad Ilyas, M.Phil8, Falak Sher Khan, MS9, Valeed Khan, MS9, Mohammad Moradi, MSc10, Muhammad Ayaz11, Farooq Naeem, PhD 11,12, Abolfazl Heidari, PhD1,13, Iltaf Ahmed, PhD14, Shirin Ghadami, PhD15, Zehra Agha, PhD3, Sirous Zeinali, PhD15, Raheel Qamar, PhD3,16, Hossein Mozhdehipanah, MD17, Peter John, PhD14, Asif Mir, PhD8, Muhammad Ansar, PhD9, Leon French, PhD18, Muhammad Ayub, MBBS, MD11,12, John B. Vincent, PhD1,2,19 1Molecular Neuropsychiatry & Development (MiND) Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; 2Institute of Medical Science, University of Toronto, Toronto, ON, Canada; 3Dept. of Biosciences, COMSATS Institute of Information Technology, Islamabad, Pakistan; 4Institute of Human Genetics, Medical University of Graz, Graz, Austria; 5Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, Canada; 6 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; 1 bioRxiv preprint doi: https://doi.org/10.1101/092346; this version posted March 15, 2017. -
Implicating Gene and Cell Networks Responsible for Differential COVID
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.07.447287; this version posted June 16, 2021. 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-NC 4.0 International license. 1 Implicating Gene and Cell Networks Responsible for 2 Differential COVID-19 Host Responses via an Interactive 3 Single Cell Web Portal 4 5 Kang Jin1,2, Eric E. Bardes1, Alexis Mitelpunkt1,3,4, Jake Y. Wang1, Surbhi Bhatnagar1,5, 6 Soma Sengupta6, Daniel Pomeranz Krummel6, Marc E. Rothenberg7, Bruce J. 7 Aronow1,8,9,* 8 9 1Division oF Biomedical InFormatics, Cincinnati Children's Hospital Medical Center, 10 Cincinnati, OH, 45229, USA 11 2Department oF Biomedical InFormatics, University oF Cincinnati, Cincinnati, OH, 45229, 12 USA 13 3Pediatric Rehabilitation, Dana-Dwek Children's Hospital, Tel Aviv Medical Center, Tel 14 Aviv, 6423906, Israel 15 4Sackler Faculty oF Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel 16 5Department oF Electrical Engineering and Computer Science, University oF Cincinnati, 17 Cincinnati, OH, 45221, USA 18 6Department oF Neurology and Rehabilitation Medicine, University oF Cincinnati College 19 oF Medicine, Cincinnati, OH, 45267, USA. 20 7Division oF Allergy and Immunology, Department oF Pediatrics, Cincinnati Children's 21 Hospital Medical Center, University oF Cincinnati, Cincinnati, OH, 45229, USA 22 8Department oF Pediatrics, University oF Cincinnati School oF Medicine, Cincinnati, OH, 23 45256, USA 24 9Lead contact 25 *Correspondence: [email protected] (B.A.) 26 27 28 29 30 31 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.07.447287; this version posted June 16, 2021. -
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. -
Structural and Functional Characterization of TMEM16 Family Members
Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2016 Structural and functional characterization of TMEM16 family members Lim, Novandy Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-129639 Dissertation Published Version Originally published at: Lim, Novandy. Structural and functional characterization of TMEM16 family members. 2016, University of Zurich, Faculty of Science. Structural and Functional Characterization of TMEM16 Family Members Dissertation zur Erlangung der naturwissenschaftlichen Doktorwürde (Dr. sc. nat.) vorgelegt der Mathematisch-naturwissenschaftlichen Fakultät der Universität Zürich von Novandy Karunia Lim aus Indonesien Promotionskomitee Prof. Dr. Raimund Dutzler (Vorsitz) Prof. Dr. Markus Seeger Prof. Dr. Martin Jinek Zürich, 2016 Acknowledgements I would like to take this opportunity to thank all the people who have supported and helped me throughout my stay and project in the lab. Firstly, I would like to thank Prof. Raimund Dutzler for the opportunity to work on this highly exciting project. I am sincerely grateful for his constant support and his faith in me throughout my time in his group. I would like to thank Prof. Martin Jinek and Prof. Markus Seeger for being part of my thesis committee. I would like to thank Janine Brunner and Stephan Schenck for the helpful discussion on TMEM16 project. A big thank you to Alessia Dürst too for helping us with the homologue screen during her Masters thesis. I am grateful to all the past and present members of the Dutzler for their friendship, support and scientific discussions. -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
S41467-020-18249-3.Pdf
ARTICLE https://doi.org/10.1038/s41467-020-18249-3 OPEN Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes with neurodegenerative disease associations in the aged brain Lei Zhao1,2,17, Zhongqi Li 1,2,17, Joaquim S. L. Vong2,3,17, Xinyi Chen1,2, Hei-Ming Lai1,2,4,5,6, Leo Y. C. Yan1,2, Junzhe Huang1,2, Samuel K. H. Sy1,2,7, Xiaoyu Tian 8, Yu Huang 8, Ho Yin Edwin Chan5,9, Hon-Cheong So6,8, ✉ ✉ Wai-Lung Ng 10, Yamei Tang11, Wei-Jye Lin12,13, Vincent C. T. Mok1,5,6,14,15 &HoKo 1,2,4,5,6,8,14,16 1234567890():,; The molecular signatures of cells in the brain have been revealed in unprecedented detail, yet the ageing-associated genome-wide expression changes that may contribute to neurovas- cular dysfunction in neurodegenerative diseases remain elusive. Here, we report zonation- dependent transcriptomic changes in aged mouse brain endothelial cells (ECs), which pro- minently implicate altered immune/cytokine signaling in ECs of all vascular segments, and functional changes impacting the blood–brain barrier (BBB) and glucose/energy metabolism especially in capillary ECs (capECs). An overrepresentation of Alzheimer disease (AD) GWAS genes is evident among the human orthologs of the differentially expressed genes of aged capECs, while comparative analysis revealed a subset of concordantly downregulated, functionally important genes in human AD brains. Treatment with exenatide, a glucagon-like peptide-1 receptor agonist, strongly reverses aged mouse brain EC transcriptomic changes and BBB leakage, with associated attenuation of microglial priming. We thus revealed tran- scriptomic alterations underlying brain EC ageing that are complex yet pharmacologically reversible. -
WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US). -
The Transcriptomic Landscape of Prostate Cancer Development and Progression: an Integrative Analysis
cancers Article The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis Jacek Marzec 1,† , Helen Ross-Adams 1,*,† , Stefano Pirrò 1 , Jun Wang 1 , Yanan Zhu 2, Xueying Mao 2, Emanuela Gadaleta 1 , Amar S. Ahmad 3, Bernard V. North 3, Solène-Florence Kammerer-Jacquet 2, Elzbieta Stankiewicz 2, Sakunthala C. Kudahetti 2, Luis Beltran 4, Guoping Ren 5, Daniel M. Berney 2,4, Yong-Jie Lu 2 and Claude Chelala 1,6,* 1 Bioinformatics Unit, Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (J.M.); [email protected] (S.P.); [email protected] (J.W.); [email protected] (E.G.) 2 Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (Y.Z.); [email protected] (X.M.); solenefl[email protected] (S.-F.K.-J.); [email protected] (E.S.); [email protected] (S.C.K.); [email protected] (D.M.B.); [email protected] (Y.-J.L.) 3 Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (A.S.A.); [email protected] (B.V.N.) 4 Department of Pathology, Barts Health NHS, London E1 F1R, UK; [email protected] 5 Department of Pathology, The First Affiliated Hospital, Zhejiang University Medical College, Hangzhou 310058, China; [email protected] 6 Centre for Computational Biology, Life Sciences Initiative, Queen Mary University London, London EC1M 6BQ, UK * Correspondence: [email protected] (H.R.-A.); [email protected] (C.C.) † These authors contributed equally to this work. -
TMEM16F Activation by Ca2+ Triggers Plasma Membrane Expansion And
www.nature.com/scientificreports OPEN TMEM16F activation by Ca2+ triggers plasma membrane expansion and directs PD-1 Received: 18 September 2018 Accepted: 3 December 2018 trafcking Published: xx xx xxxx Christopher Bricogne1, Michael Fine2, Pedro M. Pereira 3, Julia Sung4, Maha Tijani4, Youxue Wang2, Ricardo Henriques 3, Mary K. Collins1,4,5 & Donald Hilgemann2 TMEM16F is a Ca2+ -gated ion channel that is required for Ca2+ -activated phosphatidylserine exposure on the surface of many eukaryotic cells. TMEM16F is widely expressed and has roles in platelet activation during blood clotting, bone formation and T cell activation. By combining microscopy and patch clamp recording we demonstrate that activation of TMEM16F by Ca2+ ionophores in Jurkat T cells triggers large-scale surface membrane expansion in parallel with phospholipid scrambling. With continued ionophore application,TMEM16F-expressing cells then undergo extensive shedding of ectosomes. The T cell co-receptor PD-1 is selectively incorporated into ectosomes. This selectivity depends on its transmembrane sequence. Surprisingly, cells lacking TMEM16F not only fail to expand surface membrane in response to elevated cytoplasmic Ca2+, but instead undergo rapid massive endocytosis with PD-1 internalisation. These results establish a new role for TMEM16F as a regulator of Ca2+ activated membrane trafcking. Eukaryotic cells retain phosphatidylserine (PS) on the cytoplasmic face of the plasma membrane1. In response to high levels of cytoplasmic Ca2+ elevation and during apoptosis, PS is exposed on the cell surface by a process known as phospholipid scrambling2. Ca2+-activated phospholipid scrambling is mediated by a transmembrane protein, TMEM16F (also known as anoctamin 6)3,4, a widely expressed member of the TMEM16 family of ion channels5. -
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