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Lim AJW, Lim LJ, Ooi BNS, Koh ET, Tan JWL, Chong SS, Khor CC, Tucker-Kellogg L, Leong KP, Lee CG. Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients. EBioMedicine 2022; 75:103800. [PMID: 35022146 PMCID: PMC8808170 DOI: 10.1016/j.ebiom.2021.103800] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients. METHODS Exome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set. FINDINGS Significantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients. INTERPRETATION Majority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways. FUNDING Singapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.
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Affiliation(s)
- Ashley J W Lim
- Dept of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Lee Jin Lim
- Dept of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Brandon N S Ooi
- Dept of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ee Tzun Koh
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore
| | - Justina Wei Lynn Tan
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore
| | - Samuel S Chong
- Dept of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Chiea Chuen Khor
- Division of Human Genetics, Genome Institute of Singapore, Singapore
| | - Lisa Tucker-Kellogg
- Centre for Computational Biology, and Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore
| | - Khai Pang Leong
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore; Clinical Research & Innovation Office, Tan Tock Seng Hospital, Singapore.
| | - Caroline G Lee
- Dept of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Div of Cellular & Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore, Singapore; Duke-NUS Medical School, Singapore; NUS Graduate School, National University of Singapore, Singapore.
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Kinchen JM, Mohney RP, Pappan KL. Long-Chain Acylcholines Link Butyrylcholinesterase to Regulation of Non-neuronal Cholinergic Signaling. J Proteome Res 2021; 21:599-611. [PMID: 34758617 DOI: 10.1021/acs.jproteome.1c00538] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Acylcholines are comprised of an acyl chain esterified to a choline moiety; acetylcholine is the best-characterized member of this class, functioning as a neurotransmitter in the central and peripheral nervous systems as well as an inhibitor of cytokine production by macrophages and other innate immune cells. Acylcholines are metabolized by a class of cholinesterases, including acetylcholinesterase (a specific regulator of acetylcholine levels) and butyrylcholinesterase (BChE, an enigmatic enzyme whose function has not been resolved by genetic knockout models). BChE provides reserve capacity to hydrolyze acetylcholine, but its importance is arguable given acetylcholinesterase is the most catalytically efficient enzyme characterized to date. While known to be substrates of BChE in vitro, endogenous production of long-chain acylcholines is a recent discovery enabled by untargeted metabolomics. Compared to acetylcholine, long-chain acylcholines show greater stability in circulation with homeostatic levels-dictated by synthesis and clearance-suggested to impact cholinergic receptor sensitivity of acetylcholine with varying levels of antagonism. Acylcholines then provide a link between BChE and non-neuronal acetylcholine signaling, filling a gap in understanding around how imbalances between acylcholines and BChE could modulate inflammatory disease, such as the "cytokine storm" identified in severe COVID-19. Areas for further research, development, and clinical testing are outlined.
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Affiliation(s)
- Jason M Kinchen
- Owlstone Medical Inc., 600 Park Office Drive, Suite 140, Research Triangle Park, North Carolina 27709, United States
| | - Robert P Mohney
- Owlstone Medical Inc., 600 Park Office Drive, Suite 140, Research Triangle Park, North Carolina 27709, United States
| | - Kirk L Pappan
- Owlstone Medical Inc., 600 Park Office Drive, Suite 140, Research Triangle Park, North Carolina 27709, United States
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Stanaway IB, Hall TO, Rosenthal EA, Palmer M, Naranbhai V, Knevel R, Namjou-Khales B, Carroll RJ, Kiryluk K, Gordon AS, Linder J, Howell KM, Mapes BM, Lin FTJ, Joo YY, Hayes MG, Gharavi AG, Pendergrass SA, Ritchie MD, de Andrade M, Croteau-Chonka DC, Raychaudhuri S, Weiss ST, Lebo M, Amr SS, Carrell D, Larson EB, Chute CG, Rasmussen-Torvik LJ, Roy-Puckelwartz MJ, Sleiman P, Hakonarson H, Li R, Karlson EW, Peterson JF, Kullo IJ, Chisholm R, Denny JC, Jarvik GP, Crosslin DR. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet Epidemiol 2018; 43:63-81. [PMID: 30298529 PMCID: PMC6375696 DOI: 10.1002/gepi.22167] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/10/2018] [Accepted: 08/28/2018] [Indexed: 12/30/2022]
Abstract
The Electronic Medical Records and Genomics (eMERGE) network is a network of medical centers with electronic medical records linked to existing biorepository samples for genomic discovery and genomic medicine research. The network sought to unify the genetic results from 78 Illumina and Affymetrix genotype array batches from 12 contributing medical centers for joint association analysis of 83,717 human participants. In this report, we describe the imputation of eMERGE results and methods to create the unified imputed merged set of genome‐wide variant genotype data. We imputed the data using the Michigan Imputation Server, which provides a missing single‐nucleotide variant genotype imputation service using the minimac3 imputation algorithm with the Haplotype Reference Consortium genotype reference set. We describe the quality control and filtering steps used in the generation of this data set and suggest generalizable quality thresholds for imputation and phenotype association studies. To test the merged imputed genotype set, we replicated a previously reported chromosome 6 HLA‐B herpes zoster (shingles) association and discovered a novel zoster‐associated loci in an epigenetic binding site near the terminus of chromosome 3 (3p29).
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Affiliation(s)
- Ian B Stanaway
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Taryn O Hall
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Elisabeth A Rosenthal
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Melody Palmer
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Vivek Naranbhai
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington.,Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Rachel Knevel
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Bahram Namjou-Khales
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Robert J Carroll
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Krzysztof Kiryluk
- Department of Medicine, Columbia University, New York City, New York
| | - Adam S Gordon
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Jodell Linder
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Kayla Marie Howell
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Brandy M Mapes
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Frederick T J Lin
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | | | - M Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ali G Gharavi
- Department of Medicine, Columbia University, New York City, New York
| | | | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | - Soumya Raychaudhuri
- Harvard Medical School, Harvard University, Cambridge, Massachusetts.,Program in Medical and Population Genetics, Broad Institute of Massachusetts Technical Institute and Harvard University, Cambridge, Massachusetts
| | - Scott T Weiss
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Matt Lebo
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Sami S Amr
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - David Carrell
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland
| | | | | | - Patrick Sleiman
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | - Rongling Li
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Elizabeth W Karlson
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Josh F Peterson
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | | | - Rex Chisholm
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Joshua Charles Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Gail P Jarvik
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
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- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - David R Crosslin
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
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Lin CH, Lin CH, Chang YC, Huang YJ, Chen PW, Yang HT, Lane HY. Sodium Benzoate, a D-Amino Acid Oxidase Inhibitor, Added to Clozapine for the Treatment of Schizophrenia: A Randomized, Double-Blind, Placebo-Controlled Trial. Biol Psychiatry 2018; 84:422-432. [PMID: 29397899 DOI: 10.1016/j.biopsych.2017.12.006] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 12/06/2017] [Accepted: 12/08/2017] [Indexed: 12/15/2022]
Abstract
BACKGROUND Clozapine is the last-line antipsychotic agent for refractory schizophrenia. To date, there is no convincing evidence for augmentation on clozapine. Activation of N-methyl-D-aspartate receptors, including inhibition of D-amino acid oxidase that may metabolize D-amino acids, has been reported to be beneficial for patients receiving antipsychotics other than clozapine. This study aimed to examine the efficacy and safety of a D-amino acid oxidase inhibitor, sodium benzoate, for schizophrenia patients who had poor response to clozapine. METHODS We conducted a randomized, double-blind, placebo-controlled trial. Sixty schizophrenia inpatients that had been stabilized with clozapine were allocated into three groups for 6 weeks' add-on treatment of 1 g/day sodium benzoate, 2 g/day sodium benzoate, or placebo. The primary outcome measures were Positive and Negative Syndrome Scale (PANSS) total score, Scale for the Assessment of Negative Symptoms, Quality of Life Scale, and Global Assessment of Functioning. Side effects and cognitive functions were also measured. RESULTS Both doses of sodium benzoate produced better improvement than placebo in the Scale for the Assessment of Negative Symptoms. The 2 g/day sodium benzoate also produced better improvement than placebo in PANSS-total score, PANSS-positive score, and Quality of Life Scale. Sodium benzoate was well tolerated without evident side effects. The changes of catalase, an antioxidant, were different among the three groups and correlated with the improvement of PANSS-total score and PANSS-positive score in the sodium benzoate group. CONCLUSIONS Sodium benzoate adjuvant therapy improved symptomatology of patients with clozapine-resistant schizophrenia. Further studies are warranted to elucidate the optimal dose and treatment duration as well as the mechanisms of sodium benzoate for clozapine-resistant schizophrenia.
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Affiliation(s)
- Chieh-Hsin Lin
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan; Center for General Education, Cheng Shiu University, Kaohsiung, Taiwan; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Ching-Hua Lin
- Department of Adult Psychiatry, Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan
| | - Yue-Cune Chang
- Department of Mathematics, Tamkang University, Taipei, Taiwan
| | - Yu-Jhen Huang
- Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan
| | - Po-Wei Chen
- Department of Psychiatry, Taichung Chin-Ho Hospital, Taichung, Taiwan
| | - Hui-Ting Yang
- Department of Nutrition, China Medical University, Taichung, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan.
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