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Garcia-Gaona E, García-Gregorio A, García-Jiménez C, López-Olaiz MA, Mendoza-Ramírez P, Fernandez-Guzman D, Pillado-Sánchez RA, Soto-Pacheco AD, Yareni-Zuñiga L, Sánchez-Parada MG, González-Santiago AE, Román-Pintos LM, Castañeda-Arellano R, Hernández-Ortega LD, Mercado-Sesma AR, Orozco-Luna FDJ, Villa-Angulo C, Villa-Angulo R, Baptista-Rosas RC. mtDNA Single-Nucleotide Variants Associated with Type 2 Diabetes. Curr Issues Mol Biol 2023; 45:8716-8732. [PMID: 37998725 PMCID: PMC10670651 DOI: 10.3390/cimb45110548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/25/2023] Open
Abstract
Type 2 diabetes (T2D) is a chronic systemic disease with a complex etiology, characterized by insulin resistance and mitochondrial dysfunction in various cell tissues. To explore this relationship, we conducted a secondary analysis of complete mtDNA sequences from 1261 T2D patients and 1105 control individuals. Our findings revealed significant associations between certain single-nucleotide polymorphisms (SNPs) and T2D. Notably, the variants m.1438A>G (rs2001030) (controls: 32 [27.6%], T2D: 84 [72.4%]; OR: 2.46; 95%CI: 1.64-3.78; p < 0.001), m.14766C>T (rs193302980) (controls: 498 [36.9%], T2D: 853 [63.1%]; OR: 2.57, 95%CI: 2.18-3.04, p < 0.001), and m.16519T>C (rs3937033) (controls: 363 [43.4%], T2D: 474 [56.6%]; OR: 1.24, 95%CI: 1.05-1.47, p = 0.012) were significantly associated with the likelihood of developing diabetes. The variant m.16189T>C (rs28693675), which has been previously documented in several studies across diverse populations, showed no association with T2D in our analysis (controls: 148 [13.39] T2D: 171 [13.56%]; OR: 1.03; 95%CI: 0.815-1.31; p = 0.83). These results provide evidence suggesting a link between specific mtDNA polymorphisms and T2D, possibly related to association rules, topological patterns, and three-dimensional conformations associated with regions where changes occur, rather than specific point mutations in the sequence.
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Affiliation(s)
- Enrique Garcia-Gaona
- Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Puebla 72420, Mexico;
| | - Alhelí García-Gregorio
- Facultad de Enfermería Región Poza Rica-Tuxpan, Universidad Veracruzana, Veracruz 91700, Mexico;
| | - Camila García-Jiménez
- Facultad de Ciencias Médicas y Biológicas “Dr. Ignacio Chávez”, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58000, Mexico;
| | | | - Paola Mendoza-Ramírez
- Facultad de Ciencias Biológicas, Benemérita Universidad Autónoma de Puebla, Puebla 72420, Mexico;
| | | | | | - Axel David Soto-Pacheco
- Facultad de Medicina Extensión Los Mochis, Universidad Autónoma de Sinaloa, Sinaloa 81223, Mexico; (R.A.P.-S.); (A.D.S.-P.)
| | - Laura Yareni-Zuñiga
- Departamento de Ciencias de la Salud-Enfermedad como Proceso Individual, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (L.Y.-Z.); (L.M.R.-P.); (A.R.M.-S.)
| | - María Guadalupe Sánchez-Parada
- Departamento de Ciencias Biomédicas, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (M.G.S.-P.); (A.E.G.-S.); (R.C.-A.); (L.D.H.-O.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Ana Elizabeth González-Santiago
- Departamento de Ciencias Biomédicas, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (M.G.S.-P.); (A.E.G.-S.); (R.C.-A.); (L.D.H.-O.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Luis Miguel Román-Pintos
- Departamento de Ciencias de la Salud-Enfermedad como Proceso Individual, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (L.Y.-Z.); (L.M.R.-P.); (A.R.M.-S.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Rolando Castañeda-Arellano
- Departamento de Ciencias Biomédicas, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (M.G.S.-P.); (A.E.G.-S.); (R.C.-A.); (L.D.H.-O.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Luis Daniel Hernández-Ortega
- Departamento de Ciencias Biomédicas, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (M.G.S.-P.); (A.E.G.-S.); (R.C.-A.); (L.D.H.-O.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Arieh Roldán Mercado-Sesma
- Departamento de Ciencias de la Salud-Enfermedad como Proceso Individual, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (L.Y.-Z.); (L.M.R.-P.); (A.R.M.-S.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | | | - Carlos Villa-Angulo
- Laboratorio de Bioinformática y Biofotónica, Instituto de Ingeniería Universidad Autónoma de Baja California, Mexicali 21100, Mexico; (C.V.-A.); (R.V.-A.)
| | - Rafael Villa-Angulo
- Laboratorio de Bioinformática y Biofotónica, Instituto de Ingeniería Universidad Autónoma de Baja California, Mexicali 21100, Mexico; (C.V.-A.); (R.V.-A.)
| | - Raúl C. Baptista-Rosas
- Departamento de Ciencias de la Salud-Enfermedad como Proceso Individual, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (L.Y.-Z.); (L.M.R.-P.); (A.R.M.-S.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
- Hospital General de Occidente, Secretaría de Salud Jalisco, Zapopan 45170, Mexico
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Flora J, Khan W, Jin J, Jin D, Hussain A, Dajani K, Khan B. Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines. Int J Mol Sci 2022; 23:ijms23158235. [PMID: 35897804 PMCID: PMC9368306 DOI: 10.3390/ijms23158235] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/06/2022] [Accepted: 07/21/2022] [Indexed: 02/04/2023] Open
Abstract
Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted >50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).
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Affiliation(s)
- James Flora
- Department of Computer Science and Engineering, California State University San Bernardino, 5500 University Parkway, San Bernardino, CA 92407, USA; (J.F.); (J.J.); (K.D.)
| | - Wasiq Khan
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK;
| | - Jennifer Jin
- Department of Computer Science and Engineering, California State University San Bernardino, 5500 University Parkway, San Bernardino, CA 92407, USA; (J.F.); (J.J.); (K.D.)
| | - Daniel Jin
- Division of Vascular & Interventional Radiology, Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA 92354, USA;
| | - Abir Hussain
- Department of Electrical Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates;
| | - Khalil Dajani
- Department of Computer Science and Engineering, California State University San Bernardino, 5500 University Parkway, San Bernardino, CA 92407, USA; (J.F.); (J.J.); (K.D.)
| | - Bilal Khan
- Department of Computer Science and Engineering, California State University San Bernardino, 5500 University Parkway, San Bernardino, CA 92407, USA; (J.F.); (J.J.); (K.D.)
- Institute of the Environment and Sustainability, University of California Los Angeles, Los Angeles, CA 90095, USA
- Correspondence: ; Tel.: +1-(909)-537-5428
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An atlas of evidence-based phenotypic associations across the mouse phenome. Sci Rep 2020; 10:3957. [PMID: 32127602 PMCID: PMC7054260 DOI: 10.1038/s41598-020-60891-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 02/17/2020] [Indexed: 01/18/2023] Open
Abstract
To date, reliable relationships between mammalian phenotypes, based on diagnostic test measurements, have not been reported on a large scale. The purpose of this study was to present a large mouse phenotype-phenotype relationships dataset as a reference resource, alongside detailed evaluation of the resource. We used bias-minimized comprehensive mouse phenotype data and applied association rule mining to a dataset consisting of only binary (normal and abnormal phenotypes) data to determine relationships among phenotypes. We present 3,686 evidence-based significant associations, comprising 345 phenotypes covering 60 biological systems (functions), and evaluate their characteristics in detail. To evaluate the relationships, we defined a set of phenotype-phenotype association pairs (PPAPs) as a module of phenotypic expression for each of the 345 phenotypes. By analyzing each PPAP, we identified phenotype sub-networks consisting of the largest numbers of phenotypes and distinct biological systems. Furthermore, using hierarchical clustering based on phenotype similarities among the 345 PPAPs, we identified seven community types within a putative phenome-wide association network. Moreover, to promote leverage of these data, we developed and published web-application tools. These mouse phenome-wide phenotype-phenotype association data reveal general principles of relationships among mammalian phenotypes and provide a reference resource for biomedical analyses.
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Collier N, Groza T, Smedley D, Robinson PN, Oellrich A, Rebholz-Schuhmann D. PhenoMiner: from text to a database of phenotypes associated with OMIM diseases. Database (Oxford) 2015; 2015:bav104. [PMID: 26507285 PMCID: PMC4622021 DOI: 10.1093/database/bav104] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Revised: 07/24/2015] [Accepted: 08/27/2015] [Indexed: 11/27/2022]
Abstract
Analysis of scientific and clinical phenotypes reported in the experimental literature has been curated manually to build high-quality databases such as the Online Mendelian Inheritance in Man (OMIM). However, the identification and harmonization of phenotype descriptions struggles with the diversity of human expressivity. We introduce a novel automated extraction approach called PhenoMiner that exploits full parsing and conceptual analysis. Apriori association mining is then used to identify relationships to human diseases. We applied PhenoMiner to the BMC open access collection and identified 13,636 phenotype candidates. We identified 28,155 phenotype-disorder hypotheses covering 4898 phenotypes and 1659 Mendelian disorders. Analysis showed: (i) the semantic distribution of the extracted terms against linked ontologies; (ii) a comparison of term overlap with the Human Phenotype Ontology (HP); (iii) moderate support for phenotype-disorder pairs in both OMIM and the literature; (iv) strong associations of phenotype-disorder pairs to known disease-genes pairs using PhenoDigm. The full list of PhenoMiner phenotypes (S1), phenotype-disorder associations (S2), association-filtered linked data (S3) and user database documentation (S5) is available as supplementary data and can be downloaded at http://github.com/nhcollier/PhenoMiner under a Creative Commons Attribution 4.0 license. Database URL: phenominer.mml.cam.ac.uk.
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Affiliation(s)
- Nigel Collier
- The University of Cambridge, Cambridge, CB3 9DB, UK, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK,
| | - Tudor Groza
- Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia
| | - Damian Smedley
- Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Peter N Robinson
- Institute for Medical Genetics and Human Genetics, Charité-Universitatsmedizin Berlin, 13353 Berlin, Germany and
| | - Anika Oellrich
- Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
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