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Möbus L, Serra A, Fratello M, Pavel A, Federico A, Greco D. A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401754. [PMID: 38840452 PMCID: PMC11321629 DOI: 10.1002/advs.202401754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/05/2024] [Indexed: 06/07/2024]
Abstract
The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.
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Affiliation(s)
- Lena Möbus
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
| | - Angela Serra
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Tampere Institute for Advanced StudyTampere UniversityTampere33520Finland
- Division of Pharmaceutical BiosciencesFaculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
| | - Michele Fratello
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
| | - Alisa Pavel
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens Lyngby2800Denmark
| | - Antonio Federico
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Tampere Institute for Advanced StudyTampere UniversityTampere33520Finland
- Division of Pharmaceutical BiosciencesFaculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
| | - Dario Greco
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical BiosciencesFaculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
- Institute of BiotechnologyUniversity of HelsinkiHelsinki00790Finland
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2
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Chatzinikolaou G, Stratigi K, Siametis A, Goulielmaki E, Akalestou-Clocher A, Tsamardinos I, Topalis P, Austin C, Bouwman BA, Crosetto N, Altmüller J, Garinis GA. XPF interacts with TOP2B for R-loop processing and DNA looping on actively transcribed genes. SCIENCE ADVANCES 2023; 9:eadi2095. [PMID: 37939182 PMCID: PMC10631727 DOI: 10.1126/sciadv.adi2095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/05/2023] [Indexed: 11/10/2023]
Abstract
Co-transcriptional RNA-DNA hybrids can not only cause DNA damage threatening genome integrity but also regulate gene activity in a mechanism that remains unclear. Here, we show that the nucleotide excision repair factor XPF interacts with the insulator binding protein CTCF and the cohesin subunits SMC1A and SMC3, leading to R-loop-dependent DNA looping upon transcription activation. To facilitate R-loop processing, XPF interacts and recruits with TOP2B on active gene promoters, leading to double-strand break accumulation and the activation of a DNA damage response. Abrogation of TOP2B leads to the diminished recruitment of XPF, CTCF, and the cohesin subunits to promoters of actively transcribed genes and R-loops and the concurrent impairment of CTCF-mediated DNA looping. Together, our findings disclose an essential role for XPF with TOP2B and the CTCF/cohesin complex in R-loop processing for transcription activation with important ramifications for DNA repair-deficient syndromes associated with transcription-associated DNA damage.
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Affiliation(s)
- Georgia Chatzinikolaou
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, GR70013, Heraklion, Crete, Greece
| | - Kalliopi Stratigi
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, GR70013, Heraklion, Crete, Greece
| | - Athanasios Siametis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, GR70013, Heraklion, Crete, Greece
- Department of Biology, University of Crete, Heraklion, Crete, Greece
| | - Evi Goulielmaki
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, GR70013, Heraklion, Crete, Greece
| | - Alexia Akalestou-Clocher
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, GR70013, Heraklion, Crete, Greece
- Department of Biology, University of Crete, Heraklion, Crete, Greece
| | - Ioannis Tsamardinos
- Computer Science Department of University of Crete, Heraklion, Crete, Greece
| | - Pantelis Topalis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, GR70013, Heraklion, Crete, Greece
| | - Caroline Austin
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Britta A. M. Bouwman
- Division of Microbiology, Tumor and Cell Biology, Karolinska Institutet and Science for Life Laboratory, Stockholm 17177, Sweden
| | - Nicola Crosetto
- Division of Microbiology, Tumor and Cell Biology, Karolinska Institutet and Science for Life Laboratory, Stockholm 17177, Sweden
- Human Technopole, Viale Rita Levi-Montalcini 1, 22157 Milan, Italy
| | - Janine Altmüller
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Core Facility Genomics, Charitéplatz 1, 10117 Berlin, Germany
| | - George A. Garinis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, GR70013, Heraklion, Crete, Greece
- Department of Biology, University of Crete, Heraklion, Crete, Greece
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3
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Sánchez-Valle J, Valencia A. Molecular bases of comorbidities: present and future perspectives. Trends Genet 2023; 39:773-786. [PMID: 37482451 DOI: 10.1016/j.tig.2023.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023]
Abstract
Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.
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Affiliation(s)
- Jon Sánchez-Valle
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain.
| | - Alfonso Valencia
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain; ICREA, Barcelona, 08010, Spain.
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4
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Fu M, Yan Y, Olde Loohuis LM, Chang TS. Defining the distance between diseases using SNOMED CT embeddings. J Biomed Inform 2023; 139:104307. [PMID: 36738869 DOI: 10.1016/j.jbi.2023.104307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/10/2022] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
Characterizing disease relationships is essential to biomedical research to understand disease etiology and improve clinical decision-making. Measurements of distance between disease pairs enable valuable research tasks, such as subgrouping patients and identifying common time courses of disease onset. Distance metrics developed in prior work focused on smaller, targeted disease sets. Distance metrics covering all diseases have not yet been defined, which limits the applications to a broader disease spectrum. Our current study defines disease distances for all disease pairs within the International Classification of Diseases, version 10 (ICD-10), the diagnostic classification system universally used in electronic health records. Our proposed distance is computed based on a biomedical ontology, SNOMED CT (Systemized Nomenclature of Medicine, Clinical Terms), which can also be viewed as a structured knowledge graph. We compared the knowledge graph-based metric to three other distance metrics based on the hierarchical structure of ICD, clinical comorbidity, and genetic correlation, to evaluate how each may capture similar or unique aspects of disease relationships. We show that our knowledge graph-based distance metric captures known phenotypic, clinical, and molecular characteristics at a finer granularity than the other three. With the continued growth of using electronic health records data for research, we believe that our distance metric will play an important role in subgrouping patients for precision health, and enabling individualized disease prevention and treatments.
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Affiliation(s)
- Mingzhou Fu
- Movement Disorders Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA; Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Yu Yan
- Medical Informatics Home Area, Department of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Loes M Olde Loohuis
- Center for Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Timothy S Chang
- Movement Disorders Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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Arowolo O, Salemme V, Suvorov A. Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures. TOXICS 2022; 10:764. [PMID: 36548597 PMCID: PMC9784704 DOI: 10.3390/toxics10120764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/15/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multi-chemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer's disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.
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Affiliation(s)
- Olatunbosun Arowolo
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 686 North Pleasant Street, Amherst, MA 01003, USA
| | - Victoria Salemme
- Department of Pharmacology, University of California, 1275 Med Science, Davis, CA 95616, USA
| | - Alexander Suvorov
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 686 North Pleasant Street, Amherst, MA 01003, USA
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Just Add Data: automated predictive modeling for knowledge discovery and feature selection. NPJ Precis Oncol 2022; 6:38. [PMID: 35710826 PMCID: PMC9203777 DOI: 10.1038/s41698-022-00274-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 04/13/2022] [Indexed: 01/20/2023] Open
Abstract
Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.
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7
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Marshall JL, Peshkin BN, Yoshino T, Vowinckel J, Danielsen HE, Melino G, Tsamardinos I, Haudenschild C, Kerr DJ, Sampaio C, Rha SY, FitzGerald KT, Holland EC, Gallagher D, Garcia-Foncillas J, Juhl H. The Essentials of Multiomics. Oncologist 2022; 27:272-284. [PMID: 35380712 PMCID: PMC8982374 DOI: 10.1093/oncolo/oyab048] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 11/05/2021] [Indexed: 11/13/2022] Open
Abstract
Within the last decade, the science of molecular testing has evolved from single gene and single protein analysis to broad molecular profiling as a standard of care, quickly transitioning from research to practice. Terms such as genomics, transcriptomics, proteomics, circulating omics, and artificial intelligence are now commonplace, and this rapid evolution has left us with a significant knowledge gap within the medical community. In this paper, we attempt to bridge that gap and prepare the physician in oncology for multiomics, a group of technologies that have gone from looming on the horizon to become a clinical reality. The era of multiomics is here, and we must prepare ourselves for this exciting new age of cancer medicine.
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Affiliation(s)
- John L Marshall
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Beth N Peshkin
- Georgetown University, Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | | | | | - Håvard E Danielsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Radiumhospitalet, Montebello, Oslo, Norway
| | - Gerry Melino
- Department of Experimental Medicine, TOR, University of Rome Tor Vergata, Rome, Italy
| | - Ioannis Tsamardinos
- JADBio Gnosis DA, N. Plastira 100, Science and Technology Park of Crete and Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas, Heraklion, GR, Greece
| | | | - David J Kerr
- Nuffield Division of Clinical and Laboratory Sciences, Level 4, Academic Block, John Radcliffe Infirmary, Headington, Oxford, UK
| | | | - Sun Young Rha
- Yonsei Cancer Center, Yonsei University College of Medicine, Seodaemun-Ku, Seoul, Korea
| | - Kevin T FitzGerald
- Department of Medical Humanities in the School of Medicine, Creighton University, Omaha, NE, USA
| | - Eric C Holland
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - David Gallagher
- St. James’s Hospital/Trinity College Dublin, St. Raphael’s House, Dublin, Ireland
| | - Jesus Garcia-Foncillas
- Cancer Institute, Fundacion Jimenez Diaz University Hospital, Autonomous University, Madrid, Spain
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8
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Xiang J, Zhang J, Zhao Y, Wu FX, Li M. Biomedical data, computational methods and tools for evaluating disease-disease associations. Brief Bioinform 2022; 23:6522999. [PMID: 35136949 DOI: 10.1093/bib/bbac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, China
| | - Jiashuai Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, China
| | - Fang-Xiang Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada
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9
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Human microRNA similarity in breast cancer. Biosci Rep 2021; 41:229885. [PMID: 34612484 PMCID: PMC8529337 DOI: 10.1042/bsr20211123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 09/28/2021] [Accepted: 10/04/2021] [Indexed: 11/25/2022] Open
Abstract
MicroRNAs (miRNAs) play important roles in a variety of human diseases, including breast cancer. A number of miRNAs are up- and down-regulated in breast cancer. However, little is known about miRNA similarity and similarity network in breast cancer. Here, a collection of 272 breast cancer-associated miRNA precursors (pre-miRNAs) were utilized to calculate similarities of sequences, target genes, pathways and functions and construct a combined similarity network. Well-characterized miRNAs and their similarity network were highlighted. Interestingly, miRNA sequence-dependent similarity networks were not identified in spite of sequence–target gene association. Similarity networks with minimum and maximum number of miRNAs originate from pathway and mature sequence, respectively. The breast cancer-associated miRNAs were divided into seven functional classes (classes I–VII) followed by disease enrichment analysis and novel miRNA-based disease similarities were found. The finding would provide insight into miRNA similarity, similarity network and disease heterogeneity in breast cancer.
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Malliaraki N, Lakiotaki K, Vamvoukaki R, Notas G, Tsamardinos I, Kampa M, Castanas E. Translating vitamin D transcriptomics to clinical evidence: Analysis of data in asthma and chronic obstructive pulmonary disease, followed by clinical data meta-analysis. J Steroid Biochem Mol Biol 2020; 197:105505. [PMID: 31669573 DOI: 10.1016/j.jsbmb.2019.105505] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 09/29/2019] [Accepted: 10/22/2019] [Indexed: 12/29/2022]
Abstract
Vitamin D (VitD) continues to trigger intense scientific controversy, regarding both its bi ological targets and its supplementation doses and regimens. In an effort to resolve this dispute, we mapped VitD transcriptome-wide events in humans, in order to unveil shared patterns or mechanisms with diverse pathologies/tissue profiles and reveal causal effects between VitD actions and specific human diseases, using a recently developed bioinformatics methodology. Using the similarities in analyzed transcriptome data (c-SKL method), we validated our methodology with osteoporosis as an example and further analyzed two other strong hits, specifically chronic obstructive pulmonary disease (COPD) and asthma. The latter revealed no impact of VitD on known molecular pathways. In accordance to this finding, review and meta-analysis of published data, based on an objective measure (Forced Expiratory Volume at one second, FEV1%) did not further reveal any significant effect of VitD on the objective amelioration of either condition. This study may, therefore, be regarded as the first one to explore, in an objective, unbiased and unsupervised manner, the impact of VitD levels and/or interventions in a number of human pathologies.
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Affiliation(s)
- Niki Malliaraki
- Laboratory of Experimental Endocrinology, University of Crete, School of Medicine, Heraklion, Greece; Laboratory of Clinical Chemistry/Biochemistry, University Hospital, Heraklion, Greece
| | - Kleanthi Lakiotaki
- Department of Computer Science, University of Crete, School of Sciences, Heraklion, Greece
| | - Rodanthi Vamvoukaki
- Laboratory of Experimental Endocrinology, University of Crete, School of Medicine, Heraklion, Greece
| | - George Notas
- Laboratory of Experimental Endocrinology, University of Crete, School of Medicine, Heraklion, Greece
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete, School of Sciences, Heraklion, Greece; Gnosis Data Analysis PC, Heraklion, Greece
| | - Marilena Kampa
- Laboratory of Experimental Endocrinology, University of Crete, School of Medicine, Heraklion, Greece
| | - Elias Castanas
- Laboratory of Experimental Endocrinology, University of Crete, School of Medicine, Heraklion, Greece.
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