1
|
Cheng X, Meng X, Chen R, Song Z, Li S, Wei S, Lv H, Zhang S, Tang H, Jiang Y, Zhang R. The molecular subtypes of autoimmune diseases. Comput Struct Biotechnol J 2024; 23:1348-1363. [PMID: 38596313 PMCID: PMC11001648 DOI: 10.1016/j.csbj.2024.03.026] [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: 11/12/2023] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/11/2024] Open
Abstract
Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.
Collapse
Affiliation(s)
| | | | | | - Zerun Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuai Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuhao Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hao Tang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| |
Collapse
|
2
|
Rispoli MG, D'Apolito M, Pozzilli V, Tomassini V. Lessons from immunotherapies in multiple sclerosis. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:293-311. [PMID: 36803817 DOI: 10.1016/b978-0-323-85555-6.00013-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.
Collapse
Affiliation(s)
- Marianna G Rispoli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Maria D'Apolito
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valeria Pozzilli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valentina Tomassini
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy.
| |
Collapse
|
3
|
Diakou I, Papakonstantinou E, Papageorgiou L, Pierouli K, Dragoumani K, Spandidos DA, Bacopoulou F, Chrousos GP, Goulielmos GΝ, Eliopoulos E, Vlachakis D. Multiple sclerosis and computational biology (Review). Biomed Rep 2022; 17:96. [PMID: 36382258 PMCID: PMC9634047 DOI: 10.3892/br.2022.1579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/27/2022] [Indexed: 12/02/2022] Open
Abstract
Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.
Collapse
Affiliation(s)
- Io Diakou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Eleni Papakonstantinou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Louis Papageorgiou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Katerina Pierouli
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Konstantina Dragoumani
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Demetrios A. Spandidos
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Flora Bacopoulou
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
| | - George P. Chrousos
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
| | - Georges Ν. Goulielmos
- Section of Molecular Pathology and Human Genetics, Department of Internal Medicine, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Elias Eliopoulos
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of The Academy of Athens, 11527 Athens, Greece
| |
Collapse
|
4
|
López-Dorado A, Pérez J, Rodrigo M, Miguel-Jiménez J, Ortiz M, de Santiago L, López-Guillén E, Blanco R, Cavalliere C, Morla EMS, Boquete L, Garcia-Martin E. Diagnosis of multiple sclerosis using multifocal ERG data feature fusion. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2021; 76:157-167. [PMID: 34867127 PMCID: PMC8475498 DOI: 10.1016/j.inffus.2021.05.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 11/15/2020] [Accepted: 05/17/2021] [Indexed: 05/16/2023]
Abstract
The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI-port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.
Collapse
Affiliation(s)
- A. López-Dorado
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - J. Pérez
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
| | - M.J. Rodrigo
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - J.M. Miguel-Jiménez
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - M. Ortiz
- School of Physics, University of Melbourne, VIC 3010, Australia
| | - L. de Santiago
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - E. López-Guillén
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - R. Blanco
- Department of Surgery, Medical and Social Sciences, University of Alcalá, Alcalá de Henares, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - C. Cavalliere
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - E. Mª Sánchez Morla
- Department of Psychiatry, Hospital 12 de Octubre Research Institute (i+12), 28041 Madrid, Spain
- Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
- CIBERSAM: Biomedical Research Networking Centre in Mental Health, 28029 Madrid, Spain
| | - L. Boquete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - E. Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| |
Collapse
|
5
|
Jafari A, Babajani A, Rezaei-Tavirani M. Multiple Sclerosis Biomarker Discoveries by Proteomics and Metabolomics Approaches. Biomark Insights 2021; 16:11772719211013352. [PMID: 34017167 PMCID: PMC8114757 DOI: 10.1177/11772719211013352] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 04/05/2021] [Indexed: 12/22/2022] Open
Abstract
Multiple sclerosis (MS) is an autoimmune inflammatory disorder of the central nervous system (CNS) resulting in demyelination and axonal loss in the brain and spinal cord. The precise pathogenesis and etiology of this complex disease are still a mystery. Despite many studies that have been aimed to identify biomarkers, no protein marker has yet been approved for MS. There is urgently needed for biomarkers, which could clarify pathology, monitor disease progression, response to treatment, and prognosis in MS. Proteomics and metabolomics analysis are powerful tools to identify putative and novel candidate biomarkers. Different human compartments analysis using proteomics, metabolomics, and bioinformatics approaches has generated new information for further clarification of MS pathology, elucidating the mechanisms of the disease, finding new targets, and monitoring treatment response. Overall, omics approaches can develop different therapeutic and diagnostic aspects of complex disorders such as multiple sclerosis, from biomarker discovery to personalized medicine.
Collapse
Affiliation(s)
- Ameneh Jafari
- Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amirhesam Babajani
- Department of Pharmacology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | |
Collapse
|
6
|
Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital Twins for Multiple Sclerosis. Front Immunol 2021; 12:669811. [PMID: 34012452 PMCID: PMC8128142 DOI: 10.3389/fimmu.2021.669811] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/16/2021] [Indexed: 12/16/2022] Open
Abstract
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.
Collapse
Affiliation(s)
| | | | | | | | | | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
| |
Collapse
|
7
|
Cuccaro ML, Manrique CP, Quintero MA, Martinez R, McCauley JL. Understanding Participation in Genetic Research Among Patients With Multiple Sclerosis: The Influences of Ethnicity, Gender, Education, and Age. Front Genet 2020; 11:120. [PMID: 32231680 PMCID: PMC7082924 DOI: 10.3389/fgene.2020.00120] [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: 10/12/2018] [Accepted: 01/31/2020] [Indexed: 11/13/2022] Open
Abstract
This study examined reasons for participation in a genetic study of risk for multiple sclerosis (MS). Our sample consisted of 101 patients diagnosed with MS who were approached about enrolling in the Multiple Sclerosis Genetic Susceptibility Study. Participants were predominantly Hispanic (80%), female (80%), and well educated (71%), having at least some level of college education. Of these 101 individuals who were approached, 95 agreed to participate and are the focus of this report. Among enrollees, the most frequently cited reasons for participation were to find a cure for MS (56%), having MS (46%), and helping future generations (37%). Regression models comparing ethnic groups, Hispanics endorsed having MS as a reason to participate significantly more frequently than non-Hispanics (HI 52%, non-HI 19%, p = 0.015) while non-Hispanics endorsed finding new and better treatments significantly more frequently than Hispanics (Hispanic 17%, non-Hispanic 50%, p = 0.003). Among our three age groups, younger individuals endorsed finding a cure for MS significantly more frequently (74% of 18-35-year olds vs. 56% of 36-55 year olds vs. 39% of >55 year olds). Our results suggest that motivations for participation in genetic research vary by ethnicity, and that these influences need to be considered in developing more inclusive programs of disease-related genetic research. Future efforts should focus on development of standard methods for understanding participation in genetic and genomic research, especially among underrepresented groups as a catalyst for engaging all populations.
Collapse
Affiliation(s)
- Michael L Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States.,Dr. John T. Macdonald Foundation, Department of Human Genetics and Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Clara P Manrique
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Maria A Quintero
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Ricardo Martinez
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Jacob L McCauley
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States.,Dr. John T. Macdonald Foundation, Department of Human Genetics and Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| |
Collapse
|