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Manos T, Antonopoulos CG, Batista AM, Iarosz KC. Editorial: Advancing our understanding of the impact of dynamics at different spatiotemporal scales and structure on brain synchronous activity, volume II. Front Comput Neurosci 2024; 18:1386652. [PMID: 38504873 PMCID: PMC10948415 DOI: 10.3389/fncom.2024.1386652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024] Open
Affiliation(s)
- Thanos Manos
- ETIS Lab, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
| | - Chris G. Antonopoulos
- School of Mathematics, Statistics and Actuarial Science, University of Essex, Wivenhoe Park, United Kingdom
| | - Antonio M. Batista
- Department of Mathematics and Statistics, State University of Ponta Grossa, Ponta Grossa, Brazil
- Science Graduated Program, State University of Ponta Grossa, Ponta Grossa, Brazil
- Institute of Physics, University of São Paulo, São Paulo, Brazil
| | - Kelly C. Iarosz
- Science Graduated Program, State University of Ponta Grossa, Ponta Grossa, Brazil
- Institute of Physics, University of São Paulo, São Paulo, Brazil
- Exact Sciences, Natural and Engineering, University Center, Telêmaco Borba, Brazil
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2
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Shao L, Zhao Y, Heinrich M, Prieto-Garcia JM, Manzoni C. Active natural compounds perturb the melanoma risk-gene network. G3 (BETHESDA, MD.) 2024; 14:jkad274. [PMID: 38035793 PMCID: PMC10849364 DOI: 10.1093/g3journal/jkad274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/27/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023]
Abstract
Cutaneous melanoma is an aggressive type of skin cancer with a complex genetic landscape caused by the malignant transformation of melanocytes. This study aimed at providing an in silico network model based on the systematic profiling of the melanoma-associated genes considering germline mutations, somatic mutations, and genome-wide association study signals accounting for a total of 232 unique melanoma risk genes. A protein-protein interaction network was constructed using the melanoma risk genes as seeds and evaluated to describe the functional landscape in which the melanoma genes operate within the cellular milieu. Not only were the majority of the melanoma risk genes able to interact with each other at the protein level within the core of the network, but this showed significant enrichment for genes whose expression is altered in human melanoma specimens. Functional annotation showed the melanoma risk network to be significantly associated with processes related to DNA metabolism and telomeres, DNA damage and repair, cellular ageing, and response to radiation. We further explored whether the melanoma risk network could be used as an in silico tool to predict the efficacy of anti-melanoma phytochemicals, that are considered active molecules with potentially less systemic toxicity than classical cytotoxic drugs. A significant portion of the melanoma risk network showed differential expression when SK-MEL-28 human melanoma cells were exposed to the phytochemicals harmine and berberine chloride. This reinforced our hypothesis that the network modeling approach not only provides an alternative way to identify molecular pathways relevant to disease but it may also represent an alternative screening approach to prioritize potentially active compounds.
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Affiliation(s)
- Luying Shao
- Department of Pharmaceutical and Biological Chemistry, UCL School of Pharmacy, WC1N 1AX London, UK
| | - Yibo Zhao
- Department of Pharmacology, UCL School of Pharmacy, WC1N 1AX London, UK
| | - Michael Heinrich
- Department of Pharmaceutical and Biological Chemistry, UCL School of Pharmacy, WC1N 1AX London, UK
- Chinese Medicine Research Center, and Department of Pharmaceutical Sciences and Chinese Medicine Resources, College of Chinese Medicine, China Medical University, Taichung City 404333, Taiwan
| | - Jose M Prieto-Garcia
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, L3 3AF Liverpool, UK
| | - Claudia Manzoni
- Department of Pharmacology, UCL School of Pharmacy, WC1N 1AX London, UK
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Gupta NS, Kumar P. Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Comput Biol Med 2023; 162:107051. [PMID: 37271113 DOI: 10.1016/j.compbiomed.2023.107051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 06/06/2023]
Abstract
Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.
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Affiliation(s)
- Nancy Sanjay Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
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Ginsberg SD, Sharma S, Norton L, Chiosis G. Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes. Trends Pharmacol Sci 2023; 44:20-33. [PMID: 36414432 PMCID: PMC9789192 DOI: 10.1016/j.tips.2022.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/21/2022]
Abstract
Diseases are manifestations of complex changes in protein-protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes - pathological entities that enable dysfunctional rewiring of PPI networks - as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders.
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Affiliation(s)
- Stephen D Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA; NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Sahil Sharma
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY 10065, USA
| | - Larry Norton
- Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Gabriela Chiosis
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY 10065, USA; Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
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5
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Alberio T, Brughera M, Lualdi M. Current Insights on Neurodegeneration by the Italian Proteomics Community. Biomedicines 2022; 10:biomedicines10092297. [PMID: 36140397 PMCID: PMC9496271 DOI: 10.3390/biomedicines10092297] [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: 07/26/2022] [Revised: 08/29/2022] [Accepted: 09/13/2022] [Indexed: 12/02/2022] Open
Abstract
The growing number of patients affected by neurodegenerative disorders represents a huge problem for healthcare systems, human society, and economics. In this context, omics strategies are crucial for the identification of molecular factors involved in disease pathobiology, and for the discovery of biomarkers that allow early diagnosis, patients’ stratification, and treatment response prediction. The integration of different omics data is a required step towards the goal of personalized medicine. The Italian proteomics community is actively developing and applying proteomics approaches to the study of neurodegenerative disorders; moreover, it is leading the mitochondria-focused initiative of the Human Proteome Project, which is particularly important given the central role of mitochondrial impairment in neurodegeneration. Here, we describe how Italian research groups in proteomics have contributed to the knowledge of many neurodegenerative diseases, through the elucidation of the pathobiology of these disorders, and through the discovery of disease biomarkers. In particular, we focus on the central role of post-translational modifications analysis, the implementation of network-based approaches in functional proteomics, the integration of different omics in a systems biology view, and the development of novel platforms for biomarker discovery for the high-throughput quantification of thousands of proteins at a time.
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Koçoğlu C, Ferrari R, Roes M, Vandeweyer G, Kooy RF, van Broeckhoven C, Manzoni C, van der Zee J. Protein interaction network analysis reveals genetic enrichment of immune system genes in frontotemporal dementia. Neurobiol Aging 2022; 116:67-79. [DOI: 10.1016/j.neurobiolaging.2022.03.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/09/2022] [Accepted: 03/31/2022] [Indexed: 12/12/2022]
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Vavouraki N, Tomkins JE, Kara E, Houlden H, Hardy J, Tindall MJ, Lewis PA, Manzoni C. Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias. iScience 2021; 24:102484. [PMID: 34113825 PMCID: PMC8169945 DOI: 10.1016/j.isci.2021.102484] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 04/01/2021] [Accepted: 04/23/2021] [Indexed: 12/14/2022] Open
Abstract
The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Owing to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human data were used to create a protein-protein interaction network based on the causative genes. Network evaluation as a combination of topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes, such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, indicating that there is scope to further classify conditions currently described under the same umbrella-term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.
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Affiliation(s)
- Nikoleta Vavouraki
- School of Pharmacy, University of Reading, Reading, RG6 6AX, UK
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX, UK
| | | | - Eleanna Kara
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Henry Houlden
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - John Hardy
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL and Department of Neurodegenerative Disease, UCL IoN, UCL London, W1T 7NF UK
- Reta Lila Weston Institute, UCL IoN, 1 Wakefield Street, London, WC1N 1PJ, UK
- UCL Movement Disorders Centre, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Marcus J. Tindall
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX, UK
- Institute of Cardiovascular and Metabolic Research, University of Reading, Reading, RG6 6AS, UK
| | - Patrick A. Lewis
- School of Pharmacy, University of Reading, Reading, RG6 6AX, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Comparative Biomedical Sciences, Royal Veterinary College, London, NW1 0TU, UK
| | - Claudia Manzoni
- School of Pharmacy, University of Reading, Reading, RG6 6AX, UK
- School of Pharmacy, University College London, London, WC1N 1AX, UK
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Advances in protein-protein interaction network analysis for Parkinson's disease. Neurobiol Dis 2021; 155:105395. [PMID: 34022367 DOI: 10.1016/j.nbd.2021.105395] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/12/2021] [Accepted: 05/14/2021] [Indexed: 02/08/2023] Open
Abstract
Protein-protein interactions (PPIs) are a key component of the subcellular molecular networks which enable cells to function. Due to their importance in homeostasis, alterations to the networks can be detrimental, leading to cellular dysfunction and ultimately disease states. Parkinson's disease (PD) is a progressive neurodegenerative condition with multifactorial aetiology, spanning genetic variation and environmental modifiers. At a molecular and systems level, the characterisation of PD is the focus of extensive research, largely due to an unmet need for disease modifying therapies. PPI network analysis approaches are a valuable strategy to accelerate our understanding of the molecular crosstalk and biological processes underlying PD pathogenesis, especially due to the complex nature of this disease. In this review, we describe the utility of PPI network approaches in modelling complex systems, focusing on previous work in PD research. We discuss four principal strategies for using PPI network approaches: to infer PD related cellular functions, pathways and novel genes; to support genomics studies; to study the interactome of single PD related genes; and to compare the molecular basis of PD to other neurodegenerative disorders. This is an evolving area of research which is likely to further expand as omics data generation and availability increase. These approaches complement and bridge-the-gap between genetics and functional research to inform future investigations. In this review we outline several limitations that require consideration, acknowledging that ongoing challenges in this field continue to be addressed and the refinement of these approaches will facilitate further advances using PPI network analysis for understanding complex diseases.
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Termine A, Fabrizio C, Strafella C, Caputo V, Petrosini L, Caltagirone C, Giardina E, Cascella R. Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence. J Pers Med 2021; 11:280. [PMID: 33917161 PMCID: PMC8067806 DOI: 10.3390/jpm11040280] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/05/2021] [Accepted: 04/06/2021] [Indexed: 12/13/2022] Open
Abstract
In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases through the integration of all sources of biomedical data.
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Affiliation(s)
- Andrea Termine
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
| | - Carlo Fabrizio
- IRCCS Santa Lucia Foundation, Laboratory of Experimental and Behavioral Neurophysiology, 00143 Rome, Italy; (C.F.); (L.P.)
| | - Claudia Strafella
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Valerio Caputo
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Laura Petrosini
- IRCCS Santa Lucia Foundation, Laboratory of Experimental and Behavioral Neurophysiology, 00143 Rome, Italy; (C.F.); (L.P.)
| | - Carlo Caltagirone
- IRCCS Santa Lucia Foundation, Department of Clinical and Behavioral Neurology, 00179 Rome, Italy;
| | - Emiliano Giardina
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- UILDM Lazio ONLUS Foundation, Department of Biomedicine and Prevention, Tor Vergata University, 00133 Rome, Italy
| | - Raffaella Cascella
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedical Sciences, Catholic University Our Lady of Good Counsel, 1000 Tirana, Albania
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Ruffini N, Klingenberg S, Schweiger S, Gerber S. Common Factors in Neurodegeneration: A Meta-Study Revealing Shared Patterns on a Multi-Omics Scale. Cells 2020; 9:E2642. [PMID: 33302607 PMCID: PMC7764447 DOI: 10.3390/cells9122642] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/24/2020] [Accepted: 12/04/2020] [Indexed: 02/06/2023] Open
Abstract
Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) are heterogeneous, progressive diseases with frequently overlapping symptoms characterized by a loss of neurons. Studies have suggested relations between neurodegenerative diseases for many years (e.g., regarding the aggregation of toxic proteins or triggering endogenous cell death pathways). We gathered publicly available genomic, transcriptomic, and proteomic data from 177 studies and more than one million patients to detect shared genetic patterns between the neurodegenerative diseases on three analyzed omics-layers. The results show a remarkably high number of shared differentially expressed genes between the transcriptomic and proteomic levels for all conditions, while showing a significant relation between genomic and proteomic data between AD and PD and AD and ALS. We identified a set of 139 genes being differentially expressed in several transcriptomic experiments of all four diseases. These 139 genes showed overrepresented gene ontology (GO) Terms involved in the development of neurodegeneration, such as response to heat and hypoxia, positive regulation of cytokines and angiogenesis, and RNA catabolic process. Furthermore, the four analyzed neurodegenerative diseases (NDDs) were clustered by their mean direction of regulation throughout all transcriptomic studies for this set of 139 genes, with the closest relation regarding this common gene set seen between AD and HD. GO-Term and pathway analysis of the proteomic overlap led to biological processes (BPs), related to protein folding and humoral immune response. Taken together, we could confirm the existence of many relations between Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis on transcriptomic and proteomic levels by analyzing the pathways and GO-Terms arising in these intersections. The significance of the connection and the striking relation of the results to processes leading to neurodegeneration between the transcriptomic and proteomic data for all four analyzed neurodegenerative diseases showed that exploring many studies simultaneously, including multiple omics-layers of different neurodegenerative diseases simultaneously, holds new relevant insights that do not emerge from analyzing these data separately. Furthermore, the results shed light on processes like the humoral immune response that have previously been described only for certain diseases. Our data therefore suggest human patients with neurodegenerative diseases should be addressed as complex biological systems by integrating multiple underlying data sources.
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Affiliation(s)
- Nicolas Ruffini
- Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; (N.R.); (S.K.); (S.S.)
- Leibniz Institute for Resilience Research, Leibniz Association, Wallstraße 7, 55122 Mainz, Germany
| | - Susanne Klingenberg
- Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; (N.R.); (S.K.); (S.S.)
| | - Susann Schweiger
- Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; (N.R.); (S.K.); (S.S.)
| | - Susanne Gerber
- Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; (N.R.); (S.K.); (S.S.)
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