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Vankwani S, Mirza MR, Awan FR, Zafar M, Nawrocki A, Wasim M, Khan HN, Ayesha H, Larsen MR, Choudhary MI. Label free quantitative proteomic profiling of serum samples of intellectually disabled young patients revealed dysregulation of complement coagulation and cholesterol cascade systems. Metab Brain Dis 2024:10.1007/s11011-024-01351-6. [PMID: 38733546 DOI: 10.1007/s11011-024-01351-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 05/05/2024] [Indexed: 05/13/2024]
Abstract
Intellectual disability is a heterogeneous disorder, diagnosed using intelligence quotient (IQ) score criteria. Currently, no specific clinical test is available to diagnose the disease and its subgroups due to inadequate understanding of the pathophysiology. Therefore, current study was designed to explore the molecular mechanisms involved in disease perturbation, and to identify potential biomarkers for disease diagnosis and prognosis. A total of 250 participants were enrolled in this study, including 200 intellectually disabled (ID) subjects from the subgroups (mild, moderate, and severe) with age and gender matched healthy controls (n = 50). Initially, IQ testing score and biochemical profile of each subject was generated, followed by label-free quantitative proteomics of subgroups of IQ and healthy control group through nano-LC/MS- mass spectrometry. A total of 310 proteins were identified, among them198 proteins were common among all groups. Statistical analysis (ANOVA) of the subgroups of ID showed 142 differentially expressed proteins, in comparison to healthy control group. From these, 120 proteins were found to be common among all subgroups. The remaining 22 proteins were categorized as exclusive proteins found only in disease subgroups. Furthermore, the hierarchical cluster analysis (HCL) of common significant proteins was also performed, followed by PANTHER protein classification and GO functional enrichment analysis. Results provides that the datasets of differentially expressed proteins, belong to the categories of immune / defense proteins, transfer carrier proteins, apolipoproteins, complement proteins, protease inhibitors, hemoglobin proteins etc., they are known to involvein immune system, and complement and coagulation pathway cascade and cholesterol metabolism pathway. Exclusively expressed 22 proteins were found to be disease stage specific and strong PPI network specifically those that have significant role in platelets activation and degranulation, such as Filamin A (FLNA). Furthermore, to validate the mass spectrometric findings, four highly significant proteins (APOA4, SAP, FLNA, and SERPING) were quantified by ELISA in all the study subjects. AUROC analysis showed a significant association of APOA4 (0.830), FLNA (0.958), SAP (0.754) and SERPING (0.600) with the disease. Apolipoprotein A4 (APOA4) has a significant role in cholesterol transport, and in modulation of glucose and lipid metabolism in the CNS. Similarly, FLNA has a crucial role in the nervous system, especially in the functioning of synaptic network. Therefore, both APOA4, and FLNA proteins represent good potential for candidate biomarkers for the diagnosis and prognosis of the intellectual disability. Overall, serum proteome of ID patients provides valuable information of proteins/pathways that are altered during ID progression.
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Affiliation(s)
- Soma Vankwani
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Munazza Raza Mirza
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
| | - Fazli Rabbi Awan
- Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Jhang Road, P.O. Box. 577, Faisalabad, Pakistan.
| | - Muneeza Zafar
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
- Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Jhang Road, P.O. Box. 577, Faisalabad, Pakistan
| | - Arkadiusz Nawrocki
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Muhammad Wasim
- Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Jhang Road, P.O. Box. 577, Faisalabad, Pakistan
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Pakistan
| | - Haq Nawaz Khan
- Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Jhang Road, P.O. Box. 577, Faisalabad, Pakistan
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Pakistan
| | - Hina Ayesha
- Department of Pediatrics, Punjab Medical College, Allied & DHQ Hospitals, Faisalabad Medical University, Faisalabad, Pakistan
| | - Martin Rossel Larsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Muhammad Iqbal Choudhary
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
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Ramanathan S, Brilot F, Irani SR, Dale RC. Origins and immunopathogenesis of autoimmune central nervous system disorders. Nat Rev Neurol 2023; 19:172-190. [PMID: 36788293 DOI: 10.1038/s41582-023-00776-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2023] [Indexed: 02/16/2023]
Abstract
The field of autoimmune neurology is rapidly evolving, and recent discoveries have advanced our understanding of disease aetiologies. In this article, we review the key pathogenic mechanisms underlying the development of CNS autoimmunity. First, we review non-modifiable risk factors, such as age, sex and ethnicity, as well as genetic factors such as monogenic variants, common variants in vulnerability genes and emerging HLA associations. Second, we highlight how interactions between environmental factors and epigenetics can modify disease onset and severity. Third, we review possible disease mechanisms underlying triggers that are associated with the loss of immune tolerance with consequent recognition of self-antigens; these triggers include infections, tumours and immune-checkpoint inhibitor therapies. Fourth, we outline how advances in our understanding of the anatomy of lymphatic drainage and neuroimmune interfaces are challenging long-held notions of CNS immune privilege, with direct relevance to CNS autoimmunity, and how disruption of B cell and T cell tolerance and the passage of immune cells between the peripheral and intrathecal compartments have key roles in initiating disease activity. Last, we consider novel therapeutic approaches based on our knowledge of the immunopathogenesis of autoimmune CNS disorders.
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Affiliation(s)
- Sudarshini Ramanathan
- Translational Neuroimmunology Group, Kids Neuroscience Centre, Children's Hospital at Westmead, Sydney, New South Wales, Australia
- Sydney Medical School, Faculty of Medicine and Health and Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia
- Department of Neurology, Concord Hospital, Sydney, New South Wales, Australia
| | - Fabienne Brilot
- Translational Neuroimmunology Group, Kids Neuroscience Centre, Children's Hospital at Westmead, Sydney, New South Wales, Australia
- School of Medical Science, Faculty of Medicine and Health and Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Sarosh R Irani
- Oxford Autoimmune Neurology Group, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Russell C Dale
- Translational Neuroimmunology Group, Kids Neuroscience Centre, Children's Hospital at Westmead, Sydney, New South Wales, Australia.
- Sydney Medical School, Faculty of Medicine and Health and Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia.
- TY Nelson Department of Paediatric Neurology, Children's Hospital Westmead, Sydney, New South Wales, Australia.
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CNV Detection from Exome Sequencing Data in Routine Diagnostics of Rare Genetic Disorders: Opportunities and Limitations. Genes (Basel) 2021; 12:genes12091427. [PMID: 34573409 PMCID: PMC8472439 DOI: 10.3390/genes12091427] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/08/2021] [Accepted: 09/09/2021] [Indexed: 12/15/2022] Open
Abstract
To assess the potential of detecting copy number variations (CNVs) directly from exome sequencing (ES) data in diagnostic settings, we developed a CNV-detection pipeline based on ExomeDepth software and applied it to ES data of 450 individuals. Initially, only CNVs affecting genes in the requested diagnostic gene panels were scored and tested against arrayCGH results. Pathogenic CNVs were detected in 18 individuals. Most detected CNVs were larger than 400 kb (11/18), but three individuals had small CNVs impacting one or a few exons only and were thus not detectable by arrayCGH. Conversely, two pathogenic CNVs were initially missed, as they impacted genes not included in the original gene panel analysed, and a third one was missed as it was in a poorly covered region. The overall combined diagnostic rate (SNVs + CNVs) in our cohort was 36%, with wide differences between clinical domains. We conclude that (1) the ES-based CNV pipeline detects efficiently large and small pathogenic CNVs, (2) the detection of CNV relies on uniformity of sequencing and good coverage, and (3) in patients who remain unsolved by the gene panel analysis, CNV analysis should be extended to all captured genes, as diagnostically relevant CNVs may occur everywhere in the genome.
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Rojano E, Córdoba-Caballero J, Jabato FM, Gallego D, Serrano M, Pérez B, Parés-Aguilar Á, Perkins JR, Ranea JAG, Seoane-Zonjic P. Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer. J Pers Med 2021; 11:730. [PMID: 34442375 PMCID: PMC8398478 DOI: 10.3390/jpm11080730] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 07/13/2021] [Accepted: 07/20/2021] [Indexed: 12/21/2022] Open
Abstract
Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets.
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Affiliation(s)
- Elena Rojano
- Department of Molecular Biology and Biochemistry, University of Málaga, 29071 Málaga, Spain; (E.R.); (J.C.-C.); (Á.P.-A.); (J.A.G.R.); (P.S.-Z.)
- Institute of Biomedical Research in Málaga (IBIMA), 29010 Málaga, Spain;
| | - José Córdoba-Caballero
- Department of Molecular Biology and Biochemistry, University of Málaga, 29071 Málaga, Spain; (E.R.); (J.C.-C.); (Á.P.-A.); (J.A.G.R.); (P.S.-Z.)
| | - Fernando M. Jabato
- Institute of Biomedical Research in Málaga (IBIMA), 29010 Málaga, Spain;
- Supercomputation and Bioinformatics (SCBI), University of Malaga, 29071 Malaga, Spain
- LifeWatch-ERIC, 41071 Seville, Spain
| | - Diana Gallego
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), [Madrid, Málaga, Barcelona], Instituto de Salud Carlos III, 28029 Madrid, Spain; (D.G.); (M.S.); (B.P.)
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Campus de Cantoblanco, Universidad Autónoma de Madrid, 28049 Madrid, Spain
- Instituto de Investigación Sanitaria idiPAZ, 28049 Madrid, Spain
| | - Mercedes Serrano
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), [Madrid, Málaga, Barcelona], Instituto de Salud Carlos III, 28029 Madrid, Spain; (D.G.); (M.S.); (B.P.)
- Neuropediatric Department, Institut de Recerca Hospital Sant Joan de Déu, 08950 Barcelona, Spain
| | - Belén Pérez
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), [Madrid, Málaga, Barcelona], Instituto de Salud Carlos III, 28029 Madrid, Spain; (D.G.); (M.S.); (B.P.)
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Campus de Cantoblanco, Universidad Autónoma de Madrid, 28049 Madrid, Spain
- Instituto de Investigación Sanitaria idiPAZ, 28049 Madrid, Spain
| | - Álvaro Parés-Aguilar
- Department of Molecular Biology and Biochemistry, University of Málaga, 29071 Málaga, Spain; (E.R.); (J.C.-C.); (Á.P.-A.); (J.A.G.R.); (P.S.-Z.)
| | - James R. Perkins
- Department of Molecular Biology and Biochemistry, University of Málaga, 29071 Málaga, Spain; (E.R.); (J.C.-C.); (Á.P.-A.); (J.A.G.R.); (P.S.-Z.)
- Institute of Biomedical Research in Málaga (IBIMA), 29010 Málaga, Spain;
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), [Madrid, Málaga, Barcelona], Instituto de Salud Carlos III, 28029 Madrid, Spain; (D.G.); (M.S.); (B.P.)
| | - Juan A. G. Ranea
- Department of Molecular Biology and Biochemistry, University of Málaga, 29071 Málaga, Spain; (E.R.); (J.C.-C.); (Á.P.-A.); (J.A.G.R.); (P.S.-Z.)
- Institute of Biomedical Research in Málaga (IBIMA), 29010 Málaga, Spain;
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), [Madrid, Málaga, Barcelona], Instituto de Salud Carlos III, 28029 Madrid, Spain; (D.G.); (M.S.); (B.P.)
| | - Pedro Seoane-Zonjic
- Department of Molecular Biology and Biochemistry, University of Málaga, 29071 Málaga, Spain; (E.R.); (J.C.-C.); (Á.P.-A.); (J.A.G.R.); (P.S.-Z.)
- Institute of Biomedical Research in Málaga (IBIMA), 29010 Málaga, Spain;
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), [Madrid, Málaga, Barcelona], Instituto de Salud Carlos III, 28029 Madrid, Spain; (D.G.); (M.S.); (B.P.)
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