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Rajan R, Holla VV, Kamble N, Yadav R, Pal PK. Genetic heterogeneity of early onset Parkinson disease: The dilemma of clinico-genetic correlation. Parkinsonism Relat Disord 2024:107146. [PMID: 39313403 DOI: 10.1016/j.parkreldis.2024.107146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 09/05/2024] [Accepted: 09/08/2024] [Indexed: 09/25/2024]
Abstract
With advances in genetic testing increasing proportion of early onset Parkinson disease (EOPD) are being identified to have an underlying genetic aetiology. This is can be in the form of either highly penetrant genes associated with phenotypes with monogenic or mendelian inheritance patterns or those genes known as risk factor genes which confer an increased risk of PD in an individual. Both of them can modify the phenotypic manifestation in a patient with PD. This improved knowledge has helped in deciphering the intricate role of various cellular pathways in the pathophysiology of PD including both early and late and even sporadic PD. However, the phenotypic and genotypic heterogeneity is a major challenge. Different deleterious alterations in a same gene can result in a spectrum of presentation spanning from juvenile to late onset and typical to atypical parkinsonism manifestation. Similarly, a single phenotype can occur due to abnormality in two or more different genes. This conundrum poses a dilemma in the clinical approach and in understanding the clinico-genetic correlation. Understanding the clinico-genetic correlation carries even more importance especially when genetic testing is either not accessible or affordable or in many regions both. In this narrative review, we aim to discuss briefly the approach to various PARK gene related EOPD and describe in detail the clinico-genetic correlation of individual type of PARK gene related genetic EOPD with respect to their classical clinical presentation, pathophysiology, investigation findings and treatment response to medication and surgery.
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Affiliation(s)
- Roopa Rajan
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Vikram V Holla
- Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Nitish Kamble
- Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Ravi Yadav
- Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Pramod Kumar Pal
- Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, India.
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Wu YS, Zheng WH, Liu TH, Sun Y, Xu YT, Shao LZ, Cai QY, Tang YQ. Joint-tissue integrative analysis identifies high-risk genes for Parkinson's disease. Front Neurosci 2024; 18:1309684. [PMID: 38576865 PMCID: PMC10991821 DOI: 10.3389/fnins.2024.1309684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 02/22/2024] [Indexed: 04/06/2024] Open
Abstract
The loss of dopaminergic neurons in the substantia nigra and the abnormal accumulation of synuclein proteins and neurotransmitters in Lewy bodies constitute the primary symptoms of Parkinson's disease (PD). Besides environmental factors, scholars are in the early stages of comprehending the genetic factors involved in the pathogenic mechanism of PD. Although genome-wide association studies (GWAS) have unveiled numerous genetic variants associated with PD, precisely pinpointing the causal variants remains challenging due to strong linkage disequilibrium (LD) among them. Addressing this issue, expression quantitative trait locus (eQTL) cohorts were employed in a transcriptome-wide association study (TWAS) to infer the genetic correlation between gene expression and a particular trait. Utilizing the TWAS theory alongside the enhanced Joint-Tissue Imputation (JTI) technique and Mendelian Randomization (MR) framework (MR-JTI), we identified a total of 159 PD-associated genes by amalgamating LD score, GTEx eQTL data, and GWAS summary statistic data from a substantial cohort. Subsequently, Fisher's exact test was conducted on these PD-associated genes using 5,152 differentially expressed genes sourced from 12 PD-related datasets. Ultimately, 29 highly credible PD-associated genes, including CTX1B, SCNA, and ARSA, were uncovered. Furthermore, GO and KEGG enrichment analyses indicated that these genes primarily function in tissue synthesis, regulation of neuron projection development, vesicle organization and transportation, and lysosomal impact. The potential PD-associated genes identified in this study not only offer fresh insights into the disease's pathophysiology but also suggest potential biomarkers for early disease detection.
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Affiliation(s)
- Ya-Shi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
- Department of Cell Biology and Medical Genetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Wen-Han Zheng
- Department of Cell Biology and Medical Genetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Tai-Hang Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Yan Sun
- Department of Cell Biology and Medical Genetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Yu-Ting Xu
- Department of Cell Biology and Medical Genetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Li-Zhen Shao
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Qin-Yu Cai
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Ya Qin Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
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Khaled ML, Ren Y, Kundalia R, Alhaddad H, Chen Z, Wallace GC, Evernden B, Ospina OE, Hall M, Liu M, Darville LN, Izumi V, Chen YA, Pilon-Thomas S, Stewart PA, Koomen JM, Corallo SA, Jain MD, Robinson TJ, Locke FL, Forsyth PA, Smalley I. Branched-chain keto acids promote an immune-suppressive and neurodegenerative microenvironment in leptomeningeal disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572239. [PMID: 38187773 PMCID: PMC10769272 DOI: 10.1101/2023.12.18.572239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Leptomeningeal disease (LMD) occurs when tumors seed into the leptomeningeal space and cerebrospinal fluid (CSF), leading to severe neurological deterioration and poor survival outcomes. We utilized comprehensive multi-omics analyses of CSF from patients with lymphoma LMD to demonstrate an immunosuppressive cellular microenvironment and identified dysregulations in proteins and lipids indicating neurodegenerative processes. Strikingly, we found a significant accumulation of toxic branched-chain keto acids (BCKA) in the CSF of patients with LMD. The BCKA accumulation was found to be a pan-cancer occurrence, evident in lymphoma, breast cancer, and melanoma LMD patients. Functionally, BCKA disrupted the viability and function of endogenous T lymphocytes, chimeric antigen receptor (CAR) T cells, neurons, and meningeal cells. Treatment of LMD mice with BCKA-reducing sodium phenylbutyrate significantly improved neurological function, survival outcomes, and efficacy of anti-CD19 CAR T cell therapy. This is the first report of BCKA accumulation in LMD and provides preclinical evidence that targeting these toxic metabolites improves outcomes.
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Affiliation(s)
- Mariam Lotfy Khaled
- The Department of Metabolism and Physiology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
- Department of Biochemistry, Faculty of Pharmacy, Cairo University, Egypt
| | - Yuan Ren
- The Department of Metabolism and Physiology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Ronak Kundalia
- The Department of Metabolism and Physiology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Hasan Alhaddad
- The Department of Metabolism and Physiology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Zhihua Chen
- Department of Biostatistics and Bioinformatics, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Gerald C. Wallace
- Department of Hematology/Oncology, Georgia Cancer Center at Medical College of Georgia, Augusta, GA, USA
| | - Brittany Evernden
- Department of Neuro Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Oscar E. Ospina
- Department of Biostatistics and Bioinformatics, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - MacLean Hall
- Department of Immunology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Min Liu
- The Proteomics and Metabolomics Core, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Lancia N.F. Darville
- The Proteomics and Metabolomics Core, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Victoria Izumi
- The Proteomics and Metabolomics Core, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Y. Ann Chen
- Department of Biostatistics and Bioinformatics, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Shari Pilon-Thomas
- Department of Immunology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Paul A. Stewart
- Department of Biostatistics and Bioinformatics, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - John M. Koomen
- The Proteomics and Metabolomics Core, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
- Department of Molecular Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Salvatore A. Corallo
- Department of Blood and Marrow Transplant and Cellular Immunotherapy, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Michael D. Jain
- Department of Blood and Marrow Transplant and Cellular Immunotherapy, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Timothy J. Robinson
- Therapeutic Radiology, Smilow Cancer Hospital at Yale New Haven, 35 Park Street, New Haven, CT, USA
| | - Fredrick L. Locke
- Department of Blood and Marrow Transplant and Cellular Immunotherapy, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Peter A. Forsyth
- Department of Neuro Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
- The Department of Tumor Biology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Inna Smalley
- The Department of Metabolism and Physiology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
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