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Omics data integration suggests a potential idiopathic Parkinson's disease signature.

ABSTRACT: The vast majority of Parkinson's disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson's disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes.

SUBMITTER: Zagare A

PROVIDER: S-EPMC10662437 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Omics data integration suggests a potential idiopathic Parkinson's disease signature.

Zagare Alise A Preciat German G Nickels Sarah L SL Luo Xi X Monzel Anna S AS Gomez-Giro Gemma G Robertson Graham G Jaeger Christian C Sharif Jafar J Koseki Haruhiko H Diederich Nico J NJ Glaab Enrico E Fleming Ronan M T RMT Schwamborn Jens C JC

Communications biology 20231120 1

The vast majority of Parkinson's disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson's disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify dif ...[more]

PMID: 37985891

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Project description:BackgroundThe tumor immune microenvironment significantly affects tumor occurrence, progression, and prognosis, but its impact on the prognosis of low-grade glioma (LGG) patients with epilepsy has not been reported. Hence, the purpose of this study is to explore its effect on LGG patients with epilepsy.MethodsThe data of LGG patients derived from the TCGA database. The level of immune cell infiltration and the proportion of 22 immune cells were evaluated by ESTIMATE and CIBERSORT algorithms, respectively. The Cox and LASSO regression analysis was adopted to determine the DEGs, and further established the clustering and risk score models. The association between genomic alterations and risk score was investigated using CNV and somatic mutation data. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC database were used to predict the patient's response to immunotherapy and chemotherapy, respectively.ResultsThe prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were screened out. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, with a high risk-score indicating a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had the better predictive ability. Patients at high risk had a higher level of macrophage infiltration and increased inflammatory activities associated with T cells and macrophages. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were present in the low-risk patients. The signature genes participated in the regulation of immune-related pathways, such as IL6-JAK-STAT3 signaling, IFN-α response, IFN-γ response, and TNFA-signaling-via-NFKB pathways. The high-risk patients were more likely to benefit from anti-PD1 and temozolomide (TMZ) treatment.ConclusionAn immune-related gene signature was established based on ABCC3, PDPN, and INA, which can be used to predict the prognosis, immune infiltration status, immunotherapy and chemotherapy response of LGG patients with epilepsy.

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Omics data integration suggests a potential idiopathic Parkinson's disease signature.

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Omics data integration suggests a potential idiopathic Parkinson's disease signature.

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