Proteogenomic network analysis reveals dysregulated mechanisms and potential mediators in Parkinson’s disease

Doostparast Torshizi, Abolfazl; Truong, Dongnhu T.; Hou, Liping; Smets, Bart; Whelan, Christopher D.; Li, Shuwei

doi:10.1038/s41467-024-50718-x

Cited by 3 publications

References 98 publications

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Druggable targets for Parkinson’s disease: transcriptomics based Mendelian randomization study

Lyu,

Chen,

Qiu

et al. 2024

Sci Rep

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Parkinson’s disease (PD) is a prevalent neurodegenerative disorder. Currently available drugs for PD, can only relieve the symptoms of PD, but cannot prevent the progression of the disease and have serious side effects. Other new druggable therapeutic targets for PD are needed. First, six GEO datasets with transcriptomic data from the substantia nigra (SN) region of the brain were downloaded to find dysregulated druggable genes in PD. Then, Mendelian randomization (MR) and summary statistics-based MR (SMR) analysis were conducted using eQTL data from both brain tissue and blood to investigate the relationship between gene expression and PD. Next, the association between the expression of candidate druggable targets and disease stage was validated in an additional dataset GSE49036. Finally, a phenome-wide MR analysis was carried out to investigate the potential impact of candidate druggable genes on several other complex diseases or traits. Our study revealed 313 differentially expressed genes that may be directly targetable and have an impact on PD (FDR-p < 0.1). Through MR and SMR analysis, P2RX7 and RNASET2 were identified as feasible PD therapeutic targets, which were highly expressed in PD tissues and increased as the Braak stages increased. Phenome-wide MR analysis revealed other effects of targeting RNASET2. This study presents genetic support for the potential therapeutic properties of targeting P2RX7 and RNASET2, which will be useful for developing druggable therapeutic targets for PD.

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Druggable targets for Parkinson’s disease: transcriptomics based Mendelian randomization study

Lyu,

Chen,

Qiu

et al. 2024

Sci Rep

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Machine learning-based proteogenomic data modeling identifies circulating plasma biomarkers for early detection of lung cancer

Johnson,

Hou,

Huang

et al. 2024

Preprint

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Identifying genetic variants associated with lung cancer (LC) risk and their impact on plasma protein levels is crucial for understanding LC predisposition. The discovery of risk biomarkers can enhance early LC screening protocols and improve prognostic interventions. In this study, we performed a genome-wide association analysis using the UK Biobank and FinnGen. We identified genetic variants associated with LC and protein levels leveraging the UK Biobank Pharma Proteomics Project. The dysregulated proteins were then analyzed in pre-symptomatic LC cases compared to healthy controls followed by training machine learning models to predict future LC diagnosis. We achieved median AUCs ranging from 0.79 to 0.88 (0-4 years before diagnosis/YBD), 0.73 to 0.83 (5-9YBD), and 0.78 to 0.84 (0-9YBD) based on 5-fold cross-validation. Conducting survival analysis using the 5-9YBD cohort, we identified eight proteins, including CALCB, PLAUR/uPAR, and CD74 whose higher levels were associated with worse overall survival. We also identified potential plasma biomarkers, including previously reported candidates such as CEACAM5, CXCL17, GDF15, and WFDC2, which have shown associations with future LC diagnosis. These proteins are enriched in various pathways, including cytokine signaling, interleukin regulation, neutrophil degranulation, and lung fibrosis. In conclusion, this study generates novel insights into our understanding of the genome-proteome dynamics in LC. Furthermore, our findings present a promising panel of non-invasive plasma biomarkers that hold potential to support early LC screening initiatives and enhance future diagnostic interventions.

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Oligodendrocytes, the Forgotten Target of Gene Therapy

Ozgür-Gunes,

Le Stunff,

Bougnères

2024

Cells

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If the billions of oligodendrocytes (OLs) populating the central nervous system (CNS) of patients could express their feelings, they would undoubtedly tell gene therapists about their frustration with the other neural cell populations, neurons, microglia, or astrocytes, which have been the favorite targets of gene transfer experiments. This review questions why OLs have been left out of most gene therapy attempts. The first explanation is that the pathogenic role of OLs is still discussed in most CNS diseases. Another reason is that the so-called ubiquitous CAG, CBA, CBh, or CMV promoters—widely used in gene therapy studies—are unable or poorly able to activate the transcription of episomal transgene copies brought by adeno-associated virus (AAV) vectors in OLs. Accordingly, transgene expression in OLs has either not been found or not been evaluated in most gene therapy studies in rodents or non-human primates. The aims of the current review are to give OLs their rightful place among the neural cells that future gene therapy could target and to encourage researchers to test the effect of OL transduction in various CNS diseases.

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Proteogenomic network analysis reveals dysregulated mechanisms and potential mediators in Parkinson’s disease

Cited by 3 publications

References 98 publications

Druggable targets for Parkinson’s disease: transcriptomics based Mendelian randomization study

Druggable targets for Parkinson’s disease: transcriptomics based Mendelian randomization study

Machine learning-based proteogenomic data modeling identifies circulating plasma biomarkers for early detection of lung cancer

Oligodendrocytes, the Forgotten Target of Gene Therapy

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