The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism

Bressan, Elisângela; Reed, Xylena; Bansal, V.; Hutchins, Elizabeth; Cobb, Melanie M.; Webb, Michelle G.; Alsop, Eric; Grenn, Francis P.; Illarionova, Anastasia; Savytska, Natalia; Violich, Ivo; Broeer, Stefanie; Fernandes, Noémia; R, Sivakumar; Beilina, Alexandra; Billingsley, Kimberley; Berghausen, Joos; Pantazis, Caroline B.; Pitz, Vanessa; Patel, Dhairya; Daida, Kensuke; Meechoovet, Bessie; Reiman, Rebecca; Courtright-Lim, Amanda; Logemann, Amber; Antone, Jerry; Barch, Mariya; Kitchen, Robert; Li, Yan; Dalgard, Clifton L.; Rizzu, Patrizia; Hernandez, Dena G.; Hjelm, Brooke E.; Nalls, Mike A.; Gibbs, J. Raphael; Finkbeiner, Steven; Cookson, Mark; Keuren‐Jensen, Kendall Van; Craig, David W.; Singleton, Andrew B.; Heutink, Peter; Blauwendraat, Cornelis

doi:10.1016/j.xgen.2023.100261

Cited by 28 publications

(27 citation statements)

References 73 publications

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“…Relevant for PD, DDX46 has been found within a GWAS interval linked to PD, locus 28 with the index variant rs11950533 from a recent PD GWAS study (Bressan et al, 2023). Our analysis indicates that the DDX46 pre-mRNA splicing pattern is altered in the PRKN (Ex 3del) mutant mDA cells (Figure 7A).…”

Section: Resultsmentioning

confidence: 99%

Comparison of Alternative pre-mRNA Splicing and Gene Expression Patterns in Midbrain Lineage Cells Carrying Familial Parkinson’s Disease Mutations

Lee,

Syed,

Busquets

et al. 2024

Preprint

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SummaryParkinson’s disease (PD) is a devastating neurodegenerative disorder, with both genetic and environmental causes. Human genetic studies have identified ∼20 inherited familial genes that cause monogenic forms of PD. We have investigated the effects of individual familial PD mutations by developing a medium-throughput platform using genome-editing to install individual PD mutations in human pluripotent stem cells (hPSCs) that we subsequently differentiated into midbrain lineage cells including dopaminergic (DA) neurons in cell culture. Both global gene expression and pre-mRNA splicing patterns in midbrain cultures carrying inherited, pathogenic PD mutations in the PRKN and SNCA genes were analyzed. This analysis revealed that PD mutations lead to many more pre-mRNA splicing changes than changes in overall gene RNA expression levels. Importantly, we have also shown that these splicing changes overlap with changes found in PD patient postmortem brain sample RNA-seq datasets. These pre-mRNA splicing changes are in genes related to cytoskeletal and neuronal process formation, as well as splicing factors and spliceosome components. We predict that these mutation-specific pre-mRNA isoforms can be used as biomarkers for PD that are linked to the familial PD mutant genotypes.

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Section: Resultsmentioning

confidence: 99%

Comparison of Alternative pre-mRNA Splicing and Gene Expression Patterns in Midbrain Lineage Cells Carrying Familial Parkinson’s Disease Mutations

Lee,

Syed,

Busquets

et al. 2024

Preprint

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“…To facilitate accurate downstream comparisons, we addressed the substantial differentiation heterogeneity inherent in iPSC lines, even among lines derived from genetically related individuals. Previous advancements, including optimized differentiation protocols for specific iPSC lines from healthy controls, deep phenotyping of selected cell lines with the desired stem cell characteristics [36], and data atlases generated from numerous iPSCs in large-scale collaborative projects [20], may not be applicable when studying rare diseases or atypical clinical cases, as in our case. Consequently, we aimed to develop a tool capable of enriching the desired cell type, thereby reducing expected heterogeneity between patient-derived samples.…”

Section: Discussionmentioning

confidence: 99%

“…Efforts have been focused on refining differentiation protocols with timely addition of signaling molecules mimicking in vivo embryonic development [14,15], but critical events for dopaminergic patterning, such as the level of Wnt activation [16][17][18], seem to be an inherent cell line characteristic [19]. Lastly, the final fraction of DA neurons is often limited in the culture, mixed with proliferating neural progenitor cells as well as other types of cells [20]. Consequently, disease-related changes in DA neurons may sometimes be obscured by culture heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of enriched dopaminergic neurons from siblings with Gaucher disease discordant for parkinsonism

Hertz,

Perez,

Hao

et al. 2024

Preprint

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Inducible pluripotent stem cells (iPSCs) derived from patient samples have significantly enhanced our ability to model neurological diseases. Comparative studies of dopaminergic (DA) neurons differentiated from iPSCs derived from siblings with Gaucher disease discordant for parkinsonism provides a valuable avenue to explore genetic modifiers contributing to GBA1-associated parkinsonism in disease-relevant cells. However, such studies are often complicated by the inherent heterogeneity in differentiation efficiency among iPSC lines derived from different individuals. To address this technical challenge, we devised a selection strategy to enrich dopaminergic (DA) neurons expressing tyrosine hydroxylase (TH). A neomycin resistance gene (neo) was inserted at the C-terminus of the TH gene following a T2A self-cleavage peptide, placing its expression under the control of the TH promoter. This allows for TH+ DA neuron enrichment through geneticin selection. This method enabled us to generate comparable, high-purity DA neuron cultures from iPSC lines derived from three sisters that we followed for over a decade: one sibling is a healthy individual, and the other two have Gaucher disease (GD) with GBA1 genotype N370S/c.203delC+R257X (p.N409S/c.203delC+p.R296X). Notably, the younger sister with GD later developed Parkinson disease (PD). A comprehensive analysis of these high-purity DA neurons revealed that although GD DA neurons exhibited decreased levels of glucocerebrosidase (GCase), there was no substantial difference in GCase protein levels or lipid substrate accumulation between DA neurons from the GD and GD/PD sisters, suggesting that the PD discordance is related to of other genetic modifiers.

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“…As the costs of omics assays continue to drop, the standard use of high-throughput DNA, RNA, protein, and metabolomics biomarkers in the clinic need to become a reality. Largescale sequencing initiatives that focus on the genomic underpinnings of neurodegenerative diseases 41,[86][87][88][89][90] will aid in the development of more targeted and cost-effective tests such as PRSs and metabolite panels. 91 Collectively, these initiatives will enable many opportunities for biomarker identification, validation in both diagnosis and early disease detection, as well as raise important ethical and technical challenges.…”

Section: Possible Solutionsmentioning

confidence: 99%

Artificial intelligence for dementia genetics and omics

Bettencourt,

Skene,

Bandres‐Ciga

et al. 2023

Alzheimer's & Dementia

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Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine.Highlights We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

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The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism

Cited by 28 publications

References 73 publications

Comparison of Alternative pre-mRNA Splicing and Gene Expression Patterns in Midbrain Lineage Cells Carrying Familial Parkinson’s Disease Mutations

Comparison of Alternative pre-mRNA Splicing and Gene Expression Patterns in Midbrain Lineage Cells Carrying Familial Parkinson’s Disease Mutations

Comparative study of enriched dopaminergic neurons from siblings with Gaucher disease discordant for parkinsonism

Artificial intelligence for dementia genetics and omics

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