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

Disease related changes in ATAC-seq of iPSC-derived motor neuron lines from ALS patients and controls

Amyotrophic Lateral Sclerosis (ALS), like many other neurodegenerative diseases, is highly heritable, but with only a small fraction of cases explained by monogenic disease alleles. To better understand sporadic ALS, we report epigenomic profiles, as measured by ATAC-seq, of motor neuron cultures derived from a diverse group of 380 ALS patients and 80 healthy controls. We find that chromatin accessibility is heavily influenced by sex, the iPSC cell type of origin, ancestry, and the inherent variance arising from sequencing. Once these covariates are corrected for, we are able to identify ALS-specific signals in the data. Additionally, we find that the ATAC-seq data is able to predict ALS disease progression rates with similar accuracy to methods based on biomarkers and clinical status. These results suggest that iPSC-derived motor neurons recapitulate important disease-relevant epigenomic changes.

Machine learning nominates the inositol pathway and novel genes in Parkinson's disease

There are 78 loci associated with Parkinson's disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson's disease locus and identify variants and pathways potentially involved in Parkinson's disease. We trained a machine learning model to predict Parkinson's disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson's disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson's disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson's disease.

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

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.

Identification of 27 allele-specific regulatory variants in Parkinson's disease using a massively parallel reporter assay

Genome wide association studies (GWAS) have identified a number of genomic loci that are associated with Parkinson's disease (PD) risk. However, the majority of these variants lie in non-coding regions, and thus the mechanisms by which they influence disease development, and/or potential subtypes, remain largely elusive. To address this, we used a massively parallel reporter assay (MPRA) to screen the regulatory function of 5254 variants that have a known or putative connection to PD. We identified 138 loci with enhancer activity, of which 27 exhibited allele-specific regulatory activity in HEK293 cells. The identified regulatory variant(s) typically did not match the original tag variant within the PD associated locus, supporting the need for deeper exploration of these loci. The existence of allele specific transcriptional impacts within HEK293 cells, confirms that at least a subset of the PD associated regions mark functional gene regulatory elements. Future functional studies that confirm the putative targets of the empirically verified regulatory variants will be crucial for gaining a greater understanding of how gene regulatory network(s) modulate PD risk.

iSCORE-PD: an isogenic stem cell collection to research Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder caused by complex genetic and environmental factors. Genome-edited human pluripotent stem cells (hPSCs) offer the uniique potential to advance our understanding of PD etiology by providing disease-relevant cell-types carrying patient mutations along with isogenic control cells. To facilitate this experimental approach, we generated a collection of 55 cell lines genetically engineered to harbor mutations in genes associated with monogenic PD (SNCA A53T, SNCA A30P, PRKN Ex3del, PINK1 Q129X, DJ1/PARK7 Ex1-5del, LRRK2 G2019S, ATP13A2 FS, FBXO7 R498X/FS, DNAJC6 c.801 A>G+FS, SYNJ1 R258Q/FS, VPS13C A444P, VPS13C W395C, GBA1 IVS2+1). All mutations were generated in a fully characterized and sequenced female human embryonic stem cell (hESC) line (WIBR3; NIH approval number NIHhESC-10-0079) using CRISPR/Cas9 or prime editing-based approaches. We implemented rigorous quality controls, including high density genotyping to detect structural variants and confirm the genomic integrity of each cell line. This systematic approach ensures the high quality of our stem cell collection, highlights differences between conventional CRISPR/Cas9 and prime editing and provides a roadmap for how to generate gene-edited hPSCs collections at scale in an academic setting. We expect that our isogenic stem cell collection will become an accessible platform for the study of PD, which can be used by investigators to understand the molecular pathophysiology of PD in a human cellular setting.

Multiomics analysis identifies novel facilitators of human dopaminergic neuron differentiation

Midbrain dopaminergic neurons (mDANs) control voluntary movement, cognition, and reward behavior under physiological conditions and are implicated in human diseases such as Parkinson's disease (PD). Many transcription factors (TFs) controlling human mDAN differentiation during development have been described, but much of the regulatory landscape remains undefined. Using a tyrosine hydroxylase (TH) human iPSC reporter line, we here generate time series transcriptomic and epigenomic profiles of purified mDANs during differentiation. Integrative analysis predicts novel regulators of mDAN differentiation and super-enhancers are used to identify key TFs. We find LBX1, NHLH1 and NR2F1/2 to promote mDAN differentiation and show that overexpression of either LBX1 or NHLH1 can also improve mDAN specification. A more detailed investigation of TF targets reveals that NHLH1 promotes the induction of neuronal miR-124, LBX1 regulates cholesterol biosynthesis, and NR2F1/2 controls neuronal activity.

Transcriptomic changes in oligodendrocytes and precursor cells predicts clinical outcomes of Parkinson's disease

Several prior studies have proposed the involvement of various brain regions and cell types in Parkinson's disease (PD) pathology. Here, we performed snRNA-seq on the prefrontal cortex and anterior cingulate regions from post-mortem control and PD brain tissue. We found a significant association of oligodendrocytes (ODCs) and oligodendrocyte precursor cells (OPCs) with PD-linked risk loci and report several dysregulated genes and pathways, including regulation of tau-protein kinase activity, regulation of inclusion body assembly and protein processing involved in protein targeting to mitochondria. In an independent PD cohort with clinical measures (681 cases and 549 controls), polygenic risk scores derived from the dysregulated genes significantly predicted Montreal Cognitive Assessment (MoCA)-, and Beck Depression Inventory-II (BDI-II)-scores but not motor impairment (UPDRS-III). We extended our analysis of clinical outcome prediction by incorporating three separate datasets that were previously published by different laboratories. In the first dataset from the anterior cingulate cortex, we identified a correlation between ODCs and BDI-II. In the second dataset obtained from the substantia nigra (SN), OPCs displayed notable predictive ability for UPDRS-III. In the third dataset from the SN region, a distinct subtype of OPCs, labeled OPC_ADM, exhibited predictive ability for UPDRS-III. Intriguingly, the OPC_ADM cluster also demonstrated a significant increase in PD samples. These results suggest that by expanding our focus to glial cells, we can uncover region-specific molecular pathways associated with PD symptoms.

Artificial intelligence for dementia genetics and omics

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.

Functional genomics and systems biology in human neuroscience

Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.