DNA Methylation Age Acceleration Is Not Associated with Age of Onset in Parkinson's Disease

Background Epigenetic clocks using DNA methylation (DNAm) to estimate biological age have become popular tools in the study of neurodegenerative diseases. Notably, several recent reports have shown a strikingly similar inverse relationship between accelerated biological aging, as measured by DNAm, and the age of onset of several neurodegenerative disorders, including Parkinson's disease (PD). Common to all of these studies is that they were performed without control subjects and using the exact same measure of accelerated aging: DNAm age minus chronological age. Objective We aimed to assess the validity of these findings in PD, using the same dataset as in the original study, blood DNAm data from the Parkinson's Progression Markers Initiative cohort, but also including control samples in the analyses. Methods We replicated the analyses and findings of the previous study and then reanalyzed the dataset incorporating control samples to account for underlying age-related biases. Results Our reanalysis shows that there is no correlation between age of onset and DNAm age acceleration. Conversely, there is a pattern of overestimating DNAm age in younger and underestimating DNAm age in older individuals in the dataset that entirely explains the previously reported association. Conclusions Our findings refute the previously reported inverse relationship between DNAm age acceleration and age of onset in PD. We show that these findings are fully accounted for by an expected over/underestimation of DNAm age in younger/older individuals. Furthermore, this effect is likely to be responsible for nearly identical findings reported in other neurodegenerative diseases. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.publishedVersio

Ultra-deep whole genome bisulfite sequencing reveals a single methylation hotspot in human brain mitochondrial DNA

While DNA methylation is established as a major regulator of gene expression in the nucleus, the existence of mitochondrial DNA (mtDNA) methylation remains controversial. Here, we characterized the mtDNA methylation landscape in the prefrontal cortex of neurological healthy individuals (n=26) and patients with Parkinson’s disease (n=27), using a combination of whole-genome bisulphite sequencing (WGBS) and bisulphite-independent methods. Accurate mtDNA mapping from WGBS data required alignment to an mtDNA reference only, to avoid misalignment to nuclear mitochondrial pseudogenes. Once correctly aligned, WGBS data provided ultra-deep mtDNA coverage (16,723 ± 7,711) and revealed overall very low levels of cytosine methylation. The highest methylation levels (5.49 ± 0.97%) were found on CpG position m.545, located in the heavy-strand promoter 1 region. The m.545 methylation was validated using a combination of methylation-sensitive DNA digestion and quantitative PCR analysis. We detected no association between mtDNA methylation profile and Parkinson’s disease. Interestingly, m.545 methylation correlated with the levels of mtDNA transcripts, suggesting a putative role in regulating mtDNA gene expression. In addition, we propose a robust framework for methylation analysis of mtDNA from WGBS data, which is less prone to false-positive findings due to misalignment of nuclear mitochondrial pseudogene sequences.publishedVersio

Meta-analysis of whole-exome sequencing data from two independent cohorts finds no evidence for rare variant enrichment in Parkinson disease associated loci

Parkinson disease (PD) is a complex neurodegenerative disorder influenced by both environmental and genetic factors. While genome wide association studies have identified several susceptibility loci, many causal variants and genes underlying these associations remain undetermined. Identifying these is essential in order to gain mechanistic insight and identify biological pathways that may be targeted therapeutically. We hypothesized that gene-based enrichment of rare mutations is likely to be found within susceptibility loci for PD and may help identify causal genes. Whole-exome sequencing data from two independent cohorts were analyzed in tandem and by meta-analysis and a third cohort genotyped using the NeuroX-array was used for replication analysis. We employed collapsing methods (burden and the sequence kernel association test) to detect gene-based enrichment of rare, protein-altering variation within established PD susceptibility loci. Our analyses showed trends for three genes (GALC, PARP9 and SEC23IP), but none of these survived multiple testing correction. Our findings provide no evidence of rare mutation enrichment in genes within PD-associated loci, in our datasets. While not excluding that rare mutations in these genes may influence the risk of idiopathic PD, our results suggest that, if such effects exist, much larger sequencing datasets will be required for their detection.publishedVersio

Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition

The etiology of Parkinson’s disease is largely unknown. Genome-wide transcriptomic studies in bulk brain tissue have identified several molecular signatures associated with the disease. While these studies have the potential to shed light into the pathogenesis of Parkinson’s disease, they are also limited by two major confounders: RNA post-mortem degradation and heterogeneous cell type composition of bulk tissue samples. We performed RNA sequencing following ribosomal RNA depletion in the prefrontal cortex of 49 individuals from two independent case-control cohorts. Using cell type specific markers, we estimated the cell type composition for each sample and included this in our analysis models to compensate for the variation in cell type proportions. Ribosomal RNA depletion followed by capture by random primers resulted in substantially more even transcript coverage, compared to poly(A) capture, in post-mortem tissue. Moreover, we show that cell type composition is a major confounder of differential gene expression analysis in the Parkinson’s disease brain. Accounting for cell type proportions attenuated numerous transcriptomic signatures that have been previously associated with Parkinson’s disease, including vesicle trafficking, synaptic transmission, immune and mitochondrial function. Conversely, pathways related to endoplasmic reticulum, lipid oxidation and unfolded protein response were strengthened and surface as the top differential gene expression signatures in the Parkinson’s disease prefrontal cortex. Our results indicate that differential gene expression signatures in Parkinson’s disease bulk brain tissue are significantly confounded by underlying differences in cell type composition. Modeling cell type heterogeneity is crucial in order to unveil transcriptomic signatures that represent regulatory changes in the Parkinson’s disease brain and are, therefore, more likely to be associated with underlying disease mechanisms.publishedVersio

Meta-analysis of whole-exome sequencing data from two independent cohorts finds no evidence for rare variant enrichment in Parkinson disease associated loci

Parkinson disease (PD) is a complex neurodegenerative disorder influenced by both environmental and genetic factors. While genome wide association studies have identified several susceptibility loci, many causal variants and genes underlying these associations remain undetermined. Identifying these is essential in order to gain mechanistic insight and identify biological pathways that may be targeted therapeutically. We hypothesized that gene-based enrichment of rare mutations is likely to be found within susceptibility loci for PD and may help identify causal genes. Whole-exome sequencing data from two independent cohorts were analyzed in tandem and by meta-analysis and a third cohort genotyped using the NeuroX-array was used for replication analysis. We employed collapsing methods (burden and the sequence kernel association test) to detect gene-based enrichment of rare, protein-altering variation within established PD susceptibility loci. Our analyses showed trends for three genes (GALC, PARP9 and SEC23IP), but none of these survived multiple testing correction. Our findings provide no evidence of rare mutation enrichment in genes within PD-associated loci, in our datasets. While not excluding that rare mutations in these genes may influence the risk of idiopathic PD, our results suggest that, if such effects exist, much larger sequencing datasets will be required for their detection.publishedVersio

POLG genotype influences degree of mitochondrial dysfunction in iPSC derived neural progenitors, but not the parent iPSC or derived glia

Diseases caused by POLG mutations are the most common form of mitochondrial diseases and associated with phenotypes of varying severity. Clinical studies have shown that patients with compound heterozygous POLG mutations have a lower survival rate than patients with homozygous mutations, but the molecular mechanisms behind this remain unexplored. Using an induced pluripotent stem cell (iPSC) model, we investigate differences between homozygous and compound heterozygous genotypes in different cell types, including patient-specific fibroblasts, iPSCs, and iPSC-derived neural stem cells (NSCs) and astrocytes. We found that compound heterozygous lines exhibited greater impairment of mitochondrial function in NSCs than homozygous NSCs, but not in fibroblasts, iPSCs, or astrocytes. Compared with homozygous NSCs, compound heterozygous NSCs exhibited more severe functional defects, including reduced ATP production, loss of mitochondrial DNA (mtDNA) copy number and complex I expression, disturbance of NAD+ metabolism, and higher ROS levels, which further led to cellular senescence and activation of mitophagy. RNA sequencing analysis revealed greater downregulation of mitochondrial and metabolic pathways, including the citric acid cycle and oxidative phosphorylation, in compound heterozygous NSCs. Our iPSC-based disease model can be widely used to understand the genotype-phenotype relationship of affected brain cells in mitochondrial diseases, and further drug discovery applications.publishedVersio

The NADPARK study: A randomized phase I trial of nicotinamide riboside supplementation in Parkinson’s disease

We conducted a double-blinded phase I clinical trial to establish whether nicotinamide adenine dinucleotide (NAD) replenishment therapy, via oral intake of nicotinamide riboside (NR), is safe, augments cerebral NAD levels, and impacts cerebral metabolism in Parkinson’s disease (PD). Thirty newly diagnosed, treatment-naive patients received 1,000 mg NR or placebo for 30 days. NR treatment was well tolerated and led to a significant, but variable, increase in cerebral NAD levels—measured by 31phosphorous magnetic resonance spectroscopy—and related metabolites in the cerebrospinal fluid. NR recipients showing increased brain NAD levels exhibited altered cerebral metabolism, measured by 18fluoro-deoxyglucose positron emission tomography, and this was associated with mild clinical improvement. NR augmented the NAD metabolome and induced transcriptional upregulation of processes related to mitochondrial, lysosomal, and proteasomal function in blood cells and/or skeletal muscle. Furthermore, NR decreased the levels of inflammatory cytokines in serum and cerebrospinal fluid. Our findings nominate NR as a potential neuroprotective therapy for PD, warranting further investigation in larger trials.publishedVersio

Dynamical properties of gene regulatory networks involved in long-term potentiation

The significant increase in the availability of postgenomic data has stimulated the growth of hypothesis-generating strategies to unravel the molecular basis of nature. The application of systems theory to biological problems emerged in the early 1970s, and yet the computational methods developed to model biological networks and analyse their functionality have been seldom used for understanding the neurogenetic basis of cognition. The main interests of this thesis are the application of computational models to microarray expression data for the identification and analysis of biological networks related with long-term potentiation (LTP), the cellular correlate of learning and memory in mammals. The models include the analysis of co-expression and studies of dynamical stability. The thesis starts with the application of established methods on gene expression analysis on the available expression data from LTP in order to identify networks of closely correlated genes in their patterns of expression to ultimately pinpoint putative key regulators not identified previously by classical differential expression analysis. The thesis continues with the analysis of previously identified gene networks regulated 20 min, 5 h, and 24 h post-LTP induction. A dynamical stability analysis using weight matrices suggests that the early network has a significant sensitivity to perturbations compared with randomly generated networks of similar characteristics. In addition, using random Boolean networks, we study the differential sensitivity to perturbations of these networks and we find that our results are consistent with a model of LTP as a complex cellular switch. In such a scenario, earlier networks are dynamically more unstable than later regulatory networks, which are proposed to be responsible for the new homeostatic state reached by the stimulated neurons. Key genes responsible for the dynamic properties observed are identified and discussed. In particular, we found that Egr2, a member of the Egr family of transcription factors was crucial to the bistability observed in the early-response network. Other genes previously associated with LTP have a more modest contribution. A functional analysis of these networks is presented and integrated with previous knowledge on the molecular basis of LTP

Dynamical properties of gene regulatory networks involved in long-term potentiation

The significant increase in the availability of postgenomic data has stimulated the growth of hypothesis-generating strategies to unravel the molecular basis of nature. The application of systems theory to biological problems emerged in the early 1970s, and yet the computational methods developed to model biological networks and analyse their functionality have been seldom used for understanding the neurogenetic basis of cognition. The main interests of this thesis are the application of computational models to microarray expression data for the identification and analysis of biological networks related with long-term potentiation (LTP), the cellular correlate of learning and memory in mammals. The models include the analysis of co-expression and studies of dynamical stability. The thesis starts with the application of established methods on gene expression analysis on the available expression data from LTP in order to identify networks of closely correlated genes in their patterns of expression to ultimately pinpoint putative key regulators not identified previously by classical differential expression analysis. The thesis continues with the analysis of previously identified gene networks regulated 20 min, 5 h, and 24 h post-LTP induction. A dynamical stability analysis using weight matrices suggests that the early network has a significant sensitivity to perturbations compared with randomly generated networks of similar characteristics. In addition, using random Boolean networks, we study the differential sensitivity to perturbations of these networks and we find that our results are consistent with a model of LTP as a complex cellular switch. In such a scenario, earlier networks are dynamically more unstable than later regulatory networks, which are proposed to be responsible for the new homeostatic state reached by the stimulated neurons. Key genes responsible for the dynamic properties observed are identified and discussed. In particular, we found that Egr2, a member of the Egr family of transcription factors was crucial to the bistability observed in the early-response network. Other genes previously associated with LTP have a more modest contribution. A functional analysis of these networks is presented and integrated with previous knowledge on the molecular basis of LTP

Dynamical properties of gene regulatory networks involved in long-term potentiation

The long-lasting enhancement of synaptic effectiveness known as long-term potentiation (LTP) is considered to be the cellular basis of long-term memory. LTP elicits changes at the cellular and molecular level, including temporally specific alterations in gene networks. LTP can be seen as a biological process in which a transient signal sets a new homeostatic state that is remembered by cellular regulatory systems. Previously, we have shown that early growth response (Egr) transcription factors are of fundamental importance to gene networks recruited early after LTP induction. From a systems perspective, we hypothesized that these networks will show less stable architecture, while networks recruited later will exhibit increased stability, being more directly related to LTP consolidation. Using random Boolean network simulations we found that the network derived at 24 h was markedly more stable than those derived at 20 min or 5 h post-LTP. This temporal effect on the vulnerability of the networks is mirrored by what is known about the vulnerability of LTP and memory itself. Differential gene co-expression analysis further highlighted the importance of the Egr family and found a rapid enrichment in connectivity at 20 min, followed by a systematic decrease, providing a potential explanation for the down-regulation of gene expression at 24 h documented in our preceding studies. We also found that the architecture exhibited by a control and the 24 h LTP co-expression networks fit well to a scale-free distribution, known to be robust against perturbations. By contrast the 20 min and 5 h networks showed more truncated distributions. These results suggest that a new homeostatic state is achieved 24 h post-LTP. Together, these data present an integrated view of the genomic response following LTP induction by which the stability of the networks regulated at different times parallel the properties observed at the synapse