Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to ...disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological processes underlying PD using the largest currently available cohorts of genetic and gene expression data from International Parkinson’s Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership-Parkinson’s disease initiative (AMP-PD), among other sources. We applied large-scale gene-set specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk focusing on publicly annotated gene sets representative of curated pathways. We nominated specific molecular sub-processes underlying protein misfolding and aggregation, post-translational protein modification, immune response, membrane and intracellular trafficking, lipid and vitamin metabolism, synaptic transmission, endosomal–lysosomal dysfunction, chromatin remodeling and apoptosis mediated by caspases among the main contributors to PD etiology. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data and found evidence for a burden of rare damaging alleles in a range of processes, including neuronal transmission-related pathways and immune response. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for dopaminergic neurons, serotonergic neurons, hypothalamic GABAergic neurons, and neural progenitors. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of PD patients, which revealed functional enrichment in inflammatory signaling pathways, cell death machinery related processes, and dysregulation of mitochondrial homeostasis. Our analyses highlight several specific promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.
Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous ...work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.
Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS ...data to identify novel risk loci and gain further insight into the causes of Parkinson's disease.
We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation.
Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7).
These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data.
The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).
Professor Adrian Bejan on his 75th birthday Lage, José L.; Worek, William M.; Amon, Cristina H. ...
International communications in heat and mass transfer,
12/2023, Letnik:
149
Journal Article
Professor Yogesh Jaluria on his 70th Birthday Acharya, Sumanta; Amon, Cristina; Ayyaswamy, Portonovo ...
International journal of heat and mass transfer,
September 2019, 2019-09-00, 20190901, Letnik:
140
Journal Article
Objective: To evaluate the calf muscle pump function using an air plethysmograph (APG) applied to the lower leg of subjects during three different tiptoe exercises.
Design: A controlled trial design ...was selected to compare the hemodynamic effects of three exercise conditions on a group of able-bodied, healthy patients.
Setting: Testing was performed in an outpatient clinic at a rehabilitation hospital.
Subjects: Patient groups were selected from a convenience sample of 10 healthy volunteers with normal venous capacitance and no reflux, determined through impedance pleythysmography before the study.
Interventions: Three exercise conditions undertaken by each subject consisted of loaded and unloaded lower leg muscle contractions produced by (1) voluntary contraction (VOL), (2) electrical stimulation of the gastocnemius-soleus and tibialis anterior muscles (ES), and (3) combined ES and VOL (ES/VOL).
Main Outcome Measure: Hemodynamic measurements of venous filling index upon standing from the supine (VFI), ejection fraction (EF), ejection volume (EV), residual volume (RV), and residual volume fraction (RVF) were recorded after each protocol. These results were used to compare the lower leg hemodynamic effects of the treatments.
Results: Combined ES/VOL single tiptoe exercise produced the highest EV (97.8mL), followed by VOL (80.6mL) and ES (51.7mL) (
p < .0008). The EF was also highest for combined ES/VOL (73.1%), followed by VOL (64.5%) and ES (37.8%) (
p < .0001). Ten tiptoe ES exercises produced the highest RV (96.2mL), followed by ES/VOL (44.7mL) and VOL (28.2mL) (
p < .0001). RVF was also highest in the ES group (71%), followed by ES/VOL (33.4%) and VOL (22.8%) (
p < .0001).
Conclusion: Periodic single ES-induced calf muscle contractions produced significant muscle pump function and could be used to improve venous blood flow and reduce stasis in the lower leg. Continuous ES-induced contractions, on the other hand, could improve lower leg peripheral perfusion while eliciting the physiologic venous muscle pump. Higher RV and RVF after 10 ES-induced contractions in this sample of healthy subjects with normal VFI may be caused by an increase in arterial blood perfusion after repeated ES-induced contractions.
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