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Over the last two decades, we have witnessed a revolution in the field of Parkinson’s disease (PD) genetics. Great advances have been made in identifying many loci that confer a risk for PD, which has subsequently led to an improved understanding of the molecular pathways involved in disease pathogenesis. Despite this success, it is predicted that only a relatively small proportion of the phenotypic variability has been explained by genetics. Therefore, it is clear that common heritable components of disease are still to be identified. Dissecting the genetic architecture of PD constitutes a critical effort in identifying therapeutic targets and although such substantial progress has helped us to better understand disease mechanism, the route to PD disease-modifying drugs is a lengthy one. In this review, we give an overview of the known genetic risk factors in PD, focusing not on individual variants but the larger networks that have been implicated following comprehensive pathway analysis. We outline the challenges faced in the translation of risk loci to pathobiological relevance and illustrate the need for integrating big-data by noting success in recent work which adopts a broad-scale screening approach. Lastly, with PD genetics now progressing from identifying risk to predicting disease, we review how these models will likely have a significant impact in the future.

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.

A correction to this paper has been published: https://doi.org/10.1007/s00401-021-02309-z

Background The development of biological therapies which block the tumor necrosis factor alfa (TNFα) has revolutionized the treatment of various inflammatory diseases such as rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA). Nevertheless, approximately 35% of patients starting biological therapies against TNFα do not respond (primary clinical failure) [1] and it is estimated that approximately 30% of responders will present a clinical failure after having responded for at least 6 months (secondary clinical failure) [2]. Among the mechanisms responsible for the secondary clinical failure the importance of immunogenicity induced by these drugs has been suggested. Biological therapies may induce an unwanted immune response leading to the formation of antibodies against drugs (ADA). These ADA affect the bioavailability of the biological drug, frecuently leading to subtherapeutic levels and thereby causing a loss of clinical response. Objectives To assess the concordance between secondary clinical failure (definition based on DAS28 or BASDAI) and the type of mechanistic failure (definition based on drug levels) in patients with inflammatory arthropaties. Methods Immunogenicity was measured in patients with RA, AS and PsA who presented secondary clinical failure under treatment with ADL or ETN. Blood samples were collected immediately before drug administration (trough levels). Drug and ADA concentrations were determined in patient's serum by ELISA. Results 23 patients with secondary clinical failure were recruited: 73.91% had RA, 8.69% PsA and 17.39% AS. 56.52% did not respond to ADL whilst 43.48% did not respond to ETN. None of the patients treated with ETN showed either subtherapeutic drugs levels or ADA (primary mechanistic failure), whereas it was found that 17.39% of the patients treated with ADL had subtherapeutic drug levels for reasons attributable to immunogenicity (secondary mechanistic failure; p=0.000048). No correlation was found among the type of mechanistic failure and the time of clinical efficacy (p=0.469), disease duration (p=0.39), treatment with previous biological therapies (p=0.15) and the concomitant treatment with corticosteroids (CS) (p=0.24) and disease-modifying antirheumatic drugs (DMARDs) (p=0.50). Conclusions 1. The mechanistic model in patients with secondary clinical failure to ADL may present clinical utility. 2. Immunogenicity appears not to play a relevant role in the secondary clinical failure to ETN. 3. In clinical practice determining drug and ADA levels may only be useful in non-responders to ADL. References Wiens A, Venson R, Correr CJ, Otuki MF, Pontarolo R.Meta-analysis of the efficacy and safety of adalimumab, etanercept and infliximab for the treatment of rheumatoid arthritis. Pharmacotherapy 2010; 30:339-53. Remy A, Avonac J, Gossec L, Combe B. Clinical relevance of switching to a second tumour necrosis factor-alpha inhibitor after discontinuation of a first tumour necrosis factor-alpha inhibitor in rheumatoid arthritis: a systematic literature review and meta-analysis. Clin Exp Rheumatol 2011; 29:96-103. Acknowledgements The authors would like to thank the patients who offered to participate in the study. We also thank Angela Tate for assistance with the English version and IG for his time and effort. Disclosure of Interest None declared DOI 10.1136/annrheumdis-2014-eular.2563

Parkinsons disease (PD) is genetically associated with the H1 haplotype of the MAPT 17q.21.31 locus, although the causal gene and variants underlying this association have not been identified. To better understand the genetic contribution of this region to PD, we fine-mapped the 17q21.31 locus in order to identify novel mechanisms conferring risk for the disease. We identified three novel H1 sub-haplotype blocks across the 17q21.31 locus associated with PD risk. Protective sub-haplotypes were associated with increased LRRC37A/2 copy number and expression in human brain tissue. We found that LRRC37A/2 is a membrane-associated protein that plays a role in cellular migration, chemotaxis and astroglial inflammation. In human substantia nigra, LRRC37A/2 was primarily expressed in astrocytes, interacted directly with soluble alpha-synuclein, and co-localized with Lewy bodies in PD brain tissue. These data indicate that a novel candidate gene, LRRC37A/2, contributes to the association between the 17q21.31 locus and PD via its interaction with alpha-synuclein and its effects on astrocytic function and inflammatory response. These data are the first to associate the genetic association at the 17q21.31 locus with PD pathology, and highlight the importance of variation at the 17q21.31 locus in the regulation of multiple genes other than MAPT and KANSL1, as well as its relevance to non-neuronal cell types. Competing Interest Statement The authors have declared no competing interest. Footnotes * Manuscript has been revised to focus on Parkinson's disease, and includes additional validation of the association with LRRC37A expression in astrocytes

Polygenic inheritance plays a central role in Parkinson's 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 pathways underlying PD using the largest currently available cohorts of genetic data 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 placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. 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 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done. Competing Interest Statement Financial Disclosures: Mike A. Nalls participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA, as a possible conflict of interest Dr. Nalls also consults for Neuron 23s Inc, Lysosomal Therapeutics Inc, and Illumina Inc among others. C.R.S. is named as co-inventor on a US patent application on sphingolipids biomarkers that is jointly held by Brigham & Women's Hospital and Sanofi. C.R.S has consulted for Sanofi Inc.; has collaborated with Pfizer, Opko, and Proteome Sciences, and Genzyme Inc. No other disclosures were reported.

Objective: To assess genetic modifiers of Parkinsons disease (PD) age at onset (AAO) penetrance in individuals carrying common and rare LRRK2 risk alleles Methods: We analysed reported genetic modifier DNM3 rs2421947 in 724 LRRK2 p.G2019S heterozygotes using linear regression of AAO. We meta-analysed our data with previously published data (n=754). VAMP4 is in close proximity to DNM3 and is associated with PD. We analysed the effect of the rs11578699 VAMP4 variant on pG2019S penetrance in 786 LRRK2 p.G2019S heterozygotes. We also evaluated the impact of VAMP4 variants using AAO regression in 4882 patients with PD carrying a common LRRK2 risk variant (rs10878226). Results: There was no evidence for linkage disequilibrium between DNM3 rs2421947 and VAMP4 rs11578699. Our linear regression AAO of 724 p.G2019S carriers showed no relationship between DNM3 rs2421947 and AAO (beta = -1.19, p = 0.55, n =708). Meta-analysis with previously published data did not indicate a significant effect on AAO (beta = -2.21, p = 0.083, n = 1304), but there was significant heterogeneity in the analyses of new and previously published data. VAMP4 rs11578699 was nominally associated with AAO in patients dichotomized by the common LRRK2 risk variant rs10878226 (beta=1.68, se=0.81 p=0.037). Interpretation: Analysis of DNM3 in previously unpublished data does not show an interaction between DNM3 and LRRK2 G2019S for AAO, however the inter-study heterogeneity may indicate ethnic-specific effects of DNM3 rs2421947. Analysis of sporadic PD patients stratified by the PD risk variant rs10878226 indicates a possible interaction between LRRK2 and VAMP4.

In this study, we explore the neuroprotective and modulatory potential of graphenic materials (GMs) in terms of the maturation of dopaminergic neurons and their capacity to counteract the cellular stress induced by toxins such as MPP+ (1-methyl-4-phenylpyridinium) and Tunicamycin. We found that GMs promote significant morphological changes in neuronal cells after prolonged exposure, enhancing both differentiation and cellular adhesion. Through structural analysis, we unveiled a complex organization of GMs and a marked upregulation of tyrosine hydroxylase (TH), a key marker of mature dopaminergic neurons. Under oxidative stress induced by MPP+, GMs significantly reduced the release of lactate dehydrogenase (LDH), indicating protection against mitochondrial damage. Moreover, GMs substantially decreased the levels of α-synuclein (α-Syn), a protein closely associated with neurodegenerative disorders such as Parkinson’s disease. Notably, partially reduced graphene oxide (PRGO) and fully reduced graphene oxide (FRGO) films were particularly effective at reducing α-Syn-associated toxicity compared to positive controls. Under conditions of endoplasmic reticulum (ER) stress triggered by Tunicamycin, GMs—especially PRGO microflakes—modulated the unfolded protein response (UPR) pathway. This effect was evidenced by the increased expression of BIP/GRP78 and the decreased phosphorylation of stress sensors such as PERK and eIF2α; this suggests that a protective role is played against ER stress. Additionally, GMs enhanced the synthesis of Torsin 1A, a chaperone protein involved in correcting protein folding defects, with PRGO microflakes showing up to a fivefold increase relative to the controls. Through the cFos analysis, we further revealed a pre-adaptive cellular response in GM-treated cells exposed to MPP+, with PRGO microflakes inducing a significant twofold increase in cFos expression compared to the positive control, indicating partial protection against oxidative stress. In conclusion, these results underscore GMs’ capacity to modulate the critical cellular pathways involved in oxidative, mitochondrial, and ER stress responses, positioning them as promising candidates for future neuroprotective and therapeutic strategies.

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.