Genetic studies of LRRK2 and PINK1 in Parkinson's disease

Background and objectives Parkinson’s disease (PD) is a common neurodegenerative disorder affecting 1% of the elderly. The disease causes a significant burden of illness and cost to society. The causes of PD have remained unknown, and the influence of genetic factors used to be controversial. In 2004, several mutations were identified in familial PD within two genes: PINK1 and the novel gene LRRK2. The aims of this thesis were to further investigate genetic, clinical and pathological aspects of these genes in PD and other neurodegenerative disorders causing parkinsonism. Five papers based on data from studies of these genes are included in this thesis. Methods - DNA from probands of families with autosomal dominant parkinsonism were sequenced to identify novel mutations in the LRRK2 gene. After the identification of a novel heterozygous LRRK2 mutation, we assessed the frequency of this mutation in a total of 248 families from different populations. We also screened samples of patients with idiopathic PD from three populations (Norway, Ireland, and Poland). Family members of mutation carriers were examined, and analyses of segregation, mutation haplotypes and penetrance were performed (Paper I). - A clinicogenetic study of PD in Central Norway was initiated several years ago at the Department of Neurology, St. Olav’s University Hospital in Trondheim. We screened 435 Norwegian patients diagnosed with PD and 519 control subjects from this study for the presence of seven known LRRK2 mutations. The clinical presentation of disease was studied in patients with mutations (Paper II). -A series of 242 patients from a clinicogenetic study of dementia in Central Norway (Trønderbrain) were screened for the presence of seven known pathogenic mutations previously reported in the LRRK2 gene (Paper III). - We examined several brain banks for cases with clinical or pathological features of parkinsonian disorders. DNA was obtained from frozen brain tissue of cases with parkinsonism, other neurodegenerative disorders and controls (total n=1584) and genotyped for the exon 41 LRRK2 g.6055G>A (G2019S) mutation. Available medical records of mutation carriers were reviewed and neuropathological examination was performed (Paper IV). - Comprehensive PINK1 mutation analysis was performed in a total of 131 patients from Norway with early-onset parkinsonism (onset =50 years) or familial late-onset PD. Mutations identified were examined in 350 Norwegian control individuals (Paper V). Results - We identified a novel heterozygous LRRK2 g.6055G>A mutation (G2019S). Seven of 248 families with autosomal dominant parkinsonism (2.8%) and six of 806 patients with idiopathic PD (0.7%) carried this mutation. All patients with this mutation shared an ancestral haplotype, indicative of a common founder. The mutation segregates with disease (multipoint LOD score 2.41). Penetrance is age dependent, increasing from 17% at age 50 years to 85% at age 70 years (Paper I). - Ten Norwegian PD patients were found to be heterozygote carriers of the Lrrk2 G2019S mutation. The clinical features included asymmetric resting tremor, bradykinesia, and rigidity with a good response to levodopa and could not be distinguished from idiopathic Parkinson’s disease. No Parkinson’s disease patient carried any of the other LRRK2 mutations (Paper II). We did not identify LRRK2 mutations in our series of dementia patients (Paper III). - Lrrk2 G2019S was found in 2% (n=8) of the pathologically confirmed PD/Lewy body disease (LBD) cases (n=405). Neuropathological examination showed typical LBD in all cases (Paper IV). -Heterozygous missense mutations in PINK1 were found in three of 131 patients; homozygous or compound heterozygous mutations were not identified. A parkinsonian phenotype, with asymmetric onset and without atypical features, characterised these patients clinically (Paper V). Conclusions We identified a novel mutation in the LRRK2 gene, g.6055G>A (G2019S). This mutation is a relatively common cause of both familial and sporadic PD, and it is found in a number of populations from North America and Europe, including Norway. This specific mutation is today the most prevalent known cause of PD, but seems to be rare in other neurodegenerative disorders. Clinically, patients with the Lrrk2 G2019S substitution present with a levodopa–responsive parkinsonian syndrome with asymmetric resting tremor, bradykinesia, and rigidity. Both clinically and pathologically LRRK2-associated PD appears to be indistinguishable from idiopathic disease. PINK1 mutations were rare in our Norwegian population, but heterozygote mutation carriers might be at increased risk for disease.PhD i nevrovitenskapPhD in Neuroscienc

Social isolation and all-cause mortality: a population-based cohort study in Denmark.

Social isolation is associated with increased mortality. Meta-analytic results, however, indicate heterogeneity in effect sizes. We aimed to provide new evidence to the association between social isolation and mortality by conducting a population-based cohort study. We reconstructed the Berkman and Syme's social network index (SNI), which combines four components of social networks (partnership, interaction with family/friends, religious activities, and membership in organizations/clubs) into an index, ranging from 0/1 (most socially isolated) to 4 (least socially isolated). We estimated cumulative mortality and adjusted mortality rate ratios (MRR) associated with SNI. We adjusted for potential important confounders, including psychiatric and somatic status, lifestyle, and socioeconomic status. Cumulative 7-year mortality in men was 11% for SNI 0/1 and 5.4% for SNI 4 and in women 9.6% for SNI 0/1 and 3.9% for SNI 4. Adjusted MRRs comparing SNI 0/1 with SNI 4 were 1.7 (95% CI: 1.1-2.6) among men and 1.6 (95% CI: 0.83-2.9) among women. Having no partner was associated with an adjusted MRR of 1.5 (95% CI: 1.2-2.1) for men and 1.7 (95% CI: 1.2-2.4) for women. In conclusion, social isolation was associated with 60-70% increased mortality. Having no partner was associated with highest MRR

Hydronium-dominated ion transport in carbon-dioxide-saturated electrolytes at low salt concentrations in nanochannels

Nanochannel ion transport is known to be governed by surface charge at low ionic concentrations. In this paper, we show that this surface charge is typically dominated by hydronium ions arising from dissolution of ambient atmospheric carbon dioxide. Taking the hydronium ions into account, we model the nanochannel conductance at low salt concentrations and identify a conductance minimum before saturation at a value independent of salt concentration in the dilute limit. Via the Poisson-Boltzmann equation, our model self-consistently couples chemical-equilibrium dissociation models of the silica wall and of the electrolyte bulk, parametrized by the dissociation reaction constants. Experimental data with aqueous KCl solutions in 165-nm-high silica nanochannels are described well by our model, both with and without extra hydronium from added HCl

Critical reflection and dialogical learning design: moving MOOCs beyond unidirectional transmission of content

Distance education and e-learning has been around for some time now. The ubiquitous development of the internet (Sharples, 2007) has however made way for the emergence of new educational formats such as the much talked-about Massive Open Online Courses (MOOCs). Within MOOCs users have access to educational literature and tasks at all times, which allow users to fit the course into their own pace, place and Personal Learning Environment (Attwell 2007).Today MOOCs has spread across the globe, and in Denmark we now see institutions such as Aarhus University developing a new course with roots in the MOOC format, however without the ‘Massive’ part (Aarhus University, 2016).Over a 5 week period we conducted a netnographic (Kozinet, 2015) mixed methods research of the MOOC Blended Learning Essentials (https://www.futurelearn.com/courses/blended-learning-gettingstarted/2). Contrary to the acclaimed potentials of MOOCs, our research showed a pronounced lack of dialogue and a high degree of what Freire (1996) calls “the banking concept of education,” entailing a high amount of one-way knowledge transmission (Hoem, 2006). To circumvent these tendencies, the paper presents a case analysis and design framework for moving MOOCs beyond “the banking concept of education” and towards dialogue in ways that support critical thinking; a high-level cognitive skill essential to higher education (Laurillard, 2012).

Critical reflection and dialogical learning design: moving MOOCs beyond unidirectional transmission of content

Distance education and e-learning has been around for some time now. The ubiquitous development of the internet (Sharples, 2007) has however made way for the emergence of new educational formats such as the much talked-about Massive Open Online Courses (MOOCs). Within MOOCs users have access to educational literature and tasks at all times, which allow users to fit the course into their own pace, place and Personal Learning Environment (Attwell 2007).Today MOOCs has spread across the globe, and in Denmark we now see institutions such as Aarhus University developing a new course with roots in the MOOC format, however without the ‘Massive’ part (Aarhus University, 2016).Over a 5 week period we conducted a netnographic (Kozinet, 2015) mixed methods research of the MOOC Blended Learning Essentials (https://www.futurelearn.com/courses/blended-learning-gettingstarted/2). Contrary to the acclaimed potentials of MOOCs, our research showed a pronounced lack of dialogue and a high degree of what Freire (1996) calls “the banking concept of education,” entailing a high amount of one-way knowledge transmission (Hoem, 2006). To circumvent these tendencies, the paper presents a case analysis and design framework for moving MOOCs beyond “the banking concept of education” and towards dialogue in ways that support critical thinking; a high-level cognitive skill essential to higher education (Laurillard, 2012).

Essensiell tremor

Essensiell tremor rammer nesten 1 % av befolkningen og kan være svært hemmende. Fortsatt er mye uklart rundt patofysiologi, miljøfaktorer og genetiske årsaker. Sannsynligvis rommer diagnosen flere forskjellige tilstander. Vi gir her en oppdatert oversikt over essensiell tremor, medikamentell og nevrokirurgisk behandling samt gjennomgang av forslag til nye definisjoner for tremor generelt og essensiell tremor spesielt. Essensiell tremor er en av de vanligste nevrologiske sykdommene, med en anslått prevalens på nesten 1 % og økende med alder (1). Tradisjonelt ble essensiell tremor oppfattet som en monosymptomatisk sykdom med aksjonstremor, men uten andre nevrologiske symptomer. Vi vet nå at tilstanden kan være mer kompleks, med flere motoriske og ikke-motoriske symptomer (2). En oversikt over essensiell tremor ble publisert i Tidsskriftet i 2008 (3). Siden den gang er det blitt introdusert en ny sykdomsklassifikasjon, og vi har fått flere og bedre alternativer til nevrokirurgisk behandling. Vi gir her en oppdatert klinisk oversikt over tilstanden. Artikkelen bygger dels på forfatternes egne erfaringer, dels på litteratursøk i PubMed, der et skjønnsmessig utvalg av relevante artikler om diagnostikk, etiologi, patofysiologi, definisjon og behandling etter 2007 er inkludert.måsjekke

To fly, or not to fly, that is the question:A deep learning model for peptide detectability prediction in mass spectrometry

Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related with the peptide sequence and its resulting physicochemical properties. Moreover, the high variability in MS data, particularly in peptide detectability and intensity across multiple analyses and samples, makes the development of a generic model for detectability prediction unfeasible. This underlines the need for tools that can be refined for specific experimental conditions. To address this need, we present Pfly, a deep learning model developed to predicts peptide detectability based solely on peptide sequence. Pfly distinguishes itself as a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor the model to their specific experimental conditions, facilitating the creation of lab-specific models. This, in turn, can lead to more accurate results and expand the model’s applicability across various research fields. The model’s architecture is an encoder-decoder with an attention mechanism. This tool classifies peptides as either flyers or non-flyers, providing both binary probabilities and detailed categorical probabilities for four distinct classes defined in this study: non-flyer, weak flyer, intermediate flyer, and strong flyer. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias towards synthesizability, improving the predictive capacity and outperforming state-of-the-art predictors in a benchmark comparison. The study further investigates the influence of protein abundance and the search engine, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated in the DLOmix framework and it is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.Competing Interest StatementM.W. is a co-founder and shareholder of MSAID GmbH and OmicScouts GmbH, with no operational role in both companies. As such, the authors declare no conflict of interest

Evidence for More than One Parkinson's Disease-Associated Variant within the HLA Region

Parkinson's disease (PD) was recently found to be associated with HLA in a genome-wide association study (GWAS). Follow-up GWAS's replicated the PD-HLA association but their top hits differ. Do the different hits tag the same locus or is there more than one PD-associated variant within HLA? We show that the top GWAS hits are not correlated with each other (0.00≤r2≤0.15). Using our GWAS (2000 cases, 1986 controls) we conducted step-wise conditional analysis on 107 SNPs with P<10−3 for PD-association; 103 dropped-out, four remained significant. Each SNP, when conditioned on the other three, yielded PSNP1 = 5×10−4, PSNP2 = 5×10−4, PSNP3 = 4×10−3 and PSNP4 = 0.025. The four SNPs were not correlated (0.01≤r2≤0.20). Haplotype analysis (excluding rare SNP2) revealed increasing PD risk with increasing risk alleles from OR = 1.27, P = 5×10−3 for one risk allele to OR = 1.65, P = 4×10−8 for three. Using additional 843 cases and 856 controls we replicated the independent effects of SNP1 (Pconditioned-on-SNP4 = 0.04) and SNP4 (Pconditioned-on-SNP1 = 0.04); SNP2 and SNP3 could not be replicated. In pooled GWAS and replication, SNP1 had ORconditioned-on-SNP4 = 1.23, Pconditioned-on-SNP4 = 6×10−7; SNP4 had ORconditioned-on-SNP1 = 1.18, Pconditioned-on-SNP1 = 3×10−3; and the haplotype with both risk alleles had OR = 1.48, P = 2×10−12. Genotypic OR increased with the number of risk alleles an individual possessed up to OR = 1.94, P = 2×10−11 for individuals who were homozygous for the risk allele at both SNP1 and SNP4. SNP1 is a variant in HLA-DRA and is associated with HLA-DRA, DRB5 and DQA2 gene expression. SNP4 is correlated (r2 = 0.95) with variants that are associated with HLA-DQA2 expression, and with the top HLA SNP from the IPDGC GWAS (r2 = 0.60). Our findings suggest more than one PD-HLA association; either different alleles of the same gene, or separate loci

Epigenome-wide association study of peripheral immune cell populations in Parkinson’s disease

Understanding the contribution of immune mechanisms to Parkinson’s disease pathogenesis is an important challenge, potentially of major therapeutic implications. To further elucidate the involvement of peripheral immune cells, we studied epigenome-wide DNA methylation in isolated populations of CD14+ monocytes, CD19+ B cells, CD4+ T cells, and CD8+ T cells from Parkinson’s disease patients and healthy control participants. We included 25 patients with a maximum five years of disease duration and 25 controls, and isolated four immune cell populations from each fresh blood sample. Epigenome-wide DNA methylation profiles were generated from 186 samples using the Illumina MethylationEpic array and association with disease status was tested using linear regression models. We identified six differentially methylated CpGs in CD14+ monocytes and one in CD8 + T cells. Four differentially methylated regions were identified in monocytes, including a region upstream of RAB32, a gene that has been linked to LRRK2. Methylation upstream of RAB32 correlated negatively with mRNA expression, and RAB32 expression was upregulated in Parkinson’s disease both in our samples and in summary statistics from a previous study. Our epigenome-wide association study of early Parkinson’s disease provides evidence for methylation changes across different peripheral immune cell types, highlighting monocytes and the RAB32 locus. The findings were predominantly cell-type-specific, demonstrating the value of isolating purified cell populations for genomic studies

To Fly, or Not to Fly, That Is the Question:A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry

Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is an encoder-decoder with an attention mechanism, classifying peptides as flyers or non-flyers, and providing both binary and categorical probabilities for four distinct classes defined in this study. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias toward synthesizability, improving predictive capacity and outperforming state-of-the-art predictors in benchmark comparisons across different human and cross-species datasets. The study further investigates the influence of protein abundance and rescoring, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated into the DLOmix framework and is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.</p