<p>Figure 1: Oxygen consumption rate of prostate cancer cell lines with different concentrations of metformin. Error bars depict the standard deviation. Standard deviation and p-values are calculated from three biological replicates. Figure 2: Glucose (upper panel) and reductive glutamine (lower panel) contribution to fatty acids in prostate cancer cell lines treated with metformin fitted from the measured incorporation of U-13C labeled glucose or 5-13C labeled glutamine (reductive glutamine contribution (red)) into palmitate. Error bars depict the 95 % confidence interval. Confidence intervals and p-values are calculated from two biological replicates. Figure 3: Comparison of glutamine contribution to tricarboxylic acid cycle in prostate cancer cell lines treated with metformin or rotenone measured by the incorporation of U-13C labeled glutamine into alpha-ketoglutarate (upper panel) and reductive glutamine contribution to palmitate using 5-13C labeled glutamine (lower panel). Confidence intervals and p-values are calculated from two biological replicates. Figure 4: (A) Relative cell count of prostate cancer cell lines treated with a combination of metformin and the glutaminase inhibitor BPTES. Cell counts were normalized to the condition with no metformin and no BPTES added. (B) Relative cell count of Huh7 liver cancer cells, which can grow without glutamine in the presence or absence of metformin. Cell counts were normalized to the corresponding condition with no metformin added. Error bars depict the standard deviation. Standard deviation and p-values are calculated from three biological replicates. Figure 5: (A) TSC2 expression in TSC2 knockdown cells and control. Standard diviation is calculated from three technical replicates. p-values are < 0.005.(B) Metformin sensitivity (2.5mM) in LNCaP cells with and without dimethyl alpha-ketoglutarate given by cell counts normalized to the corresponding condition with no metformin. Standard deviation and p-values are calculated from at least three biological replicates. p-value is < 0.05. All error bars depict the standard deviation.</p>
For the past five years, our annual reports have been tracking the clinical development of new drug-based therapies for the neurodegenerative condition of Parkinson's disease (PD). These reviews have followed the progress both of "symptomatic treatments" (ST - improves/reduces symptoms of the condition) and "disease-modifying treatments" (DMT - attempts to delay/slow progression by addressing the underlying biology of PD). Efforts have also been made to further categorize these experimental treatments based on their mechanisms of action and class of drug.
Objective: The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age-at-onset of Parkinson's disease. Methods: We performed the first genome-wide association study of penetrance and age-at-onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non-cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age-at-onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genome-wide association study of Parkinson's disease was able to explain variability in penetrance and age-at-onset in LRRK2 mutation carriers. Results: A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; P-value=2.5E-08, beta=1.27, SE=0.23, risk allele: C) met genome-wide significance for the penetrance model. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: P-value=1.1E-07; age-at-onset top variant: P-value=9.3E-07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age-at-onset. Interpretation: This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations.
The genomic landscape of the Indian population, particularly for age-related disorders like Parkinsons disease (PD) remains underrepresented in global research. Genetic variability in PD has been studied predominantly in European populations, offering limited insights into its role within the Indian population. To address this gap, we conducted the first pan-India genomic survey of PD involving 4,806 cases and 6,364 controls, complemented by a meta-analysis integrating summary statistics from a multi-ancestry PD meta-analysis (N=611,485). We further leveraged RNA-sequencing data from lymphoblastoid cell lines of 731 individuals from the 1000 Genomes project to evaluate the expression of key loci across global populations. Our findings reveal a higher genetic burden of PD in the Indian population compared to Europeans, accounting for ~30% of the previously unexplained heritability. Thirteen genome-wide significant loci were identified, including two novel loci, with an additional three loci uncovered through meta-analysis. Polygenic risk score analysis showed moderate transferability from European populations. Our results highlight the importance of genetic loci in immune function, lipid metabolism and SNCA aggregation in PD pathogenesis, with gene expression variability emphasizing population-specific differences. We also established South Asias largest PD biobank, providing a foundation for patient-centric approaches to PD research and treatment in India.
Objective The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age‐at‐onset of Parkinson's disease. Methods We performed the first genomewide association study of penetrance and age‐at‐onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non‐cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age‐at‐onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genomewide association study of Parkinson's disease was able to explain variability in penetrance and age‐at‐onset in LRRK2 mutation carriers. Results A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; p value = 2.5E‐08, beta = 1.27, SE = 0.23, risk allele: C) met genomewide significance for the penetrance model. Co‐immunoprecipitation analyses of LRRK2 and CORO1C supported an interaction between these 2 proteins. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: p value = 1.1E‐07; age‐at‐onset top variant: p value = 9.3E‐07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age‐at‐onset. Interpretation This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations. ANN NEUROL 2021;90:82–94
In 2023, a workshop was organized by the UK charity Cure Parkinson's with The Michael J Fox Foundation for Parkinson's Research and Parkinson's UK to review the field of growth factors (GFs) for Parkinson's disease (PD). This was a follow up to a previous meeting held in 2019.1 This 2023 workshop reviewed new relevant data that has emerged in the intervening 4 years around the development of new GFs and better models for studying them including the merit of combining treatments as well as therapies that can be modulated. We also discussed new insights into GF delivery and trial design that have emerged from the analyses of completed GDNF trials, including the patient voice, as well as the recently completed CDNF trial.2 We then concluded with our recommendations on how GF studies in PD should develop going forward.
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