Large-scale proteomics analysis of five brain regions from Parkinson's disease patients with a GBA1 mutation.

Despite being the second most common neurodegenerative disorder, little is known about Parkinson's disease (PD) pathogenesis. A number of genetic factors predispose towards PD, among them mutations in GBA1, which encodes the lysosomal enzyme acid-ß-glucosidase. We now perform non-targeted, mass spectrometry based quantitative proteomics on five brain regions from PD patients with a GBA1 mutation (PD-GBA) and compare to age- and sex-matched idiopathic PD patients (IPD) and controls. Two proteins were differentially-expressed in all five brain regions whereas significant differences were detected between the brain regions, with changes consistent with loss of dopaminergic signaling in the substantia nigra, and activation of a number of pathways in the cingulate gyrus, including ceramide synthesis. Mitochondrial oxidative phosphorylation was inactivated in PD samples in most brain regions and to a larger extent in PD-GBA. This study provides a comprehensive large-scale proteomics dataset for the study of PD-GBA.

Elevation of gangliosides in four brain regions from Parkinson's disease patients with a GBA mutation.

A number of genetic risk factors have been identified over the past decade for Parkinson's Disease (PD), with variants in GBA prominent among them. GBA encodes the lysosomal enzyme that degrades the glycosphingolipid, glucosylceramide (GlcCer), with the activity of this enzyme defective in Gaucher disease. Based on the ill-defined relationship between glycosphingolipid metabolism and PD, we now analyze levels of various lipids by liquid chromatography/electrospray ionization-tandem mass spectrometry in four brain regions from age- and sex-matched patient samples, including idiopathic PD, PD patients with a GBA mutation and compare both to control brains (n = 21 for each group) obtained from individuals who died from a cause unrelated to PD. Of all the glycerolipids, sterols, and (glyco)sphingolipids (251 lipids in total), the only lipid class which showed significant differences were the gangliosides (sialic acid-containing complex glycosphingolipids), which were elevated in 3 of the 4 PD-GBA brain regions. There was no clear correlation between levels of individual gangliosides and the genetic variant in Gaucher disease [9 samples of severe (neuronopathic), 4 samples of mild (non-neuronopathic) GBA variants, and 8 samples with low pathogenicity variants which have a higher risk for development of PD]. Most brain regions, i.e. occipital cortex, cingulate gyrus, and striatum, did not show a statistically significant elevation of GlcCer in PD-GBA. Only one region, the middle temporal gyrus, showed a small, but significant elevation in GlcCer concentration in PD-GBA. We conclude that changes in ganglioside, but not in GlcCer levels, may contribute to the association between PD and GBA mutations.

Hope for male fecundity: clinically insignificant changes in semen parameters over 10 years at a single clinic while assessing an infertility population.

PURPOSE: What is the trend in sperm parameters in a group of men attending a single reproductive center, over a 10-year period? METHODS: A retrospective study was conducted on 12,188 semen samples obtained from unique individuals who attended a university reproductive clinic from 2009 to 2018, inclusively. Semen analysis was done using computer-assisted sperm analysis and verified by an andrologist. Analysis was done after dividing the dataset into two groups: above WHO 2010 lower reference limits (ARL) (N = 6325) and below the reference limits (BRL) (N = 5521). RESULTS: Volume increased slightly (ARL, p = 0.049) before returning to baseline or was stable (BRL, p = 0.59). Sperm concentration and total count of the BRL and ARL group declined initially and then recovered slightly (p < 0.0001, in all cases). Although these changes were statistically significant, this was due to the large study population; clinically, these changes were quite mild and would not have been significant for fertility. Sperm total motility and progressive motility of both the BRL group and the ARL group increased slightly from 2009 until 2015 and then decreased back to baseline (p < 0.0001). This change offset the decrease in count seen in those years. A spurious change was observed with sperm morphology that declined after the first 2 years and remained stable thereafter (p < 0.0001, in both groups). However, this change was attributed to a contemporaneous change in the method of analyzing strict morphology which happened when the change occurred. CONCLUSION: While statistically significant changes were found, clinically, these changes were quite mild and would not have been significant for fertility.

Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets.

As treatment protocols for medulloblastoma (MB) are becoming subgroup-specific, means for reliably distinguishing between its subgroups are a timely need. Currently available methods include immunohistochemical stains, which are subjective and often inconclusive, and molecular techniques-e.g., NanoString, microarrays, or DNA methylation assays-which are time-consuming, expensive and not widely available. Quantitative PCR (qPCR) provides a good alternative for these methods, but the current NanoString panel which includes 22 genes is impractical for qPCR. Here, we applied machine-learning-based classifiers to extract reliable, concise gene sets for distinguishing between the four MB subgroups, and we compared the accuracy of these gene sets to that of the known NanoString 22-gene set. We validated our results using an independent microarray-based dataset of 92 samples of all four subgroups. In addition, we performed a qPCR validation on a cohort of 18 patients diagnosed with SHH, Group 3 and Group 4 MB. We found that the 22-gene set can be reduced to only six genes (IMPG2, NPR3, KHDRBS2, RBM24, WIF1, and EMX2) without compromising accuracy. The identified gene set is sufficiently small to make a qPCR-based MB subgroup classification easily accessible to clinicians, even in developing, poorly equipped countries.

An age-based sperm nomogram: the McGill reference guide.

STUDY QUESTION: How does age affect various semen parameters? SUMMARY ANSWER: For most semen parameters, the nomogram of the entire population was biphasic, peaking around the fourth decade of life. WHAT IS KNOWN ALREADY: In clinical practice, semen quality is examined by using the WHO 2010 reference limits but these limits do not account for male age. A percentile-based, large-scale nomogram describing how different semen parameters change throughout reproductive life has been lacking. STUDY DESIGN, SIZE, DURATION: A retrospective study was conducted with 12 188 sperm samples, obtained from individuals who attended the McGill University Health Centre reproductive clinic between 2009 and 2018. PARTICIPANTS/MATERIALS, SETTING, METHODS: One sample from each individual who attended the clinic during the study period was analysed by using computer-assisted sperm analysis (CASA). The analysed parameters were human-verified and included sperm concentration, motility, progressive motility, total count, morphology and semen volume. Based on this analysis, the entire dataset (n = 12 188) was further divided into two groups of samples: samples that surpassed the WHO 2010 lower reference limits ('above reference limits' group, ARL; n = 6305), and samples that did not ('below reference limit' group, BRL; n = 5883). Regression quantiles were fitted as a function of age to generate age-dependent nomograms, and these quantiles were divided into 5th, 25th, 50th, 75th and 95th percentiles. MAIN RESULTS AND THE ROLE OF CHANCE: In the entire dataset, age had a significant influence (P < 0.001) on all parameters (except morphology) which demonstrated a biphasic trend peaking in the fourth decade of life. In the ARL group, age had a significant influence (P < 0.01) on all semen parameters except sperm concentration and morphology. However, unlike in the entire dataset, only semen volume demonstrated a biphasic trend in the ARL group (peaking in the fourth decade of life), whereas other parameters either remained unchanged (concentration and morphology) or consistently declined with age (sperm motility, progressive motility and total sperm count). Percentile-based nomograms were generated for individuals between the ages of 20 and 60 years in the entire dataset and in the ARL group. LIMITATIONS, REASONS FOR CAUTION: First, the semen samples were obtained from individuals who were referred to a fertility clinic, such that the entire dataset does not necessarily represent the general population. Second, the cross-sectional sampling design increases variance, and the nomograms are less accurate in the 5th and 95th percentiles and at the extremes of the age distributions. Third, the observed age-dependent changes in semen parameters do not necessarily indicate changes in fertility, as not all factors that affect male fertility were analysed. Fourth, some of our semen analyses employed CASA, which can have variability issues. Finally, our models did not incorporate possible secular trends. WIDER IMPLICATIONS OF THE FINDINGS: We provide the first nomogram that correlates age with semen quality parameters in different population percentiles, thus complementing the current reference limits set by the WHO in 2010. Most examined semen parameters in our study changed non-linearly with age; therefore, age should be regularly employed as a factor in the clinical analysis of semen samples. STUDY FUNDING/COMPETING INTEREST(S): The authors have not received any funding to support this study. There are no conflicts of interest to declare. TRIAL REGISTRATION NUMBER: N/A.