Dysregulation of the causative genes for hereditary parkinsonism in the midbrain in Parkinson's disease.

BACKGROUND AND OBJECTIVES: Many hereditary movement disorders with complex phenotypes without a locus symbol prefix for familial PD present as parkinsonism; however, the dysregulation of genes associated with these phenotypes in the SNpc of PD patients has not been systematically studied. METHODS: Gene set enrichment analyses were performed using 10 previously published genome-wide expression datasets obtained by laser-captured microdissection of pigmented neurons in the SNpc. A custom-curated gene set for hereditary parkinsonism consisting of causative genes (n = 78) related to disorders with a parkinsonism phenotype, but not necessarily idiopathic or monogenic PD, was constructed from the Online Mendelian Inheritance in Man database. RESULTS: In 9 of the 10 gene expression data sets, gene set enrichment analysis showed that the disease-causing genes for hereditary parkinsonism were downregulated in the SNpc in PD patients compared to controls (nominal P values <0.05 in five studies). Among the 63 leading edge subset genes representing downregulated genes in PD, 79.4% were genes without a locus symbol prefix for familial PD. A meta-gene set enrichment analysis performed with a random-effect model showed an association between the gene set for hereditary parkinsonism and PD with a negative normalized enrichment score value (-1.40; 95% CI: -1.52∼-1.28; P < 6.2E-05). CONCLUSION: Disease-causing genes with a parkinsonism phenotype are downregulated in the SNpc in PD. Our study highlights the importance of genes associated with hereditary movement disorders with parkinsonism in understanding the pathogenesis of PD. © 2017 International Parkinson and Movement Disorder Society.

RelCurator: a text mining-based curation system for extracting gene-phenotype relationships specific to neurodegenerative disorders.

BACKGROUND: The identification of gene-phenotype relationships is important in medical genetics as it serves as a basis for precision medicine. However, most of the gene-phenotype relationship data are buried in the biomedical literature in textual form. OBJECTIVE: We propose RelCurator, a curation system that extracts sentences including both gene and phenotype entities related to specific disease categories from PubMed articles, provides rich additional information such as entity taggings, and predictions of gene-phenotype relationships. METHODS: We targeted neurodegenerative disorders and developed a deep learning model using Bidirectional Gated Recurrent Unit (BiGRU) networks and BioWordVec word embeddings for predicting gene-phenotype relationships from biomedical texts. The prediction model is trained with more than 130,000 labeled PubMed sentences including gene and phenotype entities, which are related to or unrelated to neurodegenerative disorders. RESULTS: We compared the performance of our deep learning model with those of Bidirectional Encoder Representations from Transformers (BERT), Support Vector Machine (SVM), and simple Recurrent Neural Network (simple RNN) models. Our model performed better with an F1-score of 0.96. Furthermore, the evaluation done using a few curation cases in the real scenario showed the effectiveness of our work. Therefore, we conclude that RelCurator can identify not only new causative genes, but also new genes associated with neurodegenerative disorders' phenotype. CONCLUSION: RelCurator is a user-friendly method for accessing deep learning-based supporting information and a concise web interface to assist curators while browsing the PubMed articles. Our curation process represents an important and broadly applicable improvement to the state of the art for the curation of gene-phenotype relationships.

Application of poly (vinyl alcohol)-cryogels to immobilizing nitrifiers: Enhanced tolerance to shear stress-induced destruction and viability control.

The hardness of poly (vinyl alcohol)-cryogels (PVA-CGs) was improved under three parameter conditions: 7.5 %-12.5 % PVA, 1-5 freezing-thawing cycles (FTCs), and the addition of 0 %-10 % glycerol as a cryoprotectant. This study investigated the effects of shear stress-induced destruction (SSID) on mechanical strength by inducing rapid erosion with a high frictional force. Tolerance to SSID (Tol-SSID) exhibited different sensitivities and trends depending on the above three fabrication parameters. The measured Tol-SSID exhibited consistent and inconsistent trends with tensile strength and swelling, respectively. Tol-SSID evaluation provides new insights into the practically meaningful mechanical strength of PVA-CGs against strong friction, which simulates extreme shear stress in a bioreactor. A PVA-CG with a PVA concentration of 10 % and in two FTCs resulted in Tol-SSID and tensile strength of 88.3 % and 0.59 kPa, respectively. Here, 5 % glycerol was added to maintain the bacterial respiration activity of immobilized nitrifiers of 0.097 mg-O2/g-VSS·min and survival of 88.6 %. The continuous mode of nitrification using the optimized PVA-CG for 10 days resulted in an ammonia removal rate of 0.2173 kg-N/m3·d, which is an improvement over cases without glycerol addition: 0.1426 and 0.1472 kg-N/m3·d for PVA-CGs in two and three FTCs, respectively.

Accuracy of Machine Learning Using the Montreal Cognitive Assessment for the Diagnosis of Cognitive Impairment in Parkinson's Disease.

OBJECTIVE: The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson's disease (PD). Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI. METHODS: In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson's Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments. Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method. RESULTS: Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual (n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ± 0.03) and machine learning (0.78 ± 0.03). Using a more stringent dataset that excluded MoCA results (n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ± 0.05), but not that of machine learning (0.74 ± 0.07), was significantly reduced. Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87-0.89). CONCLUSION: Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.

PhenGenVar: A User-Friendly Genetic Variant Detection and Visualization Tool for Precision Medicine.

Precision medicine has been revolutionized by the advent of high-throughput next-generation sequencing (NGS) technology and development of various bioinformatic analysis tools for large-scale NGS big data. At the population level, biomedical studies have identified human diseases and phenotype-associated genetic variations using NGS technology, such as whole-genome sequencing, exome sequencing, and gene panel sequencing. Furthermore, patients' genetic variations related to a specific phenotype can also be identified by analyzing their genomic information. These breakthroughs paved the way for the clinical diagnosis and precise treatment of patients' diseases. Although many bioinformatics tools have been developed to analyze the genetic variations from the individual patient's NGS data, it is still challenging to develop user-friendly programs for clinical physicians who do not have bioinformatics programing skills to diagnose a patient's disease using the genomic data. In response to this demand, we developed a Phenotype to Genotype Variation program (PhenGenVar), which is a user-friendly interface for monitoring the variations in a gene of interest for molecular diagnosis. This allows for flexible filtering and browsing of variants of the disease and phenotype-associated genes. To test this program, we analyzed the whole-genome sequencing data of an anonymous person from the 1000 human genome project data. As a result, we were able to identify several genomic variations, including single-nucleotide polymorphism, insertions, and deletions in specific gene regions. Therefore, PhenGenVar can be used to diagnose a patient's disease. PhenGenVar is freely accessible and is available at our website.

The Protein-Protein Interaction Network of Hereditary Parkinsonism Genes Is a Hierarchical Scale-Free Network.

PURPOSE: Hereditary parkinsonism genes consist of causative genes of familial Parkinson's disease (PD) with a locus symbol prefix (PARK genes) and hereditary atypical parkinsonian disorders that present atypical features and limited responsiveness to levodopa (non-PARK genes). Although studies have shown that hereditary parkinsonism genes are related to idiopathic PD at the phenotypic, gene expression, and genomic levels, no study has systematically investigated connectivity among the proteins encoded by these genes at the protein-protein interaction (PPI) level. MATERIALS AND METHODS: Topological measurements and physical interaction enrichment were performed to assess PPI networks constructed using some or all the proteins encoded by hereditary parkinsonism genes (n=96), which were curated using the Online Mendelian Inheritance in Man database and literature. RESULTS: Non-PARK and PARK genes were involved in common functional modules related to autophagy, mitochondrial or lysosomal organization, catecholamine metabolic process, chemical synapse transmission, response to oxidative stress, neuronal apoptosis, regulation of cellular protein catabolic process, and vesicle-mediated transport in synapse. The hereditary parkinsonism proteins formed a single large network comprising 51 nodes, 83 edges, and three PPI pairs. The probability of degree distribution followed a power-law scaling behavior, with a degree exponent of 1.24 and a correlation coefficient of 0.92. LRRK2 was identified as a hub gene with the highest degree of betweenness centrality; its physical interaction enrichment score was 1.28, which was highly significant. CONCLUSION: Both PARK and non-PARK genes show high connectivity at the PPI and biological functional levels.

Combined machine learning and biomolecular analysis for stability assessment of anaerobic ammonium oxidation under salt stress.

In this study, the stability of the total nitrogen removal efficiency (TNRE) was modeled using an artificial neural network (ANN)-based binary classification model for the anaerobic ammonium oxidation (AMX) process under saline conditions. The TNRE was stabilized to 80.2 ± 11.4% at the final phase under the salinity of 1.0 ± 0.02%. The results of terminal restriction fragment length polymorphism (T-RFLP) analysis showed the predominance of Candidatus Jettenia genus. Real-time quantitative PCR analysis revealed the average abundance of Ca. Jettenia and Kuenenia spp. increased in 3.2 ± 5.4 × 108 and 2.0 ± 2.2 × 105 copies/mL, respectively. The prediction accuracy using operational parameters with data augmentation was 88.2%. However, integration with T-RFLP and real-time qPCR signals improved the prediction accuracy by 97.1%. This study revealed the feasible application of machine learning and biomolecular signals to the stability prediction of the AMX process under increased salinity.