The PRALINE database: protein and Rna humAn singLe nucleotIde variaNts in condEnsates.

SUMMARY: Biological condensates are membraneless organelles with different material properties. Proteins and RNAs are the main components, but most of their interactions are still unknown. Here, we introduce PRALINE, a database for the interrogation of proteins and RNAs contained in stress granules, processing bodies and other assemblies including droplets and amyloids. PRALINE provides information about the predicted and experimentally validated protein-protein, protein-RNA and RNA-RNA interactions. For proteins, it reports the liquid-liquid phase separation and liquid-solid phase separation propensities. For RNAs, it provides information on predicted secondary structure content. PRALINE shows detailed information on human single-nucleotide variants, their clinical significance and presence in protein and RNA binding sites, and how they can affect condensates' physical properties. AVAILABILITY AND IMPLEMENTATION: PRALINE is freely accessible on the web at http://praline.tartaglialab.com.

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.

Introduction: Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods: We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results: MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.

Transcriptomic differences in MSA clinical variants.

BACKGROUND: Multiple system atrophy (MSA) is a rare oligodendroglial synucleinopathy of unknown etiopathogenesis including two major clinical variants with predominant parkinsonism (MSA-P) or cerebellar dysfunction (MSA-C). OBJECTIVE: To identify novel disease mechanisms we performed a blood transcriptomic study investigating differential gene expression changes and biological process alterations in MSA and its clinical subtypes. METHODS: We compared the transcriptome from rigorously gender and age-balanced groups of 10 probable MSA-P, 10 probable MSA-C cases, 10 controls from the Catalan MSA Registry (CMSAR), and 10 Parkinson Disease (PD) patients. RESULTS: Gene set enrichment analyses showed prominent positive enrichment in processes related to immunity and inflammation in all groups, and a negative enrichment in cell differentiation and development of the nervous system in both MSA-P and PD, in contrast to protein translation and processing in MSA-C. Gene set enrichment analysis using expression patterns in different brain regions as a reference also showed distinct results between the different synucleinopathies. CONCLUSIONS: In line with the two major phenotypes described in the clinic, our data suggest that gene expression and biological processes might be differentially affected in MSA-P and MSA-C. Future studies using larger sample sizes are warranted to confirm these results.