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  1. Article ; Online: The PRALINE database: protein and Rna humAn singLe nucleotIde variaNts in condEnsates.

    Vandelli, Andrea / Arnal Segura, Magdalena / Monti, Michele / Fiorentino, Jonathan / Broglia, Laura / Colantoni, Alessio / Sanchez de Groot, Natalia / Torrent Burgas, Marc / Armaos, Alexandros / Tartaglia, Gian Gaetano

    Bioinformatics (Oxford, England)

    2023  Volume 39, Issue 1

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Humans ; RNA/metabolism ; Organelles ; Proteins/metabolism ; Nucleotides/metabolism
    Chemical Substances RNA (63231-63-0) ; Proteins ; Nucleotides
    Language English
    Publishing date 2023-02-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btac847
    Database MEDical Literature Analysis and Retrieval System OnLINE

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

    Arnal Segura, Magdalena / Bini, Giorgio / Fernandez Orth, Dietmar / Samaras, Eleftherios / Kassis, Maya / Aisopos, Fotis / Rambla De Argila, Jordi / Paliouras, George / Garrard, Peter / Giambartolomei, Claudia / Tartaglia, Gian Gaetano

    Alzheimer's & dementia (Amsterdam, Netherlands)

    2022  Volume 14, Issue 1, Page(s) e12300

    Abstract: 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 ... ...

    Abstract 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
    Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.
    Language English
    Publishing date 2022-04-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2832898-X
    ISSN 2352-8729
    ISSN 2352-8729
    DOI 10.1002/dad2.12300
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Transcriptomic differences in MSA clinical variants.

    Pérez-Soriano, Alexandra / Arnal Segura, Magdalena / Botta-Orfila, Teresa / Giraldo, Darly / Fernández, Manel / Compta, Yaroslau / Fernández-Santiago, Rubén / Ezquerra, Mario / Tartaglia, Gian G / Martí, M J

    Scientific reports

    2020  Volume 10, Issue 1, Page(s) 10310

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Aged ; Case-Control Studies ; Cerebellar Diseases/blood ; Cerebellar Diseases/diagnosis ; Cerebellar Diseases/genetics ; Diagnosis, Differential ; Female ; Gene Expression Profiling ; Humans ; Male ; Middle Aged ; Multiple System Atrophy/blood ; Multiple System Atrophy/diagnosis ; Multiple System Atrophy/genetics ; Parkinson Disease/blood ; Parkinson Disease/diagnosis ; Parkinson Disease/genetics ; Transcriptome
    Language English
    Publishing date 2020-06-25
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-020-66221-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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