• Proteomic profiling in multiple sclerosis clinical courses reveals potential biomarkers of neurodegeneration.

    Liguori M, Qualtieri A, Tortorella C, Direnzo V, Bagalà A, Mastrapasqua M, Spadafora P, Trojano M.
    PLoS One. 2014 Aug 6;9(8):e103984. doi: 10.1371/journal.pone.0103984. eCollection 2014.
    (PMCID: PMC4123901) – (PMID: 25098164)

    The aim of this project was to perform an exploratory analysis of the cerebrospinal fluid (CSF) proteomic profiles of Multiple Sclerosis (MS) patients, collected in different phases of their clinical course, in order to investigate the existence of peculiar profiles characterizing the different MS phenotypes.
    The study was carried out on 24 Clinically Isolated Syndrome (CIS), 16 Relapsing Remitting (RR) MS, 11 Progressive (Pr) MS patients. The CSF samples were analyzed using the Matrix Assisted Laser Desorption Ionization Time Of Flight (MALDI-TOF) mass spectrometer in linear mode geometry and in delayed extraction mode (m/z range: 1000-25000 Da). Peak lists were imported for normalization and statistical analysis. CSF data were correlated with demographic, clinical and MRI parameters. The evaluation of MALDI-TOF spectra revealed 348 peak signals with relative intensity ≥1% in the study range. The peak intensity of the signals corresponding to Secretogranin II and Protein 7B2 were significantly upregulated in RRMS patients compared to PrMS, whereas the signals of Fibrinogen and Fibrinopeptide A were significantly downregulated in CIS compared to PrMS patients. Additionally, the intensity of the Tymosin β4 peak was the only signal to be significantly discriminated between the CIS and RRMS patients. Although with caution due to the relatively small size of the study populations, and considering that not all the findings remained significant after adjustment for multiple comparisons, in our opinion this mass spectrometry evaluation confirms that this technique may provide useful and important information to improve our understanding of the complex pathogenesis of MS.

  • Genome-wide association study in musician’s dystonia: a risk variant at the arylsulfatase G locus?

    Lohmann K, Schmidt A, Schillert A, Winkler S, Albanese A, Baas F, Bentivoglio AR, Borngräber F, Brüggemann N, Defazio G, Del Sorbo F, Deuschl G, Edwards MJ, Gasser T, Gómez-Garre P, Graf J, Groen JL, Grünewald A, Hagenah J, Hemmelmann C, Jabusch HC, Kaji R, Kasten M, Kawakami H, Kostic VS, Liguori M, Mir P, Münchau A, Ricchiuti F, Schreiber S, Siegesmund K, Svetel M, Tijssen MA, Valente EM, Westenberger A, Zeuner KE, Zittel S, Altenmüller E, Ziegler A, Klein C.
    Mov Disord. 2014 Jun;29(7):921-7. doi: 10.1002/mds.25791. Epub 2013 Dec 26.
    (PMID: 24375517)

    Musician’s dystonia (MD) affects 1% to 2% of professional musicians and frequently terminates performance careers. It is characterized by loss of voluntary motor control when playing the instrument. Little is known about genetic risk factors, although MD or writer’s dystonia (WD) occurs in relatives of 20% of MD patients. We conducted a 2-stage genome-wide association study in whites. Genotypes at 557,620 single-nucleotide polymorphisms (SNPs) passed stringent quality control for 127 patients and 984 controls. Ten SNPs revealed high statistical significance and entered the replication phase including 116 MD patients and 125 healthy musicians. A genome-wide significant SNP was also genotyped in 208 German or Dutch WD patients, 1,969 Caucasian, Spanish, and Japanese patients with other forms of focal or segmental dystonia as well as in 2,233 ethnically matched controls. Genome-wide significance with MD was observed for an intronic variant in the arylsulfatase G (ARSG) gene (rs11655081). This SNP was also associated with WD but not with any other focal or segmental dystonia. The allele frequency of rs11655081 varies substantially between different populations. The population stratification in our sample was modest, but the effect size may be overestimated. Using a small but homogenous patient sample, we provide data for a possible association of ARSG with MD. The variant may also contribute to the risk of WD, a form of dystonia that is often found in relatives of MD patients.

  • Identification of miRNA:mRNA Interaction Networks.

    G. Pio, M. Ceci, D. D’Elia, D. Malerba
    Chapter in Book “Machine Learning and Knowledge Discovery in Databases”, ECML PKDD 2014, Part III, LNCS 8726, pp. 508–511. T. Calders et al. (Eds.): Springer-Verlag Berlin Heidelberg 2014. DOI 10.1007/978-3-662-44845-8

  • Learning to combine miRNA target predictions: A semi-supervised ensemble learning approach (Discussion Paper)

    G. Pio, M. Ceci, D. D’Elia, D. Malerba
    In Conference Proceeding of 22nd Italian Symposium on Advanced Database Systems, SEBD 2014; 16 June 2014 through 18 June 2014; Code 109101, ISBN: 978-163439145-0.
    Conference paper: http://www.scopus.com/record/display.url?view=basic&eid=2-s2.0-84914125251&origin=resultslist

    Link prediction in network data is a data mining task which is receiving significant attention due to its applicability in various do- mains. An example can be found in social network analysis, where the goal is to identify connections between users. Another application can be found in computational biology, where the goal is to identify previ- ously unknown relationships among biological entities. For example, the identification of regulatory activities (links) among genes would allow bi- ologists to discover possible gene regulatory networks. In the literature, several approaches for link prediction can be found, but they often fail in simultaneously considering all the possible criteria (e.g. network topol- ogy, nodes properties, autocorrelation among nodes). In this paper we present a semi-supervised data mining approach which learns to combine the scores returned by several link prediction algorithms. The proposed solution exploits both a small set of validated examples of links and a huge set of unlabeled links. The application we consider regards the identification of links between genes and miRNAs, which can contribute to the understanding of their roles in many biological processes. The specific application requires to learn from only positively labeled examples of links and to face with the high unbalancing between labeled and unla- beled examples. Results show a significant improvement with respect to single prediction algorithms and with respect to baseline combination.

  • Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach.

    Gianvito Pio, Donato Malerba, Domenica D’Elia, Michelangelo Ceci
    BMC Bioinformatics 2014, 15(Suppl 1):S4 doi:10.1186/1471-2105-15-S1-S4

    Background: MicroRNAs (miRNAs) are small non-coding RNAs which play a key role in the post-transcriptional regulation of many genes. Elucidating miRNA-regulated gene networks is crucial for the understanding of mechanisms and functions of miRNAs in many biological processes, such as cell proliferation, development, differentiation and cell homeostasis, as well as in many types of human tumors. To this aim, we have recently presented the biclustering method HOCCLUS2, for the discovery of miRNA regulatory networks. Experiments on predicted interactions revealed that the statistical and biological consistency of the obtained networks is negatively affected by the poor reliability of the output of miRNA target prediction algorithms. Recently, some learning approaches have been proposed to learn to combine the outputs of distinct prediction algorithms and improve their accuracy. However, the application of classical supervised learning algorithms presents two challenges: i) the presence of only positive examples in datasets of experimentally verified interactions and ii) unbalanced number of labeled and unlabeled examples.

    Results: We present a learning algorithm that learns to combine the score returned by several prediction algorithms, by exploiting information conveyed by (only positively labeled/) validated and unlabeled examples of interactions. To face the two related challenges, we resort to a semi-supervised ensemble learning setting. Results obtained using miRTarBase as the set of labeled (positive) interactions and mirDIP as the set of unlabeled interactions show a significant improvement, over competitive approaches, in the quality of the predictions. This solution also improves the effectiveness of HOCCLUS2 in discovering biologically realistic miRNA:mRNA regulatory networks from large-scale prediction data. Using the miR-17-92 gene cluster family as a reference system and comparing results with previous experiments, we find a large increase in the number of significantly enriched biclusters in pathways, consistent with miR-17-92 functions.

    Conclusions: The proposed approach proves to be fundamental for the computational discovery of miRNA regulatory networks from large-scale predictions. This paves the way to the systematic application of HOCCLUS2 for a comprehensive reconstruction of all the possible multiple interactions established by miRNAs in regulating the expression of gene networks, which would be otherwise impossible to reconstruct by considering only experimentally validated interactions.

  • BiP-Day 2013: “Prima Giornata della Bioinformatica Pugliese” – Workshop report.

    Domenica D’Elia, Sabino Liuni
    EMBnet.journal 20, e758. http://dx.doi.org/10.14806/ej.20.0.758

    On 5 December 2013, a regional workshop on Bioinformatics in Apulia (BiP-Day 2013) was held in Bari (IT) under the patronage of the Italian Bioinformatics Society (BITS) and EMBnet. The aim of the workshop was to stimulate tighter collaboration between life science researchers and private biotech companies in the Apulia Region around cutting-edge topics in biological and clinical research, for which bioinformatics R&D is key.The programme was structured into three main sessions: 1) Regional development programmes and major infrastructures for Bioinformatics in the Apulia Region; 2) Bioinformatics projects in bio-medicine, biodiversity, agri-food and bioinformatics training programmes; 3) Research & Business: the importance of communication. Presentations are available from the workshop website associated to the programme, and from the News section Presentations.

  • 2014 Annual General Meeting: Publicity & Public Relations Project Committee Report.

    Domenica D’Elia, Vicky Schneider-Gricar, Rubina Karla, Cesar Bonavides-Martinez, Rafael Jimenez
    EMBnet.journal 20, e758. http://dx.doi.org/10.14806/ej.20.0.800

    To properly respond to the most urgent needs raised during the EMBnet workshop held in Valencia in May 2013, the Publicity & Public Relations Project Committee (P&PR PC) established two task-forces: i) a website task-force, comprising Rafael Jimenez and Cesar Bonavides-Martinez; and ii) a communication strategy task-force, comprising Vicky Schneider and Rubina Kalra. An overview of activities and achievements was given by the PC Chair, and discussed during the EMBnet 2014 workshop held in Lyon, 30 May. The programme also included a “Website hands-on” by Rafael Jimenez on “How to use the EMBnet website, add and manage content” aiming to expose members to some of its basic functions and services, and to practise their use. This article describes the achievements of the P&PR PC since June 2013, and plans for the next year.