Matteo Giulietti

SpliceAid: a database of experimental RNA target motifs bound by splicing proteins in humans

UNLABELLED The correct post-transcriptional RNA processing is finely regulated by RNA-binding proteins. Unfortunately, there is little experimental information on target RNA sequences of RNA-binding proteins and moreover such experimentally derived target sequences are annotated in a compact form by the score matrices that overestimate the number of possible recognized sequences. We carried out an exhaustive hand curated literature search to create a database, SpliceAid, collecting all the experimentally assessed target RNA sequences that are bound by splicing proteins in humans. We built a web resource, database driven, to easy query SpliceAid and give back the results by an accurate and dynamic graphic representation. AVAILABILITY SpliceAid database is freely accessible at http://www.introni.it/splicing.html.

BAP1, PBRM1 and SETD2 in clear-cell renal cell carcinoma: molecular diagnostics and possible targets for personalized therapies

Several novel recurrent mutations of histone modifying and chromatin remodeling genes have been identified in renal cell carcinoma. These mutations cause loss of function of several genes located in close proximity to VHL and include PBRM1, BAP1 and SETD2. PBRM1 encodes for BAF180, a component of the SWI/SNF chromatin remodeling complex, and is inactivated in, on average, 36% of clear cell renal cell carcinoma (ccRCC). Mutations of BAP1 encode for the histone deubiquitinase BRCA1 associated protein-1, and are present in 10% of ccRCCs. They are largely mutually exclusive with PBRM1 mutations. Mutations to SETD2, a histone methyltransferase, occur in 10% of ccRCC. BAP1- or SETD2-mutated ccRCCs have been associated with poor overall survival, while PBRM1 mutations seem to identify a favorable group of ccRCC tumors. This review describes the roles of PBRM1, BAP1 and SETD2 in the development and progression of ccRCC and their potential for future personalized approaches.

An improved in silico selection of phenotype affecting polymorphisms in SLC6A4, HTR1A and HTR2A genes

Among the experimentally assessed DNA variations in serotonin related genes, some influence physiological expression of personality and mental disorders, others alter the responses to pharmacological and/or psychotherapeutic treatments. Because of the huge number of polymorphisms lying in genes and of the great length of time necessary to perform association studies, a selection of the variations being studied is a necessary and crucial step.

Epithelial to Mesenchymal Transition in Renal Cell Carcinoma: Implications for Cancer Therapy

Epithelial-to-mesenchymal transition (EMT) is a developmentally vital reversible process by which fully differentiated cells lose their epithelial features and acquire a migratory mesenchymal phenotype. EMT contributes to the metastatic potential of tumors. The expression profile and other biological properties of EMT suggest potential targets for cancer therapy, including in renal-cell carcinoma (RCC). The preclinical and clinical results have substantiated the promises that dysregulated elements leading to EMT can be a potential target in RCC patients. In this study, we illustrated the pathogenic and prognostic role of EMT in RCC. In addition, we reconstructed, by literature analysis, the different pathways implicated in the EMT process, thus supporting the rational for future EMT-directed therapeutic approaches for RCC patients.

Weighted gene co-expression network analysis reveals key genes involved in pancreatic ductal adenocarcinoma development.

PurposePancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Up till now, the patient’s prognosis remains poor which, among others, is due to the paucity of reliable early diagnostic biomarkers. In the past, candidate diagnostic biomarkers and therapeutic targets have been delineated from genes that were found to be differentially expressed in normal versus tumour samples. Recently, new systems biology approaches have been developed to analyse gene expression data, which may yield new biomarkers. As of yet, the weighted gene co-expression network analysis (WGCNA) tool has not been applied to PDAC microarray-based gene expression data.MethodsPDAC microarray-based gene expression datasets, listed in the Gene Expression Omnibus (GEO) database, were analysed. After pre-processing of the data, we built two final datasets, Normal and PDAC, encompassing 104 and 129 patient samples, respectively. Next, we constructed a weighted gene co-expression network and identified modules of co-expressed genes distinguishing normal from disease conditions. Functional annotations of the genes in these modules were carried out to highlight PDAC-associated molecular pathways and common regulatory mechanisms. Finally, overall survival analyses were carried out to assess the suitability of the genes identified as prognostic biomarkers.ResultsUsing WGCNA, we identified several key genes that may play important roles in PDAC. These genes are mainly related to either endoplasmic reticulum, mitochondrion or membrane functions, exhibit transferase or hydrolase activities and are involved in biological processes such as lipid metabolism or transmembrane transport. As a validation of the applied method, we found that some of the identified key genes (CEACAM1, MCU, VDAC1, CYCS, C15ORF52, TMEM51, LARP1 and ERLIN2) have previously been reported by others as potential PDAC biomarkers. Using overall survival analyses, we found that several of the newly identified genes may serve as biomarkers to stratify PDAC patients into low- and high-risk groups.ConclusionsUsing this new systems biology approach, we identified several genes that appear to be critical to PDAC development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.

How much do we know about the coupling of G-proteins to serotonin receptors?

Serotonin receptors are G-protein-coupled receptors (GPCRs) involved in a variety of psychiatric disorders. G-proteins, heterotrimeric complexes that couple to multiple receptors, are activated when their receptor is bound by the appropriate ligand. Activation triggers a cascade of further signalling events that ultimately result in cell function changes. Each of the several known G-protein types can activate multiple pathways. Interestingly, since several G-proteins can couple to the same serotonin receptor type, receptor activation can result in induction of different pathways. To reach a better understanding of the role, interactions and expression of G-proteins a literature search was performed in order to list all the known heterotrimeric combinations and serotonin receptor complexes. Public databases were analysed to collect transcript and protein expression data relating to G-proteins in neural tissues. Only a very small number of heterotrimeric combinations and G-protein-receptor complexes out of the possible thousands suggested by expression data analysis have been examined experimentally. In addition this has mostly been obtained using insect, hamster, rat and, to a lesser extent, human cell lines. Besides highlighting which interactions have not been explored, our findings suggest additional possible interactions that should be examined based on our expression data analysis.

Identification of candidate miRNA biomarkers for pancreatic ductal adenocarcinoma by weighted gene co-expression network analysis

PurposePancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a dismal prognosis which is, among others, due to a lack of suitable biomarkers and therapeutic targets. Previously, basic gene expression analysis methods have been used for their identification, but recently new algorithms have been developed allowing more comprehensive data analyses. Among them, weighted gene co-expression network analysis (WGCNA) has already been applied to several cancer types with promising results.MethodsWe applied WGCNA to miRNA expression data from PDAC patients. Specifically, we processed microarray-based expression data of 2555 miRNAs in serum from 100 PDAC patients and 150 healthy subjects. We identified network modules of co-expressed miRNAs in the healthy subject dataset and verified their preservation in the PDAC dataset. In the non-preserved modules, we selected key miRNAs and carried out functional enrichment analyses of their experimentally known target genes. Finally, we tested their prognostic significance using overall survival analyses.ResultsThrough WGCNA we identified several miRNAs that discriminate healthy subjects from PDAC patients and that, therefore, may play critical roles in PDAC development. At a functional level, we found that they regulate p53, FoxO and ErbB associated cellular signalling pathways, as well as cell cycle progression and various genes known to be involved in PDAC development. Some miRNAs were also found to serve as novel prognostic biomarkers, whereas others have previously already been proposed as such, thereby validating the WGCNA approach. In addition, we found that these novel data may explain at least some of our previous PDAC gene expression analysis results.ConclusionsWe identified several miRNAs critical for PDAC development using WGCNA. These miRNAs may serve as biomarkers for PDAC diagnosis/prognosis and patient stratification, and as putative novel therapeutic targets.

ExportAid: database of RNA elements regulating nuclear RNA export in mammals

MOTIVATION Regulation of nuclear mRNA export or retention is carried out by RNA elements but the mechanism is not yet well understood. To understand the mRNA export process, it is important to collect all the involved RNA elements and their trans-acting factors. RESULTS By hand-curated literature screening we collected, in ExportAid database, experimentally assessed data about RNA elements regulating nuclear export or retention of endogenous, heterologous or artificial RNAs in mammalian cells. This database could help to understand the RNA export language and to study the possible export efficiency alterations owing to mutations or polymorphisms. Currently, ExportAid stores 235 and 96 RNA elements, respectively, increasing and decreasing export efficiency, and 98 neutral assessed sequences. AVAILABILITY AND IMPLEMENTATION Freely accessible without registration at http://www.introni.it/ExportAid/ExportAid.html. Database and web interface are implemented in Perl, MySQL, Apache and JavaScript with all major browsers supported.

Computational analysis of the mutations in BAP1, PBRM1 and SETD2 genes reveals the impaired molecular processes in renal cell carcinoma.

Clear cell Renal Cell Carcinoma (ccRCC) is due to loss of von Hippel–Lindau (VHL) gene and at least one out of three chromatin regulating genes BRCA1-associated protein-1 (BAP1), Polybromo-1 (PBRM1) and Set domain-containing 2 (SETD2). More than 350, 700 and 500 mutations are known respectively for BAP1, PBRM1 and SETD2 genes. Each variation damages these genes with different severity levels. Unfortunately for most of these mutations the molecular effect is unknown, so precluding a severity classification. Moreover, the huge number of these gene mutations does not allow to perform experimental assays for each of them. By bioinformatic tools, we performed predictions of the molecular effects of all mutations lying in BAP1, PBRM1 and SETD2 genes. Our results allow to distinguish whether a mutation alters protein function directly or by splicing pattern destruction and how much severely. This classification could be useful to reveal correlation with patients’ outcome, to guide experiments, to select the variations that are worth to be included in translational/association studies, and to direct gene therapies.

Bioinformatic analyses to select phenotype affecting polymorphisms in HTR2C gene

Single nucleotide polymorphisms (SNPs) in serotonin related genes influence mental disorders, responses to pharmacological and psychotherapeutic treatments. In planning association studies, researchers that want to investigate new SNPs have to select some among a large number of candidates. Our aim is to guide researchers in the selection of the most likely phenotype affecting polymorphisms. Here, we studied serotonin receptor 2C (HTR2C) SNPs because, till now, only relatively few of about 2000 are investigated.