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Biology subjects

Pinoli, P.

Publications and source records attributed to Pinoli, P..

5 recordsLinked to original sources

The molecular basis of the anticancer effect of statins

Statins, one of the most used class of cardiovascular drugs with the primary function of reducing blood cholesterol levels, exert their effect by inhibiting the enzyme HMG-CoA reductase, the key player in cholesterol biosynthesis. While the primary indication for statins is the prevention of cardiovascular diseases, there has been growing interest in their potential anticancer effects. However, the current evidence on these effects is largely based on epidemiological observations and preclinical research, not yet substantiated by knowledge of the mechanisms behind it. Here we show that statins have an anticancer effect as they exploit the principle of Synthetic Lethality, a concept in which the combination of two non-lethal genetic or molecular events results in cell death or impairment. When either of these events occurs alone, it is not lethal, but when they happen coupled, they create a lethal condition for the cell. In this work we report that statins emerged from a computational data analysis that we performed on approximately 37,000 synthetic lethality couples. We performed this analysis to select repurposable drugs that could target genes involved in Synthetic Lethality couples with metastatic genes. We validated our discovery in vitro by drug tests performed on cell lines derived from cancers of the breast, ovary, and cervix. Our data-driven drug repurposing strategy allowed us to understand the molecular basis of the anticancer effect of statins, a discovery which can be directly translated into practical clinical applications in oncology.

cancer biology↗

The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles

Functional genomics experiments are invaluable for understanding mechanisms of gene regulation. However, comprehensively performing all such experiments, even across a fixed set of sample and assay types, is often infeasible in practice. A promising alternative to performing experiments exhaustively is to, instead, perform a core set of experiments and subsequently use machine learning methods to impute the remaining experiments. However, questions remain as to the quality of the imputations, the best approaches for performing imputations, and even what performance measures meaningfully evaluate performance of such models. In this work, we address these questions by comprehensively analyzing imputations from 23 imputation models submitted to the ENCODE Imputation Challenge. We find that measuring the quality of imputations is significantly more challenging than reported in the literature, and is confounded by three factors: major distributional shifts that arise because of differences in data collection and processing over time, the amount of available data per cell type, and redundancy among performance measures. Our systematic analyses suggest several steps that are necessary, but also simple, for fairly evaluating the performance of such models, as well as promising directions for more robust research in this area.

bioinformatics↗

A Non-Negative Matrix Tri-Factorization based Method for Predicting Antitumor Drug Sensitivity

Large annotated cell line collections have been proven to enable the prediction of drug response in the preclinical setting. We present an enhancement of Non-Negative Matrix Tri-Factorization method, which allows the integration of different data types for the prediction of missing associations. To test our method we retrieved a dataset from CCLE, containing the connections among cell lines and drugs by means of their IC50 values. We performed two different kind of experiments: a) prediction of missing values in the matrix, b) prediction of the complete drug profile of a new cell line, demonstrating the validity of the method in both scenarios.

bioinformatics↗

ViruSurf: an integrated database to investigate viral sequences

ViruSurf, available at http://gmql.eu/virusurf/, is a large public database of viral sequences and integrated and curated metadata from heterogeneous sources (GenBank, COG-UK and NMDC); it also exposes computed nucleotide and amino acid variants, called from original sequences. A GISAID-specific ViruSurf database, available at http://gmql.eu/virusurf_gisaid/, offers a subset of these functionalities. Given the current pandemic outbreak, SARS-CoV-2 data are collected from the four sources; but ViruSurf contains other virus species harmful to humans, including SARS-CoV, MERS-CoV, Ebola, and Dengue. The database is centered on sequences, described from their biological, technological, and organizational dimensions. In addition, the analytical dimension characterizes the sequence in terms of its annotations and variants. The web interface enables expressing complex search queries in a simple way; arbitrary search queries can freely combine conditions on attributes from the four dimensions, extracting the resulting sequences. Several example queries on the database confirm and possibly improve results from recent research papers; results can be recomputed over time and upon selected populations. Effective search over large and curated sequence data may enable faster responses to future threats that could arise from new viruses.

genomics↗

Empowering Virus Sequences Research through Conceptual Modeling

The pandemic outbreak of the coronavirus disease has attracted attention towards the genetic mechanisms of viruses. We hereby present the Viral Conceptual Model (VCM), centered on the virus sequence and described from four perspectives: biological (virus type and hosts/sample), analytical (annotations and variants), organizational (sequencing project) and technical (experimental technology). VCM is inspired by GCM, our previously developed Genomic Conceptual Model, but it introduces many novel concepts, as viral sequences significantly differ from human genomes. When applied to SARS-CoV2 virus, complex conceptual queries upon VCM are able to replicate the search results of recent articles, hence demonstrating huge potential in supporting virology research. In addition to VCM, we also illustrate the data dictionary for patients phenotype used by the COVID-19 Host Genetic Initiative. Our effort is part of a broad vision: availability of conceptual models for both human genomics and viruses will provide important opportunities for research, especially if interconnected by the same human being, playing the role of virus host as well as provider of genomic and phenotype information.

genomics↗