Biology subjects

Lucchetta, M.

Publications and source records attributed to Lucchetta, M..

4 recordsLinked to original sources

bioRxiv2023

Emergence of power-law distributions in protein-protein interaction networks through study bias

Protein-protein interaction (PPI) networks are power-law-distributed. However, the experimental procedures for detecting PPIs are affected by technical and study bias. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. This raises the question of whether PL distributions in observed PPI networks could be explained by these biases alone. To assess this question, we studied the degree distribution of thousands of PPI networks of controlled provenance. Our findings are supported by mathematical models and extensive simulations and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the degree distribution and the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.

systems biology↗

bioRxiv2020

Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method

Diseases imply dysregulation of cells functions at several levels. The study of differentially expressed genes in case-control cohorts of patients is often the first step in understanding the details of the cells dysregulation. A further level of analysis is introduced by noticing that genes are organized in functional modules (often called pathways), thus their action and their dysregulation may be better understood by the identification of the modules most affected by the disease (aka disease modules, or active subnetworks). We describe how an algorithm based on the Core&Peel method developed originally for detecting protein complexes in PPI networks, can be adapted to detect disease modules in co-expression networks of genes. We first validate Core&Peel for the easier general task of functional module detection by comparison with 42 methods participating in the Disease Module Identification DREAM challenge of 2019. Next, we use four specific disease test cases (colorectal cancer, prostate cancer, asthma and rheumatoid arthritis), four state-of-the-art algorithms (ModuleDiscoverer, Degas, KeyPathwayMiner and ClustEx), and several pathway databases to validate the proposed algorithm. Core&Peel is the only method able to find significant associations of the predicted disease module with known validated relevant pathways for all four diseases. Moreover for the two cancer data sets, Core&Peel detects further nine relevant pathways enriched in the predicted disease module, not discovered by the other methods used in the comparative analysis. Finally we apply Core&Peel, along with other methods, to explore the transcriptional response of human cells to SARS-CoV-2 infection, at a modular level, aiming at finding supporting evidence for drug repositioning efforts.

bioinformatics↗

bioRxiv2018

Different molecular signatures in lung cancer types from integrative bioinformatic analyses of RNASeq data

BackgroundGenomic initiatives such as The Cancer Genome Atlas (TCGA) project contain data on profiling of thousands of tumors with different-omics approaches, providing a valuable source of information which may be used to decipher cancer signaling and related alterations. Managing and analyzing data from large-scale projects such as TCGA is a demanding task. Indeed, it is difficult to dissect the high complexity hidden in genomic data and to adequately account for tumor heterogeneity.\n\nResultsIn this study, we used a robust statistical framework along with the integration of diverse bioinformatic tools to analyze next-generation sequencing data from more than 1000 patient samples from two different lung cancer subtypes, i.e., the lung adenocarcinoma (LUAD) and the squamous cell carcinoma (LUSC). In particular, we used RNA-Seq gene expression data to identify both co-expression modules and differentially expressed genes to accurately discriminate between LUAD and LUSC. Moreover, we identified a group of genes which could act as specific oncogenes or tumor suppressor genes in one of the two lung cancer types, as well as two dual role genes. Our results have also been cross-validated against other transcriptomics data of lung cancer patients.\n\nConclusionsOur integrative approach allowed to identify two key features: a substantial up-regulation of genes involved in O-glycosylation of mucins in LUAD, and a compromised immune response in LUSC. The immune-profile associated with LUSC is linked to the activation of three specific oncogenic pathways which promote the evasion of antitumor immune response, providing new future directions for the design of target therapies.

bioinformatics↗

bioRxiv2018

Analyses of cancer data in the Genomic Data Commons Data Portal with new functionalities in the TCGAbiolinks R/Bioconductor package

The advent of Next Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data.\n\nThe Genomic Data Commons (GDC) Data Portal is a large data collection platform that includes different genomic studies included the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data without renouncing a robust statistical and theoretical framework are needed. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from the Genotype-Tissue-Expression (GTEx) platform iv) support for other genomics datasets, here exemplified by the TARGET data.\n\nEvidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. Henceforth, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGABiolinks v 2.8 and higher released in Bioconductor version 3.7.

bioinformatics↗