Biology subjects

Mattei, G.

Publications and source records attributed to Mattei, G..

6 recordsLinked to original sources

bioRxiv2024

PoreMeth2: decoding the evolution of methylome alterations with Nanopore sequencing

In epigenetic analysis, identifying differentially methylated regions (DMRs) typically involves detecting groups of consecutive CpGs that show significant changes in their average methylation levels. However, the methylation state of a genomic region can also be characterized by a mixture of patterns (epialleles) with variable frequencies, and the relative proportions of such patterns can provide insights into its mechanisms of formation. Traditional methods based on bisulfite conversion and NGS, due to the read size (150 bp), allow epiallele frequency analysis only in high-CpG-density regions, limiting differential methylation studies to just 50% of the human methylome. Nanopore sequencing, with its long reads, enables the analysis of epiallele frequency across both high- and low-CpG-density regions. We introduce a novel computational approach, PoreMeth2, an R library that integrates epiallelic diversity and methylation frequency changes from Nanopore data to identify DMRs, assess their formation mechanisms, and annotate them to genic and regulatory elements. We applied PoreMeth2 to cancer and glial cell datasets, demonstrating its ability to distinguish epigenomic changes with a strong effect on gene expression from those with a weaker impact on transcriptional activity. PoreMeth2 is publicly available at https://github.com/Lab-CoMBINE/PoreMeth2.

bioinformatics↗

bioRxiv2023

GASOLINE: detecting germline and somatic structural variants from long-reads data.

Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30x sequencing coverage experiments requires 4-5 hours with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified 6 genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods.

bioinformatics↗

bioRxiv2022

High-resolution Nanopore methylome-maps reveal random hyper-methylation at CpG-poor regions as driver of chemoresistance in leukemias.

Aberrant DNA methylation at CpG dinucleotides is a hallmark of cancer and is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution.

genomics↗

bioRxiv2021

Third Generation Cytogenetic Analysis (TGCA): diagnostic application of long-read sequencing.

Unbalanced Structural Variants (uSVs) play important roles in the pathogenesis of several genetic syndromes. Traditional and molecular karyotyping are considered the first-tier diagnostic tests to detect macroscopic and cryptic deletions/duplications. However, their time-consuming and laborious experimental protocols protract diagnostic times from three to fifteen days. Long read sequencing approaches, such as Oxford Nanopore Technologies (ONT), have the ability to reduce time to results for the detection of uSVs with the same resolution of current state-of-the-art diagnostic tests. Here we compared ONT to molecular karyotyping for the detection of pathogenic uSVs of 7 patients with previously diagnosed causative CNVs of different sizes and allelic fractions. Larger chromosomal anomalies included trisomy 21 and mosaic tetrasomy 12p. Among smaller CNVs we tested two reciprocal genomic imbalances in 7q11.23 (1.367 Mb), a 170 kb deletion encompassing NRXN1 and mosaic 6q27 (1.231 Mb) and 2q23.1 (408 kb) deletions. DNA libraries were prepared following ONT standard protocols and sequenced on the GridION device for 48 h. Data generated during runs were analysed in online mode, using NanoGLADIATOR. We were capable to identify all pathogenic CNVs with detection time inversely proportional to size and allelic fraction. Aneuploidies were called after only 30 minutes of sequencing, while 30 hours were needed to call CNVs < 500 kb also in mosaic state (44%). These results demonstrate the clinical utility of our approach that allows the molecular diagnosis of genomic disorders within a 30 minutes to 30 hours time-frame.

genomics↗

bioRxiv2017

Functional pathways for metabolic network-based data analysis: the MetPath algorithm

Analyzing biological data using pathways helps identify trends in data tied to the function of a network. A large number of pathway-based analysis tools have been developed toward this goal. These pathways are often manually curated and thus associations are subject to the biases of the curator. A potentially attractive alternative is to define pathways based on the inherent functionality and connectivity of the network itself. Within metabolism, functionality is defined by the production and consumption of metabolites, and connectivity by metabolites participating in reactions through common enzymes. In this work, we present an algorithm, termed MetPath, that calculates pathways for production and consumption of metabolites. We show how these pathways have attractive properties, such as the ability to integrate multiple data types and weight contribution of genes within the pathway by their functional contribution to metabolite production/consumption. Pathways calculated in this manner are condition-specific and thus are custom tailored to the system of interest, in contrast to curated pathways. We find that these pathways predict gene expression correlation better compared to manually-curated pathways. Additionally, we find that these pathways can be used to understand gene expression changes between growth conditions and between cell types. This work serves to better understand the functional pathway structure underlying cell metabolism and helps to enable systems analyses of high-throughput data.

systems biology↗

bioRxiv2017

Systemic and vascular inflammation in an in-vitro model of central obesity

Metabolic disorders due to over-nutrition are a major global health problem, often associated with obesity and related morbidities. Obesity is peculiar to humans, as it is associated with lifestyle and diet, and so difficult to reproduce in animal models.\n\nHere we describe a model of human central adiposity based on a 3-tissue system consisting of a series of interconnected fluidic modules. Given the causal link between obesity and systemic inflammation, we focused primarily on pro-inflammatory markers, examining the similarities and differences between the 3-tissue model and evidence from human studies in the literature. When challenged with high levels of adiposity, the in-vitro system manifests cardiovascular stress through expression of E-selectin and von Willebrand factor as well as systemic inflammation (expressing IL-6 and MCP-1) as observed in humans. Interestingly, most of the responses are dependent on the synergic interaction between adiposity and the presence of multiple tissue types. The set-up has the potential to reduce animal experiments in obesity research and may help unravel specific cellular mechanisms which underlie tissue response to nutritional overload.

bioengineering↗