Biology subjects

De Libero, G.

Publications and source records attributed to De Libero, G..

4 recordsLinked to original sources

bioRxiv2025

Expanding the MR1 ligandome using chemical class-specific fragmentation and molecular networking

The identification and subsequent characterization of unknown analytes using mass spectrometry presents a long-standing challenge across many research fields, particularly when analyte levels are low and the compound class is underrepresented in mass spectral databases. We have developed a data analysis workflow for investigating classes of small molecules and demonstrated its application through the reanalysis of data collected to probe for modified nucleoside MR1-presented antigens. We reanalyzed the datasets to screen for additional classes of compounds within the MR1 ligandome using Compound Discoverer, a commercial software package designed for metabolomic analysis, featuring fragmentation filtering nodes, molecular networking, and spectral database searching. Our study identified two compound classes that bind to MR1. One class includes compounds characterized by the presence of a ribityl substructure and molecular formulas consistent with structural similarity to riboflavin, where the most abundant compound differs from riboflavin by two additional oxygen atoms and one fewer carbon atom. A second class comprises an adenosine monophosphate isomer and larger analytes that are putatively identified as consisting of di- and tri-covalently bound nucleotides. The application of our analytical approach to characterize the MR1 ligandome demonstrates the power of combining compound-class fragmentation, molecular networking, and mass spectral database searching in exploring receptor ligandomes and, more generally, identifying novel classes of compounds.

immunology↗

bioRxiv2022

Nucleobase adduct-containing metabolites are MR1 ligands that stimulate self-reactive MR1T cells

MR1T lymphocytes are a recently identified population of T cells that recognize unknown self-antigens presented by the non-polymorphic MHC-I-related molecule, MR1. MR1T cells can kill tumor cells and modulate the functions of other immune cells with promising therapeutic applications. By integrating genetic, pharmacological and biochemical approaches we identified carbonyl stress and alterations of nucleobase metabolism in tumor target cells that promote recognition by MR1 T cells. We dissected these pathways and found that nucleobase adduct-containing metabolites are self-antigens stimulating MR1T cells. Several nucleobase adducts are presented by MR1 molecules and stimulate individual MR1T cells. Our data suggest that MR1T cells are surveyor of cellular metabolic alterations occurring in conditions of metabolic stress, such as cancer, and lay the groundwork for the development of novel HLA-unrestricted T cell-based therapies.

immunology↗

bioRxiv2019

Histone acetylome-wide association study of tuberculosis

Host-cell chromatin changes are thought to play an important role in the pathogenesis of infectious diseases. Here, we describe the first histone acetylome-wide association study (HAWAS) of an infectious disease, based on genome-wide H3K27 acetylation profiling of peripheral granulocytes and monocytes from subjects with active Mycobacterium tuberculosis (Mtb) infection and healthy controls. We detected >2,000 differentially acetylated loci in either cell type in a Chinese discovery cohort, which were validated in a subsequent multi-ethnic cohort, thus demonstrating that HAWAS can be independently corroborated. Acetylation changes were correlated with differential gene expression in a third cohort. Differential acetylation was enriched near potassium channel genes, including KCNJ15, which modulated Akt-mTOR signaling and promoted Mtb clearance in vitro. We performed histone acetylation QTL analysis on the dataset and identified candidate causal variants for immune phenotypes. Our study serves as proof-of-principle for HAWAS to infer mechanisms of host response to pathogens.

immunology↗

bioRxiv2018

rCASC: reproducible Classification Analysis of Single Cell sequencing data

SummarySingle-cell RNA sequencing has emerged as an essential tool to investigate cellular heterogeneity, and highlighting cell sub-population specific signatures. Nowadays, dedicated and user-friendly bioinformatics workflows are required to exploit the deconvolution of single-cells transcriptome. Furthermore, there is a growing need of bioinformatics workflows granting both functional, i.e. saving information about data and analysis parameters, and computation reproducibility, i.e. storing the real image of the computation environment. Here, we present rCASC a modular RNAseq analysis workflow allowing data analysis from counts generation to cell sub-population signatures identification, granting both functional and computation reproducibility.\n\nAvailability and ImplementationrCASC is part of the reproducible bioinfomatics project. rCASC is a docker based application controlled by a R package available at https://github.com/kendomaniac/rCASC.\n\nSupplementary informationSupplementary data are available at rCASC github

bioinformatics↗