Biology subjects

Foglierini, M.

Publications and source records attributed to Foglierini, M..

4 recordsLinked to original sources

bioRxiv2022

ACE2 engagement exposes the fusion peptide to pan-coronavirus neutralizing antibodies

Coronaviruses use diverse Spike (S) glycoproteins to attach to host receptors and fuse with target cells. Using a broad screening approach, we isolated from SARS-CoV-2 immune donors seven monoclonal antibodies (mAbs) that bind to all human alpha and beta coronavirus S proteins. These mAbs recognize the fusion peptide and acquire high affinity and breadth through somatic mutations. Despite targeting a conserved motif, only some mAbs show broad neutralizing activity in vitro against alpha and beta coronaviruses, including Omicron BA.1 variant and bat WIV-1, and reduce viral titers and pathology in vivo. Structural and functional analyses show that the fusion peptide-specific mAbs bind with different modalities to a cryptic epitope which is concealed by prefusion-stabilizing 2P mutations and becomes exposed upon binding of ACE2 or ACE2-mimicking mAbs. This study identifies a new class of pan-coronavirus neutralizing mAbs and reveals a receptor-induced conformational change in the S protein that exposes the fusion peptide region.

immunology↗

bioRxiv2021

Clonal dissection of immunodominance and cross-reactivity of the CD4+ T cell response to SARS-CoV-2

The identification of CD4+ T cell epitopes is essential for the design of effective vaccines capable of inducing neutralizing antibodies and long-term immunity. Here we demonstrate in COVID-19 patients a robust CD4+ T cell response to naturally processed SARS-CoV-2 Spike and Nucleoprotein, including effector, helper and memory T cells. By characterizing 2,943 Spike-reactive T cell clones, we found that 34% of the clones and 93% of the patients recognized a conserved immunodominant region encompassing residues S346-365 in the RBD and comprising three nested HLA-DR and HLA-DP restricted epitopes. By using pre- and post-COVID-19 samples and Spike proteins from alpha and beta coronaviruses, we provide in vivo evidence of cross-reactive T cell responses targeting multiple sites in the SARS-CoV-2 Spike protein. The possibility of leveraging immunodominant and cross-reactive T helper epitopes is instrumental for vaccination strategies that can be rapidly adapted to counteract emerging SARS-CoV-2 variants.

immunology↗

bioRxiv2020

AncesTree: an interactive immunoglobulin lineage tree visualizer

High-throughput sequencing of human immunoglobulin genes allows analysis of antibody repertoires and the reconstruction of clonal lineage evolution. Phylip, an algorithm that has been originally developed for applications in ecology and macroevolution, can also be used for the phylogenic reconstruction of antibodies maturation pathway. The study of antibodies (Abs) affinity maturation is of specific interest to understand the generation of Abs with high affinity or broadly neutralizing activities. Phylogenic analysis enables the identification of the key somatic mutations required to achieve optimal antigen binding. To complement Phylip algorithm, we developed AncesTree, a graphic user interface (GUI) that aims to give researchers the opportunity to interactively explore antibodies clonal evolution. AncesTree displays interactive immunoglobulins (Ig) phylogenic tree, Ig related mutations and sequence alignments using additional information coming from specialized antibody tools (such as IMGT(R)). The GUI is a Java standalone application allowing interaction with Ig-tree that can run under Windows, Linux and Mac OS.

bioinformatics↗

bioRxiv2019

Machine learning predicts immunoglobulin light chain toxicity through somatic mutations

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LCs) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage. However, delays in diagnosis are common, with a consequent poor patients prognosis, as symptoms usually appear only after strong organ involvement. Here, we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. LICTOR achieved a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieved a prediction accuracy of 83%. Furthermore, we were able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL.

bioinformatics↗