Biology subjects

Foglierini, M.

Publications and source records attributed to Foglierini, M..

2 recordsLinked to original sources

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↗