Biology subjects

Giovannetti, A.

Publications and source records attributed to Giovannetti, A..

4 recordsLinked to original sources

bioRxiv2026

STRmie-HD enables interruption-aware HTT repeat genotyping and somatic mosaicism profiling across sequencing platforms

Short tandem repeat expansions in exon 1 of the HTT gene drive Huntingtons disease (HD) pathogenesis, with disease onset and progression heavily influenced by somatic mosaicism and sequence interruptions. While sequencing technologies enable repeat sizing, many computational tools lack the resolution to capture subtle interruption motifs and allele-specific somatic variation. We present STRmie-HD, an alignment-free, de novo framework for interruption-aware genotyping and quantitative profiling of somatic mosaicism at single-read resolution. The tool parses individual reads to quantify uninterrupted CAG tract length, CCG repeat content, and critical interruption variants, including Loss of Interruption (LOI) and Duplication of Interruption (DOI). Validated across Illumina, PacBio SMRT, and Oxford Nanopore platforms, STRmie-HD demonstrates high concordance with reference genotypes and superior sensitivity in identifying rare interruption patterns that conventional tools often overlook. Furthermore, it implements somatic mosaicism metrics to characterize repeat dynamics, successfully distinguishing the higher somatic expansion burden in brain tissues compared to peripheral blood. STRmie-HD offers a comprehensive and extensible solution for high-resolution molecular characterization of HTT variation, providing a robust framework for patient stratification and genetic research in HD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/713334v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17a54aforg.highwire.dtl.DTLVardef@4dcfc5org.highwire.dtl.DTLVardef@8398edorg.highwire.dtl.DTLVardef@1acefde_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract: STRmie-HD flowchart. STRmie-HD is a comprehensive analytical framework that processes sequencing reads to analyze CAG/CCG trinucleotide repeats, interruption variants, and somatic mosaicism in the HTT gene. The workflow begins with sequencing reads (FASTA/FASTQ) that can undergo optional custom processing eq]based on the sequencing design. These reads are then fed into a regular expression-based engine (STRmie-HD) to identify CAG and CCG motifs. The identified motifs lead to the estimation of CAG/CCG alleles, visualized as distinct peaks representing different allele sizes, interruption variant assessment, and somatic mosaicism quantification. STRmie-HD produces an HTML output that wraps this information into a report. C_FIG

bioinformatics↗

bioRxiv2025

nAPOGEE: A machine-learning platform for clinically actionable pathogenicity assessment of all mitochondrial noncoding variants

Mitochondrial noncoding variants, particularly those in tRNA and rRNA genes, pose significant challenges for clinical interpretation due to heteroplasmy, broad phenotypic heterogeneity where symptoms can overlap with other conditions, and the limited availability of well-established genotype-phenotype correlations. Despite their central role in mitochondrial translation, these variants have remained largely unexplored by the existing variant-effect predictors. Here, we present nAPOGEE, a novel machine-learning framework specifically designed to assess the pathogenicity of all possible single-nucleotide variants in human mitochondrial noncoding RNAs. nAPOGEE integrates two specialized predictors: tAPOGEE, which outperforms existing tools for tRNAs, and rAPOGEE, the first dedicated classifier for mitochondrial rRNA variants. Using curated training datasets, phylogenetic conservation metrics, secondary structure modeling, RNA-specific embeddings, and thermodynamic features, nAPOGEE provides biologically interpretable predictions and posterior probabilities aligned with the ACMG/AMP guidelines. Applied to both curated variant sets and population-scale data, nAPOGEE revealed consistent spatial correlation of the predicted pathogenicity, reflecting underlying structural and evolutionary constraints. This study addresses a longstanding gap in mitochondrial genomics and offers a clinically applicable tool for variant prioritization, reclassification, and research into mitochondrial disease mechanisms.

bioinformatics↗

bioRxiv2023

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

APOGEE 2 is a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP), APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

bioinformatics↗

bioRxiv2023

Modelling drug responses and evolutionary dynamics using triple negative breast cancer patient-derived xenografts

Triple negative breast cancers (TNBC) exhibit inter- and intra-tumour heterogeneity, which is reflected in diverse drug responses and interplays with tumour evolution. Here, we use TNBC patient-derived tumour xenografts (PDTX) as a platform for co-clinical trials to test their predictive value and explore the molecular features of drug response and resistance. Patients and their matched PDTX exhibited mirrored drug responses to neoadjuvant therapy in a clinical trial. In parallel, additional clinically-relevant treatments were tested in PDTXs in vivo to identify alternative effective therapies for each PDTX model. This framework establishes the foundation for anticipatory personalised therapies for those patients with resistant or relapsed tumours. The PDTXs were further explored to model PDTX- and treatment-specific behaviours. The dynamics of drug response were characterised at single-cell resolution revealing a novel mechanism of response to olaparib. Upon olaparib treatment PDTXs showed phenotypic plasticity, including transient activation of the immediate-early response and irreversible sequential phenotypic switches: from epithelial to epithelial-mesenchymal-hybrid states, and then to mesenchymal states. This molecular mechanism was exploited ex vivo by combining olaparib and salinomycin (an inhibitor of mesenchymal-transduced cells) to reveal synergistic effects. In summary, TNBC PDTXs have the potential to help design individualised treatment strategies derived from model-specific evolutionary insights.

cancer biology↗