Biology subjects

Napoli, A.

Publications and source records attributed to Napoli, A..

9 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↗

bioRxiv2024

Statins inhibit onco-dimerization of the 4Ig isoform of B7-H3

B7-H3 (CD276), a member of the B7-family of immune checkpoint proteins, has been shown to have immunological and non-immunological effects promoting tumorigenesis [1, 2] and expression correlates with poor prognosis for many solid tumors, including cervical, ovarian and breast cancers [3-6]. We recently identified a tumor-cell autochthonous tumorigenic role for dimerization of the 4Ig isoform of B7-H3 (4Ig-B7-H3) [7], where 4Ig-B7-H3 dimerization in cis activated tumor-intrinsic cellular proliferation and tumorigenesis pathways, providing a novel opportunity for therapeutic intervention. Herein, a live cell split-luciferase complementation strategy was used to visualize 4Ig-B7-H3 homodimerization in a high-throughput small molecule screen (HTS) to identify modulators of this protein-protein interaction (PPI). Notably, the HTS identified several compounds that converged on lipid metabolism (including HMG-CoA reductase inhibitors, also known as statins) as significant inhibitors of 4Ig-B7-H3 dimerization (p < 0.01). In vitro and in vivo murine studies provided evidence that statin-mediated disruption of 4Ig-B7-H3 dimerization was associated with anti-tumor effects. Statin-mediated anti-cancer efficacy was selective for B7-H3-expressing tumors and retrospective analysis of clinical tumor specimens supported the hypothesis that concurrent statin use enhanced clinical outcomes for patients in a B7-H3 restricted manner. Thus, disruption of 4Ig-B7-H3 dimerization provides an unanticipated molecular mechanism linking statin use in cancer therapy and prevention with immune checkpoint.

cancer biology↗

bioRxiv2023

NMDA receptors regulate neural progenitor cell proliferation and maturation in the zebrafish forebrain.

Developmental neurogenesis is a tightly regulated spatiotemporal process with its dysregulation implicated in neurodevelopmental disorders. NMDA receptors are glutamate-gated ion channels that are widely expressed in the early nervous system, yet their contribution to neurogenesis is poorly understood. Notably, a variety of mutations in genes encoding NMDA receptor subunits are associated with neurodevelopmental disorders. To rigorously define the role of NMDA receptors in developmental neurogenesis, we used a mutant zebrafish line (grin1-/-) that lacks all NMDA receptors yet survives to 10 days post-fertilization, offering the opportunity to study post-embryonic neurodevelopment in the absence of NMDA receptors. Focusing on the forebrain, we find that these fish have a progressive supernumerary neuron phenotype confined to the telencephalon at the end of embryonic neurogenesis, but which extends to all forebrain regions during postembryonic neurogenesis. This enhanced neuron population does not arise directly from increased numbers or mitotic activity of radial glia cells, the principal neural stem cells. Rather, it stems from a lack of timely maturation of transit-amplifying neuroblasts into post-mitotic neurons, as indicated by a decrease in expression of the ontogenetically-expressed chloride transporter, KCC2. Pharmacological blockade with MK-801 recapitulates the grin1-/- supernumerary neuron phenotype, indicating a requirement for ionotropic signaling. Thus, NMDA receptors are required for suppression of indirect, transit amplifying cell-driven neurogenesis by promoting maturational termination of mitosis. Loss of suppression results in neuronal overpopulation that can fundamentally change brain circuitry and may be a key factor in pathogenesis of neurodevelopmental disorders caused by NMDA receptor dysfunction.

neuroscience↗

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

Investigating mitochondrial gene expression patterns in Drosophila melanogaster using network analysis to understand aging mechanisms

The process of aging is a complex phenomenon that involves a progressive decline in physiological functions required for survival and fertility. To better understand the mechanisms underlying this process, the scientific community has utilized several tools. Among them, mitochondrial DNA has emerged as a crucial factor in biological aging, given that mitochondrial dysfunction is thought to significantly contribute to this phenomenon. Additionally, Drosophila melanogaster has proven to be a valuable model organism for studying aging due to its low cost, capacity to generate large populations, and ease of genetic manipulation and tissue dissection. Moreover, graph theory has been employed to understand the dynamic changes in gene expression patterns associated with aging and to investigate the interactions between aging and aging-related diseases. In this study, we have integrated these approaches to examine the patterns of gene co-expression in Drosophila melanogaster at various stages of development. By applying graph-theory techniques, we have identified modules of co-expressing genes, highlighting those that contain a significantly high number of mitochondrial genes. We found important mitochondrial genes involved in aging and age-related diseases in Drosophila melanogaster, including UQCR-C1, ND-B17.2, ND-20, and Pdhb. Our findings shed light on the role of mitochondrial genes in the aging process and demonstrate the utility of Drosophila melanogaster as a model organism and graph theory in aging research.

bioinformatics↗

bioRxiv2022

Integrative Cut&Tag/RNA-Seq analysis of histone variant macroH2A1-dependent orchestration of human iPSCs reprogramming

Human-induced pluripotent stem cells (iPSCs) can be derived from adult stem cells by forced expression of defined transcription factors. This paves the way for autologous iPSC-derived therapies, which, however, are not yet considered safe. Moreover, reprogramming of somatic cells into iPSCs is an inefficient process, in the range of 0.1%-1%. The epigenetic mechanisms implicated in iPSCs reprogramming are not well understood. The substitution of canonical histone H2A with macroH2A1 histone variant exon-spliced isoforms (macroH2A1.1 and macroH2A1.2) appears as an emerging regulator of iPSCs identity. In particular, we have previously shown that overexpression of macroH2A1.1 led to a more efficient iPSCs reprogramming, by not fully defined mechanisms. Cleavage under targets and tagmentation (CUT&Tag) is a recent methodology used for robust epigenomic profiling of a limited amount of cells. Here, we performed the first integrative CUT&Tag/RNA-Seq analysis of the histone variant macroH2A1-dependent orchestration of iPSCs reprogramming using human umbilical vein endothelial cells (HUVEC) during their reprogramming into iPSC over-expressing tagged macroH2A1.1 or macroH2A1.2. Our results demonstrate a higher and more widespread genome occupancy and a greater number of differentially expressed genes orchestrated by macroH2A1.1 in HUVEC undergoing reprogramming as compared to macroH2A1.2, which involved pervasive functions related to the three embryonic germ layers and increased overlap with CTCF, FOS, GATA2, and POLR2A transcription factor binding sites. In particular, all predicted macroH2A1.1 activating pathways were related to ectoderm/neural processes. As macroH2A1 isoforms have been previously associated with pathologies of the nervous system, our findings may provide relevant molecular insights for modeling neurodegenerative diseases using iPSCs.

bioinformatics↗

bioRxiv2019

A model to study NMDA receptors in early nervous system development

NMDA receptors (NMDARs) are glutamate-gated ion channels that play critical roles in neuronal development and nervous system function. Pharmacological antagonism is an invaluable tool to study NMDARs, but is experimentally limited. Here, we developed a model to study NMDARs in early development in zebrafish, by generating CRISPR-mediated lesions in the NMDAR genes, grin1a and grin1b, which encode the obligatory GluN1 subunits. While receptors containing grin1a or grin1b show high Ca2+ permeability, like their mammalian counterpart, grin1a is expressed earlier and more broadly in development than grin1b. Both grin1a-/- and grin1b-/- zebrafish are viable as adults. Unlike in rodents, where the grin1 knockout is embryonic lethal, grin1 double mutant fish (grin1a-/-; grin1b-/-), which lack all NMDAR-mediated synaptic transmission, survive until about 10 days post fertilization, providing a unique opportunity to explore NMDAR function during development and in generating behaviors. Many behavioral defects in the grin1 double mutant larvae, including abnormal evoked responses to light and acoustic stimuli, prey capture deficits and a failure to habituate to acoustic stimuli, are replicated by short-term treatment with the NMDAR antagonist MK-801, suggesting they arise from acute effects of compromised NMDAR-mediated transmission. Other defects, however, such as periods of hyperactivity and alterations in place preference, are not phenocopied by MK-801, suggesting a developmental origin. Taken together, we have developed a unique model to study NMDARs in the developing vertebrate nervous system.

neuroscience↗

bioRxiv2018

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

BackgroundPharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant - drug - adverse event. Such a relationship states that an adverse event may occur for patients having the speciifed gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks \"clinical counterpart\" or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources.\n\nResultsIn this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and level of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2,701), the literature (65,720) and from discoveries reported from EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD (PGx Linked Open Data) the resulting knowledge base that represents and reconciles knowledge units of those various origins.\n\nConclusionsThe proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources.

bioinformatics↗