Biology subjects

Marchetti, F.

Publications and source records attributed to Marchetti, F..

12 recordsLinked to original sources

bioRxiv2026

A robust mutation spectrum analysis framework for high-depth error-corrected sequencing data

Comparing mutational spectra across experimental groups requires inference at the level of the biological replicate. Error-corrected sequencing (ECS) generates hundreds to thousands of mutations per replicate, yet classical pooled G2 tests treat individual mutations as independent observations and ignore replicate-level variability, inflating statistical significance (pseudo-replication). We present a replicate-aware Ridge-Penalized Dirichlet-Multinomial (RP-DMM) framework that models biological replicates directly and accounts for overdispersion. An L2 (ridge) penalty, with strength chosen by leave-one-out cross-validation, stabilizes spectrum estimates by shrinking weakly supported mutation categories toward a common baseline, and inference uses a parametric bootstrap. We also provide a complementary non-parametric rank-permutation test for sparse datasets. In simulations, the pooled G2 test became increasingly anti-conservative as mutation depth increased, with false-positive rates approaching 100% under the null hypothesis. RP-DMM maintained Type I error near the nominal 5% level and had substantially greater power than the rank-permutation test. Regularization was most consequential for high-dimensional spectra. False positive rates approached the nominal 5% level once the lower-mutation group contained approximately 300-500 mutations, depending on the number of biological replicates. A practical target of approximately 400 mutations in the lower-mutation group provided robust Type I error control across all scenarios examined; below this, the rank-permutation test performed robustly and is preferred. In Duplex Sequencing data from benzo[a]pyrene-exposed mice, RP-DMM distinguished exposure-associated mutational signatures from comparisons with higher mutation counts but null effect size, whereas the pooled G2 test declared all comparisons significant. The cross-validated penalty decreased as mutation depth increased, indicating that regularization strength should be tuned to the data. The framework is implemented in the MutSeqR R package.

bioinformatics↗

bioRxiv2026

A biologically-grounded cerebellar spiking network model with realistic synaptic transmission captures complex circuit dynamics.

Cerebellar neural circuit dynamics rely on a rich repertoire of synaptic and excitability mechanisms, which are thought to determine network computation in physiological and pathological conditions. In this work, we develop and validate a biologically-grounded spiking neural network of the cerebellar cortex, embedding key mechanisms of cellular excitability and synaptic transmission, and assess their impact on signal processing. Neuronal input-output functions, short-term synaptic plasticity, receptor-specific kinetics, and NMDA channel voltage-dependent gating were calibrated against detailed multicompartmental models through automatic tuning procedures. Incorporating these realistic biological properties allowed the network model to simulate key features observed in recordings from acute cerebellar slices. The neuronal discharge and local field potentials elicited by mossy fiber stimulation faithfully reproduced the natural patterns with millisecond precision. Then, selective receptor switch-off revealed the contribution of NMDA, GABA, and AMPA receptors to the frequency-dependent input-output function of the granular layer and Purkinje cells, linking previous empirical findings to specific synaptic mechanisms. This model combines high computational performance with biological realism and offers a computationally efficient framework to investigate neurophysiological phenomena and the neural correlates of behavior in large-scale long-lasting simulations, such as those needed to address the neural underpinnings of learning and of cerebellar pathologies.

neuroscience↗

bioRxiv2024

Dose-related Mutagenic and Clastogenic Effects of Benzofluoranthene in Mouse Somatic Tissues Detected by Duplex Sequencing and the Micronucleus Assay

Polycyclic aromatic hydrocarbons (PAHs) are common environmental pollutants that originate from the incomplete combustion of organic materials. We investigated the clastogenicity and mutagenicity of benzo[b]fluoranthene (BbF), one of 16 priority PAHs, in MutaMouse males after a 28-day oral exposure. BbF causes robust dose-dependent increases in micronucleus frequency in peripheral blood, indicative of chromosome damage. Duplex Sequencing (DS), an error-corrected sequencing technology, reveals that BbF induces dose-dependent increases in mutation frequencies in bone marrow (BM) and liver. Mutagenicity is increased in intergenic relative to genic regions, suggesting a role for transcription-coupled repair of BbF-induced DNA damage. At higher doses, the maximum mutagenic response to BbF is higher in liver, which has a lower mitotic index but higher metabolic capacity than BM; however, mutagenic potency is comparable between the two tissues. BbF induces primarily C:G>A:T mutations, followed by C:G>T:A and C:G>G:C, indicating that BbF metabolites mainly target guanines and cytosines. The mutation spectrum of BbF correlates with cancer mutational signatures associated with tobacco exposure, supporting its contribution to the carcinogenicity of combustion-derived PAHs in humans. Overall, BbFs mutagenic effects are similar to benzo[a]pyrene, a well-studied mutagenic PAH. Our work showcases the utility of DS for effective mutagenicity assessment of environmental pollutants. SynopsisWe used Duplex Sequencing to study the mutagenicity of benzo[b]fluoranthene across the mouse genome. Dose-dependent changes in mutation frequency and spectrum quantify its role in PAH-induced carcinogenicity.

pharmacology and toxicology↗

bioRxiv2023

Decrypting the languages of allostery in membrane-bound K-Ras4B using four complementary in silico approaches

Proteins are evolved molecular machines whose diverse biochemical functions can be dynamically regulated by allostery, through which even faraway protein residues can conformationally communicate. Allostery can express itself in different ways, akin to different "languages": allosteric control pathways predominating in an unperturbed protein are superseded by others as soon as a perturbation arises--e.g., a mutation--that alters its function (pathologically or not). Accurately modeling these often-unintuitive phenomena could therefore help explain functional changes in a specific protein whenever they are unclear. Unbiased molecular dynamics (MD) simulations are a possibility; however, since allostery can operate at longer timescales than those accessible by MD, simulations require integration with a reliable method able to, e.g., detect regions of incipient allosteric change or likely perturbation pathways. Several methods exist but are typically applied singularly: we argue their joint application, in a "multilingual" approach, could significantly enrich this picture. To prove this, we perform unbiased MD simulations ([~]100 {micro}s) of the widely studied, allosterically active oncotarget K-Ras4B, solvated and embedded in a phospholipid membrane, proceeding to decrypt its allostery using four showcase "languages": Distance Fluctuation analysis and the Shortest Path Map capture allosteric communication hotspots at equilibrium; Anisotropic Thermal Diffusion and Dynamical Non-Equilibrium MD simulations assess them once the GTP that oncogenically activates K-Ras4B is, respectively, either superheated or hydrolyzed. "Languages" provide a uniquely articulate, mutually coherent, experimentally consistent picture of allostery in K-Ras4B. At equilibrium, pathways stretch from the membrane-embedded hypervariable region all the way to the active site, touching known flexible allosteric "switches" and proposed pockets. Upon GTP cleavage/perturbation, known to inactivate K-Ras4B, allosteric signals most reverberate on switches and interfaces that recruit effector proteins. Our work highlights the benefits of integrating different allostery detection methods with unbiased MD.

biochemistry↗

bioRxiv2023

Duplex Sequencing Provides Detailed Characterization of Mutation Frequencies and Spectra in the Bone Marrow of MutaMouse Males Exposed to Procarbazine Hydrochloride

Mutagenicity testing is an essential component of health safety assessment. Duplex Sequencing (DS), an emerging high-accuracy DNA sequencing technology, may provide substantial advantages over conventional mutagenicity assays. DS could be used to eliminate reliance on standalone reporter assays and provide mechanistic information alongside mutation frequency (MF) data. However, the performance of DS must be thoroughly assessed before it can be routinely implemented for standard testing. We used DS to study spontaneous and procarbazine (PRC)-induced mutations in the bone marrow (BM) of MutaMouse males across a panel of 20 diverse genomic targets. Mice were exposed to 0, 6.25, 12.5, or 25 mg/kg-bw/day for 28 days by oral gavage and BM sampled 42 days post-exposure. Results were compared with those obtained using the conventional lacZ viral plaque assay on the same samples. DS detected significant increases in mutation frequencies and changes to mutation spectra at all PRC doses. Low intra-group variability within DS samples allowed for detection of increases at lower doses than the lacZ assay. While the lacZ assay initially yielded a higher fold-change in mutant frequency than DS, inclusion of clonal mutations in DS mutation frequencies reduced this discrepancy. Power analyses suggested that three animals per dose group and 500 million duplex base pairs per sample is sufficient to detect a 1.5-fold increase in mutations with >80% power. Overall, we demonstrate several advantages of DS over classical mutagenicity assays and provide data to support efforts to identify optimal study designs for the application of DS as a regulatory test.

genomics↗

bioRxiv2023

Error-corrected Duplex Sequencing enables direct detection and quantification of mutations in human TK6 cells with remarkable inter-laboratory consistency

Error-corrected Duplex Sequencing (DuplexSeq) enables direct quantification of low-frequency mutations and offers tremendous potential for chemical mutagenicity assessment. We investigated the utility of DuplexSeq to quantify induced mutation frequency (MF) and spectrum in human lymphoblastoid TK6 cells exposed to a prototypical DNA alkylating agent, N-ethyl-N-nitrosourea (ENU). Furthermore, we explored appropriate experimental parameters for this application, and assessed inter-laboratory reproducibility. In two independent experiments in two laboratories, TK6 cells were exposed to ENU (25-200 {micro}M) and DNA was sequenced 48, 72, and 96 h post-exposure. A DuplexSeq mutagenicity panel targeting twenty 2.4-kb regions distributed across the genome was used to sample diverse, genome-representative sequence contexts. A robust increase in MF that was unaffected by time was observed in both laboratories. Concentration-response in the MF from the two laboratories was strongly positively correlated (R2=0.95). C:G>T:A, T:A>C:G, T:A>A:T, and T:A>G:C mutations increased in consistent, concentration-dependent manners in both laboratories, with high proportions of C:G>T:A at all time points. The target sites responded similarly between the two laboratories and revealed a higher average MF in intergenic regions. These results, demonstrating remarkable reproducibility across time and laboratory for both MF and spectrum, support the high value of DuplexSeq for characterizing chemical mutagenicity in both research and regulatory evaluation.

genomics↗

bioRxiv2021

SARS-CoV-2 Spike Protein Mutations and Escape from Antibodies: a Computational Model of Epitope Loss in Variants of Concern

The SARS-CoV-2 spike (S) protein is exposed on the viral surface and is the first point of contact between the virus and the host. For these reasons it represents the prime target for Covid-19 vaccines. In recent months, variants of this protein have started to emerge. Their ability to reduce or evade recognition by S-targeting antibodies poses a threat to immunological treatments and raises concerns for their consequences on vaccine efficacy. To develop a model able to predict the potential impact of S-protein mutations on antibody binding sites, we performed unbiased multi-microsecond molecular dynamics of several glycosylated S-protein variants and applied a straightforward structure-dynamics-energy based strategy to predict potential changes in immunogenic regions on each variant. We recover known epitopes on the reference D614G sequence. By comparing our results, obtained on isolated S-proteins in solution, to recently published data on antibody binding and reactivity in new S variants, we directly show that modifications in the S-protein consistently translate into the loss of potentially immunoreactive regions. Our findings can thus be qualitatively reconnected to the experimentally characterized decreased ability of some of the Abs elicited against the dominant S-sequence to recognize variants. While based on the study of SARS-CoV-2 Spike variants, our computational epitope-prediction strategy is portable and could be applied to study immunoreactivity in mutants of proteins of interest whose structures have been characterized, helping the development/selection of vaccines and antibodies able to control emerging variants.

biophysics↗

bioRxiv2020

Asciminib mitigates DNA damage stress signalling induced by cyclophosphamide in the ovary

Cancer treatments often have adverse effects on the quality of life for young women. One of the most relevant negative impacts is the loss of fertility. Cyclophosphamide is one of the most detrimental chemotherapeutic drugs for the ovary. Cyclophosphamide may induce the destruction of dormant follicles while promoting follicle activation and growth. Herein, we demonstrate the in vivo protective effect of the allosteric Bcr-Abl tyrosine kinase inhibitor Asciminib on signalling pathways activated by cyclophosphamide in mouse ovaries. Besides, we provide evidence that Asciminib did not interfere with the cytotoxic effect of cyclophosphamide in MCF7 breast cancer cells. Our data indicate that concomitant administration of Asciminib mitigates the cyclophosphamide-induced ovarian reserve loss without preventing the anticancer potential of cyclophosphamide. Altogether these observations are relevant for the development of effective ferto-protective adjuvants to preserve the ovarian reserve from the damaging effect of cancer therapies.

cell biology↗

bioRxiv2020

Mutagenicity of folic acid deficiency and supplementation is tissue-specific and results in distinct mutation profiles

Cancer incidence varies by tissue due to differences in environmental risk factor exposure, gene variant inheritance, and lifetime number of stem cell divisions in a tissue. Folate deficiency is associated with increased risk for colorectal cancer (CRC) and acute lymphocytic leukemia. Conversely, high folic acid (FA) intake has been associated with higher CRC risk. However, the mutagenic potential of FA intake in different tissues has not been characterized. Here we quantified mutations in folate-susceptible somatic tissues, namely bone marrow and colon, from the same MutaMouse mice and determined the FA-induced mutation profiles of both tissues using next generation sequencing. FA-induced mutagenesis was tissue- and dose-specific: FA deficiency increased mutant frequency (MF) in bone marrow while FA supplementation increased MF in colon. Analyses of mutation profiles suggested that FA interacted with mutagenic mechanisms that are unique to each tissue. These data illuminate potential mechanisms underpinning differences in susceptibility to FA-related cancers.

genetics↗

bioRxiv2020

The answer lies in the energy: how simple atomistic molecular dynamics simulations may hold the key to epitope prediction on the fully glycosylated SARS-CoV-2 spike protein

Betacoronavirus SARS-CoV-2 is posing a major threat to human health and its diffusion around the world is having dire socioeconomical consequences. Thanks to the scientific communitys unprecedented efforts, the atomic structure of several viral proteins has been promptly resolved. As the crucial mediator of host cell infection, the heavily glycosylated trimeric viral Spike protein (S) has been attracting the most attention and is at the center of efforts to develop antivirals, vaccines, and diagnostic solutions. Herein, we use an energy-decomposition approach to identify antigenic domains and antibody binding sites on the fully glycosylated S protein. Crucially, all that is required by our method are unbiased atomistic molecular dynamics simulations; no prior knowledge of binding properties or ad hoc combinations of parameters/measures extracted from simulations is needed. Our method simply exploits the analysis of energy interactions between all intra-protomer aminoacid and monosaccharide residue pairs, and cross-compares them with structural information (i.e., residueresidue proximity), identifying potential immunogenic regions as those groups of spatially contiguous residues with poor energetic coupling to the rest of the protein. Our results are validated by several experimentally confirmed structures of the S protein in complex with anti- or nanobodies. We identify poorly coupled sub-domains: on the one hand this indicates their role in hosting (several) epitopes, and on the other hand indicates their involvement in large functional conformational transitions. Finally, we detect two distinct behaviors of the glycan shield: glycans with stronger energetic coupling are structurally relevant and protect underlying peptidic epitopes; those with weaker coupling could themselves be poised for antibody recognition. Predicted Immunoreactive regions can be used to develop optimized antigens (recombinant subdomains, synthetic (glyco)peptidomimetics) for therapeutic applications.

biochemistry↗

bioRxiv2020

Differential Antibody Recognition by Novel SARS-CoV-2 and SARS-CoV Spike Protein Receptor Binding Domains: Mechanistic Insights

The appearance of the novel betacoronavirus SARS-CoV-2 represents a major threat to human health, and its diffusion around the world is causing dramatic consequences. The knowledge of the 3D structures of SARS-CoV-2 proteins can facilitate the development of therapeutic and diagnostic molecules. Specifically, comparative analyses of the structures of SARS-CoV-2 proteins and homologous proteins from previously characterized viruses, such as SARS-CoV, can reveal the common and/or distinctive traits that underlie the mechanisms of recognition of cell receptors and of molecules of the immune system. Herein, we apply our recently developed energy-based methods for the prediction of antibody-binding epitopes and protein-protein interaction regions to the Receptor Binding Domain (RBD) of the Spike proteins from SARS-CoV-2 and SARS-CoV. Our analysis focusses only on the study of the structure of RBDs in isolation, without making use of any previous knowledge of binding properties. Importantly, our results highlight structural and sequence differences among the regions that are predicted to be immunoreactive and bind/elicit antibodies. These results provide a rational basis to the observation that several SARS-CoV RDB-specific monoclonal antibodies fail to appreciably bind the SARS-CoV-2 counterpart. Furthermore, we correctly identify the region of SARS-CoV-2 RBD that is engaged by the cell receptor ACE2 during viral entry into host cells. The data, sequences and structures we present here can be useful for the development of novel therapeutic and diagnostic interventions.

biochemistry↗

bioRxiv2019

SEQUENCING CHEMICALLY INDUCED MUTATIONS IN THE MUTAMOUSE LACZ REPORTER GENE IDENTIFIES HUMAN CANCER MUTATIONAL SIGNATURES

Transgenic rodent (TGR) models use bacterial reporter genes to quantify in vivo mutagenesis. Pairing TGR assays with next-generation sequencing (NGS) enables comprehensive mutation spectrum analysis to inform mutational mechanisms. We used this approach to identify 2,751 independent lacZ mutations in the bone marrow of MutaMouse animals exposed to four chemical mutagens: benzo[a]pyrene, N-ethyl-N-nitrosourea, procarbazine, and triethylenemelamine. We also collected published data for 706 lacZ mutations from eight additional environmental mutagens. We demonstrate that lacZ gene sequencing generates chemical-specific mutation signatures observed in human cancers with established environmental causes. For example, the mutation signature of benzo[a]pyrene, a potent carcinogen in tobacco smoke, matched the signature associated with tobacco-induced lung cancers. Our results show that the analysis of chemically induced mutations in the lacZ gene shortly after exposure provides an effective approach to characterize human-relevant mechanisms of carcinogenesis and identify novel environmental causes of mutation signatures observed in human cancers.

genetics↗