Biology subjects

Angaroni, F.

Publications and source records attributed to Angaroni, F..

8 recordsLinked to original sources

bioRxiv2024

Kinbiont: From time series to ecological and evolutionary responses in microbial systems

Microbial behavior is quantitatively characterized by observables inferred from kinetics experiments. Growth rate and biomass yield, for example, are used to map response patterns across different conditions including antibiotic growth inhibition and yield dependence on substrate. As microbial kinetics datasets grow, there is immense potential to advance our understanding of ecological and evolutionary processes. But how can we turn these data into actionable insights about microbial responses? Here we introduce Kinbiont - an ecosystem of numerical methods integrating advanced ordinary differential equation solvers, non-linear optimization, signal processing, and interpretable machine learning algorithms. Kinbiont offers a model-based data analysis pipeline covering all aspects of microbial kinetics, from pre-processing to result interpretation. We demonstrate Kinbionts performance using synthetic and real datasets, including bacterial growth, diauxic curves, phage-bacteria co-cultures, and ecotoxicological responses. Kinbiont can aid biological discovery through data-driven generation of hypotheses that can be tested in targeted experiments.

microbiology↗

bioRxiv2023

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients

By leveraging the ever-increasing availability of cancer omics data and the continuous advances in cancer data science and machine learning, we have discovered the existence of cancer type-specific evolutionary signatures associated with different disease outcomes. These signatures represent "favored trajectories" of acquisition of driver mutations that are repeatedly detected in patients with similar prognosis. In this work, we present a novel framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) that extracts such signatures from NGS experiments generated by different technologies such as bulk and single-cell sequencing data. In our study, we applied ASCETIC to (i) single-cell sequencing data from 146 patients with distinct myeloid malignancies and bulk whole-exome sequencing data from 366 acute myeloid leukemia patients, (ii) multi-region sequencing data from 100 early-stage lung cancer patients from the TRACERx project, (iii) whole-exome/genome sequencing data from more than 10,000 Pan-Cancer Atlas samples, and (iv) targeted bulk sequencing data from more than 25,000 MSK-MET metastatic patients (both datasets including multiple cancer types). As a result, we extracted different cancer (sub)type-specific single-nucleotide variants evolutionary signatures associated with clusters of patients with statistically significant different prognoses. In addition, we conducted several validations using diverse and previously unexplored datasets to evaluate the reliability and applicability of the evolutionary signatures extracted by ASCETIC. Such analyses provided evidence of the robustness and generalizability of the identified evolutionary patterns.

genetics↗

bioRxiv2021

Large-scale analysis of synonymous viral variants reveals global adaptation of the SARS-CoV-2 to the human codon usage

Many large national and transnational studies have been dedicated to the analysis of SARS-CoV-2 genome, most of which focused on missense and nonsense mutations. However, approximately 30% of the SARS-CoV-2 variants are synonymous, therefore changing the target codon without affecting the corresponding protein sequence. By performing a large-scale analysis of sequencing data generated from almost 400,000 SARS-CoV-2 samples, we show that silent mutations increasing the similarity of viral codons to the human ones tend to fixate in the viral genome over-time. This indicates that SARS-CoV-2 codon usage is adapting to the human host, likely improving its effectiveness in using the human aminoacyl-tRNA set through the accumulation of deceitfully neutral silent mutations. One-Sentence SummarySynonymous SARS-CoV-2 mutations related to the activity of different mutational processes may positively impact viral evolution by increasing its adaptation to human codon usage.

bioinformatics↗

bioRxiv2021

Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines

AO_SCPLOWBSTRACTC_SCPLOWMatters Arising from: Sharma, A., Cao, E.Y., Kumar, V. et al. Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy. Nat Commun 9, 4931 (2018). https://doi.org/10.1038/s41467-018-07261-3. In Sharma, A. et al. Nat Commun 9, 4931 (2018) the authors employ longitudinal single-cell transcriptomic data from patient-derived primary and metastatic oral squamous cell carcinomas cell lines, to investigate possible divergent modes of chemo-resistance in tumor cell subpopulations. We integrated the analyses presented in the manuscript, by performing variant calling from scRNA-seq data via GATK Best Practices. As a main result, we show that an extremely high number of singlenucleotide variants representative of the identity of a specific patient is unexpectedly found in the scRNA-seq data of the cell line derived from a second patient, and vice versa. This finding likely suggests the existence of a sample swap, thus jeopardizing some of the translational conclusions of the article. Our results prove the efficacy of a joint analysis of the genotypic and transcriptomic identity of single-cells.

genomics↗

bioRxiv2020

Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity

AO_SCPLOWBSTRACTC_SCPLOWTo dissect the mechanisms underlying the inflation of variants in the SARS-CoV-2 genome, we present one of the largest up-to-date analyses of intra-host genomic diversity, which reveals that most samples present heterogeneous genomic architectures, due to the interplay between host-related mutational processes and transmission dynamics. The deconvolution of the set of intra-host minor variants unveils the existence of non overlapping mutational signatures related to specific nucleotide substitutions, which prove that distinct hosts respond differently to SARS-CoV-2 infections, and which are likely ruled by APOBEC, Reactive Oxygen Species (ROS) and ADAR. Thanks to a corrected-for-signatures dN/dS analysis we demonstrate that the mutational processes underlying such signatures are affected by purifying selection, with important exceptions. In fact, several mutations linked to low-rate mutational processes appear to transit to clonality in the population, eventually leading to the definition of new clonal genotypes and to a statistically significant increase of overall genomic diversity. Importantly, the analysis of the phylogenetic model shows the presence of multiple homoplasies, due to mutational hotspots, phantom mutations or positive selection, and supports the hypothesis of transmission of minor variants during infections. Overall, the results of this study pave the way for the integrated characterization of intra-host genomic diversity and clinical outcome of SARS-CoV-2 hosts.

genetics↗

bioRxiv2020

Characterization of intra-host SARS-CoV-2 variants improves phylogenomic reconstruction and may reveal functionally convergent mutations

We introduce VERSO, a two-step framework for the characterization of viral evolution from sequencing data of viral genomes, which improves over phylogenomic approaches for consensus sequences. VERSO exploits an efficient algorithmic strategy to return robust phylogenies from clonal variant profiles, also in conditions of sampling limitations. It then leverages variant frequency patterns to characterize the intra-host genomic diversity of samples, revealing undetected infection chains and pinpointing variants likely involved in homoplasies. On simulations, VERSO outperforms state-of-the-art tools for phylogenetic inference. Notably, the application to 6726 Amplicon and RNA-seq samples refines the estimation of SARS-CoV-2 evolution, while co-occurrence patterns of minor variants unveil undetected infection paths, which are validated with contact tracing data. Finally, the analysis of SARS-CoV-2 mutational landscape uncovers a temporal increase of overall genomic diversity, and highlights variants transiting from minor to clonal state and homoplastic variants, some of which falling on the spike gene. Available at: https://github.com/BIMIB-DISCo/VERSO.

evolutionary biology↗

bioRxiv2020

Longitudinal cancer evolution from single cells

The rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution in single tumors and to investigate intra-tumor heterogeneity. This is particularly relevant when assessing the efficacy of therapies over time on the clonal composition of a tumor and in the identification of resistant subclones. We here introduce LACE (Longitudinal Analysis of Cancer Evolution), the first algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. Our approach solves a Boolean matrix factorization problem with phylogenetic constraints, by maximizing a weighted likelihood function computed on multiple time points, and we show with simulations that it outperforms state-of-the-art methods for both bulk and single-cell sequencing data. Remarkably, as the results are robust with respect to high levels of data-specific errors, LACE can be employed to process single-cell mutational profiles as generated by calling variants from the increasingly available scRNA-seq data, thus obviating the need of relying on rarer and more expensive genome sequencing experiments. This also allows to investigate the relation between genomic clonal evolution and phenotype at the single-cell level. To illustrate the capabilities of LACE, we show its application to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, in which we characterize the impact of concurrent BRAF/MEK-inhibition on clonal evolution, also by showing that distinct genetic clones reveal different sensitivity to the therapy. Furthermore, the analysis of a longitudinal dataset of breast cancer PDXs from targeted scDNA-sequencing experiments delivers a high-resolution characterization of intra-tumor heterogeneity, also allowing the detection of a late de novo subclone.

bioinformatics↗

bioRxiv2019

Personalized therapy design for liquid tumors via optimal control theory

One of the key challenges in current cancer research is the development of reliable methods for the definition of personalized therapeutic strategies, based on increasingly available experimental data on single patients. To this end, methods from control theory can be effectively employed on patient-specific pharmacokinetic and pharmacodynamic models to generate robust data-driven experimental hypotheses.\n\nHere we introduce the Control Theory for Therapy Design (CT4TD) theoretical framework for the generation of optimized personalized therapeutic strategies in cancer patients, based on optimal control theory and population dynamics modeling. The CT4TD framework can help clinicians in designing patient-specific therapeutic regimens, with the specific goal of optimizing the efficacy of the cure while reducing the costs, especially in terms of toxicity and adverse effects. CT4TD can be used at the time of the diagnosis in order to set optimized personalized therapies to reach selected target drug concentrations. Furthermore, if longitudinal data on patients under treatment are available, our approach introduces the possibility of adjusting the therapy with the explicit goal of minimizing the tumor burden measured in each case.\n\nAs a case study, we present the application of CT4TD to Imatinib administration in Chronic Myeloid Leukemia, in which we show that the optimized therapeutic strategies are extremely diversified among patients, and display improvements with respect to the currently employed regimes. Interestingly, we prove that much of the variance in therapeutic response observed among patients is due to the individual differences in pharmacokinetics, rather than in pharmacodynamics.

bioinformatics↗