Biology subjects

Lazzari, G.

Publications and source records attributed to Lazzari, G..

5 recordsLinked to original sources

bioRxiv2025

Gempipe: a tool for drafting, curating and analyzing pan and multi-strain genome-scale metabolic models.

Genome-scale metabolic models (GSMMs) can mechanistically explain phenotypic differences among closely-related bacterial strains. However, high-throughput multi-strain reconstructions of GSMMs are still challenging: reference-based methods inherit curated information while missing new contents; alternatively, (universe-based) reference-free methods could cover strain-specific reactions, but they disregard curated information. Ideally, references should be curated pan-GSMMs for species (or genus), but their reconstruction is extremely demanding, making them still rare in literature. Here Gempipe is presented, a computational tool streamlining the multi-strain reconstruction and analysis of GSMMs, going through the production of a pan-GSMM. Its reconstruction method is hybrid, as an optional reference GSMM is automatically expanded with extra reactions taken from a reference-free reconstruction. Gempipe also downloads, filters and annotates genomes; performs in-depth gene recovery; annotates models contents; predicts strain-specific capabilities. The companion programming interface includes functions ranging from the (pan-)GSMMs curation to the multi-strain analysis. Gempipe was validated using multi-strain datasets, showing improved accuracy when compared with state-of-the-art tools. Moreover, metabolic diversities within Limosilactobacillus reuteri were explored, grouping strains into metabolically coherent clusters and systematically predicting health-related metabolites biosynthesis. IMPORTANCEAvailable GSMM reconstruction tools present major limitations in the context of multi-strain modeling. Gempipe surpasses these limitations by implementing a novel, hybrid reconstruction strategy. Not only it produces more accurate strain-specific GSMMs, but also pan-GSMMs when the only available reference is a manually curated model for a single strain, which is currently the most common case. With the vast availability of genome sequences, the high-throughput, multi-strain GSMM reconstruction and analysis approach provided by Gempipe will facilitate large-scale studies of the exploration and bioprospecting of strain-level bacterial metabolic diversity, moving a step forward in strains screening and rational selection.

microbiology↗

bioRxiv2022

A stem cell zoo uncovers intracellular scaling of developmental tempo across mammals

Differential speeds in biochemical reactions have been proposed to be responsible for the differences in developmental tempo between mice and humans. However, the underlying mechanism controlling the species-specific kinetics remains to be determined. Using in vitro differentiation of pluripotent stem cells, we recapitulated the segmentation clocks of diverse mammalian species varying in body weight and taxa: marmoset, rabbit, cattle and rhinoceros. Together with the mouse and human, the segmentation clock periods of the six species did not scale with the animal body weight, but were rather grouped according to phylogeny. The biochemical kinetics of the core clock gene HES7 displayed clear scaling with the species-specific segmentation clock period. However, the cellular metabolic rates did not show an evident correlation. Instead, genes involving biochemical reactions showed an expression pattern that scales with the segmentation clock period. Altogether, our stem cell zoo uncovered general scaling laws governing species-specific developmental tempo.*

developmental biology↗

bioRxiv2022

MetaPhage: an automated pipeline for analyzing, annotating, and classifying bacteriophages in metagenomics sequencing data.

In the last decades, a great interest has emerged in the study and characterisation of the microbiota, especially the human gut microbiota, demonstrating that commensal microorganisms play a pivotal role in normal anatomical development and physiological function of the human body. To better understand the complex bacterial dynamics that characterize different environments, bacteriophage predation and gene transfer need to be considered as well, as they are important factors that may contribute to controlling the density, diversity, and network interactions among bacterial communities. To date, a variety of bacteriophage identification tools have been developed, differing on phage mining strategies, input files requested and results produced; however, new users approaching the bacteriophage analysis might struggle in untangling the variety of methods and comparing the different results produced. Here we present MetaPhage, a comprehensive reads-to-report pipeline that streamlines the use of multiple miners and generates an exhaustive report to both summarize and visualize the key findings and to enable further exploration of specific results with interactive filterable tables. The pipeline is implemented in Nextflow, a widely adopted workflow manager, that enables an optimized parallelization of the tasks on different premises, from local server to the cloud, and ensures reproducible results using containerized packages. MetaPhage is designed to allow scalability, reproducibility and to be easily expanded with new miners and methods, in a field that is constantly expanding. MetaPhage is freely available under a GPL-3.0 license at https://github.com/MattiaPandolfoVR/MetaPhage.

bioinformatics↗

bioRxiv2020

Breaking Apart Contact Networks with Vaccination

Infectious diseases can cause large disease outbreaks due to their transmission potential from one individual to the next. Vaccination is an effective way of cutting off possible chains of transmission, thereby mitigating the outbreak potential of a disease in a population. From a contact network perspective, vaccination effectively removes nodes from the network, thereby breaking apart the contact network into a much smaller network of susceptible individuals on which the disease can spread. Here, we look at the continuum of small world networks to random networks, and find that vaccination breaks apart networks in ways that can dramatically influence the maximum outbreak size. In particular, after the removal of a constant number of nodes (representing vaccination coverage), the more clustered small world networks more readily fall apart into many disjoint and small susceptible sub-networks, thus preventing large outbreaks, while more random networks remain largely connected even after node removal through vaccination. We further develop a model of social mixing that moves small world networks closer to the random regime, thereby facilitating larger disease outbreaks after vaccination. Our results show that even when vaccination is entirely random, social mixing can lead to contact network structures that strongly influence outbreak sizes. We find the largest effects to be in the regime of relatively high vaccination coverages of around 80%, where despite vaccination being random, outbreak sizes can vary by a factor of 20.

ecology↗

bioRxiv2017

Assessing The Dynamics And Control Of Droplet- and Aerosol-Transmitted Influenza Using An Indoor Positioning System

There is increasing evidence that aerosol transmission is a major contributor to the spread of influenza. Despite this, virtually all studies assessing the dynamics and control of influenza assume that it is transmitted solely through direct contact and large droplets, requiring close physical proximity. Here, we use wireless sensors to measure simultaneously both the location and close proximity contacts in the population of a US high school. This dataset, highly resolved in space and time, allows us to model both droplet and aerosol transmission either in isolation or in combination. In particular, it allows us to computationally assess the effectiveness of overlooked mitigation strategies such as improved ventilation that are available in the case of aerosol transmission. While the effects of the type of transmission on disease outbreak dynamics appear to be weak, we find that good ventilation could be as effective in mitigating outbreaks as vaccinating the majority of the population. In simulations using empirical transmission levels observed in households, we find that bringing ventilation to recommended levels has the same mitigating effect as a vaccination coverage of 50% to 60%. Our results therefore suggest that improvements of ventilation in public spaces could be an important and easy-to-implement strategy supplementing vaccination efforts for effective control of influenza spread.

epidemiology↗