Biology subjects

Riccardi, C.

Publications and source records attributed to Riccardi, C..

5 recordsLinked to original sources

bioRxiv2023

Supervised White Matter Bundle Segmentation in Glioma Patients with Transfer Learning

In clinical neuroscience, the segmentation of the main white matter bundles is propaedeutic for many tasks such as preoperative neurosurgical planning and monitoring of neuro-related diseases. Automating bundle segmentation with data-driven approaches and deep learning models has shown promising accuracy in the context of healthy individuals. The lack of large clinical datasets is preventing the translation of these results to patients. Inference on patients data with models trained on healthy population is not effective because of domain shift. This study aims to carry out an empirical analysis to investigate how transfer learning might be beneficial to overcome these limitations. For our analysis, we consider a public dataset with hundreds of individuals and a clinical dataset of glioma patients. We focus our preliminary investigation on the corticospinal tract. The results show that transfer learning might be effective in partially overcoming the domain shift.

neuroscience↗

bioRxiv2022

Metabolic robustness to growth temperature of cold adapted bacterium

Microbial communities experience continuous environmental changes, among which temperature fluctuations are arguably the most impacting. This is particularly important considering the ongoing global warming but also in the "simpler" context of seasonal variability of sea-surface temperature. Understanding how microorganisms react at the cellular level can improve our understanding of possible adaptations of microbial communities to a changing environment. In this work, we investigated which are the mechanisms through which metabolic homeostasis is maintained in a cold-adapted bacterium during growth at temperatures that differ widely (15 and 0{degrees}C). We have quantified its intracellular and extracellular central metabolomes together with changes occurring at the transcriptomic level in the same growth conditions. This information was then used to contextualize a genome-scale metabolic reconstruction and to provide a systemic understanding of cellular adaptation to growth at two different temperatures. Our findings indicate a strong metabolic robustness at the level of the main central metabolites, counteracted by a relatively deep transcriptomic reprogramming that includes changes in gene expression of hundreds of metabolic genes. We interpret this as a transcriptomic buffering of cellular metabolism, able to produce overlapping metabolic phenotypes despite the wide temperature gap. Moreover, we show that metabolic adaptation seems to be mostly played at the level of few key intermediates (e.g. phosphoenolpyruvate) and in the cross-talk between the main central metabolic pathways. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the leveraging of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations.

systems biology↗

bioRxiv2022

The selective force driving metabolic operon assembly

The evolution of operons has puzzled evolutionary biologists since their discovery and many theories exist to explain their emergence and spreading. The presence of several plausible hypotheses dealing with operon emergence/evolution/spreading is indicative of the absence of a universal causal factor for this evolutionary process. Here, we argue that the way in which DNA replication and cell division are coupled in microbial species introduces an additional selective force that may be responsible for the clustering of functionally related genes on chromosomes. We interpret this as a preliminary and necessary step in operon formation. Specifically, we start from the observation that during DNA replication differences in copy number of genes that are found at distant loci on the same chromosome arm exist. We provide theoretical considerations suggesting that, when genes of the same metabolic process are far away on the chromosome, this results in perturbations to metabolic homeostasis. By formalizing the effect of DNA replication on metabolic homeostasis based on Metabolic Control Analysis, we show that the above situation provides a selective force that can drive the formation of gene clusters and operons. Finally, we confirmed that, in present-day genomes, this force is significantly stronger in those species where the average number of active replication forks is larger and quantify the theoretical contribution of this feature on the distribution of extant gene clusters and operons.

systems biology↗

bioRxiv2022

Modelling the metabolic consequences of antimicrobial exposure

Besides genetic mutations, the metabolic state of bacterial cells represents another driving factor in the emergence of antimicrobial resistance and in the actual efficacy of treatments. In this direction, studying how bacteria reprogram their metabolism when facing antimicrobial exposure is crucial to enhance our ability to limit the development and spread of antibiotic resistance. Here we have studied the metabolic consequences of antimicrobial exposure in bacteria using an integrated approach that exploits transcriptomics and computational modelling. Specifically, we asked whether common metabolic strategies emerge during the exposure to antimicrobials, regardless of the kind of antimicrobial used or, on the contrary, antimicrobial-specific pathways exist. To this purpose, we have used an heterogeneous dataset from six published studies on Escherichia coli exposed to different concentrations/types of compounds. We show that experimental condition, not antimicrobial exposure, is the factor that influences the most the resulting metabolic networks. However, despite condition-dependent metabolic signatures being evident, specific changes in flux distributions by antimicrobial exposed cells could be identified. In particular, purine and pyrimidine biosynthesis, and cofactor and prosthetic group biosynthesis were commonly affected by all considered antimicrobials. This suggests the presence of general metabolic strategies to face the stress posed by antimicrobial exposure and that, in turn, may represent an untapped resource for the fight against microbial infections. Finally, our analysis predicted an overall metabolic rewiring following bacteriostatic vs. bactericidal drug exposure that is in line with the current knowledge about the effects of these two classes of compounds on microbial metabolic phenotypes. IMPORTANCEA mechanistic understanding of microbial metabolic reprogramming during antimicrobial exposure is key to facilitate the discovery of new resistance mechanisms and to identify novel areas of intervention to face microbial infections. This study shows how the integration of transcriptomic data and genome-scale metabolic modelling can be used to address this critical issue, and to trace general metabolic strategies exploited by bacteria to face the stress posed by antimicrobial drugs.

systems biology↗

bioRxiv2022

Extraction and analysis of methylation features from Pacific Biosciences SMRT reads using MeStudio

MotivationDNA methylation is the most relevant epigenetic information, present in eukaryotes and prokaryotes, and is related to several biological phenomena, from cellular differentiation to control of gene flow, pathogenesis and virulence. The widespread use of third-generation sequencing technologies allows direct and easy detection of genome-wide methylation profiles, offering increasing opportunities to understand and exploit the epigenomics landscape. ResultsWe introduce MeStudio, a pipeline which allows to analyse and combine genome-wide methylation profiles with genomic features. Outputs report the presence of DNA methylation in coding sequences, noncoding sequences, intergenic sequences, and sequences upstream to CDS. We show the usage and performances of MeStudio on a set of single-molecule real time sequencing outputs from the bacterial species Sinorhizobium meliloti. Availability and ImplementationMeStudio is written in Python, Bash and C and is freely available under an open source GPLv3 license at https://github.com/combogenomics/MeStudio Supplementary informationSupplementary data are available at Bioinformatics online. Contactcombo.unifi@gmail.com

bioinformatics↗