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Pulvirenti, A.

Publications and source records attributed to Pulvirenti, A..

5 recordsLinked to original sources

Root-associated microbial community recruitment in two citrus rootstocks subjected to water and salinity stresses

Background and AimsAbiotic stress is a major constraint for citrus production in Mediterranean environments, where water deficit and salinity frequently occur. This is particularly relevant for perennial crops, like citrus, where limited options for stress avoidance exist. Rootstocks are extensively employed to enhance stress resilience; however, their influence on the root microbiome under abiotic stress remains largely unexplored. Here, we investigated the effects of water stress and salinity on the diversity, composition, and interactions of bacterial and fungal communities in two citrus rootstocks with reported contrasting phenotypes, such as Bitters, which has been described as exhibiting a promising tolerance to both water and salt stress, and Carrizo, which is generally reported to be highly sensitive to these conditions. MethodsThe distinct rootstocks have been subjected to either water stress or salt stress and compared with the non-stressed rootstocks. At the end of stress period, they were profiled and then integrated with recorded plant morphological (i.e. root volume), physiological (water potential, abscisic acid, chlorophyll and chlorophyll content meter) and biochemical measurements (abscisic acid and catalase). In parallel, we used a high-throughput amplicon sequencing to profile bacterial and fungal communities inhabiting the rhizosphere and endorhizosphere microhabitats of the rootstocks in both stresses and in non-treated conditions. Finally, we used correlations and multivariate analysis to determine relationships between plant performance and microbiome putatively underpinning stress adaptation and tolerance. ResultsAcross all treatments, microbial community composition was primarily shaped by microhabitat, with clear differentiation between rhizosphere and endorhizosphere. Abiotic stress significantly restructured microbial communities, particularly in the rhizosphere, while the endorhizosphere exhibited stronger genotype-dependent patterns. Bacterial communities showed pronounced stress-driven enrichments of taxa belonging to the main phyla (such as Proteobacteria, Actinobacteriota and Bacteroidota), with selective recruitment of taxa putatively associated with stress adaptation, whereas the response of fungal taxa (more represented by Ascomycota, Basidiomycota and Glomeromycota phyla) was less consistent and mainly microhabitat-driven. Notably, the two rootstocks exhibited distinct physiological strategies, with Bitters by increased proline accumulation and root volume and Carrizo characterized by enhanced ABA and catalase. ConclusionsOur findings showed Bitters outperform Carrizo in terms of tolerance to both water and salinity stress. In both rootstocks, specific bacterial taxa such as high abundant core or rare members, were associated with distinct phenotypic parameters, highlighting the importance of integrating plant and microbiome perspectives for improving stress resilience in citrus.

microbiology↗

geneslator: an R package for comprehensive gene identifier conversion and annotation

MotivationHigh-throughput sequencing generates large gene lists, making data interpretation challenging. Accurate gene annotation and reliable conversion between identifiers (e.g., gene symbols, Ensembl GeneIDs, Entrez GeneIDs) are essential for integrating datasets, conducting functional analyses, and enabling cross-species comparisons. Existing tools and databases facilitate annotation but often suffer from inconsistencies, missing mappings, and fragmented workflows, limiting reproducibility and interpretability. ResultsTo address these limitations, we developed geneslator, an R package that unifies gene identifier conversion, orthologs mapping, and pathway annotation across eight model organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Saccharomyces cerevisiae, Caenorhabditis elegans, Arabidopsis thaliana). geneslator provides an up-to-date, precise, and coherent framework that preserves data integrity, enables cross-species analyses, and facilitates robust interpretation of gene function and regulation, outperforming state-of-the-art gene annotation tools. Availabilitygeneslator is available at https://github.com/knowmics-lab/geneslator. Contactgrete.privitera@unict.it

bioinformatics↗

Clonal evolutionary analysis reveals patterns of malignant transformation in pancreatic cancer from Intraductal Papillary Mucinous IPMN Neoplasms (IPMN)

Intraductal papillary mucinous neoplasms (IPMNs) are critical precursors to pancreatic ductal adenocarcinoma (PDAC), a highly lethal cancer due to late detection and rapid progression. Using multi-region whole-genome and transcriptome sequencing, we traced the evolution of PDAC from IPMN, constructing detailed phylogenetic trees to provide insights into subclonal architectures and progression pathways. Our analysis identified two distinct evolutionary trajectories: one driven by a single ancestral clone, and another involving multiple independent ancestral clones, potentially influencing the timing and nature of PDAC onset. We further explored the roles of mutational signatures and structural variants (SVs) in promoting clonal evolution. Complementing these genomic findings, our transcriptomic analysis revealed unique gene expression profiles and variations in the immune landscape, correlating with the different progression stages from IPMN to PDAC. These insights reveal the complex molecular dynamics of IPMN progression to PDAC, highlighting the need to refine early detection and treatment strategies.

cancer biology↗

Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

genomics↗

PHENSIM: Phenotype Simulator

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach in order to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules and does so by exploiting signaling pathways. Our tools applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approachs reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.atlas.dmi.unict.it/.

bioinformatics↗