Biology subjects

Alaimo, S.

Publications and source records attributed to Alaimo, S..

5 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2021

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↗

bioRxiv2020

Divergent use of metabolic fluxes in breast cancer metastasis

Breast cancers can metastasize to many organs. But how do disseminated cells from a primary tumor adapt to distal tissues? Here we combined metabolomics, flux measurements, and mathematical modeling to study metabolic fluxes in breast cancer cells adapted to home to different organs. We found that lung-homing cells maintain high glycolytic flux despite low levels of glycolytic intermediates, by constitutively activating a pathway sink into lactate. Their distinct behavior--a strong Warburg effect--has a gene expression signature: a high ratio of lactate dehydrogenase to pyruvate dehydrogenase gene expression, which also correlates with lung metastases in patients with breast cancer. Surprisingly, this strong Warburg effect does not necessarily increase cellular growth rate, suggesting that lactate secretion may be a trait under selection in lung metastasis. Our results stress that metabolic fluxes may not correlate with metabolic intermediates, a finding relevant for metastatic tropism.

cancer biology↗

bioRxiv2020

IWS1 phosphorylation promotes cell proliferation and predicts poor prognosis in EGFR mutant lung adenocarcinoma patients, through the cell cycle-regulated U2AF2 RNA splicing.

The authors have withdrawn their manuscript. While attempting to reproduce the data on the alternative splicing of exon 2 of U2AF2, they observed that the proposed splicing mechanism could not give rise to a functional U2AF2 protein. In addition, they observed evidence of manipulation in the electropherogram of the splicing junction between exons 1 and 3 and in the primary data on which this electropherogram was based, which were deposited in Mendeley by the first author. These observations raise questions on the integrity of the reported results. In light of this information, the authors have no confidence in the key findings of the paper, and therefore, do not wish it to be cited. If you have any questions, please contact the corresponding author.

cancer biology↗

bioRxiv2020

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↗