Biology subjects

Calogero, R. A.

Publications and source records attributed to Calogero, R. A..

5 recordsLinked to original sources

bioRxiv2026

Ex vivo stem-like cell families model evolution of glioblastoma therapeutic resistance

Glioblastoma (GBM) arises from stem-like cells (GSCs) that exhibit intrinsic therapeutic resistance and can be positively selected by treatment, rendering recurrent GBM intractable. Mechanistic dissection of therapeutic resistance evolution has been limited by scarce matched primary/recurrent tractable models. To address this gap, we developed "resistant GSC families", a within-patient matched platform that models therapy-driven selection by ex vivo deriving, from the same primary whole GBM, temozolomide (TMZ)- and ionizing radiation (IR)-selected GSCs, alongside a treatment-naive control (CTRL-GSC). This design enables pressure-specific dissection of resistance evolution, separating chemotherapy- and radiotherapy-associated genetic alterations and adaptations that are often confounded in primary-versus-recurrent comparisons. Using this framework, we link TMZ-driven relapse-like states to either mismatch repair (MMR)-dependent stable resistance or O6-methylguanine-DNA methyltransferase (MGMT)-independent drug tolerance, and identify adaptive DNA damage response and cell-cycle changes as a route to increased radioresistance. Across pressures, treatment-emergent GSCs accumulate chromosomal alterations and exhibit adaptive phenotypic remodeling, including increased receptor tyrosine kinase activity. Resistant GSC families represent a model enabling mechanistic studies and hypothesis-driven testing of strategies aimed at preventing or treating GBM recurrence.

cancer biology↗

bioRxiv2024

UnifiedGreatMod: A New Holistic Modeling Paradigm for Studying Biological Systems on a Complete and Harmonious Scale

MotivationComputational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognisable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, for example, to the cancer evolution study. ResultsTo address this aspect, we propose a new modelling paradigm, UnifiedGreatMod, which allows modellers to integrate fine-grained and coarse-grained biological information into a unique model. It allows for functional studies, in which the understanding of the systems multi-level stable condition and the systems fluctuating condition are combined to investigate the functional dependencies among the biological entities in the system under study. This is achieved thanks to the hybridisation of two analysis approaches that capture a systems different granularity levels. The proposed paradigm was then implemented into the open-source, general modelling framework GreatMod, in which a graphical meta-formalism is exploited to simplify the model creation phase and R languages to define user-defined analysis workflows. The proposals effectiveness was demonstrated by mechanistically simulating the metabolic output of Echerichia coli under environmental nutrient perturbations and integrating a gene expression dataset. Additionally, the UnifiedGreatMod was used to examine the responses of luminal epithelial cells to Clostridium difficile infection.

systems biology↗

bioRxiv2022

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

The transition from the evaluation of a single time point to the examination of the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and data clustering challenging. Here we present CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft (PDX) growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. Through the lens of a new perspective of mechanism interpretation, CONNECTOR shed light onto novel model aggregations and identified unanticipated molecular associations with response to clinically approved therapies.

bioinformatics↗

bioRxiv2018

Complementary information on single nucleotide variants, INDELs and functional translocations can be obtained with RNAseq using different library preparations

BackgroundRNA-seq represents an attractive methodology for the detection of functional genomic variants because it allows the integration of variant frequency and their expression. However, although specific statistic frameworks have been designed to detect SNVs/INDELS/gene fusions in RNA-seq data, very little has been done to understand the effect of library preparation protocols on transcript variant detection in RNA-seq data.\n\nResultsHere, we compared RNA-seq results obtained on short reads sequencing platform with two protocols: one based on polyA+ RNA selection protocol (POLYA) and the other based on exonic regions capturing protocol (ACCESS). Our data indicate that ACCESS detects 10% more coding SNV/INDELs with respect to POLYA, making this protocol more suitable for this goal. Furthermore, ACCESS requires less reads for coding SNV detection with respect to POLYA. On the other hand, if the analysis aims at identifying SNV/INDELs also in the 5and 3 UTRs, POLYA is definitively the preferred method. No particular advantage comes from the usage of ACCESS or POLYA in the detection of fusion transcripts.\n\nConclusionData show that a careful selection of the \"wet\" protocol adds specific features that cannot be obtained with bioinformatics alone.

genomics↗

bioRxiv2017

Reproducible Bioinformatics Project: A community for reproducible bioinformatics analysis pipelines

BackgroundReproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and a list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools and/or of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues.\n\nResultsTo address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate her/his workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover she/he has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization.\n\nConclusionsReproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.

bioinformatics↗