Biology subjects

Barberis, M.

Publications and source records attributed to Barberis, M..

8 recordsLinked to original sources

bioRxiv2026

Synthetic Combinatorial Minimisation of Cell Cycle Control in Yeast

The eukaryotic cell cycle, with its inherent regulatory redundancy, provides an ideal target for exploring genome modularisation and minimisation through synthetic genomics. Building upon principles established by the Synthetic Yeast Genome (Sc2.0) project, we used CRISPR-mediated genome engineering to relocate nine key cell cycle genes into a synthetic gene cluster in the S. cerevisiae genome, to allow combinatorial study of these genes in yeast. We employed Cre/loxP recombination to rapidly generate hundreds of strains with different gene deletion combinations in the module, with the objective of identifying minimal gene sets that permit robust cell cycle function. Using FACS-sorting and POLAR (Pool of Long Amplified Reads) sequencing, we conducted high-throughput analysis of gene deletion combinations in large cell pools, detecting approximately 80% of theoretically possible gene combinations, including those predicted from prior mathematical modelling studies. Our findings demonstrate that the cell cycle gene set can be minimised while maintaining viability, though only select combinations of gene deletions ensure robust fitness across different conditions. This work establishes a framework for genome minimisation, opening the door to the design of simplified, modular synthetic genomes for diverse applications.

synthetic biology↗

bioRxiv2025

The yeast phosphofructokinase β-subunit has ATP-dependent RNA unwinding activity and modulates cell cycle progression

Phosphofructokinase (PFK) is a rate-limiting glycolytic enzyme that also possesses an unexplored RNA binding activity. Here, we show that the - and {beta}-subunits of yeast PFK, encoded by PFK1 and PFK2, respectively, bind hundreds of mRNAs in cells, including ones coding for proteins involved in regulation of mitotic cell cycle. Pkf2p directly binds in a Mg-ATP-dependent manner to short GA-, UC-, AU- and U-rich motifs overrepresented in its mRNA targets. Strikingly, Pfk2p displays directional 5 - 3 double-stranded RNA unwinding activity not seen with Pfk1p. Furthermore, Pfk2p dynamically associates with ribosomes and promotes translation of cell cycle genes. Consequently, pfk2{Delta}, but not pfk1{Delta}, mutant cells show severely delayed G1/S phase transition which is independent of the enzymes glycolytic activity. Our results uncovered a hidden function for the Pfk2 subunit as a translational activator of mitotic cell cycle gene transcripts possibly through energy-dependent RNA unwinding activity.

molecular biology↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2022

miRNome profiling of lung cancer metastases revealed a key role for miRNA-PD-L1 axis in the modulation of chemotherapy response

Locally-advanced non-small-cell lung cancer (NSCLC) is frequent at diagnosis and requires multimodal treatment approaches. Neoadjuvant chemotherapy (NACT) followed by surgery is the treatment of choice for operable locally-advanced NSCLC (Stage IIIA). However, the majority of patients are NACT-resistant and shows persistent lymph nodal metastases (LNmets) and an adverse outcome. Therefore, the identification of mechanisms and biomarkers of NACT resistance is paramount for ameliorating prognosis of patients with Stage IIIA NSCLC. Here, we investigated the miRNome and transcriptome of chemo naive LNmets collected from patients with Stage IIIA NSCLC (N=64). We found that a microRNA signature accurately predicts NACT response. Mechanistically, we discovered a miR-455-5p/PD-L1 regulatory axis which drives chemotherapy resistance, hallmarks metastases with active IFN-{gamma} response pathway (an inducer of PD-L1 expression), and impacts T cells viability and relative abundances in tumor-microenviroment (TME). Our data provides new biomarkers to predict NACT response and adds molecular insights relevant for improving the management of patients with locally-advanced NSCLC.

cancer biology↗

bioRxiv2020

Extracellular Vesicles Hijack the Autophagic Pathway to Induce Tau Accumulation in Endolysosomes

Clinical progression of tauopathies is reflected by the transcellular propagation of pathogenic Tau seeds with the possible involvement of extracellular vesicles as transport vectors. However, the mechanism regulating extracellular vesicle cargo delivery to recipient cells is poorly understood. We established a cell model for investigating extracellular vesicle-delivery of membranes and proteins. In this model, extracellular vesicles are readily internalized and accumulate in endolysosomes. For the first time, we show that in this acidic compartment of recipient cells, extracellular vesicle-delivered Tau seeds cause the accumulation and abnormal folding of normal Tau by a process that requires the participation of autophagy. Endolysomes represent thus a cross-road where Tau seeds released from extracellular vesicles propagate on cellular Tau on its route for autophagy-mediated degradation, ultimately driving its accumulation, endolysosomal stress and cytotoxicity. Whilst, autophagy stimulation is considered as a viable solution to protect neurons from harmful cytosolic protein inclusions, our data suggest that this approach may favour the aberrant accumulation of neurodegeneration-associated proteins induced by exogenous pathogenic protein forms, with possible implications in the spreading of the disease.

cell biology↗

bioRxiv2020

Multi-Approach and Multi-Scale Model of CD4+ T Cells Predicts Switch-Like and Oscillatory Emergent Behaviors in Inflammatory Response to Infection

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells which uses four modeling approaches to integrate processes taking place at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility, and facilitates data integration. Our technology helps capture emergent behaviors that arise from nonlinear dynamics interwoven across three scales. Multi-scale insights added to single-scale studies allowed us to identify switch-like and oscillatory behaviors of CD4+ T cells at the population level, which are both novel and immunologically important. We envision our model and the generic framework encompassing it to become the foundation of a more comprehensive model of the human immune system.

systems biology↗

bioRxiv2020

Integrative computational approach identifies new targets in CD4+ T cell-mediated immune disorders

CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune related diseases. CD4+ T cells metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naive, Th1, Th2 and Th17 CD4+ T cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs, and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validations together with literature-based evidence showed that modulation of fifty percent of identified drug targets has been observed to lead to suppression of CD4+ T cells, further increasing their potential impact as therapeutic interventions. The used approach can be generalized in the context of other diseases, and novel metabolic models can be further used to dissect CD4+ T cell metabolism.

systems biology↗

bioRxiv2019

Design principles of ROS dynamic networks relevant to precision therapies for age-related diseases

The eminently complex regulatory network protecting the cell against oxidative stress, surfaces in several disease maps, including that of Parkinsons disease (PD). How this molecular networking achieves its various functionalities and how processes operating at the seconds-minutes time scale cause a disease at a time scale of multiple decennia is enigmatic.\n\nBy computational analysis, we here disentangle the reactive oxygen species (ROS) regulatory network into a hierarchy of subnetworks that each correspond to a different functionality. The detailed dynamic model of ROS management obtained integrates these functionalities and fits in vitro data sets from two different laboratories.\n\nThe model shows effective ROS-management for a century, followed by a sudden systems collapse due to the loss of p62 protein. PD related conditions such as lack of DJ-1 protein or increased -synuclein accelerated the systems collapse. Various in-silico interventions (e.g. addition of antioxidants or caffeine) slowed down the collapse of the system in silico, suggesting the model may help discover new medicinal and nutritional therapies.

systems biology↗