Biology subjects

Farina, L.

Publications and source records attributed to Farina, L..

4 recordsLinked to original sources

bioRxiv2026

Agrochemical-responsive gene expression control systems for modulating plant development and metabolism

Precise temporal control of gene expression is critical for studying plant biology and engineering complex crop traits. Current systems enable chemically inducible regulation but rely on costly or agriculturally impractical inducers and lack the flexibility needed to regulate combinations of native loci and transgenes. In this work, we elucidate the design rules for control systems, based on Cas9 and Cre recombinase fused to the ecdysone receptor (EcR), which respond to a widely used agrochemical methoxyfenozide (MF). First, we validated the function of both circuits in transient assays and explored how transduction properties can be modulated by engineering nuclear trafficking dynamics. We next characterized both the Cas9-based and recombinase-based systems by using them to regulate fluorescent reporters in transgenic Arabidopsis thaliana plants. Here, we demonstrate systemic activation following root application of MF, validating the use of an agriculturally compatible inducer for whole-plant gene regulation. Finally, we validated the utility of the inducible Cas-based SynTF system to regulate multigene pathways and control both metabolic flux and developmental circuits. Together, these results establish design principles for agrochemical-inducible control systems and demonstrate their utility for temporally regulating plant phenotypes. These synthetic circuits provide a versatile framework for engineering complex traits using an agriculturally compatible inducer.

synthetic biology↗

bioRxiv2025

Molecular mechanisms of sexual dimorphism in cancer through improved miRNA regulation-level network-based approach: MIRROR2

A current open challenge in precision medicine is sex-specific medicine: the study of how sex-based biological differences influence peoples health. With recent advancements in high-throughput technologies, large-scale molecular data are being generated for individual cancer patients, however, extracting meaningful insights from these complex datasets remains a challenge. Network-based approaches, being inherently holistic, can lead to a better understanding of the molecular mechanisms underlying a disease. For this reason, this study focuses on the development of a network-based method to investigate sexual dimorphism in cancer using transcriptomic data. Previous studies have already shown how miRNAs are involved in differentiating patients by sex in different types of cancer; however, they focused only on evaluating changes in the expression level, without conducting a more comprehensive analysis of miRNA expression or investigating miRNAs targets. The aim of this study is therefore to carry out a multi-layer study involving both miRNAs and their target genes expression data. In particular, we developed a generalizable algorithm (MIRROR2), which can be used on cancer patients to help identify key regulatory mechanisms and molecules that act as differentiators between males and females. Here we implemented and tested MIRROR2 on three different cancers (colon adenocarcinoma, hepatocellular carcinoma, and low-grade gliomas) and assessed its performance by comparing it to state-of-the-art approaches. This revealed MIRROR2s efficacy in identifying sex-specific key genes (and how to integrate them with clinical features), presenting it as a viable alternative to state-of-the-art methods which fail to capture these differences.

bioinformatics↗

bioRxiv2023

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

IntroductionNeural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. Virtual brain modelling (TVB), by exploiting structural and functional MRI, yields mesoscopic parameters of connectivity and synaptic transmission. MethodsWe used TVB to simulate brain networks, which are key for human brain function, in Alzheimers disease (AD) and Frontotemporal Dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. ResultsThe pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. DiscussionThese TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

neuroscience↗

bioRxiv2019

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called \"switch genes\" - for disease. Genes contributing to common biological processes or define given cell types are frequently co-regulated and co-expressed, giving rise to expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.

bioinformatics↗