Biology subjects

Vitali, F.

Publications and source records attributed to Vitali, F..

3 recordsLinked to original sources

bioRxiv2026

Association between soil properties and Grapevine Leaf Stripe Disease (Esca Complex) symptom expression. Part I: soil physical, hydrological and chemical characteristics

This study is the first of two companion papers (Part II addresses rhizosphere microbial communities) and aims to investigate the influence of soil properties on GLSD foliar symptom expression by comparing high recurrence of symptomatic vines areas (HRSVAs) and low recurrence of symptomatic vines areas (LRSVAs). The research was conducted in an approximately 1.75-hectare Cabernet Sauvignon vineyard in Tuscany, Italy. Electromagnetic induction (EMI) proximal sensing was combined with kriging-based maps of grapevine leaf stripe disease (GLSD) symptom recurrence over a five-year period, allowing the identification of four vineyard sectors representing contrasting levels of symptom recurrence (high vs. low) and soil apparent electrical conductivity (ECa; medium vs. high). One soil profile was excavated in each sector and characterised through pedological description and physicochemical analysis. GLSD symptoms showed a heterogeneous yet spatially structured distribution, with distinct zones exhibiting contrasting recurrence. Soil properties differed markedly between HRSVAs and LRSVAs. LRSVAs were characterised by clay loam to silty clay loam textures, high lime content, alkaline pH, higher available water capacity (AWC), and lower available manganese concentrations (4.5-4.8 mg/kg). In contrast, HRSVAs had sandy loam to sandy clay loam textures, subalkaline pH, greater soil fertility (higher nitrogen and organic matter contents), deeper root distribution, and higher available manganese concentrations (14.7-14.8 mg/kg). These findings highlight the importance of investigating soil physicochemical properties in relation to GLSD symptom expression, as both macro- and micronutrient availability may be associated with disease dynamics. The higher manganese availability observed in HRSVAs, together with recent evidence that manganese can stimulate the wood-degrading activity of Fomitiporia mediterranea, warrants further investigation into the potential association between soil manganese bioavailability and disease expression. The spatial correspondence between soil heterogeneity and GLSD symptom recurrence highlights the value of high-resolution investigations of soil-plant interactions for improving our understanding of this complex disease.

plant biology↗

bioRxiv2019

Autologous micrograft accelerates endogenous wound healing response via ERK-induced cell migration

Defective fibroblast migration causes delayed wound healing (WH) and chronic skin lesions. Autologous micrograft (AMG) therapies have recently emerged as a new effective treatment able to improve wound healing capacity. However, the molecular mechanisms connecting their beneficial outcomes with the wound healing process are still unrevealed. Here, we show that AMG modulates primary fibroblast migration and accelerates skin re-epithelialization without affecting cell proliferation. We demonstrate that AMG is enriched in a pool of WH-associated growth factors that may provide the initiation signal for a faster endogenous wound healing response. This, in turn leads to increased cell migration rate by elevating activity of extracellular signal-regulated kinase (ERK) pathway and subsequent activation of matrix metalloproteinase expression and their extracellular enzymatic activity. Moreover, AMG-treated wounds showed increased granulation tissue formation and organized collagen content. Overall, we shed light on AMG molecular mechanism supporting its potential to trigger a highly improved wound healing process.

molecular biology↗

bioRxiv2018

Protease target prediction via matrix factorization

MotivationProtein cleavage is an important cellular event, involved in a myriad of processes, from apoptosis to immune response. Bioinformatics provides in silico tools, such as machine learning-based models, to guide target discovery. State-of-the-art models have a scope limited to specific protease families (such as Caspases), and do not explicitly include biological or medical knowledge (such as the hierarchical protein domain similarity, or gene-gene interactions). To fill this gap, we present a novel approach for protease target prediction based on data integration.\n\nResultsBy representing protease-protein target information in the form of relational matrices, we design a model that: (a) is general, i.e., not limited to a single protease family; and (b) leverages on the available knowledge, managing extremely sparse data from heterogeneous data sources, including primary sequence, pathways, domains, and interactions from nine databases. When compared to other algorithms on test data, our approach provides a better performance even for models specifically focusing on a single protease family.\n\nAvailabilityhttps://gitlab.com/smarini/MaDDA/ (Matlab code and utilized data.)\n\nContactsmarini@med.umich.edu, or takutsu@kuicr.kyoto-u.ac.jp

bioinformatics↗