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Neofusicoccum parvum is a fungal pathogen associated with a wide range of plant hosts. Despite being widely studied, the molecular mechanism of infection of N. parvum is still far from being understood. Analysis of N. parvum genome lead to the identification of six putative genes encoding necrosis and ethylene-inducing proteins (NLPs). The sequence of NLPs genes (NprvNep 1-6) were analyzed and four of the six NLP genes were successfully cloned, expressed in E. coli and purified by affinity chromatography. Pure recombinant proteins were characterized according to their phytotoxic and cytotoxic effects to tomato leaves and to mammalian Vero cells, respectively. These assays revealed that all NprvNeps tested are cytotoxic to Vero cells and also induce cell death in tomato leaves. NprvNep2 was the most toxic to Vero cells, followed by NprvNep1 and 3. NprvNep4 induced weaker, but, nevertheless, still significant toxic effects to Vero cells. A similar trend of toxicity was observed in tomato leaves: the most toxic was NprvNep 2 and the least toxic NprvNep 4. This study describes for the first time an overview of the NLP gene family of N. parvum and provides additional insights into its pathogenicity mechanism.

The interactome - the network of protein-protein interactions (PPIs) within a cell or organism - is technically difficult to assess. Bioinformatic tools can, not only, identify potential PPIs that can be later experimentally validated, but also be used to assign functional meaning to PPIs. Saliva's potential as a non-invasive diagnostic fluid is currently being explored by several research groups. But, in order to fully attain its potential, it is necessary to achieve the full characterization of the mechanisms that take place within this ecosystem. The onset of omics technologies, and specifically of proteomics, delivered a huge set of data that is largely underexplored. Quantitative information relative to proteins within a given context (for example a given disease) can be used by computational algorithms to generate information regarding PPIs. These PPIs can be further analyzed concerning their functional meaning and used to identify potential biomarkers, therapeutic targets, defense and pathogenicity mechanisms. We describe a computational pipeline that can be used to identify and analyze PPIs between human and microbial proteins. The pipeline was tested within the scenario of human PPIs of systemic (Zika Virus infection) and of oral conditions (Periodontal disease) and also in the context of microbial interactions (Candida-Streptococcus) and showed to successfully predict functionally relevant PPIs. The pipeline can be applied to different scientific areas, such as pharmacological research, since a functional meaningful PPI network can provide insights on potential drug targets, and even new uses for existing drugs on the market., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Lasiodiplodia theobromae (Botryosphaeriaceae, Ascomycota) is a plant pathogen and human opportunist whose pathogenicity is modulated by temperature. The molecular effects of temperature on L. theobromae are mostly unknown, so we used a multi-omics approach to understand how temperature affects the molecular mechanisms of pathogenicity. The genome of L. theobromae LA-SOL3 was sequenced (Illumina MiSeq) and annotated. Furthermore, the transcriptome (Illumina TruSeq) and proteome (Orbitrap LC-MS/MS) of LA-SOL3 grown at 25 °C and 37 °C were analysed. Proteins related to pathogenicity (plant cell wall degradation, toxin synthesis, mitogen-activated kinases pathway and proteins involved in the velvet complex) were more abundant when the fungus grew at 25 °C. At 37 °C, proteins related to pathogenicity were less abundant than at 25 °C, while proteins related to cell wall organisation were more abundant. On the other hand, virulence factors involved in human pathogenesis, such as the SSD1 virulence protein, were expressed only at 37 °C. Taken together, our results showed that this species presents a typical phytopathogenic molecular profile that is compatible with a hemibiotrophic lifestyle. We showed that L. theobromae is equipped with the pathogenesis toolbox that enables it to infect not only plants but also animals.

The characterisation of the secretome of phytopathogenic fungi may contribute to elucidate the molecular mechanisms of pathogenesis. This is particularly relevant for Diplodia corticola, a fungal plant pathogen belonging to the family Botryosphaeriaceae, whose genome remains unsequenced. This phytopathogenic fungus is recognised as one of the most important pathogens of cork oak, being related to the decline of cork oak forests in the Iberian Peninsula. Unfortunately, secretome analysis of filamentous fungi is limited by the low protein concentration and by the presence of many interfering substances, such as polysaccharides, which affect the separation and analysis by 1D and 2D gel electrophoresis. We compared six protein extraction protocols concerning their suitability for further application with proteomic workflows. The protocols involving protein precipitation were the most efficient, with emphasis on TCA-acetone protocol, allowing us to identify the most abundant proteins on the secretome of this plant pathogen. Approximately 60% of the spots detected were identified, all corresponding to extracellular proteins. Most proteins identified were carbohydrate degrading enzymes and proteases that may be related to D. corticola pathogenicity. Although the secretome was assessed in a noninfection environment, potential virulence factors such as the putative glucan-β-glucosidase, neuraminidase, and the putative ferulic acid esterase were identified. The data obtained forms a useful basis for a deeper understanding of the pathogenicity and infection biology of D. corticola. Moreover, it will contribute to the development of proteomics studies on other members of the Botryosphaeriaceae., (Copyright © 2014 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.)