Swart, V. | Search bioRxiv

bioRxiv2025

South African brown locusts, Locustana pardalina, hosts fluconazole resistant Candidozyma (Candida) auris (Clade III)

The environmental niche and mode of transmission from the environment to humans of the emerging pathogenic yeast, Candidozyma (Candida) auris is a subject of speculation, with hypotheses including avian species and marine environments. Interestingly, yeasts related to C. auris have been repeatedly observed associated with various insects. This lprompted us to investigate a thermophilic insect, Locustana pardalina as possible host for C. auris. Here we report the isolation and identification of three C auris strains from the gut of L. pardalina as well as the phenotypic characterisation of one of these isolates. Interestingly, the isolate was able to survive at 50oC and grew at 15% NaCl. In addition, it was susceptible to the tested disinfectants and antifungals, except fluconazole. Genome sequencing and SNP analyses placed the isolate in Clade III, which is common is South Africa. This highlights the role of insects in the evolution and dissemination of emerging pathogenic yeasts.

microbiology↗

bioRxiv2024

What NLR you recognizing? Predicted binding affinities- and energies may be used to identify novel NLR-effector interactions

Nucleotide binding Leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity in plants following pathogen infection. Advances in genome sequencing have led to the identification of a myriad NLRs in numerous agriculturally important plant species. However, deciphering which NLR proteins recognize specific pathogen effectors remains a challenge. Predicting NLR-effector interactions in silico would provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using AlphaFold2-Multimer for all experimentally validated NLR-effector interactions. Binding affinities- and energies were predicted using 97 machine learning models from Area-affinity. We show that predicted structures with an AlphaFold confidence score > 0.42 have acceptable accuracy, and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol, depending on the Area-Affinity model used. For 2427 "forced" NLR-effector complexes, these estimates showed larger variability, enabling the identification of novel NLR-effector complexes with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for true interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all plant-pathogen interactions. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for providing resistance against plant pathogens, advancing our understanding of plant immunity.

plant biology↗