Biology subjects

Maclean, D.

Publications and source records attributed to Maclean, D..

3 recordsLinked to original sources

bioRxiv2024

The blast effector Pwl2 is a virulence factor that modifies the cellular localisation of host protein HIPP43 to suppress immunity.

The rice blast fungus Magnaporthe oryzae secretes a battery of effector proteins to facilitate host infection. Among these effectors, Pwl2 was first identified as a host specificity determinant for infection of weeping lovegrass (Eragrostis curvula) and is also recognised by the barley Mla3 resistance gene. However, its biological activity is not known. Here we show that PWL2 expression is regulated by the Pmk1 MAP kinase during cell-to-cell movement by M. oryzae at plasmodesmata (PD)-containing pit field sites. Consistent with its regulation, we provide evidence that Pwl2 binds to a barley heavy metal-binding isoprenylated protein HIPP43, which results in its displacement from plasmodesmata. Transgenic barley lines overexpressing either PWL2 or HIPP43 exhibit attenuated immune responses and increased disease susceptibility. By contrast, a Pwl2SNDEYWY mutant that does not interact with HIPP43, fails to alter the PD localisation of HIPP43. Targeted deletion of three copies of PWL2 in M. oryzae results in a{Delta} pwl2 mutant showing gain-of-virulence to weeping lovegrass and barley Mla3 lines, but also a reduction in severity of blast disease on susceptible host plants. Taken together, our results provide evidence that Pwl2 is a virulence factor that acts by suppressing host immunity through perturbing the plasmodesmatal deployment of HIPP43.

plant biology↗

bioRxiv2022

The transcriptional landscape of plant infection by the rice blast fungus Magnaporthe oryzae reveals distinct families of temporally co-regulated and structurally conserved effectors

The rice blast fungus Magnaporthe oryzae causes a devastating disease which threatens global rice production. In spite of intense study, the biology of plant tissue invasion during blast disease remains poorly understood. Here we report a high resolution, transcriptional profiling study of the entire plant-associated development of the blast fungus. Our analysis revealed major temporal changes in fungal gene expression during plant infection. Pathogen gene expression could be classified into 10 modules of temporally co-expressed genes, providing evidence of induction of pronounced shifts in primary and secondary metabolism, cell signalling and transcriptional regulation. A set of 863 genes encoding secreted proteins are differentially expressed at specific stages of infection, and 546 were predicted to be effectors and named MEP (Magnaporthe effector protein) genes. Computational prediction of structurally-related MEPs, including the MAX effector family, revealed their temporal co-regulation in the same co-expression modules. We functionally characterised 32 MEP genes and demonstrate that Mep effectors are predominantly targeted to the cytoplasm of rice cells via the biotrophic interfacial complex (BIC), and use a common unconventional secretory pathway. Taken together, our study reveals major changes in gene expression associated with blast disease and identifies a diverse repertoire of effectors critical to successful infection.

plant biology↗

bioRxiv2018

DeepStomata: Facial Recognition Technology for Automated Stomatal Aperture Measurement

Stomata are an attractive model for studying the physiological responses of plants to various environmental stimuli1-3. Of the morphological parameters that represent the degree of stomatal opening, the length of the minor axis of the stomatal pore (the stomatal aperture) has been most commonly used to dissect the molecular basis of its regulation. Measuring stomatal apertures is time consuming and labour intensive, preventing their use in large-scale studies. Here, we completely automated this process by developing a program called DeepStomata, which combines stomatal region detection and pore isolation by image segmentation. The former, which comprises histograms of oriented gradients (HOG)-based stomatal detection and the convolutional neural network (CNN)-based classification of open/closed-state stomata, acts as an efficient conditional branch in the workflow to selectively quantify the pores of open stomata. An analysis of batches of images showed that the accuracy of our automated aperture measurements was equivalent to that of manual measurements, however had higher sensitivity (i,e., lower false negative rate) and the process speed was at least 80 times faster. The outstanding performance of our proposed method for automating a laborious and repetitive task will allow researchers to focus on deciphering complex phenomena.

bioinformatics↗