The Fungal Effector Mlp37347 Alters Plasmodesmata Fluxes and Enhances Susceptibility to Pathogen.

Melampsora larici-populina (Mlp) is a devastating pathogen of poplar trees, causing the defoliating poplar leaf rust disease. Genomic studies have revealed that Mlp possesses a repertoire of 1184 small secreted proteins (SSPs), some of them being characterized as candidate effectors. However, how they promote virulence is still unclear. This study investigates the candidate effector Mlp37347's role during infection. We developed a stable Arabidopsis transgenic line expressing Mlp37347 tagged with the green fluorescent protein (GFP). We found that the effector accumulated exclusively at plasmodesmata (PD). Moreover, the presence of the effector at plasmodesmata favors enhanced plasmodesmatal flux and reduced callose deposition. Transcriptome profiling and a gene ontology (GO) analysis of transgenic Arabidopsis plants expressing the effector revealed that the genes involved in glucan catabolic processes are up-regulated. This effector has previously been shown to interact with glutamate decarboxylase 1 (GAD1), and in silico docking analysis supported the strong binding between Mlp37347 and GAD1 in this study. In infection assays, the effector promoted Hyalonoperospora arabidopsidis growth but not bacterial growth. Our investigation suggests that the effector Mlp37347 targets PD in host cells and promotes parasitic growth.

Unrelated Fungal Rust Candidate Effectors Act on Overlapping Plant Functions.

Rust fungi cause epidemics that threaten the production of important plant species, such as wheat and soy. Melampsora larici-populina (Mlp) causes the poplar rust and encodes at least 1184 candidate effectors (CEs) whose functions are poorly known. In this study, we sequenced the transcriptome and used mass spectrometry to analyze the metabolome of Arabidopsis plants constitutively expressing 14 Mlp CEs and of a control line to discover alterations leading to plant susceptibility. We found 2299 deregulated genes across the experiment. Genes involved in pattern-triggered immunity, such as FRK1, PR1, RBOHD, and WRKY33, as well as AUX/IAA genes were down-regulated. We further observed that 680 metabolites were deregulated in at least one CE-expressing transgenic line, with "highly unsaturated and phenolic compounds" and "peptides" enriched among down- and up-regulated metabolites. Interestingly, transgenic lines expressing unrelated CEs had correlated patterns of gene and metabolite deregulation, while expression of CEs belonging to the same family deregulated different genes and metabolites. Thus, our results uncouple effector sequence similarity and function. This supports that effector functional investigation in the context of their virulence activity and effect on plant susceptibility requires the investigation of the individual effector and precludes generalization based on sequence similarity.

Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis.

BACKGROUND: RNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes. RESULTS: Here, we report an R-based pipeline to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFS script to calculate the cut-off. It then selects as references the genes with lowest TPM covariance. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower covariance and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the custom selected genes were more stably expressed. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. were more stably expressed) than commonly used reference genes. CONCLUSIONS: The proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.