Summary Rust fungi (Pucciniales) are the largest group of plant pathogens and represent one of the most devastating threats to agricultural crops worldwide. Despite the economic importance of these highly specialized pathogens, many aspects of their biology remain obscure, largely because rust fungi are obligate biotrophs. The rise of genomics and advances in high‐throughput sequencing technology have presented new options for identifying candidate effector genes involved in pathogenicity mechanisms of rust fungi. Transcriptome analysis and integrated bioinformatics tools have led to the identification of key genetic determinants of host susceptibility to infection by rusts. Thousands of genes encoding secreted proteins highly expressed during host infection have been reported for different rust species, which represents significant potential towards understanding rust effector function. Recent high‐throughput in planta expression screen approaches (effectoromics) have pushed the field ahead even further towards predicting high‐priority effectors and identifying avirulence genes. These new insights into rust effector biology promise to inform future research and spur the development of effective and sustainable strategies for managing rust diseases.

Pterodon emarginatus Vogel is a Brazilian species that belongs to the family Fabaceae, popularly known as sucupira. Its oil has several biological activities, including potent larvicidal property against Aedes aegypti. This insect is the vector of dengue, a tropical disease that has been considered a critical health problem in developing countries, such as Brazil. Most of dengue control methods involve larvicidal agents suspended or diluted in water and making active lipophilic natural products available is therefore considered a technological challenge. In this context, nanoemulsions appear as viable alternatives to solve this major problem. The present study describes the development of a novel nanoemulsion with larvicidal activity against A. aegypti along with the required Hydrophile Lipophile Balance determination of this oil. It was suggested that the mechanism of action might involve reversible inhibition of acetylcholinesterase and our results also suggest that the P. emarginatus nanoemulsion is not toxic for mammals. Thus, it contributes significantly to alternative integrative practices of dengue control, as well as to develop sucupira based nanoproducts for application in aqueous media.

The basidiomycete Melampsora larici-populina causes poplar rust disease by invading leaf tissues and secreting effector proteins through specialized infection structures known as haustoria. The mechanisms by which rust effectors promote pathogen virulence are poorly understood. The present study characterized Mlp124478, a candidate effector of M. larici-populina. We used the models Arabidopsis thaliana and Nicotiana benthamiana to investigate the function of Mlp124478 in plant cells. We established that Mlp124478 accumulates in the nucleus and nucleolus, however its nucleolar accumulation is not required to promote growth of the oomycete pathogen Hyaloperonospora arabidopsidis. Stable constitutive expression of Mlp124478 in A. thaliana repressed the expression of genes involved in immune responses, and also altered leaf morphology by increasing the waviness of rosette leaves. Chip-PCR experiments showed that Mlp124478 associats'e with the TGA1a-binding DNA sequence. Our results suggest that Mlp124478 exerts a virulence activity and binds the TGA1a promoter to suppress genes induced in response to pathogen infection.

Chaga (Inonotus obliquus) is a medicinal fungus used in traditional medicine of Native American and North Eurasian cultures. Several studies have demonstrated the medicinal properties of chaga’s bioactive molecules. For example, several terpenoids (e.g., betulin, betulinic acid and inotodiol) isolated from I. obliquus cells have proven effectiveness in treating different types of tumor cells. However, the molecular mechanisms and regulation underlying the biosynthesis of chaga terpenoids remain unknown. In this study, we report on the optimization of growing conditions for cultured I. obliquus in presence of different betulin sources (e.g., betulin or white birch bark). It was found that better results were obtained for a liquid culture pH 6.2 at 28 °C. In addition, a de novo assembly and characterization of I. obliquus transcriptome in these growth conditions using Illumina technology was performed. A total of 219,288,500 clean reads were generated, allowing for the identification of 20,072 transcripts of I. obliquus including transcripts involved in terpenoid biosynthesis. The differential expression of these genes was confirmed by quantitative-PCR. This study provides new insights on the molecular mechanisms and regulation of I. obliquus terpenoid production. It also contributes useful molecular resources for gene prediction or the development of biotechnologies for the alternative production of terpenoids.

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 cutoff. 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.

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.

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 script 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 expression of custom selected genes was more stable. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. expression more stable) 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.

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 expression of custom selected genes was more stable. geNorm produced a similar result in which most custom selected genes ranked higher ( i.e. expression more stable) 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.