Genome-wide identification and characterization of the RIO atypical kinase family in plants

Exploiting plant transcriptomic databases: Resources, tools, and approaches

There are now more than 300 000 RNA sequencing samples available, stemming from thousands of experiments capturing gene expression in organs, tissues, developmental stages, and experimental treatments for hundreds of plant species. The expression data have great value, as they can be re-analyzed by others to ask and answer questions that go beyond the aims of the study that generated the data. Because gene expression provides essential clues to where and when a gene is active, the data provide powerful tools for predicting gene function, and comparative analyses allow us to study plant evolution from a new perspective. This review describes how we can gain new knowledge from gene expression profiles, expression specificities, co-expression networks, differential gene expression, and experiment correlation. We also introduce and demonstrate databases that provide user-friendly access to these tools.

Transcriptomic and genomic analysis provides new insights in molecular and genetic processes involved in zucchini ZYMV tolerance

BACKGROUND

Cucurbita pepo is highly susceptible to Zucchini yellow mosaic virus (ZYMV) and the resistance found in several wild species cannot be considered as complete or broad-spectrum resistance. In this study, a source of tolerance introgressed in C. pepo (381e) from C. moschata, in True French (TF) background, was investigated 12 days post-inoculation (DPI) at transcriptomic and genomic levels.

RESULTS

The comparative RNA-sequencing (RNA-Seq) of TF (susceptible to ZYMV) and 381e (tolerant to ZYMV) allowed the evaluation of about 33,000 expressed transcripts and the identification of 146 differentially expressed genes (DEGs) in 381e, mainly involved in photosynthesis, transcription, cytoskeleton organization and callose synthesis. By contrast, the susceptible cultivar TF triggered oxidative processes related to response to biotic stimulus and activated key regulators of plant virus intercellular movement. In addition, the discovery of variants located in transcripts allowed the identification of two chromosome regions rich in Single Nucleotide Polymorphisms (SNPs), putatively introgressed from C. moschata, containing genes exclusively expressed in 381e.

CONCLUSION

381e transcriptome analysis confirmed a global improvement of plant fitness by reducing the virus titer and movement. Furthermore, genes implicated in ZYMV tolerance in C. moschata introgressed regions were detected. Our work provides new insight into the plant virus recovery process and a better understanding of the molecular basis of 381e tolerance.

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

Targeted Mutagenesis of the Rice FW 2.2-Like Gene Family Using the CRISPR/Cas9 System Reveals OsFWL4 as a Regulator of Tiller Number and Plant Yield in Rice

The FW2.2-like (FWL) genes encode cysteine-rich proteins with a placenta-specific 8 domain. They play roles in cell division and organ size control, response to rhizobium infection, and metal ion homeostasis in plants. Here, we target eight rice FWL genes using the CRISPR/Cas9 system delivered by Agrobacterium-mediated transformation. We successfully generate transgenic T₀ lines for 15 of the 16 targets. The targeted mutations are detected in the T₀ lines of all 15 targets and the average mutation rate is found to be 81.6%. Transfer DNA (T-DNA) truncation is a major reason for the failure of mutagenesis in T₀ plants. T-DNA segregation analysis reveals that the T-DNA inserts in transgenic plants can be easily eliminated in the T₁ generation. Of the 30 putative off-target sites examined, unintended mutations are detected in 13 sites. Phenotypic analysis reveals that tiller number and plant yield of OsFWL4 gene mutants are significantly greater than those of the wild type. Flag leaves of OsFWL4 gene mutants are wider than those of the wild type. The increase in leaf width of the mutants is caused by an increase in cell number. Additionally, grain length of OsFWL1 gene mutants is higher than that of the wild type. Our results suggest that transgene-free rice plants with targeted mutations can be produced in the T₁ generation using the Agrobacterium-mediated CRISPR/Cas9 system and that the OsFWL4 gene is a negative regulator of tiller number and plant yield.