Searching for a Match: Structure, Function and Application of Sequence-Specific RNA-Binding Proteins.

Plants encode over 1800 RNA-binding proteins (RBPs) that modulate a myriad of steps in gene regulation from chromatin organization to translation, yet only a small number of these proteins and their target transcripts have been functionally characterized. Two classes of eukaryotic RBPs, pentatricopeptide repeat (PPR) and pumilio/fem-3 binding factors (PUF), recognize and bind to specific sequential RNA sequences through protein-RNA interactions. These modular proteins possess helical structural units containing key residues with high affinity for specific nucleotides, whose sequential order determines binding to a specific target RNA sequence. PPR proteins are nucleus-encoded, but largely regulate post-transcriptional gene regulation within plastids and mitochondria, including splicing, translation and RNA editing. Plant PUFs are involved in gene regulatory processes within the cell nucleus and cytoplasm. The modular structures of PPRs and PUFs that determine sequence specificity has facilitated identification of their RNA targets and biological functions. The protein-based RNA-targeting of PPRs and PUFs contrasts to the prokaryotic cluster regularly interspaced short palindromic repeats (CRISPR)-associated proteins (Cas) that target RNAs in prokaryotes. Together the PPR, PUF and CRISPR-Cas systems provide varied opportunities for RNA-targeted engineering applications.

Genome-wide association of carbon and nitrogen metabolism in the maize nested association mapping population.

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C4 metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.

Fake news blues: A GUS staining protocol to reduce false-negative data.

The ß-glucuronidase gene, uidA (GUS), has remained a favorite reporter gene in plants since its introduction in 1987 for its stability and versatility in a variety of fluorometric, spectrophotometric, and histochemical techniques. One of the most popular uses is as a reporter gene for visualizing endogenous promoter activities within plant tissues. Despite this popularity, specific protocols for minimizing nonrepresentative staining patterns, including false negatives, in challenging tissue types are not common. This became a large issue during our work on dark-grown Arabidopsis hypocotyls, and we set out to develop a protocol that would ensure accurate staining in a tissue that is biologically resistant to reagent penetration. Through extensive testing using a variety of constitutive and endogenous promoter::GUS fusion lines, we have developed an optimized GUS staining protocol that combines the use of acetone as a fixative, deliberate physical damage, and proper positive and negative controls to help ensure accurate staining along the hypocotyl while minimizing false negatives. Hopefully, our recommendations will allow for improved staining that more accurately reflects the true activity of cloned endogenous promoters and thus facilitate a more accurate understanding of promoter activity in Arabidopsis hypocotyls and other hard-to-stain tissues.

Comparative analyses of C4 and C3 photosynthesis in developing leaves of maize and rice.

C4 and C3 photosynthesis differ in the efficiency with which they consume water and nitrogen. Engineering traits of the more efficient C4 photosynthesis into C3 crops could substantially increase crop yields in hot, arid conditions. To identify differences between C4 and C3 photosynthetic mechanisms, we profiled metabolites and gene expression in the developing leaves of Zea mays (maize), a C4 plant, and Oryza sativa (rice), a C3 plant, using a statistical method named the unified developmental model (UDM). Candidate cis-regulatory elements and transcription factors that might regulate photosynthesis were identified, together with differences between C4 and C3 nitrogen and carbon metabolism. The UDM algorithms could be applied to analyze and compare development in other species. These data sets together with community viewers to access and mine them provide a resource for photosynthetic research that will inform efforts to engineer improvements in carbon fixation in economically valuable grass crops.

A low-cost library construction protocol and data analysis pipeline for Illumina-based strand-specific multiplex RNA-seq.

The emergence of NextGen sequencing technology has generated much interest in the exploration of transcriptomes. Currently, Illumina Inc. (San Diego, CA) provides one of the most widely utilized sequencing platforms for gene expression analysis. While Illumina reagents and protocols perform adequately in RNA-sequencing (RNA-seq), alternative reagents and protocols promise a higher throughput at a much lower cost. We have developed a low-cost and robust protocol to produce Illumina-compatible (GAIIx and HiSeq2000 platforms) RNA-seq libraries by combining several recent improvements. First, we designed balanced adapter sequences for multiplexing of samples; second, dUTP incorporation in 2(nd) strand synthesis was used to enforce strand-specificity; third, we simplified RNA purification, fragmentation and library size-selection steps thus drastically reducing the time and increasing throughput of library construction; fourth, we included an RNA spike-in control for validation and normalization purposes. To streamline informatics analysis for the community, we established a pipeline within the iPlant Collaborative. These scripts are easily customized to meet specific research needs and improve on existing informatics and statistical treatments of RNA-seq data. In particular, we apply significance tests for determining differential gene expression and intron retention events. To demonstrate the potential of both the library-construction protocol and data-analysis pipeline, we characterized the transcriptome of the rice leaf. Our data supports novel gene models and can be used to improve current rice genome annotation. Additionally, using the rice transcriptome data, we compared different methods of calculating gene expression and discuss the advantages of a strand-specific approach to detect bona-fide anti-sense transcripts and to detect intron retention events. Our results demonstrate the potential of this low cost and robust method for RNA-seq library construction and data analysis.