Coordination of Chloroplast Development through the Action of the GNC and GLK Transcription Factor Families.

Fundamental questions regarding how chloroplasts develop from proplastids remain poorly understood despite their central importance to plant life. Two families of nuclear transcription factors, the GATA NITRATE-INDUCIBLE CARBON-METABOLISM-INVOLVED (GNC) and GOLDEN TWO-LIKE (GLK) families, have been implicated in directly and positively regulating chloroplast development. Here, we determined the degree of functional overlap between the two transcription factor families in Arabidopsis (Arabidopsis thaliana), characterizing their ability to regulate chloroplast biogenesis both alone and in concert. We determined the DNA-binding motifs for GNC and GLK2 using protein-binding microarrays; the enrichment of these motifs in transcriptome datasets indicates that GNC and GLK2 are repressors and activators of gene expression, respectively. ChIP-seq analysis of GNC identified PHYTOCHROME INTERACTING FACTOR and brassinosteroid activity genes as targets whose repression by GNC facilitates chloroplast biogenesis. In addition, GNC targets and represses genes involved in ERECTA signaling and thereby facilitates stomatal development. Our results define key regulatory features of the GNC and GLK transcription factor families that contribute to the control of chloroplast biogenesis and photosynthetic activity, including areas of independence and cross talk.

Cytokinin induces genome-wide binding of the type-B response regulator ARR10 to regulate growth and development in Arabidopsis.

The plant hormone cytokinin affects a diverse array of growth and development processes and responses to the environment. How a signaling molecule mediates such a diverse array of outputs and how these response pathways are integrated with other inputs remain fundamental questions in plant biology. To this end, we characterized the transcriptional network initiated by the type-B ARABIDOPSIS RESPONSE REGULATORs (ARRs) that mediate the cytokinin primary response, making use of chromatin immunoprecipitation sequencing (ChIP-seq), protein-binding microarrays, and transcriptomic approaches. By ectopic overexpression of ARR10, Arabidopsis lines hypersensitive to cytokinin were generated and used to clarify the role of cytokinin in regulation of various physiological responses. ChIP-seq was used to identify the cytokinin-dependent targets for ARR10, thereby defining a crucial link between the cytokinin primary-response pathway and the transcriptional changes that mediate physiological responses to this phytohormone. Binding of ARR10 was induced by cytokinin with binding sites enriched toward the transcriptional start sites for both induced and repressed genes. Three type-B ARR DNA-binding motifs, determined by use of protein-binding microarrays, were enriched at ARR10 binding sites, confirming their physiological relevance. WUSCHEL was identified as a direct target of ARR10, with its cytokinin-enhanced expression resulting in enhanced shooting in tissue culture. Results from our analyses shed light on the physiological role of the type-B ARRs in regulating the cytokinin response, mechanism of type-B ARR activation, and basis by which cytokinin regulates diverse aspects of growth and development as well as responses to biotic and abiotic factors.

Analysis and visualization of RNA-Seq expression data using RStudio, Bioconductor, and Integrated Genome Browser.

Sequencing costs are falling, but the cost of data analysis remains high, often because unforeseen problems arise, such as insufficient depth of sequencing or batch effects. Experimenting with data analysis methods during the planning phase of an experiment can reveal unanticipated problems and build valuable bioinformatics expertise in the organism or process being studied. This protocol describes using R Markdown and RStudio, user-friendly tools for statistical analysis and reproducible research in bioinformatics, to analyze and document the analysis of an example RNA-Seq data set from tomato pollen undergoing chronic heat stress. Also, we show how to use Integrated Genome Browser to visualize read coverage graphs for differentially expressed genes. Applying the protocol described here and using the provided data sets represent a useful first step toward building RNA-Seq data analysis expertise in a research group.