Pinghua Li

The developmental dynamics of the maize leaf transcriptome

We have analyzed the maize leaf transcriptome using Illumina sequencing. We mapped more than 120 million reads to define gene structure and alternative splicing events and to quantify transcript abundance along a leaf developmental gradient and in mature bundle sheath and mesophyll cells. We detected differential mRNA processing events for most maize genes. We found that 64% and 21% of genes were differentially expressed along the developmental gradient and between bundle sheath and mesophyll cells, respectively. We implemented Gbrowse, an electronic fluorescent pictograph browser, and created a two-cell biochemical pathway viewer to visualize datasets. Cluster analysis of the data revealed a dynamic transcriptome, with transcripts for primary cell wall and basic cellular metabolism at the leaf base transitioning to transcripts for secondary cell wall biosynthesis and C4 photosynthetic development toward the tip. This dataset will serve as the foundation for a systems biology approach to the understanding of photosynthetic development.

Reference Genome Sequence Of The Model Plant Setaria

We generated a high-quality reference genome sequence for foxtail millet (Setaria italica). The ∼400-Mb assembly covers ∼80% of the genome and >95% of the gene space. The assembly was anchored to a 992-locus genetic map and was annotated by comparison with >1.3 million expressed sequence tag reads. We produced more than 580 million RNA-Seq reads to facilitate expression analyses. We also sequenced Setaria viridis, the ancestral wild relative of S. italica, and identified regions of differential single-nucleotide polymorphism density, distribution of transposable elements, small RNA content, chromosomal rearrangement and segregation distortion. The genus Setaria includes natural and cultivated species that demonstrate a wide capacity for adaptation. The genetic basis of this adaptation was investigated by comparing five sequenced grass genomes. We also used the diploid Setaria genome to evaluate the ongoing genome assembly of a related polyploid, switchgrass (Panicum virgatum).

Setaria viridis and Setaria italica, model genetic systems for the Panicoid grasses

Setaria italica and its wild ancestor Setaria viridis are diploid C(4) grasses with small genomes of ∼515 Mb. Both species have attributes that make them attractive as model systems. Setaria italica is a grain crop widely grown in Northern China and India that is closely related to the major food and feed crops maize and sorghum. A large collection of S. italica accessions are available and thus opportunities exist for association mapping and allele mining for novel variants that will have direct application in agriculture. Setaria viridis is the weedy relative of S. italica with many attributes suitable for genetic analyses including a small stature, rapid life cycle, and prolific seed production. Setaria sp. are morphologically similar to most of the Panicoideae grasses, including major biofuel feedstocks, switchgrass (Panicum virgatum) and Miscanthus (Miscanthus giganteus). They are broadly distributed geographically and occupy diverse ecological niches. The cross-compatibility of S. italica and S. viridis also suggests that gene flow is likely between wild and domesticated accessions. In addition to serving as excellent models for C(4) photosynthesis, these grasses provide novel opportunities to study abiotic stress tolerance and as models for bioenergy feedstocks.

Exploring plant transcriptomes using ultra high-throughput sequencing

Ultra high-throughput sequencing (UHTS) technologies offer the potential to interrogate transcriptomes in detail that has traditionally been restricted to single gene surveys. For instance, it is now possible to globally define transcription start sites, polyadenylation signals, alternative splice sites and generate quantitative data on gene transcript accumulation in single tissues or cell types. These technologies are thus paving the way for whole genome transcriptomics and will undoubtedly lead to novel insights into plant development and biotic and abiotic stress responses. However, several challenges exist to making this technology broadly accessible to the plant research community. These include the current need for a computationally intensive analysis of data sets, a lack of standardized alignment and formatting procedures and a relatively small number of analytical software packages to interpret UHTS outputs. In this review we summarize recent findings from UHTS and discuss potential opportunities and challenges for broad adoption of these technologies in the plant science community.

Model-based clustering for RNA-seq data

MOTIVATION RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. RESULTS In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. AVAILABILITY AND IMPLEMENTATION An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

Developmental dynamics of Kranz cell transcriptional specificity in maize leaf reveals early onset of C4-related processes

Summary The measured differential expression of genes between bundle sheath and mesophyll cells at successive developmental stages of the maize leaf is used to identify C4-photosynthesis-related candidates.

Genome-Wide Association of Carbon and Nitrogen Metabolism in the Maize Nested Association Mapping Population

Genetic variants of maize identify genes and regions that control core carbon and nitrogen metabolism. 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.

Identification of Photosynthesis-Associated C4 Candidate Genes through Comparative Leaf Gradient Transcriptome in Multiple Lineages of C3 and C4 Species

Leaves of C4 crops usually have higher radiation, water and nitrogen use efficiencies compared to the C3 species. Engineering C4 traits into C3 crops has been proposed as one of the most promising ways to repeal the biomass yield ceiling. To better understand the function of C4 photosynthesis, and to identify candidate genes that are associated with the C4 pathways, a comparative transcription network analysis was conducted on leaf developmental gradients of three C4 species including maize, green foxtail and sorghum and one C3 species, rice. By combining the methods of gene co-expression and differentially co-expression networks, we identified a total of 128 C4 specific genes. Besides the classic C4 shuttle genes, a new set of genes associated with light reaction, starch and sucrose metabolism, metabolites transportation, as well as transcription regulation, were identified as involved in C4 photosynthesis. These findings will provide important insights into the differential gene regulation between C3 and C4 species, and a good genetic resource for establishing C4 pathways in C3 crops.

Transcriptional response to petiole heat girdling in cassava

To examine the interactions of starch and sugar metabolism on photosynthesis in cassava, a heat-girdling treatment was applied to petioles of cassava leaves at the end of the light cycle to inhibit starch remobilization during the night. The inhibition of starch remobilization caused significant starch accumulation at the beginning of the light cycle, inhibited photosynthesis, and affected intracellular sugar levels. RNA-seq analysis of heat-treated and control plants revealed significantly decreased expression of genes related to photosynthesis, as well as N-metabolism and chlorophyll biosynthesis. However, expression of genes encoding TCA cycle enzymes and mitochondria electron transport components, and flavonoid biosynthetic pathway enzymes were induced. These studies reveal a dynamic transcriptional response to perturbation of sink demand in a single leaf, and provide useful information for understanding the regulations of cassava under sink or source limitation.

Transcriptome response of cassava leaves under natural shade

Cassava is an important staple crop in tropical and sub-tropical areas. As a common farming practice, cassava is usually cultivated intercropping with other crops and subjected to various degrees of shading, which causes reduced productivity. Herein, a comparative transcriptomic analysis was performed on a series of developmental cassava leaves under both full sunlight and natural shade conditions. Gene expression profiles of these two conditions exhibited similar developmental transitions, e.g. genes related to cell wall and basic cellular metabolism were highly expressed in immature leaves, genes involved in lipid metabolism and tetrapyrrole synthesis were highly expressed during the transition stages, and genes related to photosynthesis and carbohydrates metabolism were highly expressed in mature leaves. Compared with the control, shade significantly induced the expression of genes involved in light reaction of photosynthesis, light signaling and DNA synthesis/chromatin structure; however, the genes related to anthocyanins biosynthesis, heat shock, calvin cycle, glycolysis, TCA cycle, mitochondrial electron transport, and starch and sucrose metabolisms were dramatically depressed. Moreover, the shade also influenced the expression of hormone-related genes and transcriptional factors. The findings would improve our understanding of molecular mechanisms of shade response, and shed light on pathways associated with shade-avoidance syndrome for cassava improvement.