Global characterization of Artemisia annua glandular trichome transcriptome using 454 pyrosequencing

<p>Abstract</p> <p>Background</p> <p>Glandular trichomes produce a wide variety of commercially important secondary metabolites in many plant species. The most prominent anti-malarial drug artemisinin, a sesquiterpene lactone, is produced in glandular trichomes of <it>Artemisia annua</it>. However, only limited genomic information is currently available in this non-model plant species.</p> <p>Results</p> <p>We present a global characterization of <it>A. annua </it>glandular trichome transcriptome using 454 pyrosequencing. Sequencing runs using two normalized cDNA collections from glandular trichomes yielded 406,044 expressed sequence tags (average length = 210 nucleotides), which assembled into 42,678 contigs and 147,699 singletons. Performing a second sequencing run only increased the number of genes identified by ~30%, indicating that massively parallel pyrosequencing provides deep coverage of the <it>A. annua </it>trichome transcriptome. By BLAST search against the NCBI non-redundant protein database, putative functions were assigned to over 28,573 unigenes, including previously undescribed enzymes likely involved in sesquiterpene biosynthesis. Comparison with ESTs derived from trichome collections of other plant species revealed expressed genes in common functional categories across different plant species. RT-PCR analysis confirmed the expression of selected unigenes and novel transcripts in <it>A. annua </it>glandular trichomes.</p> <p>Conclusion</p> <p>The presence of contigs corresponding to enzymes for terpenoids and flavonoids biosynthesis suggests important metabolic activity in <it>A. annua </it>glandular trichomes. Our comprehensive survey of genes expressed in glandular trichome will facilitate new gene discovery and shed light on the regulatory mechanism of artemisinin metabolism and trichome function in <it>A. annua</it>.</p

Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

<p>Abstract</p> <p>Background</p> <p>Microarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data.</p> <p>Results</p> <p>In this study, we propose a new discretization method "bikmeans", and compare its performance with four other widely-used discretization methods using different datasets, modeling algorithms and number of intervals. Sensitivities, specificities and total accuracies were calculated and statistical analysis was carried out. Bikmeans method always gave high total accuracies.</p> <p>Conclusions</p> <p>Our results indicate that proper discretization methods can consistently improve gene regulatory network inference independent of network modeling algorithms and datasets. Our new method, bikmeans, resulted in significant better total accuracies than other methods.</p

Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants

Proteins are modular functionalities regulating multiple cellular activities in prokaryotes and eukaryotes. As a consequence of higher plants adapting to arid and thermal conditions, C4 photosynthesis is the carbon fixation process involving multi-enzymes working in a coordinated fashion. However, how these enzymes interact with each other and whether they co-evolve in parallel to maintain interactions in different plants remain elusive to date. Here, we report our findings on the global protein co-evolution relationship and local dynamics of co-varying site shifts in key C4 photosynthetic enzymes. We found that in most of the selected key C4 photosynthetic enzymes, global pairwise co-evolution events exist to form functional couplings. Besides, protein–protein interactions between these enzymes may suggest their unknown functionalities in the carbon delivery process. For PEPC and PPCK regulation pairs, pocket formation at the interactive interface are not necessary for their function. This feature is distinct from another well-known regulation pair in C4 photosynthesis, namely, PPDK and PPDK-RP, where the pockets are necessary. Our findings facilitate the discovery of novel protein regulation types and contribute to expanding our knowledge about C4 photosynthesis

Beta Distribution-Based Cross-Entropy for Feature Selection

Analysis of high-dimensional data is a challenge in machine learning and data mining. Feature selection plays an important role in dealing with high-dimensional data for improvement of predictive accuracy, as well as better interpretation of the data. Frequently used evaluation functions for feature selection include resampling methods such as cross-validation, which show an advantage in predictive accuracy. However, these conventional methods are not only computationally expensive, but also tend to be over-optimistic. We propose a novel cross-entropy which is based on beta distribution for feature selection. In beta distribution-based cross-entropy (BetaDCE) for feature selection, the probability density is estimated by beta distribution and the cross-entropy is computed by the expected value of beta distribution, so that the generalization ability can be estimated more precisely than conventional methods where the probability density is learnt from data. Analysis of the generalization ability of BetaDCE revealed that it was a trade-off between bias and variance. The robustness of BetaDCE was demonstrated by experiments on three types of data. In the exclusive or-like (XOR-like) dataset, the false discovery rate of BetaDCE was significantly smaller than that of other methods. For the leukemia dataset, the area under the curve (AUC) of BetaDCE on the test set was 0.93 with only four selected features, which indicated that BetaDCE not only detected the irrelevant and redundant features precisely, but also more accurately predicted the class labels with a smaller number of features than the original method, whose AUC was 0.83 with 50 features. In the metabonomic dataset, the overall AUC of prediction with features selected by BetaDCE was significantly larger than that by the original reported method. Therefore, BetaDCE can be used as a general and efficient framework for feature selection

Transcriptome and DNA Methylome Analysis of Two Contrasting Rice Genotypes under Salt Stress during Germination

With climate change and labor shortages, direct-seeding rice cultivation is becoming popular worldwide, especially in Asia. Salinity stress negatively affects rice seed germination in the direct-seeding process, and the cultivation of suitable direct-seeding rice varieties under salinity stress is necessary. However, little is known about the underlying mechanism of salt responses during seed germination under salt stress. To investigate the salt tolerance mechanism at the seed germination stage, two contrasting rice genotypes differing in salt tolerance, namely, FL478 (salt-tolerant) and IR29 (salt-sensitive), were used in this study. We observed, that compared to IR29, FL478 appeared to be more tolerant to salt stress with a higher germination rate. GD1 (germination defective 1), which was involved in seed germination by regulating alpha-amylase, was upregulated significantly in the salt-sensitive IR29 strain under salt stress during germination. Transcriptomic data showed that salt-responsive genes tended to be up/downregulated in IR29 but not in FL478. Furthermore, we investigated the epigenetic changes in FL478 and IR29 during germination under saline treatment using whole genome bisulfite DNA sequencing (BS-seq) technology. BS-seq data showed that the global CHH methylation level increased dramatically under salinity stress in both strains, and the hyper CHH differentially methylated regions (DMRs) were predominantly located within the transposable elements regions. Compared with FL478, differentially expressed genes with DMRs in IR29 were mainly related to gene ontology terms such as response to water deprivation, response to salt stress, seed germination, and response to hydrogen peroxide pathways. These results may provide valuable insights into the genetic and epigenetic basis of salt tolerance at the seed germination stage, which is important for direct-seeding rice breeding

Identification of Two Flip-Over Genes in Grass Family as Potential Signature of C4 Photosynthesis Evolution

In flowering plants, C4 photosynthesis is superior to C3 type in carbon fixation efficiency and adaptation to extreme environmental conditions, but the mechanisms behind the assembly of C4 machinery remain elusive. This study attempts to dissect the evolutionary divergence from C3 to C4 photosynthesis in five photosynthetic model plants from the grass family, using a combined comparative transcriptomics and deep learning technology. By examining and comparing gene expression levels in bundle sheath and mesophyll cells of five model plants, we identified 16 differentially expressed signature genes showing cell-specific expression patterns in C3 and C4 plants. Among them, two showed distinctively opposite cell-specific expression patterns in C3 vs. C4 plants (named as FOGs). The in silico physicochemical analysis of the two FOGs illustrated that C3 homologous proteins of LHCA6 had low and stable pI values of ~6, while the pI values of LHCA6 homologs increased drastically in C4 plants Setaria viridis (7), Zea mays (8), and Sorghum bicolor (over 9), suggesting this protein may have different functions in C3 and C4 plants. Interestingly, based on pairwise protein sequence/structure similarities between each homologous FOG protein, one FOG PGRL1A showed local inconsistency between sequence similarity and structure similarity. To find more examples of the evolutionary characteristics of FOG proteins, we investigated the protein sequence/structure similarities of other FOGs (transcription factors) and found that FOG proteins have diversified incompatibility between sequence and structure similarities during grass family evolution. This raised an interesting question as to whether the sequence similarity is related to structure similarity during C4 photosynthesis evolution

Investigation of glandular trichome proteins in Artemisia annua L. using comparative proteomics.

Glandular secreting trichomes (GSTs) are called biofactories because they are active in synthesizing, storing and secreting various types of plant secondary metabolites. As the most effective drug against malaria, artemisinin, a sesquiterpene lactone is derived from GSTs of Artemisia annua. However, low artemisinin content (0.001%~1.54% of dry weight) has hindered its wide application. We investigate the GST-expressed proteins in Artemisia annua using a comparative proteomics approach, aiming for a better understanding of the trichome proteome and arteminisin metabolism. 2D-electrophoresis was employed to compare the protein profiles of GSTs and leaves. More than 700 spots were resolved for GSTs, of which ∼93 non-redundant proteins were confidently identified by searching NCBI and Artemisia EST databases. Over 70% of these proteins were highly expressed in GTSs. Functional classification of these GSTs enriched proteins revealed that many of them participate in major plant metabolic processes such as electron transport, transcription and translation