Tea is the world's oldest and most popular caffeine-containing beverage with immense economic, medicinal, and cultural importance. Here, we present the first high-quality nucleotide sequence of the repeat-rich (80.9%), 3.02-Gb genome of the cultivated tea tree Camellia sinensis. We show that an extraordinarily large genome size of tea tree is resulted from the slow, steady, and long-term amplification of a few LTR retrotransposon families. In addition to a recent whole-genome duplication event, lineage-specific expansions of genes associated with flavonoid metabolic biosynthesis were discovered, which enhance catechin production, terpene enzyme activation, and stress tolerance, important features for tea flavor and adaptation. We demonstrate an independent and rapid evolution of the tea caffeine synthesis pathway relative to cacao and coffee. A comparative study among 25 Camellia species revealed that higher expression levels of most flavonoid- and caffeine- but not theanine-related genes contribute to the increased production of catechins and caffeine and thus enhance tea-processing suitability and tea quality. These novel findings pave the way for further metabolomic and functional genomic refinement of characteristic biosynthesis pathways and will help develop a more diversified set of tea flavors that would eventually satisfy and attract more tea drinkers worldwide.

Summary: Tea is the world's widely consumed nonalcohol beverage with essential economic and health benefits. Confronted with the increasing large‐scale omics‐data set particularly the genome sequence released in tea plant, the construction of a comprehensive knowledgebase is urgently needed to facilitate the utilization of these data sets towards molecular breeding. We hereby present the first integrative and specially designed web‐accessible database, Tea Plant Information Archive (TPIA; http://tpia.teaplant.org). The current release of TPIA employs the comprehensively annotated tea plant genome as framework and incorporates with abundant well‐organized transcriptomes, gene expressions (across species, tissues and stresses), orthologs and characteristic metabolites determining tea quality. It also hosts massive transcription factors, polymorphic simple sequence repeats, single nucleotide polymorphisms, correlations, manually curated functional genes and globally collected germplasm information. A variety of versatile analytic tools (e.g. JBrowse, blast, enrichment analysis, etc.) are established helping users to perform further comparative, evolutionary and functional analysis. We show a case application of TPIA that provides novel and interesting insights into the phytochemical content variation of section Thea of genus Camellia under a well‐resolved phylogenetic framework. The constructed knowledgebase of tea plant will serve as a central gateway for global tea community to better understand the tea plant biology that largely benefits the whole tea industry.

Significance: Asian rice ( Oryza sativa ) is among the world’s most important crops. The genus Oryza has become a model for the study of plant genome structure, function, and evolution. We have undertaken de novo, full-genome sequence analysis of five diploid AA-genome species that are closely related to O. sativa . These species are native to quite different environments, representing four continents, thus exhibiting very different adaptations. Our studies identify specific genetic changes, in both gene copy number and the degree of diversifying natural selection, that indicate specific genes responsible for these adaptations, particularly in genes related to defense against pathogens and reproductive diversification. This genome discovery and comparative analysis provide a powerful tool for future Oryza study and rice improvement.

Polyploidy, or whole-genome duplication (WGD), serves as a key innovation in plant evolution and is an important genomic feature for all eukaryotes. Neopolyploids have to overcome difficulties in meiosis, genomic alterations, changes of gene expression, and epigenomic reorganization. However, the underlying mechanisms for these processes are poorly understood. One of the most interesting aspects is that genome doubling events increase the dosage of all genes. Unlike allopolyploids entangled by both hybridization and polyploidization, autopolyploids, especially artificial lines, in relatively uniform genetic background offer a model system to understand mechanisms of genome-dosage effects. To investigate DNA methylation effects in response to WGD rather than hybridization, we produced autotetraploid rice with its diploid donor, Oryza sativa ssp. indica cv. Aijiaonante, both of which were independently self-pollinated over 48 generations, and generated and compared their comprehensive transcriptomes, base pair-resolution methylomes, and siRNAomes. DNA methylation variation of transposable elements (TEs) was observed as widespread in autotetraploid rice, in which hypermethylation of class II DNA transposons was predominantly noted in CHG and CHH contexts. This was accompanied by changes of 24-nt siRNA abundance, indicating the role of the RNA-directed DNA methylation pathway. Our results showed that the increased methylation state of class II TEs may suppress the expression of neighboring genes in autotetraploid rice that has obtained double alleles, leading to no significant differences in transcriptome alterations for most genes from its diploid donor. Collectively, our findings suggest that chromosome doubling induces methylation variation in TEs that affect gene expression and may become a “genome shock” response factor to help neoautopolyploids adapt to genome-dosage effects.

The bioactive peptide has wide functions, such as lowering blood glucose levels and reducing inflammation. Meanwhile, computational methods such as machine learning are becoming more and more important for peptide functions prediction. Most of the previous studies concentrate on the single-functional bioactive peptides prediction. However, the number of multi-functional peptides is on the increase; therefore, novel computational methods are needed. In this study, we develop a method MLBP (Multi-Label deep learning approach for determining the multi-functionalities of Bioactive Peptides), which can predict multiple functions including anti-cancer, anti-diabetic, anti-hypertensive, anti-inflammatory and anti-microbial simultaneously. MLBP model takes the peptide sequence vector as input to replace the biological and physiochemical features used in other peptides predictors. Using the embedding layer, the dense continuous feature vector is learnt from the sequence vector. Then, we extract convolution features from the feature vector through the convolutional neural network layer and combine with the bidirectional gated recurrent unit layer to improve the prediction performance. The 5-fold cross-validation experiments are conducted on the training dataset, and the results show that Accuracy and Absolute true are 0.695 and 0.685, respectively. On the test dataset, Accuracy and Absolute true of MLBP are 0.709 and 0.697, with 5.0 and 4.7% higher than those of the suboptimum method, respectively. The results indicate MLBP has superior prediction performance on the multi-functional peptides identification. MLBP is available at https://github.com/xialab-ahu/MLBP and http://bioinfo.ahu.edu.cn/MLBP/.

The evolution of genes and genomes after polyploidization has been the subject of extensive studies in evolutionary biology and plant sciences. While a significant number of duplicated genes are rapidly removed during a process called fractionation, which operates after the whole-genome duplication (WGD), another considerable number of genes are retained preferentially, leading to the phenomenon of biased gene retention. However, the evolutionary mechanisms underlying gene retention after WGD remain largely unknown. Through genome-wide analyses of sequence and functional data, we comprehensively investigated the relationships between gene features and the retention probability of duplicated genes after WGDs in six plant genomes, Arabidopsis (Arabidopsis thaliana), poplar (Populus trichocarpa), soybean (Glycine max), rice (Oryza sativa), sorghum (Sorghum bicolor), and maize (Zea mays). The results showed that multiple gene features were correlated with the probability of gene retention. Using a logistic regression model based on principal component analysis, we resolved evolutionary rate, structural complexity, and GC3 content as the three major contributors to gene retention. Cluster analysis of these features further classified retained genes into three distinct groups in terms of gene features and evolutionary behaviors. Type I genes are more prone to be selected by dosage balance; type II genes are possibly subject to subfunctionalization; and type III genes may serve as potential targets for neofunctionalization. This study highlights that gene features are able to act jointly as primary forces when determining the retention and evolution of WGD-derived duplicated genes in flowering plants. These findings thus may help to provide a resolution to the debate on different evolutionary models of gene fates after WGDs.