Chromosome-scale genome assembly of Camellia sinensis combined with multi-omics provides insights into its responses to infestation with green leafhoppers.

The tea plant (Camellia sinensis) is an important economic crop, which is becoming increasingly popular worldwide, and is now planted in more than 50 countries. Tea green leafhopper is one of the major pests in tea plantations, which can significantly reduce the yield and quality of tea during the growth of plant. In this study, we report a genome assembly for DuyunMaojian tea plants using a combination of Oxford Nanopore Technology PromethION™ with high-throughput chromosome conformation capture technology and used multi-omics to study how the tea plant responds to infestation with tea green leafhoppers. The final genome was 3.08 Gb. A total of 2.97 Gb of the genome was mapped to 15 pseudo-chromosomes, and 2.79 Gb of them could confirm the order and direction. The contig N50, scaffold N50 and GC content were 723.7 kb, 207.72 Mb and 38.54%, respectively. There were 2.67 Gb (86.77%) repetitive sequences, 34,896 protein-coding genes, 104 miRNAs, 261 rRNA, 669 tRNA, and 6,502 pseudogenes. A comparative genomics analysis showed that DuyunMaojian was the most closely related to Shuchazao and Yunkang 10, followed by DASZ and tea-oil tree. The multi-omics results indicated that phenylpropanoid biosynthesis, α-linolenic acid metabolism, flavonoid biosynthesis and 50 differentially expressed genes, particularly peroxidase, played important roles in response to infestation with tea green leafhoppers (Empoasca vitis Göthe). This study on the tea tree is highly significant for its role in illustrating the evolution of its genome and discovering how the tea plant responds to infestation with tea green leafhoppers will contribute to a theoretical foundation to breed tea plants resistant to insects that will ultimately result in an increase in the yield and quality of tea.

Identification of the active compounds and their mechanisms of medicinal and edible Shanzha based on network pharmacology and molecular docking.

Shanzha (Crataegus pinnatifida Bunge), an edible traditional Chinese medicine (TCM), has an effect on dyspepsia. However, the investigations of the pharmacological effects have not been carried out. This study aimed to identify the potential targets and pharmacological mechanisms of Shanzha in the treatment of dyspepsia by network pharmacology and molecular docking. Five active compounds and 13 key targets were obtained by a set of bioinformatics assays. Vitexin 7-glucoside, suchilactone, and 20-hexadecanoylingenol were the main compounds acting on dyspepsia. The key targets were prostaglandin-endoperoxide synthase 2 (PTGS2), serine/threonine-protein kinase mTOR (MTOR), heat shock protein HSP 90-alpha (HSP90AA1), mitogen-activated protein kinase 1 (MAPK1), MAPK3, E3 ubiquitin-protein ligase Mdm2 (MDM2), receptor tyrosine-protein kinase erbB-2 (ERBB2), caspase-3 (CASP3), matrix metalloproteinase-9 (MMP9), estrogen receptor (ESR1), tumor necrosis factor (TNF), phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA), and peroxisome proliferator-activated receptor gamma (PPARG), which played the vital roles in TNF, prostate cancer, thyroid hormone, hepatitis B and estrogen signaling pathway. The molecular mechanisms of Shanzha regulating dyspepsia were mainly related to reduction of inflammatory response, controlling cell proliferation and survival, increasing intestinal moisture, and promoting intestinal motility. PRACTICAL APPLICATIONS: Shanzha has been used as an edible TCM to improve digestion for a long time. However, the ingredients and mechanisms of Shanzha in the treatment of dyspepsia are not clear. In this research, network pharmacological analysis integrated with molecular docking was conducted to investigate the molecular mechanism. The results suggested that the core targets alleviated dyspepsia by reducing the intestinal inflammatory response, increasing intestinal movement, controlling cell physiological activities, and reducing constipation. In summary, this study demonstrated the multiple compounds, targets, and pathways characteristics of Shanzha in the treatment of dyspepsia, which may provide guidance and foundations for further application of edible medicine.

Transcriptome profiling analysis of tea plant (Camellia sinensis) using Oxford Nanopore long-read RNA-Seq technology.

Transcriptome profiles have been widely captured using short-read sequencing technology, but there are still limitations partially due to the read length. Here, we generated long reads using Oxford Nanopore PromethION™ technology and short reads using the Illumina sequencing platform to study the transcriptome of root, stem, and leaf of Camellia sinensis cv. Fudingdabai. We mapped the Nanopore reads to the Shuchazao of C. sinensis genome sequence, and the mapping rates ranged from 82.63% to 90.59% (average 86.44%); this is lower than that of the Illumina reads which was 87.83% to 91.14% (average 90.12%). Gene expression level was quantified using the Nanopore and Illumina data and we observed a good agreement. The same tea leaf flavor synthesis pathways were highlighted using both sequencing technologies when analyzing the differentially expressed genes between leaf and root. Alternative splicing was then analyzed, and the intron-retention was observed as the most common alternative splicing. Moreover Nanopore long reads could correct transcript isoform annotation for differential expression investigation purposes. Nanopore sequencing techniques can provide a novel reference basis for molecular analysis of tea plants.

Complement Genome Annotation Lift Over Using a Weighted Sequence Alignment Strategy.

With the broad application of high-throughput sequencing, more whole-genome resequencing data and de novo assemblies of natural populations are becoming available. For a particular species, in general, only the reference genome is well established and annotated. Computational tools based on sequence alignment have been developed to investigate the gene models of individuals belonging to the same or closely related species. During this process, inconsistent alignment often obscures genome annotation lift over and leads to improper functional impact prediction for a genomic variant, especially in plant species. Here, we proposed the zebraic striped dynamic programming algorithm, which provides different weights to genetic features to refine genome annotation lift over. Testing of our zebraic striped dynamic programming algorithm on both plant and animal genomic data showed complementation to standard sequence approach for highly diverse individuals. Using the lift over genome annotation as anchors, a base-pair resolution genome-wide sequence alignment and variant calling pipeline for de novo assembly has been implemented in the GEAN software. GEAN could be used to compare haplotype diversity, refine the genetic variant functional annotation, annotate de novo assembly genome sequence, detect homologous syntenic blocks, improve the quantification of gene expression levels using RNA-seq data, and unify genomic variants for population genetic analysis. We expect that GEAN will be a standard tool for the coming of age of de novo assembly population genetics.

Prediction and characterization of protein-protein interaction networks in swine.

BACKGROUND: Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. RESULTS: We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. CONCLUSION: The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).