Divergent Response Strategies of CsABF Facing Abiotic Stress in Tea Plant: Perspectives From Drought-Tolerance Studies.

In plants, the bZIP family plays vital roles in various biological processes, including seed maturation, flower development, light signal transduction, pathogen defense, and various stress responses. Tea, as a popular beverage, is widely cultivated and has withstood a degree of environmental adversity. Currently, knowledge of the bZIP gene family in tea plants remains very limited. In this study, a total of 76 CsbZIP genes in tea plant were identified for the whole genome. Phylogenetic analysis with Arabidopsis counterparts revealed that CsbZIP proteins clustered into 13 subgroups, among which 13 ABFs related to the ABA signaling transduction pathway were further identified by conserved motif alignment and named CsABF1-13, these belonged to the A and S subgroups of CsbZIP and had close evolutionary relationships, possessing uniform or similar motif compositions. Transcriptome analysis revealed the expression profiles of CsABF genes in different tissues (bud, young leaf, mature leaf, old leaf, stem, root, flower, and fruit) and under diverse environmental stresses (drought, salt, chilling, and MeJA). Several CsABF genes with relatively low tissue expression, including CsABF1, CsABF5, CsABF9, and CsABF10, showed strong expression induction in stress response. Thirteen CsABF genes, were examined by qRT-PCR in two tea plant cultivars, drought-tolerant "Taicha 12" and drought-sensitive "Fuyun 6", under exogenous ABA and drought stress. Furthermore, CsABF2, CsABF8, and CsABF11, were screened out as key transcription factors regulating drought tolerance of tea cultivars. Subsequently, some potential target genes regulated by CsABFs were screened by co-expression network and enrichment analysis. This study update CsbZIP gene family and provides a global survey of the ABF gene family in tea plant. The resolution of the molecular mechanism of drought resistance in different varieties could be helpful for improving stress resistance in tea plant via genetic engineering.

Prediction of Lymph-Node Metastasis in Cancers Using Differentially Expressed mRNA and Non-coding RNA Signatures.

Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96-92.19%), 81.97% (70.83-95.24%), and 80.78% (69.61-90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.

Non-Coding RNAs Regulate the Resistance to Anti-EGFR Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is the third prevalent cancer worldwide, the morbidity and mortality of which have been increasing in recent years. As molecular targeting agents, anti-epidermal growth factor receptor (EGFR) monoclonal antibodies (McAbs) have significantly increased the progression-free survival (PFS) and overall survival (OS) of metastatic CRC (mCRC) patients. Nevertheless, most patients are eventually resistant to anti-EGFR McAbs. With the intensive study of the mechanism of anti-EGFR drug resistance, a variety of biomarkers and pathways have been found to participate in CRC resistance to anti-EGFR therapy. More and more studies have implicated non-coding RNAs (ncRNAs) primarily including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are widely involved in tumorigenesis and tumor progression. They function as essential regulators controlling the expression and function of oncogenes. Increasing data have shown ncRNAs affect the resistance of molecular targeted drugs in CRC including anti-EGFR McAbs. In this paper, we have reviewed the advance in mechanisms of ncRNAs in regulating anti-EGFR McAbs therapy resistance in CRC. It provides insight into exploring ncRNAs as new molecular targets and prognostic markers for CRC.

Gene Coexpression Network Reveals Insights into the Origin and Evolution of a Theanine-Associated Regulatory Module in Non-Camellia and Camellia Species.

Theanine (thea) is one of the most important plant-derived characteristic secondary metabolites and a major healthcare product because of its beneficial biological activities, such as being an antianxiety agent, promoting memory, and lowering blood pressure. Thea mostly accumulates in Camellia plants and is especially rich in Camellia sinensis (tea plant). Although some functional genes (e.g., TS, GOGAT, and GS) attributed to thea accumulation have been separately well explored in tea plants, the evolution of a regulatory module (highly interacting gene group) related to thea metabolism remains to be elaborated. Herein, a thea-associated regulatory module (TARM) was mined by using a comprehensive analysis of a weighted gene coexpression network in Camellia and non-Camellia species. Comparative genomic analysis of 84 green plant species revealed that TARM originated from the ancestor of green plants (algae) and that TARM genes were recruited from different evolutionary nodes with the most gene duplication events at the early stage. Among the TARM genes, two core transcription factors of NAC080 and LBD38 were deduced, which may play a crucial role in regulating the biosynthesis of thea. Our findings provide the first insights into the origin and evolution of TARM and indicate a promising paradigm for identifying vital regulatory genes involved in thea metabolism.

Time-series transcriptomic analysis reveals novel gene modules that control theanine biosynthesis in tea plant (Camellia sinensis).

Theanine (thea) is a unique non-protein amino acid in tea plant (Camellia sinensis) and one of the most important small molecular compounds for tea quality and health effects. The molecular mechanism that maintains thea biosynthesis is not clear but may be reflected in complicated biological networks as other secondary metabolites in plants. We performed an integrative transcriptomic analysis of tea seedlings bud and leave over the time-course of ethylamine (EA) treatment that activated thea pathway. We identified 54 consistent differentially expressed genes (cDEGs, 25 upregulated and 29 downregulated) during thea activation. Gene Ontology (GO) functional enrichment analysis of upregulated genes and downregulated genes showed that they may function as a cascade of biological events during their cooperative contribution to thea biosynthesis. Among the total cDEGs, a diversity of functional genes (e.g., enzymes, transcription factors, transport and binding proteins) were identified, indicating a hierarchy of gene control network underlying thea biosynthesis. A gene network associated with thea biosynthesis was modeled and three interconnected gene functional modules were identified. Among the gene modules, several topologically important genes (e.g., CsBCS-1, CsRP, CsABC2) were experimentally validated using a combined thea content and gene expression analysis. Collectively, we presented here for the first time a comprehensive landscape of the biosynthetic mechanism of thea controlled by a underling gene network, which might provide a theoretical basis for the identification of key genes that contribute to thea biosynthesis.

Identification of MYB Transcription Factors Regulating Theanine Biosynthesis in Tea Plant Using Omics-Based Gene Coexpression Analysis.

Theanine (thea) is the most abundant free amino acid in tea plant (Camellia sinensis) and one of the most important secondary metabolites conferring tea quality and health benefits. Great effort has recently been made to functionally dissect enzyme genes (e.g., GS, GDH, GOGAT) responsible for in vivo thea accumulation. However, the transcriptional regulation of its biosynthesis remains to be explored. Starting from publicly available (condition-independent) tea transcriptome data, we performed an exhaustive coexpression analysis between transcription factor (TF) genes and thea enzyme genes in tea plant. Our results showed that two typical plant-specialized (secondary) metabolites related TF families, such as MYB, bHLH, together with WD40 domain proteins, were prominently involved, suggesting a potential MYB-bHLH-WD40 (MBW) complex-mediated regulatory pattern in thea pathway. Aiming at the most involved MYB family, we screened seven MYB genes as thea candidate regulators through a stringent multistep selection (e.g., filtering with condition-specific nitrogen-treated transcriptome data). The control of MYB regulators in thea biosynthesis was further demonstrated using an integrated analysis of thea accumulation and MYB expression in several major tea tissues, including leave, bud, root, and stem. Our investigation aided tea researchers in having a comprehensive view of transcriptional regulatory landscape in thea biosynthesis, serving as the first platform for studying molecular regulation in thea pathway and a paradigm for understanding the characteristic components biosynthesis in nonmodel plants.