RESULT: SPME GC-MS analysis showed the highest terpenoid accumulation on the 6th day post-inoculation (dpi) compared to the other treatment time points (0 dpi, 3 dpi, and 9 dpi). Among the increased terpenoid compounds, α-cedrene, valencene and β-bisabolene were prominent. P. minor inoculated for 6 days was selected for miRNA library construction using next generation sequencing. Differential gene expression analysis showed that 58 miRNAs belonging to 30 families had significantly altered regulation.
Among these 58 differentially expressed genes (DEGs), 27 [corrected] miRNAs were upregulated, whereas 31 [corrected] miRNAs were downregulated. Two putative novel pre-miRNAs were identified and validated through reverse transcriptase PCR. Prediction of target transcripts potentially involved in the mevalonate pathway (MVA) was carried out by psRobot software, resulting in four miRNAs: pmi-miR530, pmi-miR6173, pmi-miR6300 and a novel miRNA, pmi-Nov_13. In addition, two miRNAs, miR396a and miR398f/g, were predicted to have their target transcripts in the non-mevalonate pathway (MEP). In addition, a novel miRNA, pmi-Nov_12, was identified to have a target gene involved in green leaf volatile (GLV) biosynthesis. RT-qPCR analysis showed that pmi-miR6173, pmi-miR6300 and pmi-nov_13 were downregulated, while miR396a and miR398f/g were upregulated. Pmi-miR530 showed upregulation at 9 dpi, and dynamic expression was observed for pmi-nov_12. Pmi-6300 and pmi-miR396a cleavage sites were detected through degradome sequence analysis. Furthermore, the relationship between miRNA metabolites and mRNA metabolites was validated using correlation analysis.
CONCLUSION: Our findings suggest that six studied miRNAs post-transcriptionally regulate terpenoid biosynthesis in P. minor. This regulatory behaviour of miRNAs has potential as a genetic tool to regulate terpenoid biosynthesis in P. minor.
METHOD: In this study, P. minor plants were exogenously elicited with MeJA and leaf samples were subjected to SWATH-MS proteomics analysis. A previously published translated transcriptome database was used as a reference proteome database for a comprehensive protein sequence catalogue and to compare their differential expression.
RESULTS: From this proteomics informed by transcriptomics approach, we have successfully profiled 751 proteins of which 40 proteins were significantly different between control and MeJA-treated samples. Furthermore, a correlation analysis between both proteome and the transcriptome data sets suggests that significantly upregulated proteins were positively correlated with their cognate transcripts (Pearson's r = 0.677) while a weak correlation was observed for downregulated proteins (r = 0.147).
DISCUSSION: MeJA treatment induced the upregulation of proteins involved in various biochemical pathways including stress response mechanism, lipid metabolism, secondary metabolite production, DNA degradation and cell wall degradation. Conversely, proteins involved in energy expensive reactions such as photosynthesis, protein synthesis and structure were significantly downregulated upon MeJA elicitation. Overall protein-transcript correlation was also weak (r = 0.341) suggesting the existence of post-transcriptional regulation during such stress. In conclusion, proteomics analysis using SWATH-MS analysis supplemented by the transcriptome database allows comprehensive protein profiling of this non-model herbal species upon MeJA treatment.
METHODS: In this study, we performed a hybrid assembly of 454 and Illumina sequencing reads from Polygonum minus root and leaf tissues, respectively, to generate a combined transcriptome library as a reference.
RESULTS: A total of 34.37 million filtered and normalized reads were assembled into 188,735 transcripts with a total length of 136.67 Mbp. We performed a similarity search against all the publicly available genome sequences and found similarity matches for 163,200 (86.5%) of Polygonum minus transcripts, largely from Arabidopsis thaliana (58.9%). Transcript abundance in the leaf and root tissues were estimated and validated through RT-qPCR of seven selected transcripts involved in the biosynthesis of phenylpropanoids and flavonoids. All the transcripts were annotated against KEGG pathways to profile transcripts related to the biosynthesis of secondary metabolites.
DISCUSSION: This comprehensive transcriptome profile will serve as a useful sequence resource for molecular genetics and evolutionary research on secondary metabolite biosynthesis in Polygonaceae family. Transcriptome assembly of Polygonum minus can be accessed at http://prims.researchfrontier.org/index.php/dataset/transcriptome.