Plant responses to drought stress are regulated at the transcriptional and post-transcriptional levels through noncoding endogenous microRNAs. These microRNAs play key roles in gene expression, mainly by down-regulating target mRNAs. In this work, an in silico search and validation for microRNAs related to drought response in peach ('G.H. Hill'), almond ('Sefied') and an interspecific peach-almond hybrid ('GN 15') has been performed. We used qPCR to analyse the gene expression of several miRNAs described as being related to drought response in peach, including miR156, miR159, miR160, miR167, miR171, miR172, miR398, miR403, miR408, miR842 and miR2275 under mild and severe water deficit. These miRNAs were in silico selected on the basis of previous works, their conservation in plants and their drought response. qPCR analysis confirmed the implication of these miRNAs in the dehydration stress response in the three assayed genotypes. Comparison of miRNA expression patterns in the three evaluated genotypes indicated that the hybrid GN 15 showed higher expression levels of specific miRNAs which should be related to the observed drought tolerance. mRNA target transcripts of the miRNAs studied were predicted using the Rose database, which includes transcription factors that regulate plant growth and development. In addition, results showed that the promoter region contains responsive elements to hormone-mediated regulatory elements. Network analysis not only unravelled the interaction between miRNAs and their predicted gene targets but also highlighted the roles of miRNAs in response to drought stress.

Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.

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