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Dynamic architecture and regulatory implications of the miRNA network underlying the response to stress in melon.

Alejandro Sanz-CarbonellMaria Carmen MarquésGerman MartinezGustavo Gomez
Published in: RNA biology (2019)
miRNAs are small RNAs that regulate mRNAs at both transcriptional and posttranscriptional level. In plants, miRNAs are involved in the regulation of different processes including development and stress-response. Elucidating how stress-responsive miRNAs are regulated is key to understand the global response to stress but also to develop efficient biotechnological tools that could help to cope with stress. Here, we describe a computational approach based on sRNA sequencing, transcript quantification and degradome data to analyse the accumulation, function and structural organization of melon miRNAs reactivated under seven biotic and abiotic stress conditions at two and four days post-treatment. Our pipeline allowed us to identify fourteen stress-responsive miRNAs (including evolutionary conserved such as miR156, miR166, miR172, miR319, miR398, miR399, miR894 and miR408) at both analysed times. According to our analysis miRNAs were categorized in three groups showing a broad-, intermediate- or narrow- response range. miRNAs reactive to a broad range of environmental cues appear as central components in the stress-response network. The strictly coordinated response of miR398 and miR408 (broad response-range) to the seven stress treatments during the period analysed here reinforces this notion. Although both, the amplitude and diversity of the miRNA-related response to stress changes during the exposition time, the architecture of the miRNA-network is conserved. This organization of miRNA response to stress is also conserved in rice and soybean supporting the conservation of miRNA-network organization in other crops. Overall, our work sheds light into how miRNA networks in plants organize and function during stress.
Keyphrases
  • cell proliferation
  • long non coding rna
  • long noncoding rna
  • stress induced
  • gene expression
  • machine learning
  • risk assessment
  • drug delivery
  • climate change
  • genome wide
  • deep learning
  • network analysis