bioRxivMolecular BiologyPreprint
MicroRNAome of Spodoptera frugiperda in Response to SfMNPV Infection
Santiago M. Gómez BergnaiD, Leslie C. Amorós MoralesiD, Abril González AbadiD, Josefina VilchesiD, Silvana E. Tongiani, Ricardo SalvadoriD, Vı́ctor RomanowskiiD, Matías L. PidreiD, María Leticia FerrelliiD
Abstract
Spodoptera frugiperda is one of the most important agronomical pests due to its migratory capacity and broad host range. Since it is resistant to several insecticides, novel control strategies are being explored to control it. In this way, Spodoptera frugiperda Multiple Nucleopolyhedrovirus, a natural pathogen, has been proposed for its biocontrol. In this work, we performed a small RNA-seq on uninfected larvae and larvae infected with SfMNPV to identify expressed miRNA, characterize them, and identify differentially expressed (DE) miRNA in the infected condition. We identified several known and putative novel miRNAs, some of which are encoded in multiple copies and may be expressed within miRNA clusters. We also found 13 DE miRNA, most of them previously reported, two of them are putative novel miRNAs identified in this work. We predicted miRNA targets and found that their putative biological role could be related with processes relevant to the infection such as proliferative and apoptotic pathways, cell cycle regulation, autophagy, DNA damage response (DDR), vesicle transport, cytoskeleton remodelling, JAK/STAT and Toll signaling pathway, and immune response activation, among others. Moreover, we observed that several of the putative targets were hub genes in a predicted protein - protein interaction network. Finally, we found DE miRNA putatively associated with the regulation of viral gene expression, suggesting they might have a role in modulating the infection. Our results contribute to better understanding the miRNA landscape in S. frugiperda, and their putative role upon SfMNPV infection.
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: protein structure prediction
- approach: statistical model
- approach: experimental assay
- Citations
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