Soybean (Glycine max) is a major plant protein source and oilseed crop. However, plant-parasitic nematodes (PPNs) affect its annual yield. In the current study, in order to better understand the regulation of defense mechanism against PPNs in soybean, we investigated the role of long non-coding RNAs (lncRNAs) in response to two nematode species, Heterodera glycines (SCN: soybean cyst nematode) and Rotylenchulus reniformis (reniform). To this end, two publicly available RNA-seq data sets (SCN data set and RAD: reniform-associated data set) were employed to discover the lncRNAome profile of soybean under SCN and reniform infection, respectively. Upon identification of unannotated transcripts in these data sets, a seven-step pipeline was utilized to sieve these transcripts, which ended up in 384 and 283 potential lncRNAs in SCN data set and RAD, respectively. These transcripts were then used to predict cis and trans nematode-related targets in soybean genome. Computational prediction of target genes function, some of which were also among differentially expressed genes, revealed the involvement of putative nematode-responsive genes as well as enrichment of multiple stress responses in both data sets. Finally, 15 and six lncRNAs were proposed to be involved in microRNA-mediated regulation of gene expression in soybean in response to SNC and reniform infection, respectively. Collectively, this study provides a novel insight into the signaling and regulatory network of soybean-pathogen interactions and opens a new window for further research.
Keywords: bioinformatics; long non-coding RNA; nematode; plant defense; regulation; soybean.
Copyright © 2021 Khoei, Karimi, Karamian, Amini and Soorni.