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Fusarium wilt disease, caused by  Fusarium oxysporum  f.sp.  cubense  ( Foc ), has been recognized as the most devastating disease to banana. The regulatory role of long non-coding RNAs (lncRNAs) in plant defense has been verified in many plant species. However, the understanding of their role during early  Foc TR4 ( Foc  tropical race 4) infection stage is very limited. In this study, lncRNA sequencing was used to reveal banana root transcriptome profile changes during early  Foc TR4 infection stages. Quantitative real time PCR (qRT-PCR) was performed to confirm the expression of eight differentially expressed (DE) lncRNAs (DELs) and their predicted target genes (DETs), and three DE genes (DEGs). Totally, 12,109 lncRNAs, 36,519 mRNAs and 2642 novel genes were obtained, of which 1398 (including 78 DELs, 1220 DE known genes and 100 DE novel genes) were identified as  Foc TR4 responsive DE transcripts. Gene function analysis revealed that most DEGs were involved in biosynthesis of secondary metabolites, plant–pathogen interaction, plant hormone signal transduction, phenylalanine metabolism, phenylpropanoid biosynthesis, alpha-linolenic acid metabolism and so on. Coincidently, many DETs have been identified as DEGs in previous transcriptome studies. Moreover, many DETs were found to be involved in ribosome, oxidative phosphorylation, lipoic acid metabolism, ubiquitin mediated proteolysis, N-glycan biosynthesis, protein processing in endoplasmic reticulum and DNA damage response pathways. QRT-PCR result showed the expression patterns of the selected transcripts were mostly consistent with our lncRNA sequencing data. Our present study showed the regulatory role of lncRNAs on known biotic and abiotic stress responsive genes and some new-found  Foc TR4 responsive genes, which can provide new insights into  Foc TR4-induced changes in the banana root transcriptome during the early pathogen infection stage.

Identification and characterization of early Fusarium wilt responsive mRNAs and long non-coding RNAs in banana root using high-throughput sequencing