Newly emerging or re-emerging diseases are a constant and significant threat to agricultural production, so prompt and accurate identification of the causative agents is required for rapid and appropriate disease management. Classical methods of pathogen detection can be successfully supplemented by next-generation sequencing (NGS), whereby sequence analysis can help in the discovery of new or emerging diseases. In 2007, hop growers in Slovenia reported the appearance of severely stunted hop plants, a phenomenon that spread rapidly within hop gardens and among farms. Classical diagnostic methods were unable to detect a new pathogen; therefore, single step high-throughput parallel sequencing of total RNA and small RNAs from plants with and without symptoms was employed to identify a novel pathogen. The sequences were assembled de novo and also mapped to reference genomes, resulting in identification of a novel sequence of Citrus bark cracking viroid (CBCVd) in the stunted hop plants. Furthermore, the presence of this novel pathogen on hop was confirmed by RT-PCR analysis of 59 plants with symptoms from 15 hop gardens, representing the main outbreak locations identified by systematic disease monitoring, and small RNA Illumina sequencing of the bulked RNA sample. The high infectivity of the newly identified CBCVd was also confirmed by biolistic inoculation of two hop cultivars, which developed aggressive symptoms in controlled conditions. This study shows the feasibility of deep sequencing for the identification of causative agents of new diseases in hop and other plants.

Viroids, the smallest known pathogens, unable to encode any proteins, can cause severe diseases in their host plants. One of the proposed mechanisms of their pathogenicity includes silencing the host’s genes via viroid-derived small RNAs, which are products of the host’s immune response to the viroid’s double stranded RNA. Humulus lupulus (hop) plants are hosts to several viroids; two of them, HLVd and CBCVd, are interesting models for studying host-viroid interactions, due to the symptomless infection of the former and severe stunting disease caused by the latter. To study these interactions, we constructed a deep hop NGS transcriptome based on 35 Gb paired-end sequencing data assembled into over 74 Mb of contigs. These transcripts were used for in-silico prediction of target transcripts of vd-sRNA of the two aforementioned viroids, using two different software tools. Prediction models revealed that 1062 and 1387 hop transcripts share nucleotide similarities with HLVd- and CBCVd-derived small RNAs, respectively, so they could be silenced in an RNA interference process. Furthermore, we selected 17 transcripts from 4 groups of targets involved in the metabolism of plant hormones, small RNA biogenesis, transcripts with high complementarity with viroid-derived small RNAs and transcripts targeted by CBCVd-derived small RNAs with high cellular concentrations. Their expression was monitored by reverse transcription quantitative PCR performed using leaf, flower and cone samples. Additionally, the expression of 5 pathogenesis related genes was monitored. Expression analysis confirmed high expression levels of four pathogenesis related genes in leaves of HLVd and CBCVd infected hop plants. Expression fluctuations were observed for the majority of targets, with possible evidence of downregulation of GATA transcription factor by CBCVd- and of linoleate 13S-lipoxygenase by HLVd-derived small RNAs. These results provide a deep transcriptome of hop and the first insights into complex viroid-hop plant interactions.