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Long non-coding RNAs (lncRNAs) play important roles in response to various biotic and abiotic stresses. So far, systematic identification and characterization of lncRNAs have been reported in a few model plant species and major crops, but their roles in abiotic stress response have not yet been reported in common wild rice (Oryza rufipogon). Dongxiang wild rice (DXWR) possesses a high degree of drought resistance and has been well recognized as a precious genetic resource for drought resistant rice breeding. We presented the reference catalog of 1 655 novel lncRNA transcripts in DXWR using strand-specific RNA sequencing and bioinformatics approaches. Meanwhile, a total of 1 092 lncRNAs were determined as differentially expressed lncRNAs under drought stress. Quantitative real-time PCR results exhibited a high concordance with RNA sequencing data, which confirmed that the expression patterns of lncRNAs based on RNA sequencing were highly reliable. Furthermore, 8 711 transcripts were predicted as target genes of the differentially expressed lncRNAs. Functional annotation analysis based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases showed that the target genes were significantly enriched in cellular and metabolic processes, cell part, binding and plant hormone signal transduction, as well as many other terms related to abiotic stress resistance. These results expanded our understanding of lncRNA biology and provided candidate regulators for genetic improvement of drought resistance in rice cultivars. Keywords: Dongxiang wild rice, Drought stress, Long non-coding RNA, Systematic characterization

Background: Plant chloroplast DNA (cpDNA) typically has a circular structure, including a large single-copy region (LSC), a small single-copy region (SSC) and two inverted repeats (IR1 and IR2). The organization of these four elementary regions LSC-IR1-SSC-IR2 is highly conserved across all plant cpDNAs. Only very a few structural variations (SVs) occurring at the elementary-region level have been reported. Results: In the present study, we assembled the full-length cpDNA of Dongxiang wild rice line 159 (DXWR159). Using the long PacBio subreads, we discovered a large inversion of SSC and a large duplication of IR in DXWR159 cpDNAs. The large inversion of SSC results in a reverse orientation of SSC. According to the orientation of SSC, the structures of rice cpDNAs can be classified into the forward SSC (SSC-F) and reverse SSC (SSC-R) types. As the most significant finding, both SSC-F and SSC-R type cpDNAs were detected in several seedlings of DXWR159. The inversion of SSC was named as SSC switching. Conclusions: The frequencies of short tandem repeats (STRs) and large SVs were under-estimated in plant cpDNAs. STRs may not be useful as molecular markers in phylogenetic studies, particularly at low taxonomic levels. SSC switching is not a rarely occurring event and is active in cell growth. We propose that SSC switching ubiquitous occurs in plant cpDNAs and further investigation may reveal novel and important functions associated with SSC switching.

Additional file 2. Table S1. Molecular markers used for gene mapping in this study.

Additional file 2. Fig. S1: Genome-wide association (GWA) mapping results for rice sheath blight disease ratings based on an imputed 3,463,224 SNP dataset. Field and greenhouse studies were conducted in both Stuttgart, Arkansas (AR) USA and Nanning, China (NC) with Arkansas field studies being conducted in 2016 and 2017, and Nanning field studies in 2018. Manhattan (left) and Q-Q (right) plots are grouped by study environment a) Arkansas field 2016, b) Arkansas field 2017, c) Arkansas field both years combined, d) Nanning field 2018, e) Arkansas greenhouse, and f) Nanning greenhouse. Within each environment the plots are arranged in the following order by panel Indica subspecies (INDAUS), aus (AUS), Indica (IND), Japonica subspecies (JAP), temperate japonica (TEJ), tropical japonica (TRJ) and all RDP1 accessions (395). In the Manhattan plots the X axis shows the SNP positions across the 12 rice chromosomes and the Y axis is the –log10 (p) value for each SNP. The black horizontal line represents the –log10(p) significance threshold at 5. Black arrows identify the significant peak SNPs with regions >100 Kb comprising the 18 GWA ShB-QTL. The 21 target SNPs listed in Table 3 are identified by red arrows. (If two or more peak SNPs were in close proximity in a given QTL region, the peaks were denoted by a single arrow.) Fig. S2: Genome-wide association (GWA) mapping results for days to 50% heading, plant height and culm habit based on an imputed 3,463,224 SNP dataset. The data was collected from field studies conducted at Stuttgart, Arkansas (AR) in 2016 and 2017, and Nanning, China (NC) in 2018. Manhattan (left) and Q-Q (right) plots are grouped by the trait evaluated in the field study as follows: a) days to heading, b) plant height and c) culm habit. Within each trait the plots are arranged by field study environment Arkansas field 2016 (AR16), Arkansas field 2017 (AR17), Arkansas field both years (ARall) and Nanning field 2018 (NC18), Culm habit was only recorded from the Arkansas field study in 2017. In the Manhattan plots the X axis shows the SNP positions across the 12 rice chromosomes and the Y axis is the –log10 (p) value for each SNP. The black horizontal line represents the –log10(p) significance threshold at 5. Black arrows indicate the significant peak SNPs identified by the GWA mapping of the RDP1 accessions (395) with a region >100 Kb. Details are in Table S3. (If two or more peak SNPs were in close proximity, they were denoted by a single arrow.) Fig. S3: Manhattan (left) and Q-Q (right) plots identifying the 15 TN and 14 PN QTL regions identified by GWA analyses using an imputed 3,463,244 SNP dataset (Table 4). In the Manhattan plots the X axis shows the SNP positions across the 12 rice chromosomes and the Y axis is the –log10 (p) value for each SNP. The black horizontal line represents the –log10(p) significance threshold at 5. Black arrows indicate peak SNPs within the indicated QTL regions. The plots are organized by trait with the TN results in plots A through F and PN results in plots in G through L. Subpopulation abbreviations are INDAUS (Indica subspecies), JAP (Japonica subspecies), AUS (aus), IND (Indica), TEJ (temperate japonica), and TRJ (tropical japonica)

Additional file 4. Table S3. The relative contents of up-regulation metabolites and FC in rice hulls at different stages.

Additional file 1. Fig. S1. Orthogonal projection to latent structure discriminant analysis (OPLS-DA) model verification diagram between BP, DP and Y. The horizontal lines represent R2 and Q2 of the original model, and the red and blue dots represent R2’ and Q2’ of the model after Y substitution, respectively. BP = pg-10d; DP = pg-20d; Y = pg-30d. Fig. S2. Heat map visualization of relative flavonoids in the different stages of hulls filling. W = pg-0d; BP = pg-10d; DP = pg-20d; Y = pg-30d.

Additional file 1: Supplemental Table 1. Details and sources of the 46 Monochasma savatieri samples analyzed in this study. Supplemental Fig. 1. The representative gel pictures with scoring by the EST-SSR markers. The patterns of bands were analyzed by a gel imaging analysis system (version Tocan 240). Supplemental Fig. 2. The photos of some representative samples in this study.

MicroRNAs (miRNAs) are non-coding small RNAs, which play important regulatory roles in response to biotic and abiotic stresses. Dongxiang wild rice (Oryza rufipogon, DXWR) can survive in extreme drought environment, but its molecular mechanism of drought resistance is still largely unknown. To further explore miRNA regulatory mechanisms involved in drought resistance, we identified 138 novel miRNAs in DXWR using small RNA sequencing and bioinformatics approaches, and found that the expression levels of 67 novel miRNAs were significantly affected by drought stress. In total, 200 candidate target genes were predicted and annotated for the drought stress-responsive novel miRNAs. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways suggested that most of the target genes were related to metabolism. Stem-loop quantitative real-time PCR (qRT-PCR) results exhibited high concordance with sequencing data, which confirmed that miRNA expression patterns based on small RNA sequencing in the present study were reliable. Meanwhile, qRT-PCR validated the inverse expression patterns between several miRNAs and their target genes. These results will enhance our understanding of miRNA regulatory mechanisms in response to drought stress in DXWR, and can serve as an important reference for the protection and utilization of this valuable genetic resource. Keywords: Dongxiang wild rice, drought stress, genetic resource, novel microRNA, small RNA sequencing