Research Report

Long non-coding RNAs Profiling of Qingke Barley in Tibet under the Condition of Blumeria graminis Infection  

Lingling Wei1,2 , Yulin Wang1,3 , Hongjun Yuan1,3 , Mu Wang1,4 , Xingquan Zeng1,4
1 State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, Lhasa, 850002
2 Tibet Agriculture and Animal Husbandry University, Linzhi, 86000
3 Research Institute of Agriculture, Tibet Academy of Agriculture and Animal Husbandry Sciences, Lhasa, 850002
4 Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa, 850000
Author    Correspondence author
Triticeae Genomics and Genetics, 2020, Vol. 11, No. 4   doi: 10.5376/tgg.2020.11.0004
Received: 06 Sep., 2020    Accepted: 09 Sep., 2020    Published: 25 Sep., 2020
© 2020 BioPublisher Publishing Platform
This article was first published in Molecular Plant Breeding in Chinese, and here was authorized to translate and publish the paper in English under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Wei L.L., Wang Y.L., Yuan H.J., Wang M., and Zeng X.Q., 2020, Long non-coding RNAs profiling of qingke barley in tibet under the condition of Blumeria graminis infection, Triticeae Genomics and Genetics, 11(4): 1-11 (doi: 10.5376/tgg.2020.11.0004)


Powdery mildew, known as one of the most devastating diseases in cereal crops, causes serious losses in grain yield and quality. In order to clarify the pathogenic molecular mechanism of qingke barley to powdery mildew, In the present study, the DEGs expression patterns of Tibetan barley (also called qingke) cultivars G7 (showed complete penetration resistance to powdery mildew) and Z13 (showed complete penetration sensitivity to powdery mildew) were compared after inoculation with Blumeria graminis f. sp. Hordei (Bgh). The genes coding for calcium-dependent protein kinase, glutathione S-transferase, plasminogen activator inhibitor 1, serine-type endopeptidase, and MADS-box transcription factor were expressed at higher levels in the G7, indicating their possible roles in the resistance to Bgh. The highly expressed genes in G7 take a part in the biological processes including ion channels, calcium-dependent protein kinase, and plant hormone pathway. Sixteen lncRNAs and their 21 candidate target genes were identified to exhibit coordinated expression patterns, which were enriched in plant hormone signal transduction and ion binding processes. This study casts an insight into the molecular mechanisms to control powdery mildew in barley and relative species.


Qingke; Powdery mildew (Blumeria graminis); Long non cording RNA; Plant hormone pathway

Powdery mildew is one of the most serious plant diseases that commonly occur in monocot and dicot species (Praz et al., 2017). Living an obligate biotrophic life style, B. g. can be divided in different formae speciales corresponding to the specific host species (Schulze-Lefert and Panstruga, 2011), such as B. g. hordei (Bgh) to infect barley, B. g. tritici to wheat (Silvar et al., 2010), B. g. secalis to rye (Troch et al., 2014), and B. g. triticale to triticale (Menardo et al., 2016). Although disease control is usually mediated by the application of fungicides, breeding of resistant cultivars is preferred owing to the cost-effectiveness and environmentally friendly nature.


The resistance of plant host against powdery mildews fits the gene-for-gene hypothesis, in which a resistance protein gene (R) in host and a cognate avirulence effector gene (AVR) in pathogen are involved. The R proteins, represented by the family of intracellular nucleotide-binding domain and leucine-rich repeat proteins (NLRs) (Jacob et al., 2013), Interact directly or indirectly with cognate pathogen AVRs. The barley resistance genes that have been located at present, such as Mla (Bai, 2013), Mlg (Meng, 2008), Mlp1 (Li et al., 2002), all follow the gene-gene hypothesis, but such resistance is often defeated by the rapid evolution of powdery mildew (Zhu et al., 2014). The interaction between host and powdery mildew pathogen is very complex. The resistance of barley to powdery mildew can be divided into three types. The first type is specific resistance, which must depend on the small species-specific resistance gene. The second type is broad-spectrum resistance, which is dependent on the non-small-species-specific resistance gene mlo. The third is partial resistance, which relies on non-subspecies-specific resistance genes. The resistance of barley to Bgh is mainly controlled by the first type of gene, that is, the resistance of barley to Bgh is mainly realized by the specific resistance generated by the small species-specific disease resistance gene (Zhu et al., 2006).


Long non-coding RNA (lncRNA) is a kind of RNA that is longer than 200 nucleotides and has no protein-coding ability. However, they are abundant in organisms and carry out biological functions (Zhang et al., 2020). LncRNAs can be derived from intron regions, exon regions, intergenomic regions, intron regions, promoter regions, 3 'and 5' UTR regions and enhancers, and can be transcribed in just or antisense directions (Nie et al., 2012). LncRNA regulates gene transcription through a variety of mechanisms.Its regulatory mechanism is largely determined by the location of the genome transcribed by lncRNA. It has been reported that lncRNA can enhance the access of target genes to RNA polymerase rather than compete with RNA polymerase (Hirota et al., 2008). Some lncrnas can bind to target gene promoter DNA to form RNA-DSDNA triad, thus physically blocking the pre-promoter complex of target gene promoter (Martianov et al., 2007). Other lncrnas may also regulate target genes at the transcriptional level by controlling transcription factor subcellular localization or inhibiting RNA polymerase activity (Nguyen et al., 2001;Willingham et al., 2005;Mariner et al., 2008).In addition to regulation at the transcriptional level, lncRNAs also regulate various aspects of Post-transcriptional mRNA processing, including pre-mrna selective splicing, transport, translation and degradation. LncRNA may also regulate the stability of target mRNA through trans-action. LncRNA and target mRNA are complementary to form double stranded RNA double stranded, and then the double stranded RNA is processed into endogenous siRNA to degrade the target mRNA (Golden et al., 2008). There is evidence that lncRNA may play an important role in epigenetic control of gene expression, including Tsix/RepA/Xist, HOTAIR and COLDAIR (Rinn et al., 2007; Zhao et al., 2008; Tsai et al., 2010; Heo and Sung, 2011). In recent years, more and more evidence has shown that lncRNA plays a significant role in plant response to biotic and abiotic stresses. LncRNA may be involved in regulating plant flowering, male sterility, nutrient metabolism, biological and abiotic stress response, etc. (Zhang et al., 2013). For example, lncRNA plays an important role in Arabidopsis thaliana (Zhang et al., 2020) in response to cold stress and salicylic acid (SA) stress seed germination (Heo and Sung, 2011), paulownia plants in response to paulownia bush disease (Wang et al., 2017) and sexual reproduction in rice (Zhang et al., 2014).


Studies of lncRNA in Arabidopsis thaliana (Ben Amor et al., 2009), Zea mays L. (Boerner and Mcginnis, 2012), Triticum aestivum L. (Xin et al., 2011), Oryza sativa L (Lu et al., 2012), Setaria italica (Xin et al., 2011) and Populus Trichocarpa (Shuai et al., 2014) are relatively mature. Under different types of stress conditions, plant RNA-SEQ data were also used to detect stress-responsive lncRNA (Chekanova, 2015; Shafiq et al., 2016; Zhang et al., 2013). They identified 584 lncRNAs responding to drought stress in Setaria italica (Xin et al., 2011). In wheat, 125 hypothesized lncRNAs were found to respond to powdery mildew infection and heat stress.Detailed tests of a large number of RNA-SEQ data from Populus Trichocarpa showed drought response to 504 lncRNAs (Shuai et al., 2014). In addition, Huang et al. (2016) reported more than 12,000 barley lncRNAs, 604 of which were immune responses to Fusarium head blight. However, so far, no such study has been conducted on the expression level of lncRNA in xizang naked barley after powdery mildew infection. Xizang naked barley (Hordeum Vulgare L. var. Nudum), also known as highland barley (2n=7x=14), is an important dietary and dietary crop widely cultivated on the Qinghai-Tibet Plateau (Yuan et al., 2018). However, its output and quality were severely damaged by Bgh. Exploring the response of highland barley to Bgh infection is of great significance for disease control and disease resistance breeding. Transcriptional data of resistant Gannunda 7 (G7) and susceptible Tibetan Qingqing 13 (Z13) genotype highland barley after Bgh vaccination were previously studied (Yuan et al., 2018). In this study, lncRNA in G7 was identified, and based on cluster analysis, the biological role of lncRNA in the resistance of qingke barley to powdery mildew was deduced, which undoubtedly played a certain promoting role in the research on the prevention and treatment of powdery mildew in related species such as qingke barley.


1 Results and Analysis

1.1 Identification of Qingke barley lncRNA

To identify novel lncRNAs in G7, transcripts in short lengths (<200 nt), long ORFs (>300 bp) and transcripts including <2 exons were excluded (Kong et al. 2007). CPC (Coding Potential Calculator) was then used to evaluate the coding potential of the remaining transcripts. Only transcripts with a score of <0 were retained. As a result, 1465 lncRNA transcripts (Table 1) were identified in 10 samples, and mapped to the 7 chromosomes of qingke without preference for location in either the controls or treated samples (Figure 1a). These lncRNAs were mainly 200–1600 bp in length (Figure 1b) and contained 2 to 6 exons (Figure 1c), which were less  than those of mRNAs. the number of lncRNAs containing 2 exons was the highest, 778.



Figure 1 Characteristics of lncRNAs and mRNAs in qingke

Note: a: Distribution of lncRNAs along each chromosome in G7; b: Length distribution of the lncRNAs and mRNAs; c: Exon distribution of the lncRNAs and mRNAs



Table 1 The profile of 16 differentially expressed lncRNAs related to plant immunity


1.2 LncRNA-mRNA correlation analysis predicted the function of target genes

In this study, the relationships between the lncRNA and mRNA of DEGs identified above, and their co-expression correlation of lncRNA-mRNA were analyzed. After filtering, 274 746 and 758 lncRNA–mRNA pairs were identified to be related to trans- and cis- regulation, respectively. These RNAs interacted in one to one or one to multiple ways. For example, the gene HVUL0H00147.2 was targeted by 59 lncRNAs via trans-acting regulation; while lncRNA (MSTRG.109) regulated 3542 genes by trans-acting mechanism. The lncRNA functions in response to Bgh were then predicted based on the GO and KEGG analyses. Most of the trans- and cis-acting targets were related to the terms "catalytic activity", "heterocyclic compound binding", "binding", and "ion binding" of the category "molecular function" and "metabolic process" and "organic substance metabolic process" of the category "biological process". And these lncRNAs were most likely related to the metabolisms of glutathione, carbon, and other secondary metabolites as well as calcium signaling pathway (KO:04020) and plant hormone signal transduction (KO:04075) based on the KEGG pathway analysis (Figure 2a).


Further analysis of these lncRNAs involved in plant immunity was performed based on the co-expression correlation of lncRNA-mRNA. 16 lncRNAs were detected at all time points between resistant G7 and susceptible Z13 (Table 1), and 16 of these target 21 genes were involved in the "plant-pathogen interaction", "phenylalanine, tyrosine and tryptophan biosynthesis", "regulation of autophagy", "drug metabolism-cytochrome P450", "folding, sorting and degradation", "peptidases", "plant hormone signal transduction", "glucosinolate biosynthesis" and "drug metabolism-other enzymes" based on GO and KEGG analyses (Figure 2a). Moreover, all the genes were directly or indirectly related to RPM1 (Table 1), a disease resistance gene in many plants, "calcium ion binding" and "sulfotransferase activity", and "hydrolase activity" and "protein tyrosine kinase activity".



Figure 2 Functional analysis of lncRNA-mRNA and expression of target genes

Note: a: Function analysis of lncRNA-mRNA networks; b~g: The relative expression of lncRNAs involved in disease resistant and their target genes in G7 and Z13; The green polygons represent mRNAs; The red polygons represent lncRNAs; The error bars indicate the standard deviation of three bqiological replicates from different single plant and three technical replicates


1.3 Validation of the expression levels of lncRNA and their target genes

To better understand how these lncRNA regulated gene expression, six pathology-related lncRNAs (MSTRG.10049, MSTRG.8506, MSTRG.13211, MSTRG.16853, MSTRG.567, and MSTRG.7472) and their target genes were selected for qRT-PCR analysis. As shown in Figure 2; Figure 2C; Figure 2D, coordinated expression was found between five lncRNAs and their targets. In comparison to susceptible Z13, MSTRG.10049, MSTRG.16853, MSTRG.8506, and MSTRG.567 caused the up-regulation of the target genes HVUL3H21236.2, HVUL4H05888.2, HVUL5H30954.2 and HVUL1H16871.2, while MSTRG.13211 down-regulated the gene HVUL3H42334.2 in resistant G7 at all time points. It's remarkable that the expression levels of all these lncRNAs and their target genes except for MSTRG.7472 experienced down (6 hpi), up (36 hpi), down (72 hpi) and up (168 hpi) changes. It indicated that these lncRNAs may play different roles in the plant disease resistance.


2 Discussion

Li (2002) research has identified a lncRNA associated with resistance to powdery mildew, this study identified many of the genes involved in plant immune system fight bacterial pathogens, including "ion channels" and "calcium dependent protein kinase", "by MADS - box transcription factor activity", "protein kinase activity" and "metabolism" related genes, and in the identification of the targets in the enrichment of lncRNA. In addition, 21 target genes associated with plant immunity were detected at all time points in the resistant and susceptible materials, and the "molecular function" of these lncRNA targets included genes associated with plant immunity, such as RPM1 (disease-resistant protein RPM1), BAK1 (Bri1-associated receptor kinase 1) and BRI1 (BRI). RPM1 is an HSP90 host protein involved in the identification of pathogens carrying avrRpm1 or avrB in crop plants (Dangl et al., 1992). Studies have shown that BAK1, a LRR receptor-like protein kinase of Arabidopsis thaliana, plays a functional role in PRR-dependent signaling by activating the innate immune plant hormone receptor BRI1 (Chinchilla et al., 2007). In this study, lncRNAs may participate in the plant immune process through the function of target genes, and these lncRNAs and their targets may be related to plant pathology and immunity, whose role needs further verification.


In plants, Ca2+ signaling pathway plays pivotal roles in abiotic and biotic stress responses (Zhao et al., 2006). The KEGG pathway analysis indicated that HVUL4H44925.2 and HVUL5H07008.2 target MSTRG.8824.1 and MSTRG.12264.1, respectively, which were involved in the "calcium signaling pathway" (KO: 04020). These results suggested that lncRNAs may participate in the Ca2+ signaling pathway to facilitate the defense response of qingke to powdery mildew. In this study, dozens of predicted cis- and trans-regulatory targets of identified lncRNAs were involved in "Plant hormone signal transduction" (KO:04075), including eight cis-regulatory target genes (e.g., HVUL5H05813.2; the target of MSTRG.10110.1) and ten trans-regulatory target genes (e.g., HVUL7H06762.2; the target of MSTRG.10939) based on the KEGG pathway analysis. It has been reported that phytohormones play an important role in plant immunity (Ford et al., 2010). SA inhibits the growth and reproduction of arabidopsis thaliana. BAK1 (BRI 1-associated receptor kinase 1) is involved in brassinosteroid (BR)-dependent growth and development, innate immunity, and cell death control via a protein complex that includes the leucine-rich repeat receptor-like protein kinase (LRR-RLK) brassinosteroid-insensitive 1 (BRI1) (Zeng et al., 2011). In this study, the differentially expressed lncRNAs MSTRG.17900 and MSTRG. 299 targeting HVUL7H07144.2 and HVUL1H46537.2 were predicted as BAK1 and BRI1 respectively. In addition, we found that some DEGs may participate in plant defense responses via the up- or down-regulation of genes involved in auxin, jasmonic acid, and abscisic acid signaling pathways. This results are consistent with previous results. Therefore, we speculated that these plant hormone pathways affected by lncRNAs might play important roles in powdery mildew disease resistance.


At present study, two kinds of highland barley G7 (showed complete penetration resistance to powdery mildew) and Z13 (showed complete penetration sensitivity to powdery mildew) were inoculated with Bgh, and then the expression pattern of lncRNAs was studied. The results show that ion channel, calcium dependent protein kinase, plant hormone pathway related lncRNA and their target genes play an important role in highland barley resistance to Bgh. These findings will contribute to the further understanding of the role of lncRNA in qingke barley mildew, and may provide valuable insights into the molecular mechanism of qingke barley resistance to powdery mildew.


3 Materials and Methods

3.1 Sample collection and library sequencing

Two Tibetan barley cultivars, resistant Gannongda 7 (G7) and sensitive Zangqing 13 (Z13), were stocked in the Tibet Academy of Agricultural and Animal Husbandry Sciences and used in this study. Gannongda No. 7 (G7) and Z13 (Z13) were provided by the Agricultural Research Institute of the Academy of Agriculture and Animal Husbandry of the Tibet Autonomous Region. G7 and Z13 were seeded in the greenhouse under the same conditions, and after the plants grew to the 4-leaf stage, they were inoculated with albino bacteria by smear method. Then 5 seedling leaves of G7 and Z13 were collected at 0 h, 6 h, 36 h, 72 h and 168 h respectively, and all samples were immediately frozen in liquid nitrogen and stored at -80°C for RNA extraction. Total RNA was extracted using Illumina TruSeq RNA sample preparation kit (Illumina, Inc., San Diego, California, USA). A total of 30 PE libraries (3 biological replicates per sample) were constructed using the Illumina paired end sample preparation kit and sequenced on the Illumina HiSeq X platform. Transcriptomic sequencing data for all samples can be downloaded from the National Center for Biotechnology Information (NCBI) SRA data, accession is SRP148116.


3.2 Differential expression and functional analysis of lncRNA

After the original sequencing data of 10 samples were quality-controlled and the sequences with low quality and joints were removed. Genome mapping of high-quality reads was executed using the HISAT (v 2.0.6) (Kim), with the Tibetan qingke draft genome assembly for barley ( as the reference. Reads that can be mapped to a single location in the reference genome (unique hits) were kept for further analysis. Kallisto software was used to estimate the gene expression levels in each sample (Yuan et al., 2018). TPM (transcripts per million) was used to estimate the transcript expression levels in all samples, and genes with expression levels higher than 1 TPM in at least one sample were used for further analysis. The remaining transcripts were subjected to an HMMER (Eddy, 2009) analysis to remove those containing any known protein domains cataloged in the Pfam database (Bateman et al. 2004). And the transcripts that remained were considered reliably expressed lncRNAs(DOl:10.1038/s41598-018-33113-7)(Yuan et al. 2018). LncRNAs with differentially expressed were screened according to fold change > 1.5 and P- value <0.05 as the standard.


3.3 LncRNA target gene prediction and interaction analysis

Studies have shown that lncRNAs regulate gene expression by two mechanisms. lncNATs (intragenic) regulate the expression of neighboring genes or genes that overlap with the lncRNAs in the antisense direction, while lncRNAs (intergenic) regulate gene expression via the sequence matches. Thus, we predicted the targets by scanning the overlapping genes with lncNATs of 100 kb up- or down-stream of corresponding lncNATs. The target genes of lincRNAs were predicted by the expression correlation with mRNAs (Pearson r>0.95). The co-expression of lncRNAs and mRNAs was calculated by the Pearson correlation coefficients (PCC). A value of P < 0.05 was considered statistically significant. Based on the correlations between lncRNAs and target mRNAs, a lncRNA-mRNA network was constructed using the Cytoscape v 3.0.2 (Cline et al. 2007). 


3.4 Functional classification of lncRNA target genes

DEGs between resistant and susceptible materials and the target genes of candidate lncRNAs were subjected to a GO (Gene Ontology) analysis (Ashburner et al. 2000). Three major categories Molecular Function, Biological Process, and Cellular Component were condisdered. The pathways of lncRNAs co-expressed with mRNAs and DEGs between resistant and susceptible genotypes were analyzed by a KEGG pathway analysis (Kanehisa et al. 2004). These analyses allowed us to predict the enriched functional element and biological pathways of co-expressed mRNAs and differentially expressed lncRNAs. A P-value of <0.05 was considered statistically significant for the correlations.


3.5 Confirmation of gene expression using real-time qRT-PCR

Expression levels of differentially expressed lncRNAs and mRNAs were validated by RT-qPCR analysis. Total RNA was collected, and cDNA was synthesized using the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Shiga, Japan). DEGs were validated using a CFX96 Touch™ Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) with SYBR Green II (TaKaRa) (Table 2). For real-time qRT-PCR of lncRNA, cDNA was synthesized using the lnRcute lncRNA First-Strand cDNA Synthesis Kit (with gDNase) (TIANGEN, Beijing, China). LncRNAs were validated with lnRcute lncRNA qPCR Kit (SYBR Green) (TIANGEN). Expression levels of genes were normalized using the endogenous 18S rRNA sequence of H. vulgare (Scholtz and Visser, 2013) with specific primers (5′-TTGTGGAGGCACTACTTC-3′and 5′-AGCAATACGGTCTCTGTC-3′); the relative expression levels were calculated using the 2−ΔΔCt method. In this study, lincRNAs and UTR associated lncRNAs were preferred for qPCR analysis. Primer sequences were designed using the Primer3 input v 4.0.0 ( and the primer specificity for lncRNA and targets was checked using the Integrative Genomic Viewer (IGV) ( (Table 3). All qRT-PCR amplifications were carried out in triplicate, with the standard reaction program. The specificity of the amplified fragments was checked using the generated melting curve. The real-time data were analyzed using the Opticon Monitor Analysis Software v 3.1 (Bio-Rad) tool. The primers used for the qRT-PCRs are listed in Supplementary Table 4.



Table 2 Primers designed for lncRNAs and their targets



Table 3 Primers designed for resistant-related genes



Table 4 Specific primers of DEGs for qRT-PCR


Authors’ contributions

X.Z. and M.W. conceived the idea of the work and designed the research; Y.W. and H.Y. produced and analyzed the data; L.W. wrote the paper; L.W. and H. Y. participated in the experimental design and analysis of the experimental results; X. Z. was the initiator and responsible person of the project, directing experimental design, data analysis, paper writing and modification. All authors read and approved the final manuscript.



The work was supported by the National Key R&D Program of China (2018YFD1000703) and the Tibet Financial Special Fund (XZNKY-2019-C-051).


Conflicts of Interest

The authors declare no conflict of interest.



Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.T., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J.C., Richardson J.E., Ringwald M., and Rubin G.M., 2000, Gene ontology: tool for the unification of biology, Nat. Genet., 25(1): 25-29

PMid:10802651 PMCid:PMC3037419


Bateman A, Coin L., and Durbin R., 2004, Sonnhammer ELL: the pfam protein families database, Nucleic Acids Res, 2(246): D222–D230

Ben Amor B., Wirth S., Merchan F., Laporte P., d’Aubenton-Carafa Y., Hirsch J., Maizel A., Mallory A., Lucas A., Deragon J.M., Vaucheret H., Thermes C., and Crespi C., 2009, Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses, Genome Res., 19(1): 57-69

PMid:18997003 PMCid:PMC2612962


Boerner S., and Mcginnis K.M., 2012, Computational identification and functional predictions of long noncoding RNA in Zea mays, Plos One, 7(8):e43047

PMid:22916204 PMCid:PMC3420876


Chekanova J.A., 2015, Long non-coding RNAs and their functions in plants, Curr. Opin. Plant Biol., 27(8): 207-216



Chinchilla D., Zipfel C., Robatzek S., Kemmerling B., Nürnberger T., Jones J.D.G., Felix G., and Boller T., 2007, A flagellin-induced complex of the receptor FLS2 and BAK1 initiates plant defence, Nature, 448(7152):497-500



Cline M.S., Smoot M., Cerami E., Kuchinsky A., Landys N., Workman C., Christmas R., Avila-Campilo I., Creech M., Gross B., Hanspers K., Isserlin R., Kelley R., Killcoyne S., Lotia S., Maere S., Morris J., Ono K., Pavlovic V., Pico A.R., Vailaya A., Wang P.L., Adler A., Conklin B.R., Hood L., Kuiper M., Sander C., Schmulevich I., Schwikowski B., Warner G.J., Ideker T., and Bader G.D., 2007, Integration of biological networks and gene expression data using Cytoscape, Nat. Protoc., 2(10): 2366-2382

PMid:17947979 PMCid:PMC3685583


Dangl J., Ritter C., Gibbon M.J., Mur L.A., Wood J.R., Goss S., Mansfield J., Taylor J.D., and Vivian A., 1992, Functional homologs of the Arabidopsis RPM1 disease resistance gene in bean and pea, Plant Cell, 4(11): 1359-1369

PMid:1477552 PMCid:PMC160224


Eddy S.R., 2009, A new generation of homology search tools based on probabilistic inference, Genome Inform., 23(1):205-211



Ford K.A., Casida J.E., Chandran D., Gulevich A.G., Okrent R.A., Durkin K.A., Sarpong R.,; Bunnelle E.M., and Wildermuth M.C., 2010, Neonicotinoid insecticides induce salicylate-associated plant defense responses, Proc. Natl. Acad. Sci. USA, 107(41): 17527-17532

PMid:20876120 PMCid:PMC2955088


Golden D.E., Gerbasi V.R., and Sontheimer E.J., 2008, An inside job for siRNAs, Mol. cell, 31(3): 309-312

PMid:18691963 PMCid:PMC2675693


Heo J.B., and Sung S., 2011, Vernalization-mediated epigenetic silencing by a long intronic noncoding RNA, Science, 331(6013): 76-79



Hirota K, Miyoshi T., Kugou K., Hoffman C.S., Shibata T., and Ohta K., 2008, Stepwise chromatin remodelling by a cascade of transcription initiation of non-coding RNAs, Nature, 456(7218): 130-134



Huang Y.D., Li L., Smith K.P., and Muehlbauer G.J., 2016, Differential transcriptomic responses to Fusarium graminearum infection in two barley quantitative trait loci associated with Fusarium head blight resistance, BMC Genom., 17(1): 387

PMid:27206761 PMCid:PMC4875680


Jacob F., Vernaldi S., and Maekawa T., 2013, Evolution and conservation of plant NLR functions, Front. Immunol., 29(4): 284-297

PMid:24093022 PMCid:PMC3782705


Kanehisa M., Goto S, Kawashima S, Okuno Y., and Hattori M., 2004, The KEGG resource for deciphering the genome, Nucleic Acids Res., 32(D): 277-280

PMid:14681412 PMCid:PMC308797


Kong L., Zhang Y., Ye Z.Q., Liu X.Q., Zhao S.Q., Wei L.P., and Gao G., 2007, CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine, Nucleic Acids Res., 35(W): 345-349

PMid:17631615 PMCid:PMC1933232


Li J., Wen J., and Lease K.A., 2002, Modulates brassinosteroid signaling, Cell, 110(24): 397-404

Lu T.T., Zhu C.R., Lu G.J., Guo Y.L., Zhou Y., Zhang Z.Y., Zhao Y., Li W.J., Lu Y., Tang W.H., Feng Q., and Han B., 2012, Strand-specific RNA-seq reveals widespread occurrence of novel cis-natural antisense transcripts in rice, BMC Genom., 13(721): 721

PMid:23259405 PMCid:PMC3549290


Mariner P.D., Walters R.D., Espinoza C.A., Drullinger L.F., Wagner S.D., Kugel J.F., and Goodrich J.A., 2008, Human Alu RNA is a modular transacting repressor of mRNA transcription during heat Shock, Mol. cell, 29(4): 499-509



Martianov I., Ramadass A., Serra Barro A., Chow N., and Akoulitchev A., 2007, Repression of the human dihydrofolate reductase gene by a non-coding interfering transcript, Nature, 445(7128): 666-670



Nguyen V.T., Kiss T., Michels A.A., and Bensaude O., 2001, 7SK small nuclear RNA binds to and inhibits the activity of CDK9/cyclin T complexes, Nature, 414(6861): 322-325



Nie L., Wu H.J., Hsu J.M., Chang S.S., Labaff A.M., Li C.W., Wang Y., Hsu J.L., and Hung M.C., 2012, Long non-coding RNAs: versatile master regulators of gene expression and crucial players in cancer, Am. J. Transl. Res., 4(2): 127-150


Praz C.R., Bourras S., Zeng F.S., Sánchez-Martín, J., Menardo F., Xue M.F., Yang L.J., Roffler S., Boni R., Herren G., McNally K.E., Ben-David R., Parlange F., Oberhaensli S., Flückiger S., Schafer L.K., Wicker T., Yu D.Z., and Keller B., 2017, AvrPm2 encodes an RNase-like avirulence effector which is conserved in the two different specialized forms of wheat and rye powdery mildew fungus, New Phytol., 213: 1301-1314

PMid:27935041 PMCid:PMC5347869


Rinn J.L., Kertesz M., Wang J.K., Squazzo S.L., Xu X., Brugmann S.A., Goodnough L.H., Helms J.A., Farnham P.J., Segal E., and Chang H.Y., 2007, Functional demarcation of active and silent chromatin domains in human HOX loci by no ncoding RNAs, Cell, 129(7): 1311-1323

PMid:17604720 PMCid:PMC2084369


Scholtz J.J., and Visser B., 2013, Reference gene selection for qPCR gene expression analysis of rust-infected wheat, Physiol. Mol. Plant Pathol., 81: 22-25


Schulze-Lefert P., and Panstruga R.A., 2011, Molecular evolutionary concept connecting nonhost resistance, pathogen host range, and pathogen speciation, Trends Plant Sci., 16(3): 117-125



Shafiq S., Li J.R., and Sun Q.W., 2016, Functions of plants long non-coding RNAs, Biochim. Biophys. Acta Gene Regul. Mech., 1859(1): 155-162



Shuai P., Liang D., Tang S., Zhang Z.J., Ye C.Y., Su Y.Y., Xia X.L., and Yin W.L., 2014, Genome-wide identification and functional prediction of novel and drought responsive lincRNAs in Populus trichocarpa, J. Exp. Bot., 65(17): 4975-4983

PMid:24948679 PMCid:PMC4144774


Silvar C., Casas A.M., Kopahnke D., Habekuß A. Schweizer G., Gracia M.P., Lasa J.M., Ciudad F.J., Molina-Cano J.L., Igartua E., and Ordon F., 2010, Screening the spanish barley core collection for disease resistance, Plant Breed., 129(1): 45-52


Tsai M.C., Manor O., Wan Y., Mosammaparast N., Wang J.K., Lan F., Shi Y., Segal E., and Chang H.Y., 2010, Long noncoding RNA as modular scaffold of histone modification complexes, Science, 329(5992): 689-693

PMid:20616235 PMCid:PMC2967777


Willingham A., Orth A., Batalov S., Peters E.C., Wen B.G., Aza-Blanc P., Hogenesch J.B., and Schultz P.G., 2005, A strategy for probing the function of noncoding RNAs finds a repressor of NFAT, Science, 309(5740): 1570-1573



Xin M.M., Wang Y., Yao Y.Y., Song N., Hu Z.R., Qin D.D., Xie C.J., Peng H.R., Ni Z.F., and Sun Q.X., 2011, Identification and characterization of wheat long non-protein coding RNAs responsive to powdery mildew infection and heat stress by using microarray analysis and SBS sequencing, BMC Plant Biol., 11(1): 61

PMid:21473757 PMCid:PMC3079642


Yuan H.J., Zeng X.Q.,Yang Q.F., Xu Q.J., Wang Y.L., Jabu D.Z., Sang Z., and Tashi N., 2018, Gene coexpression network analysis combined with metabonomics reveals the resistance responses to powdery mildew in Tibetan hulless barley, Sci. Rep., 8(1): 14928

PMid:30297768 PMCid:PMC6175840


Zhang H., Chen Z.H., Wang X.X., Huang Z.N., He Z.W., and Chen Y.Q., 2013a, Long non-coding RNA: a new player in cancer, J. Hematol. Oncol., 6(37): 2-7

PMid:23725405 PMCid:PMC3693878


Zhang J., Mujahid H., Hou Y.X., and Nallamilli B.R., Peng Z.H., 2013b, Plant long ncRNAs: a new frontier for gene regulatory control, Am. J. Plant Sci., 4(15): 1038-1045


Zhang Y.C., Liao J.Y., Li Z.Y., Yu Y., Zhang J.P., Li Q.F., Qu L.H., Shu W.S., and Chen Y.Q., 2014, Genome-wide screening and functional analysis identify a large number of long noncoding RNAs involved in the sexual reproduction of rice, Genome Biol., 15(12): 1-16

PMid:25517485 PMCid:PMC4253996


Zhao J., Sun B.K., Erwin J.A, Song J.J., and Lee J.T., 2008, Polycomb proteins targeted by a short repeat RNA to the mouse X chromosome, Science, 322(5902): 750-756

PMid:18974356 PMCid:PMC2748911

Triticeae Genomics and Genetics
• Volume 11
View Options
. PDF(541KB)
. Online fPDF
Associated material
. Readers' comments
Other articles by authors
. Lingling Wei
. Yulin Wang
. Hongjun Yuan
. Mu Wang
. Xingquan Zeng
Related articles
. Qingke
. Powdery mildew ( Blumeria graminis )
. Long non cording RNA
. Plant hormone pathway
. Email to a friend
. Post a comment