激光植物miRNA

1、UV-B Responsive MicroRNA Genes in ArabidopsisthalianaXuefeng Zhou , Guandong Wang and Weixiong Zhang 1, , Department of Computer Science and Engineering1Department of GeneticsWashington University in Saint Louis Saint Louis, MO 63130-4899, USAAbstractMicroRNAs are small, non-coding RNAs that play cr

2、itical roles in post-transcriptional gene regulation. In plants, mature microRNAs pair with complementary sites on mRNAs and subse-quently lead to cleavage and degradation of the mRNAs. Many microRNAs target mRNAs that encode transcription factors, therefore, they reg-ulate the expression of many do

3、wn-stream genes. In this study, we carry out a survey of Arabidop-sis microRNA genes in response to UV-B radia-tion, an important adverse abiotic stress. We de-velop a novel computational approach to identify microRNA genes induced by UV-B radiation and characterize their functions in regulating gen

4、e ex-pression. We report that in A. thaliana 21mi-croRNA genes in 11microRNA families are up-regulated under UVB stress condition. We also discuss putative transcriptional down-regulation pathways triggered by the induction of these mi-croRNA genes. Moreover, our approach can be directly applied to

5、miRNAs responding to other abiotic and biotic stresses and extended to miR-NAs in other plants and metazoans.1IntroductionMicroRNAs are approximately 22-nucleotide long, non-coding RNAs that play critical roles in regulating gene expression at the post-transcriptional level (Bartel,2004; He and Han-

6、non, 2004. The discovery of miRNAs (mi-croRNAs has broadened our perspectives on the mechanisms of repression of gene expression, which is an important regulatory mechanism me-diating many biological processes such as de-velopment, cell proliferation and differentiation. In plants, mature miRNAs bas

7、e-pair with com-plementary sites on target mRNAs and subse-quently direct the mRNAs to be cleaved or de-graded. Plant miRNAs regulate many genes that are involved in developmental control, for example, auxin signaling (Bonnetet al., 2004; Jones-Rhoades and Bartel, 2004, organ polar-ity (Eshedet al.,

8、 2001; Kidner and Martienssen, 2004; McConnell et al., 2001, development tran-sitions (Aukermanand Sakai, 2003; Chen, 2004, leaf growth (Palatniket al., 2003 and RNA metabolism (Vaucheretet al., 2004; Xie et al., 2003. Several recent studies showed important functions of miRNAs in response to advers

9、e abi-otic stresses (Bariet al., 2006; Jones-Rhoades and Bartel, 2004; Lu et al., 2005; Sunkar and Zhu, 2004. In Arabidopsis, miR399was identiedto be highly expressed under phosphate starva-These authors contributed equally to this research. 1induced under sulfate starvation (Jones-Rhoadesand Bartel

10、, 2004. Furthermore, quantitative experimental analysis proved that miR393was strongly induced under cold stress (Sunkarand Zhu, 2004. In Populus, moreover, some miR-NAs can be induced by mechanical stress and may function in critical defense systems for structural and mechanical tness(Luet al., 200

11、5.Many targets genes of miRNAs encode tran-scription factors as well (Bartel,2004, each of which further regulates a set of downstream genes. Thus the activation of miRNA genes un-der abiotic stresses will lead to the repression of many downstream protein-coding genes and affect physiological respon

12、ses. Among various environmental factors, light plays a particularly important role. Sunlight is not only the energy source for plant photosynthesis, but also regulates several plant developmental processes and some physiological processes, such as photosynthesis, seasonal and diurnal time sensing (

13、Baroliet al., 2004; Chattopadhyay et al., 1998; Chory et al., 1996; Jiao et al., 2005. Similar to adverse en-vironmental factors, such as drought and salinity, light can also have stress effect on plants. It inter-acts with endogenous developmental programs, hence affect plant growth and development

14、. In order to acclimate under such conditions, spe-cicphotoreceptor systems have been developed and evolved to monitor changes of light com-position (Dunaevaand Adamska, 2001; Harvaux and Kloppstech, 2001; Shao et al., 2006. With complex photoreceptors, plant can register UV-B radiation and transduc

15、e the information to nu-cleus, hence affect gene expression (Chattopad-hyay et al., 1998; Jiao et al., 2005; Kimura et al., 2003b. Changes in gene expression in response to UV-B radiation include reduction in expression and synthesis of key photosynthetic proteins as well as perturbation of expressi

16、ons of the genes involved in defense mechanisms (Chattopadhyayet al., 1998; Jiao et al., 2005; Kimura et al., 2003b.Regulation of gene expression plays an impor-tant role in a variety of biological processes, such as development and responses to environmental stimuli. In plants, transcriptional regu

17、lation is2mediated by a large number of transcription fac-tors (TFscontrolling the expression of tens or hundreds of target genes in various, sometimes intertwined, signal transduction cascades (Ven-ter and Botha, 2004; Wellmer and Riechmann, 2005. Transcription factor binding sites (TF-BSs are the

18、functional short DNA sequences (cis -elements that determine the timing and lo-cation of transcriptional activities. Many com-putational methods have been developed to re-veal relationships between gene expression pat-terns and TFBSs in the proximal upstream regu-latory regions of the genes of inter

19、est. In yeast, motifs with known functions have been related to transcriptional pathways by statistical analysis of the occurrence of known motifs in the promoters of coregulated genes (Bussemakeret al., 2001. However, the presence of individual motifs is only marginally indicative of a genesexpress

20、ion pat-tern. Extended strategies pursue to optimally pre-dict gene expression patterns with promoter cis -elements and their combinations. With a system-atic strategy, the expression of a large proportion of genes in S. cerevisiae was accurately predicted based on promoter sequences (Beerand Tavazo

21、ie, 2004. Although distal regulatory elements other than those in proximal upstream promoter regions can modulate gene expression, a recent study em-phasized that the sequences in the 5-upstreamregions of genes were of primary importance in Arabidopsis gene regulation (Leeet al., 2006. Specically,pr

22、omoter sequences were sufcientto recapture the mRNA expression levels for 80%of the TFs studied. This study conrmedthe im-portant role of promoter regions in Arabidopsis gene expression.In light of this transcriptome-based perspec-tive and by taking advantage of the vast available data of genome-sca

23、le microarray expression pro-leof protein-coding genes, we develop an inno-vative computational approach to explore the ex-pression activity of miRNA genes under certain conditions. We focus on identifying and anno-tating miRNAs in A. thaliana which are respon-sive to UV-B radiation, and further con

24、sider the regulatory pathways that are probably affected by the putative UV-B inducible miRNA genes. Ourapproach is based on the following two observa-tions. First, plant miRNAs generally direct en-donucleolytic cleavage of target mRNAs (Llaveet al., 2002; Schwab et al., 2005, hence enable rapid cle

25、arance of target mRNAs when they are expressed (Axtelland Bartel, 2005; Bartel, 2004. Under a particular condition, if an miRNA is up-regulated, its targets are most likely to be co-herently down-regulated. Second, miRNA genes are transcribed by RNA polymerase II (Houbaviyet al., 2005; Lee et al., 2

26、004; Xie et al., 2005; Zhou et al., 2007. Hence the 5proximal pro-moters of miRNA genes are the most important regulatory regions, and signicantcis -elements in these regions are important in determining the spatial and temporal expression patterns of the miRNA genes. Therefore, miRNA and protein-co

27、ding genes carrying the same or similar cis -elements in their promoters are very likely to be co-regulated under the same condition and conse-quently very likely to be co-expressed.Although we focus on Arabidopsis UV-B re-sponding miRNA genes in this study, our ap-proach can be directly applied to

28、plant miRNA genes functioning under other abiotic or biotic stress conditions.Table 1:Putative UV-B responsive miRNAs. (a:standard deviations of p valuesgene idmiR159/319miR160miR165/166miR167miR169miR170/171miR172miR393miR398miR401cosine similarityp value 7.11E-031.42E-021.17E-026.10E-023.60E-051.6

29、9E-024.25E-044.33E-022.44E-042.96E-02stdv 4.30E-051.94E-041.11E-043.47E-047.00E-061.61E-041.70E-052.67E-046.00E-052.71E-0422.1Results and discussionsUV-B responsive miRNAsOne of the bases of our method for ndingstress-responsive miRNA genes is that protein-coding genes targeted by the same miRNA are

30、 likely to have coherently down-regulated expression pat-terns. We consider an miRNA to be putatively stress-inducible if the expressions of its target genes are coherently repressed and the coherence is statistically signicantabove a threshold. In this study, we only considered bona detarget genes

31、reported in the literature. For each miRNA, pairwise cosine similarities of the expressions of its target genes were computed. We measured the coherence of the expressions of its target genes by the average pairwise similarity. The statistical signicanceof the coherence was assessed with a3p value f

32、rom a Monte Carlo simulation. Briey,for each miRNA with n target genes, we rstcal-culated the average pairwise cosine similarity of the expressions of the target genes. We then ran-domly sampled n genes from the whole set of genes that were proled,and calculated their av-erage pairwise cosine simila

33、rity. We repeated the sampling a large number of times, for instance one million times in our study, and took as an em-pirical p value the frequency of observing a sim-ilarity value larger than that of the target genes. For each miRNA, we repeated this simulation 100times, and calculated the average

34、 p value and the standard deviation.Table 1shows putative UV-B responsive miR-NAs. For each of these miRNAs, its target genes are coherently down-regulated, and the coherence of their expression patterns is statistically signi-cant. Except miR167whose p value is less than 0.07, all candidates have p

35、 values smaller than 0.05.For miR158, miR162, miR163, miR168, miR395, miR402, miR403, miR404, miR405and miR406, we only found one bona detarget gene in the microarray data set, and could not test their coherence, thus excluded them from our study.2.2UV-B responsive miRNA genesWe applied our computat

36、ional approach, dis-cussed in Section 3.2, to the microarray gene expression data under UV-B radiation treat-ment from the Arabidopsis AtGenExpressFor each miRNA gene, we analyzed whether the combination of signicantmotifs in its pro-moter was statistically relevant to the UV-B stress. First, we exa

37、mined all protein-coding genes in the whole set of gene proledin the microarray experiments, and found those genes that contain the same or very similar motifs in their proxi-mal promoter regions. We then tested whether these protein-coding genes were enriched with up-regulated genes (seeSections 3.

38、2and 3.5. We further imposed on miRNA genes a crite-rion of anti-correlation between the inferred ex-pression of an miRNA gene and the expressions of its mRNA targets, in order to lterout pos-sible false predictions. Since we did not rely on any direct information of miRNA expression, we used the in

39、ferred expression of an miRNA gene and the expressions of its targets to com-pute their anti-correlation (seeSection 3.4. In our study, we chose the vebest protein-coding genes whose 5proximal promoters contain ar-rays of cis -elements that most resemble that of the corresponding miRNA genes. These

40、vegenes are most likely to be co-regulated with the corre-sponding miRNA gene, thus their expression pat-terns are most likely to be similar to the expres-sion pattern of the miRNA gene. In the rest of our discussion, we refer to the average expression pattern of the top veco-regulated protein-codin

41、g genes of an miRNA as its inferred expression pat-tern or expression pattern for short.Before we inferred expression patterns of miRNA genes, we applied the inferring proce-dure to 100randomly selected protein-coding genes with known expression patterns, and then4Table 2:Putative UV-B responsive mi

42、RNA genes. (a:p values for assessing the enrichment of UVB up-regulated genes in the set of coding genes that contain the same arrays of motifs as the miRNA genes. (bp values for assessing the signicanceof the cosine similaries. (c:Standard deviations of the p values (b .gene id miR156e miR156f miR1

43、56h miR157c miR159a miR159b miR160c miR165a miR166c miR166f miR167d miR169d miR169j miR170miR171a miR172c miR172e miR393a miR398a miR401miR395c miR395ep value 2.48E-059.39E-021.67E-068.93E-028.51E-029.94E-043.48E-042.11E-114.55E-022.50E-072.52E-061.79E-025.36E-101.27E-024.66E-021.16E-028.21E-051.08E

44、-054.89E-021.02E-128.09E-028.63E-03cosine similarity-0.42-0.42-0.41-0.32-0.41-0.48-0.53-0.52-0.47-0.47-0.72-0.41-0.41-0.69-0.75-0.75-0.77-0.60-0.78-0.71p value 5.81E-022.69E-025.76E-028.32E-023.09E-021.25E-027.14E-025.24E-027.50E-029.45E-027.81E-029.10E-029.37E-022.82E-028.32E-031.15E-032.60E-044.52

45、E-025.94E-028.51E-02stdv 1.76E-041.05E-042.73E-042.76E-041.95E-041.11E-043.51E-042.10E-041.60E-043.64E-041.77E-043.21E-041.93E-041.83E-049.10E-054.20E-051.80E-051.66E-042.13E-041.97E-04assessed the similarities between their inferred and actual expression patterns. For all these 100genes, the cosine

46、 similarity values of their in-ferred and actual expression patterns are between 0.3and 0.89, and the average of these values is 0.51. Figure 1shows the inferred expression pat-tern and the actual expression pattern of protein coding gene, At1g19770. This guregives a pic-torial view of the similarit

47、y of the inferred and original expression patterns. The cosine simi-larity value of these two expression patterns of At1g19770is 0.76.For each putative UV-B responsive miRNA gene, we calculated the average cosine similar-ity between its inferred expression and the ex-pressions of its targets. We ass

48、essed the statisti-cal signicanceof the similarity with a p value. Similar to the analysis of expression coherence of target genes in Section 2.1, the p value was also Expression pattern of AT1G19770 and its top 5 correlated genesFigure 1:The expressions of At1g19770in root and shoot, respectively,

49、the expressions of the veprotein-coding genes that are most correlated to it, and their mean expression. obtained by a Monte Carlo simulation. We took as an empirical p value the frequency to observe a cosine similarity value smaller than that in the real data. For each miRNA, the simulation was als

50、o repeated 100times to obtain an average p value and a standard deviation.Forty miRNA genes satisfy the rstcriterion. However, as shown in Table 2, inferred expres-sions of 21miRNA genes are anti-correlated to the expressions of their target genes (cosinesim-ilarity less than 0, and the anti-correla

51、tions re-ectedby the average cosine similarities of in-ferred expressions and expressions of target genes are statistically signicant.These 21genes are our predicted UV-B responsive miRNA genes. In all putative UV-B responsive miRNA fami-lies shown in Table 1, at least one member gene from each fami

52、ly was predicted to be up-regulated under UV-B radiation. However, none of the members in other miRNA families was predicted to be UV-B responsive. Three miRNA genes, miR168a , miR395c , and miR395e , might also be5UV-B responsive. The arrays of motifs in their proximal promoter regions are statisti

53、cally signif-icantly relevant to UV-B stress, shown by small p values obtained from an accumulative hyper-geometric test. Protein-coding genes sharing the same array of motifs with them have enriched GO (GeneOntology terms that are related to stress response (seediscussion in Section 2.3. How-ever,

54、since these miRNAs have fewer than two experimental validated target genes, the coher-ence of their target gene expressions and the anti-correlations between their expressions and ex-pressions of their targets could not be analyzed. Hence these genes will not be discussed further.Table 3:Stress-rela

55、ted GO terms are enriched in the annotations of protein-coding genes that contain all the motifs present in corresponding putative UV-B responsive miRNA genes.2.2E-52.3E-52.4E-53.1E-53.9E-51.4E-56.8E-56.5E-104.2E-87.8E-71.2E-61.3E-67.9E-62.5E-55.0E-51.6E-47.0E-64.8E-57.3E-59.4E-53.2E-83.8E-89.5E-71.

56、2E-61.4E-63.3E-64.2E-63.4E-53.4E-58.2E-55.0E-51.0E-41.5E-41.7E-55.5E-51.3E-51.6E-46.8E-51.1E-181.1E-186.9E-131.1E-121.3E-124.8E-124.9E-111.3E-051.5E-41.5E-41.8E-4binding/ligandtranscription factor activity transcription regulator activityhydrolase activity, acting on glycosyl bonds/N-glycosylase/gly

57、cosylasecatalytic activity/enzymeactivityhydrolase activity, hydrolyzing O-glycosyl compounds/O-glucosylhydrolase copper, zinc superoxide dismutase activity/zincsuperoxide oxidoreductase oxidoreductase activity/redoxactivity indole derivative metabolism response to wounding response to stressrespons

58、e to external stimulus oxidoreductase activity, acting on diphenols and related substances as donors, oxygen as acceptor oxidoreductase activity, acting on diphenols and related substances as donors oxidoreductase activity, acting on diphenols and related substances as donors, oxygen as acceptor oxi

59、doreductase activity, acting on diphenols and related substances as donors transcription regulator activityavonoid3-monooxygenaseactivity/avonoid3-hydroxylaseDNA bindingresponse to stimulus response to pest, pathogene or parasite response to external biotic stimulus response to biotic stimulus anthranilate synthase activity response to stressresponse to external stimulus oxo-acid-lyase activityethylene biosyn