JXB Advance Access originally published online on July 4, 2006
Journal of Experimental Botany 2006 57(11):2601-2612; doi:10.1093/jxb/erl013
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© 2006 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details)
RESEARCH PAPER |
Characterization of five microRNA families in maize
Department of Biomolecular Sciences and Biotechnology, Università degli Studi di Milano, Italy
*To whom correspondence should be addressed at: Via Celoria, 26, 20133 Milano, Italy. E-mail: enrico.pe{at}unimi.it
Received 5 January 2006; Accepted 12 April 2006
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Abstract |
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In recent years, microRNAs (miRNAs) have polarized the interest of the scientific community as a new category of gene expression regulators, present in both plants and animals. Plant miRNAs are involved in processes such as plant development, organ identity, and stress response. Nonetheless, knowledge of their functions is still incomplete, and it is conceivable that further new processes in which they are involved will be discovered. For these reasons, structural and functional characterization of MIR genes, that are also in crop species such as Zea mays L., becomes instrumental in addressing genetic and molecular mechanisms controlling phenotype determination and phenotypic adaptation to growing conditions. The present study contributes to the characterization of five miRNA families in maize, from the determination of their expression pattern in different maize tissues and genotypes, to the identification of putative targets by bioinformatic means and subsequent experimental validation of three targets by modified 5' RACE experiments. Furthermore, 30 different MIR genes belonging to these five miRNA families were analysed by their attribution to maize chromosomes using oat–maize addition lines and by investigating their phylogenetic relationship with genes from other cereals. In particular, sequence homology was determined by the reciprocal best BLAST hit approach, to define groups of homologous genes between maize, rice, and sorghum.
Key words: Expression analysis, maize, microRNAs, MIR genes, orthologous genes, sorghum
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Introduction |
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Non-coding RNAs represent a large group of molecules in a eukaryotic cell (Meyers et al., 2004). Among these molecules, in recent years, microRNAs (miRNAs) have polarized the interest of the scientific community as new categories of gene expression regulators (Carrington and Ambros, 2003; Hake, 2003); they are short RNAs, 21–24 nucleotides in length, which play an important role in post-transcriptional gene regulation in animals and plants, where several examples of miRNA-mediated gene regulation have been described (Bartel, 2004; Kidner and Martienssen, 2005; Zhang et al., 2005).
miRNAs are encoded by MIR genes that resides in distinct genomic regions. A single miRNA can be produced by the processing of one to several longer primary transcripts. Primary transcripts fold into secondary stem–loop structures which are processed in a two-step manner, through RNase III-like enzymes such as Drosha (Lee et al., 2003), Dicer, and Dicer-like enzyme (Bernstein et al., 2001; Kurihara and Watanabe, 2004). The resulting single-stranded miRNA is loaded in a ribonucleoproteic complex, called RISC (Hammond et al., 2000).
In plants, miRNAs regulate the transcript level mostly by promoting, upon base pairing, the degradation of their target mRNA molecules. This function is performed by an enzyme with a ‘slicer activity’ (Liu et al., 2004) that belongs to the RISC complex. However, in plants too there are a few examples of translational control performed by miRNAs (Aukerman and Sakai, 2003; Chen, 2003).
miRNAs in plants were first described very recently (Reinhart et al., 2002; Llave et al., 2002a; Bartel and Bartel, 2003). Searching for these molecules was accomplished by direct cloning together with genetic approaches and bioinformatic analyses, resulting in the identification, mainly in Arabidopsis thaliana and Oriza sativa (rice), of several dozen miRNAs, their corresponding precursors, and MIR genes (Palatnik et al., 2003; Rhoades and Bartel, 2004; Sunkar and Zhu, 2004; Wang et al., 2004; Sunkar et al., 2005).
More recently, other plant species such as Glycine max, Medicago truncatula, Saccharum officinalis, Sorghum bicolor, and Zea mays (Maher et al., 2004; Bedell et al., 2005) were also investigated and new miRNA families have been deposited in the RNA Registry (www.sanger.ac.uk/Software/Rfam/).
Computational analysis based on sequence similarity proved to be a reliable and successful way to identify target genes, since the number of mismatches allowed between the small RNA and its target in plants is low. Identified target genes of plant miRNAs are often transcription factors, involved in organ morphogenesis and plant development (Rhoades et al., 2002; Bonnet et al., 2004; Mallory et al., 2004; Vaucheret et al., 2004; Guo et al., 2005; Lauter et al., 2005).
For example, miR165/miR166 is involved in the determination of the adaxial/abaxial pattern in developing leaves (Kidner and Martienssen, 2004), miR172 governs floral organ development (Aukerman and Sakai, 2003), and miR-JAW regulates the level of TCP-family transcripts regulating leaf development (Palatnik et al., 2003). It is remarkable that homologous miRNAs in different species have similar targets and conserved regulatory roles: miR165/miR166, for instance, regulates the expression pattern of the HD-ZIP III gene family in Arabidopsis and maize (Juarez et al., 2004). In addition, it has recently been shown in Arabidopsis, rice, and Populus trichocarpa that some miRNAs are stress regulated and could be involved in cell responses to abiotic stresses such as salinity, cold, and dehydration (Rhoades and Bartel, 2004; Sunkar and Zhu, 2004; Lu et al., 2005). It is believed, however, that our current knowledge on plant miRNAs represents only a snapshot of the functions that miRNAs might perform in a plant cell: given their role as regulators of transcription factors and, more generally speaking, as transcription level regulators, it is conceivable that new processes in which miRNAs are involved will be discovered. This thesis is also supported by the indication that miRNAs can represent up to 1% of all predicted genes in animals and in plants (Bartel, 2004).
For the reasons mentioned above, structural and functional characterization of MIR genes, also in crop species such as Z. mays L. (maize), becomes instrumental in addressing genetic and molecular mechanisms controlling phenotype determination and phenotypic adaptation to growing conditions, on which yield potential, yield stability, and yield quality depend. In maize, where the phenomenon of heterosis finds its best examples, these phenotypic characteristics are strongly influenced by heterozygosity. With the long-term goal of exploring the possibility of the involvement of miRNAs in the heterotic phenomenon, some putative MIR genes coding for five miRNAs in maize were studied here. To explore a possible function for these miRNAs, the expression pattern of mature miRNAs was analysed by RNA gel blot analysis of RNA purified from different maize tissues in different genotypes: two inbred lines and their heterotic F1 hybrid. To investigate miRNA involvement in plant architecture further, putative targets for all these five miRNAs were also identified, and some of these were experimentally confirmed. Finally, MIR gene sequences were assigned to maize chromosomes utilizing oat–maize addition lines (OMA lines) (Kynast et al., 2001; Okagaki et al., 2001) to analyse the distribution of these gene families over the maize genome.
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Materials and methods |
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Plant material
Plant material was collected from maize inbred lines B73, H99, and their F1 hybrid B73xH99. For each genotype, the three tissues examined were derived from at least 10 independent plants. Seedlings were obtained from plants grown in a growth chamber; in particular, seeds were sown in 4 cm diameter well plateaux on an inert substrate (Agriperlite, BPB Italia SpA, Italy) and incubated in a growth chamber with a 14 h light/10 h dark photoperiod at 26 °C. Plant material was collected from each individual plant when the third adult leaf apex became visible. The term seedling refers to any vegetative tissues above the first adult leaf, which was excluded. Immature ears were harvested from plants cultivated in the open field, selecting those whose silks reached no more than two-thirds of the ear length; silks and ear apexes were discarded. Developing kernels were collected from plants cultivated in the open field, 10 d after controlled pollination (sibbing for inbred lines and cross-pollination with B73 as female parent for the hybrid).
All plant material was immediately frozen in liquid nitrogen and stored at –80 °C until used.
RNA extraction and RNA gel blot
Total RNA was extracted with TRizol® reagent (Invitrogen), as described in the user's manual, and subsequently low molecular weight RNA (LW-RNA) was isolated using the DNA/RNA midi kit (Qiagen) as described by Carrington et al. (2002). A 20 µg aliquot of LW-RNA was loaded on a 10% polyacrylamide denaturing gel, electroblotted on an N+ nylon membrane (Amersham). Filters were hybridized with oligo probes, end-labelled with [
-32P]ATP, using T4 polynucleotide kinase (Roche). Blots were prehybridized for 30 min at 65 °C using a solution containing 6x SSPE, 5x Denhardt's, 0.5% SDS, and salmon sperm DNA (200 µg ml–1). Subsequently, blots were hybridized for 4 h at a temperature 15 °C below the oligonucleotide dissociation temperature (Td) [Td (°C)=4(C+G)+2(A+T)]. Membranes were washed three times with 6x SSPE at room temperature and once at a temperature 10 °C below the Td. After air-drying, the hybridization signal was detected using a phospho-imager (Typhoon 8600TM, Amersham). Computer-generated images were analysed using Image Quant software (Amersham), in order to quantify the optical density for each spot. These measures were normalized against the corresponding optical density for loaded RNA. The values were exported in an Excel file and used to generate histograms, where the highest signal was arbitrarily set as 100. Background and values not significantly different from the background were set as zero.
miRNA target gene prediction and 5' RACE
Target gene identification have been conducted analysing public maize expressed sequence tag (EST) databases (http://www.ncbi.nlm.nih.gov), and The Institute for Genomic Research (http://www.tigr.org) databases using the BLASTN program (Altschul et al., 1990). When necessary, parameters were set to allow the program to deal with short nucleic acid sequences.
In addition, a new software, specifically designed for plant genomes, was utilized to find miRNA targets in maize. This software is available at the miRU web server http://bioinfo3.noble.org/miRU.htm (Zhang, 2005).
To validate target gene prediction, a rapid amplification of 5' cDNA ends (5' RACE) assay was performed, using the First-Choice RLM-RACE kit (Ambion). Total RNA was extracted with TRizol® reagent (Invitrogen) from tissues where abundant miRNA expression was detected, and poly(A)+ mRNA was purified with an mRNA Purification kit (Amersham); poly(A)+ mRNA was directly ligated, without other enzymatic pretreatment, to the 45 nucleotide adaptor from the kit. Subsequent steps were according to the manufacturer's instructions. For each annotated tentative contig (TC), two reverse primers were designed, with the respective forward primer used to check the presence of the specific cDNA molecules, and to assess that the working conditions were correct (supplementary Table S1 with gene-specific primers is available at JXB online); nested PCRs were performed with reverse primers and adaptor-specific primers. PCR products were subsequently sequenced.
Chromosome mapping
Lyophilized green tissues of OMA lines were kindly provided by Professor Ronald Phillips of the Department of Agronomy and Plant Genetics, University of Minnesota. DNA was extracted with the CTAB method and a PCR (35 cycles, 57 °C as annealing temperature, 50 ng of genomic DNA from OMA lines per reaction tube) was performed with pairs of primers specifically designed for this protocol (see supplementary Table S2 at JXB online). PCR products, 
200 nucleotides long, were run on a 1% agarose gel and stained with ethidium bromide.
Phylogenetic analysis
MIR gene sequences from maize, rice, and sorghum were collected, and the reciprocal best BLAST hit (RBH) method was performed running a BLASTN algorithm and selecting the first three best matches, always setting the lowest acceptable E-value limit to 1E-9, 1E-7, and 1E-06 when blasting on sorghum, rice, and maize databases, respectively. The data sources for the genomic sequences used in this analysis are the following: TIGR database (http://maize.tigr.org/release4.0/assembly.shtml) for maize, the NCBI database (www.ncbi.nlm.nih.gov) for rice, and the GSS division of GenBank (www.ncbi.nlm.nih.gov) for sorghum.
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Results |
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Research effort was focused on the following five miRNA families: miR156, miR160, miR166, miR167, and miR169, for which, upon searching the Rfam database (www.sanger.ac.uk/Software/Rfam), 30 different pre-miRNAs were identified.
Similarity between maize, rice, and sorghum MIR genes
Data on these five miRNA families in maize were mostly derived by bioinformatics analyses, which hypothesized pre-miRNA molecules as putative precursors from an in silico prediction.
Since phylogenetic conservation of the 21 nucleotide maize miRNA sequence and its predicted precursor secondary structure is considered an important validation for MIR genes (Ambros et al., 2003), the relationship between maize, rice, and sorghum MIR genes was addressed.
The very limited sequence conservation among different precursors of the same miRNA both within and among species did not allow a direct investigation on the phylogenetic relationship existing between MIR genes. Conventional PHYLIP analysis was in fact unable to estimate phylogenetic distances existing between sequences in these three evolutionarily related species. Using the evidence that loop sequence is less conserved than stem sequence, phylogenetic analysis was restricted to the sequence of the region comprising the miRNA and the anti-miRNA (60–80 nucleotides). Again, even if a higher degree of similarity was found in these short sequences, phylogenetic analysis was still meaningless. However, the application of the RBH method provided a useful hint regarding putative homology among a number of maize, rice, and sorghum MIR genes. According to this method, two sequences A and B are reciprocal best hits if sequence B is the best hit from the organism of sequence B when sequence A is the query sequence, and vice versa. Several databases that collect orthologous sequences, such as cluster of orthologous groups (COG), use this method both in prokaryotes and in eukaryotes as the basis for several algorithms (Li et al., 2003). The method was applied to find RBHs between maize and sorghum and between maize and rice, in order to define groups of orthologous genes. The results of this analysis are summarized in Tables 1 and 2, where it can be observed that many maize precursors have a putative orthologue in both rice and sorghum. Twenty-three maize genes were found to have a rice orthologue, and for 14 of them there is also a sorghum orthologue. For zma-MIR166f and zma-MIR166h, only the sorghum orthologue was found. In addition, only one putative common orthologue in sbi-MIR166b was found for these two genes, and three other sorghum genes have a double orthologue in maize (sbi-MIR169a, sbi-MIR166a, and sbi-MIR167a). The results are only partial, due to the largely incomplete sorghum database; however, interesting clues can be derived from the maize–rice comparison. For example, two maize genes—zma-MIR156b and zma-MIR156c—positioned in tandem configuration, identified two rice genes also in tandem configuration 250 bp apart. In addition, in six other cases, two maize precursors correspond to a single rice precursor, according to the tetraploid origin of maize (Gaut and Doebley, 1997; Swigonova et al., 2004).
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Chromosome mapping
As a first step for mapping MIR genes in maize, OMA lines were used to place MIR sequences on individual maize chromosomes (Okagaki et al., 2001). Specific primers were designed for each miRNA precursor and standard PCR was performed on the genomic DNA of OMA lines (see Fig. 1, for one example).
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This approach allowed 23 MIR genes to be assigned to a chromosome, as summarized in Table 3, whereas seven sequences were not amplified on OMA lines.
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Tissue and genotypic expression profile
The expression of the five miRNAs was tested by RNA gel blot analysis on samples from seedlings, developing ear, and developing kernels (10 d after pollination) derived from two inbred lines and their corresponding F1 hybrid, as shown in Fig. 2 (upper part of each panel). The intensity of each spot has been subsequently measured and normalized, as described in the Materials and methods, to create the histograms shown in the lower part of the panels in Fig. 2.
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All five miRNAs are present in the RNA fractions, with a size of
20 nucleotides, showing tissue and genotype specificity of the expression pattern. With the exception of miR156, which is expressed mainly in seedlings, the other miRNAs are present in two or all the analysed tissues; for example, miR160 and miR169 are mainly expressed in seedlings and developing ears, and miR167 is present in developing kernels and seedlings. Genotypic specificity is also evident, indicating that different genotypes express the same miRNA in the same tissues, but at different levels. miR166, for instance, shows higher expression in kernels from inbred line H99 than B73 and the B73xH99 F1 hybrid. Furthermore, miR167 is expressed at similar levels in seedlings and kernels in both inbred lines, but shows a different pattern in the F1 hybrid; similarly, miR169 has a similar expression pattern in the B73 inbred line and F1 hybrid, which is different in the H99 inbred line.
Target gene identification
Given the high homology between miRNAs and their targets in plants, the maize EST database was searched for homology to the five miRNAs sequences using a BLASTN algorithm.
In 41 ESTs, regions complementary to the five miRNA sequences were identified, in which fewer than three mismatches were present. This search was refined comparing the 41 ESTs with the TIGR database (http://www.tigr.org/tdb/tgi/plant.shtml) in which most of the maize ESTs are assembled into TCs. Twelve TCs and a few singletons have been found; for some of these TCs, a function has been annotated. The same procedure was applied to the sorghum data set. Six TCs resulted as putative targets for the five miRNAs analysed.
Very recently, the miRU software was developed (Zhang, 2005) allowing an automated on-line search of miRNA targets to be performed. This was searched for both maize and sorghum miRNA targets, using rice as the reference organism for gene function conservation. Targets with a function not conserved between the two plant species are defined as possible false positives. A large list of putative target genes was thus produced, containing all the putative target genes previously identified by means of a manual search. In Table 4, conclusive results are shown. It appears that the putative functions of target TCs are the same as those reported for Arabidopsis and rice (Bartel and Bartel, 2003).
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To verify the nature of the predicted miRNA targets, a modified 5' RACE experiment was set up, as described in the Materials and methods. This is one of the most common and widely used methods in the literature (Llave et al., 2002b; Kasschau et al., 2003; Rhoades and Bartel, 2004; Lauter et al., 2005; Lu et al., 2005) to support bioinformatics data. The 5' RACE analysis is based on the evidence that miRNA-cleaved transcripts have a phosphorylated 5' end, which can be directly ligated to the RNA adaptor, and that maps to the 10th nucleotide of the coupled miRNA. Given the clear tissue-specific pattern of expression of miR156 (Fig. 2), these analyses were performed on a few of its putative targets, using the RNA extracted from B73 seedlings, where miR156 is more abundantly expressed. TC294022 and TC280157 were confirmed as real miR156 targets, since the 5' end of the RACE product maps to the 9th and 10th nucleotide, respectively, of the coupled miRNA (Fig. 3A, B). These TCs are similar to two rice proteins coding for squamosa promoter-binding proteins. Similarly, TC306179 was confirmed as a real target for miR169 in B73 seedlings. In fact, the 5' end of the RACE product maps to the 10th nucleotide of the coupled miRNA (Fig. 3C). TC306179 codes for a protein highly homologous to a CCAAT-binding transcription factor.
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Discussion |
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Evolutionary conservation of MIR genes between maize and rice
miRNAs have recently emerged as important players in plant development and in participating in the regulation of plant response to stresses (Carrington and Ambros, 2003; Hake, 2003; Sunkar and Zhu, 2004; Kidner and Martienssen, 2005).
Most current knowledge on plant miRNAs and their corresponding MIR genes derives directly from studies on the model plant Arabidopsis and from a bioinformatics scan on the rice genome. Therefore, for a better understanding of phenotypic features crucial for the economic success of crop species, it is of relevance to identify and determine the function of miRNAs in other crops. In this respect, maize and sorghum are of double interest, since their evolutionary closeness to rice could prove important for addressing the function of miRNAs and their evolution in grasses, not yet investigated by other groups.
It was challenging to establish a degree of correlation between miRNA ‘families’ among these three grasses, because only the rice genome has been completely sequenced and it is known that grasses exhibit a high rate of duplication events (Gaut, 2001). In addition, miRNAs do not show clear patterns of sequence conservation, making it very difficult to define the evolutionary history of these sequences.
It is possible that some MIR genes might have a common ancestor, but many might have evolved recently by duplication and/or translocation events or, as recently hypothesized, might have been generated ex novo from target duplication (Allen et al., 2004).
Despite all the limitations of the analysis conducted here, the finding that at least some precursors of the same miRNA are very similar among species is of relevance. In the literature, there are no reports on the analysis of evolutionary relationship among MIR genes in rice, maize, and sorghum. For the features of the sequences analysed, the RBH proved the most reliable and useful method. E-values together with bit scores reported for the BLAST analysis corroborate the conclusion. In three cases (Table 1), blasting on maize database E-values was higher, even if the alignment and bit score are acceptable. This depends on the presence in those sequences of regions rich in short repeats.
The data summarized in Tables 1 and 2 show that there are groups of homologous genes among the five different miRNAs ‘families’. A number of maize MIR genes found an orthologue in the rice genome, and a smaller number found an orthologue in the sorghum genome, despite the closer relationship between sorghum and maize. This is obviously due to the largely incomplete sorghum data set.
As expected from the allotetraploid origin of maize, two different maize MIR genes often correspond to a unique rice or sorghum gene. There is one particular example of a duplication event which occurred prior to speciation: MIR156b and MIR156c in tandem configuration are present both in maize and in rice at a comparable distance (100 and 250 nucleotides). This observation is in agreement with the work of Guddeti et al. (2005) assessing that many rice MIR genes are organized in clusters.
Another interesting result is that relating to zma-MIR166f and zma-MIR166h, which seem to have an orthologues only in sorghum but not in rice. This group might have been generated after divergence between rice and the progenitor of both maize and sorghum. A more specific algorithm should be developed and further analysis should be performed to define the nature of these events precisely.
Expression profile of maize miRNAs
RNA blot analysis clearly demonstrates that 20–21 nucleotide long RNA molecules, possibly derived from one or more of the putative pre-miRNAs identified, are expressed in various maize tissues. These analyses indicate that there are differences in the amount of the various miRNAs in the different tissues tested, also depending on the genotype utilized. For instance, miR156 gave a positive signal only in seedlings, whereas the other four miRNAs are expressed, although at different levels, in two or three tissues. As for genotypic differences, it appears that the F1 hybrid shows a signal intensity similar to that of the parental inbred line with the strongest signal (see miR167 in 10 kernels 10 d after pollination and miR169 in developing ears), although this is not the case for miR166 expression in kernels 10 d after pollination.
miRNA target prediction
To assess and define a putative function of miRNA molecules in the grasses, and specifically in maize, a further step is represented by target identification.
The most efficient tool available is certainly the bioinformatics approach that in plants is facilitated by the high degree of homology between miRNA and target sequences (Rhoades et al., 2002; Bartel and Bartel, 2003). In fact this was also the case in our analysis where a long list of putative targets was obtained. Our analysis reveals, as expected, that many of the predicted targets, both in maize and in sorghum, have a conserved function with other plant miRNA targets. Nonetheless, some TCs with unknown function or with a function distinct from Arabidopsis or rice genes are also present, and they could be targets involved in processes that are species specific, or tissues specific (Lu et al., 2005).
Three target genes were experimentally validated, two for miR156 (TC294022 and TC280157) and one (TC306179) for miR169. The validation was obtained by performing the modified 5' RACE protocol on mRNA extracted from seedlings, where it was previously demonstrated that both miR156 and miR169 are abundant. miR156 targets code for proteins similar to two rice proteins (Q6H508 and Q6Z461) homologous to squamosa promoter-binding protein, which is a plant-specific family of transcript factors involved in early flower development. This evidence is in agreement with the expression pattern of miR156 that is present at a very low level, if any, in developing ears (Fig. 2). Also miR169, which is expressed mainly in seedlings and in immature ears, has a validated target. TC306179 codes for a protein highly homologous to a rice protein (Q851D5) that is the B subunit of the CCAAT. This protein belong to a family common to many eukaryotes (human, mammals, yeast, nematodes, fungi, and green plants). In Arabidopsis, many of the CCAAT-binding transcription factors such as leafy cotyledon 1 and 2 (LEC1 and LEC2) are involved in embryogenesis induction and embryo development. It is interesting to notice that in maize, miR169 has a lower expression in kernels at 10 d after pollination (Fig. 2) where CCAAT-binding factor should be actively present. Obviously the present data have to be taken solely as suggestions and intriguing clues about a possible role for these miRNA families in maize. Further analysis should be performed to define and investigate these aspects.
Four additional TCs, considered as putative targets of miR156 and miR169, were also tested. Since no results were obtained, despite always using the same tissue as poly(A)+ RNA source, no conclusions can be drawn regarding target validation.
Given the present limited knowledge on MIR gene function in maize, it is premature to speculate about a possible role for miRNAs in the phenomenon of heterosis. However, reports in the literature suggest that quantity, timing, and quality of gene expression could account for phenotypic differences between inbred lines and their corresponding F1 hybrid (Romagnoli et al., 1990; Leonardi et al., 1991; Osborn et al., 2003; Song and Messing, 2003). Since a single miRNA could be produced by post-transcriptional processing of different primary transcripts, it would be of interest to verify if different primary transcripts are used in different tissues and/or in different growing conditions. In addition, cis regulation of allele-specific expression seems to be quite a common phenomenon in maize hybrids (Guo et al., 2004). It is not unreasonable to hypothesize that a similar mechanism is also acting on MIR genes in heterozygous plants.
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Acknowledgements |
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We thank Dr D Horner, for useful discussion on the analysis of homologies between sequences, Dr I Salomoni for her help on OMA lines, and Dr G Pea for helpful discussion and advice. A special thanks to Professor R Phillips of the University of Minnesota for providing us with OMA lines. This work was supported by MIUR Cofin Program 2003 ‘Molecular and quantitative analysis of heterosis in maize’.
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Abbreviations |
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AZM, assembled Zea mays; EST, expressed sequence tag; LW-RNA, low molecular weight RNA; miRNA, microRNA; OMA lines, oat–maize addition lines; pre-miRNA, microRNA precursor; RACE, rapid amplification of cDNA ends; RBH, reciprocal best BLAST hit; siRNA, small interfering RNA; TC, tentative contig; Td, dissociation temperature.
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