tolerant. High-throughput sequencing technology [28] was used to identify known and novel miRNAs in the tolerant line at the early stage of waterlogging stress treatment. Putative targets of these miRNAs were predicted by computational methods. We devel- oped expression signatures for a set of miRNAs and their putative targets under waterlogging treatment. Comparisons of these signatures among the three inbred lines provided insight into the role of these factors in waterlogging survival. These observations, along with computational identification of stress and development related cis-regulatory elements in the promoters of the differen- tially expressed miRNA genes, enabled the construction of a miRNA-mediated regulatory network model. This model will serve as a basis for future research to determine mechanisms of anaerobic stress tolerance in maize. Results Experimental Design The flow chart of the experimental design is shown in Figure 1. Initially phenotype screening of three inbred lines (Hz32, B73, Mo17) based on the sensitivity to waterlogging tolerance was done. Small RNA library was constructed for the most tolerant line Hz32 under 4 h waterlogging treatment. From the small RNA library, 56 known mature miRNA signatures (representing 142 miRNA loci) and 10 novel mature miRNA signatures (represent- ing 20 miRNA loci) were identified. Out of 56, 22 known mature miRNA signatures (representing 64 miRNA loci) and 2 novel miRNAs (representing 2 miRNA loci) were selected. These 24 mature miRNA signatures, and their computational predicted targets were tested by qRT-PCR assay in three inbred lines that showed different tolerance to waterlogging. Computational approaches were used to predict the stress and development related cis-regulatory elements on the promoters of these differentially expressed miRNAs in the B73 reference maize genome and a miRNA-mediated gene regulatory network was constructed. The results are organized below based on the flow chart of the experimental design. Characterization of Tolerance in Three Inbred Lines Following the screening methods for maize waterlogging tolerance described by Liu et al [26] for each of the three inbred lines Hz32, B73 and Mo17, our results showed that the Hz32 is the most tolerant line while Mo17 is the most sensitive and B73 is in the middle range (Figure 2, Table S1). Comparison of the waterlogging tolerance coefficient (WTC) data and the phenotypes with previous studies in Hz32 and Mo17 [23,24,25,26] confirmed the tolerance of these two inbred lines. Known miRNA Profiling of Tolerant Inbred Line As Hz32 is a waterlogging tolerant line, we selected this inbred line for small RNA profiling to get the expression of waterlogging tolerance related miRNAs in Hz32. From our previous unpublished work on 55 K oligo microarray study of two inbred lines, Hz32 and Mo17 at five different time points of waterlogging treatment (1 h, 2 h, 4 h, 24 h, and 48 h), the gene expression changes reached the peak at 4 h treatment for early responses. Therefore, we picked 4 h as the reference time point for sequencing. To investigate the role of miRNAs under hypoxia stress in Hz32, size fractionated RNAs (18–28 nucleotides) were isolated from Hz32 root tissues subjected to 4 h (signal transduction stage) of waterlogging treatment and control sample (no waterlogging treatment) and sequenced by Illumina high- throughput sequencing [28]. The total number of sequence reads was 9,251,451 for the treatment sample and 9,541,782 for the Characterization of Hypoxia Induced Maize miRNAs control sample (Table S2). After removing adapter-only reads and sequences of sizes ,18 nt or .26 nt, there were 4,111,022 and 3,802,934 unique sequences (without any duplicates), respectively. The RNA sequences were mainly within a range of 20–24 nt (79.9% and 75.1% for treatment and control samples, respective- ly), which is the size of most known small RNAs (Figure S1). Overall, the size distribution was similar to previous studies done in angiosperms [29,30,31]. Sequence reads that perfectly matched known maize miRNAs [11] were considered. A total of 56 unique mature miRNA signatures, which are specified at 142 miRNA gene loci, matched with known miRNAs from miRBase release 18 [32] (Table S3). Out of 172 miRNA loci described in miRBase release 18 [32], all but 30 were detected in these small RNA libraries. High-throughput sequencing can both identify miRNAs and measure miRNA expression [33]. The abundance of miRNAs was found to vary greatly within each sample as well as within miRNA families (Table S3). To select putative differentially expressed miRNA genes for detailed study, we set a threshold of 1.5 fold difference in reads per million (RPM) between treated and control and applied a statistical filter (P value ,0.01; .50 RPM). Out of 56 unique mature miRNA signatures, 22 known mature miRNA signatures (representing 64 miRNA loci) passed the filtering criteria and were selected for validation using SLRT-PCR (Stem- Loop Real Time PCR) for Hz32 at 4 h waterlogging treatment. Detection of Novel Waterlogging Induced miRNAs and their Putative Targets The total numbers of 7,337,393 unique sequences from the treatment and control libraries (mentioned above) were also used to identify novel miRNAs specific to waterlogging treatment. After applying various filtering criteria such as removing repeats and filtering out the known miRNAs and other noncoding RNAs (ncRNAs), 106 unique sequences were identified as potentially novel miRNA candidates. Out of 106 putative novel miRNA sequences, only 10 miRNA sequences passed the secondary structure evaluation pipeline (Figure 3). These 10 miRNAs, representing 20 miRNA loci, were selected as waterlogging induced novel miRNAs (Table S4) and the putative targets of these miRNA families were predicted using computational target prediction pipeline (Table S5) [11]. To select the differentially expressed novel miRNAs, same filtering parameters were used as mentioned above for known miRNAs. Only 2 novel miRNAs, miRn6 and miRn7, passed the filtering criteria and these miRNAs were selected for validation using SLRT-PCR. Eight putative targets of these 2 novel miRNAs were also selected to preform the qRT-PCR assay (Figure S5). Quantitative Validation of Mature miRNA Signatures The Quantitative SLRT-PCR protocol [34,35,36] was used to measure mature miRNA expression for three biological replicates at 4 h treatment in Hz32. SLRT-PCR was conducted for the 22 mature miRNA sequences and 2 novel mature miRNA sequences. Four low abundance (,50RPM) mature miRNAs (156j, 169l, 171c, 171l/m) were also selected as negative control. Only miRNAs with single amplicon were considered for results. The SLRT-PCR results confirmed most of the miRNAs showed up- regulation under 4 h treatment while only miR172b/c/d, miR393a/c, miRn6, and miRn7 were down regulated in both assays (Figure 4). The low abundance four mature miRNAs were hardly detected by SLRT-PCR, showed either low expression level (,1.2 fold changes; 156j, 169l, 171l/m) or have the opposite expression to the deep sequencing results (171c, 171l/m) (Figure 4). Therefore, these 24 mature miRNA signatures (representing 66 PLoS ONE | www.plosone.org 2 June 2012 | Volume 7 | Issue 6 | e39786