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. 2014 Feb 10;15:117.
doi: 10.1186/1471-2164-15-117.

Systematic Characterization of Small RNAome During Zebrafish Early Developmental Stages

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Free PMC article

Systematic Characterization of Small RNAome During Zebrafish Early Developmental Stages

Yuangen Yao et al. BMC Genomics. .
Free PMC article

Abstract

Background: During early vertebrate development, various small non-coding RNAs (sRNAs) such as MicroRNAs (miRNAs) and Piwi-interacting RNAs (piRNAs) are dynamically expressed for orchestrating the maternal-to-zygotic transition (MZT). Systematic analysis of expression profiles of zebrafish small RNAome will be greatly helpful for understanding the sRNA regulation during embryonic development.

Results: We first determined the expression profiles of sRNAs during eight distinct stages of early zebrafish development by sRNA-seq technology. Integrative analyses with a new computational platform of CSZ (characterization of small RNAome for zebrafish) demonstrated an sRNA class transition from piRNAs to miRNAs as development proceeds. We observed that both the abundance and diversity of miRNAs are gradually increased, while the abundance is enhanced more dramatically than the diversity during development. However, although both the abundance and diversity of piRNAs are gradually decreased, the diversity was firstly increased then rapidly decreased. To evaluate the computational accuracy, the expression levels of four known miRNAs were experimentally validated. We also predicted 25 potentially novel miRNAs, whereas two candidates were verified by Northern blots.

Conclusions: Taken together, our analyses revealed the piRNA to miRNA transition as a conserved mechanism in zebrafish, although two different types of sRNAs exhibit distinct expression dynamics in abundance and diversity, respectively. Our study not only generated a better understanding for sRNA regulations in early zebrafish development, but also provided a useful platform for analyzing sRNA-seq data. The CSZ was implemented in Perl and freely downloadable at: http://csz.biocuckoo.org.

Figures

Figure 1
Figure 1
Comparison of ZmirP with triplet-SVM [[21]], MiPred [[22]] and HeteroMirPred [[23]]. To evaluate the performance ZmirP, the 10-fold cross-validations were performed. For the comparison, we directly submitted the training data set to other tools for calculating the performance values. (A) ZmirP with zebrafish-specific; (B) ZmirP with human-specific.
Figure 2
Figure 2
The computational pipeline in CSZ. First, total reads were mapped to reference genome, while mapped reads were successively mapped to miRBase, Rfam, repeat annotations, RefSeq mRNAs, and piRNABank to identify miRNAs, ncRNAs (including rRNA, tRNA, and snRNA/snoRNA), repeats, mRNAs and piRNAs. Based on the annotation information for genomic repeats, the ncRNAs were recalled and repeat-associated piRNAs were characterized from remaining repetitive sequences. For the unclassified reads, MIREAP and miRDeep2 were used for the prediction of novel miRNAs, which were further validated by ZmirP to reduce potentially false positive hits.
Figure 3
Figure 3
The summary of sRNA-seq data. (A) Number of different types of reads in eight libraries. High quality reads represent reads without N characters, or without quality scores lower than 10 for > 4 bases or 13 for > 6 bases. Clean reads represent reads without adaptors and contaminants. Reads with size ranging from 18 to 35 nt and observed more than three times was considered as reliable reads. Only one nucleotide mismatch was allowed for mapping reads to the reference genome; (B) The length distribution of mappable reads in eight libraries; (C) The proportion of total mappable reads with different length during development; (D) The proportion of unique reads with different length.
Figure 4
Figure 4
The analyses of known miRNAs in early zebrafish development. (A) The nucleotide preferences of 218 identified miRNAs; (B) The identified number of miRNAs under different thresholds for mapped reads (≥ 3, ≥ 10, ≥ 100, and ≥ 1000). The proportion of miRNAs in total sRNA-seq data was also shown; (C) The proportion of total mappable reads for different types of sRNAs; (D) The distribution of unique mapped reads for different types of sRNAs.
Figure 5
Figure 5
The clustering analysis of miRNA families. (A) The RPM-normalized expression profiles of known and novel miRNA families in eight developmental stages; (B) The RPM-normalized expression profiles of known miRNA families were clustered into three distinct groups with the k-means clustering algorithm in Cluster 3.0 [35]; (C) Top 5 mostly expressed miRNA families were shown for each stage.
Figure 6
Figure 6
Experimental validation of expression profiles for four know miRNAs with qRT-PCR. Each experimental validation was repeated three times, whereas the error bars were added for qRT-PCR experiments. (A) dre-miR-456; (B) dre-miR-22a; (C) dre-miR-206 and (D) dre-miR-192.
Figure 7
Figure 7
Confirmation of potentially novel miRNAs through non-isotopic northern blots. (A) The analyses of secondary structures for m0027-5p, chr6_7844-5p and m0026-5p revealed that the three sRNAs had canonical single stem-loop structures. The potentially mature miRNAs were marked in blue. (B) The experiments identified that m0027-5p and chr6_7844-5p are expressed in zebrafish 16-cell stage.
Figure 8
Figure 8
Computational analysis of piRNAs. (A) The normalized number of piRNA clusters in either plus or minus strand was calculated for each stage; (B) The length distribution of piRNAs from different sources; (C) The sequence logos of piRNAs from different sources; (D) The distribution of piRNA clusters originated from repeat-associated or non-repeat-associated piRNAs.
Figure 9
Figure 9
The length distribution of different types of sRNAs at eight stages. (A) 1-cell, (B) 16-cell, (C) 512-cell, (D) oblong, (E) 5.3 hpf, (F) 6-somite, (G) 24 hpf and (H) 48 hpf stages.

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