MicroRNA expression profiling of the fifth-instar posterior silk gland of Bombyx mori
- Jisheng Li†1, 2,
- Yimei Cai†3,
- Lupeng Ye1,
- Shaohua Wang1,
- Jiaqian Che1,
- Zhengying You1,
- Jun Yu3Email author and
- Boxiong Zhong1Email author
© Li et al.; licensee BioMed Central Ltd. 2014
Received: 19 December 2013
Accepted: 7 May 2014
Published: 29 May 2014
The growth and development of the posterior silk gland and the biosynthesis of the silk core protein at the fifth larval instar stage of Bombyx mori are of paramount importance for silk production.
Here, aided by next-generation sequencing and microarry assay, we profile 1,229 microRNAs (miRNAs), including 728 novel miRNAs and 110 miRNA/miRNA* duplexes, of the posterior silk gland at the fifth larval instar. Target gene prediction yields 14,222 unique target genes from 1,195 miRNAs. Functional categorization classifies the targets into complex pathways that include both cellular and metabolic processes, especially protein synthesis and processing.
The enrichment of target genes in the ribosome-related pathway indicates that miRNAs may directly regulate translation. Our findings pave a way for further functional elucidation of these miRNAs and their targets in silk production.
KeywordsMicroRNA Silkworm Posterior silk gland Target gene
The silkworm Bombyx mori is the most economically important holometabolous lepidopteran and has recently became an experimental model for molecular entomology [1, 2]. Its silk gland is the most efficient protein synthesis machine among all organisms, which makes silkworm a desirable model for studying its mechanism. As the largest and most important part of the silk gland, the posterior compartment is most attractive since it synthesizes the silk core protein that determines the quality of silk cocoons. A recent proteomic study, using two-dimensional gel electrophoresis (2-DE) coupled with matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry (MALDI-TOF MS), has identified 93 major proteins in the silk gland, of which there are several phosphorylated fibroin L-chain and P25 isoforms . The posterior silk gland of the fifth instar has been further surveyed systematically for the understanding of molecular basis and regulatory mechanism of the posterior silk gland development and fibroin synthesis . A recent transcriptomic survey has revealed a total of 10,393 active genes differentially expressed in multiple silkworm tissues on the third day of the fifth-instar larva, of which 412 and 109 are up-regulated in the anterior-middle and the posterior silk glands, respectively . These findings all provide basic data for studying the growth of the posterior silk gland and fibroin synthesis. However, microRNAs (miRNAs)-based study has not been done for the silk gland and its developing and functionally important compartments albeit justifiable for necessity [6–8].
As a large family of endogenous small non-coding RNAs, miRNAs are common regulatory RNAs of eukaryotic organisms and play important roles in a wide range of biological processes under physiological and pathological conditions [9–14]. Despite lack of empirical data, computational approaches have made initial contributions to miRNA study in B. mori[15–18], followed by next-generation sequencing efforts that profiled miRNAs for different developmental stages and tissues [19–22]. Nevertheless, specifically focused study is still necessary since the expression of miRNAs is largely temporal-spatial [22–25].
Here, we report our miRNA profiling of the fifth-instar posterior silk gland, using next-generation sequencing and microarray technologies. We show that 728 out of 1,229 miRNAs are novel and 430 of the total are identified in the third day of the posterior silk gland development. Our GO (Gene Ontology)-based pathway assignment provides the first comprehensive categorization of B. mori miRNAs in the posterior silk gland.
Results and discussion
Next-generation sequencing and data processing
Data summary of sequenced small RNAs based on DSAP
Cleaned sequence tags
Reads in cleaned sequence tags
% reliable reads #
Unique Sequence Clusters (USC)
Matched ncRNA in Rfam
USC matched to Rfam
Reads matched to Rfam
% reads matched to Rfam
Matched miRNAs in miRBase
USC matched to miRBase
Reads matched to miRBase
% reads matched to miRNAs
USC Unmatched reads
Known and novel miRNAs based on sequence data
The read count for different miRNA is rather variable (Additional file 4: Table S2). For instance, the number of reads for bmo-miR-263a is extremely high as compared to other miRNAs in all libraries; it may play a very important role in the posterior gland development and the result is in agreement with a previous report . We also found 50 pairs of miRNA/miRNA* duplexes, in addition to 24 miRNA*s without the corresponding miRNAs. Although most miRNAs are more abundant than their corresponding miRNA*s, there are exceptional cases, where bmo-miR-10, bmo-miR-276, bmo-miR-305, bmo-miR-33, and bmo-miR-34 are less abundant than their miRNA* counterparts. Similar findings have also been reported in other deep sequencing experiments and are suspected to be a result of incorrect annotations in miRBase [34–36].
Having filtered the known non-coding RNAs, we predicted novel candidate miRNAs using the mireap package  and classified 1,427 candidate miRNAs (Additional file 7: Table S3). Given the fact that there are many random inverted repeats (termed pseudo-hairpins) in eukaryotic genomes and they can also fold into dysfunctional hairpins and undistinguished sequences, we took extra cautions to classify non-conserved miRNAs. We used mirident classifier to identify the miRNA candidates, which has been reported to achieves 99.2% specificity and 97.6% sensitivity on a human test data set . We also evaluated two other SVM-based prediction programs, Triplet-SVM and PmirP, together with mirident and using miRbase datasets that include data from 24 insect species [38–40]. Mirident classifier gave rise to better results for insect pre-miRNA identification in our own hands . Using mirident classifier, we obtained 613 novel miRNAs, corresponding to 590 unique sequences after filtering pseudo-pre-miRNAs (Additional file 7: Table S3).
Microarray-based miRNA profiling
Since the third day of fifth-instar larva (V3) is a key time point for silk synthesis and rapid cell growth, we evaluated its miRNA expression profile using 3,077 custom-designed probes (Additional file 1: Figure S1 and Additional file 8: Table S4) that are classified into four group: (1) 1,006 known miRNAs from miRBase, which consist of miRNAs from several species, including silkworm (559 probes) and 10 flies (447 probes); (2) 1,427 predicted novel miRNAs; (3) 425 probes based on data from four publications [17, 19, 22, 42]; and (4) 219 control sequences. To ensure reproducibility, we double-gridded 841 sequences with read coverage of >5 from 1,427 custom-designed probes for each chip.
We used 16 chips (probe sets) for the study and normalized the data in log2 transformation. Both technical (all R2 > 0.97) and biological repeats (all R2 > 0.8) showed consistent results (Additional file 9: Figure S5 and Additional file 10: Table S5). As miRNA-based microarray experiments in general are reproducible [43–45], we readily identified 430 mature miRNAs. Among them, 239 are previously known and the remaining 191 include 19 conserved in Drosophila and 172 novel ones (Additional file 11: Table S6). Of the 239 known miRNAs, 187 are from a thorough collection from literature search and the remaining 52 are directly from the miRBase. These miRNAs showed different expression patterns among the samples and did not exhibit any obvious correlation to information sources. For instance, bmo-bantam, bmo-miR-12, bmo-miR-263a, bmo-miR-263b, bmo-miR-278, and bmo-miR-8 are both literature-based and database collections but the miRBase-collected miRNAs showed stronger signals. The contrary results were found among bmo-let-7, bmo-miR-1, bmo-miR-100, bmo-miR-124, bmo-miR-137, bmo-miR-14, bmo-miR-252, bmo-miR-275, bmo-miR-305, bmo-miR-307, bmo-miR-34, and bmo-miR-279c, where the literature-based collections showed higher expression levels (Additional file 12: Table S7). We inferred that this non-uniformity might be a result of different technical platforms. One discussion point on this study is the validation rate: only 172 novel miRNAs (~14%) from the sequencing data are confirmed in the microarray experiment. The reasons for such low confirmation are multifold. First, 430 mature miRNAs are detected on the third day of fifth instar but not on the entire stage, where samples are collected and pooled from the fourth instar molting to the fifth instar day 8 before spinning. The false negative results for some of the miRNAs are largely due to the dilution of the time-sensitive specific miRNAs over pooling [46, 47]. Second, the marginal level of miRNA expression is pushing the detection limit so that some of the signals may not be consistently detected even when the same experiments are repeated. Third, sampling bias may be inherent from the sequencing approach, where sampling bias is obvious for low copy transcripts [27, 43].
There are 257 miRNA genes whose expression patterns have been reported to correspond to 324 loci in the B. mori genome . After the removal of redundant sequences, we found that the two datasets shared 197 miRNAs (16 sequences showing discrepancy in their sequences were not accounted for). Among these miRNAs, 75 genes showed posterior gland expression in the current study but have not been detected in the previous study. Conversely, from 173 miRNAs identified previously in two public datasets for posterior-silk-gland expression, 37 were negative in our study. In addition, seven (bmo-miR-2846, bmo-miR-2850a, bmo-miR-2853, bmo-miR-2854, bmo-miR-2858*, bmo-miR-2858, and bmo-miR-2859) out of 21 posterior-gland specific miRNAs defined previously were not detected in our microarray experiment (Additional file 13: Table S8). These results suggest that our experiment covered most of the B mori miRNAs but inconsistency does exist, attributable to the difference between technical platforms.
Target gene prediction and pathway analysis
Combining results from both deep sequencing and microarray, we identified 1,229 miRNAs expressed in the posterior silk gland in the period of the fourth-instar molting to the fifth-instar (day 8 before) spinning, and among which 728 are novel, named as bmo-miR-Pxxx-xp series (from No.1 to No.728 at Additional file 14: Table S9), and 110 are miRNA/miRNA* duplexes (Additional file 15: Table S10). We also profiled 430 miRNAs for the third day of the fifth-instar larva (Additional file 11: Table S6). We subsequently predicted potential targets using miRanda v3.3a  and the effort yielded 14,222 targets in the entire stage, corresponding to 1,195 miRNAs. The rest 34 miRNAs did not yield target genes due to low scores (Additional file 16: Table S11). We associated 12,675 and 12,948 target genes to 423 known and 696 novel miRNAs in the third day of the fifth instar, respectively (Additional file 17: Table S12 and Additional file 18: Table S13).
Other involved pathways are also informative. First, 99 target genes are related to RNA transport pathway, and 47 target genes are mapped to RNA degradation pathway (Additional file 19: Table S14 and Additional file 22: Figure S8). Nearly 90 and 50 target genes are involved in purine and pyrimidine metabolisms, respectively (Additional file 19: Table S14, Additional file 23: Figure S9 and Additional file 24: Figure S10 ). The results indicate active regulations of transcription and nucleotide metabolism. Second, 79 target genes are found to be involved in oxidative phosphorylation pathway (Additional file 19: Table S14 and Additional file 25: Figure S11). The ATP production pathway may coordinate with nucleotide metabolic pathways for energy generation. Third, 66 and 33 target genes are related to ubiquitin mediated proteolysis and proteasome pathways, respectively (Additional file 19: Table S14 and Additional file 26: Figure S12). Since ubiquitin proteolytic system plays an important role in a broad array of basic cellular processes including regulation of cell cycle, modulation of the immune and inflammatory responses, control of signal transduction pathways, development and differentiation, and these complex processes are controlled by specific degradation of a single or a subset of proteins , the discovery of such a significant involvement is of importance. Fourth, we observed 64 target genes mapped to cell cycle pathway (Additional file 19: Table S14 and Additional file 27: Figure S13) which suggests that these miRNAs may be regulators of cell cycle. It has been well established that cell division only occurs during the embryonic development, and the number of cells in the posterior silk gland no longer increases throughout the larval life . Finally, pathway analysis results showed highly consistency between the three datasets: the entire stage, the novel miRNAs, and the V3 group.
From 10 small RNA libraries, we acquired ~93 million processed reads ranging from 18–30 nt in length, identified 1,229 miRNAs (110 miRNA/miRNA* duplexes), and profiled 430 miRNAs at the third day of the fifth instar larva. We also found 728 novel miRNAs (including 55 miRNA/miRNA* duplex and 709 Bm-specific miRNAs . Our findings expanded the collection of B. mori miRNAs in miRBase and covered most miRNAs of the posterior silk gland. Moreover, on the discovery of target genes [51–53], we predicted 14,222 targets matching 1,195 miRNAs, which are classified into many important pathways including protein synthesis, energy supply, and cell cycle control. Our results underscore the key regulatory roles that miRNAs play in the fifth instar posterior silk gland for silk production.
Silkworm rearing and sample preparation
For better miRNA profiling and eliminating strain-specific effects, we selected six domesticated silkworm strains (Q, Qiufeng; B, Baiyu; QB, Qiufeng × Baiyu; and BQ, Baiyu × Qiufeng, R1, and J1) and reared them on fresh mulberry leaves under standard condition. We used three sets of samples according to genes expression cluster analysis : (1) Stage 1: fourth instar molting to day 2 of fifth instar from Q, B, QB, and BQ); (2) Stage 2: fifth instar day 3 to day 8 before spinning; and (3) Entire stage: Stage 1 + 2 from R1 and J1. The samples were collected daily and dissected and stored at low temperature in 0.7% NaCl. Samples were subsequently rinsed and stored in liquid nitrogen.
Small RNA library construction and solexa sequencing
Total RNA was extracted from the posterior silk gland with Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. For miRNA-seq, total RNA of the desired size range (18–30 nt) was size-fractionated on a 15% PAGE gel and ligated with to sequence adapters (T4 RNA ligase). After amplified for 15 cycles, the products were separated on agarose gels and the RT-PCR products were sequenced on the Illumina platform (Beijing Genomics Institute or BGI, Shenzhen) [55, 56].
Sequence analysis and microRNA prediction
Raw sequence reads of 35 nt in size were generated and unique reads of full-length small RNA sequences (≥18 nt) were analyzed with deep sequencing small RNA analysis pipeline (DSAP) (http://dsap.cgu.edu.tw/dsap.html). Unique reads matching silkworm non-coding RNA (rRNA. tRNA, sRNA, snoRNA and other non-coding RNA) deposited at the NCBI GenBank database and Rfam 10.0 were removed. The clean reads from raw dataset were matched to the known miRNA in miRBase 16.0 (http://microrna.sanger.ac.uk) to identify conserved miRNAs and annotated stem-loop sequences. After strict screening, the remaining sequences were regarded as candidate miRNAs for further analysis.
To determine potential novel miRNAs, we identified candidate miRNAs using the mireap program (http://sourceforge.net/projects/mireap), which is an algorithm developed by BGI, which can be used to identify known miRNAs and novel candidates with canonical hairpin structure and sufficiently supported by sequencing data. In the present study, mireap parameters were set to match the condition of animal miRNAs identification as follows: (1) the length range of the miRNA sequence: 20–24 nt; (2) the maximal free energy allowed for an miRNA precursor: -18 kcal/mol; (3) the minimal common base pairs between miRNA and miRNA*: 14 with no more than four bulges; and (4) the maximal asymmetry of miRNA/miRNA* duplex: 5 nt. Following miRNA prediction, secondary structures were predicted by using the Zuker algorithm that evaluates hairpin forming potential (http://rna.urmc.rochester.edu/rnastructure.html).
To determine comprehensive miRNA expression profiles on the third day of fifth instar larvae, we collaborated with LC Bio Co. Ltd (LC sciences, USA) developed and designed miRNA probes. Considering that miRNA expression profiles may vary in different varieties and genders , we collected both male and female silkworms from four stains (Q, B, QB, and BQ) in duplicates. The small RNA fraction was extracted with Trizol reagent (Invitrogen, Carlsbad, CA, USA). To ensure the quality of the RNA, we used checked RNA quality and quantity with spectrophotometer and size-fractionated it using YM-100 Microcon centrifugal filter (Millipore). After adding poly-A tails, hybridization (10 μg probe) was used was carried out on a μParaflo™ microfluidic chip (Atactic Technologies) . After imagine acquisition (GenePix 4000B, Molecular Device; Media Cybernetics) and background removal, we normalized the signals using a LOWESS (Locally-weighted Regression) method , classified the data using a hierarchical clustering method and average linkage and Euclidean distance metric, and visualized the results with TIGR MeV (Multiple Experimental Viewer; Institute for Genomic Research).
Target gene prediction analysis
Due to lack of available 3’-utr database, we first estimated the unigenes from NCBI (release date: Mar 30, 2006) and considered 1 kb as a suitable length for silkworm 3’-utr. Then, according to the annotation of silkdb2.0 (http://www.silkdb.org/silkdb), 1 kb sequences after the last exon of annotated genes were selected as target gene region. Finally, we used miRanda v3.3a (http://cbio.mskcc.org/microrna_data/manual.html) to predict potential targets. The thresholds for candidate target sites were set at S ≥ 140 and ΔG < -20 kcal/mol .
Gene ontology and KEGG pathways analysis
We analyzed the function of miRNA targets based on Gene Ontology through searching against InterPro and KEGG databases (http://www.genome.jp/kegg/), using InterProScan, WEGO (http://wego.genomics.org.cn/), and UniProtKB (http://pir.georgetown.edu/pirwww/search/blast.shtml).
JL: 1College of Animal Sciences, Zhejiang University, Hangzhou 310058, P.R. China, 2Institute of Sericulture, Chengde Medical University, Chengde 067000, P.R. China. YC: Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100029, China.
two-dimensional gel electrophoresis
- MALDI-TOF MS:
Matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry
Kyoto encyclopedia of genes and genomes
Deep-sequencing small RNA analysis pipeline
Qiufeng × Baiyu
Baiyu × Qiufeng.
This work was supported by the grants from the National Basic Research Program of China (No. 2012CB114601), Projects of Zhejiang Provincial Science and Technology Plans (No.2013C32048 and No.2012C12910) and Project of Hebei Provincial Department of Education (No. Q2012026).
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