MicroRNA-mediated gene regulation plays a minor role in the transcriptomic plasticity of cold-acclimated Zebrafish brain tissue
© Yang et al; licensee BioMed Central Ltd. 2011
Received: 25 May 2011
Accepted: 14 December 2011
Published: 14 December 2011
MicroRNAs (miRNAs) play important roles in regulating the expression of protein-coding genes by directing the degradation and/or repression of the translation of gene transcripts. Growing evidence shows that miRNAs are indispensable player in organismal development with its regulatory role in the growth and differentiation of cell lineages. However, the roles of miRNA-mediated regulation in environmental adaptation of organisms are largely unknown. To examine this potential regulatory capability, we characterized microRNAomes from the brain of zebrafish raised under normal (28°C) and cold-acclimated (10°C, 10 days) conditions using Solexa sequencing. We then examined the expression pattern of the protein-coding genes under these two conditions with Affymetrix Zebrafish Genome Array profiling. The potential roles of the microRNAome in the transcriptomic cold regulation in the zebrafish brain were investigated by various statistical analyses.
Among the total 214 unique, mature zebrafish miRNAs deposited on the miRBase website (release 16), 175 were recovered in this study. In addition, we identified 399 novel, mature miRNAs using multiple miRNA prediction methods. We defined a set of 25 miRNAs differentially expressed under the cold and normal conditions and predicted the molecular functions and biological processes that they involve through Gene Ontology (GO) annotation of their target genes. On the other hand, microarray analysis showed that genes related to mRNA processing and response to stress were overrepresented among the up-regulated genes in cold-stress, but are not directly corresponding to any of the GO molecular functions and biological processes predicted from the differential miRNAs. Using several statistical models including a novel, network-based approach, we found that miRNAs identified in this study, either individually or together, and either directly or indirectly (i.e., mediated by transcription factors), only make minor contribution to the change in gene expression patterns under the low-temperature condition.
Our results suggest that the cold-stress response of mRNA expression may be governed mainly through regulatory modes other than miRNA-mediated regulation. MiRNAs in animal brains might act more as developmental regulators than thermal adaptability regulators.
MicroRNAs (miRNAs) are a class of ~22 nt, non-coding RNAs present in plants and animals that play important roles in regulating gene expression by directing translational repression and/or mRNA destabilization [1–3]. Recognition and interaction between miRNA and mRNA take effect only if the 5' end seed (6-8 nt) of the miRNA is complementary to the 3' UTR site of the mRNA. A miRNA typically targets multiple, even hundreds of, genes , which makes miRNAs potentially global regulators responsible for spatio-temporal gene expression patterns.
Like let-7 [5, 6], some miRNAs are highly conserved throughout evolution and participate in pivotal biological processes . Lineage- or species-specific miRNAs may contribute to the uniqueness of organisms [8, 9]. With recent advancements in miRNA microarrays and next-generation sequencing, miRNA expression profiles of many species are being generated at a rapid pace. These profiles are invaluable resources for better understanding gene expression dynamics [10–16]. Some miRNAs have been well characterized and have been shown to have important functions in normal development [17–21]. The deficient expression of some miRNAs also presents severe phenotypic abnormalities [22, 23]. The role of miRNAs in responses to cellular and environmental stress has been discovered in a variety of organisms [24–28]. A few of these miRNAs have been characterized as key regulators of the stress response . For example, miR-8 family miRNAs were shown to be indispensable in the response of zebrafish embryos to osmotic stress . Despite this progress, most studies to date have only examined miRNAs' functions at individual level. The global contributions of microRNAomes (miRNAomes) to environmental adaptations remain largely unexplored.
The zebrafish, an important model organism, is an eurythermal fish with temperature tolerance ranging from approximately 7°C to 40°C , making it a useful experimental subject for exploring the molecular mechanisms underpinning thermal responses. Almost all of the previous work on the molecular underpinnings of thermal responses has focused on the fluctuated expression of protein-coding genes in response to warm or cold acclimation [31, 32]. Many miRNAs have been discovered in zebrafish [33, 34], and their expression profiles  and functions in developmental regulation have been studied . Our goal was to determine how the zebrafish miRNAome responds to thermal acclimation and to assess the roles of miRNAs in thermally challenged transcriptomic plasticity. Toward this end, we systematically characterized the miRNAomes of zebrafish brains under normal and cold-acclimated conditions using Solexa sequencing. We also identified genome-wide gene expression patterns under each of these conditions using microarray profiling. We then examined the potential links between the changes in miRNA levels and the transcriptomic shifts and found that miRNAs may play minor roles in transcriptomic plasticity during cold acclimation in the zebrafish brain.
Deep sequencing of the zebrafish brain small RNA and miRNA prediction
SOAP mapping results of reads with or without clustering.
The miRNA expression profile in cold acclimation
Differentially expressed miRNAs of zebrafish brain under cold-acclimated and normal conditions.
Differentially expressed miRNAs and their functions.
Phosphoinositide metabolic process
ribonucleotide binding, ATP binding
protein tyrosine/serine/threonine phosphatase activity
glucose metabolic process
regulation of developmental process
aminoacyl-tRNA ligase activity
tRNA aminoacylation for protein translation
receptor signaling protein activity
cytoskeletal protein binding
Gene expression profiling and differentially expressed genes
Gene transcription changes between the cold-acclimated and normal samples were obtained by microarray-based hybridization using three sets of RNA samples prepared from fishes of three acclimation experiments. The gene expression level was measured as the intensity of probe sets on an Affymetrix microarray. After filtering the probe sets and retrieving the map relationship between the probe sets and the genes, we obtained gene expression profile recordings of 7356 protein-coding genes. The differential genes were determined using a two-class, unpaired comparison in the SAM (version 3.0) program . Using a 5% false discovery rate (FDR) cut-off, we found that 311 genes were down-regulated and 315 genes were up-regulated in the cold-acclimated samples (Additional file 7). There were 127 and 85 genes with more than 2-fold changes in mRNA abundance that were down- and up-regulated, respectively.
Functional enrichment of differentially expressed mRNAs.
cellular macromolecular complex subunit organization
Up-regulated & >2-fold change
response to stress
response to temperature stimulus
Down-regulated & >2-fold change
cellular macromolecular complex assembly
Verification of the expression pattern of the protein-coding genes and miRNAs by quantitative RT-PCR (q RT-PCR)
Statistical tests for the potential correlation between the expression levels of miRNAs and protein-coding genes
Mounting evidence has shown that miRNAs can orchestrate the expression of mRNAs and that the well-balanced interplay between the miRNAome and protein-coding gene expression plays a crucial role in an organism's development [48–50]. To investigate the influence of miRNAs on the global expression of protein-coding genes in cold stress, we adopted four different approaches. First, we estimated the effect of miRNAs on differentially regulated genes using Fisher's exact test. Using PITA target prediction methods, we obtained 18665 pairs of miRNA-target interactions in total. Our results showed only one miRNA (dre-mir-34-3p) with target genes overrepresented in the down-regulated gene set. No miRNAs showed targets enrichment or depletion in the up-regulated gene set (FDR < 5%). The miRNA dre-mir-34-3p was expressed at very low levels (29 in the cold library and 20 in the control library) in the brain and was not significantly different between the two conditions. We speculate that it is not probable that dre-mir-34-3p plays an important role in the changed gene expression patterns observed under cold stress. To address concerns over potential target prediction bias by PITA, we performed target prediction by miRanda software . Under this methodology, we obtained 56670 pairs of miRNA-target interactions, which is about 3-fold of the number (18665) obtained by PITA prediction method. We then examined the composition of targets predicted by the two methods for each miRNA. Our results showed that 615 miRNAs shared at least one predicted target between PITA and miRanda; however, as a whole, the overlap is relatively low (Additional file 8, Table S3-S6). For example, among a group of 370 miRNAs for which each has more than ten targets by whatever target prediction methods, the median value of the proportion of overlap is only 0.396 (Additional file 9, Figure S3). In average, each miRNA targets ~80 protein-coding genes and each gene is likely to be regulated by 7-8 miRNAs in return. Based on these interaction data, our statistical analyses recaptured the fore-mentioned miRNA-mRNA interacting pattern, i.e., no miRNAs targeted over- or under-represented genes in either up- or down-regulated gene set.
Recently, Chen et al.  proposed a novel concept for the regulatory effect score (RE score) to measure the inhibition capability of a miRNA on its targets. In principle, the RE score is calculated as the average difference in expression levels between a target and a non-target group. By calculating the RE scores on the PITA and miRanda methodology, respectively, we checked whether some of the miRNAs would changed regulatory effects on their targets between the two conditions. Consistent with previous results, we again found that no miRNAs exhibited a significant change in their regulatory effect after correcting the raw t-test p-values by the multiple hypothesis test (FDR < 0.05) whatever the target prediction method was used. Taken together, our results indicate that individual miRNAs are unlikely to be major factors leading to the changed transcriptomic phenotype of zebrafish brain under cold stress.
To further reduce the concerns over target-prediction bias induced errors, we applied the Sylamer algorithm  to re-examine the potential relationship between the miRNAome and transcriptome under cold acclimation. We sorted the protein-coding genes from the most significantly up-regulated to the most down-regulated. We then calculated the distribution of the enrichment/depletion of words in the 3' UTR sequences that could be complementary to the seed sequences of miRNAs. Since positions 1-7 and 2-8 of the 5' end of a mature miRNA are the most important sequences for target recognition , we took the 7 nt region as a miRNA seed in this study and examined whether some over- or under-represented words corresponded to certain miRNAs.
Mathematical models  and statistical associations [56, 57] have shown that miRNAs frequently intertwine with transcription factors (TFs) in regulatory circuits and play essential roles in signal transduction. Recently, Tu et al.  proposed a two-layer network wherein miRNA-initiated regulation was mediated and propagated through the miRNAs' targeted transcription factors. We investigated whether the miRNAs had indirectly, through their transcription factors, led to the observed transcriptomic plasticity. We first filtered the transcription factors from the arrayed gene list according to the GO annotation. We treated the genes with any one of the GO terms, "DNA binding", "transcription factor activity", "transcription activator activity" or "transcription repressor activity", as putative transcription factors. Accordingly, we predicted 796 TF-coding genes among the 7356 protein-coding genes. Notably, only 46 of these TFs showed differential expression in cold acclimation, and they were significantly underrepresented in the differential expressed gene list (46 out of 626 differential genes versus 796 TFs in the 7356 arrayed genes, Fisher's exact test: P < 0.01). Among the differential TF genes, only 6 were predicted to be the targets of 5 differential miRNAs (the ◆-▲ pair in Figure 6). Of the 6 TFs, 3 showed coherent regulatory direction with their respective miRNA partners (miR_13→emx2; dre-mir-183-5p→ikzf5; miR_46, miR_48→h3f3c. see Figure 6). Given that the gene emx2 represented slightly up-regulatoin (fold-change: 1.214; q-value: 4.376%) under cold-acclimated condition, we speculate that it was not a driven gene responsible for the changed mRNA expression pattern. There is no evidence that the ikzf5 (fold-change: 1.994; q-value: 0) and h3f3c (fold-change: 2.245; q-value: 2.144%) are related to stress response when we examined their annotations in multiple databases and in the literature. Additionally, almost no differential TF-coding genes were targeted by differential miRNAs. Taken together, we conclude that the mRNA phenotypic plasticity seen under the cold environment was not dominantly modulated by miRNAs, even if these factors were directly or indirectly mediated by transcriptional factors.
The repertoires of miRNAs have been constantly revised and updated with the advance of in silico prediction methods and more intensive cloning and sequencing efforts in broader tissues and developmental stages. The number of unique, mature human miRNAs is now up to 1223 in the miRBase database (version 16). In contrast, there are only 214 mature miRNAs now registered for zebrafish. This relatively low number could be the result of genomic between-species differences or incomplete miRNA identification in zebrafish. By Solexa sequencing, we comprehensively profiled the miRNA repertoire in the zebrafish brain. The number of predicted miRNAs in this tissue reached 700 (574 in high-stringency criteria) in this study and greatly expanded the miRNAome in zebrafish. Of note is that the expression levels of the newly identified miRNAs in the zebrafish brain are mostly low. The authenticity of these newly identified miRNAs remains to be further investigated to determine their biological functions. However, a population analysis on 7 randomly selected novel miRNAs currently carried out in the laboratory showed that almost half of them are subjected to purified selection suggesting that many newly identified miRNAs may have high potentials to be functional in vivo despite their low expression levels. It is possible that some of these newly identified, low-expressed miRNAs could be expressed at higher levels in tissues other than the brain and play roles in gene regulation. The miRNAome of the zebrafish is much larger than those of the lower chordates such as the amphioxus species, which had been determined by small RNA library sequencing  and solexa sequencing . This suggests the expansion of the miRNA regulome in the evolution of vertebrates.
Thermally invoked responses can be generally classified as cellular stress responses, homeostatic responses or body remodeling-related responses that correspond to short-term, mid-term or long-term acclimation processes, respectively [63–65]. Our samples were taken after 10-day cold acclimation, which can be classified as mid-term acclimation. We reason that the current results reflect only the gene regulatory patterns of the zebrafish brain at this temporal scale of acclimation. The involvement of miRNAs as a pivotal regulator in an earlier or later cold adaptation phase remains to be further investigated.
Persistent efforts have identified and characterized some cold stress-induced protein-coding genes [66, 67]; among them, rbm3, which encodes a glycine-rich RNA binding protein, has been suggested to regulate global protein synthesis by affecting miRNA expression levels . In the present study, we also observed two homologous genes in zebrafish, cirbp (fold-change: 2.244; q-value: 0) and zgc:112425 (fold-change: 1.961; q-value: 1.598%), with significantly up-regulated expression levels under cold-acclimated condition.
Lopez-Maury et al.  have pointed out that post-transcriptional regulatory mechanisms, compared to transcriptional mechanisms, are largely unknown under stress conditions. To our knowledge, there are few studies jointly analyzing miRNAs and mRNA profiles to explore the roles of miRNAs on the cold adaptation of fish. Such a study would extend our understanding of miRNAs and environmental adaptation and undoubtedly lead to a more challenging and interesting question: whether and how miRNAs have integrated into cold-adaptive processes and contributed to the unique physiology of fishes endemic to extreme temperature conditions.
In this study, we systematically characterized the miRNAome of zebrafish brain under normal and cold-acclimated conditions using Solexa sequencing. Our data has greatly extended the zebrafish miRNAs repertory from the 214 unique, mature miRNAs (miRBase databse, release 16 version) to more than 574 ones. Besides, our results showed that the miRNAs, either individually or together, either directly or indirectly play only a minor role in transcriptomic plasticity during cold acclimation in the zebrafish brain, suggesting that the cold-stress response of mRNA expression may be governed mainly through regulatory modes other than post-transcriptional regulation by miRNAs. MiRNAs in animal brains may act more as developmental regulators than thermal adaptability regulators.
Zebrafish treatment and brain RNA preparation
Adult zebrafish (Danio rerio) were alternatively divided into two groups: a control group and an experimental group. In each group, the sex ratio was 1:1. The control group was raised under the normal laboratory breeding temperature (28°C), and the experimental group was subjected to cold (10°C) stress. An abrupt transfer of zebrafish from normal to cold (10°C) environments will cause most of them to die. To prevent this, the temperature of the cold group was decreased from 28°C to 10°C at a rate of one degree per hour. After 10 days of cold adaption, the brains of the zebrafish were dissected, and total RNAs were extracted from these brains using the total RNA isolation kit (Promega, Madison, USA). All experimental research on animals was conducted with the approval of the Animal Research Ethics Committee of our institute.
To evaluate the genome-wide change of gene transcription in cold stress, we performed hybridization analysis using the Zebrafish Genome Array (Affymetrix) as previously described . Briefly, mRNAs were isolated from 10 μg of total RNAs derived from pooled zebrafish brains of acclimated and control samples using the Eukaryotic Poly-A RNA Control Kit (Affymetrix) in an RNase-free environment. Subsequently, mRNAs were subjected to double-strand cDNA synthesis using a one-cycle cDNA synthesis kit (Affymetrix). The resulting double-strand cDNAs were in vitro transcribed into biotin-labeling cRNAs using the IVT Labeling kit (Affymetrix). After that, biotin-labeling cRNAs were fragmented by heating and hybridized with the Affymetrix Zebrafish Genome Arrays. Finally, the signal intensity of the chip was scanned using the Gene Chip Scanner 3000 (Affymetrix) and analyzed with the Gene Chip Operating Software (Affymetrix). The data derived from the genome-wide expressional analysis was deposited in the GEO bank with the accession number GSE32092.
Small-RNA deep sequencing
For small-RNA Solexa sequencing, 20 μg of total RNAs were subjected to electrophoresis on a polyacrylamide gel under denaturing conditions. The small RNAs ranging from 18-30 nt were excised and purified. The resulting small RNAs were sequentially ligated to a 5' adaptor and a 3' adaptor. The products were then reverse transcribed (RT) and amplified by polymerase chain reaction (PCR). The RT-PCR products of approximately 90 bp (small RNA + adaptors) were isolated and subjected to sequencing analysis using the Illumina Genome Analyzer (Illumina, San Diego, USA) according to the manufacturer's instructions.
MiRNA identification and annotation
High-quality, small-RNA reads larger than 18 nt were extracted from raw reads and mapped to zebrafish genome sequences (Danio_rerio.Zv8.56.dna.toplevel.fa.gz) using SOAP2 . The miRNAs and their precursor structures were predicted by MIREAP (https://sourceforge.net/projects/mireap/) under the default settings using the perfect-matched reads. Like the miRDeep software , MIREAP integrates miRNA biogenesis, sequencing depth and structural features to identify miRNAs and further interrogate their expression level from deep sequenced small RNA libraries. Stem-loop hairpins were retained only when they comply with: 1) the mature miRNAs-associated reads are mapped in the arm region of the precursors; 2) the free energy of the secondary structure calculated by RNAfold  is lower than -18 kcal/mol. In detail, the potential mature miRNA is defined as the most abundant read sequence that aligns to the potential precursor sequence; the expression level of a miRNA is then specified as the sum of an ensemble of reads that align with the potential mature molecules, allowing three nucleotides sliding beyond the position of the potential mature miRNA at the 5' end. We then performed blast analyses on the predicted miRNAs precursors and mature miRNAs against sequences deposited in the miRBase (release 16) database to annotate these miRNAs. Novel miRNAs were further tagged as "exon-derived", "intron-derived" or "intergenic" miRNAs according to the loci of their precursors on the genome. In addition, MiPred  was used to distinguish between genuine and pseudo miRNA precursors. This software incorporates the local, contiguous structure-sequence composition and the minimum free energy (MFE) of the secondary structure to eliminate potential false positive predictions.
Frequency-based small-RNA reads clustering
Following the method proposed by Qu et al. , the raw reads were clustered according to their read numbers. After clustering, the accuracy of mapping short reads can be improved.
Gene expression validation by q RT-PCR analysis
Total RNA (8 μg) from the cold and normal fishes was respectively reverse transcribed by Takara PrimeScript RT-PCR Kit (Takara, CN) with OligodT primers, according to the protocol the manufacturer recommended. Another 4 μg total RNA from the two treatments was used for small RNA (< 200 bp) isolation by Ambion mirVana miRNA Isolation Kit. Poly (A) tail was added to the small RNAs by Ambion Poly (A) Polymerase (Ambion, CN). 2 pairs of primers for each selected gene or miRNA were designed and tested for amplification efficacy and specificity by agarose electrophoretic analysis of the PCR products. The primer pair with the best specificity was selected for further use in q-PCR. Q-PCR was run by using the SYBR RT-PCR kit in a Bio-rad CFX 96 (Bio-rad) machine and results were analyzed by the CFX Manager software. The PCR thermal cycling was performed using 45 cycles of 95°C for 15 seconds, 72°C for 30 seconds and 60°C for 30 seconds. The PCR reaction for each gene was performed in triplet with either the housekeeping gene Beta-actin (for arrayed genes) or U6 RNA (for miRNAs) as control.
Word enrichment/depletion in 3' UTR
The mRNAs were sorted according to their significance of expression levels and their corresponding 3' UTRs. The longest sequences were retrieved from the Ensembl website using Biomart tools . The word enrichment/depletion patterns of these genes were generated by Sylamer .
Regulatory effect score
The regulatory effect score (RE score) was defined as the inhibitory effect of a miRNA on the mRNAs in a sample, according to the average difference in expression of its target versus non-target mRNAs . For each miRNA, we first calculated the rank-based RE score for every sample and then compared the difference between the normal and cold-acclimated groups using a two-sample t-test. The FDR values were calculated using the R multtest  package.
GO enrichment test
For the list of mRNAs, we tested whether each had enriched GO terms in biological processes and molecular functions using the GO:TermFinder package . The GO annotation file was downloaded from http://zfin.org/zf_info/downloads.html#go/on January 29, 2010. The GO ontology file was downloaded from http://www.geneontology.org/ on January 23, 2008.
miRNA target predictions
Currently, there are several popular tools for predicting miRNA targets, including PITA , PicTar , miRanda , mirWIP  and TargetScan . These tools can be generally divided into two classes, methods that integrate the information of sequence conservation across multiple genomes and those that do not take this into account. Because of the limited genome data for zebrafish relatives, we predicted the miRNA targets by two methods, PITA and miRanda, independently. PITA scores the miRNA-target interactions based on a thermodynamic model. A salient feature of this program is that it considers the site accessibility of the surrounding mRNA sequence but disregards sequence conservation between genomes. Thus, non-conserved or even species-specific target genes could be identified using this method. For each miRNA, the target genes are defined as those predicted to contain at least one binding site (△△G ≤ -10) at their 3' UTR. We also ran miRanda (August 2010 Release) to predict the target genes with default parameters except that the energy parameter is set to -20.
where #CE (#IE) denotes the number of coherent (incoherent) edges.
To estimate the significance of the observed RE, we generated 1,000 cohorts of random networks and recalculated the RE by shuffling the original network while keeping the degree preservation according to the published method .
We are indebted to two anonymous reviewers for their constructive comments and suggestions that have substantially improved the paper. This work is financially supported by grants: 2010CB126304 from Ministry of Science and Technology of China, and 30910103906, 30971625 from Natural Science Foundation of China to Liangbiao Chen.
- Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature. 2005, 433 (7027): 769-773.PubMedView Article
- Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP: The impact of microRNAs on protein output. Nature. 2008, 455 (7209): 64-71.PubMed CentralPubMedView Article
- Hendrickson DG, Hogan DJ, McCullough HL, Myers JW, Herschlag D, Ferrell JE, Brown PO: Concordant regulation of translation and mRNA abundance for hundreds of targets of a human microRNA. PLoS Biol. 2009, 7 (11): e1000238-PubMed CentralPubMedView Article
- Bartel DP: MicroRNAs: target recognition and regulatory functions. Cell. 2009, 136 (2): 215-233.PubMed CentralPubMedView Article
- Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993, 75 (5): 843-854.PubMedView Article
- Roush S, Slack FJ: The let-7 family of microRNAs. Trends Cell Biol. 2008, 18 (10): 505-516.PubMedView Article
- Cordes KR, Sheehy NT, White MP, Berry EC, Morton SU, Muth AN, Lee TH, Miano JM, Ivey KN, Srivastava D: miR-145 and miR-143 regulate smooth muscle cell fate and plasticity. Nature. 2009, 460 (7256): 705-710.PubMed CentralPubMed
- Zhang R, Wang YQ, Su B: Molecular evolution of a primate-specific microRNA family. Mol Biol Evol. 2008, 25 (7): 1493-1502.PubMedView Article
- Li J, Liu Y, Dong D, Zhang Z: Evolution of an X-linked primate-specific microRNA cluster. Mol Biol Evol. 2009
- Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M, et al: A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007, 129 (7): 1401-1414.PubMed CentralPubMedView Article
- Liang Y, Ridzon D, Wong L, Chen C: Characterization of microRNA expression profiles in normal human tissues. BMC Genomics. 2007, 8: 166-PubMed CentralPubMedView Article
- Hua YJ, Tang ZY, Tu K, Zhu L, Li YX, Xie L, Xiao HS: Identification and target prediction of miRNAs specifically expressed in rat neural tissue. BMC Genomics. 2009, 10: 214-PubMed CentralPubMedView Article
- Chen X, Li Q, Wang J, Guo X, Jiang X, Ren Z, Weng C, Sun G, Wang X, Liu Y, et al: Identification and characterization of novel amphioxus microRNAs by Solexa sequencing. Genome Biol. 2009, 10 (7): R78-PubMed CentralPubMedView Article
- Chen PY, Manninga H, Slanchev K, Chien M, Russo JJ, Ju J, Sheridan R, John B, Marks DS, Gaidatzis D, et al: The developmental miRNA profiles of zebrafish as determined by small RNA cloning. Genes Dev. 2005, 19 (11): 1288-1293.PubMed CentralPubMedView Article
- Zhang J, Xu Y, Huan Q, Chong K: Deep sequencing of Brachypodium small RNAs at the global genome level identifies microRNAs involved in cold stress response. BMC Genomics. 2009, 10: 449-PubMed CentralPubMedView Article
- Jagadeeswaran G, Zheng Y, Sumathipala N, Jiang H, Arese E, Soulages JL, Zhang W, Sunkar R: Deep sequencing of small RNA libraries reveals dynamic regulation of conserved and novel microRNAs and microRNA-stars during silkworm development. BMC Genomics. 11 (1): 52-
- Yoo AS, Staahl BT, Chen L, Crabtree GR: MicroRNA-mediated switching of chromatin-remodelling complexes in neural development. Nature. 2009, 460 (7255): 642-646.PubMed CentralPubMed
- Johnston RJ, Chang S, Etchberger JF, Ortiz CO, Hobert O: MicroRNAs acting in a double-negative feedback loop to control a neuronal cell fate decision. Proc Natl Acad Sci USA. 2005, 102 (35): 12449-12454.PubMed CentralPubMedView Article
- Schratt GM, Tuebing F, Nigh EA, Kane CG, Sabatini ME, Kiebler M, Greenberg ME: A brain-specific microRNA regulates dendritic spine development. Nature. 2006, 439 (7074): 283-289.PubMedView Article
- Conaco C, Otto S, Han JJ, Mandel G: Reciprocal actions of REST and a microRNA promote neuronal identity. Proc Natl Acad Sci USA. 2006, 103 (7): 2422-2427.PubMed CentralPubMedView Article
- Makeyev EV, Zhang J, Carrasco MA, Maniatis T: The MicroRNA miR-124 promotes neuronal differentiation by triggering brain-specific alternative pre-mRNA splicing. Mol Cell. 2007, 27 (3): 435-448.PubMed CentralPubMedView Article
- Giraldez AJ, Cinalli RM, Glasner ME, Enright AJ, Thomson JM, Baskerville S, Hammond SM, Bartel DP, Schier AF: MicroRNAs regulate brain morphogenesis in zebrafish. Science. 2005, 308 (5723): 833-838.PubMedView Article
- Hansen T, Olsen L, Lindow M, Jakobsen KD, Ullum H, Jonsson E, Andreassen OA, Djurovic S, Melle I, Agartz I, et al: Brain expressed microRNAs implicated in schizophrenia etiology. PLoS One. 2007, 2 (9): e873-PubMed CentralPubMedView Article
- Jian X, Zhang L, Li G, Wang X, Cao X, Fang X, Chen F: Identification of novel stress-regulated microRNAs from Oryza sativa L. Genomics. 2010, 95 (1): 47-55.PubMedView Article
- Zhao M, Ding H, Zhu JK, Zhang F, Li WX: Involvement of miR169 in the nitrogen-starvation responses in Arabidopsis. New Phytol. 2011
- Frazier TP, Sun G, Burklew CE, Zhang B: Salt and Drought Stresses Induce the Aberrant Expression of microRNA Genes in Tobacco. Mol Biotechnol. 2011
- Biggar KK, Storey KB: The emerging roles of microRNAs in the molecular responses of metabolic rate depression. J Mol Cell Biol. 2010
- Simone NL, Soule BP, Ly D, Saleh AD, Savage JE, Degraff W, Cook J, Harris CC, Gius D, Mitchell JB: Ionizing radiation-induced oxidative stress alters miRNA expression. PLoS One. 2009, 4 (7): e6377-PubMed CentralPubMedView Article
- Flynt AS, Thatcher EJ, Burkewitz K, Li N, Liu Y, Patton JG: miR-8 microRNAs regulate the response to osmotic stress in zebrafish embryos. J Cell Biol. 2009, 185 (1): 115-127.PubMed CentralPubMedView Article
- Cortemeglia , Cheryl , Beitinger , Thomas LG: Temperature tolerances of wild-type and red transgenic zebra danios. 2005, Bethesda, MD, ETATS-UNIS: American Fisheries Society, 134: 7-
- Vergauwen L, Benoot D, Blust R, Knapen D: Long-term warm or cold acclimation elicits a specific transcriptional response and affects energy metabolism in zebrafish. Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology. 2010, 157 (2): 149-157.View Article
- Malek RL, Sajadi H, Abraham J, Grundy MA, Gerhard GS: The effects of temperature reduction on gene expression and oxidative stress in skeletal muscle from adult zebrafish. Comp Biochem Physiol C Toxicol Pharmacol. 2004, 138 (3): 363-373.PubMedView Article
- Soares AR, Pereira PM, Santos B, Egas C, Gomes AC, Arrais J, Oliveira JL, Moura GR, Santos MA: Parallel DNA pyrosequencing unveils new zebrafish microRNAs. BMC Genomics. 2009, 10: 195-PubMed CentralPubMedView Article
- Thatcher EJ, Bond J, Paydar I, Patton JG: Genomic organization of zebrafish microRNAs. BMC Genomics. 2008, 9: 253-PubMed CentralPubMedView Article
- Wienholds E, Kloosterman WP, Miska E, Alvarez-Saavedra E, Berezikov E, de Bruijn E, Horvitz HR, Kauppinen S, Plasterk RH: MicroRNA expression in zebrafish embryonic development. Science. 2005, 309 (5732): 310-311.PubMedView Article
- Flynt AS, Li N, Thatcher EJ, Solnica-Krezel L, Patton JG: Zebrafish miR-214 modulates Hedgehog signaling to specify muscle cell fate. Nat Genet. 2007, 39 (2): 259-263.PubMed CentralPubMedView Article
- Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J: SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009, 25 (15): 1966-1967.PubMedView Article
- Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, D154-158. 36 Database
- Friedlander MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N: Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol. 2008, 26 (4): 407-415.PubMedView Article
- Stark A, Kheradpour P, Parts L, Brennecke J, Hodges E, Hannon GJ, Kellis M: Systematic discovery and characterization of fly microRNAs using 12 Drosophila genomes. Genome Res. 2007, 17 (12): 1865-1879.PubMed CentralPubMedView Article
- Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z: MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res. 2007, W339-344. 35 Web Server
- Erlich Y, Mitra PP, delaBastide M, McCombie WR, Hannon GJ: Alta-Cyclic: a self-optimizing base caller for next-generation sequencing. Nat Methods. 2008, 5 (8): 679-682.PubMed CentralPubMedView Article
- Qu W, Hashimoto S, Morishita S: Efficient frequency-based de novo short-read clustering for error trimming in next-generation sequencing. Genome Res. 2009, 19 (7): 1309-1315.PubMed CentralPubMedView Article
- Koh W, Sheng CT, Tan B, Lee QY, Kuznetsov V, Kiang LS, Tanavde V: Analysis of deep sequencing microRNA expression profile from human embryonic stem cells derived mesenchymal stem cells reveals possible role of let-7 microRNA family in downstream targeting of hepatic nuclear factor 4 alpha. BMC Genomics. 11 (Suppl 1): S6-
- Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E: The role of site accessibility in microRNA target recognition. Nat Genet. 2007, 39 (10): 1278-1284.PubMedView Article
- Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G: GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics. 2004, 20 (18): 3710-3715.PubMed CentralPubMedView Article
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001, 98 (9): 5116-5121.PubMed CentralPubMedView Article
- Nielsen JA, Lau P, Maric D, Barker JL, Hudson LD: Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis. BMC Neurosci. 2009, 10: 98-PubMed CentralPubMedView Article
- Manakov SA, Grant SG, Enright AJ: Reciprocal regulation of microRNA and mRNA profiles in neuronal development and synapse formation. BMC Genomics. 2009, 10: 419-PubMed CentralPubMedView Article
- Nunez-Iglesias J, Liu CC, Morgan TE, Finch CE, Zhou XJ: Joint genome-wide profiling of miRNA and mRNA expression in Alzheimer's disease cortex reveals altered miRNA regulation. PLoS One. 5 (2): e8898-
- John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS: Human MicroRNA targets. PLoS Biol. 2004, 2 (11): e363-PubMed CentralPubMedView Article
- Cheng C, Fu X, Alves P, Gerstein M: mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer. Genome Biol. 2009, 10 (9): R90-PubMed CentralPubMedView Article
- van Dongen S, Abreu-Goodger C, Enright AJ: Detecting microRNA binding and siRNA off-target effects from expression data. Nat Methods. 2008, 5 (12): 1023-1025.PubMed CentralPubMedView Article
- Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP: MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 2007, 27 (1): 91-105.PubMed CentralPubMedView Article
- Goentoro L, Shoval O, Kirschner MW, Alon U: The incoherent feedforward loop can provide fold-change detection in gene regulation. Mol Cell. 2009, 36 (5): 894-899.PubMed CentralPubMedView Article
- Tsang J, Zhu J, van Oudenaarden A: MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell. 2007, 26 (5): 753-767.PubMed CentralPubMedView Article
- Martinez NJ, Ow MC, Barrasa MI, Hammell M, Sequerra R, Doucette-Stamm L, Roth FP, Ambros VR, Walhout AJ: A C. elegans genome-scale microRNA network contains composite feedback motifs with high flux capacity. Genes Dev. 2008, 22 (18): 2535-2549.PubMed CentralPubMedView Article
- Tu K, Yu H, Hua YJ, Li YY, Liu L, Xie L, Li YX: Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms. Nucleic Acids Res. 2009, 37 (18): 5969-5980.PubMed CentralPubMedView Article
- Dai Z, Chen Z, Ye H, Zhou L, Cao L, Wang Y, Peng S, Chen L: Characterization of microRNAs in cephalochordates reveals a correlation between microRNA repertoire homology and morphological similarity in chordate evolution. Evol Dev. 2009, 11 (1): 41-49.PubMedView Article
- Chen K, Rajewsky N: The evolution of gene regulation by transcription factors and microRNAs. Nat Rev Genet. 2007, 8 (2): 93-103.PubMedView Article
- Herranz H, Cohen SM: MicroRNAs and gene regulatory networks: managing the impact of noise in biological systems. Genes Dev. 24 (13): 1339-1344.
- Hilgers V, Bushati N, Cohen SM: Drosophila microRNAs 263a/b confer robustness during development by protecting nascent sense organs from apoptosis. PLoS Biol. 2010, 8 (6): e1000396-PubMed CentralPubMedView Article
- Vergauwen L, Benoot D, Blust R, Knapen D: Long-term warm or cold acclimation elicits a specific transcriptional response and affects energy metabolism in zebrafish. Comp Biochem Physiol A Mol Integr Physiol. 2010, 157 (2): 149-157.PubMedView Article
- Logan CA, Somero GN: Transcriptional responses to thermal acclimation in the eurythermal fish Gillichthys mirabilis (Cooper 1864). Am J Physiol Regul Integr Comp Physiol. 2010, 299 (3): R843-852.PubMedView Article
- Kultz D: Molecular and evolutionary basis of the cellular stress response. Annu Rev Physiol. 2005, 67: 225-257.PubMedView Article
- Gualerzi CO, Giuliodori AM, Pon CL: Transcriptional and post-transcriptional control of cold-shock genes. J Mol Biol. 2003, 331 (3): 527-539.PubMedView Article
- Yamaguchi-Shinozaki K, Shinozaki K: Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses. Annu Rev Plant Biol. 2006, 57: 781-803.PubMedView Article
- Dresios J, Aschrafi A, Owens GC, Vanderklish PW, Edelman GM, Mauro VP: Cold stress-induced protein Rbm3 binds 60S ribosomal subunits, alters microRNA levels, and enhances global protein synthesis. Proc Natl Acad Sci USA. 2005, 102 (6): 1865-1870.PubMed CentralPubMedView Article
- Lopez-Maury L, Marguerat S, Bahler J: Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nat Rev Genet. 2008, 9 (8): 583-593.PubMedView Article
- Zheng D, Kille P, Feeney GP, Cunningham P, Handy RD, Hogstrand C: Dynamic transcriptomic profiles of zebrafish gills in response to zinc depletion. BMC Genomics. 2010, 11: 548-PubMed CentralPubMedView Article
- Zuker M, Stiegler P: Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 1981, 9 (1): 133-148.PubMed CentralPubMedView Article
- Shalgi R, Lieber D, Oren M, Pilpel Y: Global and local architecture of the mammalian microRNA-transcription factor regulatory network. PLoS Comput Biol. 2007, 3 (7): e131-PubMed CentralPubMedView Article
- Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, et al: Combinatorial microRNA target predictions. Nat Genet. 2005, 37 (5): 495-500.PubMedView Article
- Hammell M, Long D, Zhang L, Lee A, Carmack CS, Han M, Ding Y, Ambros V: mirWIP: microRNA target prediction based on microRNA-containing ribonucleoprotein-enriched transcripts. Nat Methods. 2008, 5 (9): 813-819.PubMed CentralPubMedView Article
- Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB: Prediction of mammalian microRNA targets. Cell. 2003, 115 (7): 787-798.PubMedView Article
- Maslov S, Sneppen K: Specificity and stability in topology of protein networks. Science. 2002, 296 (5569): 910-913.PubMedView Article
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.