Deep Sequencing of Chicken microRNAs
© Burnside et al; licensee BioMed Central Ltd. 2008
Received: 10 November 2007
Accepted: 22 April 2008
Published: 22 April 2008
The use of new, deep sequencing technologies has greatly accelerated microRNA discovery. We have applied this approach to the identification of chicken microRNAs and to the comparison of microRNAs in chicken embryo fibroblasts (CEF) infected with Marek's disease virus (MDV) to those present in uninfected CEF.
We obtained 125,463 high quality reads that showed an exact match to the chicken genome. The majority of the reads corresponded to previously annotated chicken microRNAs; however, the sequences of many potential novel microsRNAs were obtained. A comparison of the reads obtained in MDV-infected and uninfected CEF indicates that infection does not significantly perturb the expression profile of microRNAs. Frequently sequenced microRNAs include miR-221/222, which are thought to play a role in growth and proliferation. A number of microRNAs (e.g., let-7, miR-199a-1, 26a) are expressed at lower levels in MDV-induced tumors, highlighting the potential importance of this class of molecules in tumorigenesis.
Deep sequencing technology is highly suited for small RNA discovery. This approach is independent of comparative sequence analysis, which has been the primary method used to identify chicken microRNAs. Our results have confirmed the expression of many microRNAs identified by sequence similarity and identified a pool of candidate novel microRNAs.
MicroRNAs are small (about 22 nt) RNAs that play important regulatory roles by targeting mRNAs for degradation or translational repression. MicroRNAs were first identified in Caenorhabditis elegans  but high evolutionary conservation eventually led to the identification of microRNAs in other species. This, coupled with conventional sequencing of small RNA libraries, has greatly expanded the list of known microRNAs. The most recent release of the microRNA database, miRBase 10.0 , contains over 5000 microRNA gene loci in a wide variety of animal, plant and viral genomes.
Conventional sequencing favors identification of highly expressed species, and comparative genomics will not identify nonconserved microRNAs. In order to enhance discovery of small RNA species, massively parallel signature sequencing (MPSS) was used to identify small RNAs in Arabidopsis thaliana , and the results showed that the diversity of small RNAs exceeded previous estimates. More recently, newer deep sequencing technologies have been used to profile microRNAs in Arabidopsis DICER and RDR2 mutants [4, 5], and others have applied this technology to various samples including human and chimpanzee brain  and Chlamydomonas reinhardtii . These approaches have the advantage that they not only provide sequence of low abundance species, but also provide quantitative data since the frequency of sequencing reads reflects the abundance of microRNAs in the population.
We previously reported on the use of deep sequencing technologies for identification of microRNAs encoded by Marek's disease virus (MDV), an economically important pathogenic herpesvirus of chickens [8, 9]. In an extension of the pilot study, we sequenced additional reads from both MDV-infected chicken embryo fibroblasts (CEF) and uninfected CEF and now report on the identification of potential novel host microRNAs. In addition, the sequence of several new MDV-encoded microRNAs were discovered by deeper sequencing.
Small RNA libraries
Distribution of small RNAs from uninfected CEF and CEF infected with MDV
MDV infected CEF
High quality/both adapters
Exact match to chicken genome
Match to known miRNAs
Match to other chicken smalls1
Match to MDV
Other potential smalls
microRNA profiling by analysis of read counts
Relative abundance of differentially expressed microRNAs
# Reads in MDV infected CEF
# Reads in uninfected CEF
Ratio Infected/Uninfected (Normalized)
Most frequently sequenced microRNAs in CEFs
# Reads in MDV infected CEF
# Reads in uninfected CEF
Ratio Infected/Uninfected (normalized)
MDV encoded microRNAs
Name and Sequence (5' -> 3')
Potential novel microRNAs
About 10% of the reads matched the chicken genome but not other known small RNAs and were considered candidates for novel microRNAs. The presence of hairpin structures containing these reads was evaluated using RNAfold , and those present in hairpins were further filtered according to established criteria . First, the candidate microRNA is entirely within the arm of the hairpin that has the lowest free energy among all sliding windows containing the candidate microRNA. Second, at least sixteen of 22 nucleotides of the candidate microRNA must match the other arm of the hairpin. Third, the hairpin should not contain any large (> 5 nt) internal loops or bulges. Matches to repeats or regions of low complexity were eliminated. Additional File 2 lists 63 candidate novel microRNAs passing these criteria. Uracil, which is preferentially found in the first position of known chicken microRNAs, is also first in 48% of the candidate novels. None contain a seed sequence that is identical to already established microRNA families. Three of the candidates (ID #26/27, 38/39 and 50/51) are found in the same stem loop, making it likely that they are mature and '*' strands of premicroRNAs. One (ID #10) is clustered 96 nt upstream of gga-miR-7-2, and one (ID#31) is immediately upstream of gga-let-7a-2.
Curiously, one of the potential novels (#ID14) is found within the highly evolutionarily conserved coding region of DCGR8 (DiGeorge syndrome critical region gene 8), which interacts with Drosha in the processing of pri-microRNAs .
Expression of known host microRNAs in MDV-induced tumors
Our deep sequencing approach to microRNA discovery in the chicken confirms the expression of 112 known microRNAs and identifies a pool of 63 candidate novel microRNAs. The majority of the known chicken microRNAs have been identified by sequence comparison with microRNAs from other species , and the expression of many has been confirmed by analysis of EST data and in situ hybridization [14, 15]. Cloning from small RNA libraries also validates the expression of microRNAs. A recent study of chicken microRNA cloning  used conventional technology to confirm expression of 25 of the known chicken microRNAs and identified one possible novel microRNA. Our study adds to the confirmation of expression of predicted microRNAs, and greatly expands the list of potential novel microRNAs in the chicken.
A large majority (86%) of the reads from chicken CEF small RNA libraries matched known microRNAs. Similar numbers were obtained in deep sequencing of human brain , where 80% of the reads matched known human microRNAs. Thus, it could be argued that we are approaching saturation of microRNA discovery. However, it is possible that only highly and ubiquitously expressed microRNAs have been found, and less abundant or tissue-specific microRNAs may still be revealed by deep sequencing of different tissues. This is clearly the case in plants, where a large set of small RNAs have been discovered by deep sequencing . In an analysis of the human 'colorectal microRNAome', SAGE was used as a deep sequencing approach, and matches to 200 microRNAs in miRbase were found, as well as 100 previously unrecognized microRNA* strands and 133 candidate novels . MPSS analysis of mouse embryo small RNA discovered over 60 potential novel microRNAs, some of which are rodent specific . We have identified a pool of 63 candidate novel chicken microRNAs and have confirmed expression of several candidates by northern blot analysis. Some are expressed in a tissue-specific manner, while others are more ubiquitously expressed. Further analysis of these and other candidates with respect to temporal and tissue expression will be a first step to understanding function. Overall, the deep sequencing approach to microRNA discovery suggests that a significant number of novel microRNAs remain to be discovered and characterized.
In addition to identifying novel chicken microRNAs, deep sequencing of MDV infected CEF has revealed previously uncharacterized MDV-encoded microRNAs, bringing the total of MDV microRNAs to 18. Other herpesviruses encode microRNAs, and these are thought to play a role in immune evasion, apoptosis and cell cycle control [18, 19]. MDV causes a well- characterized, virally induced T cell lymphoma of chickens and represents an excellent model system for analyzing the function of viral microRNAs in the pathogenesis of cancer. Many recent studies implicate microRNAs as either tumor suppressors or oncogenes , and host encoded microRNAs can act in cis (on viral target genes) or trans (on host encoded genes) to affect phenotypic changes . Moreover, virally infected cells are stressed, and it has been proposed that microRNAs play an important role in the stress response . A comparison of the reads of MDV-infected CEF versus uninfected CEF indicates that the majority of the microRNAs are expressed at similar levels. However, CEF are used to propagate virus, and viral infection occurs in a very small percentage of cells, thus making it difficult to observe changes when analyzing whole cultures. In addition, CEF are not the in vivo target of the virus, and we might expect a more critical layer of regulation in T cells. A small set of microRNAs appeared to be differentially expressed in infected vs. uninfected cells, but this was not confirmed by northern blot analysis. This lack of concordance between the two techniques is not uncommon when expression levels are very low, or when cross-hybridization with similar species can confound the results. Additionally, in our system, MDV infects only a small percentage of the cells and infections vary considerably from culture to culture. This biological noise could also hamper the ability to reproduce differences noted in the sequencing data set.
The two most frequently sequenced microRNAs were gga-miR-222 and -221, which also share sequence identity in the seed region. These are located within a 3000 nt region of Chromosome 1, where there are two copies of gga-miR-222 followed by one copy of gga-miR-221. In human, miR-221 and -222 are coordinately expressed from a single primary transcript . We see about 1.7-fold higher abundance of ggg-miR-222 compared to gga-miR-221, consistent with their sharing a transcript that is highly expressed in CEF. Computational analysis has predicted that p27Kip1 protein, a key inhibitory regulator of the cell cycle , is a potential target for this cluster. Down regulation of p27Kip1 by miR-221/222 promotes growth and proliferation of cancer cells, and could play a similar role in dividing CEF . miR-125b and 21 were also abundant in our libraries. miR-125b is critical for the proliferation of some human cell lines , and mir-21 is thought to function as an oncogene by decreasing apoptosis . The high levels in rapidly dividing CEF could play a permissive role in the cell cycle in CEF.
Our northern analysis of MDV-induced tumors shows several host microRNAs that were noticeably less abundant in MDV-induced tumor tissue compared to normal spleen, consistent with previous reports of a general down-regulation of microRNAs in tumors . Among those down regulated, let7 was particularly interesting. The let7 microRNA is known to down-regulate Hmga2 , which is a small, non-histone, chromatin-associated protein that is believed to influence chromatin remodeling . Hmga2 is expressed robustly in undifferentiated proliferating cells, and its expression during embryogenesis and in a variety of benign and malignant tumors has been characterized . Down-regulation of let7 should lead to increased expression of Hmga-2, and such a scenario would be consistent with the cell proliferation that characterizes tumors. miR-16 is considered a tumor suppressor , which acts by targeting BCL-2, and repressed expression is consistent with tumorigenesis. MiR-181s were down-regulated in gliobastoma compared to normal brain controls , and miR-199a was down-regulated in hepatocelluar carcinoma . Thus, it is likely that in MDV-induced tumors, as in other tumors, many microRNAs act collectively to facilitate cellular transformation and proliferation. More information on the perturbations of host microRNAs will come from a deep sequencing analysis of microRNAs in tumors.
Understanding the biological function of microRNAs first requires identification of all microRNAs within a genome. Here we have described the application of deep sequencing technology for the identification of many candidate novel chicken microRNAs from a single tissue source. The application of this technology to other tissues will no doubt lead to the identification of other novel microRNAs, which will improve the annotation of the chicken genome and further our understanding of this important class of regulatory molecules.
Cloning, sequencing and analysis of chicken microRNAs
Secondary CEF, prepared by routine techniques, were infected with the RB1B strain of MDV as described previously . Protocols developed previously in the Green lab were used to construct the small RNA libraries . cDNA inserts were amplified by PCR, and amplicons were sequenced by 454 Life Sciences . Sequence data were filtered for adapter sequences, clustered (allowing a 4-base overhang or mismatch at either end), and insert sequence was analyzed by comparing to the chicken and MDV genomes, chicken ncRNA (Ensembl 12/06) and to the microRNA database  using Perl string matching. The remaining sequences were analyzed using RNAfold  to identify the loop structure of minimum free energy containing the microRNAs. This list was further curated to eliminate highly repetitive sequences .
Northern blot analysis of microRNAs
RNA was fractionated using PEG or through use of the FlashPage system (Ambion) as described previously . The low molecular weight fractions were electrophoresed on a 15% denaturing polyacrylamide gel, electroblotted to charged nylon, and hybridized to 32P-labeled antisense primers complementary to the microRNAs. Hybridization to an antisense primer for gga-miR-221 or U6 was used as a loading control. A 10-bp DNA ladder was used to approximate size.
Funding for this work was provided by the USDA NRI. We would like to thank Evgeny.Glazov (CSRIO) for stimulating discussions and data sharing.
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