Quantification and accurate normalisation of small RNAs through new custom RT-qPCR arrays demonstrates Salmonella-induced microRNAs in human monocytes
© Sharbati et al; licensee BioMed Central Ltd. 2012
Received: 5 September 2011
Accepted: 16 January 2012
Published: 16 January 2012
Small interfering and non-coding RNAs regulate gene expression across all kingdoms of life. MicroRNAs constitute an important group of metazoan small RNAs regulating development but also disease. Accordingly, in functional genomics microRNA expression analysis sheds more and more light on the dynamic regulation of gene expression in various cellular processes.
We have developed custom RT-qPCR arrays allowing for accurate quantification of 31 small RNAs in triplicate using a 96 well format. In parallel, we provide accurate normalisation of microRNA expression data based on the quantification of 5 reference snRNAs. We have successfully employed such arrays to study microRNA regulation during human monocyte differentiation as well as Salmonella infection. Besides well-known protagonists such as miR-146 or miR-155, we identified the up-regulation of miR-21, miR-222, miR-23b, miR-24, miR-27a as well as miR-29 upon monocyte differentiation or infection, respectively.
The provided protocol for RT-qPCR arrays enables straight-forward microRNA expression analysis. It is fully automatable, compliant with the MIQE guidelines and can be completed in only 1 day. The application of these arrays revealed microRNAs that may mediate monocyte host defence mechanisms by regulating the TGF-β signalling upon Salmonella infection. The introduced arrays are furthermore suited for customised quantification of any class of small non-coding RNAs as exemplified by snRNAs and thus provide a versatile tool for ubiquitous applications.
KeywordsmicroRNA RT-qPCR array normalisation isomiR Salmonella macrophage
Metazoan regulation of gene expression relies on an interwoven network of e.g. DNA methylation, transcription factors, mRNA degradation or translational control. Recent research has shown that translational regulation as well as mRNA degradation is controlled by RNA interference (RNAi). The main class of intrinsic small regulating RNAs concerting these effects in eukaryotes is constituted of microRNAs (miRNAs). Mature miRNAs (about 20 nt length) derive from a hairpin. The active strand is loaded to an Argonaute family protein to form the miRNA induced silencing complex (miRISC), which recognises the target site within a 3' UTR. Animal miRISC was originally thought to repress target translation rather than mRNA degradation. However, recent data suggest that mRNA degradation may be the predominant mode of miRNA mediated regulation of gene expression . Accordingly, various studies have shown negatively correlated expression of miRNAs and their targets [2, 3]. Based on these observations, in silico tools such as MAGIA  were developed to link target prediction to the expression analysis of miRNAs and their target mRNAs. Connecting negatively correlated miRNA and target mRNA expression with target prediction allows for the identification of aberrations in miRNA mediated regulation among various disease related pathways. The role of miRNA mediated gene regulation in development and disease such as cancer or viral infections was recognised very early. However, very recent studies suggest that miRNAs are also involved in the specific host response to bacterial pathogens such as Mycobacteria or Salmonella [5–8]. In this regard, integrated miRNA- as well as mRNA-transcriptome analysis by means of microarrays and reverse transcription quantitative PCR (RT-qPCR) allowed us to show that in mycobacterial infections of human macrophages caspases 3 and 7 are under the control of let-7e and miR-29a, respectively .
Many methods such as microarrays, RNAseq or RT-qPCR are used to study mRNA as well as miRNA expression of cells or tissues under a given condition. Quantification of miRNAs but also other small non-coding RNAs by means of RT-qPCR was established during the last decade based on several detection strategies. In this regard, we developed a protocol called miR-Q, which relies on reverse transcription (RT) of miRNAs using specific oligonucleotides. The generated cDNA is quantified by means of a novel qPCR protocol using three oligonucleotides based on SYBR Green detection chemistry . The protocol can easily be customised to detect and quantify any class of non-coding small RNAs. Besides the frequent application of miR-Q in our lab, the protocol was adopted by others for quantification of small RNAs e.g. in immunity-, virus-, metabolism- or cancer-related RNAi research [10–13]. The miR-Q protocol enables highly specific quantification of single miRNAs providing a high sensitivity. Since RT-qPCR remains the gold-standard for accurate quantification of gene expression, such arrays are increasingly employed for mid-throughput quantification of gene expression providing high sensitivity coupled with accuracy. While arrayed quantification of mRNAs is performed easily, the short length of miRNAs and underlying detection chemistries require well-conceived strategies. A few commercial miRNA RT-qPCR arrays are currently available, which are rather costly. Based on the stem-looped qPCR approach of Applied Biosystems Tang and colleagues reported the 220-plex miRNA expression of a single cell . Another group reported a multiplex quantification of miRNAs involving the purification of multiplex PCR amplicons followed by uniplex analysis on microfluidic chips . However, until today there are no non-commercial and custom protocols for miRNA RT-qPCR arrays. This prompted us to advance the existing miR-Q protocol allowing more versatile and fully automatable arrayed quantification of any class of eukaryotic small non-coding RNAs. Compliant with the MIQE guidelines and nomenclature , we provide here a reliable and cost-effective method enabling arrayed quantification combined with accurate normalisation of miRNA expression.
Results and discussion
Multiplexed reverse transcription of miRNAs enables miR-Q arrays
Oligonucleotides for miR-Q arrays.
We used total RNA isolated from differentiated human THP-1 to prove the repeatability of miR-Q arrays. Figure 2B shows the triplicate Cq values using the layout shown in Figure 2A. Except for 10 miRNAs (let-7 family, miR-106a, miR-141, miR-143, miR-145, miR-27a and miR-29b) all other miRNAs and reference snRNAs were detected in differentiated THP-1 showing minor Cq variation in triplicate measurements and demonstrating strong repeatability of miR-Q arrays (Figure 2B). Commercially available RT-qPCR arrays often employ single measurement for individual miRNAs, so that the lack of intra-array replicates weakens the repeatability of assessed data. Furthermore, there is no opportunity to evaluate if an undetermined Cq is based on target absence or failed reaction. For generation of highly reliable results we encourage intra-array triplicate measurements of each target.
A set of 5 snRNAs allows for accurate normalisation of miRNA expression
miR-Q arrays are highly reproducible and accurate
Additionally, we have performed spike-in experiments to check for the accuracy of the miR-Q assays. For this purpose, we have selected three miRNAs (let-7e, miR-143 and miR-145) that were proven to be absent in differentiated THP-1 (Figure 4A). RT reactions as performed for the validation of reproducibility were spiked with synthetic let-7e, miR-143 and miR-145 to give a final concentration in RT-qPCRs at 3.3, 33 and 330 pM, respectively. According to the MIQE guidelines, accuracy of a RT-qPCR refers to the difference between experimentally measured and actual concentrations as fold difference . Accordingly, we performed miR-Q arrays for all concentrations and determined the fold differences between 3.3 and 33 pM as well as 33 and 330 pM applying the ΔΔCq algorithm, respectively. The reactions revealed 12 fold difference of both let-7e and miR-143 and 20 fold difference of miR-145 between 3.3 and 33 pM spike-in (Figure 4B). While detection of all other miRNAs remained unaffected, in the 33 pM spiked sample there was a slight cross reaction between let-7e spike-in and other let-7 family members resulting in 1.3 to 1.9 fold difference compared to the 3.3 pM control. Higher dosage of spike-in however led to a higher cross reaction within the let-7 family. As shown in Figure 4C, let-7e spike-in at a final concentration of 330 pM created 5 fold increased detection of let-7a and 6.5 fold detection of the specific target let-7e compared to the 33 pM spiked sample. Furthermore, there was a 22.4 fold difference of miR-143 and a 34 fold difference of miR-145 between both samples (Figure 4C). As we discussed earlier, the higher the concentration of the spike-in, the higher is the cross reaction between the assays and paralogous targets of a miRNA family. These data verify the accuracy of miR-Q arrays especially at low spike-in concentrations, which rather represent cellular and physiological miRNA concentrations compared to higher concentrations producing increased cross reactivity.
Recent deep sequencing efforts of small RNAs have shown that many mature miRNAs are either trimmed alternatively resulting in 5' and 3' variations or additional 3' non-template adenosines or uridines are transferred by nucleotidyl transferases post-transcriptionally . These variants of the canonical miRNA are often referred to as 'isomiRs'. We were interested how far the miR-Q approach is biased toward detecting only the canonical mature miRNA without any additions or if it is possible to quantify the whole population of a given miRNA including isomiRs. Therefore, we performed additional experiments evaluating the bias of our approach by non-template adenosines or uridines present at the 3' end of non-canonical isomiRs based on miR-24 variants. Recently, this miRNA was shown to possess additional 3' adenosine or uridine modifications . We performed comparative and paralleled spike-in experiments using synthetic canonical miR-24 as well as the A or U isomiRs, respectively. As it is shown in Figure 5C, the design of the miR-Q approach allowed the detection of isomiRs regardless of an additional adenosine or uridine. All variants were detected over the same linear range proving the coverage of the entire population. We outlined a correlation matrix by computing the Pearson correlation coefficient (r) for every pair of data sets. The analysis revealed high linearity between all data sets possessing r < 0.9969 and two-tailed P values < 0.0001 (Figure 5D). The miR-Q approach provides an advantage over other detection chemistries based e.g. on hairpin reverse primers allowing to capture the entire expression of a given miRNA including the canonical molecule as well as isomiRs.
Salmonella infection of human macrophages results in specific miRNA response proven by miR-Q arrays
The presented approach is the first custom and non-commercial protocol for RT-qPCR arrays based on a miRNA specific RT-qPCR called miR-Q. The developed miR-Q arrays enable reliable and feasible quantification of small non-coding RNAs as exemplified by miRNA expression analysis during monocyte differentiation and infection. On one hand, well known miRNA dysregulation verified the accuracy of the presented method. On the other hand, several dysregulated miRNAs were newly identified in differentiated as well as infected human monocytes by means of the presented method and will provide a basis for our future studies.
Cell culture and RNA isolation
The human acute monocytic leukemia cell line THP-1 (DSMZ ACC 16) was cultured in suspension using RPMI 1640 (Biochrom AG) supplemented with 10% fetal bovine serum superior (Biochrom AG) and 10 μg/ml gentamicin (Biochrom AG) and was passaged twice weekly. THP-1 were in vitro differentiated into macrophages by adding 250 ng/ml phorbol-12-myristate-13-acetate (PMA). Differentiated cells were lysed after 28 h and total RNA was isolated using the miRVana Isolation Kit (Life Technologies). RNA integrity numbers (RIN) were determined using an Agilent 2100 Bioanalyzer and RNA Nano Chips (Agilent). All RNA isolations used in this study possessed RINs higher than 9.
Monocytic THP-1 were seeded in 6-well plates without gentamicin at a density of 5 × 105 cells per well. Afterwards, they were stimulated with viable Salmonella serovar Typhymurium DT104 at a multiplicity of infection (MOI) of 10 in triplicate. 2 h post stimulation adherent as well as non-adherent cells were washed twice with warm PBS and lysed for total RNA isolation. Non-treated THP-1 served as controls. Macrophages derived from monocytic THP-1 were also infected with Salmonella serovar Typhymurium DT104. For this purpose, 3.5 × 105 cells per well were seeded in 6-well plates and differentiated as described above. Macrophages remained either non-infected or were infected with exponential-phase Salmonella serovar Typhymurium DT104 at an MOI of 10. 2 h post infection supernatants were removed and adherent cells were washed twice with warm PBS. Adherent macrophages were lysed and total RNA was isolated as described above.
All miRNA specific oligonucleotides for RT as well as qPCR were designed as described earlier  and were synthesised and HPLC purified by Metabion AG and Eurogentec Deutschland GmbH. The miRNA sequences were taken from the miRNA database miRBase 17 .
Individual and multiplexed reverse transcription
RT of miRNAs for single RT-qPCR was performed as described earlier . For multiplexed RT, a pool of miRNA- as well as reference snRNA-specific DNA-oligonucleotide (RT6-'miRNA' or RT6-'snRNA' and the RevertAid™ M-MuLV Reverse Transcriptase (Fermentas GmbH) were employed to transcribe miRNAs and reference snRNAs into cDNA. The RT-6-Pool represented a blend of DNA-oligonucleotides specific for 26 miRNAs as well as 5 snRNAs serving as reference genes for normalisation. The final concentration of the RT-6-Pool was 1 μM representing 32.3 nM of each DNA-oligonucleotide. The reaction was performed in 200 μl PCR tubes using the Veriti® Thermal Cycler (Life Technologies). Between 500 ng and 1 μg total RNA and 1 μl of the RT-6-Pool was first prepared in a 5 μl volume. The mixture was incubated at 70°C for 5 min and chilled on ice. After adding 5× RT-Buffer, 1 mM dNTPs and 200 U Reverse Transcriptase, the volume was adjusted to 10 μl by adding nuclease-free water. The reaction was started at 37°C for 5 min followed by 42°C for 2 h. The enzyme was inactivated by heating at 95°C for 5 min. Afterwards the cDNA pool was cooled down for at least 1 min on ice and 90 μl of nuclease-free water was added yielding the template for qPCR arrays. RT reactions serving for comparative analyses were always run in parallel using identical amounts of total RNA.
RT-qPCR arrays and conventional miR-Q
Arrayed triplicate measurement of 26 miRNAs and 5 reference snRNAs was performed in 10 μl final reaction volume by means of SYBR Green detection chemistry using the SensiMix SYBR Hi-ROX Kit (Bioline GmbH) and the Step One Plus Cycler (Life Technologies). All reactions were carried out using clear MicroAmp® Fast 96-Well Reaction Plates (Life Technologies) that were sealed with adhesive films. Firstly, a master mix was prepared in a 945 μl volume consisting of 2× SensiMix SYBR Hi-ROX, 100 nM of each universal primer (MP-fw and MP-rev) and 100 μl of diluted RT-reaction. After dispensing 9 μl of the master mix in wells of a 96 well plate, 1 μl of each miRNA- or snRNA-specific reverse primer ('miRNA or snRNA'-rev) was added to respective wells of the array at a final concentration of 5 nM. The amplification was carried out via the first step at 95°C for 10 min, followed by 40 cycles with 15 s at 95°C, 20 s at 60°C and 20 s at 72°C. The fluorescence signal was acquired at 60°C. Quality control was performed by subsequent melting curve analysis by 95°C for 1 min, 60°C for 2 min, ramping from 60°C to 95°C at 2°C/min and acquiring the signal. Samples containing water instead of the specific 'miRNA or snRNA'-rev primer served as negative controls (NC). Conventional miR-Q was performed as described previously .
Normalisation of miRNA expression and comparative quantification
Normalisation of miRNA expression was performed using a set of snRNAs: SNORD44 (NR_002750.2), SNORD47 (NR_002746.1), SNORD48 (NR_002745.1), SNORD52 (NR_002742.2) and RNU6 (NR_004394). After calculating the Cq mean of each reference snRNA, the Cq geometric mean of all reference snRNAs was used to normalise the miRNA expression values. The difference between the Cq of the miRNA of interest (Cq miRoI) and the calculated geometric mean (Cq norm) was calculated yielding the ΔCq sample or ΔCq calibrator, respectively. The relative quantification (ΔΔCq) was performed by determining the difference between ΔCq sample and ΔCq calibrator. Fold differences were determined by calculating 2 to the power of-ΔΔCq.
Acknowledgements and funding
The authors are grateful to Barbara Kutz-Lohroff for excellent technical assistance. Furthermore we thank Dr. Pawel Janczyk for providing Salmonella serovar Typhimurium DT104. This work was supported by the Deutsche Forschungsgemeinschaft (SH 465/1-1 and SFB 852 project B4).
- Huntzinger E, Izaurralde E: Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nat Rev Genet. 2011, 12 (2): 99-110. 10.1038/nrg2936.View ArticlePubMedGoogle Scholar
- Brennecke J, Stark A, Russell RB, Cohen SM: Principles of microRNA-target recognition. PLoS Biol. 2005, 3 (3): e85-10.1371/journal.pbio.0030085.PubMed CentralView ArticlePubMedGoogle Scholar
- Farh KK, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, Burge CB, Bartel DP: The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science. 2005, 310 (5755): 1817-1821. 10.1126/science.1121158.View ArticlePubMedGoogle Scholar
- Sales G, Coppe A, Bisognin A, Biasiolo M, Bortoluzzi S, Romualdi C: MAGIA, a web-based tool for miRNA and Genes Integrated Analysis. Nucleic acids research. 2010, 352-359. 38 Suppl
- Guenin-Mace L, Carrette F, Asperti-Boursin F, Le Bon A, Caleechurn L, Di Bartolo V, Fontanet A, Bismuth G, Demangel C: Mycolactone impairs T cell homing by suppressing microRNA control of L-selectin expression. Proceedings of the National Academy of Sciences of the United States of America. 2011, 108 (31): 12833-12838. 10.1073/pnas.1016496108.PubMed CentralView ArticlePubMedGoogle Scholar
- Ma F, Xu S, Liu X, Zhang Q, Xu X, Liu M, Hua M, Li N, Yao H, Cao X: The microRNA miR-29 controls innate and adaptive immune responses to intracellular bacterial infection by targeting interferon-gamma. Nature immunology. 2011, 12 (9): 861-869. 10.1038/ni.2073.View ArticlePubMedGoogle Scholar
- Schulte LN, Eulalio A, Mollenkopf HJ, Reinhardt R, Vogel J: Analysis of the host microRNA response to Salmonella uncovers the control of major cytokines by the let-7 family. The EMBO journal. 2011, 30 (10): 1977-1989. 10.1038/emboj.2011.94.PubMed CentralView ArticlePubMedGoogle Scholar
- Sharbati J, Lewin A, Kutz-Lohroff B, Kamal E, Einspanier R, Sharbati S: Integrated microRNA-mRNA-analysis of human monocyte derived macrophages upon Mycobacterium avium subsp. hominissuis infection. PloS one. 2011, 6 (5): e20258-10.1371/journal.pone.0020258.PubMed CentralView ArticlePubMedGoogle Scholar
- Sharbati-Tehrani S, Kutz-Lohroff B, Bergbauer R, Scholven J, Einspanier R: miR-Q: a novel quantitative RT-PCR approach for the expression profiling of small RNA molecules such as miRNAs in a complex sample. BMC Mol Biol. 2008, 9 (1): 34-10.1186/1471-2199-9-34.PubMed CentralView ArticlePubMedGoogle Scholar
- Basso K, Sumazin P, Morozov P, Schneider C, Maute RL, Kitagawa Y, Mandelbaum J, Haddad J, Chen CZ, Califano A: Identification of the human mature B cell miRNome. Immunity. 2009, 30 (5): 744-752. 10.1016/j.immuni.2009.03.017.PubMed CentralView ArticlePubMedGoogle Scholar
- Gurlevik E, Woller N, Schache P, Malek NP, Wirth TC, Zender L, Manns MP, Kubicka S, Kuhnel F: p53-dependent antiviral RNA-interference facilitates tumor-selective viral replication. Nucleic acids research. 2009, 37 (12): 84-10.1093/nar/gkp374.View ArticleGoogle Scholar
- Li J, Guo H, Shi Z, Tu C: In vitro inhibition of CSFV replication by retroviral vector-mediated RNA interference. Journal of virological methods. 2010, 169 (2): 316-321. 10.1016/j.jviromet.2010.07.036.View ArticlePubMedGoogle Scholar
- Ramachandran D, Roy U, Garg S, Ghosh S, Pathak S, Kolthur-Seetharam U: Sirt1 and mir-9 expression is regulated during glucose-stimulated insulin secretion in pancreatic beta-islets. The FEBS journal. 2011, 278 (7): 1167-1174. 10.1111/j.1742-4658.2011.08042.x.View ArticlePubMedGoogle Scholar
- Tang F, Hajkova P, Barton SC, O'Carroll D, Lee C, Lao K, Surani MA: 220-plex microRNA expression profile of a single cell. Nature protocols. 2006, 1 (3): 1154-1159. 10.1038/nprot.2006.161.View ArticlePubMedGoogle Scholar
- Moltzahn F, Olshen AB, Baehner L, Peek A, Fong L, Stoppler H, Simko J, Hilton JF, Carroll P, Blelloch R: Microfluidic-based multiplex qRT-PCR identifies diagnostic and prognostic microRNA signatures in the sera of prostate cancer patients. Cancer research. 2011, 71 (2): 550-560. 10.1158/0008-5472.CAN-10-1229.PubMed CentralView ArticlePubMedGoogle Scholar
- Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL: The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clinical chemistry. 2009, 55 (4): 611-622. 10.1373/clinchem.2008.112797.View ArticlePubMedGoogle Scholar
- Castro FO, Sharbati S, Rodriguez-Alvarez LL, Cox JF, Hultschig C, Einspanier R: MicroRNA expression profiling of elongated cloned and in vitro-fertilized bovine embryos. Theriogenology. 2010, 73 (1): 71-85. 10.1016/j.theriogenology.2009.08.003.View ArticlePubMedGoogle Scholar
- Sharbati-Tehrani S, Kutz-Lohroff B, Scholven J, Einspanier R: Concatameric cloning of porcine microRNA molecules after assembly PCR. Biochem Biophys Res Commun. 2008, 375 (3): 484-489. 10.1016/j.bbrc.2008.08.048.View ArticlePubMedGoogle Scholar
- Sharbati S, Friedlander MR, Sharbati J, Hoeke L, Chen W, Keller A, Stahler PF, Rajewsky N, Einspanier R: Deciphering the porcine intestinal microRNA transcriptome. BMC Genomics. 2010, 11: 275-10.1186/1471-2164-11-275.PubMed CentralView ArticlePubMedGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3 (7):
- Wyman SK, Knouf EC, Parkin RK, Fritz BR, Lin DW, Dennis LM, Krouse MA, Webster PJ, Tewari M: Post-transcriptional generation of miRNA variants by multiple nucleotidyl transferases contributes to miRNA transcriptome complexity. Genome research. 2011, 21 (9): 1450-1461. 10.1101/gr.118059.110.PubMed CentralView ArticlePubMedGoogle Scholar
- Guo L, Yang Q, Lu J, Li H, Ge Q, Gu W, Bai Y, Lu Z: A comprehensive survey of miRNA repertoire and 3' addition events in the placentas of patients with pre-eclampsia from high-throughput sequencing. PloS one. 2011, 6 (6): e21072-10.1371/journal.pone.0021072.PubMed CentralView ArticlePubMedGoogle Scholar
- Taganov KD, Boldin MP, Chang KJ, Baltimore D: NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proceedings of the National Academy of Sciences of the United States of America. 2006, 103 (33): 12481-12486. 10.1073/pnas.0605298103.PubMed CentralView ArticlePubMedGoogle Scholar
- Cekaite L, Clancy T, Sioud M: Increased miR-21 expression during human monocyte differentiation into DCs. Frontiers in bioscience (Elite edition). 2010, 2: 818-828.View ArticleGoogle Scholar
- Forrest AR, Kanamori-Katayama M, Tomaru Y, Lassmann T, Ninomiya N, Takahashi Y, de Hoon MJ, Kubosaki A, Kaiho A, Suzuki M: Induction of microRNAs, mir-155, mir-222, mir-424 and mir-503, promotes monocytic differentiation through combinatorial regulation. Leukemia. 2010, 24 (2): 460-466. 10.1038/leu.2009.246.View ArticlePubMedGoogle Scholar
- Yuan B, Dong R, Shi D, Zhou Y, Zhao Y, Miao M, Jiao B: Down-regulation of miR-23b may contribute to activation of the TGF-beta1/Smad3 signalling pathway during the termination stage of liver regeneration. FEBS letters. 2011, 585 (6): 927-934. 10.1016/j.febslet.2011.02.031.View ArticlePubMedGoogle Scholar
- Ashcroft GS: Bidirectional regulation of macrophage function by TGF-beta. Microbes and infection/Institut Pasteur. 1999, 1 (15): 1275-1282. 10.1016/S1286-4579(99)00257-9.View ArticlePubMedGoogle Scholar
- Buck AH, Perot J, Chisholm MA, Kumar DS, Tuddenham L, Cognat V, Marcinowski L, Dolken L, Pfeffer S: Post-transcriptional regulation of miR-27 in murine cytomegalovirus infection. RNA. 2010, 16 (2): 307-315. 10.1261/rna.1819210.PubMed CentralView ArticlePubMedGoogle Scholar
- Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic acids research. 2011, D152-157. 39 Database
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.