Biology:MicroRNA sequencing

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MicroRNA sequencing (miRNA-seq), a type of RNA-Seq, is the use of next-generation sequencing or massively parallel high-throughput DNA sequencing to sequence microRNAs, also called miRNAs. miRNA-seq differs from other forms of RNA-seq in that input material is often enriched for small RNAs. miRNA-seq allows researchers to examine tissue-specific expression patterns, disease associations, and isoforms of miRNAs, and to discover previously uncharacterized miRNAs. Evidence that dysregulated miRNAs play a role in diseases such as cancer[1] has positioned miRNA-seq to potentially become an important tool in the future for diagnostics and prognostics as costs continue to decrease.[2] Like other miRNA profiling technologies, miRNA-Seq has both advantages (sequence-independence, coverage) and disadvantages (high cost, infrastructure requirements, run length, and potential artifacts).[3]

Introduction

MicroRNAs (miRNAs) are a family of small ribonucleic acids, 21-25 nucleotides in length, that modulate protein expression through transcript degradation, inhibition of translation, or sequestering transcripts.[4][5][6] The first miRNA to be discovered, lin-4, was found in a genetic mutagenesis screen to identify molecular elements controlling post-embryonic development of the nematode Caenorhabditis elegans.[7] The lin-4 gene encoded a 22 nucleotide RNA with conserved complementary binding sites in the 3’-untranslated region of the lin-14 mRNA transcript[8] and downregulated LIN-14 protein expression.[9] miRNAs are now thought to be involved in the regulation of many developmental and biological processes, including haematopoiesis (miR-181 in Mus musculus[10]), lipid metabolism (miR-14 in Drosophila melanogaster[11]) and neuronal development (lsy-6 in Caenorhabditis elegans[12]).[6] These discoveries necessitated development of techniques able to identify and characterize miRNAs, such as miRNA-seq.

History

MicroRNA sequencing (miRNA-seq) was developed to take advantage of next-generation sequencing or massively parallel high-throughput sequencing technologies in order to find novel miRNAs and their expression profiles in a given sample. miRNA sequencing in and of itself is not a new idea, initial methods of sequencing utilized Sanger sequencing methods. Sequencing preparation involved creating libraries by cloning of DNA reverse transcribed from endogenous small RNAs of 21–25 bp size selected by column and gel electrophoresis.[13] However, this method is exhaustive in terms of time and resources, as each clone has to be individually amplified and prepared for sequencing. This method also inadvertently favors miRNAs that are highly expressed.[6] Next-generation sequencing eliminates the need for sequence specific hybridization probes required in DNA microarray analysis as well as laborious cloning methods required in the Sanger sequencing method. Additionally, next-generation sequencing platforms in the miRNA-SEQ method facilitate the sequencing of large pools of small RNAs in a single sequencing run.[14]

miRNA-seq can be performed using a variety of sequencing platforms. The first analysis of small RNAs using miRNA-seq methods examined approximately 1.4 million small RNAs from the model plant Arabidopsis thaliana using Lynx Therapeutics' Massively Parallel Signature Sequencing (MPSS) sequencing platform. This study demonstrated the potential of novel, high-throughput sequencing technologies for the study of small RNAs, and it showed that genomes generate large numbers of small RNAs with plants as particularly rich sources of small RNAs.[15] Later studies used other sequencing technologies, such as a study in C. elegans which identified 18 novel miRNA genes as well as a new class of nematode small RNAs termed 21U-RNAs.[16] Another study comparing small RNA profiles of human cervical tumours and normal tissue, utilized the Illumina Genome Analyzer to identify 64 novel human miRNA genes as well as 67 differentially expressed miRNAs.[17] Applied Biosystems SOLiD sequencing platform has also been used to examine the prognostic value of miRNAs in detecting human breast cancer.[18]

Methods

miRNA Library Preparation

Small RNA Preparation

Sequence library construction can be performed using a variety of different kits depending on the high-throughput sequencing platform being employed. However, there are several common steps for small RNA sequencing preparation.[19][20]

Total RNA Isolation

In a given sample all the RNA is extracted and isolated using an isothiocyanate/phenol/chloroform (GITC/phenol) method or a commercial product such as Trizol (Invitrogen) reagent. A starting quantity of 50-100 μg total RNA, 1 g of tissue typically yields 1 mg of total RNA, is usually required for gel purification and size selection.[20] Quality control of the RNA is also measured, for example running an RNA chip on Caliper LabChipGX (Caliper Life Sciences).

Size Fractionation of small RNAs by Gel Electrophoresis

Isolated RNA is run on a denaturing polyacrylamide gel. An imaging method such as radioactive 5’-32P-labeled oligonucleotides along with a size ladder is used to identify a section of the gel containing RNA of the appropriate size, reducing the amount of material ultimately sequenced. This step does not have to be necessarily carried out before the ligation and reverse transcription steps outlined below.[19][20]

Ligation

The ligation step adds DNA adaptors to both ends of the small RNAs, which act as primer binding sites during reverse transcription and PCR amplification. An adenylated single strand DNA 3’adaptor followed by a 5’adaptor is ligated to the small RNAs using a ligating enzyme such as T4 RNA ligase2. The adaptors are also designed to capture small RNAs with a 5’ phosphate group, characteristic microRNAs, rather than RNA degradation products with a 5’ hydroxyl group.[19][20]

Reverse Transcription and PCR Amplification

This step converts the small adaptor ligated RNAs into cDNA clones used in the sequencing reaction. There are many commercial kits available that will carry out this step using some form of reverse transcriptase. PCR is then carried out to amplify the pool of cDNA sequences. Primers designed with unique nucleotide tags can also be used in this step to create ID tags in pooled library multiplex sequencing.[19][20]

Sequencing

The actual RNA sequencing varies significantly depending on the platform used. Three common next-generation sequencing[21] platforms are Pyrosequencing on the 454 Life Sciences platform,[22] polymerase-based sequence-by-synthesis on the Illumina platform,[23] or sequencing by ligation on the ABI Solid Sequencing platform.[24]

Data Analysis

Central to miRNA-seq data analysis is the ability to 1) obtain miRNA abundance levels from sequence reads, 2) discover novel miRNAs and then be able to 3) determine the differentially expressed miRNA and their 4) associated mRNA gene targets.

miRNA-seq Data Analysis

miRNA Alignment & Abundance Quantification

miRNAs may be preferentially expressed in certain cell types, tissues, stages of development, or in particular disease states such as cancer.[1] Since deep sequencing (miRNA-seq) generates millions of reads from a given sample, it allows us to profile miRNAs; whether it may be by quantifying their absolute abundance, to discover their variants (known as isomirs[25]) Note that given that the average length of sequence reads are longer than the average miRNA (17-25 nt), the 3’ and 5’ ends of the miRNA should be found on the same read. There are several miRNA abundance quantification algorithms.[21][26] Their general steps are as follows:[27]

  1. After sequencing, the raw sequence reads are filtered based on quality. The adaptor sequences are also trimmed off the raw sequence reads.
  2. The resulting reads are then formatted into a fasta file where the copy number and sequence is recorded for each unique tag.
  3. Sequences that may represent E. Coli contamination are identified by a BLAST search against an E. Coli database and are removed from analysis.
  4. Each of the remaining sequences are aligned against a miRNA sequence database (such as miRBase[28]) In order to account for imperfect DICER processing, a 6nt overhang on the 3’ end, and 3nt on the 5’ end are allowed.
  5. The reads that do not align to the miRNA database are then loosely aligned to miRNA precursors to detect miRNAs that might carry mutations or those that have gone through RNA editing.
  6. The read counts for each miRNA are then normalized to the total number of mapped miRNAs to report the abundance of each miRNA.

Novel miRNA Discovery

Another advantage of miRNA-seq is that it allows the discovery of novel miRNAs that may have eluded traditional screening and profiling methods.[27] There are several novel miRNA discovery algorithms. Their general steps are as follows:

  1. Obtain reads that did not align to known miRNA sequences, and map them to the genome.
  2. RNA Folding Method
    1. For the miRNA sequences were an exact match is found, obtain the genomic sequence including ~100bp of flanking sequence on either side, and run the RNA through RNA folding software such as the Vienna package.[29]
    2. Folded sequences that lie on one arm of the miRNA hairpin and have a minimum free energy of less than ~25kcal/mol are shortlisted as putative miRNA.
    3. The shortlisted sequences are trimmed down to include only the possible precursor sequence and are then refolded to ensure that the precursor was not artificially stabilized by neighbouring sequences.
    4. The resulting folded sequences are considered novel miRNAs if the miRNA sequence falls within one arm of the hairpin, and are highly conserved between species.
  3. Star Strand Expression Method (miRdeep[30])
    1. Novel miRNA sequences are identified based on the characteristic expression pattern that they display due to DICER processing: higher expression of the mature miRNA over the star strand and loop sequences.

Differential Expression Analysis

After the abundances of miRNAs are quantified for each sample, their expression levels can be compared between samples. One would then be able to identify miRNA that are preferentially expressed that particular time points, or in particular tissues or disease states. After normalizing for the number of mapped reads between samples, one can use a host of statistical tests (like those used in gene expression profiling) to determine differential expression

Target Prediction

Identifying a miRNA's mRNA targets will provide an understanding of the genes or networks of genes whose expression they regulate.[31] Public databases provide predictions of miRNA targets. But to better distinguish true positive predictions from false positive predictions, miRNA-seq data can be integrated to mRNA-seq data to observe for miRNA:mRNA functional pairs. RNA22,[32] TargetScan,[33][34][35][36][37][38] miRanda,[39] and PicTar[40] are software designed for this purpose. A list of prediction software is given here. The general steps are:

  1. Determine miRNA:mRNA binding pairs, complementarity between the miRNA sequences at the 3’-UTR of the mRNA sequence is identified.
  2. Determine the degree of conservation of miRNA:mRNA binding pairs across species. Typically, more highly binding pairs are less likely to be false positives of prediction.
  3. Observe for evidence of miRNA targeting in mRNA-seq or protein expression data: where the miRNA expression is high, the gene and protein expression of its target gene should be low.

Target Validation for Cleaved mRNA Targets

Many miRNAs function to direct cleavage of their mRNA targets; this is particularly true in plants, and thus high-throughput sequencing methods have been developed to take advantage of this property of miRNAs by sequencing the uncapped 3' ends of cleaved or degraded mRNAs. These methods are known as Degradome sequencing or PARE.[41][42] Validation of target cleavage in specific mRNAs is typically performed using a modified version of 5' Rapid Amplification of cDNA Ends with a gene-specific primer.

Applications

Identification of Novel miRNAs

miRNA-seq has revealed novel miRNAs that were previously eluded in traditional miRNA profiling methods. Examples of such findings are in embryonic stem cells,[25] chicken embryos,[43] acute lymphoblastic leukaemia,[44] diffuse large b-cell lymphoma and b-cells,[45] acute myeloid leukemia,[46] and lung cancer.[47]

Disease biomarkers

Micro RNAs are important regulators of almost all cellular processes such as survival, proliferation, and differentiation. Consequently, it is not unexpected that miRNAs are involved in various aspects of cancer through the regulation of onco- and tumor suppressor gene expression. In combination with the development of high-throughput profiling methods, miRNAs have been identified as biomarkers for cancer classification, response to therapy, and prognosis.[48] Additionally, because miRNAs regulate gene expression they can also reveal perturbations in important regulatory networks that may be driving a particular disorder.[48] Several applications of miRNAs as biomarkers and predictors of disease are given below.

Table 1: Cancer subtypes distinguished by microRNAs
Cancer type miRNAs α Ref.
Breast
ER Status miR-26a/b, miR-30 family, miR-29b, miR-155, miR-342, miR-206, miR-191
PR status let-7c, miR-29b, miR-26a, miR-30 family, miR-520g [49][50]
HER2/neu status miR-520d, miR-181c, miR-302c, miR-376b, miR-30e [51][50]
Lung
Squamous vs non-squamous cell miR-205 [52]
Small cell vs non-small cell miR-17-5p, miR-22, miR-24, miR-31 [48]
Gastric
Diffuse vs intestinal miR-29b/c, miR-30 family, miR-135a/b [53]
Endometrial
Endometrioid vs uterine papillary miR-19a/b, miR-30e-5p, miR-101, miR-452, miR-382, miR-15a, miR-29c [54]
Renal
Clear cell vs papillary miR-424, miR-203, miR-31, miR-126 [55]
Oncocytoma vs chromophobe miR-200c, miR-139-5p [55]
Myeloma
with t(14;16) miR-1, miR-133a [56]
with t(4;14) miR-203, miR-155, miR-375 [56]
with t(11;14) miR-125a, miR-650, miR-184 [56]
Acute myeloid leukemia
with t(15;17) miR-382, miR-134, miR-376a, miR-127, miR-299-5p, miR-323 [57]
with t(8;21) or inv(16) let-7b/c, miR-127 [57]
with NPM1 mutations miR-10a/b, let-7, miR-29, miR-204, miR-128a, miR-196a/b [57][58]
with FLT3 ITD miR-155 [57][58][59]
Chronic lymphocytic leukemia
ZAP-70 levels and IgVH status miR-15a, miR-195, miR-221, miR-155, miR-23b [60]
Melanoma
with BRAF V600E miR-193a, miR-338, miR-565 [61]
Lymphoma
Diffuse Large B Cell Lymphoma has-miR-128, has-miR-129-3p, has-miR-152, has-miR-155, has-miR-185, has-miR-193a-5p, has-miR-196b, has-miR-199b-3p, has-miR-20b, has-miR-23a, has-miR-27a, has-miR-28-5p, has-miR-301a, has-miR-331-3p, has-miR-365, has-miR-625, has-miR-9 [45]

αThis is not a comprehensive list of miRNAs involved with these malignancies.

Comparison With Other Methods of miRNA Profiling

The disadvantages of using miRNA-seq over other methods of miRNA profiling are that it is more expensive, generally requires a larger amount of total RNA, involves extensive amplification, and is more time-consuming than microarray and qPCR methods.[3] As well, miRNA-seq library preparation methods seem to have systematic preferential representation of the miRNA complement, and this prevents accurate determination of miRNA abundance.[62] At the same time, the approach is hybridization independent and therefore does not require a priori sequence information. Because of this, one can obtain sequences of novel miRNAs and miRNA isoforms (isoMirs), distinguish sequentially similar miRNAs, and identify point mutations.[63]

Platform Comparison of miRNA Profiling

[3]

Table 2: Platform Comparison of miRNA Profiling
qPCR Microarray Sequencing
Throughput time ~6 hours ~2 days 1–2 weeks
Total RNA required 500 ng 100-1,000 ng 500-5,000 ng
Dynamic range detected Six orders of magnitude Four orders of magnitude Five or more orders of magnitude
Infrastructure and technical requirements Few Moderate Substantial
Cost per sample (USD) $400 $250–$350 $500–$700

References

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  49. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named Iorio2005
  50. ↑ 50.0 50.1 Lowery, Aoife J; Miller, Nicola; Devaney, Amanda; McNeill, Roisin E; Davoren, Pamela A; Lemetre, Christophe; Benes, Vladimir; Schmidt, Sabine et al. (2009). "MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer". Breast Cancer Research 11 (3): R27. doi:10.1186/bcr2257. ISSN 1465-5411. PMID 19432961. 
  51. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named BlenkironGoldstein2007
  52. ↑ Lebanony, D.; Benjamin, H.; Gilad, S.; Ezagouri, M.; Dov, A.; Ashkenazi, K.; Gefen, N.; Izraeli, S. et al. (2009). "Diagnostic Assay Based on hsa-miR-205 Expression Distinguishes Squamous From Nonsquamous Non-Small-Cell Lung Carcinoma". Journal of Clinical Oncology 27 (12): 2030–2037. doi:10.1200/JCO.2008.19.4134. ISSN 0732-183X. PMID 19273703. 
  53. ↑ Ueda, Tetsuya; Volinia, Stefano; Okumura, Hiroshi; Shimizu, Masayoshi; Taccioli, Cristian; Rossi, Simona; Alder, Hansjuerg; Liu, Chang-gong et al. (2010). "Relation between microRNA expression and progression and prognosis of gastric cancer: a microRNA expression analysis". The Lancet Oncology 11 (2): 136–146. doi:10.1016/S1470-2045(09)70343-2. ISSN 1470-2045. PMID 20022810. 
  54. ↑ Ratner, Elena S.; Tuck, David; Richter, Christine; Nallur, Sunitha; Patel, Rajeshvari M.; Schultz, Vince; Hui, Pei; Schwartz, Peter E. et al. (2010). "MicroRNA signatures differentiate uterine cancer tumor subtypes". Gynecologic Oncology 118 (3): 251–257. doi:10.1016/j.ygyno.2010.05.010. ISSN 0090-8258. PMID 20542546. 
  55. ↑ 55.0 55.1 Fridman, Eddie; Dotan, Zohar; Barshack, Iris; David, Miriam Ben; Dov, Avital; Tabak, Sarit; Zion, Orit; Benjamin, Sima et al. (2010). "Accurate Molecular Classification of Renal Tumors Using MicroRNA Expression". The Journal of Molecular Diagnostics 12 (5): 687–696. doi:10.2353/jmoldx.2010.090187. ISSN 1525-1578. PMID 20595629. 
  56. ↑ 56.0 56.1 56.2 Gutiérrez, N C; Sarasquete, M E; Misiewicz-Krzeminska, I; Delgado, M; De Las Rivas, J; Ticona, F V; Fermiñán, E; Martín-Jiménez, P et al. (2010). "Deregulation of microRNA expression in the different genetic subtypes of multiple myeloma and correlation with gene expression profiling". Leukemia 24 (3): 629–637. doi:10.1038/leu.2009.274. ISSN 0887-6924. PMID 20054351. 
  57. ↑ 57.0 57.1 57.2 57.3 Jongen-Lavrencic, M.; Sun, S. M.; Dijkstra, M. K.; Valk, P. J. M.; Lowenberg, B. (2008). "MicroRNA expression profiling in relation to the genetic heterogeneity of acute myeloid leukemia". Blood 111 (10): 5078–5085. doi:10.1182/blood-2008-01-133355. ISSN 0006-4971. PMID 18337557. 
  58. ↑ 58.0 58.1 Garzon, R.; Garofalo, M.; Martelli, M. P.; Briesewitz, R.; Wang, L.; Fernandez-Cymering, C.; Volinia, S.; Liu, C.-G. et al. (2008). "Distinctive microRNA signature of acute myeloid leukemia bearing cytoplasmic mutated nucleophosmin". Proceedings of the National Academy of Sciences 105 (10): 3945–3950. doi:10.1073/pnas.0800135105. ISSN 0027-8424. PMID 18308931. Bibcode: 2008PNAS..105.3945G. 
  59. ↑ Marcucci, Guido; Radmacher, Michael D.; Maharry, Kati; Mrózek, Krzysztof; Ruppert, Amy S.; Paschka, Peter; Vukosavljevic, Tamara; Whitman, Susan P. et al. (2008). "MicroRNA Expression in Cytogenetically Normal Acute Myeloid Leukemia". New England Journal of Medicine 358 (18): 1919–1928. doi:10.1056/NEJMoa074256. ISSN 0028-4793. PMID 18450603. 
  60. ↑ Calin, George Adrian; Ferracin, Manuela; Cimmino, Amelia; Di Leva, Gianpiero; Shimizu, Masayoshi; Wojcik, Sylwia E.; Iorio, Marilena V.; Visone, Rosa et al. (2005). "A MicroRNA Signature Associated with Prognosis and Progression in Chronic Lymphocytic Leukemia". New England Journal of Medicine 353 (17): 1793–1801. doi:10.1056/NEJMoa050995. ISSN 0028-4793. PMID 16251535. 
  61. ↑ Caramuta, Stefano; Egyházi, Suzanne; Rodolfo, Monica; Witten, Daniela; Hansson, Johan; Larsson, Catharina; Lui, Weng-Onn (2010). "MicroRNA Expression Profiles Associated with Mutational Status and Survival in Malignant Melanoma". Journal of Investigative Dermatology 130 (8): 2062–2070. doi:10.1038/jid.2010.63. ISSN 0022-202X. PMID 20357817. 
  62. ↑ Linsen, Sam E V; de Wit, Elzo; Janssens, Georges; Heater, Sheila; Chapman, Laura; Parkin, Rachael K; Fritz, Brian; Wyman, Stacia K et al. (2009). "Limitations and possibilities of small RNA digital gene expression profiling". Nature Methods 6 (7): 474–476. doi:10.1038/nmeth0709-474. ISSN 1548-7091. PMID 19564845. 
  63. ↑ Git, A.; Dvinge, H.; Salmon-Divon, M.; Osborne, M.; Kutter, C.; Hadfield, J.; Bertone, P.; Caldas, C. (2010). "Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression". RNA 16 (5): 991–1006. doi:10.1261/rna.1947110. ISSN 1355-8382. PMID 20360395. 



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