Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Summary

Luminescent identification of functional elements in 3’ untranslated regions (3’UTRs) (3’LIFE) is a technique to identify functional regulation in 3’UTRs by miRNAs or other regulatory factors. This protocol utilizes high-throughput methodology such as 96-well transfection and luciferase assays to screen hundreds of putative interactions for functional repression.

Abstract

Luminescent Identification of Functional Elements in 3’UTRs (3’LIFE) allows the rapid identification of targets of specific miRNAs within an array of hundreds of queried 3’UTRs. Target identification is based on the dual-luciferase assay, which detects binding at the mRNA level by measuring translational output, giving a functional readout of miRNA targeting. 3’LIFE uses non-proprietary buffers and reagents, and publically available reporter libraries, making genome-wide screens feasible and cost-effective. 3’LIFE can be performed either in a standard lab setting or scaled up using liquid handling robots and other high-throughput instrumentation. We illustrate the approach using a dataset of human 3’UTRs cloned in 96-well plates, and two test miRNAs, let-7c and miR-10b. We demonstrate how to perform DNA preparation, transfection, cell culture and luciferase assays in 96-well format, and provide tools for data analysis. In conclusion 3'LIFE is highly reproducible, rapid, systematic, and identifies high confidence targets.

Introduction

The overall goal of this method is to detect and precisely map microRNA (miRNA) targets in high-throughput. MiRNAs are endogenous non-coding RNAs ~22 nucleotides in length. Following transcription and processing, mature miRNAs are incorporated in a protein complex called the RNA induced silencing complex (RISC). Each miRNA guides the RISC to target elements located primarily in the 3’untranslated regions (3’UTRs) of messenger RNAs (mRNAs), resulting in either translation repression or mRNA cleavage ¹. MiRNA recognize target sites based on standard Watson-Crick and G:U wobble base pairing, and are degenerate in nature, containing multiple mismatched base pairs and bulged regions. Many miRNAs are broadly conserved from plants to humans ^2,3, where they play a diverse range of biological roles. In metazoans miRNAs can influence multiple biological processes including cell fate decisions ⁴, developmental timing ⁵, and frequently exhibit tissue specific expression patterns ^6,7. MiRNA misexpression can also result in aberrant gene regulation, which can have substantial influence on cell behaviour based solely on the function of target genes. As such, miRNAs are linked to a wide range of diseases, including neurodegeneration ^8,9, diabetes ¹⁰ and cancer ¹¹. Bioinformatic and wet-bench approaches suggest that each miRNA may be capable of targeting hundreds to thousands of distinct mRNAs ^12-14, indicating that high-throughput or genome wide approaches are required to probe this large pool of potential interactions.

Identifying target genes is a critical component of mechanistically defining miRNA function, and to do so researchers must be able to reveal targets on a large scale. Several approaches have been developed to identify miRNA targets, including bioinformatic prediction algorithms, high-throughput sequencing of targeted mRNAs, and reporter based assays. Each of these approaches has inherent strengths and weaknesses. Given that miRNA targeting is guided by sequence specificity, most notably of nucleotides 2-6 of the miRNA (termed the seed region), several algorithms have been developed to predict miRNA targets throughout the genome of many organisms. These algorithms are trained using the observed base-pairing motifs of validated miRNA targets, and frequently utilize parameters such as stringent seed pairing, site conservation, and/or thermodynamic stability ¹⁵. While these filters refine the large number of putative targets with sufficient complementarity to only high confidence targets, they may exclude species specific and non-canonical miRNA target sites, which recent evidence suggests are widespread ^16-24. Furthermore, these predictions do not take into account mechanisms of mRNA processing that exclude miRNA target sites, such as alternative polyadenylation ²⁵, RNA editing ²⁶, RNA methylation ²⁷, and cooperative binding. As such, high false positive and false negative rates have been reported for many algorithms ^22,24,28. While these algorithms are useful to identify candidate miRNA targets for subsequent experimental validation, these high error rates limit the efficacy of bioinformatic approaches for systematic miRNA target detection.

To systematically probe for interactions between a given miRNA and potentially targeted 3’UTRs we have developed a high-throughput assay called Luminescent Identification of Functional Elements in 3’UTRs (3’LIFE) ²⁴. This assay measures direct interactions and translational repression of the test 3’UTR by a query miRNA using a dual luciferase reporter system. In this system, the 3’UTR of a gene of interest is cloned downstream of the firefly luciferase (fluc) reporter reading frame. The reporter construct is cotransfected with a query miRNA in HEK293T cells. MiRNA targeting is determined by measuring the relative change between the test fluc::3’UTR reporter and a second non-specific Renilla luciferase reporter. Importantly, luciferase assays detect functional miRNA/mRNA interactions that influence the translational output of the reporter. This is a key advantage over traditional methods to detect miRNA regulation, such as RT-qPCR and Western blots, in that this bypasses differences in mRNA degradation and translational repression, as well as changes in protein abundance independent of 3’UTR based regulation.

Luciferase assays are widely utilized to validate direct miRNA targets because of their relative simplicity and sensitivity, yet their use in high-throughput screens is limited by high costs associated with consumable reagents, the lack of 3’UTR libraries from public sources, and the absence of standardized luciferase protocols, leading to difficulties in comparing functional repression across multiple datasets. To facilitate the use of the 3’LIFE assay, we have placed emphasis on simplification of experimental design, utilization of non-commercial transfection ²⁴ and luciferase reagents ²⁹, creating a 3’UTR library which is regularly updated and expanded, and is available through a public plasmid repository ³⁰.

The scalability of the 3’LIFE assay allows screening of a large 3’UTR library for targeting by a given miRNA without biasing the screen towards bioinformatically identified genes. In addition to testing canonical and predicted interactions, this systematic approach allows the identification of novel targets driven via non-canonical and/or species-specific interactions. Importantly, the effect of miRNA targeting on protein production is generally understood to result in modest translational repression ^15,31, suggesting that a primary role of miRNA regulation is to fine-tune protein output, protect against aberrant levels of gene expression, and provide robustness to cell specific programs ^32,33. The sensitivity of the luciferase assay combined with the inherently large number of negative miRNA/mRNA interactions in the 3’LIFE screen allows the detection of subtle effects of miRNA targeting on a large number of genes, and the identification of multiple components of gene networks that are regulated by a given miRNA ²⁴.

Here we describe the 3'LIFE protocol, and demonstrate it’s feasibility by screening two well characterized miRNAs, miR-10b and let-7c against a panel of 275 human 3'UTRs (Figure 1).

Protocol

1. Cell Culture (24-48 hr prior to transfection) 24-48 hr prior to transfection seed a sufficient quantity of HEK293T cells based on the number of 96-well plates being transfected. NOTE: For consistent transfections, plate cells at a sufficient density to favor rapid division, yet not be at more than 70-90% confluency at the time of transfection. Each 96-well plate requires 9 x 106 cells (75,000 cells per well, and 120 wells per plate to account for use of reservoir and multic…

Representative Results

The luminometer output file contains raw measurements for both firefly and Renilla luciferase proteins. This raw format is compatible with the “3’LIFE – single plate analysis” and “3’LIFE – multiplate analysis” spreadsheets available from the Mangone lab website (www.mangonelab.com). The single plate analysis spreadsheet automatically calculates firefly/Renilla ratio, normalizes each miRNA to the appropriate negativ…

Discussion

The 3’LIFE assay identifies functional miRNA targets in 3’UTRs in high-throughput. This assay is useful for researchers who wish to experimentally identify a large number of putative targets for their miRNA of interest. The 3’LIFE assay is a powerful approach to query for 3’UTR driven regulation, in that the assay provides a functional measure of miRNA targeting, and the binary testing of a single reporter::3’UTR against a single miRNA can confidently address the targeting status of individu…

Materials

Reagents
glycylglycine	Sigma	G1127-25G
Kx PO4	Sigma	P2222
EGTA	Sigma	E3889
ATP	Sigma	FLAAS
DTT	Sigma	D0632
MgSO4	Sigma	M7506
CoA	Sigma	C4282
luciferin	Sigma	L9504
NaCl	Sigma	S7653
Na2EDTA	Sigma	E0399
K H2 P O4	Sigma	1551139
BSA	Sigma	A2153
NaN3	Sigma	S2002
Coelenterazine	Sigma	C3230
PBS/HEPES	Corning	21-040-CV
DMEM	Sigma	D5546
FBS	Sigma	F2442
Pennicilin/Streptomicin	Sigma	P4333
Trypsin		T2600000
Consumables
MaxiPrep Kit	Promega	A2392
96-well miniprep plate	Pall	8032
96-well shuttle plates	Lonza	V4SP-2096
5x Lysis Buffer	Promega	E1941
Instruments
96-well GloMax Plate Reader	Promega	E9032
Biomech FX Liquid Handler Robot	Beckmann	A31842
4D-Nucleofector Core Unit	Lonza	AAF-1001
96-well Shuttle System	Lonza	AAM-1001
Cell Counter Countess	Invitrogen	C10227