DNA Microarrays

At the beginning of this work little was known about the miRNA dependent effects on mRNA levels.

The current opinion is that miRNAs primarily repress protein levels with little effect on mRNA levels (Olsen and Ambros, 1999; Wightman et al., 1993). Nevertheless, translational repression also leads to a slight destabilization of mRNA transcripts, that can be detected in microarrays. DNA microarrays are used to analyze changes in mRNA levels globally across the whole genome. Indeed, several studies using DNA microarrays to analyze miRNA targets have been previously performed (Giraldez et al., 2006; Krutzfeldt et al., 2005; Lim et al., 2005; Rehwinkel et al., 2006). Detailed analysis of miRNA mediated mRNA destabilization demonstrated that mRNA decrease is associated with poly(A)-tail shortening, de-capping and higher mRNA turnover (Behm-Ansmant et al., 2006; Eulalio et al., 2009; Giraldez et al., 2006; Wu et al., 2006).

In this work dual color whole human expression arrays from Agilent were used to analyze transcriptoms of miRNA expressing cells in comparison to control cells. To analyze the effects of all EBV miRNAs, BJAB and Beas-2b stable cell lines as well as adenovirus infected HNEpC were analyzed by transcriptome microarrays. The first analysis was performed with BJAB stable cell lines and with BJAB stable cells expressing only GFP as a control. Technical duplicates were performed and resulted in a large number of regulated genes being identified. One minor technical problem that was encountered was that GFP expression was higher in the control cells than in the miRNA expressing cells. To minimize false positives due to this, two additional arrays were performed using

Discussion

BJAB stable cells expressing IRES-GFP or just BJAB cells as controls. The common overlap of all experiments was interpreted as the most probable miRNA targets.

Analysis of array data was performed using the Genepix program and the Genome browser, a program written by Adam Grundhoff. Genepix allows identification of stained spots, mapping to the genomic region and normalization of all data. The genome browser then includes standard deviations and cut off values to be calculated. Normally 2x standard deviation is used to define the cut off for minimizing false positive signals resulting from fluctuations of background. The identification of genes passing this criteria from the three independent BJAB stable cell line set-ups was then evaluated. Furthermore, with the 2x stdv. cut off genes were enriched that were very strong differently expressed. It is frequently believed that mRNA levels are directly correlated with protein levels. A study investigating protein and mRNA expression from freshly isolated human monocytes for example displayed a good correlation for all studied genes (Guo et al., 2008). In contrast, other studies investigating protein and mRNA expression from arabidopsis or human prostate tissues were unable to identify a significant correlation (Greenbaum et al., 2003; Pascal et al., 2008). Therefore it remains disputed if miRNA and protein levels must correlate. The lack of correlation in the forementioned studies may be due to diverse reasons. For example, post-translational modifications, protein half-lifes or even methodical difficulties could explain the very different findings. Independent from general correlations between mRNA and protein levels, several other publications investigating miRNA dependent decrease of mRNA levels detected a wide range of regulation including a great amount of possible target mRNAs only slightly regulated (Guo et al., 2010; Hendrickson et al., 2008). The assumption that miRNA primarily inhibit translation and that mRNA destabilization is a secondary effect and not very pronounced, lead to the decision to set the cut off to 1x stdv. This was on the one hand believed to increase false positives, but on the other hand to also increase sensitivity. But since three independent controls were used, it was suggested to obtain a managable list of putative target mRNAs, which then could be further investigated experimentally. The overlap lead to 56 and 20 genes regulated in BJAB arrays and in epithelial arrays, respectively (4-23).

A detailed look at the genes regulated in my experiments identified some of the previously published targets (table 1-2), however they were to a large extent found close to the background. For example, PUMA is a published miRNA target that was identified in all of my microarray experimentes, but was only considered to be significantly down regulated in the BJAB-IRES arrays. Therefore it was identified in all experiments but only passed the threshold in one type of experimental set-up. Another example is MICB, which was previously shown by Nachmani and colleagues (Nachmani et al., 2009) to be a target of different miRNAs. In my studies it was also regulated but it was found to be slightly upregulated. This is surprising and inconsistent with the published literature, because if MICB is a target of EBV miRNAs then one would assume that it should be down regulated. Perhaps one of the best matching targets I found was TOMM22, which was identified recently in co-immunoprecipitation of RISC complexes and confirmed to be targeted by miRNA-BART-16 (Dolken et al., 2010) TOMM22 was consistent and reproducible down regulated in all experimental set-ups arguing strongly that it is a target of EBV miRNAs. Although this observation was reproducable, due to the

stringent criteria, in two set ups BJAB-GFP and HNEpC 4 d p.i., TOMM22 did not pass the threshold.

Similar to TOMM22, IPO7 was also identified as a miRNA target in co-IP (Dolken et al., 2010). In three of my array ups it was identified to be down regulated, but not in all of the experimental set-ups. The lack of IPO7 regulation in these other arrays may be due to technical difficulties such as bad spot performance or it may be related to the abundance of the IPO7 mRNA making it difficult to see modest changes. Dicer is another published target of EBV-miRNA-BART-6 (Iizasa et al., 2010), which was found in the epithelial array set-ups but not in the BJAB set-ups. Lastly CXCL11 was found by to be a target of EBV-miRNA-BHRF1-3 but it was not confirmed in any of my arrays.

Together these findings demonstrate that the identification of host target genes regulated by miRNAs may fluctuate or be entirely different depending on the source of the miRNA and the cell line / cell type analyzed.

In general, DNA microarrays are useful in the global analysis of miRNA targets but should be very stringently controlled. Replicates are essential to reduce the amount of false positives even at the expense of increasing false neagtives. In this work different controls were used in the BJAB set-ups leading to partially different transcriptomes of the analyzed cells. One challenge in analyzing targets that are only marginally regulated is that it increases the amount of false positives, which can be reduced again by replicate experiments. The controls should also be carefully chosen to compensate for changes only due to slight differences in the samples. For example, using antagomirs (miRNAs that are 100% complementary to mature miRNAs) which inhibit the function of a certain miRNA and therefore to rescue the effect of a given miRNA or scrambled miRNA (a miRNA consisting of the same set of nucleotides randomly ordered), which shoould loose the function of the real miRNA, are alternative experiments. Of course it is also possible that your control miRNAs have unwanted cellular effects and therefore it is wise to incorporate several controls into the design of the DNA microarray.

Using a very stringent approach, Ziegelbauer and colleagues were able to identify several miRNA targets of KSHV miRNAs (Ziegelbauer et al., 2009). In this study, they transfected BJAB B-cells with single miRNAs, they stably transduced cells with retroviruses expressing clusters of KSHV miRNAs, they infected primary endothelial cell lines with KSHV and they inhibited single miRNAs in latently infected B-cell line (Bcbl-1). For each miRNA 10-30 mRNAs were consistently identified that passed all sets of conditions. As a proof of principle for the functionality of this high throughput screening they confirmed the mRNA target of miR-K5 using luciferase reporter assay and Western blot analysis.

A very recent study investigated the molecular consequences of miRNA repression by ribosome profiling (Guo et al., 2010), a procedure, which determines the positions of ribosomes on cellular mRNAs with sub-codon resolution. The data were analyzed by deep sequencing of ribosome-protected mRNA fragments and provided quantitative data on thousands of genes. With that approach it was demonstrated that lowered mRNA levels account for most of the decreased protein production and additionally that changes in mRNA levels closely reflected the impact of miRNAs on gene expression.

The implementation of data resulting from SILAC (stable isotope labeling with amino acids in cell culture) allowed comparison of miRNA dependent changes on protein level with the mRNA data and

Discussion

expressed targets. This is the first experimental proof pointing towards destabilization of target mRNA as the predominant reason for reduced protein output. However, as to be expected, there are also exceptions were translational repression is coupled with mRNA destabilization (Coller and Parker, 2004). Guo and colleagues argued that even if destabilization is a secondary effect, it is the mRNA destabilization that would exert the greatest impact on protein level.