Interference Inteference

A recent publication in Nucleic Acids Research (a very fine journal which is now all open-access) highlights an underappreciated (IMHO) aspect of RNA interference, or RNAi, studies of gene function, and may also be relevant to the therapeutic application of RNAi.

RNAi is another one of those amazing bits of biology (with restriction enzymes another obvious example) which seem too good to be true: short, computational bits of nucleotide sequence can specifically knock down the expression of targeted genes. Much of the work in the field has been on attempting to identify and control so-called off-target effects, as the specificity is not perfect. In the worst case, all of your novel hits may turn out to simply be off-target effects back to a not-at-all novel gene for the function of interest.

One strategy widely employed to reduce off-target effects is to use pools of siRNAs, with the general thought that if the on-target effects are additive and each siRNA has its own idiosyncratic list of off-targets, then the off-target effects will be diluted but the on-target ones amplified. There is more than just hope to support this, but a possible problem emerges: can the individual siRNAs interfere with each other. In particular, could one bad siRNA in the pool clobber the effects of the others, as siRNA design isn't quite perfect.

One way such an effect could be realized is if all the siRNAs are competing for a limited resource. siRNA does not work by magic, but rather by utilizing built-in cellular machinery. If the excess capacity of that machinery, above the load already placed by normal cellular processes, is soaked up by the applied siRNAs, then interference between siRNAs could result.

One key result in the new paper is that the levels of RISC, the key RNAi-executing complex, vary across cell lines. Biology tends to be a synonym with variability, but this isn't always accounted for in experimental designs. This may translate into experiments behaving very differently by cell line, and given the somewhat shadowy understanding of cell lines, this is not great news.

The paper goes on to identify Ago2 as the key protein whose levels affect siRNA competition. By tinkering with Ago2 expression, either up or down, the interference effects can also be modulated.

As the authors summarize, this all stresses the need for being cautious in designing & interpreting RNAi experiments and in extrapolating results in one cell line to others. At my previous posting I looked at a lot of RNAi papers, and as in the days of microarrays there was a worrisome low degree of overlap in the hits between ostensibly equivalent screens. In one case, two papers claiming to use the same cell line came up with incompatible phenotypes for one particular gene knockdown. Measuring Ago2 levels is a control which should be strongly considered for these experiments, and results from pooled siRNA experiments without deconvolution into individual siRNAs aren't to be trusted (I'm not sure I've seen such published, but I'm sure people are tempted). RNAi is a powerful means to functional analysis & potentially a useful therapeutic modality, but it's not quite as clean & simple as one might dream of.

A good RNAi guide

Cell Cycle is an interesting little journal that publishes many papers open access. A nice little review of the statistical treatment of genome-wide RNAi is available freely.

The review focuses on noise and variance in RNAi screens, and doesn't explore some of the other key issues such as off-target effects, interferon response and appropriate cell lines. So it isn't a complete guide but a sharply focused one.

A recent Nature has a paper on genome-wide RNAi for targets increasing sensitivity to the key antitumor agent paclitaxel (alas, not free).

Genome-wide RNAi with siRNAs is a powerful technology, but it requires a pretty large investment in automation to make it work. That will slow the widespread adoption of the technology, which isn't entirely bad. In some ways, mRNA microarray technology spread too far too fast leading to many bad papers being published before the methodologies were well worked out. Of course, there are still lots of bad microarray papers being published, but you can't make the horse drink. Some bad papers have poor microarray analysis, and others are just atrocious experimental design. In the end, the technology has been besmirched, generally unfairly.