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UNC s Debra Taxman on Examining siRNA-Design Algorithms for shRNA Design

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Debra Taxman
Research Associate
Ting lab

Name: Debra Taxman

Position: Research Associate, Ting lab

Background: Postdoc, Ting lab, University of North Carolina — 1994-2003

PhD, molecular and cell biology, Pennsylvania State University — 1994

Technician, Arizona State University — 1987-1989

BA, biology, Washington University in St. Louis — 1987


In the Jan. 24 issue of BMC Biotechnology, researchers at the University of North Carolina described their efforts to determine whether published siRNA-design algorithms could be applied to shRNAs. The researchers concluded that the algorithms tested showed "little or no efficacy at predicting shRNA knockdown outcome."

Recently, RNAi News spoke with Debra Taxman, lead author of the paper, about the research.

Let's start with an overview of the lab and the work done there.

It's quite a diverse lab — we have 25 people — and there are several different lines of work going on. The largest part of the lab is the study of a set of immune genes called Caterpiller, and our lab was the first to describe this family of genes. Now we're working on characterizing different members of the genes.

What are these genes? Are the associated with a particular disease?

There are three diseases associated with members of this family of genes. One is cold urticaria, which is an inflammatory disease that is induced by being in cold. The other is Crohn's disease, and the third disease is called bare lymphocyte syndrome. All three are immune disorders, and it turns out that there are more than three members of this family — there are about 20 similar genes.

Is that the area that you work in?

Yes. That's one aspect [of the lab.] Another is neuroscience, and then there is a cancer component. So we're very diverse.

So where did RNAi come into play?

My project was to create stable shRNA lines with a variety of these genes, either Caterpiller genes or [genes] involved in TLR signaling pathways. I made about 30 of these shRNA lines.

Around this time, people started publishing a lot of rules for algorithms to design optimal sequences for siRNAs. What I discovered was shRNA [design] didn't really match these published algorithms.

What was the variance? How far apart were the two?

The published algorithms are very different from each other, first of all. So there's really not a good consensus, at least at the time I was doing this work. But I found that really there was no statistical predictive capability of any of the algorithms [for shRNA.] Applying the algorithms toward shRNA really did not work, although some were better than others.

Do you recall where the algorithms were from?

I tested six different algorithms from different publications, although now there are many more out there.

When were you doing this work?

I started about a year and a half ago making and characterizing the knockdowns. Then, I noticed that many of them didn't work, and if they worked or not didn't seem to apply to any published algorithms.

Do you have any clue as to why they were so ineffective?

For shRNA, there are many more steps that go into processing it down to the double-stranded RNA that then goes and inhibits. So it could be somewhere within the mechanism there are different criteria.

When you were making your shRNAs, how did you approach the design?

We haven't designed new shRNAs according to our algorithm. [The BMC Biotechnology paper] was more an analysis. But when we do design them, we will use the modified algorithm that we published.

So after doing this work, looking at the algorithms, you created your own?

Right. We tested six [siRNA-design] algorithms and none of them really explained the results we were getting. We noticed that a lot of the false positives occurred with siRNAs that had the central region of the 19-mer … [that] was correlated with a thermodynamic stability. So basically we added a restriction to [an existing] algorithm.

We determined that three of [the siRNA algorithms] worked better than the other three. So those three we were able to modify, and the modified algorithm seemed to work well to explain our results. Then we took the modified algorithm and tested it against a larger set of other published shRNA information that we pooled together, and we were able to tell from that that the modified algorithms would apply, generally.

And these are what you are planning to apply to the shRNA lines for the Caterpiller genes?

Right. Many of them we're already created, and now we're doing downstream assays.

Were there no shRNA algorithms available?

That's right. All the algorithms are for siRNAs. So we were wondering if the reason none of them seemed to apply [to shRNAs] was that there are different criteria that are necessary for shRNAs.

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