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Q&A: Colorado State Scientists Size Up Affy in Attempt to Build Rice Mutation Database


myron_bruce.jpgName: Myron Bruce

Title: Researcher, Bioagricultural Sciences and Pest Management, Colorado State University

In an article published last month in BMC Genomics, a team of researchers from Jan Leach's lab in the Bioagricultural Sciences and Pest Management program at Colorado State University decided to evaluate Affymetrix's Rice GeneChip for its ability to locate deletions in rice mutants.

In the study, the team used the RiceChip to identify deleted genomic regions in rice mutants selected from a large collection generated by gamma ray or fast neutron treatment. According to the authors, the use of array technology should make it feasible to create a database of known deletions in mutant rice that could benefit rice breeding programs.

To get a better sense of how this could impact the broader agbio community, BioArray News spoke with lead author Myron Bruce, a PhD candidate in Leach's lab, about the study last week. Below is an edited transcript of that interview.

What is your background and why are you involved in rice research?

Well I have been working on rice research since about 2002 or 2003 when I was an undergraduate at Kansas State University. Then I came up to Colorado State to start working on my PhD with Jan Leach. I fell in love with rice. It's a beautiful system and I just decided to stick with it.

At the beginning of the paper you discuss the use of alternative platforms like PCR. I wanted to get a sense of what you used before you decided to evaluate array platforms.

The first thing we did was recombination mapping to look for deletions, which is tedious, time consuming, and can be pretty expensive, especially in IR64 indica rice because the regeneration time is so long.

We started with expression arrays. We had a project with Agilent Technologies when they released their 20K rice array. We hybridized some mutant cDNA to that array to look for very high down-regulation as an indication of a deletion. We included that as a supplementary figure in the paper. The problem with that platform was that their coverage was too low. That was before they introduced their second-generation rice array.

Then we started to pursue the location of these deletions. We got in touch with Syngenta. They had a proprietary Affymetrix array and we tested it out and it worked really well. When the public array became available, we just started working with it.

Could you describe how this particular study evolved?

Well, the mutant collection was generated at the International Rice Institute in the Philippines. They used fast neutron, gamma ray, dioxybutane, and EMF to give us a range of mutation sizes and types, from big deletions with the radiation, to smaller deletions with DEB, all the way down to point mutations with EMF. That way we could use the collection to address different questions we had.

One of the serendipitous things that came out of that was that we started getting queries on a number of genes. Having a big list of mutations allows us to narrow down the region. We have a list of genes from the radiation mutants and then we have candidates to test in the smaller allelic mutants. So that worked out really well having different sizes of mutations in that collection.

And you chose indica because it is the most commonly produced variety of rice?

We chose indica and IR64 specifically because IR64 is the semi-dwarf megavariety that is largely grown in Southeast Asia. The reason that we chose an indica variety for the deletion collection generation is because indica is notoriously not amenable to transformation. We have a really hard time doing transgenics in indica. So a deletion collection was an easier way to go to get a large collection of mutants relatively inexpensively.

But you had no idea where the mutations were. The point was to use the Affy platform to find out where they were located.

Exactly. We labeled and hybridized genomic DNA directly to the arrays and then examined reduction in hybridization.

So you used a reverse genetics approach.

One of the beautiful things about the way we are doing this is that you can use the approach either way; you can use it for forward or reverse genetics. You can hybridize the genomic DNA, look for deletions, and then go from genotype to phenotype. Also, for forward genetics, if you have got a phenotype, go ahead and hybridize the genomic DNA and then get a genotype that way.

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You mentioned that you had been using the Agilent platform in the past but switched to the Affy rice chip. Why did you do that?

The reason that we switched from Agilent and cDNA to Affymetrix and genomic DNA is coverage. The other nice thing about the Affy chip is that you have multiple probes per gene, so you can get a little more confident in your predictions. I would like to have a look at the second-generation Agilent array, but I haven't had the opportunity.

What do you think about the tools that are being made available to rice researchers like yourself by Affymetrix and Agilent and others? Are you pleased with them?

Yes, I think they are doing a great job. I am uncertain whether or not there is a rice tiling array coming out, but that would be nice for deep-transcript profiling, though maybe not for the kind of work we did in this paper, perhaps because the tiling arrays are just so expensive.

With the Affy array, the quality is high, and it [was] easy to work with once we got our protocols optimized. We had really good data coming out of our hybridization. We have had the same experience with Agilent. It is a nice array; it's just not suited for this purpose.

I think a lot of times that the plant world gets glossed over, with the exception of Arabidopsis, just because we don't have the money that some of the higher-profile study areas, like human or mouse or rat, do. But I think these firms are doing a good job to get us what we need.

In the paper you described the use of the array to locate mutations within the samples. How successful were you in using Affy for this?

I think we had a respectable true prediction rate and really low false prediction rate. After we hybridized the arrays, we put them through diagnostics to make sure that the data would be acceptable for us, and then after that performed the analysis. I think the Affy arrays performed really well.

But you also discussed some limitations in the paper.

And those limitations are going to be hard to overcome. If we have a deletion in a region that is repeated elsewhere in the genome, obviously that is going to hybridize and mask out the deletion that we could have predicted. The nice thing is that we'll often have single-copy genes surrounding those repeat regions so that we can get a little more confidence about our prediction.

Sometimes it makes it hard to call one end of a deletion if it is in a highly repetitive region. The same thing is true for gene families that are repeated throughout the genome. I think that those are some of the biggest limitations to this application, and that is what made the radiation mutants so much easier to work with. The deletions were large and contiguous and one of our co-authors helped to autonomously predict regions that are probably deleted. Repeat regions are going to be a pain that way, but I think we have worked around it pretty well.

Your main goal is to create a database of rice mutations. Could you give me an update on the status of that effort?

Well, we were talking with a core facility in Taiwan that was ready to start receiving materials from IRI for hybridization. That is one part of it. But anybody who wants to can pick up the technology, buy the arrays, get the seed from IRI because it is publicly available, and upload all their information to our genome browser so that everyone can have access to that material. I see it as value adding to deletion collection that already exists and everyone has access to.

As we move forward with more hybridizations, we get closer and closer to saturation. Our prediction was that we have a 91 percent probability of hitting every gene in the genome with 3,000 hybridizations. That is a lot of arrays. But as more people do it, there will be enough information available so that a researcher can say, "I want a mutation in this gene. Do you have it?" and then go ahead and get the seed from IRI.

I saw that you had some USDA support and I am interested in the connection between what you are doing and the larger agbio community.

One of the hardest things to get at are quantitative trait loci. I see this as being particularly useful in figuring out which genes within a QTL are particularly important, and that can help towards direct breeding programs to breed for genes that are important within QTL. If we know what genes are important for the trait governed by that QTL, then our mapping can be more precise. All that information can be fed directly into breeding programs.

What is next in your list of things to do?

I am working on an extension from this paper. I want to go in and characterize one of the mutants that we worked with in this study. We are a rice pathology lab specifically, and we didn't really talk about phenotype on a lot of those mutants. We are going to look at some of the genes deleted and see how they play a role in more quantitative resistance, not so much qualitative resistance in plants; contributing small effects to a disease phenotype.

You mentioned before that other organisms have gotten a bit more attention from array vendors. Within the community, do you think that information is flowing smoothly with fellow researchers?

I think the rice community is extremely close-knit and everyone loves to share. Our aims are different sometimes, but what we really want to do is make sure everyone has something to eat. Competition for funding occurs, but collaborations are widespread and the networks I have participated in are extremely friendly and helpful. I have yet to come across someone who doesn't want to share their research and come up with new ideas to solve problems that are out there.

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