Name: Louis Kunkel
Position: Professor, pediatrics, Children’s Hospital/Harvard Medical School
Background: Associate professor, pediatrics, Harvard Medical School — 1987-1990
Assistant professor, pediatrics, Harvard Medical School — 1984-1986
Instructor, pediatrics, Harvard Medical School — 1982-1984
Research fellow, Harvard Medical School — 1980-1982
Fellow, University of California, San Francisco — 1978-1980
PhD, biology, Johns Hopkins University — 1978
BA, Gettysberg College — 1971
In this week’s issue of The Proceedings of the National Academy of Sciences, Louis Kunkel and colleagues at the Children’s Hospital in Boston detailed experiments that revealed the down- or up-regulation of a number of microRNA in muscle samples from patients with various muscular dystrophies.
Specifically, the investigators found that 185 miRNAs are regulated in Duchenne muscular dystrophy, Becker muscular dystrophy, facioscapulohumeral muscular dystrophy, limb-girdle muscular dystrophies types 2A and 2B, Miyoshi myopathy, nemaline myopathy, polymyositis, dermatomyositis, and inclusion body myositis.
Of that total, five miRNAs were found to be consistently regulated in almost all tissue samples. The others, however, were only dysregulated in individual diseases.
This week, RNAi News spoke with Kunkel about the findings.
Let’s begin with an overview of your lab and the research that goes on there.
My lab focuses mostly on the muscular dystrophies, and it was my lab that actually cloned the gene responsible for the Duchenne type dystrophy more than 20 years ago.
So we’ve been interested in the underlying basis of the different muscular dystrophies and how one might alleviate the symptoms of the diseases.
When did the idea of looking at microRNA come into the picture?
It was about three years ago that we started thinking about this [as] it became clear that microRNA are a major regulator in development. The question was, “Can we see any differences in the microRNA in different muscle diseases compared to control?”
What technologies did you use to conduct the experiments described in PNAS?
We out-sourced it to a company, Asuragen, to do the microRNA analysis. They have a special panel that they do. Basically, we prepared the RNA here.
The hardest part was actually capturing the biopsies of all those different forms of dystrophy and then being able to prepare good RNA to ship off to the company. Many of the co-authors on the paper were people who contributed samples to us.
What were the key findings?
What we saw was that there are 185 microRNA that changed gene expression from control in the overall set [of muscular dystrophies]. Some of them were specific for each disease and some were general, crossing lots of them. For the [five] that are general, it means the disease process has a commonality [with the other conditions]. The [rest] that are specific for each disease are probably [reflecting] the specificity of that disorder.
[Additionally], if there was a major immune response in the disorder, you would see a clustering of microRNA that are expressed more readily in the immune system or [with] an inflammatory response.
What happens in the muscle of [muscular dystrophy] patients is that there is a degeneration of the myofibers caused by a primary genetic defect. The fibers regenerate, and in the process of regenerating you have an inflammatory response. So the [makeup of] the microRNA population is a bit of a reflection of the disease process that is taking place.
For the miRNA that were up- or down-regulated, were these ones that had previously been identified as associated with these disorders?
Some of them had previously been identified to be expressed in muscle, but none had been linked to any specific disease.
Where there any surprises in your findings?
We really didn’t know what to expect. I was a bit surprised that the diseases clustered relative to one another. That is, you could see that the Duchenne type dystrophy was different than [facioscapulohumeral muscular] dystrophy — they actually separated from one another.
I thought there would be a lot less specificity to this.
Were you surprised by the number of microRNA that were changed in these conditions?
No I wasn’t. We’ve done expression profiling of RNA in a lot of these disorders, and there is a substantial change in RNA expression. That would predict that a lot of master regulators, such as the microRNA, would also be changing as well.
Indeed, using the Duchenne type dystrophy we predicted what the targets would be based on the change in microRNA expressions, and then went back and looked at the actual changes in gene expression that we saw at the RNA level. Many of the predicted effects were seen at the RNA level.
What’s the next step? You’re looking to confirm these findings?
We’re trying to take a select few [miRNA] and modulate their expression in muscle cell culture and zebrafish muscle.
How did you decide which miRNA you were going to focus on for this follow-up work?
It was partly based on where they are thought to be expressed and partly based on whether they were specific for a particular disease.
Has that work started?
We’re in the process of starting it. It took a while to get the [PNAS] paper together. Once that was done, we … could start the experiments.
What are your thoughts on using microRNA as a target for drug intervention?
Well, I look at them as good possibilities. If you can modulate their activity, you might be able to change some of the disease process that’s occurring. I think until we do experiments and look at what downstream effects they have in cell culture, we can’t be predictive of what the targets might be.
Given the success with this work, are there other areas of research where you’re looking at microRNA?
We’ve been looking in autism spectrum disorders. We haven’t gotten far enough along to say anything about it.
Are you using the same approach?
No, a different one. We’re actually sequencing them. When we started this, the high-throughput sequencers weren’t available, but now you can actually sequence samples from autistic versus controls and get a very good readout of the microRNA population. And it’s a real readout — you’re actually counting the number of microRNA that are in your RNA sample based on sequence.