Dr. Mark R. Cookson is a cell biologist whose current research interests include the effects of mutations in the genes associated with neurodegeneration at the cellular and molecular level. His laboratory efforts are directed at finding the underlying pathways that lead to Parkinson’s disease and related disorders. Dr. Cookson received both his B.Sc. and Ph.D. degrees from the University of Salford, UK in 1991 and 1995, respectively. His postdoctoral studies included time spent at the Medical Research Council laboratories and at the University of Newcastle, Newcastle, UK. He joined the Mayo Clinic, Jacksonville, Florida, as an Assistant Professor in 2000 and moved to the NIA in February 2002. Within the Laboratory of Neurogenetics, Dr. Cookson’s group works on the effects of mutations associated with Parkinson’s disease on protein function.
The following has been paraphrased from an interview with Dr. Mark Cookson on April 12th, 2018.
(Click here for the full audio version)
Could you describe genome-wide association studies (GWAS) and what impact they will have on our ability to treat neurodegenerative disorders?
GWAS try to understand what causes disease by looking at multiple aspects of our inheritance and genetic variance in very large numbers of people. Bringing these together allows us to estimate and assay (measure the presence, amount, or functional activity of a target) the inheritable components of disease.
In GWAS we collect samples from people that have a disease and compare them to samples from healthy people by examining the DNA sequences of the two groups. We assay them and compare around half a million single nucleotide polymorphisms (a variation in a single base pair in a DNA sequence, commonly called SNPs). We then check if any SNPs are more present in one group or the other. What we get is a map of some of the inheritance factors associated with the disease.
One thing to keep in mind is that what we see are not single cause genes, instead they are factors that shift your risk of getting a disease by a small percent. We can add them all together and get a reasonable account of a person’s lifetime risk of getting a certain disease. But added all together, genetics usually can only account for about 1/3rd of a person’s lifetime risk.
If you know what genes are associated with a disease, you can organize them into biologically meaningful pathways and start to think about how to intervene in disease progression by identifying drug-able targets. That is really why we are doing these studies. For example, one gene that we work on a lot is called LRRK2, and GWAS suggest it might be a target in Parkinson’s disease.
How is our improved understanding of LRRK2 associated PD improving our understanding and our ability to treat sporadic PD?
The assumption we make with rare cases is that they are related in some meaningful way to the people who have these diseases but who don’t carry the same penetrant mutations (mutations associated with a high risk of disease). We know that LRRK2 plays a role in families that have this mutation and the clinical data supports the idea that it also plays a role in the sporadic form of the disease because the symptoms of the disease look very similar between LRRK2 PD and sporadic PD. The other aspect that supports this idea is that when we assay sporadic cases from GWAS, the LRRK2 gene shows up as a target. But the only way we will really know if LRRK2 is important is when we get drugs that target LRRK2 and try them in people with the mutation and without.
What are the differences and relative advantages of RNA sequencing vs. DNA sequencing?
Watson and Crick showed that the structure of DNA enables us to pass on information from generation to generation. It is essentially a library with books that contain instructions, and it is largely the same throughout a person’s lifetime.
However, at a given moment, we need to translate that genetic information into something that is useful, that useful end product is a protein. For example, LRRK2 genes encode for LRRK2 proteins, and generally we design drugs to target the protein, not the gene.
RNA is the intermediate copy of DNA that actually translates the instructions to make the protein. Sequencing RNA tells us what parts of the DNA a cell is using at a given time. DNA sequencing just tells us what the sequence is, RNA sequencing lets us know what parts of the DNA sequence are actually being used. So, RNA sequencing gives us much more information about what the cell is doing under given circumstances.
Could you explain the RNA World Hypothesis and do you share this belief?
One of the reasons we do science is to explain the world and our place in it, and one of the greatest ideas in human history is evolution, the idea that life has been on this planet for billions of years and has changed over time due to heredity. A very interesting question is, what was the first thing that we would recognize as life? Well, the thing that connects all life on earth is DNA, which survived by passing its information from generation to generation. The question then becomes, where did DNA come from?
DNA has a lot of very unusual properties; one is that it is a very stable molecule, which is probably why it was selected by evolution. But, it also has to pair up with other strands of DNA to reproduce, and people have suggested that chemically, that would have been a very unlikely event at the beginning of evolutionary history. Many think that RNA, which resembles a single strand of DNA, formed first because RNA chains can replicate and evolve by themselves. The idea is that in the primordial world there were a lot of chemicals available, and by chance, enough reactions happened that molecules of RNA formed. Some were stable enough to make copies of itself, which became life as we know it.
I can’t say yet whether I believe it. It is certainly plausible, but whether it is testable is another matter. It will take somebody very clever to think of a way to test this.
One interesting project that you are a part of is called the International Parkinson’s Disease Genomics Consortium (IPDGC), and one tool that came from it is called NeuroX. Could you explain NeuroX and how it is accelerating our understanding of neurodegenerative diseases?
All credit for this goes to Andy Singleton, who led the creation of NeuroX. The idea was, when we genotype very large numbers of people, we can’t sequence every one of the 3 billion bases for everybody, so instead, he developed a small DNA chip capable of looking at large sample sizes. The NeuroX chip checks for most of the known mutations associated with neurodegenerative diseases, allowing us to assay a DNA sample quickly and cheaply. That has opened up and democratized many of these large sample studies and should accelerate the pace of discovery.
You state a need for ‘unbiased approaches’ to understanding complex diseases, how do you ensure that you’ve effectively removed bias from a study?
The idea is that we should be removing as many sources of technical and cognitive bias as we can. When studying human genetics, it is important to look as broadly as possible without specifying ahead of time what you think you are going to find. For example, when looking at a family where multiple members have Parkinson’s disease, you need to look across the whole genome of both affected and unaffected members. But, you should not assume to know where you’ll find mutations, you might guess right occasionally, but in general you would be wrong more often than right.
That’s the idea behind GWAS, we don’t go in ahead of time and decide what we think is important, we look at everything evenly and then ask where is the signal. That requires us to use techniques that are as unbiased as possible, and for the researcher to let go of their cognitive biases and let the data tell them where to go.
Other parts of biology can be approached in the same unbiased way, like when studying how proteins interact with each other. It doesn’t entirely remove the hypothesis because you still believe that there is something to find, but it does make the hypothesis as broad as possible. This has lead us to findings that we never would have expected and which have pushed the field in new directions several times. It is also the reason why we have had a high hit rate of being right. As scientists, that is really our measure, it is not about how excited the media gets over a particular study, it is about reproducibility, can someone else build on it, and can we use it in our collective goal to impact disease progression.
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