A water flea's phenotypic plasticity and HDL-cholesterol in humans

This week marked the announcement of the completion of the genome sequence of the water flea Daphnia pulex. I remember peering through a microscope in my first biology classes amazed at the activity and diversity of structures of these creatures. Now, the 200-megabase genome has been deduced. One of the startling discoveries is the small D. pulex genome is packed full with more than 30000 genes, far exceeding the number in the human genome. Some 13000 genes were identified in the paper by Colbourne, et al. as paralogs - arising from gene duplication.

Here is part A of figure 1 from the paper illustrating major differences in gene numbers between D. pulex and other animal genomes.

So, why all these paralogous genes? Well, the upshot here is one of likely gene duplication as a means to build an inventory of possibilities for a wide range of phenotypes. This scenario is spelled out rather nicely by Dieter Ebert in an accompanying overview. The water flea is remarkably able to sense its predators in a very precise manner and in turn activate any of a number of genes that direct expression of defense mechanisms. Some of these are structural features such as protective helmets, tail spines and neck teeth. Herein is the water flea's phenotypic plasticity - different environments induce expression of different subsets of the vast genome for the purpose of evading the predator. A gene for each bad guy swimming nearby.

Now, let's consider humans and their environment. In particular, I'd like to offer the example of diet, for most this is high in fat and sugar, and the important blood lipid of HDL-cholesterol, so-called "good cholesterol." Regular readers of this blog know that our research expends a good deal of effort in describing gene-environment interactions (GxEs). This is a situation where one allele of a genetic variant like a SNP associates with disease risk only when a given environmental factor passes a certain threshold. We have compiled a series of these GxEs for phenotypes pertinent to metabolic syndrome - phenotypes such as body weight, BMI, blood lipids, blood pressure, glucose and insulin levels, as well as heart disease and type 2 diabetes risk. Those data are available here. If you mine those data, you'll notice that by far there are more GxEs reported in the literature for HDL-cholesterol than any other commonly measured phenotype.

Thus, it seems to me that the water flea has a lot of very similar genes, mostly in paralogous pairs to cope with slight changes in its environment. Humans do not. Eating a sub-optimal diet will likely drive HDL levels down (unhealthy). There are also age-related, natural declines in HDL. At the same time, there are a number of variants in our genomes that show an environmental sensitivity with respect to HDL - there are many ways to activate a program of increased risk (by lowering HDL levels). And similar cases can be presented for LDL, triglycerides, total cholesterol, blood pressure, waist circumference, body weight, etc. So, while it may take years of indulging in a sub-optimal diet before an adverse event such as diabetes of atherosclerosis is diagnosed, perhaps our (relatively) small number of genes, each with a collection of variants, that sets us up for sensitivity to what we put in our mouths. If we can't eat right, then perhaps more genes would be the answer to a better defense against a poor diet.

Synonymous SNPs are not so synonymous

Early this week, an excellent paper by Brest, Darfeuille-Michaud, Hofman, et al. in Nature Genetics provides a prime example of going beyond genome-wide association studies (GWAS) to dissect the functional consequences of a genetic variant associated with disease risk. In so doing, the authors provide another case of synonynous SNPs not being so synonymous.

Here are what I find to be the key points of the research presented in this report:

1. The exonic SNP c.313C>T (rs10065172) is in perfect linkage disequilibrium (r²=1.0) with a deletion polymorphism of 20 kbp mapping upstream of the IRGM gene. This deletion has been strongly associated with Crohn's disease in several European populations or those with European ancestry. What is important here is a SNP can act as a tag or proxy for the deletion.

2. The c.313C>T variant alters codon 105 of the IRGM protein from CTG>TTG. Both codons call for leucine upon translation and so this SNP is classified as synonymous. The authors speculate that there could be allele-specific consequences to protein expression. Based on two other reports from other groups, the authors decided to investigate whether allele-specific interactions between the IRGM transcript and a microRNA could be at play here. They observed a predicted binding between microRNA-196 (or miR-196, both miR-196A encoded by A1 and A2 genes and by miR-196B) that was affected by the variation at SNP c.313C>T. Importantly, they show that not only is the miR-196-IRGM interaction real but that expression of miR-196 is elevated in inflammatory epithelia from Crohn's sufferers. These results underscore the point that synonymous SNPs are not so synonymous. The different alleles can exhibit different functions that have health consequences.

3. From GWAS to function. Although this paper does not report original results from GWAS, it builds on those results in an important way. There are four key papers reporting GWAS results for IRGM and Crohn's disease. These papers are by Parkes et al (2007), the Wellcome Trust Case Control Consortium (2007), Barrett et al (2008) and Franke et al (2010). So, in just over three years from the initial discovery of association of this once rather unremarkable gene (only 5 papers were published on IRGM prior to the initial GWAS report of 2007, most reporting a role in autophagy), we now have a much deeper understanding how a synonymous variant leads to the disease condition.

This is very nice work indeed and can be held up as an example of the success of GWAS in laying a foundation for getting at the mechanism of a disease.

One size does not fit all

On January 11th of this year, 23andMe, one of several companies offering direct-to-consumer genotyping (or genetic testing), put out a press release entitled, "23andMe Presents Top Ten Most Interesting Genetic Findings of 2010."

I found number 5 on that list to be quite appealing. It reads, in part:

One size doesn’t fit all — personalizing treatment

The old adage, “take two aspirin and call me in the morning,” doesn’t work as well as we might think. It turns out that one size doesn’t fit all when it comes to drug response, and for some people, certain drugs might be more effective, not work at all, or even produce serious side effects. The growing body of pharmacogenomics research has helped us understand that, at least in part, genetics play a role in how well some drugs work for different people. The 23andMe Drug Response reports link customers’ genetics to the way they might respond to certain drugs and medications. The results range from whether you’re likely to benefit from a drug, need a different dose due to sensitivity, experience toxic or adverse effects, or even have increased risk for other conditions. 23andMe cautions that its Drug Response reports should not be used to independently establish, abolish, or adjust medical treatment and medications but should be discussed with your physician. Only a medical professional can determine whether a particular drug or dose is appropriate for you.

The piece goes on to describe, briefly, two genes, CYP2C9 and VKORC1 and the role of variants of these genes in warfarin dosing.

OK, so this is all neat but really only represents the tip of the tip of the iceberg. There are many more examples of one size not fitting all and reaching far beyond pharmaceuticals. We and many others have reported on many such interactions between certain genetic variants and diet which affect disease risk. On this blog, I have listed some examples pertaining to HDL-cholesterol. And those variants that show interactions with physical activity as modifiers of disease risk are really interesting. And not to forget other environmental factors or lifestyle choices of sleep, latitude and altitude of residence (how much seasonality you experience, oxygen tension), use of alcohol, use of tobacco, and so forth.

For scientists, medical professionals and the general public alike, clearly a greater understanding of elements at the basis of "one size does not fit all" would be welcome. Stay tuned, it's happening - more of these elements, the gene-environment interactors, are being described and collated into databases.

Take your family's medical history!

Yesterday, my parents, both over age 70, visited me. While we sat around the table over coffee and cookies, I decided that this would be a perfect time to ask them about our family's medical history. I felt that this is important information for the times when I may need to supply such to a doctor of theirs or to my own physician. Also, I have recently submitted a sample so that my genomic DNA can be genotyped and I feel that if I wish to put any kind of risk assessment into perspective, I need to do so with my family medical history in mind.

So, what did I learn? Well, I learned a lot about going back to my great-grandparents, but the particular diseases and ailments are not important to my readers. I will share this though - I feel really good at having done this and sharing this information with my siblings. I encourage everyone to do the same. Sit down, ask and take notes. Then, share this information with those not at the table and with your own physician. It is important.

Gene-HDL associations modified by physical activity

A brief post here. I am simply listing a few genes/SNPs that associate with HDL-cholesterol in a manner modified by physical activity.



One can see from the above table (click for a larger view) that results of physical activity modifying the effects of APOE alleles is not consistent across populations. There are different risk alleles in the different studies. The EUROSPAN study under PubMed ID 20066028 did not give specifics of levels of physical activity nor identify the risk alleles.

If there is something that interests you in terms of measures of metabolic health (along the lines of heart disease, diabetes, blood lipids), just ask and I'll see what I can provide.