Variable Genome

A bitter taste for the microbiome

2011-12-12T11:32:00.000-08:00

Taste is a fine thing - nuanced by the presence of many compounds, their ratios to one another, and past experience. That is true of foods that enter the mouth. Could this also be true of the mix of food waste and bacteria in the colon? Although sampling a spoonful of Firmicutes or Bacteroidetes is admittedly something quite revolting, my thinking is leaning toward "yes." The brain likely has an idea of who is in residence in the colon and which metabolic byproducts are present.

A stir was created in 2007 when it was reported that two taste receptors and gustducin have a role in glucose-mediated responses, suggesting function as a previously undescribed glucose sensor in the gut lumen (Jang, Kokrashvili, et al. 2007 Proc Natl Acad Sci USA 104:15069–15074). One can find many news articles from that time highlighting this finding. It seems that the repertoire of taste receptors expressed in the gut, particularly in the colon, is much more extensive.

The ATLAS gene expression tool at the EBI is a semantically enriched database of meta-analysis based summary statistics over a curated subset of ArrayExpress gene expression data. ATLAS supports queries for condition-specific gene expression patterns as well as broader exploratory searches for biologically interesting genes. Using ATLAS, I found that the following ten taste receptors are expressed either in the small intestine or the colon:

TAS1R1
TAS2R13
TAS2R14
TAS2R16
TAS2R3
TAS2R38
TAS2R4
TAS2R7
TAS2R8
TAS2R9

Every one of these, except for one, are described as responding primarily to bitter tastants. TAS2R38 is sensitive to glucosinolate, a plant derived family of compounds, which do have a bitter-like taste. TAS1R1 is a type of sweet receptor, detecting primarily L-enantiomers of certain amino acids. It is highly likely that querying other gene expression databases will turn up other members of the taste receptor family as expressed in the lower gastrointestinal tract.

It makes sense to me that proteins that are designed to communicate what is in essence the composition of the external environment should be given such roles as sentry or monitor beyond the taste buds on the tongue. Taste receptors are designed to sense specific classes of chemicals and to relay a signal to the brain. Fecal fermentation of proanthocyanins, phytochemicals and other complex molecules is likely to be monitored in some way for the benefit of the human host. Perhaps taste receptors have a role in that process.

The curious case of SNP rs2880301

2011-10-26T09:31:00.000-07:00

"Wow! I've never seen anything like that before," my colleague Chao-Qiang Lai exclaimed when examining output from his analysis of genome-wide association (GWAS) data. He was looking for genetic markers influencing the level of triglyceride in serum as part of the GOLDN study. GOLDN is looking at the genetics of the response to lipid-lowering medication. The result of Chao's preliminary analysis indicated that SNP rs2880301 associated with TG levels with a p-value of 10^-218. He showed me some data from the scan of the Affymetrix 6.0 genotyping chip and we postulated that we could be looking at some type of CNV (copy number variant) or deletion, but the lack of minor allele homozygotes troubled us.

What intrigued us right from the start was our colleagues at other institutions who are also analysis the GOLDN GWAS data did not report this SNP in their initial findings. The dbSNP entry for rs2880301 indicates a C to T variant with an allele frequency of 0.24 in the four primary HapMap populations from USA, Nigeria, China and Japan. No differences in allele frequency means no chance of positive (or negative) selection on this variant. No, none indeed as we were to learn later.

So, Chao dug deeper into his data and he and I shot ideas back and forth. After my suggestion to look at sex, he saw that when the SNP and sex are together in the same model, the analysis did not complete. Then, looking at the individual genotypes, he saw that all the men had genotype CT and all the women CC. This is from a total of just over 800 subjects.

OK, time for me to step in and see where this "SNP" maps in the genome. My first query was the flanking sequence supplied by Affymetrix. This 33-bp segment maps nearly perfectly to both chromosome 13, within intron 1 of the TPTE2 gene and agreeing with both dbSNP and Affyemtrix's annotation of the SNP, and curiously to a spot on the Y chromosome. (TPTE2 is a membrane-associated phosphatase which acts on the 3-position phosphate of inositol phospholipids and could be argues as relevant to TG biology.) The only residue not matching is the "polymorphic" base of the "SNP." A C is found on chr13 and a T is found on Y. Thus, the SNP becomes a marker of sex and Chao was right - it is a type of deletion (females carry no Y chromosome) - but a deletion he had not envisioned.

Is rs2880301 then a marker for gender? Not really. I compared the genomic regions where the homologous SNP sequences were found on both chromosomes, extending over 6 kbp in each direction. I saw a large region of sequence identity between 13 and Y - over 96% - for a ~5 kbp segment. Running RepeatMasker indicated that rs2880301 falls within an L1 LINE, a common repeat element. Thus, while it is intriguing that an array of repeats (70% of the 13-kbp segment of chr13 is masked by RepeatMasker) are conserved between chromosomes 13 and Y, and in order, SNP rs2880301 is not really a SNP. All subjects are C on chr13 and all Y chromosomes are T.

What we then had in our data were five genotypes: CC on chr13 for all women, CC on chr13 for all men and T on Y. Thus, the "allele frequencies" of C between 0.75 and 0.80 and T between 0.20 and 0.25 seen by us and others, including the HapMap data, roughly correspond to populations that are half to slightly more than half women.

Genetics of hypertension

2011-09-13T11:36:00.000-07:00

An important paper describing genetic factors involved in blood pressure and heart disease was published this week. 29 loci were identified - 16 of these novel. This is impressive, but not so surprising as both diastolic and systolic blood pressure are complex, heritable traits. Many, many factors are at play here. In fact, the authors describe a risk score based on genotypes at 29 genome-wide significant variants, which was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function.

Some time ago, I wrote about the genome of the SHR (spontaneous hypertensive rat). This rat and the FHH strain are models for hypertension. The genome of the SHR strain showed that 788 genes contained variants with respect to the reference rat genome. Whether any or all of those variants function in producing the hypertension phenotype is not clear, but reasoning went that these variants would be a logical place from which to build list of candidate genes.

Well, I thought it would be a fun and quick exercise to see how many of these 29 loci detected by genome-wide associations in nearly 70,000 individuals of European ancestry (with validation of top signals in up to 133,000 additional individuals of European descent) compare to those genes that possess genetic variants in either the FHH or SHR rat strains. This may give insight into the applicability of sequencing the genomes of specific model organism strains and into how many or how often genes are identified by both GWAS and whole-genome sequencing. To do this, I used the Rat Genome Browser hosted by the Medical College of Wisconsin.

Of the 29 human loci, 23 map in the rat genome within a QTL for blood pressure. This looks good, but consider that there are numerous blood pressure (BP) QTL mapped throughout the rat genome. In fact, the rat Adm gene maps within 27 different BP QTL and three other gene regions, Furin - Fes, Plekha7 and Mov10, map within more than 20 different BP QTL. Some of these QTL are large, spanning many genes, which means that fine-mapping is needed - such as a GWAS - to identify more precisely candidate loci.

Only 18 of the human BP genes identified in the paper contain SNPs in either the FHH or SHR strains. Often, the variants are shared in both strains. Both synonymous and nonsynonymous SNPs were noted, but synonymous far outnumbered those variants that altered the underlying amino acid sequence. No SNPs in gene control regions were noted, which may indeed be the case or a limitation of the data sources used here.

The human genes whose rat versions contain SNPs in the hypertensive-susceptible strains are:

SLC39A8
ATP2B1
GNAS - EDN3
MTHFR - NPPB
FGF5
CYP1A1 - ULK3
FURIN - FES
FLJ32810 - TMEM133
NPR3 - C5orf23
EBF1
PLCE1
BAT2-BAT5
ZNF652
TBX5 - TBX3
JAG1
GUCY1A3 - GUCY1B3
MECOM
ULK4

SNPs altering gene expression still need to be added to this analysis. Nonetheless, the numbers and types of genes that share genetic variation in hypertensive mammals (human, rat) is revealing. It is likely that the 788 identified genes with variation in the SHR rat are not all important for hypertension, but that strain does carry variants in 17 of these new BP genes. Or is that just 17?

More on microRNAs - a nutrition connection

2011-05-24T13:09:00.000-07:00

The landscape at the intersection of microRNA (miR) expression and diet is sparse. This is even more so concerning the consequence of bioactive food components in affecting the physical aspects of the miR-mRNA interaction.

Nonetheless, evidence has been reported to suggest that miRs are key metabolic regulators. In adipose of mice, expression of miRs was shown to be sensitive to conjugated linoleic acid in the diet. In rats fed a diet of corn oil/fish oil with pectin/cellulose and in which colonic tumors were induced, a number of miRs, including miR-16, miR-19b, miR-21, miR-26b, miR-27b, miR-93 and miR-203, exhibited altered expression and were linked to oncogenic signaling pathways. Also in rats, downregulation in the liver of three miRs (miR-122, miR-451 and miR-27) and upregulation of miR-200a, miR-200b and miR-429 was noted after feeding of either a high-fat or high-fructose diet with consequences of diet-induced nonalcoholic fatty liver disease.

In mice, pregnant and lactating dams fed a high-fat diet displayed reduced expression of miR-26a, miR-122, miR-192, miR-194, miR-709 and the let-7 family with a common predicted target of methyl-CpG binding protein 2 (Mecp2).

A comparison of miR expression profiles in subcutaneous adipose of women highlighted eleven miRNAs as significantly deregulated in obese subjects with and without type 2 diabetes. Many of the same miRs also showed significant deregulation during adipocyte differentiation. The role of diet in regulating miR expression in prostate cancer has been reviewed. MiR-33, encoded in an intron of SERBF1/SREBF2, cooperatively regulates cholesterol homeostasis via targeting of ABCA1 and NPC1. The FXR/SHP signaling cascade regulates miR-34a and its target SIRT1, which likely functions as either a regulator of epigenetic gene silencing or an intracellular regulatory protein with mono-ADP-ribosyltransferase activity.

Using a mouse diet-induced obesity model, it was shown that hepatic expression of miR-107 decreases while its target FASN, encoding fatty acid synthase, increases.

In summary, there is a growing body of evidence to strongly implicate microRNAs as having significant functions in regulating the metabolic-based response of a number of cell types.

CDKN2A and its response to diet

2011-04-01T08:41:00.000-07:00

Although it is April 1st here, there is some serious business taking place on my desktop: Cleanup day. I'm reading through an electronic pile of papers and news items that have gathered over the last weeks.

Here's an interesting bit about human gene CDKN2A. This gene encodes cyclin-dependent kinase inhibitor 2A and is also known as p16^INK4a. Suppression of CDKN2A by glucose restriction in human cells (fetal lung fibroblasts) was shown by Li & Tollefsbol to contribute to lifespan extension via epigenetic and genetic mechanisms that were mediated by SIRT1.

A year ago, we published a paper showing the effects on gene expression in mononuclear cells in metabolic syndrome subjects after intake of phenol-rich virgin olive oil. We noted repressed expression of several pro-inflammatory genes. Interestingly, CDKN2A was also significantly repressed. Thus, two dietary conditions - low glucose and phenol-rich olive oil - repress expression of this gene, albeit in different cell types and under different circumstances.

MicroRNAs and glucose metabolism

2011-03-28T06:13:00.000-07:00

A noteworthy article released yesterday in Nature Cell Biology reports that in mice, in liver, microRNA miR-143 induces the down-regulation of oxysterol binding protein-like 8 (OSBPL8, ORP8). This leads to an impaired ability of insulin to induce AKT activation. AKT is a protein kinase. What is most interesting about this work is the presentation of evidence that microRNA-based regulation of gene activity is important in glucose homeostasis and perhaps onset of type 2 diabetes.

Before I get into what else is known about miR-143/MIRN143, it is interesting to note that OSBPL8 suppresses ABCA1 expression and cholesterol efflux from macrophages, as reported by Yan et al. (2008). ABCA1 is itself regulated, in part, by microRNA MIR33A encoded within SREBF2 to regulate both HDL biogenesis in the liver and cellular cholesterol efflux.

MIRN143:
MIRN143 is frequently observed to be downregulated in colorectal (Ng Sung 2009 Br J Cancer 101:699) and gastric cancers (Takagi Akao 2009 Oncology 77:12)

MIRN143 is frequently downregulated in pancreatic cancer cells (Kent Mendell 2009 Cancer Biol Ther 8:2013)

MIRN143 was a transcriptional target of myocardin and other transcriptional factors involved in smooth muscle cell fate (Cordes Srivastava 2009 Nature 460:705)

MIRN143 has also been found to play a role in adipocyte differentiation (Xie Lodish 2009 Dibetes 58:1050, Walden Cannon 2009 J Cell Physiol 218:444, Takanabe Hasegawa 2008 Biochem Biophys Res Commun 376:728, Esau Griffey 2004 J Biol Chem 279:52361))

Expression of MIRN143 was elevated in differentiating adipocytes and inhibition of MIRN143 could suppress differentiation of adipocytes (Esau Griffey 2004 J Biol Chem 279:52361)

Ectopically expressed MIRN143 in preadipocyte 3T3-L1 cells has been found to accelerate adipogenesis (Xie Lodish 2009 Diabetes 58:1050)

In addition, MIRN145, neighboring MIRN143 in the human genome is also a participant to this regulatory network:

IRS1 translation is downregulated by MIRN145 (Shi B, Baserga R, et al J. Biol. Chem. 282:32582-32590, 2007)

MIRN145 regulates actin cytoskeletal dynamics (Xin 2009 Genes Dev 23:2166)

stem cell pluripotency is regulated by MIRN145 (Xu 2009 Cell 137:647)

Genetics of coronary heart disease

2011-03-10T05:48:00.000-08:00

Note: This is a guest-post, authored by geneticist and molecular biologist Dr. Chao-Qiang Lai; with edits added by LP.

Last week, Nature Genetics published three letters reporting results from genome-wide association studies (GWAS) for coronary heart disease (CAD). The studies reported a number of markers that reached the threshold of statistical significance for association to CAD with concomitant association to traditional biomarkers of disease risk, such as elevated LDL-cholesterol (LDL-C), elevated total cholesterol, decreased HDL-cholesterol (HDL-C), hypertension, obesity (as measured by elevated body mass index), or type 2 diabetes. However, the two larger and more highly powered GWAS (C4D Genetics Consortium, Schunkert, et al.) also identified CAD-associated variants that are not associated with traditional biomarkers. The third study is of interest because it examines CAD in Chinese populations, but beginning with a discovery set of 130 cases and 130 controls leaves it a bit under-powered. They report a unique association between a SNP in C6orf105 and CAD, which is not found in European or south Asian populations. Curiously, this gene has also been implicated in non-syndromic oral cleft.

There are many sources of CAD. Blood lipids are most commonly thought of as the prime source, but blood pressure in the form of hypertension is also a source. Traditional biomarkers such as LDL-C, HDL-C, triglycerides, and hypertension have been used almost as the sole surrogates for measuring the devolvement and progression of CAD over the course of some 50 years. Meta-analyses of GWAS based on over 100,000 subjects (22,233 cases and 64,762 controls from 14 GWAS) thus far have identified 23 genetic variants associating with CAD. The eye-opening aspect to this is these variants account for about 10% of CAD cases with the shocking observation that 17 of 23 confirmed loci appear to have no association with traditional markers. This observation then suggests two possible explanations.

One possibility is when we assume that the remainder of the CAD cases (90%) contribute to risk associated with traditional markers, such genetic factors cannot be detected based on current GWAS methodology. This is likely to be true because of to the effect sizes of these variants are too small, or their effects are camouflaged by gene-gene (GxG) and gene-environment (GxE) interactions or by epigenetic mechanisms.

This second possibility rests on the fundamental premise that all markers associating with CAD have more or less equal chance to be detected. It then follows that a majority of genetic factors that contribute to CAD has nothing to do with traditional markers. If this is indeed the case, it opens a new avenue to identify the new mechanism(s) and new biomarkers that lead to CAD. In fact, this possibility is supported by many observations. For example, 50% of those individuals who have CAD have low LDL-C (Braunwald & Shattuck; Ridker).

These genes – for example, ADAMTS7, PDGFD, ABO and PPAP2B – point to new mechanisms. While GxG, GxE and epigenetic interactions remain as viable contributors to CAD risk, the path to better understanding of the other component(s) to CAD risk will likely transit through metabolic profiling to identify the compounds that distinguish elevated from nominal risk. Furthermore, research will need to be conducted in model organisms based on these newly discovered genes, perhaps in pig as this is a good model for heart function and disease in human.

10 years with the human genome

2011-02-18T13:44:00.000-08:00

This week marks the 10-year anniversary of the publications of a (nearly) completed human genome sequence. Much has been made already of this passage of time, as well as what we can look forward to in the next ten years.

What I thought I would do in this space is share a little personal story on my connection with this achievement. In late spring of 2001, I happened to search the Internet and PubMed for my name because I wanted to check to see if any presentations at conference or publications from previous laboratories in which I had worked had been released. To my surprise, I found a website in Japan with the title of something like "list of authors" which contained a collection of names of former colleagues from my days in the Genome Sequencing Center at Cold Spring Harbor Laboratory. That seemed strange and so investigating a bit I learned that we were included on the Nature paper describing the human genome - along with some 5000+ other authors (hence the special listing on this website, and no hits in PubMed). Well, needless to say but that was quite a thrill. I quickly updated my CV to include this landmark publication.

Back in 1997 to 1999, as the publicly funded project to sequence the human genome was ramping up and dollars were dangled in front of genome centers around the USA and the globe, we at CSHL were trying to deposit as much finished sequence into GenBank as possible. Monthly and quarterly totals of base pairs deposited were key to securing grant money. An introduction to all this came within my first two weeks as the Computational Fellow (post-doc) with Dick McCombie when I was told I would be leading the analysis segment of his Genome Sequencing course. I learned the ins and outs of a new computer system and new software tools (I came from a cell biology lab) just in time to teach the students. We worked hard during that 2-week course to sequence a 143-kbp BAC clone containing some critical HIV/AIDS-relevant genes: CCR2, CCR5 and CCR6. You can view the sequence entry I deposited to GenBank here, accession U95626.

From this initial BAC, we worked on many more to try to show that we could put high-quality sequence data together and to get as much sequence finished as possible. Of course, our main funding was to contribute to the Arabidopsis thaliana genome and so the human projects (BACs and cosmid/fosmid clones) took second priority. But we did contribute enough sequence to warrant inclusion on the paper and Dick was kind enough to remember everyone who had passed through his lab during those years.

PCSK, cholesterol homeostasis and osteoporosis

2011-02-16T12:14:00.000-08:00

Today, I saw a news release on a series of articles concerning the PCSK gene family published by Dr. Nabil Seidah's group at the Institut de recherches cliniques de Montréal. The combined body of work suggests that the PCSK enzymes could influence health from cholesterol homeostasis to osteoporosis.

PCSK stands for proprotein convertase subtilisin/kexin. This means that it enzymatically converts a larger proprotein into a smaller functional entity. PCSK9 is certainly the most well publicized member of this family with much known about genetic variants associating with myocardial infarction, heart disease and plasma lipid levels, particularly LDL-cholesterol. PCSK9 interacts with the LDL-cholesterol receptor.

PCSK9 also shows decreased expression in a circadian rhythmic fashion in mouse liver depleted for Mir122. This comes from a report by Gatfield, Schibler, et al. 2009 Genes Dev. 23:1313-26.

Here are some other interesting bits about members of the PCSK gene family.

PCSK2 - homolog of nematode gene C51E3.7 which is involved in determination of adult lifespan. SNPs in PCSK2 may increase susceptibility to myocardial infarction and type 2 diabetes, which are both age-related afflictions. A QTL for HDL has been mapped to the vicinity of Pcsk2 in mouse: Hdlq19.

Interestingly, some of my own work on literature mining with Biomax BioLT tool indicated that both PCSK7 and PCSK1N have relationships with HDL-cholesterol. A QTL for HDL at the PCSK7 locus has been described.

Heterozygous knock-out mice for Pcsk1 show increased adipose mass. Transgenic expression in mice of Pcsk1n driven by an actin promoter yielded adult-onset obesity. This gene, in human, was recently proposed as a candidate obesity/type 2 diabetes (T2DM) genes by Chang Hsu (2011 Diabetes, in press) but did not pass their test for Fst measures of positive selection.

An interesting paper by Tiffin, Hide, et al. (2006) suggested that PCSK2 and PCSK7 are candidate obesity and T2DM genes.

Certainly interesting phenotypes here. Keep your eyes on these genes.

Transcription factor databases

2011-02-10T12:10:00.000-08:00

The following is a guest-post by my colleague Jacqueline Lane (with some editing by me). She has been interested in identifying novel transcription factors (TF) involved in obesity and genetic variants in their binding sites as well as in the TF genes themselves.

Jackie has put together a list of TF-gene interaction databases she is willing to share here. There are three types of data:

1) TF-gene interaction
This is a compilation of databases with TF-gene interaction data. This might be of the most interest because it lists many databases. See http://www.pazar.info/. There is also the oRegAnno database, which is easy to view if you click on the tfview link on the right-hand side; see http://www.oreganno.org/oregano/. Lastly, TF-gene binding data can also be found at http://www.tfcat.ca/.

2) TF-TF interactions
This is a database of TF-TF co-activators and co-repressors (TFs that direct transcription of a gene in concert). This helps with determining tissue/temporal specific combinatorial regulation. See http://www.cell.com/retrieve/pii/S0092867410000796.

3) TF co-activators
The TF co-factor database lists proteins that bind to TFs, but not directly to DNA. These protein interactions can give a better picture of the full interaction. Find the data at http://nar.oxfordjournals.org/content/early/2010/10/20/nar.gkq945.full.

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

2011-02-04T17:17:00.000-08:00

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

2011-02-02T16:47:00.000-08:00

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

2011-01-21T08:23:00.000-08:00

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!

2010-12-10T06:06:00.000-08:00

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

2010-12-02T11:29:00.000-08:00

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.

ASHG 2010 conference notes - 5 Nov 2010

2010-11-05T12:32:00.000-07:00

Notes from ASHG 2010 (American Society of Human Genetics)
Washington, D.C. 5 November 2010

E. Kang – Reliable eQTL mapping with F1 generations of inbred mice by measuring allele-specific differential expression

Inbred A:
nnnAnnnnnCnnnnnAnnnnnnGnnn (variant positions showing alleles)
nnnAnnnnnCnnnnnAnnnnnnGnnn

Inbred B:
nnnTnnnnnGnnnnnAnnnnnnCnnn
nnnTnnnnnGnnnnnAnnnnnnCnnn

Inbred C:
nnnAnnnnnGnnnnnTnnnnnnGnnn
nnnAnnnnnGnnnnnTnnnnnnGnnn

Then, the inbred F1s:

AB F1:
nnnAnnnnnCnnnnnAnnnnnnGnnn – high expressor of a given gene
nnnTnnnnnGnnnnnAnnnnnnCnnn – low expressor

BC F1:
nnnTnnnnnGnnnnnAnnnnnnCnnn – low expressor
nnnAnnnnnGnnnnnTnnnnnnGnnn – high expressor

CA F1:
nnnAnnnnnGnnnnnTnnnnnnGnnn – high expressor
nnnAnnnnnCnnnnnAnnnnnnGnnn – high expressor

Thus, the possible causal alleles are the A at SNP 1 and the G at SNP 4.

They worked with 71 million SNPs from six F1 strains built from four parental lines.

--------------------

S. Montgomery – eQTL discovery with RNAseq

Regulatory haplotypes found with HapMap3 data were essentially concordant with 1000G data. So, getting closer to the causal variant? Yes, he states, because p-values are getting stronger.

More rare variants were observed in outliers of expression of a given gene.

For RNAseq, look for many individuals with heterozygous haplotypes. The putative regulatory SNPs they discover are just upstream of the gene to a point within the gene. The magnitude: 60,000 with p-value < 0.05 and 10 or more RNAseq reads (at a total of 3500 genes).

--------------------

P. ‘t Hoen – Expression association with fasting glucose levels

See their recent paper in Nucl Acid Res 38:e165, entitled "Tissue-specific transcript annotation and expression profiling with complementary next-generation sequencing technologies."

~62% of transcript reads from blood samples encode hemoglobin. Still, 9562 genes are expressed at > 0.3 transcripts per cell.

SNP rs11605924 maps within intron 1 of CRY2 and associates with higher expression when glucose plasma is low – but this is a circadian rhythm gene and makes things quite interesting.

--------------------

V. Strumba – cis eQTLs across ten brain regions

170 humans – psychiatric disorders + controls

The region is 500 kbp upstream and downstream of the gene, including the gene, too. 45,000 SNP-gene expression pairs passed FDR of 0.05 in at least one brain region. 58% of SNP-gene expression pairs are specific to one of the ten brain regions tested.

--------------------

A. Dimas – Sex-specific eQTLs

After identification, they did follow-up in twins for replication.

An interesting example is SPO11, a gene with a sex-specific eQTL each for males and females. The two eQTL SNPs are ~760 kbp apart: the female SNP maps to PCK1 and the male eQTL maps to RAB22A. Importantly, the eQTL is not observed when the sexes are mixed, analyzed together.

---------------------

T. Zeller – Cardiovascular disease-associated eQTLs

Of 950 CAD-associated SNPs, 34 SNPs associated with expression at p LIPA increases expression of LIPA, associates with lower HDL-C, associates with lower systolic blood pressure. But there is no difference in expression in CAD subjects vs controls. But it did in 21,428 CAD cases vs 38361 controls in a meta-analysis.

LYZ encodes lysozyme. Lower expression of LYZ associates with CAD. They identified an intergenic SNP that associates with LYZ mRNA levels – rs11166777.

--------------------

J. Curran - Selenoprotein S and cardiovascular disease risk

A SNP at position -105, changing G to A, associates with differential expression of the SELS gene when cells are treated with tunicamycin, an endoplasmic reticulum stressor, but show no differences in mRNA levels under basal conditions. The G allele shows the higher expression.

--------------------

E. Gamazon (abstract 195) – High proportion of transcripts associated with insulin sensitivity in fat and muscle are associated with eQTLs

SCAN is a SNP and CNV annotation database that they built and used in the following analyses.

Top GWAS hits are significantly enriched for eQTL SNPs (see Nicolae, Gamazon et al. 2010 PLoS Genet).

From 184 subjects, they looked at fat and muscle biopsies plus their insulin sensitivity data (in order to classify individuals as insulin sensitive or insulin resistant). Of those, 167 were selected for genotyping (Affymetrix 6.0) and gene expression (Agilent array). In adipose, there is a significant enrichment for eQTL SNPs, Some T2DM SNPs were shown to have eQTL characteristics. For example, rs864745 associates with expression of JAZF1, a T2DM locus, in muscle.

In muscle, ten genes are differentially expressed between the insulin sensitive and the insulin resistant individuals. One of these is PPARGC1A. In adipose, the story is one of more genes – 172 genes are differentially expressed between the insulin sensitive and the insulin resistant subjects at greater than or equal to 1.5-fold. However, few eQTL SNPs were identified from these 182 events. They conclude that transcript regulation is mostly trans. Many, nearly all of the cis eQTL candidates did not hold up to further analysis.

--------------------

J. Zhao – TCF7L2 variants and functional consequences

They used ChIP-seq but observed nothing from extracts from pancreatic islet cells. They noted (from the literature?) a connection between TCF7L2 and cancer. For example, TCF7L2 binds in the region far upstream of the MYC oncogene.

[LP: Are any of the 1095 TCF7L2 binding sites they observe (within 50 kbp of 866 genes) disrupted by SNPs?]

--------------------

J. Florez – Meta-analysis of proinsulin levels

The phenotype is fasting proinsulin adjusted for fasting insulin in a manner that seemed to require a fair amount of thought on their part. Then, they did the GWAS – where TCF7L2 and SLC30A8 served as positive controls. They noted six loci:

ARAP1
VPS13C / C2CD4A / C2CD4B
PCSK1
MADD
SGSM2
LARP6

A seventh locus is SNP rs306549 in DDX31 where the association is found only in women.

--------------------

N. Palmer – Loci for type 2 diabetes in African-Americans

14.7% of African-American adults have T2DM and one in four elderly women suffer from the disease or end-stage kidney disease.

They used principal component analysis to model the admixture.

The original cohort was 965 cases and 1029 controls. The replication population was 709 cases and 690 controls. For the meta-analysis, they had ~3100 cases and ~3100 controls.

754 SNPs were selected for replication. 122 SNPs were nominally and directionally consistent to proceed with validation. They found loci in:

MTR / RYR2
SNX13
PARD3
ZBED5 / GALNTL4
MAF

During the Q&A, the issue was raised that some controls will go on to develop T2DM in the future. [LP: Rather unfair question as this can be the case for so many studies that were presented at ASHG. In fact, you can control for this, somewhat, with age-matched controls.]

--------------------

W. Wei (Institute for Genetics and Molecular Medicine) – Epistasis and genetic control of BMI

Pairwise genome scan identified seven gene-gene pairs reaching statistical significance. A significant number of genes in the 35 gene-gene pairs (the seven above plus another 28 based on candidate approaches) have a role in smoking and alcohol addiction. He showed some gene-gene interaction networks – nice and very similar to what we are doing.

See, for example, his paper in Heredity entitled, "Controlling false positives in the mapping of epistatic QTL."

--------------------

N. Timpson – Effect of BMI on risk of heart disease

They segmented the population by ~4 units of BMI because this is the standard deviation for this population between heart disease and not showing heart disease. After showing a lot of analysis methods and approaches, there was the point that an increase in BMI of about four units leads to an OR of ~1.52 in risk for ischemic heart disease. Thus, BMI is causally related to ischemic heart disease (OR ~1.5). He used an allele score to represent lifescore changes in BMI.

--------------------

E. Speiliotes – GWAS for fatty liver disease

Five loci identified:

PPP1R3B
GCKR
LYPLAL1
NCAN
PNPLA3

ASHG 2010 conference notes - 4 Nov 2010

2010-11-04T13:33:00.000-07:00

Notes from ASHG 2010 (American Society of Human Genetics)
Washington, D.C. 4 November 2010

A Goldstein – Challenges to identification of high-risk alleles

High-risk alleles are rare to very rare and typically have a penetrance greater than 5.

Challenges to finding high-risk alleles
There really is no major high-risk gene
Lack of power or informativeness
Underlying complexity of genetics
Clinical and epidemiological heterogeneity and/or misclassification
Follow-up of linkage results

Illustrations of challenges
BRCA1 – 10% of risk of breast cancer
BRCA2 – 12% of risk of breast cancer
Existence of a "BRCA3" with high-risk is rather unlikely

CDKN2A/ARF – ~20% risk for melanoma
CDK4 – ~1% risk for melanoma

So, increase power of the study. Better use or incorporate:
Molecular genetic data
Functional genomics data
Epidemiological and clinical data

New technology may help – such as NextGen sequencing

--------------------

J. Bailey-Wilson – Complex traits really are complex

Major environmental risk factors may be common
Major genetic risk alleles for serious diseases tend to be rare in population
- Due to selection
- A major locus may have many “risk” alleles

She offers breast cancer as a model. Traditional approaches identified BRCA1 and BRCA2, but then came GWAS.

Linkage is very powerful to detect high penetrance risk alleles in families. Association is very powerful to detect common risk alleles but – if each family has a different, rare or private allele/variant, association will not succeed.

Why has “the gene” not been found?
- False positive linkage
- Have the right gene but don’t understand it yet
- Haven’t yet sequenced fully the region defined by the linkage study
- It is not a gene but a regulatory region
- Could be a long, non-coding RNA
- MicroRNAs and intronic variants, too

Synonymous variants are interesting – change the kinetics of translation!

She is hopeful that more sequencing will be done under broad linkage peaks. But need to phenotype well to fully test for GxE influence.

--------------------

E. Wijsman – Cardiovascular QTLs and large pedigrees

They are looking at familial combined hyperlipidemia (FCHL) in 4 families with 253 subjects. They looked at 600 STRs and 48K SNPs on CVD chip. The phenotype of choice is plasma APOB. For plasma APOB levels, they noted a LOD score of 3.1 on chromosome 4.

Across this large APOB linkage peak, they used each SNP as a covariate to see which one(s) abolish the peak. Then, which gene? Do exome sequencing. All this identified a SNP in LRBP but direct genotyping of the entire pedigree brought the variance from 0.4 to ~0.18 – killed it. So, need to generate many candidate variants for quick screening by genotyping the entire pedigree – because finding one SNP and testing it in a one-by-one manner is not efficient.

The exome data may identify a haplotype which extends to the non-exome.

--------------------

N. Camp – Analytical strategies to identify rare risk variants using extended high-risk pedigrees

They use Utah family data: 2.2 million individuals over three to eleven gnerations, with hospital records.

--------------------

J. Degner – Using genome-wide sensitivity data to infer transcription factor binding

Transcription factor binding sites (TFBS) are poorly annotated. They use ENCODE’s DNase I data. See http://centipede.uchicago.edu for their tool – it uses 230 position weight matrices, 800,000 sites. They also have an article in press at Genome Research. So, use this to check GWAS hits. An example is a binding site QTL for PEBPI.

--------------------

I Aneas – What are the downstream targets of Tbx20?

- differential expression in Tbx20 wildtype vs knockout mice, in heart tissue
- ChIP-seq data from embryo gives 2000 binding sites, from adult gives 4000 binding sites

Combining the above gives 2000 genes. This set is enriched for ion transport and calcium homeostasis functions.

--------------------

A Letourneau – Effect of trisomy 21 on gene expression

They used a twin study – monozygotic twins where one is trisomic for Chr21 and the other not. Many genes on Chr21 and elsewhere in the genome show differential expression. Many Chr21 genes show >1.5-fold increase in expression for trisomic:normal comparison. 58 genes show Chr21-trisomy-specific alternate splicing. [LP: This has got to be a harbinger of what is possible with careful analysis of the effect of CNVs.]

--------------------

T. Teslovich – Sequencing of 400 cases, 200 controls at 26 genes for type 2 diabetes

Goal: Identify rare variants in genes implicated by GWAS.

To date, the most interesting finding is GCKR variant E584X (stop codon). In study #1, the minor allele frequency (MAF) was 0.56% in cases and 0.80% in controls. In study #2, the MAF was 0.08% in cases and 0.15% in controls. (I missed values for study #3.) The point here is one of where the differences in allele frequencies are not significant. So, go to the Metabolo-chip with 14,000 cases and 17,000 controls. This is on-going…

--------------------

H. Daoud – Exome sequencing in ALS families

Six candidate genes were identified that are shared in two ALS families, but none are shared in three families. This is indicative of the heterogeneity of ALS.

--------------------

D. MacArthur – Loss-of-function mutations in healthy human genomes

LOF is a premature stop, splice site disruption, small indel leading to a frameshift, others.

Data from the 1000G pilot:
- 1088 stop SNPs
- 643 splice disruptors
- 956 small (< 40 bp) frameshift indels
- 147 genes disrupted by large indels

Implication is each person has many of these types of variant. ~25% (453 of ~1743) LOF variants did not pass manual validation. OK, so a few of these LOF variants actually are from RefSeq errors and gene model errors. Gene models will be corrected in the next release of Gencode so that subsequent clinical sequencing won’t have to deal with this. In other words, there will be no error.

The estimate is there are ~140 true LOF variants per individual and about 35 or these are homozygous.

ASHG 2010 conference notes - 3 Nov 2010

2010-11-03T08:56:00.000-07:00

Notes from ASHG 2010 (American Society of Human Genetics)
Washington, D.C.
3 November 2010

John Rossi (City of Hope National Medical Center) – SNPs in human microRNA genes affect biogenesis and function

miRNAs regulate translation and degradation of mRNAs. Identifying targets of the miRNAs is a major challenge.

--------------------

Euan Ashley (Stanford University) – What to do with all the sequence data?

Examine the genome of S. Quake with its 6 billion data points.

A rare variants algorithm – tough because a single database does not exist or is private and in varying format. Thus, they use catalogs of common variants for this Patient Zero prototype. With common variants, they need genotype frequencies much more than odds ratio or p-value of association (in the population) when applying population data to the individual.

Dealing with novel variants presents another challenge but some new tools were built by their team (e.g., using SNP-based changes in free energy of RNA folding).

They want to put the genetic risk of the individual in the context of risk for that patient – a 40-yr old White male. For example, he already has a 50% increased risk for obesity given certain non-genetic parameters. It is also necessary to consider environmental risk. Below is an example figure of how such information on risk can be presented to the patient, where the bar indicates how risk changes for this person. In this case, there is an increase in risk of obesity from about 10% to about 60%.
Summary:
- Data are coming, lots and lots!
- We need to deal with large amounts of data
- Databases need to be reconfigured to facilitate genome interpretation
- Physicians need to learn how to communicate such genetic results with patients

--------------------

Russ Altman (Stanford University) – Pharmacogenomics

He started with a screenshot of www.pharmgkb.org and used it to highlight a few SNPs relevant to warfarin dosing.

The focus of the talk was to analyze S. Quake’s genome and evaluate ~2500 SNPs and CNVs with pharmacological implications. They used common variants. Within CYP2C19, Quake has a known variant resulting in 50% reduction in metabolizing rate (he’s heterozygous). He then presented a table with column headers of: Drug, Summary, Level of evidence, PMID, Gene, rsID.

Then on to the novel SNPs found in the Quake genome and organized in the same type of table. The focus was on those SNPs that change an amino acid and are predicted to be deleterious, with predicted potential drug impact. He, as a physician, cannot say, “These SNPs have not been studied before and we will ignore the data (on predicted impact).” Instead, acknowledge those SNPs and genes and drugs and go in a different but equivalent direction with regard to advice and treatment.

--------------------

Job Dekker (University of Massachusetts Medical School) - HiC and higher order folding of the human genome

Started with chromosome 21 to identify higher order organization of the genome. The 5C method was employed to identify millions of chromatin-chromatin interactions across the entire genome. Their finding is genes often become physically close to elements that are 1 to 10 MB away from that gene. This is a long-range distance but mapping to the same chromosome. They have identified some 3000 such examples.

--------------------

Arend Sidow (Stanford University) – What is the functional fraction of the portion of the variable part of the human genome?

How big is the functional fraction of our total genetic variation? “Our” is a key word: It could relate to population or to a single person or haploid genome. For the amount of total genetic variation, consider derived alleles.

0.5% of haploid genome is deviant – but what fraction is functional?

He used p53 (TP53) as an example with its SNPs and repeats to suggest to him that 10% of variants are functional. They use GERP – genomic evolutionary rate profiling (Cooper 2005 Genome Res). See Davydov (PLoS Comp Biol, in press). That work shows that 225 MB, 7.3% of the genome, is functional.

What is the functional fraction of the variation in human?

0.5% of the genome, 3 million variants. Functional: 3-8%, 300,000 to 1,000,000 bp, with most (~90%) mapping to non-coding sites.

--------------------

Erin Kaminsky (Emory University) – Towards evidence-based criteria for clinical interpretation of CNVs

15,749 subjects (from 7 different studies) were genotyped for CNVs as were ~10,400 controls. I think the pathology was for neurological disorders. Pathogenic CNVs were identified in ~17% of cases.

She presented a table of CNV deletions at 22q11.2 (found in 93 cases and 0 controls), 15q13.2-q13.3 (epilepsy, 46 cases, 0 controls), 15q11.2-q13.3 (Angelman, 41 cases, 0 controls), 16p11.2 (autism, 67 cases, 5 controls), and 1q21.1 (microcephaly, 55 cases, 3 controls). The group also looked at duplications.

They used p-value to classify the CNV as pathogenic or not. There was nothing like pathway analysis or gene expression data to go along with this.

--------------------

N. Wasserman – MYC, GWAS for cancer and the nearby gene desert

This region near to MYC is a gene desert but it is a region of regulation (see Wasserman 2010 Genome Res).

How then to identify such long-range regulatory potential? They use BACs (bacterial artificial chromosomes) as enhancer traps!

FTO. The obesity associations fall within a 50-kbp block of LD that includes the last half of intron 1, exon 2 and most of intron 2. Fto-/- mice are smaller and leaner, and have less adipose than control. Thus, tissue-specific upregulation of FTO should lead to the obese condition. The result is enhancers in this 50-kbp region enhance expression in many tissues just like normal Fto (mouse).

They then used 13 different contigs spanning this 50 kbp region to tile across the LD block to find tissue-specific enhancer elements in zebrafish, then to mouse. They found a brain enhancer and then deleted that enhancer from the BAC enhancer trap to show that that small segment is necessary to drive expression in brain.

--------------------

Jared Maguire (Broad Institute) – Using conditional mutation rate to interpret variation in the genome

They use adjacent bases as an explanation for local variability. They look at 3-mers in the coding sequence but he offered an example of GCG > GTG as a known sequence-context-driven C > T change from CpG islands. (I thought CpG islands were not typically found in coding sequence.)

They look for genes with higher SNP burden than others. No specific genes were given.

--------------------

M. Eberle (Illumina, Inc) – Illumina NextGen genotype arrays

15-20% increase in the number of common variants based on latest NextGen and 1000G data. Can they build haplotypes? They use 1.4 million SNPs for imputation based on 60 CEPH samples. He thinks this will improve when more samples are added. This process gives 7.7 million total SNPs. Many show concordance. Genotype calls for rare variants are very accurate: Rare variants show similar accuracy to common variants and overall concordance is 99.96%.

--------------------

Li – Global patterns of RNA editing in humans

RDDs = RNA-DNA differences

Traditional RNA editors are the ADARs (A>I) and APOBECs (C>U). RDDs are not traditional.

RNA preps from 27 CEU B cell samples were sequenced along with the genomic DNA. From the DNA side, they retained only monomorphic sites not in dbSNP, HapMap, 1000G data. From the RNA side, they required greater than 20 reads per position, greater than 20% of those reads with sequence different than the DNA.

They find 3762 (+/-1647) RDD events per subject. Overall, there were 20,753 events in 4507 genes. When requiring that the event/gene be present in more than half the subjects, there were 10,117 events and 3776 events detected in all the subjects.

30.8% of the 101,574 grand total events were A>G or T>C. 19.3% were C>T or G>A. But all others were seen. About 25% of the events are in coding sequence.

What percent of the reads show the RDD? Of all 101,574 events, median level is 97%! These affect splicing. These affect disease susceptibility. These modify disease manifestation. The question remains if these mRNAs are degraded or translated.

--------------------

J. Knight – Psoriasis susceptibility loci and genetic interaction between HLA-C and ERAP1.

Their GWAS identified many immune system genes. They then looked for pair-wise interactions between SNPs that replicated and those concordant with other studies. They used a dominant model to do this.

--------------------

M. Hannibel – Identification of a gene involved in Kabuki syndrome

This is a rare syndrome and so they began the search by looking for a SNP in exome data but in HapMap or dbSNP. 78% of 104 kindreds have MLL2 mutations. MLL2 methylates histone H3 on lysine 4, H3K4.

ASHG 2010 conference notes - 2 Nov 2010

2010-11-03T06:57:00.000-07:00

Notes from ASHG 2010 (American Society of Human Genetics)
Washington, D.C.
November 2, 2010

Eric Lander (Broad Institute) – The human genome project: A decade later

The draft (~90% complete) of the human genome was announced in June, 2000 and published in February, 2001. The finished (~99.3%) sequence was announced in April, 2003 and published in October, 2004.

With the sequence available, we can now build maps of all kinds. Some types include structure maps, maps of molecular function and disease maps. We can also put together a catalog of signatures – allowing us to build platforms for gene expression and proteomics.

In 2000, the completed eukaryotic genomes numbered four (S. cerevisiae, C. elegans, D. melanogaster, A. thaliana). 38 prokaryotic genomes were known. In 2010, the genomes of 250 eukaryotes are complete, 4000 bacteria/viruses and at least 500 human genomes. This has happened for various reasons, a primary one being the drop in cost of sequencing; it fallen ~100,000-fold since 1999.

Understanding the genome. In 2000, the thought was there are 35,000 to 100,000 protein-coding genes, regulatory sequences were not so numerous, there was some non-coding sequence, and transposons and such were considered junk. In 2010, the gene count is 21,000, much more information is in the genome than we thought (~25% of evolutionarily conserved sequences are non-coding and number about 3 million elements (by sequencing and comparing the genomes of 29 mammals)), transposons are big players in the dissemination of these conserved elements, the epigenome, and the approximate 5000 large inter-genic non-coding RNAs.

Mendelian traits. In 1990, we knew the source of 70. In 2000, that number was 1300. In 2010 that stands at 2900 Mendelian disorders identified (see OMIM). There are about 1800 more to know.

The basis of disease – complex diseases and traits. In 1990, we knew only about HLA, number = 1. In 2000, that was ~25, with things like APOE and Alzheimer disease. In 2010 that has risen to ~1100 with respect to 165 common disease traits. But there is disappointment in GWAS because the effect size is small and there is this missing heritability. He thinks that rare variants are not needed because heritability increases as the number of subjects in the GWAS increases, because population genetics suggests that for many common diseases rare variants explain less than other variants, (point #3 I missed), and epistasis hugely distorts the estimate of variance (a – GWAS finds all loci, b – but the loci explain 33% of variance, c – thus we need to use GWAS to identify the biology and then look at variance).

Cancer. In 1990 we knew of 12 solid tumor cancer genes. In 2000 that number was 80. In 2010 it is 240. New pathways are being discovered as pertinent in certain concerns.

History of human populations. He rushed through this and did not really provide any information that is not widely published.

--------------------

John Stamatoyannopoulos (University of Washington) – Using ENCODE to read the human genome: Function and disease

ENCODE is used to guide interpretation of disease-associated genetic variation (GWAS). Many GWAS point to non-coding GWAS SNPs – 47% in introns, 2% in promoters, 7% coding, 14% are 50-100 kbp from nearest known gene, 10% are 1-50 kbp from nearest known gene, 18% are >100 kbp from nearest known gene.

DNase I hypersensitivity site (D1HS) maps overlayed on inflammatory bowel disease GWAS near PTGER4. He uses data from relevant cell lines Th2, Th1, B lymphocytes and sees signals of histone marks in those cells.

Cancer GWAS at 8q24 (upstream of MYC). One SNP lands in a H3K27Ac site, a binding site for TCF7L2 (in colonic cells) and a D1HS.

26% of GWAS SNPs fall in D1HSs. This is ~2.5-fold enrichment. GWAS SNPs for cognition, Parkinson disease, bipolar disorder, and others, map to D1HSs found only in brain. He sees a similar result for heart with Q-T interval, atrial fibrillation, EKG traits and response to statin therapy.

ENCODE is heading to a point of nucleotide resolution in order to better define the regulatory genome.

--------------------

Nathalie Cartier (INSERM) – Gene therapy for neurodegenerative diseases

Brain: 2% of body weight but 25% of all cholesterol.
LP: Hence the Alzheimer-lipid links

--------------------

Michael Meaney – Environmental regulation of the neural epigenome

Environmental factors are social (parental) and economic (food, shelter, safety).

Parental care leads to epigenetic marks which lead to changes in gene expression which then leads to a phenotype. His example is licking of young rat pups (in the first one to two weeks of life) by rat mothers. This licking (care) leads to changes in phenotypic responses to stress, neural development, female reproduction and metabolism. He intends to discuss the endocrine response to stress. Expression of specific genes in specific brain region(s).

[Cool stuff – but delivered like a speed reading of a journal article...]

S1PR5 - Dad's diet and CNVs

2010-10-25T06:30:00.000-07:00

A paper just out a couple weeks ago on the effects on beta cell gene expression in daughter rats due to the high-fat diet of their fathers has turned some attention of those who are interested in epigenetics to contributions from males. This is apt as many genes are known or predicted to be imprinted in human males.

One gene shown by Ng, et al. to be 1.23-fold down-regulated in beta cells of daughters whose fathers were fed a high-fat diet is S1pr5. In humans, S1PR5 encodes a sphingosine-1-phosphate receptor. The ligand of this receptor, lysosphingolipid sphingosine 1-phosphate (S1P), regulates cell proliferation, apoptosis, motility, and neurite retraction and its actions may be both intracellular as a second messenger and extracellular as a receptor ligand [RefSeq].

It is highly relevant that Glessner, et al. (2010) identified a CNV (copy number variant) in S1PR5 that associates with childhood obesity. That CNV is a deletion but whether this leads to down-regulation of S1PR5 is not known. That is likely but not a sure bet until experimental data are taken. Nonetheless, this is an interesting gene and may steal some of the spotlight that Ng, et al. shine on IL13RA2, the human ortholog of the rat gene showing the greatest fold-change in expression between daughter rats whose fathers were fed different diets.

Paternal-linked programming: High-fat diet and a daughter's obesity

2010-10-22T06:34:00.000-07:00

A recent article by Ng, Morris, et al. describes a situation in rats where a father's high-fat diet promotes a phenotype relevant to obesity in the daughter offspring. There is also an interesting review of this report at Nutritional Blogma.

Here, I wished to point out that there are likely to be found many other examples of paternal-linked effects on offspring health. In this regard, Luedi, Hartemink, et al. published a list of computationally predicted instances of imprinting. Many of these are paternal in origin and some served as their training set. In all there are actually 71 genes with known or predicted paternal imprinting. I list those here:

APBA1 amyloid beta (A4) precursor protein-binding, family A, member 1 (X11)
BMP8A bone morphogenetic protein 8a
BRP44L brain protein 44-like
C9orf116 chromosome 9 open reading frame 116
C9orf85 chromosome 9 open reading frame 85
CCDC85A coiled-coil domain containing 85A
CDH18 cadherin 18, type 2
CYP1B1 cytochrome P450, family 1, subfamily B, polypeptide 1 (putative obesity gene (Tiffin, Hide 2006 Nucleic Acids Res. 34:3067))
DGCR6 DiGeorge syndrome critical region gene 6
DKFZp761D1918 hypothetical protein DKFZp761D1918
DLGAP2 discs, large (Drosophila) homolog-associated protein 2
DLK1 delta-like 1 homolog (Drosophila) (Constituitive expression of mouse Pref-1 (DLK1) inhibits, whereas anitsense Pref-1 enhances, 3T3-L1 adipocyte differentiation (Wang, Sul 2006 J Nutrition 136:2953))
EGFL7 EGF-like-domain, multiple 7
EVX1 even-skipped homeobox 1
FAM174A family with sequence similarity 174, member A
FAM59A family with sequence similarity 59, member A
FERMT2 fermitin family homolog 2 (Drosophila)
FLJ20464 hypothetical protein FLJ20464
FLJ25694 hypothetical protein FLJ25694
FLJ42875 FLJ42875 protein
FOXG1 forkhead box G1 (FOXG1 is implicated in epilepsy and Rett syndrome (Le Guen, Bienvenu, et al. 2010 Neurogenetics. in press; Pintaudi, Veneselli, et al. 2010 Epilepsy Behav. in press))
FUCA1 fucosidase, alpha-L- 1, tissue
GATA3 GATA binding protein 3 (Defects in GATA3 are the cause of hypoparathyroidism with sensorineural deafness and renal dysplasia)
GFI1 growth factor independent 1 transcription repressor
GNAS GNAS complex locus
HES1 hairy and enhancer of split 1, (Drosophila)
HYMAI hydatidiform mole associated and imprinted
IGF2 insulin-like growth factor 2 (somatomedin A)
IGF2AS insulin-like growth factor 2 antisense
INS insulin
IPW imprinted in Prader-Willi syndrome
ISM1 isthmin 1 homolog (zebrafish)
KBTBD3 kelch repeat and BTB (POZ) domain containing 3
L3MBTL l(3)mbt-like (Drosophila)
LDLRAP1 low density lipoprotein receptor adaptor protein 1 (genetic variants have been described affecting LDLRAP1 expression which associate with total cholesterol and LDL-C)
LOC51145 erythrocyte transmembrane protein
LY6D lymphocyte antigen 6 complex, locus D
MAGEL2 MAGE-like 2
MEST mesoderm specific transcript homolog (mouse) (MEST is a PPARG target; expression in adipose is 3-fold higher in control-fed vs under-nourished animals (Kozak Koza 2010 PLoS ONE 5:e11015))
MKRN3 makorin, ring finger protein, 3
MRAP2 melanocortin 2 receptor accessory protein 2
MYEOV2 myeloma overexpressed 2
NDN necdin homolog (mouse)
NDUFA4 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4, 9kDa
NKAIN3 Na+/K+ transporting ATPase interacting 3
NNAT neuronatin
NTM neurotrimin
OBSCN obscurin, cytoskeletal calmodulin and titin-interacting RhoGEF
OR11L1 olfactory receptor, family 11, subfamily L, member 1
PEG10 paternally expressed 10 (upregulated in PI3K lung cancer pathway (Gustafson Spira 2010 Science Translational Medicine 2:26ra25))
PLAGL1 pleiomorphic adenoma gene-like 1
PRDM16 PR domain containing 16 (PRDM16 forms a transcriptional complex with the active form of CEBPB (C/EBP-beta, also known as LAP), acting as a critical molecular unit that controls the cell fate switch from myoblastic precursors to brown fat cells (Kajimura, Spiegelman 2009 Nature))
PURG purine-rich element binding protein G
PYY2 peptide YY, 2 (seminalplasmin)
RBP5 retinol binding protein 5, cellular
SGCE sarcoglycan, epsilon
SIM2 single-minded homolog 2 (Drosophila)
SLC22A2 solute carrier family 22 (organic cation transporter), member 2 (identified in a GWAS for prostate cancer)
SNURF SNRPN upstream reading frame
SOX8 SRY (sex determining region Y)-box 8
SPON2 spondin 2, extracellular matrix protein
TIGD1 tigger transposable element derived 1
TMEM52 transmembrane protein 52
TMEM60 transmembrane protein 60
TNFRSF18 tumor necrosis factor receptor superfamily, member 18
TSHZ3 teashirt family zinc finger 3
WT1 Wilms tumor 1
ZIM2 zinc finger, imprinted 2 (ZIM2 shares 7 exons with PEG3 (Kim, Bergmann, Stubbs. 2000 Genomics. 64:114-8), which has been described as an obesity gene in mouse)
ZNF225 zinc finger protein 225
ZNF267 zinc finger protein 267
ZNF738 zinc finger protein 738

IGF2 and INS (insulin) are noted with interest given the Ng paper and its implications.

Notes from INCON 2010

2010-09-28T13:29:00.000-07:00

The INCON conference in Brazil is an International Conference on Nutrigenomics, held in Guarujá in conjunction with ICMAA.

A former colleague of mine, Bibiana Garcia-Bailo, has graciously provided her notes on this conference. I post them here. Later, these will appear in edited form on Jim Kaput's NutriAlerts.

Notes From the MGP Workshop. September 26^th, 2010. INCON/ICMAA 2010 – Brazil.

Welcome and Introduction

Lucia Regina Ribeiro gave the first welcome address to the workshop. She stressed that the work of the MGP will be important to Latin America particularly in the following areas:

1. 1. Multiple micronutrient deficiencies.

2. 2. Interactions between nutritional status and infectious diseases. For example, attempts to increase iron status conflict with the fact that excess iron can precipitate infections. Supplementation is beneficial in promoting resistance against infection, but pathogens also require micronutrients for growthà so we must critically evaluate supplementation in developing countries to make sure it does not affect pathogens and diseases such as malaria, pneumonia, tuberculosis or HIV

3. 3. Micronutrient supplementation

4. 4. Individual and ancestral genetic variability

A hearty congratulations and thanks must be extended to Lucia for organizing this event, where so many scientists and students from around the world have gathered to learn from one another, exchange ideas and establish connections.

After Lucia’s welcome address, John Hesketh introduced the idea and history of the MGP and stressed its nature as a community effort. He presented the topic of variation in micronutrient requirements among individuals, and how one size does not fit all. The current, population-derived recommendations for micronutrient intakes may not target the health needs of individuals, who have different requirements based on their genetic background, lifestyle, physiologic status, etc.

In light of this idea, the MGP was formed. The MGP had three drivers to it:

· Human genome and human variome information
· Omic data, transcriptomic,s proteomics, metabolomics
· Bioinformatics

The idea was, then, to combine expertise in these areas and to move the science forward as a group. The MGP has been conceived as a community bioinformatic resorce and an online portal for access to a comprehensive database of micronutrient ‘omics’ information. It links to existing tools and databases, and has several components:

· A genetic variation portal
· Knowledge of all micronutrient-relevant variations
· A micronutrient pathway portal with gene micronutrient interactions;
· A database of array data, other omics data, phenotypes and study design information

The goal is to put together a single-source portal of information on micronutrient pathways, metabolism, genetics, omics and microarrays data. The MGP’s overall aims are the following:

· Creation of a public portal and bioinformatics toolbox for all ‘omics’ information on micronutrients and related pathways
· New generation of micronutrient research to improve understanding of individual requirements and health outcomes
· Individualized strategies based on micronutrients to improve health

To this end, expert groups have been assembled for each micronutrient and are taking charge of developing gene lists and pathway information to begin populating the MGP portal. John stressed the importance of collaboration and extended an invitation to researchers to participate in these expert groups. He then presented preliminary work on Selenium as an exemplar of how the MGP is working,

As a conclusion, John emphasized the MGP as a way to gather the various ‘omics’ information available in a central point of access, through the MGP website. The MGP is a community bioinformatic resource that links to existing tools and databases. In terms of future developments, John mentioned the EU-fnded MICROGENNET project for global collaboration in this area.

Micronutrient Genomics Pathway Portal – Comparing the Needles in Two Haystacks

During this talk, Chris Evelo introduced some of the bioinformatic aspects behind the MGP work.

He stressed that the MGP expert micronutrient teams must produce gene lists for two reasons:

· To be able to start epidemiological studies, and get companies to develop technology to sequence those genes cheaply and effectively
· To start looking at nutritional phenotypes associated with those genes so that we can begin filling the genetic portal.

He then introduced WikiPathways, to which there is a link from the MGP website, as well as its new features.

Beyond building pathways by hand, in the traditional ways, one can also use the tool in an automated way through Webservice. Webservice is intended for use by software, rather than users. For example, Webservice can automatically integrate data from ArrayExpress Atlas with WikiPathways pathways.

Wikipathways now also has a Cytoscape plugin, so one can upload a pathway from Wikipathways into Cytoskape. Based on the connections formed, one can access a great amount of information, search and open pathways from Wiki into Cytoscape.

With another tool, SSOAP (http://www.omegahat.org/SSOAP/), one can upload the pathways from Wikipathways into R, allowing for the ability to do things like GSEA in R on Wikipathways using webservices.

Chris also presented an example of the applications of this technology through a gene list for vitamin D. The list is actually pathway data, and can be used to show information directly on the pathway. A number of sources were used to get the list, such as Wikipathways, KEGG Pathway and Gene Ontology. The list can be used immediately to load data onto it. One can connect to the network, and can for example connect SNP data to candidate gene studies. Clicking on any of the genes gives the user all the information, which is in part contained in the pathway and has enough information to connect to the online network in order to obtain even more information. One can also open the data in a pathway editing program, and also see the literature references where the genes came from. There is also the possibility of extensive curation of the list.

A few good questions were asked after Chris’s presentation. One was whether biochemical pathways are included in the Wiki. Chris responded that Wikipathways accepts any definition of a pathway that the user has. So ‘pathway’ is a combination of biochemical pathways, network interactions, transport processes, protein-protein interactions, and gene lists. One suggestion was made that the pathways be able to show transcriptomics information and different versions of pathways.

Overall, the point was stressed that MICROGENNET will be able to facilitate exchanges between individuals and groups from all the expert teams, to share methods and educate on approaches among the different groups.

New Pathways: The EURRECA Network

Suzan Wopereis introduced the EURRECA Network to the audience.

EURRECA is a European-funded FW6 program consisting of scientists, universities, small companies, etc. Suzan highlighted the fact that every country in Europe has its own micronutrient recommendations, and the aim of EURRECA is to develop tools to align micronutrient recommendations across Europe, with a special focus on vulnerable groups and consumer understanding. Suzan is involved in Integrating Activity 3, whose goal is to go from dietary recommendations for one micronutrient for the whole population towards recommendations for multiple micronutrients for one person.

Suzan presented her group’s work by first elaborating on the idea that nutrition-related health is a balance of three overarching processes: oxidative stress, inflammatory stress, and metabolic stress. If these processes are imbalanced, nutrition-related diseases occur. Micronutrients contribute towards the balance of the three processes. Suzan and her collaborators are creating micronutrient-related networks by searching the literature for micronutrient status parameters. They then collect information on micronutrient function parameters such as enzymes and cofactors, to get an idea of micronutrient function. They then collected health parameters.

All this information is to be available in the EURRECA website, with all different micronutrients and links to specific wikis. For each micronutrient, one can see a table with the three overarching stresses (oxidative, inflammatory, metabolic). The table contains biomarkers for these processes, and for each specific micronutrient it shows the relationship with established biomarkers as extracted from the literature, plus a score for the ‘trustworthiness’ of the relationship.

This sort of information allows users to begin creating micronutrient networks, observing the interactions between various micronutrient networks, and observing how the micronutrients relate to inflammation, oxidation and metabolic stress. Suzan mentioned that they have created a standard way of drawing pathways using systems biology graphical notation. These pathways can be edited by everyone, using PathVisio. She highlighted some of the work done on selenium, folic acid and vitamin B₁₂ pathways. All of these are available on the Wikipathways micronutrient portal.

Suzan’s talk elicited some good discussion. The point was brought forward that, from the point of view of the MGP, adding genetic information to these networks is key. In turn, the pathways can help identify genes associated with the micronutrient of interest. One interesting addition in the future would be to include information on kinetic dynamics and substrate dependence. Another useful addition would be the ability to see the overlap in pathways. This can already be done in a rudimentary fashion, but will be developed further in the future.

Micronutrient Genetic Variation Portal

Jim Kaput’s talk on the Micronutrient Genetic Variation Portal highlighted the importance of looking at genotype – environment interactions. Jim started by exposing the fact that current, powerful database repositories such as in the NCBI website contain a wealth of genetic and molecular databases and tools, but they fail to include important information on nutrition and lifestyle. This omission is fatal, since nutrients constitute the most long-lasting environmental influence on our biology, from the uterine environment to the end of life, interacting with the genome and leading to different responses in individuals depending on their genetic background and other environmental factors. Therefore, we as nutrition scientists MUST include nutrition and lifestyle data in databases such as NCBI, and put our tools on these websites. Jim mentioned the PhenX Project as one example that aims to analyze phenotype and prioritizes 20 research domains.

In the US, a recent meeting resulted in a paper in Journal of Nutrition, 2010 (in press), where experts got together to assess resources and see what we need as a group to go forward to make nutritional and lifestyle information available together with current genetic and molecular databases and tools.

The Nutritional Phenotype Database, dbNP, is one major effort to create a toolbox for nutrigenomics research in order to link environmental with genetic information. Pathway maps are merged with genetic information, in order to build a genetic module that covers both. Jim showed a screenshot from the database. The genetic module is divided into various tabs or sections. The tabs so far included are pathways, variant information, gene, genetic, epigenetic, and epistatic. One can download the information from the database for statistical analysis, so it’s not just a visualizing tool. Epistasis is very important because of gene-gene interactions.

Jim then illustrated a potential use for the database. One could select candidate genes for analysis by looking into nutrient pathway maps, to then test whether they are involved in the complex phenotype of interest. In such a way, we combine nutrient pathway and genetic information to answer the question of how to bring this information into the clinic and health outcomes.

Jim also spent some time discussing how genetic variation in individuals makes it difficult to design case-control studies or interventions. In designing studies, we must take into account different genetic backgrounds , cultures and diet around the world, which may lead to different results in different regions. This idea was summarized in the paper ‘Planning the Human Variome Project: The Spain Report,’ Hum Mutat Res 2009. This is of particular importance for nutrigenomics projects. Along these lines, Jim brought up the shortcomings of GWAS research, since this technique does not take into account epistasis or epigenetics. One must take into account the entire genome of the individual and the whole set of interactions that may be going on between genes, many of which may not be represented in the platform used depending on the population under study.

Finally, Jim suggested future steps for dbNP. These would include reaching a consensus on the public data elements that should be available, as well as the allocation of tasks to specific groups. Funding of the work to make dbNP take off should also be considered.

The talk provided a note of resonance on the idea that we should be assessing variation in different populations across the globe, as well as considering that gene silencing occurs with aging, and this will become an emerging issue with aging populations.

Genomic Perspective on Vitamin D Signaling

Carsten Carlberg provided a summary of the current genomics research on vitamin D signalling. The case of vitamin D is interesting because it is both a micronutrient that can be obtained from the skin or the diet, and also, in its bioactive form, a transcription factor that activates genes in numerous pathways, such as inflammation, cell growth and metabolism, directly through binding to the vitamin D receptor (VDR). Therefore, the bioactive form of vitamin D can have an impact for various diseases and should be a major target for nutrigenomics research.

However, there are major challenges. For example, the whole genome consists of 22,000 genes. A few percent are targets of vitamin D. Furthermore, there are 250 different tissues, all with the same genome but a different, tissue-specific transcriptome. In addition, there is time-dependent regulation, with dynamics ranging from minutes to days.

Carsten and his group have been conducting research on 1,25(OH)D –the bioactive form of vitamin D- in human cell lines such as monocytes, non-malignant human breast and prostate. For example, they examined genome-wide VDR binding in THP-1 cells. They exposed the cells to 1,25(OH)D for just 40 minutes of treatment, in order to understand regulation of pro- and anti-inflammatory genes by VDR. In untreated cells, they found 1,406 sites occupied by VDR. In treated cells, VDR was on 2,700 binding sites. In addition, many of these sites were found far away from the transcription binding site. VDR was found to bind to the proximity of genes participating in RNA-related functions, vitamin D metabolism, the inflammatory response and insulin signalling. And this happened very quickly, after just 40 minutes of exposure.

After illustrating the importance of vitamin D as a key player in important physiologic processes through his research on cell lines, Carsten highlighted the complexity of studying genomics associated with this micronutrient. There are millions of SNPs, both functional and regulatory, in coding regions, synonymous, non-synonymous, coding and non-coding. Regulatory SNPs can have a variety of effects, ranging from slightly reducing to completely preventing transcription factor binding, so that there is no gene expression whatsoever. There are a number of common traits that show associations with VDR binding. These include type 1 diabetes, Crohn’s Disease, lupus, colorectal cancer, chronic lymphocytic leukemia, tanning, hair colour, height, rheumatoid arthritis, multiple sclerosis, etc.

Carsten suggested two different approaches to genomics research. One is biological hypothesis generation a priori, with genome-wide screening for SNPs in regulatory elements, followed by selection of promising candidate SNPs for population-based studies. Another is an a posteriori approach, with refinement or generation of biological hypotheses of SNPs that are significantly associated with specific outcomes.

Carsten’s talk highlighted the importance of integrative bioinformatics in the genomics research process. In this case, this includes linking one’s information on transcription sites, regulatory SNPs, etc. with curated annotations and data sets, followed by the synergistic administration of public and in-house data.

General Discussion

One of the main points driven forth from the workshop was that expert teams are already making gene lists on various micronutrients, and any individuals who are interested in contributing to any of the micronutrients should contact the team leaders, whose information can be found in the MGP website. A number of representatives from the Human Variome Project (HVP), such as Richard Cotton, were present at the MGP workshop and also put out a call for interested individuals to participate in that endeavour.

The shortcomings of NCBI and EBI were discussed. While both databases have a wealth of information, the data are not readily available and contain little dietary information. The MGP and HVP are collecting material and wrapping it around the existing databases, but there needs to be an even greater concerted effort to contribute to these projects in order to cover the deficiencies of the existing databases.

Also raised was the point that the concepts and the expert teams are already in place, but not much face-to-face discussion has happened yet. At the next workshop, the teams should be brought together in one room to start tracking progress. However, moving the teams forward and having successful workshops in the future will require funding, and it is critical to identify funding sources, since this has been a ‘weekend’ effort for a lot of people so far. Recently obtained funding, such as MICROGENNET, has begun to allow people to exchange ideas, visit one another and collaborate on small bits of projects, but more needs to come.

In regards to this, it was discussed that breaking tasks into small pathway parts might make it easier to assign them so that they can be carried out with less money. Teams could meet in subgroups, and every team needs to chip in to bring their own bit of funding. To this effect, involving students is key since they bring great energy and passion to the project without requiring large salaries.

Another suggestion came through for selling the MGP under a genomic medicine umbrella, since the EU is currently offering funding for this. At this point, a discussion began about the potential Latin American, and in particular Brazilian, contribution to the MGP. Lucia Regina Ribeiro elaborated on this, stating that Brazil can contribute in two ways. One is through vitamin D, where a team is already being coordinated in cooperation with Carsten Carlberg. The other is through a lab with a study of variation in genes that work to transform beta-carotene into vitamin A. The work to screen for these gene variants is being carried out not just in Brazil, but also elsewhere in Latin America.

A discussion was also had on the major roadblocks to the success of the MGP. Focus is one. The group should not lose sight of the main point, which is about determining requirements for an individual for each micronutrient. The project’s goal should be to define, using ‘omics’, what is deficiency and what is excess. The teams should identify key practical outputs and tangible outcomes with respect to how to move the research forward. This is not to say that micronutrient recommendations should be made at this state of knowledge. The objective at this point should be to increase knowledge and collect more data – genomic, physiologic and environmental. Along the process, ways to properly store and filter these data should be identified so that we are not buried in abundance without being able to make sense of the available information.

Five domains enroute to personalized nutrition

2010-09-10T07:00:00.000-07:00

Currently, the Cold Spring Harbor Laboratory meeting on personal genomes is underway. One can follow tweets from the meeting with the hashtag #cshpg.

A keynote speaker in today's morning session is Eric Green, Director of the National Human Genome Research Institute (NHRGI) in the United States. In his talk, as tweeted by Greg Biggers, Green put forth five key domains by which we will achieve personalized medicine. Here, I take liberty to modify these for personalized nutrition, which often can stand upstream of medical intervention in preventing or delaying the onset of a disease condition.

Green's five points:

1 Genome Structure
2 Genome Biology
3 Disease Biology
4 Science of Medicine
5 Healthcare Delivery

My five for personalized nutrition:

1 Genome Structure
2 Genome Biology
3 Biology of the Disease-Nutrition Interface
4 Science of Nutrition & Nutrigenomics
5 Healthcare Delivery as Disease Prevention

Agenda for NuGOweek 2010

2010-08-24T08:08:00.000-07:00

The following is the agenda for the nutrigenomics conference NuGOweek 2010. For more information on NuGO, see this link. I will try to provide updates and notes from the conference as long as wireless is functional...

Tuesday 31st of August 2010
Welcome and opening lectures
Welcome: Dr Baukje de Roos, University of Aberdeen, UK
In short introductory remarks, she noted that this is the first NuGOweek conference without FP6 funding. Thus, overall number of registrants is down from about 250 to about 130. The conference is funded in part by NuGO and Unilever.

Professor Naveed Sattar, Glasgow University, UK
Nutrigenomics - A perspective from the world of metabolic disease

NS was invited to kick things off and to provide the perspective of the physician who is seeing and treating patients with metabolic-based diseases such as type 2 diabetes (T2DM) and cardiovascular disease (CVD).

Amid rising rates worldwide for obesity, T2DM and CVD, the challenge becomes to slow obesity. Not only does obesity lead to increased risk of CVD and T2DM, but also to fatty liver, sleep apnea, some cancers and fertility and pregnancy complications. This said, CVD death rates are falling. Thus, the challenge is to slow the age-relted weight gain trajectory.

Using a biochemical marker (i.e., phenotype), we can screen for CVD risk quite well, but T2DM is complicated by an oft-changing marker. Perhaps that will be HbAc (acetylated hemoglobin). He uses a scoring system incorporating sex, age, BMI, blood pressure, family history of T2Dm and coronary heart disease, ethnicity, smoking status. Something similar can be found at www.qdscore.org.

If nutrigenomics research is to identify a new predictor of disease risk, that marker (or panel of markers) must be cost-effective because the above test is 80-85% accurate. One way of putting this is weight gain pulls the T2DM trigger. Perhaps slowly... So, which other marker might he wish to add to a T2DM test? One could be GGT.

CVD risk. Only when the increase in CVD risk exceeds 20% is the patient treated - with statins. However, most events occur when the risk is elevated by just 10-20% and these people are not treated. (LP - Is this where lifestyle intervention could help?) Thus, we need new phenotypes for risk factors. CRP associates with CVD but even after long, expensive studies it remains unclear if elevated CRP in the plasma enhances prediction. So, use genomics/proteomics - e.g., peptide patterns in urine of coronary artery stenosis.

T2DM risk. T2Dm trials are coming: 12 trials are now ongoing with from 5000 to 20000 subjects, 5-7 years duration. This is tough, costly, long.

Confounders. In Glasgow, vitamin D levels show a seasonal fluctuation, but also are linked to wealth - poorest subjects have lower levels than most affluent subjects.

A big chalenge is to link omics results to disease outcome / risk.

Conclusions. The real goal is to prevent obesity as this leads to other complications. Omics research, while in its infancy, shows promise. We need lots more data keeping in mind both the clinical questions and the translational potential of the results.

Wednesday 1st September 2010
Plenary Session 1: Nutrigenomics and novel biomarkers of health
Chairs: Professor Christian A. Drevon and Dr Lorraine Brennan

Professor Barbara Cannon, Stockholm University, Sweden
The adipostat hypothesis for body-weight control

Certain chemicals are mitochondrial uncouplers. One is DNP, dinitrophenol acting by proton leakage and she wonders if this could counteract obesity. Work done in chicken and quail showed that RQ is down and fat is burned when DNP is added to diet. In 1933, Cutting et al. showed that in humans metabolism increases and body weight decreases after DNP in the diet. Tainter, et al. (1933) showed weight loss of 0.5 - 1.0 kg/week, mostly around the hips/waist. Side effects were catarats/blindness, skin rash, loss of taste. This work, however, is proof that in humans thermogenesis works against obesity.

So, adipostat set point must be flexible.

Since 2007 it is clear that humans have brown adipose tissue (BAT). Questions: How many people have it? How much do they have? Does it matter? (She cannot answer this last question yet...)

Christiensen et al (2006) showed a temperature dependence to the ability to detect BAT in humans. See Zingaretti et al (2009) where BAT is densely assocaited with nerves. Human BAT contains UCP1, as in rodents. But does it matter? Look in rodents and Ucp1 -/- mouse. There is no thermogenesis in BAT when mice housed at thermoneutrality (30 oC). Similar phenotypes were observed in UCP1-/- mice on obesity-prone (C57) and obesity-resistant (129SV) backgrounds.

After norepinepherine treatment, respiration increases in normal vs Ucp1-/- mouse. There is no increase or difference in VO2 max in Ucp1-/- mice on chow vs high-fat diets. Basal metabolism is unchanged in these animals. Thus, there is an adaptive thermogenesis dependent on Ucp1.

Humans always go around with clothing - more or less in a thermoneutral state. OK, the above animals are obesity-prone

So, is more BAT good? Only if it is activated. Lower human BMI and age correlate with presence of BAT (Zingaretti et al (2009)). Hence, there is a diet-induced thermogenesis (DIT) and decreased DIT may cause obesity. Adaptive thermogenesis counteracts obesity.

Professor Helga Refsum, University of Oslo, Norway
Cysteine in relation to body composition

Cysteine gives rise to taurine and glutathione (an anti-oxidant). Cysteine is converted to glutathione by GGT (GGT1 and GGT2). But why look at cysteine? Change in BMI predicted change in total cysteine levels in plasma over time in a Swedish study. This change associated with fat mass and not lean mass. But are other sulfur amino acids involved (e.g., taurine, glutathione, methionine)? She showed that it is not the case, only cysteine.

Does high cysteine lead to obesity? Or does obesity lead to high cysteine? Or are there confounding factors? No: dietary factors and energy intake, physical activity, lipid related factors, serum glucose, GGT levels all show no confounding effects. Baritric surgery with rapid weight loss suggests that high cysteine levels lead to obesity.

CBS (gene) deficient humans are thin and CBS in excess in humans leads to overweight. Numerous genes are implicated: SCD-1, PLTP, ABCA1, et al.

Adding cysteine to rodents fed a methionine-restricted diet reverses the phenotypes. Fatty acid synthesis increased in diet supplemented with cysteine, as suggested by gene expression analysis.

Hannelore Daniel asked about cysteine oxidation - it is not impaired in the mouse experiments.

What about dieting (e.g., Atkins and high-protein). Diets fail. Soy is low in sulfur amino acids but associates with satiety. Need weight maintenance and not weight loss.

Dr Lorraine Brennan, University College Dublin, Ireland
Nutrityping and phenotyping people using metabolomics

She wishes to understand the interactions between lifestyle factors and nutrition-based phenotypes. She uses cluster analysis to find three dietary patterns in her group of about 160 Irish. She uses NMR to find differences in biomarkers of intake: fatty acids, O-acetylcarnitine in the urine and phenyl... in plasma. One of the latter two is a marker of red meat intake and the other of vegetable intake.

Phenotyping - an intervention study was conducted for 4 weeks with vitamin D. They found 5 clusters by k-means based on 14 biomarkers. One is 25(OH)D (vitamin D). So, which biomarkers respond? Cluster 5 responded by healthier profiles in adiponectin, HOMA, insulin. Metabolites altered in cluster 5 are VLDL/LDL (decreased), glucose (d), lactate (d) and glutamine (increased).

Using one biomarker is not sufficient and dividing a population based on a number (n>1) is necessary.

Professor Hannelore Daniel, Technical University Munich, Germany
The human metabolic accordion

The normal human metabolome is boring, right? Not really because of the time-component. [LP: she did not go into detail, but assumed one such t-c. I see several that I believe she would acknowledge: after a meal, post-exercise, throughout aging, etc.)

Up-front questions: Urine and plasma represent what? What is normal in the face of physical constitution, genetic heterogeneity, etc.? Is the static metabolome a good measure of health vs. disease?

Experiment: Young men, all within BMI of 23.7 +/- 1.7 (or so), were put through a battery of tests, beginning with a ~36-hr fast, glucose tolerance, exercise test, etc. etc, over the course of 4 days. During this time, blood was taken at many time points, urine, too. many metabolites were measured and many observations were made. For example, several amino acids change in remarkable ways during this treatment.

Summary:

- Metabolic plasticity is important to evolution in order to rapidly respond in time/space (=organs, cells) to catabolic vs. anabolic states.

- Don't know what is "normal" when taking one snapshot after a overnight fast. Is this the best reference?

- Based on enormous plasticity of metabolic responses, it seems more advised to "titrate" the capacity of adaptation in time and space by defined and standardized changes for identifying deviations from normal.

Plenary Session 2: Modeling human metabolism
Chair: Professor Hannelore Daniel and Dr Grietje Holtrop

Dr Kevin Hall, National Institutes of Health, Bethesda, USA
Modeling Metabolism of Mice and Men

Modeling can be thought of as mathematics or of using a surrogate organism to learn about the human condition. He uses math. We can take longitudinal (i.e., long-term) measures of body weight, fat mass, lean mass, even food intake. However, getting long-term measures of energy expenditure is tricky. So, use short-term, then ask if mathematical modeling helps to get long-term values in numbers that easily, directly relate to values of food intake (kJ/kg body wt/day).

d(pBW)/dt = I - E,

where the change of body weight (with some factor rho) over time equals Intake - Expenditure. This is the energy balance equation.

Food intake and physical activity both allow mathematical modeling of human metabolism. This in turn allows calculation or determination of fluxes and changes of various sorts, e.g., metabolism of carbohydrates and lipids, energy expenditure, et al.

He used such to assess the USDA/ERS calculation that placing a tax on soda would lead to a linear weight loss over 5 years of about 10 kg for a 100 kg person. He found that this weight loss reaches a plateau and amounts to just 2 kg because the model, which uses more complicated mathematics than shown here, has 1) an exponent and thus reaches saturation; 2) a long time constant of 410 days.

Human weight change is dynamic and occurs over a long time scale. See their paper.

Professor Claudio Cobelli, University of Padova
Glucose Metabolism in Health and Diabetes: Necessity of Models

He takes the engineering approach - a simple experiment using complex mathematics to model it - as opposed to a biologist's complex experiment with a simple model. He uses the IVGTT - to measure glucose, insulin, C-peptide. A meal or OGTT is too complex because one needs to consider gut influences in order to model the observations.

He has moved to cellular models of insulin secretion. See the paper from 2008. Insulin sensitivity x beta-cell function = a constant. So, some people have low insulin sensitivity and need a boost with therapy, while others have reduced beta-cell response requiring a different therapeutic approach.
Dr Gerald Lobley & Dr Grietje Holtrop, University of Aberdeen, UK
Theoretical and practical considerations for measurement of glucose and protein kinetics

Moderated poster session 1

Scientific Session 1: Inflammation, metabolic health and obesity
Chairs: Professor Aldona Dembinska-Kiec and Professor John Mathers

Nadja Schulz, German Institute of Human Nutrition, Potsdam, Germany
Adp3, a protein involved in beta-oxidation is a putative regulator of insulin secretion

[LP: This is continuation of work I have seen from some 3 years ago, with reference to a gene that is not defined in literature nor in EntrezGene. Perhaps it is in patent applications.]

They began to work on this protein after a screen of C. elegans genes. Adp3-/- knock out mice show reduced body weight gain, but no differences in food intake. Some differences were noted in the light phase in locomotor activity. Increased body temperature in Adp3-/- in both phases was a key to the metabolism issue. These mice have impaired oral glucose tolerance tests but the insulin response and fat tolerance are like wildtype.

- Decreased insulin secretion in response to glucose in the KOs

- Increased insulin secretion in response to fat in the KOs.

Hannah R. Elliott, Newcastle University, UK
Novel epigenetic biomarkers of T2D susceptibility

Three questions:

- Do DNA methylation patterns associate with T2DM traits?

- Do such methylation patterns alter with age?

- Does #2 above (altered patterns) associate with T2DM severity?

She looks at the first question using the RISC cohort and CpG islands in the promoter and exon-1 regions of FTO and ADCY5. Specifically, she is most interested in CpG islands in the promoter and transcription factor binding sites. They use a MALDI-TOF approach to get a percentage of differential mass, which is an indicator of methylation.

- BMI and age correlated positively with ACY5 methylation.

- No correlation was observed between FTO methylation and age.

Thomas Skurk, Technische Universität München, Germany
Cell size of human adipocytes affects endocrine and metabolic functions

Fat cell size in adipose tissue. Adipocytes increase in size as BMI increases. He size-fractionated adipocytes. There is a shift to pro-inflammation mode in larger fat cells, assessed by measures of cytokines. It looks like ER-stress is not the only relevant factor but he is looking at more genes. Small adipocytes are insulin sensitive; large cells appear insulin resistant, but this is really true only when the person is a type 2 diabetic.

James C. McConnell, Newcastle University, UK
Genome wide DNA methylation is associated with lipid profiles at age 50

They used the Newcastle Thousand Families Study, a longitudinal birth cohort from 1947. Global DNA methylation was assessed by pyrosequencing in 231 individuals at 3 CpG islands in LINE-1 retrotransposon elements. Significant positive correlations were observed between methyl-DNA and levels of fasting glucose and C-peptide. Also, blood lipids of total cholesterol, LDL-cholesterol (increased), APOB, triglycerides (increased) and HDL-cholesterol (decreased). Thus, a perturbed pattern of DNA methylation is suggested in pathogenesis of common complex diseases.

Miguel A. Lucena, IMABIS Foundation, Malaga, Spain
Metabolic alterations in the abdominal muscle of obese rats - a proteomic approach

In obese rats, muscle saw decreased levels of glycolysis-related enzymes: glucose-6-phosphate isomerase, alpha-enolase and lactate dehydrogenase. Increased levels of FABP3 and FABP4 were noted as well as B-crystallin and HP (haptoglobin). It looks like glucose and fatty acid metabolism are affected by obesity in skeletal muscle.

Andreas Kolb, University of Aberdeen, UK
B-vitamin deficiency and phenotypic variation in vascular cells

They used A7r5 cells. Treatment was high folate, 100 ng/ml. This induced expression of many cholesterol and lipid metabolism genes - more so than any other pathways or funcitonal group. However, some genes were up-regulated and some were down-regulated. (It wasn't entirely clear to me, but I believe that these genes function in both synthesis and metabolism.) B-vitamin deficiency increased expression of pro-inflammation cytokines and decreased NO production.

Scientific Session 2: Novel food models to investigate metabolic health
Chairs: Dr Suzan Woperies and Professor Edwin Mariman

Suzan Wopereis, TNO Quality of Life, Zeist, the Netherlands
Postprandial challenge test to demonstrate subtle dietary effects on human health

MPO and MDC show less increase after the high-fat challenge (these are AIDM genes). VCAM1 showed greater reduction. ACE was reduced compared to the placebo at baseline.

Claire Merrifield, Imperial College, London, United Kingdom
NMR-based urinary metabolic profiling of the pig reveals a sustainable metabolic reprogramming event related to weaning diet

Laurence D. Parnell, Tufts University, Boston, MA, United States
Network analysis defines the impact of gene-physical activity interactions

Mark Boekschoten, Netherlands Nutrigenomics Centre, Wageningen, Netherlands
Effect of dietary fat on the transcriptome in white adipose tissue of C57BL/6J mice

Thursday 2nd September 2010
Plenary Session 3: Inflammation, metabolic health and obesity
Chairs: Professor Michael Muller and Professor Harry McArdle

Professor Michael Muller, Wageningen University, the Netherands
Metabolism and Inflammation

NAFLD = non-alcoholic fatty liver diseases, is a component/manifestation of metabolic syndrome where PPARA plays a role, especially in Kupffer cells.

NASH = non-alcoholic steatosis hepatitis.

Their goal is to isolate biomarkers of NASH. BLACK 6 mice develop NASH on a high-fat diet (45% fat vs 10% fat for control). Many genes show altered expression in the high-fat/high-responder group. This is about twice the number of genes as in the high-fat/low-responder and low-fat/high-responder groups. Many genes fall into three categories: fibrosis, inflammation, lipid metabolism.

Furthermore, changes in gene expression indicate adipose dysfunction. This is emphasized by macrophage infiltration.

The search for a plasma biomarker: CRP, haptoglobin, IL1B, MIP-1alpha - early markers of NASH development.

The Angptl4-/- mouse on a high-fat diet is very ill. But adipose tissue and liver are small. They detect systemic inflammation. Saa2, haptoglobin and this is independent of microbiota. This is observed only when the fat source is lard or palm oil, not with safflower oil.

Angptl4, under control of PPARD, represses LPL (lipoprotein lipase). In the Angptl4-/- KO, triglycerides in the chylomicrons go to fatty acids. This is in press in Cell Metabolism.

Dr Matthijs Hesselink, University of Maastricht, the Netherlands
Muscle physiology in insulin resistance and type 2 diabetes

Fatty acid derivatives disrupt IRS-PI3K-SLC2A4 signaling but evidence for such is lacking in T2DM subjects. Skeletal muscle is responsible for about 40% of postprandial glucose uptake. The focus is on storage of fat in muscle (ectopic fat). The balance between fat storage and fat metabolism in muscle is indicative of cell function. The literature shows that more fat there is in muscle, the more insulin resistance there is. Muscle triglyceride (TG) storage is augmented by increases in free fatty acids and the TG levels decrease after exercise, but there is a differential effect on insulin sensitivity.

Lipid droplet (LD) proteins (also known as perilipins or PAT proteins) are indeed important in muscle: PLIN 5 (OXPAT), PLIN2 (adipophilin, ADRP), PLIN3 (Tip47). PLIN4 (S3-12) is also expressed in muscle. Expression of PAT genes in muscle of T2DM subjects vs those without T2DM: control for age, BMI: (see Meex 2010 Diabetes). PLIN2 and PLIN5 showed no differences in expression levels, but PLIN3 is down-regulated in T2DM skeletal muscle. In this case, new LDs are not made. Gene PNPLA2 (ATGL) shows no difference. Now add exercise training. Of those genes reported, only PLIN2 and PLIN5 are up-regulated in both muscle types (T2DM and non-T2DM) post-exercise. PNPLA2 is up-regulated only in T2DM post-exercise.

The adaptive response of PLIN5 and PLIN2 may improve fuel selection or use in hyperinsulinemic subjects.

Dr Lydia Afman, Wageningen University, the Netherlands
The challenge of nutritional phenotyping in human nutrigenomics

Her goal is to identify early biomarkers of disease at a time when nutrition can be used to treat the pre-disease state.

1) Gene expression in PBMCs comparing MUFA-based diet vs. diet high in EPA/DHA. This was done for long-term (20-26 weeks). The main finding: a diet high in EPA/DHA elicited an anti-inflammation anti-atherogenic gene expression profile.

2) Adipose gene expression after 8 weeks on one of three diets: Mediterranean, high MUFA (20%), high SFA (20%). She presented only on the MUFA:SFA comparison. There was no difference in insulin sensitivity; no effect on HOMA was observed. Both the MUFA and saturated fat diets were about 40% in fat, with 20% of energy coming from the respective fat type. SFA increased expression of many inflammation pathways, notably T- and B-cell receptor signaling, leukocyte extravasation and complement. The SFA diet induced a pro-inflammatory, obesity-linked gene profile. MUFA showed a reduced inflammatory profile.

Professor Christian A. Drevon, University of Oslo, Norway
New myokines and potential actions

He began with a list reviewing literature of positive, beneficial effects of exercise on a number of diseases. He also mentioned the review of BK Pedersen (Physiol Rev 2008) describing contraction-induced release of IL6 leading to increased glucose uptake (via PI3-K) and increased fat oxiation (via STAT3).

IL7 is secreted from skeletal muscle cells. IL7 mRNA increases linearly with myogenic differentiation. LPS increased IL7 mRNA but not protein levels.

IL7 is localized to myotubes expressing myosin heavy chain. Like myostatin, IL7 decreases expression by about 35% of myosin heavy chain (MYH2) and MYOG. IL7 enhanced myotube migration.

In human subjects undergoing strength training for 2 and 11 weeks, increased expression of IL7 was noted in skeletal muscle. Also increased were IL8, TLR1, TLR2, TLR3, TLR4, TL5, TLR6, TLR7; not TLR9. See their paper.

Plenary Session 4: Gut metabolism and chronic disease development
Chairs: Professor Harry Flint and Dr Elizabeth Lund

Professor Michael Blaut, DIFE, Potsdam, Germany
Impact of food ingredients on intestinal microbiota-associated obesity development in mice

Dr Patrice Cani, University Catholique de Louvain, Belgium
The contribution of gut micro-organisms in promoting and preventing insulin resistance

Dr Petra Louis, University of Aberdeen, UK
Impact of diet upon the human gut microbiota and gut metabolism in obese subjects

Moderated poster session 2

Scientific Session 3: Food, nutrigenomics, biomarkers and health
Chairs: Dr Jill McKay and Professor Sean Strain

Jill McKay, Newcastle University, Newcastle upon Tyne, United Kingdom
Folate depletion during development and high fat intake from weaning: consequences for DNA methylation and gene regulation

Maryam Rakhshandehroo, Nutrigenomics Consortium, Wageningen, the Netherlands
Mannose binding lectin is a circulating mediator of hepatic PPARα activity in human

The aim was to screen for novel circulating mediators of PPARA activity in human. They identified MBL2 as a circulating mediator of PPARA likely affecting innate immunity.

Jildau Bouwman, TNO Quality of Life, Zeist, the Netherlands
Let's visualize personalized health

Luisa M. Ostertag, University of Aberdeen, UK
Dark secrets of chocolate, platelet function and cardiovascular health

Emilie Martinez, INRA, Clermont-Ferrand, Auvergne, France
Changes in the myocardium proteome of rat pups after maternal deficiency of methyl donors

Siv Kjølsrud Bøhn, University of Oslo, Oslo, Norway
Bilberry and grape juice decreases plasma biomarkers of inflammation in aged men with subjective memory impairment

Results: Compared to placebo, plasma biomarkers of inflammation (EGF, VEGF, IL6, MIP1b, IL10, IL9 and TNF) and a biomarker of tissue damage (LDH) significantly decreased after bilberry/grape consumption while several plasma polyphenols increased.

Debate: The future of personalised nutrition
Moderator: Dr Ben van Ommen

Argument 1: Personalized nutrition is alive and kicking
Personal health monitoring will be daily practice. Everyone has smartphone and internet access to his health status, based on electronic health records, a series of frequent bioassays in the home setting, genomics information, coupled to life style and dietary advice. Industry has skipped the concept of functional foods, and provides tailored foods in the context of life style coaching, integrated with personal health monitoring. Nutrition science has finally understood how to deal with genetic variation, that is, of course not by further refining epidemiology but by exploiting systems biology modeling. Also, major breakthroughs in mechanistic nutrition research embedded in the biology revolution provided a wealth of knowledge on food bioactives. Healthy ageing is a reality!
Speakers: George Lietz, Barbara Stewart-Knox & Christian Drevon

Argument 2: Personalized nutrition is dead, long live nutrition
Although mechanistic nutrition has provided a lot of new views on modes of actions, this appeared to have no real impact whatsoever on actual health, except for some fine-tuning. The obesity outbreak made diet the 'silent killer,' which made nutrition research split into two mainstream lines, driven by health care costs. One side merged with biomedical research focusing on prevention of pathologies. The other side merged with social science to focus on 'social engineering' of food intake control. Food intake quantification has improved and epidemiology readily incorporated this, to finally optimize public health dietary recommendations.
Speakers: Piero Dolara & Anne-Marie Minihane

Argument 3 – Nutrigenomics is a waste of money
While a lot of money has been burned on high-tech nutrition research, the marginal advances in health optimization did not justify further spending. In fact, this money could have been more wisely spent on international nutrition, as more than half of the global population still receives an inadequate diet. Anyhow, a series of events caused the decay of nutrition research. EFSA regulations in the end depressed food industry, which stopped submitting claims but rather returned to consumer persuasion via commercials. This was encouraged by the media coverage of many conflicting messages from the nutrition research community. In the end, funding for nutrition research diminished and mainstream biological research absorbed diet as one of the 'environmental factors.'
Speakers: Hannelore Daniel, Helen Roche & Duccio Cavalieri

Friday 3rd September 2010
Plenary Session 5: Insulin resistance and the brain
Chairs: Dr Ben van Ommen and Dr Lynda Williams

Dr Kenneth Kornman, Interleukin Genetics
Genetic patterns predict weight loss success at 12 months: The right diet does matter

KK: You have to reduce calories to lose weight, but how much you lose is genetically determined.

They looked at three gene variants: rs1799883 in FABP2, rs 1801282 in PPARG, rs1042714 in ADRB2 because these had substantial data from the literature and are functional (i.e., amino acid change. See Gardner, et al 2007).

Individuals (all females and overweight to obese, n=~140) were randomly assigned to one of four diets for a period of 12 months. For the first two months, they came into the clinic once a week. After that, they were contacted by phone to assess eating behaviors and status with respect to the diet. Diet types were either low-fat, low-carbohydrate or neither. Diets included Atkins, the Zone and Ornish. Genotyping of individuals was done after completion of the study!

The hypothesis is, of course, there is a diet to match the genotype of the individual. Diets for the appropriate genotype lost 2- to 3-fold more weight (closer really to 2-fold) at 6 and 12 months after initiation of the study than those on the inappropriate diet. Weight loss at 6 months was about 5.5 kg on the appropriate diet and about 4.5 kg at 12 months. Waist, triglycerides also dropped; HDL-cholesterol went up. Weight loss was steeper for both groups (on appropriate and on inappropriate diets) at 2 months and this is likely due to the weekly clinic visits.

The data at two months shows something on satiety. Subjects on the appropriate diets took in ~100 cal less (but this was not explained further in response to my question).

Professor Oren Froy, The Hebrew University of Jerusalem, Israel
Metabolism and Circadian Rhythms--Implications for Obesity

See paper by Froy in Clin. Sci. (2010) on core clock components and metabolism factors. While that must be in press, one can view this paper. There is a master clock in the brain and peripheral clocks in many organs/tissues. Only the master clock appears to sensitive to feeding.

Mice were put on restricted feeding for four months. Restricted feeding is allowing the animals to eat as much as they want but only during the 3-5 hours that food is available during each 24-hr period. They found that this feeding regimen attenuates the peripheral clock and lowers inflammation markers. See this paper for details.

Restricted feeding stimulated the food entrained oscillator, leading to high amplitude circadian rhythms and reduced levels of inflammation markers.

A high-fat diet disrupts and flattens the circadian rhythms (Barnea, et al. 2009). [LP: I asked if it is intake of calories or even water that can trigger these observations. Response: It must be calories as water had no effect.]

Dr Lynda Williams, University of Aberdeen, UK
Novel biomarkers of inflammation and leptin sensitivity

Does early onset sensitivity to leptin really matter? A high-fat diet compromises leptin action in the hypothalamus and not via the JAK/STAT signaling pathway.

Leptin is a potent insulin sensitizer acting on the hypothalamus and is necessary for the full response to glucose and glucose homeostasis. This is not due to caloric intake, but to high-fat diets.

Dr Ineke Klopping, TNO, the Netherlands
HPA linking metabolism, brain and psychological stress

Her two main points were nutrigenomics research needs to consider the stress level of the individual and timing of sampling (due to seasonal or circadian fluctuations).

Scientific Session 4: Gut metabolism and chronic disease development
Chairs: Professor Piero Dolara and Dr Robert Kleemann

Piero Dolara, University of Florence, Italy
Sodium butyrate enemas modify gene expression, atrophy and inflammation in mucosal enterostomy pouches

Lisa Gruber, Technische Universität München, Freising, Germany
The effect of high-fat feeding in a mouse model of inflammatory bowel disease

Didier Attaix, INRA/Clermont Université, Clermont-Ferrand, France
GLP-2 inhibits intestinal lysosomal proteolysis and improves skeletal muscle recovery in the starved/refed rat

Ben van Ommen, TNO Quality of Life, Zeist, the Netherlands
The nutritional phenotype database in practice

Tuberculosis susceptibility SNP under selective pressure

2010-08-11T09:06:00.000-07:00

A recent publication by Thye, Vannberg, Horstmann, Hill, et al. reports on a genome-wide association study in Africans on association to the susceptibility of tuberculosis. The major finding is the identification of a susceptibility locus in a gene-poor region on chromosome 18q11.2 centered on SNP rs4331426.

My colleague Chao-Qiang Lai used Jonathan Pritchard's Haplotter data to discern that a comparison of YRI to CEU with Fst shows that this SNP, centered within a window of 151 SNPs, is within a group of variants (n=151) where 98% of the SNPs have Fst values greater than 95% of all SNPs. This is an indication that rs4331426 is under selection. With G as the minor allele and a minor allele frequency (MAF) in CEU of 0.025 and a MAF in YRI of 0.508, this difference in allele frequencies is noteworthy. In two Asian populations, MAFs are 0.03-0.04. Not surprisingly, we do not detect significant Fst between CEU and either of these two populations.

This is an important example, of which there is a growing body, in which genetic variation under selective pressure and disease phenotype(s) are linked. As diet constitutes a key aspect of the environment, we feel that there will be interesting findings at the intersection of selective pressure and metabolic-based disease.

More on the microRNAs that control SIRT1 expression

2010-08-09T07:37:00.000-07:00

In the journal Aging, a report has just been published describing the control of expression of SIRT1 by microRNAs. See Lee and Kemper (2010).

Three microRNAs implicated in their study are MIRN34A, MIRN132 and MIRN199A. To add to the conversation, I provide some other details of these microRNAs:

MIRN34A - expression in subcutaneous fat is correlated positively with BMI (Ortega, et al. 2010 PLoS ONE 5: e9022); the pre-adipocyte from an obese individual has 1.23-fold higher expression than from a lean person (Ortega, et al.); furthermore, the gene is upregulated during adipocyte differentiation (Ortega, et al.); lastly, confluent fibroblasts show 4.1-fold higher expression than sub-confluent cells (Hwang, et al. 2009 PNAS 106: 7016-7021)

MIRN132 - no other data to offer

MIRN199A - similarly, expression in subcutaneous fat is correlated positively with BMI (Ortega, et al. 2010 PLoS ONE 5: e9022); there is 2.3 to 3-fold higher expression in confluent fibroblasts than in sub-confluent cells (Hwang, et al.)

The two genes above, those with data, are certainly interesting and strengthen the metabolic links involving SIRT1.

In brief – A diet of insects, a fatty acid perspective

2010-07-15T09:38:00.000-07:00

Essential for life, fatty acids are crucial in energy metabolism, cell and membrane structure, and physiological regulation to all organisms, including Drosophila [Stanley-Samuelson, et al. 1988]. The fatty acid compositions of all insect orders are fairly similar, in a qualitative way. Biochemical profiles include about eight components with chain lengths of 12 to 18 carbons, most of these saturated and monounsaturated fatty acids (MUFAs) plus two polyunsaturated fatty acids (PUFAs) - Linoleic acid (LA, l8:2n-6) and а-linolenic acid (ALA,18:3n-3 ) [Stanley-Samuelson, et al. 1988]. The lipids of aquatic insects and some Antarctic beetles are abundant in long chain PUFAs with a chain length of 20 or 22 carbons. Certain C20 and C22 PUFAs play important regulatory roles in reproductive biology of some insect species. For example, arachidonic acid (AA, 20:4n-6) or certain structurally related C20 and C22 PUFAs are essential nutrients for several mosquito species [Stanley-Samuelson, et al. 1988], which may be reflective of the mosquito diet compared to that of fruit flies.

------------------
Reference

D.W. Stanley-Samuelson, R. A. Jurenka, C. Cripps. Fatty acids in insects: Composition, metabolism,and biological significance. Archives of Insect Biochemistry and Physiology. 9 (1988): l -33.

ISMB 2010

2010-07-12T04:07:00.000-07:00

Throughout the ISMB (Intelligent Systems for Molecular Biology) conference, I will add some notes and observations. Check for updates.

ISCB Overton Prize Lecture - Steven Brenner, Univ. of California, Berkeley (USA). Ultraconserved nonsense - Gene regulation by splicing and RNA surveillance. A nonsense mutation leads to a stop codon and a dominant negative mutant. NMD = nonssense-mediated mRNA decay, where mRNAs with a premature stop codon are recognized and the entire mRNA molecule is destroyed. For this reason, a 3'-UTR is rarely interrupted by an intron and why premature stop codons within the last exon can have much more dramatic or severe phenotypic consequences compared to premature stops further upstream.

Exon junction complexes (EJC) retain information on exon-exon joins. The EJCs are needed for transport from the nucleus and to mark the last exon (where the termination codon should be located). If the termination codon is greater than 50 nt upstream of the EJC, the rule of thumb states that the RNA is degraded.

So, why are so many genes transcribed with "poison" exons (those that produce a premature stop codon)? The tissue-specific splicing factors that produce the mRNA with premature stop functionally act as a transcriptional repressor via NMD.

In gene SRp55 (encoding a splicing factor) the poison cassette exon is 100% identical over more than 200 nt between human and mouse. Why so conserved? It does not encode protein, nor has a conserved RNA folding structure, no over-representation of known binding sites, no repetitive elements, no similarity elsewhere in the genome (except in retropseudogenes). The answer appears to lie in regulation of gene expression.

This mode of gene regulation appears ancient and repeated often. Possible role for ultraconserved region proposed.

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Ross Curtis, Carnegie Mellon Univ., Pittsburgh, PA (USA)
GenAMap: An integrated and analytic visualization software platform for structured GWAS and eQTL analysis. Association with a genetic network and SNP-based perturbation of a network. Structured association mapping:

Advantages: greater power to detect weak association signals, fewer false positives, joint association to multiple correlated phenotypes.
Disadvantages: computatinally intensive algorithm, specialized software.

Build and explore a genetic-phenotype network while looking at the association data. They are looking for beta users - contact the Sailing Lab, rcurtis@cs.cmu.edu.

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Enoch Huang, Pfizer
Computational Biology at Pfizer. The two main decision points in drug research and development: Target selection and compound selection.

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Charles Vaske,
Inference of specific-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. They treat the pathway as a single genetic unit. Calculate the log-likelihood ratio of three possible states of an entity: up, same, down. This is the basis of their "integrated pathway activities."

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Joel Dudley, Stanford Univ. (USA)
The robustness of disease signatures across tissues and experiments. How well do different experiments agree by correlation?

- same disease (D+) / same tissue (T+)
- same disease (D+) / different tissue (T-)
- different disease (D-) / same tissue (T+)
- different disease (D-) / different tissue (T-)

See Dudley, et al (2009) Disease signatures are robust across tissues and experiments. Molec. Sys. Biol. 5:307.

Now, considering global markers of disease and molecular systems of disease.

This short talk seemed to be mostly a rehash of the papers they've published and next to nothing on the last sentence above.

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Rune Linding,
Comparative Network analysis of complex diseases. Tyrosine kinases with less (vs. greater) specificity at site of phosphorylation are more likely to have an oncogenic effect when that gene is altered. This makes sense because the lower specificity will have a greater effect on the interaction network (kinase and substrates).

60-80% of cellular kinase specificity is determined by network context.

Evolutionarily conserved phosphorylation networks link multiple diseases.

Less specific kinases tend to be causally linked to cancer.

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Susan Lindquist, Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Howard Hughes Medical Institute, Cambridge, MA (USA)
Protein folding and environmental stress REDRAW the relationship between genotype and phenotype. How do organisms stay the same? How do they change (rapidly, in some cases)?

Consider that Jean-Bapiste Lamarck and Conrad Waddington were not so crazy after all. They proposed that inheritance could occur via environmentally acquired traits.

She pointed out that a protein must fold ina very harsh, populated, complex environment. She demonstrated this with an image from David Goodsell. An example of his work is here. The concentration of protein in a cell is about the same as in a packed crystal.

Hsp90 - very abundant (~2% of protein)
- acts as a buffer of protein folding homeostasis
- has specialized clients: at later stages of folding, many of these clients are signal transducers whose folding is only completed when the signal comes in; these are proteins in meta-stable states

Drosophila Hsp90 mutants at half activity of wildtype show >100 phenotypes, pleiotropic, depending on the genetic background of the strain used. A similar result was noted for Arabidopsis thaliana.

Hsp90 acts a capacitor for some variation, allowing organisms to accumulate lots of genetic variation which is released when a stressor is applied.

Hsp90 also acts as a potentiator for other variation.
- in mammals, Hsp90 complexes with inactive hormone receptors, with inactive oncogenic kinases

She then described work with gal-induced v-Src. v-Src lacks the auto-inhibitory function found in c-Src. The Hsp90 down mutant lost all v-Src-induced phosphorylation, while c-Src-induced phosphorylation showed very little to no changes when Hsp90 was downregulated. Hsp90 stabilizes v-Src and gets it to the membrane, leaving it in place and in an active state.

She then went on to tell the story of Candida albicans and fluconazole resistance. Fluconazole resistance come about quite readily and thus is a serious clinical issue. So, reduce the Hsp90-specific buffering capacity and this leads to the observation of no fluconazole resistance. The buffer is the extra capacity of Hsp90 to fold proteins for normal activity. It is this buffering capacity that they knock down with treatment of a small molecule. In other words, the C. albicans are no longer primed to tolerate or adapt to the stress.

Hsp90 genotypes and phenotypes

- transforms adaptive value of large amounts of standing variation.

- affects polymorphisms throughout the genome, even non-coding (Hsp90 can assist folding of a 3'-UTR-binding protein that recognizes a structure of an mRNA with a variant base), in a combinatorial way.

- simple environmental stresses exert similar effects.

- has likely sculpted the standing variation that exists today in yeast genomes.

- matters to human health in many ways.

It was especially with regards to this last point that I wonder what her thoughts are on the stress-induced changes after a change in diet, say from a high-fat meal. This could alter the population of gut microbiome which could then exert a given amount of stress on cells lining the intestine. If I learn anything, I'll share.

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Svante Pääbo, Max Planck Institute for Evolutionary Anthropology, Leipzig (Germany).

Analyzing pleistocene hominid genomes. Human to human comparisons show differences of 1 per every 1000 bases; compared to chimp that 1 every 100. But this is not the best way to describe all we want to know about human lineage and human evolution.

He described the extraction of DNA for the first time from a mummy, a 2700-year old mummy from Egypt. The DNA is so highly fragmented that most of this work has been done with mitochondrial DNA.

Neandertal existed from 200,000 or 400,000 years ago to their extinction about 30,000 years ago, according to the fossil record. Two hypothesis: Replacement (all modern humans are equally distant from Neandertal) or Assimilation (some modern humans will closer to Neandertal (N) than other humans). The common ancestor of H. sapiens and N is ~0.6 mya. With second generation sequencing his group can now tackle the N nuclear genome in order address evolution questions.

A bone fragment from Vindija Cave, Croatia gave them their source material. Only ~3.5% of the sequence is primate. Most of the rest is bacterial and fungal from organisms that invaded the bone of the deceased individual. In the end, they have ~1.5x coverage with most of the reads coming approximately equally from three sources in Vindija.

Any position in N genome carries a risk of a bit less than 1% that the data are really from modern human (contamination). He talked about deamination of C to U, which is then read at T. This occurs mostly at fragment ends. They devised a manner to remove these ends. They aligned their N sequence fragments to both human and chimp in order to remove biases of a simple alignment to human.

Divergence to human reference genome shows 12.7%, translating to ~825,000 years ago. They looked at points fixed in the human, post-N lineage. There are 78 amino acid substitutions from about half of the N genome (thus the number will double), but is down to 50 because of very rare variants found from 1000 Genomes Project. Number is in flux.

Positive selection. ~10% of SNPs in human show that N has the derived allele. Did they see a depletion of derived alleles in N? THADA shows the most extreme variation from this - hence under selection. He showed Table 3 from their paper on the top 20 such selected regions. Humans have a 9-bp insertion in an intron of THADA. However, about 3-4% of Europeans have no 9-bp insertion and they are protected against type 2 diabetes.

RUNX2. Cleidocranial dysplasia. He mentioned the clavial, cranial frontal bulging and rib cage shape. So, differences in the RUNX2 may have to do with these skeletal differences between N and present day humans.

What does N have at SNPs that tag for Eurasia? For ten of twelve analyzed, N shows the non-African human allele. He compared N to Venter's genome (someone they believe to be a fully modern human). Some points showed a sharing of derived alleles. In the end, a series of comparisons indicate that ~90,000 years ago, humans met N and interbred. Thus, ~1-4% of H. sapiens DNA is derived from N.

Of those who have written email to Pääbo regarding their own ancestry and affiliation, 46 men claim to Neandertal and 3 women claim to be Neandertal. Furthermore, many more women claim to be married to a Neandertal than men claiming their spouse to be a Neandertal.

FOXP2 speech gene. Cannot make a transgenic human with the chimp FOXP2 gene and vice versa. So, look for FOXP2 "backmutations" in humans, particularly in families with speech ailments. Humanized the mouse Foxp2 gene. They could not speak to this mouse. Took the transgenics to the GSF in Neuherberg, Germany where they looked at 323 phenotypic traits, many of which are related. Two traits were different: the transgenics were slightly more cautious in the first couple minutes in a new environment; the transgenics had different vocalizations (Enard, et al. (2009) Cell). Does this encode muscle movement in the pharynx? There are longer neurites in the humanized mouse in the pharynx muscles (Enard, et al. (2009) Cell). There are also decreased dopamine levels in the cortex/brain stem.

So, what next? They now look at other homids. Denisova cave in Siberia. A bone fragment. Regarding mitochondria DNA (mtDNA), there are about 375 differences, on avg. between Denisova and human, with about 200 between human and Neandertal. That puts the Denisova branch about 1 mya, with Neandertal about 0.5 mya. This is based on the mtDNA sequences.

Lots more to consider.

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Chris Sander, Memorial Sloan Kettering Cancer Center, New York, NY (USA)
Systems biology of cancer - Diversity & simplicity - Integrated molecular profiling and clinical implications and new algorithms for perturbation cell biology.

There were two main parts to his talk: Cancer genomics and Perturbation cell biology.

Cancer genomics. Map alterations from 200 glioblastoma (GBM) samples. Use a GBM pathway that was published in 2007 in Genes & Development. For GBM there is no single molecular cause, but there is a common oncogenic program when you look at modules. For example, there are altered RTK/RAS/PI-3K signals in 85% of cases, altered p53 signaling in 86% of cases, and altered RB signals in 77% of cases. The challenge: use this information for network pharmacology and personalized therapy.

He gave two examples of taking on this challenge with prostate and ovarian cancers. With respect to prostate cancer and altered network signals, for RB 74% of metastatic cases and 34% of primary cases show alterations, for PI-3K 100% of met and 42% of 1^o show alterations, and for RAS/RAF 90% of met and 43% of 1^o show alterations. He showed data from changes in DNA copy number, many times CNV data were is data of choice.

Ovarian cancer. How many patients have damaged homologous repair mechanisms in ovarian cancer? This is one sub-project in the grander Cancer Genome Atlas. BRCA1 is inactivated in 21% of cases.

1. germline or somatic mutation
2. epigenetic silencing
3. homozygous deletion

47% of cases have at least one altered HR gene. Thus, drug trials with PARP inhibitors are under consideration. HR genes include BRCA1, BRCA2, PTEN, EMSY, ATM, ATR, RAD51C and FA genes (e.g., FANCA, FANCD2).

He then gave a quick overview of cbio.mskcc.org/cancergenomics

Perturbation cell biology. Form and consider a network module with input -> network -> output. When we don’t know the input, then we are talking about control. When we don’t know the network and its (inter)relationships, we are talking about interpretation. When we don’t know the output, we are talking about prediction. All of this is with an eye toward therapy.

He refered to published work. But, the question is, does it scale? Not to larger systems.

Use statistical physics to generate a series of probability distributions for all possible pairs W_ij interacting over good solutions. Then, draw the network model. This is in place of going straight to all possible networks via Monte Carlo descent. Then, use this model to predict the effects of drug and genetic perturbations s in cancer.

- - - - - - - - - - - - - - - - - - - - - - - - - - - -

Olive oil and cancer

2010-07-08T06:06:00.000-07:00

Could a diet where olive oil is the primary source of fat assist in delaying or preventing the onset of cancer? That's a tempting question and certainly a good one for nutrigenomics. As one might expect, the answer is both a yes and a no.

Yes. A recent study by Hirsch, Struhl, et al. used two isogenic cancer models to uncover the transcript profile and gene signature linking cancer with inflammatory and metabolic diseases. This group identified 345 genes whose expression signature is also involved in inflammation and metabolic diseases such as type 2 diabetes and cardiovascular disease. In fact, within these 345 genes are genes identified by GWAS (and other types of studies) for HDL-cholesterol (ABCA1 and GALNT2), obesity (NPC1), stroke (AIM1), and celiac disease (PTPN2, PTPRK, SCHIP1 and ZMIZ1), among others. Curiously, there is substantial sharing of genes between those upregulated in this cancer set and those that we identified as downregulated after acute intake of phenol-rich olive oil. Ten genes are shared. This is a 4.2-fold enrichment over what one would expect by chance given the size of the two gene sets. That sounds quite strong and the genes look mighty interesting:

ANXA3 - annexin A3
CXCL3 - chemokine (C-X-C motif) ligand 3
DUSP1 - dual specificity phosphatase 1
EREG - epiregulin
IER2 - immediate early response 2
IL1B - interleukin 1, beta
IL6 - interleukin 6 (interferon, beta 2)
JUNB - jun B proto-oncogene
SOCS3 - suppressor of cytokine signaling 3
SOD2 - superoxide dismutase 2, mitochondrial

These are some big players and so perhaps there is an olive oil-cancer prevention link.

However...

No. Because not everyone living in countries with heavy use of olive oil in the diet, countries such as Spain and Italy, adheres to a typical or Mediterranean diet, population data on cancer rates are not really an accurate way to assess that an olive oil-rich or Mediterranean diet lowers one's risk of cancer. Besides, cancer is too general a term - risk of specific types of cancer should be measured. For example, adherence to the traditional Mediterranean diet is associated with reduced risk of upper aerodigestive tract cancers and reduced risk of colorectal cancer has been observed in those who follow a diet higher in fruits/vegetables, lower in fat and more toward a Mediterranean diet.

The list of common cancer pathway genes is much greater than 10. Some 240 genes are upregulated and 105 are downregulated. Thus, while the 10 cancer pathway, olive oil-sensitive genes listed above are a highly interesting list, this is by no means sufficient to unequivocally state that a diet high in phenol-rich olive oil will prevent cancer.

Furthermore, many of the genes in this list of 10 are common to several important pathways. IL1B is a pro-inflammatory mediator and is also involved in the postprandial response of triglyderides. The floxed Socs3 gene in mouse gives an animal that is resistant to diet-induced obesity and this gene has been assigned to an insulin resistance inflammation network. One major point of our olive oil paper was the anti-inflammation nature of the response to the phenol-rich olive oil on gene expression in PBMCs. A recent paper essentially confirms this finding. Hence, the dual assignment of many genes to a cancer pathway and something else like inflammation is highly intriguing, but caution is, as always, warranted in condensing the complexities of metabolism, inflammation and cancer to a single kernel of dietary advice.

The genetics of O

2010-07-06T09:37:00.000-07:00

Because this post is without data, it is certain to be disappointing to many. Nonetheless, investigation begins somewhere - a thought, an idea, a simple observation - and then may blossom into a complete project. Speaking of which, it would be a rather involved project to collect all published data and all of our unpublished observations regarding the genetics of O - overweight vs. obesity.

People who know our work, know that we have examined a lot of genotype-phenotype associations. While overweight/obesity is not our forte in the manner that blood lipids are, suffice to say that we have spent a fair amount of effort looking at genetic factors contributing to BMI. See, for example, this paper on APOA2 and this on PER2, relating chronobiology to obesity. Others have shown that variants in genes FTO, MC4R, LAMA2, SOX6, NEGR1, NPC1, TMEM18 and many others contribute to the risk of obesity.

Our basic observation is - we rarely see a genetic variant that associates with both overweight and obesity. Here, overweight in most populations is defined as a body mass index, or BMI, between 25 and 30 kg/m² (where the kg reflects body weight in kilograms and m² is the square of body height in meters). Obese is a BMI above 30. For some populations, these numbers are different due to different basic characteristics of body size.

So, what could this observation imply? My personal opinion is one centered on differences in the metabolic and biochemical (inflammation, adipocytokine) profiles of adipose tissue in the overweight vs. the obese individual. Thus, there may be an entirely different set of genetic variants, in combination with lifestyle choices of diet, exercise, lark-vs-owl chronotype and such, that contribute to the overweight situation compared to those that magnify the condition to one of obesity.

To me, this is an interesting topic and I would greatly appreciate your thoughts and comments.

Maps: McDonalds and obesity

2010-06-18T04:25:00.000-07:00

A recently published map entitled "The Contiguous United States Visualized by distance to the nearest McDonald's" correlates somewhat but overall not so well with the 2008 map published by the CDC of obesity trends across the United States.

For example, the New England states show close proximity to the restaurants but some of the lowest percentages of obese individuals among the population. In South Carolina, over 30% of the population is considered obese (BMI > 30 kg/m^2) and greater than its neighboring states, but the proximity to McDonald's seems no different than in Georgia, North Carolina and other states in the region. Alabama and Mississippi have some of the highest obesity trends in the USA, but lower prximity to McDonald's. Of note, there is a high concentration of McDonald's franchises in the Chicagoland area because this is the location of the company's headquarters and test kitchen. Lastly, there are high concentrations of the restaurants in the main population centers of Utah (Salt Lake City) and Colorado (Denver), but both these states show rather low population trends of obesity.

Where there is one, you will find many more. In many instances the presence of one type of establishment acts as a seed for others. Thus, the above in essence uses the location of and priximity to McDonald's as a proxy for other fast food restaurants. The National Health and Nutrition Examination Survey (NHANES), with data from 2007 - 2008, can give some information on meals eaten in the home, meals eaten with the family and money spent on meals outside the home. Those data can be found here. I have not analyzed these data nor have seen a published analysis. I'll have to take a look.

Genome compexity and the number of genes

2010-06-16T19:13:00.000-07:00

This month marks the 10th anniversary of (one of) the announcement(s) of the completion of the human genome. Several have taken this occasion to comment on the successes of genome-based biomedical research, or lack thereof.

At "The Loom," Carl Zimmer has a neat graphic depicting estimates of the number of genes present in the human genome. This number has, more or less, steadily fallen as progress in sequencing and then in filling in remaining gaps in the reference genome sequence has moved forward. My comments to that blog entry are:

The reduction in the estimates of the number of protein-coding genes in the human genome parallels our increased understanding of the complexities involved in regulation of gene activity. For example, many types of non-coding RNAs have been described as well as their roles in modulating the information flow from DNA to protein.

At the same time, I believe that the human genome’s reduced “tool kit” (in terms of number of protein-coding genes) shows a certain level of our genome’s sophistication. Think of the many different ways one can use a screwdriver – say to open a can of paint, or its handle as a hammer. In other words, different proteins can join to different networks in a tissue- or developmental-specific manner. In conjunction with this are the alternatively spliced mRNAs, which often lead to different protein isoforms (proteins that are mostly the same, but with perhaps one different functional subdomain). Think of a Phillips vs. regular screwdriver.

Thus, fewer genes has not meant there are fewer protein isoforms nor less complex protein-protein or protein-small molecule interaction networks. To the contrary, there is an increased complexity and that is one reason it has been difficult to define all the players in a particular human affliction such as type 2 diabetes or cancer.

Here, I would like to add a couple of other points:

1. Genetic variation, whether common, moderately rare or even unique to an individual or family, no doubt has a role in adding to the complexity of interactions among the (relatively) small number of genes and small molecules. For example, our research is considering transcription factor binding sites and seed sites for mRNA-mRNA interactions that are created by minor alleles of SNPs.

2. Where much of the above leads is toward differential pathway dynamics. Because the number of protein-coding genes is low while the human organism, its response to a number of different situations (consider how long tobacco smoking or a poor diet must be endured, on average, before life-threatening phenotypes emerge), and its great capacity to develop, survive and even thrive with numerous genetic aberrations are all complex, many of the answers we research seek simply remain to be discovered. We just do not know all the players - proteins, RNAs, genome state (e.g., methylation) and small molecules - and so cannot fully describe a type 2 diabetes pathway in a series of affected tissues, or a given cancer for that matter. Progress is being made - sequencing of the genomes of a tumor and healthy tissue from the same individual have uncovered common pathways and perhaps drug targets. There, however, remains much more to describe before the full potential of human genomics research will be realized.

Clusterin variants extend links between Alzheimer disease and blood lipids

2010-05-27T07:48:00.000-07:00

A recent report in the American Journal of Clinical Nutrition shows that genetic variation in the gene encoding clusterin (CLU or APOJ) showed strong and significant association with plasma fatty acids in an Alaskan Eskimo population. This is relevant to liver function, heart disease and Alzheimer disease (AD). In fact, other characteristics of CLU serve to strengthen the links between AD and blood lipids in a manner similar to APOE (apolipoprotein E).

There are in excess of 100 scientific publications describing different aspects of CLU gene and protein function. Those are not easily summarized here but can be accessed here. Briefly, the protein has no known function and seems to be involved in several basic biological events such as cell death, tumor progression, and neurodegenerative disorders (provided by RefSeq).

I have collected some interesting data on CLU:

Two separate GWAS have shown an association with Alzheimer disease. These are papers by Harold, et al. (2009) and Lambert, et al. (2009) in populations in Europe or of European ancestry. Otowa, et al (2009) showed also by GWAS an association to panic disorder in a Japanese population.

According to SymAtlas, this gene is very highly expressed in human liver.

Our preliminary analysis indicates that CLU is under positive selection in the human lineage (based on amino acid substitution rates). This could indicate responsiveness to some character of the environment. Diet perhaps?

Proteomic analysis of aortas of Apoe -/- mice showed a large increase in Clu protein expression (Wu, Tan, et al. (2007) J. Proteome Res. 6:4728).

Lastly and perhaps most interestingly, CLU is differentially expressed in human individuals with low vs high response to caloric restriction in terms of weight loss (Bouchard Vohl 2010 Am J Clin Nutr 91:309).

Lipoprotein-associated phospholipase A2 and heart disease-risk

2010-05-21T10:40:00.000-07:00

Researchers at UC Davis have discovered that a substance found in blood, which is linked with inflammation, serves as a predictor of coronary artery disease in African-Americans. These results have been published recently in J. Clinical Endocrinology and Metabolism.

The compound in question is lipoprotein-associated phospholipase A2 (Lp-PLA2). This is also known as PLA2G7. While this blood factor is also associated with risk of heart disease in Whites, that association is not always accurate.

A colleague of mine offers that this result is interesting. Publication in JCEM rather than a cardiology journal may be related to the relatively small samples - "336 Caucasians and 224 African-Americans who were about to undergo diagnostic coronary arteriography."

With respect to the differences, obesity prevalence is 51% greater in African Americans than Whites, which could be relevant to inflammation. Alternatively, coronary disease in African Americans may be more advanced than in Whites at the point at which arteriography is performed.

I agree - especially in terms of disparities in health care among groups of ethnic minority in the USA.

----------
Reference

Enkhmaa B, Anuurad E, Zhang W, Pearson TA, Berglund L. (2010) Association of Lp-PLA(2) activity with allele-specific Lp(a) levels in a bi-ethnic population. Atherosclerosis. in press.

The Complexity of a Complex Disorder

2010-04-29T06:29:00.000-07:00

A group of researchers led by Timothy Aitman of the MRC Clinical Sciences Centre and Imperial College London used the SHR (spontaneously hypertensive rat) to identify the few genes they thought predisposed this strain to hypertension (several genes had already been identified but the group knew or felt a few others remained to be discovered). Sequencing this SHR rat with the NextGen approach and comparing those data to the rat reference genome, led to quite a surprise. 788 genes are mutated in SHR compared to the reference genome, including 60 that are deleted altogether.

My take on this is many genes are likely involved in a complex disorder and many genes - with specific variants - may work in concert to produce the disease phenotype – either via gene-gene interactions or affecting interconnecting pathways.

This type of result is very likely to be repeated with other metabolically sensitive disorders and afflictions such as dyslipidemia, obesity and type 2 diabetes. A series of variations in genes combined with deviations from a standard environment - both in terms of diet and microbiome - are likely to combine to tip the balance and enhance onset and/or progression of said affliction.

----------

Reference

EurekAlert: Hypertensive rat genome sequence expected to uncover genetic basis of human hypertension

Atanur SS, Birol I, Guryev V, Hirst M, Hummel O, Morrissey C, Behmoaras J, Fernandez-Suarez XM, Johnson MD, McLaren WM, Patone G, Petretto E, Plessy C, Rockland KS, Rockland C, Saar K, Zhao Y, Carninci P, Flicek P, Kurtz T, Cuppen E, Pravenec M, Hubner N, Jones SJM, Birney E, Timothy J. Aitman TJ. (2010) The genome sequence of the spontaneously hypertensive rat: Analysis and functional significance. Genome Res. (in press).

Optimal values

2010-04-22T07:57:00.001-07:00

If a little of this or that is good, then more must be better for me, right? No, but unfortunately, many of us think this way. Still others with some knowledge of how things are in a biological or physiological or medical context understand that a plateau of effect is often reached whereby a given compound or nutrient is no longer effective. But often ignored is the adverse effect of taking in too much of that substance. Hence, there is an inverse bell curve, as shown below, that most probably explains the effects of most substances we each encounter whether at the dining table or elsewhere in the environment.

Allow me to provide a short example. Athletes who take oxygen from the sidelines ostensibly to "recover." A higher intake of oxygen will actually saturate the hemoglobin in the red blood cells and hinder exchange of carbon dioxide transfer from peripheral tissues. Sure, breathing pure oxygen reduces respiration rate - less huffing and puffing after scoring that touchdown - but has little value in allowing the muscles to recover.

Thus, as has been so often said - everything in moderation.

Cultural evolution and the road to obesity

2010-04-06T12:31:00.000-07:00

It seems that little is known about a critical time in human civilization some 7500 to 6000 years ago, a time when groups began coalescing into societies with distinct hierarchies and structure. During this time in Mesopotamia the Ubaid culture took root and appears from recent surveys of a critically important site called Tell Zeidan in Syria to represent an especially transcendent time in human cultural evolution. The initial report, from 2008, of the Oriental Institute of the University of Chicago, which is conducting archeological excavations at Tell Zeidan, can be found here. The formation of ever larger towns and cities and trading centers certainly required advancements in agriculture. This in turn exerted an impact on the nutritional status of those inhabitants.

As I read an article this morning in the New York Times on this topic, the phrase "cultural evolution" drew to mind a very well written recent review by Kevin Laland and colleagues in Nature Reviews Genetics in which they present the argument that human evolution can be rapidly shaped by culture. This is not culture of the high-art type, but is described by the authors in terms of human activities that affect an entire group. These are activities such as farming, animal husbandry, and, in short, behavior - knowledge, skills, beliefs or values - acquired from other individuals. I believe the authors to be correct when stating that there can exist rapid changes in allele frequencies arising via positive selection on variants involved in gene-culture interactions. Table 2 of the review lists genes under recent rapid selection with an inferred cultural selection pressure. Of interest to our research are those genes involved in metabolic disorders, such as:

LEPR, PON1, RAPTOR, MAPK14, CD36, DSCR1, FABP2, SOD1, CETP, EGFR, NPPA, EPHX2, MAPK1, UCP3, LPA, MMRN1 all pertaining to energy metabolism, hot or cold tolerance; heat-shock genes and arising from dispersal and subsequent exposure to novel climates.

Thus, some number of human genes are undergoing rather rapid changes due to cultural shifts in society. The same appears to have taken place in chickens. Around 1900 separate chicken breeds began to be established for broiler (meat) production and egg-laying. An analysis of these breeds in comparison to Red Jungle fowl (the main wild ancestor) and Rhode Island Red standards revealed that just a small number of changes in the genome occurred in the production breeds to presumably yield the characteristics sought by those in the business. Of particular note are those sweeps affecting thyroid hormone function, photoperiod sensitivity, appetite and metabolism. Details of this exciting and very pertinent study are in the paper.

OK, it seems that the genome can adapt rather quickly given extreme pressures of selective breeding in the case of chickens or cultural adaptation in the case of humans. Now, consider changes in the diet of Americans over the last 100 years. A report from the Economic Research Service of the US Dept. of Agriculture shows how meat consumption in the USA has changed since 1909. Sometime in the 1950's per capita chicken consumption went from about 15 pounds/year to over 60. Over this same period, cheese consumption saw a similar 4-fold increase. Other interesting changes in beef, sweet potato and flour/grain intake can be viewed in the report.

With such rapid changes to the human diet, and this is without discussion of sugars and salt and the impacts of peer pressure and advertising, it really is no wonder that type 2 diabetes, obesity and cardiovascular diseases are on the rise.

----------
References

Laland KN, Odling-Smee J, Myles S. (2010) How culture shaped the human genome: bringing genetics and the human sciences together. Nat Rev Genet. 11:137-48.

Mentzer Morrison R, Buzby JC, Hodan Farah Wells HF (2010) Amber Waves 8:12-19.

More for data capture and mining

2010-03-23T17:24:00.001-07:00

Nearly three weeks have passed since my last entry. A big reason for that is the number of exciting papers and pertinent reviews recently published. In this regard, joining Twitter and being connected to the right people has been nothing short of amazing. Yes, I have learned much over the past three weeks, but have also been swimming in a stream of data, incorporating pathways, gene expression and proteomics data lists from supplemental files into my human genome database.

And all of that brings to mind what some elder statesmen in the nutrigenomics field have said - that one needs an army of data collectors and miners. This would be at the expense of some significant portion of the wet-lab work. Well, I agree. There are a lot of experiments that have already been done or two or three that when combined can give an insightful view into, say type 2 diabetes or obesity. This is essentially what Tiffin, Hyde et al. did a few years back. But with so much data, so many ideas to follow, too few partners (zero!) - swimming can easily become drowning. But it is really fun because our lab is looking into things and taking a view not often pursued.

Next I should write something about evolution of culture and chicken breeds.

Drug side effects and genetic and lifestyle factors

2010-03-04T05:36:00.000-08:00

I just found a neat database (SIDER) from Peer Bork's group at EMBL: http://sideeffects.embl.de. This is a database one can query for drug indications and side effects. Using the data here, I find that there are 301 drugs with side effect or indication to alter body weight. Of those, 126 (41%) show both weight gain and decrease. This makes one wonder about genetic and environmental effects of those in the drug development trials. Therefore, there is a need to control things beforehand by partitioning based on genetics, if not also based on some lifestyle parameters.

The group's publication describing SIDER can be accessed here. The citation is Kuhn, et al. (2010) Molecular Systems Biology 6:343.

MicroRNAs and BMI

2010-02-03T11:48:00.001-08:00

A new paper in PLoS One by Ortega, et al. examines expression of human microRNAs in adipose tissue of lean vs. obese individuals (n is small!) and in differentiating adipocytes. A few interesting observations emerge when one downloads and integrates the data into a larger genome database:

1. MIRN145, which downregulates IRS1 translation, is downregulated during adipocyte differentiation.

2. MIRN23B, whose expression is curtailed by MYC (thereby increasing mitochondrial glutaminase and up-regulating glutamine catabolism, a mechanism behind altered glucose metabolism in cancer cells), is also downregulated during adipocyte differentiation.

3. MIRN337 has been reported to show expression levels negatively correlated with BMI in osteoarthritic chondrocytes and is also downregulated during adipocyte differentiation.

4. Similar to MINR337, MIRN22 expression levels positively correlated with BMI in osteoarthritic chondrocytes. MIRN22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. Ortega and colleagues show that MIRN22 is upregulated during adipocyte differentiation.

5. MIRN22, MIRN29A and MIRN337 are all downregulated in subcutaneous fat of obese individuals (Ortega, et al. 2010).

6. MIRN146a positively correlates with triglyceride (TG) levels in subcutaneous fat (Ortega, et al 2010), while MIRN210 and MIRN99B negatively correlate with TG. Nine miRNAs positively correlate with BMI (MIRN10A, MIRN34A, MIRN99A, MIRN100, MIRN125B, MIRN129, MIRN199A, MIRN199B, MIRN221) and five (MIRN92A, MIRN130B, MIRN142, MIRN210, MIRN484) correlate negatively. (Data from Table S3.)

So, it would seem logical that there is a role, a significant one at that, for microRNAs in obesity. What is interesting to me is considering the prospects of small molecules, say from the diet because it abounds with so many different molecules, interacting with miRNAs and altering their interactions with target genes. Furthermore, some of the miRNAs listed here and others contain SNPs that we could easily genotype in any of a number of populations to test for associations to clinical measures of obesity, dyslipidemia, vascular diseases or type 2 diabetes. We would naturally also look for those associations that are modulated by environmental or dietary factors. Now, that would make for a very nice report!

Homeostasis

2010-01-30T17:13:00.000-08:00

It's been a while since the last post - business travel and then the inevitable catching up.

I have been thinking about homeostasis lately and why GWAS results don't describe all that much regarding the total amount of variability. Perhaps it takes many hits ("disease" or "risk" alleles) in the same operational unit (e.g., a pathway) to see disease or biomarker thereof as a measurable phenotype. And so I wonder if the organism has a much stronger drive to maintain homeostasis than we realize. In other words, the body can absorb many small defects to a given pathway so long as the environmental conditions do not go awry such as might happen with years of poor nutrition. The body can even operate well within a certain range and that brings to mind a term I heard the other day - homeodynamics. It is true. Running a marathon won't make someone collapse, neither will a hot day and neither will a low intake of fluids. The combination, however, will greatly increase the risk of that taking place.

So, with respect to my research and examining GWAS data, I am more convinced than ever that we must look at an enrichment of pathways hit by the association data. I'm excited to give that a try.

Migraine and cardiovascular disease risk

2010-01-19T06:52:00.000-08:00

A few recent papers report associations between migraine and cardiovascular disease (CVD) risk. This is particularly true for migraine with aura. Another study shows that this link is found in women. What is most exciting is the recent publication by Markus Schürks and colleagues reporting findings from a candidate gene study on the genetic basis of migraine. The data imply that polymorphisms of genes encoding constituents of inflammation pathways and migraine in women are associated. These genes include TNF, CCR2, TGFB1, NOS3, and IL9. We have looked at a few of these genes with respect to dyslipidemia, obesity and metabolic syndrome.

While it would be nice to look for direct connections between biomarkers of CVD and migraine, the phenotype collection of two of the largest and most useful populations with which we work do not contain information from the subjects on migraine or pain in general. These populations are GOLDN and BPRHS. This is where the proposed Nutrition Phenotype database project could come in handy. This group is calling for more extensive phenotyping of subjects. The project is in its infancy but an introductory paper has been accepted at Genes and Nutrition. The paper is titled "The Nutritional Phenotype database to store, share and evaluate nutritional systems biology studies" and was authored by B. van Ommen, J. Bouwman, L. Dragsted, C. A. Drevon, R. Elliott, P. de Groot, J. Kaput, J. C. Mathers, M. Müller, F. Pepping, J. Saito, A. Scalbert, M. Radonjic, P. Rocca-Serra, T. Travis , S. Wopereis and C. Evelo.

TXNIP - lots of data

2010-01-11T11:40:00.000-08:00

A new report by Zhou, et al. (Nature Immunolog, http://www.nature.com/ni/journal/vaop/ncurrent/abs/ni.1831.html) links thioredoxin (TRX)-interacting protein (TXNIP) to oxidative stress and inflammation. This gene has been connected to insulin resistance and a host of other activities. These include:

mouse knockouts show increased fat to muscle ratio

TXNIP is a rat hyperlipidemia gene (rat genome database)

highly expressed in mouse adipose (3.23-fold over average of all other tissues)

TXNIP expression was inversely correlated to total body measures of glucose uptake (Parikh, Mootha 2007 PLos Med)

forced expression of TXNIP in cultured adipocytes significantly reduced glucose uptake, while silencing with RNA interference in adipocytes and in skeletal muscle enhanced glucose uptake, confirming a role as a regulator of glucose uptake (Parikh, Mootha 2007 PLos Med)

TXNIP expression is consistently elevated in the muscle of prediabetics and diabetics, although in a panel of 4,450 Scandinavian individuals, we found no evidence for association between common genetic variation in the TXNIP gene and T2DM (Parikh, Mootha 2007 PLos Med)

TXNIP regulates both insulin-dependent and insulin-independent pathways of glucose uptake in human skeletal muscle (Parikh, Mootha 2007 PLos Med)

this is gene #135 in a published PPARA signaling network analysis (Cavalieri, Muller 2009 BMC Genomics 10:596)

TXNIP is 1.5-fold increased expression in young vs. old mouse skeletal muscle. In old muscle, calorie restriction turns up the gene (young phenotype) also ~1.5-fold over normal diet (GDS2612)

So, perhaps the newest paper is not surprising, but welcome data to integrate into the big picture.

2010-01-07T08:05:00.000-08:00

Blaine at The Genetic Geneologist (www.thegeneticgenealogist.com/2010/01/07/personalized-genomics-a-very-personal-post/) has posted a very personal story about his genotype at three loci for risk of type 2 diabetes. The SNPs are rs5219 in KCNJ11, rs1801282 in PPARG and rs7903146 in TCF7L2. With risk alleles for all six alleles, his risk, as calculated by the services he used, is significantly elevated.

A genotype is, as he writes, merely a suggestion of a possible future outcome. Lifestyle intervention before disease onset is clearly warranted. We have shown, for example, that variants at TCF7L2 associate with postprandial lipemia (after-meal levels of blood lipids) but are modulated by PUFA (polyunsaturated fatty acid) intake. See www.ncbi.nlm.nih.gov/pubmed/19141698, where my colleagues wrote "high (n-6) PUFA intakes (> or = 6.62% of energy intake) were associated with atherogenic dyslipidemia in carriers of the minor T allele at the TCF7L2 rs7903146 SNP and may predispose them to MetS, diabetes, and cardiovascular disease." Links between dyslipidemia and T2DM are well established.

In other words, the interplay between environment and genome is an important consideration that is often overlooked.

Positive selection and risk of heart disease

2010-01-06T05:28:00.000-08:00

We have just submitted a review article discussing the role of genetic variants under positive selection that have roles in dyslipidemia, obesity or heart disease. Many of the identified SNPs show interactions with environmental or dietary factors, which means that one allele of the SNP, typically the risk allele, shows an effect on the phenotype only when the environment or diet passes a certain threshold. Specifically, SNP 3111 T>C (rs1801260) of the CLOCK gene shows significantly increased waist circumference for the C allele only when the saturated fat constituent of the diet is above 11.8% of total energy. Our results from variants in the CLOCK gene are published at Am J Clin Nutr 2009;90:1466–75 (www.ncbi.nlm.nih.gov/pubmed/19846548). This SNP is also under positive selection or can be called an adaptive variant. Think of natural selection.

The implication that genetic variants under positive selection have a role in human disease is profound. These polymorphisms sense the local environment - diet, sunlight, sleep, exercise, alochol and tobacco use, exposure to pathogen, etc. - and participate in an allele-specific response of the cell, tissue, organism to that stimulus if you will. Thus, modification of one's lifestyle choices based on genotypes may be a viable choice to maintain a heathly condition. Before that, though, it will be necessary to define the effects of many, many more genetic polymorphisms, both SNPs and copy number variants (CNVs). To date we've compiled over 650 such examples where the SNP-phenotype association is modified by environment/diet. These pertain mostly to blood lipids such as LDL-cholesterol, HDL-cholesterol, triglycerides and total cholesterol. CNVs are generally under-studied in such work.

A summary of the review article is here:
Purpose of review: Obesity, cardiovascular disease and unhealthy levels of blood lipids known as dyslipidemia are complex and arise from both genetic and environmental factors as well as their interrelationships. Both large-scale genetic experiments and approaches focused on a single gene have resulted in identification of many genetic variants that are related to lipid and obesity measures. However, these variants still account for only a small fraction of the observed differences in these measures.

Recent findings: That many such genetic associations to obesity and lipid measures involve genetic variants under natural selection and that those variants are sensitive to environmental cues together suggest prominent roles for both natural selection and the interaction with the environment. Genetic variants under natural selection which interact with the environment modulate susceptibility to disease but the level to which those variants contribute to dyslipidemia and obesity and how environmental factors, especially diet, alter the genetic relationship is not yet completely described.

Summary: It is evident that genetic variants under natural selection make important contributions to obesity and heart disease risk. Advances in resequencing the entire human genome will enable accurate identification of genetic variants under positive selection. This will add power to large-scale genetic studies and allow for characterization of the relationship between natural selection and the obese and dyslipidemic conditions.

2009-12-21T17:40:00.000-08:00

Finally, I initiated my blog on variation within the human genome. This is something I have wanted to do for a long time.

Other genomes will be discussed as well. I use Twitter a couple/few times a day, but this space will be used for longer posts and more detail. I will give my own thoughts and opinions and will not disclose the sensitive aspects of the research for which I am paid.

OK, on to the science. Today, I began to read a very interesting article in Nature on a risk SNP for type 2 diabetes in which the paternal risk allele confers ~30% increased risk of T2DM while the maternal version of that same allele confers ~10% decreased risk. Furthermore, the C/T SNP alters a methylation-sensitive transcription factor binding site. This may help explain some of the missing heritability not observed in many genome-wide association studies (GWAS) and others of a smaller scale. The paper is by Kong, Steinthorsdottir, et al. (2009, Nature 462:868 (www.nature.com/nature/journal/v462/n7275/abs/nature08625.html)).

This is neat stuff, deserving of more thought.