My agenda for ASHG 2012

This week I will attend the annual conference of the American Society of Human Genetics, ASHG. It was suggested that we who will broadcast observations, comments, invites, critiques and musings on Twitter should also post an agenda of those sessions we feel are important to attend. I've done just that, listing below those sessions I plan to attend.

Hashtag will be #ASHG2012

All talks are tweetable, opt-out, meaning if the speaker says nothing to the contrary, one can tweet

Wednesday, November 7
8:00 am - 10:00 am
5. Gene Regulatory Change: The Engine of Human Evolution? Room 135, Lower Level North
9. Surveying Customer Responses to Personal Genetic Services Room 132, Lower Level North

10:30 am - 12:45 pm
15. New Loci for Obesity, Diabetes, and Related Traits Gateway Ballroom 104, Lower Level South

2:15 pm - 4:15 pm
Poster session 1

Thursday, November 8
8:00 am - 10:00 am
22. Common and Rare CNVs: Genesis, Patterns of Variations and Human Diseases Hall D, Lower Level North

10:30 am - 12:45 pm
32. Cardiovascular Genetics: GWAS and Beyond Room 134, Lower Level North
37. Metabolic Disease Discoveries Room 123, Lower Level North

2:15 pm - 4:15 pm
Poster session 2

4:30 pm - 6:45 pm
44. Tools for Phenotype Analysis Room 132, Lower Level North

Friday, November 9
8:00 am - 10:30 am
47. Structural and Regulatory Genomic Variation Hall D, Lower Level North
53. From SNP to Function in Complex Traits Room 132, Lower Level North

2:15 pm - 4:15 pm
Poster session 3

4:30 pm - 6:45 pm
61. Missing Heritability, Interactions and Sequencing Room 135, Lower Level North
63. Transcriptional Regulation, Variation and Complexity Gateway Ballroom 104, Lower Level South
64. Epigenetics Room 124, Lower Level North

Saturday, November 10
9:40 am - 11:40 am
76. The Functional Consequences of microRNA Dysregulation in Human Disease Room 134, Lower Level North

NuGO Week 2012

The following are what I took away as highlights from NuGO Week 2012, held from 28 to 31 August 2012 in Helsinki, Finland. I felt that this conference showed a marked maturity in research accomplishments of the nutrigenomics community. In the past, this conference and others, as well as personal communications, were quite often invoked intent to use omics platforms without showing much in the way of data. That changed dramatically at this conference – there were presentations with a lot of data.

Some of the top themes were: Networks and GxEs, metabolic profiling done to quantify metabolites, either known or as discovery of a metabolic process, or done to quantify adherence to a given diet/food type intake, eg plant polyphenolics, aging and health.

Specific notes:

My take-away lessons from Marju Orho-Melander’s talk (Lund University): Your genetic susceptibility is affected by what you eat/how you eat/what and how much you exercise/etc. She uses the Malmö Diet and Cancer Study n=28499. 1750 have incident T2DM. Protein from animal sources increases risk of T2DM, while whole grain/high-fiber intake decreases the risk. Using the epidemiology data (eg, animal protein intake increases risk) to focus or inform the interaction work may be something worth looking into. She’s ready to perform a GxE analysis using the GWAS data from the Malmö Diet and Cancer Study. Instead of using a genetic risk score for the disease, use a pathway approach to consider a marker for the disease, prior to the endpoint of disease itself. So, look at interactions for glucose and glucose homeostasis in place of interactions for T2DM.

Aldons “Jake” Lusis of UCLA. It is difficult to go from a GWAS hit to a mechanism in humans. He uses a systems genetics approach: integrate clinical traits and intermediate phenotypes across a population using correlation and gene mapping. He uses mice because this offers a controlled environment, tissue access, and deep biochemical profiling. They use 100 classical inbred mouse strains, genome sequenced and GWAS-like association mapping. His group is looking at genetics of dietary response in 6 to 8 mice/strain. Some mice have no change in body fat going from chow to high-fat/high-sugar diet, others have substantial change. They also looked at food consumption. They always look at males and females separately. Food intake may be more strongly related to lean body mass according to the stronger correlation between food intake and body weight over food intake with fat mass. They use a T-test on 135,000 SNPs in a GWAS. Threshold is determined by permutation or simulation. Interactions will be identified using association and correlation in his systems genetics strategy. Visit http://systems.genetics.ucla.edu to see the loci that control body fat or look gene by gene to see what traits are associated with that gene.

Melissa Morine of University of Trento. Within a network, one can perform a modularity calculation – whereby members are highly connected to each other and rather unconnected to nodes outside the module.

Marjukka Kolehmainen of University of Eastern Finland. Of 82 individuals who were obese, only 34 donated abdominal subcutaneous tissue both before and after very low-calorie diet. All 3 PPAR pathways were down-regulated in the subcutaneous adipose during the very low calorie diet intervention. Energy metabolism was also strongly down-regulated. Both pathways returned to near normal levels during the maintenance period.

ETHERPATHS. Anne-Marja Aura of VTT Technical Research Centre of Finland: There is a set of known and detectable metabolites of given fatty acids that can be used as biomarkers of intake. It seems important to assess both microbial metabolites and those found in the serum. Robert Caesar (University of Gothenburg) examines diet-microbial regulation of liver and adipose transcriptomics. Macrophages from germ-free (obese-resistant) mice have decreased expression of Ccr2 chemokine receptor. Compare WAT and liver gene expression in response to metabolites from the gut microflora. Liver should be more responsive because of close link via vena cava. Liver pathways altered: Up: lymphocyte mediated immunity, adaptive immune response, innate immune response, immune effector process, cell activation, chemotaxis, positive regulation in response to stimulus; Down: sterol metabolic process, cell adhesion, lipid metabolic process, etc. Gut microbiota increases liver inflammation during high-fat diet independent of dietary lipid quality. Tuulia Hyötyläinen (VTT Technical Research Centre of Finland) looks at lipoprotein lipids and polar lipids in the lipoprotein fractions. Some of this work is published in Mol Biosyst in 2012. N-3 intervention caused TG levels to go down in females, no change in males. There were also sex differences for metabolites seen in lipoprotein fractions.

Mark Boekschoten of Wageningen University. PLS-path model gives them 44 liver and 69 adipose genes important in body weight gain. Variation in these genes in humans could manifest as GxEs for total caloric intake or saturated fat intake on body weight.

Jessica Schwarz of Wageningen University. Her poster shows that a high-protein diet restores VLDL production (which is lowered with a high-fat diet) and prevents fat accumulation in the liver in mice. She got onto this project from the observation that a high-protein diet showed lower oil-red staining in liver and lower TG levels.

Hector Keun of Imperial College of London. He has two objectives: Testing for association within and between data types; incorporating background knowledge to enhance our ability to interpret associations. A multivariate model example is O2-PLS, which can be used to compare two data blocks, reducing it to the simplest list of associations. This was developed by Trygg & Wold. It is also of interest to describe what variation is not common to the two data blocks. For example, there could be variation that is specific to the metabolomic data that does not show in the proteomic data from those same animals. Pathway significance is calculated using the hypergeometric distribution test, when comparing a set of up-regulated genes with genes in a given pathway to see if that pathway is over-represented, by chance, in the set of up-regulated genes. It is also possible to use this analysis approach on the FFQ data. See Kamburov and Cavill for access to their webtools. His adjustment for background incorporates the fact that the number of observations or tests really for genes is much higher than for metabolites, for example.

Willem de Vos of Wageningen University. The Bacterioides/Firmicutes ratio found in the gut microbiome is not helpful with regard to diet, interventions and obesity. This group uses the log [CFU/g feces] on x-axis on a graph to look at correlation with some factor (he used LPS binding proteins) with changes to the microbiota.

Jacqueline Monteiro of University of São Paulo. There is a correlation or relationship between calcium in the diet and adipocyte differentiation. Kids in the lowest quintile for milk intake were in the highest quintile for BMI in their Delta Project.

ASHG 2010 conference notes - 5 Nov 2010

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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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?]

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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.

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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.]

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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."

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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.

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E. Speiliotes – GWAS for fatty liver disease

Five loci identified:

PPP1R3B
GCKR
LYPLAL1
NCAN
PNPLA3

ASHG 2010 conference notes - 4 Nov 2010

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

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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.

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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.

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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.

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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.

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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.

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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.]

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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…

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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.

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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

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.

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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%.

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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.

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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.

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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.

Five domains enroute to personalized nutrition

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

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