Friday, December 10, 2010

Take your family's medical history!

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

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

Thursday, December 2, 2010

Gene-HDL associations modified by physical activity

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

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

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

Friday, November 5, 2010

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)

Inbred B:

Inbred C:

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:

/ C2CD4A / C2CD4B

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:


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:


Thursday, November 4, 2010

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


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 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.

Wednesday, November 3, 2010

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.


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%.
- 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 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

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...]

Monday, October 25, 2010

S1PR5 - Dad's diet and CNVs

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.

Friday, October 22, 2010

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

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.

Tuesday, September 28, 2010

Notes from INCON 2010

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 26th, 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 (, 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.

Friday, September 10, 2010

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

Tuesday, August 24, 2010

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

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.


- 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