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.
