Proteomics of fasting

It was with keen interest that I read the article by Bouwman, et al. entitled “2D-electrophoresis and multiplex immunoassay proteomic analysis of different body fluids and cellular components reveal known and novel markers for extended fasting,” which appeared in BMC Medical Genomics last week. As we are interested in the genetic basis for the differential response to diet, we view perturbations to the system, either by a high-fat intervention or fasting/calorie restriction, as instrumental in deciphering this response. Overall, I found this to be nice work and deserving of a wide audience.

The authors report that “[p]rincipal component analysis applied to the multiplex immunoassay (RBM) data set revealed that each of the subjects could be identified based on levels of 89 plasma proteins (see figure 3). It appears that such data can be used to provide a metabolic fingerprint of the individual volunteers participating in this intervention study. However, this demonstrates that the between-subject effects are larger than that of the fasting effect.”

This is not surprising given that of the 44 different proteins identified as responding to extended fasting (see tables 2 & 3, figure 4), nine are encoded by genes harboring variants responding differentially to environmental factors such as dietary intake and physical activity. The dietary component most often modulating the association between those genes (and their variants) and a phenotype pertinent to metabolic syndrome is fat. Physical activity is also a wide-reaching modulator of the association between genetic variation and various phenotypes pertinent to metabolic syndrome. In other words, a combination of genetic variation between study participants in combination with each individual’s lifestyle choices (say, more or less exercise) could indeed influence the levels of certain proteins found to respond to the fasting intervention.

At the same time, I cannot dispute, as the authors write, that the between-subject variation may have arisen from heterogeneity of the study cohort “with regard to various parameters, including gender and BMI.” This is logical, but again other factors such as habitual diet and exercise, even sleep patterns could be at work here. Another source of between-subject variation is certainly genetic.

The authors observe that “[m]ost interesting biomarkers are involved in metabolic pathways, as well as those related to inflammation and oxidative stress.” This is where my quite minor complaint with the work arises – I would have liked to see more interpretation of the results from a biological or even medical perspective. Thus, I note that IL10, IL1B, TNF, SERPINE1, INS and CCL2 respond to extended fasting and are members of the Insulin resistance inflammation network (Olefsky, Glass (2010) in a review of Macrophages, Inflammation and Insulin Resistance (Annu Rev Physiol 72:219-46)). Furthermore, VCAM1, APCS, CRP, IL1B, TNF, IL18 and CCL2 are assigned an inflammation role within the set of PPARA target genes (Rakhshandehroo, Kersten 2010 PPAR Research pii: 612089).

A second comparison I undertook was to look at the number of genes responding to the fasting intervention and to an intervention termed AIDM: Anti-inflammatory dietary mix (Bakker, et al 2010 Am J Clin Nutr 91:1044). Large-scale assays of genes, proteins, and metabolites in plasma, urine, and adipose tissue showed that an intervention with selected dietary components influenced inflammatory processes, oxidative stress and metabolism in humans. Eight genes are in common and we’d expect about one by chance. These eight genes are FABP3, VCAM1, IL12A, AFP, FTH1, IL18, APOA1 and F7. Most of these eight were described in the AIDM article as down-regulated (lower levels) in plasma by the dietary intervention, similar to the response to fasting. This raises the intriguing hypothesis that the AIDM diet at least partially mimics fasting.

Adipokines are signaling proteins that are secreted from adipocytes. It is an interesting observation, then, that four of the altered proteins seen during fasting are described by Rosenow, et al as adipokines. These are SERPINE1, SERPINF1, C3 and TIMP1. Perhaps fasting-induced changes to the signaling potential of adipose tissue should focus on these four proteins.

CDKN2A and its response to diet

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

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

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

Olive oil and cancer

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

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

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

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

However...

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

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

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

Lipoprotein-associated phospholipase A2 and heart disease-risk

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

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

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

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

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

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Reference

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

MicroRNAs and BMI

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

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

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

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

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

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

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

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