ARAP1 and type 2 diabetes - a circadian connection?

A new report in the American Journal of Human Genetics shows a variant with association to type 2 diabetes and insulin levels functions within an allele-specific motif for islet cell transcription factors PAX4 and PAX6. Specifically, "measurement of allele-specific mRNA levels in human pancreatic islet samples heterozygous for rs11603334 showed that the T2D-risk and proinsulin-decreasing allele (C) is associated with increased ARAP1 expression (p < 0.02). We evaluated four candidate functional SNPs for allelic effects on transcriptional activity by performing reporter assays in rodent pancreatic beta cell lines. The C allele of rs11603334, located near one of the ARAP1 promoters, exhibited 2-fold higher transcriptional activity than did the T allele (p < 0.0001); three other candidate SNPs showed no allelic differences. Electrophoretic mobility shift assays demonstrated decreased binding of pancreatic beta cell transcriptional regulators PAX6 and PAX4 to the rs11603334 C allele."

Interestingly, Pax4 is abundantly over-represented within cis-regulatory motifs in mouse clock-controlled genes, in liver and muscle, per Table 1, and relative to peak Per2 expression in mice, the Pax4 protein recognizes a motif over-represented in the suprachiasmatic nucleus (SCN, the "master clock" in the brain) at phase 12, per Table 2. These data are from Bozek, Relogio, et al 2009 PLoS One4:e4882. That same study showed similar activity for Pax6: relative to peak Per2 expression in mice, Pax6 protein also recognizes a motif over-represented in the SCN at phase 12, per Table 2 (Bozek Relogio 2009 PLoS One 4:e4882, PMID 19287494).

This makes one wonder about the timing of the food intake that might exacerbate glucose homeostasis in carriers of the risk allele.

The WHO's report on noncommunicable diseases

The World Health Organization of the United Nations has released a report titled "Global status report on noncommunicable diseases." Access to the report and its individual chapters is at this link. I was particularly interested in Chapter 1 and the major contributing factors to noncommunicable diseases (NCD).

According to the above report and others from the WHO, the four primary contributors to global increases in NCDs, such as type 2 diabetes, cancer, and cardiovascular diseases, are:

tobacco

harmful use of alcohol

unhealthy diet

physical inactivity

While such a list is really not surprising, what I do take from this, with respect to my own research on the genetic basis for the differential response to the diet as it pertains to metabolic diseases, is these are our key environmental factors used to assess gene by environment, or GxE, interactions. In other words, while these factors are strong contributors to NCD onset and progression, genetic differences exert different influences on the disease risk, onset and progression in different individuals. That influence could be negative - increasing risk - or positive - being more protective.

Thus, the importance of GxE identification cannot be overlooked, and ought really to be emphasized in genetic association studies.

POTW: Exercise and gene methylation

The Paper of the Week for 9 March 2012 was entitled "Acute Exercise Remodels Promoter Methylation in Human Skeletal Muscle" by Barres, et al. It appeared in Cell Metabolism as a Short Article.

The exercise test was performed on a stationary bicycle. One cohort of subjects were exercised until reaching either 40% or 80% of VO₂ peak. A second cohort was exercised until 1,674 kJ were expended. These were acute interventions, making the findings all the more remarkable.

I found the following to be key points of this paper:

1. In both healthy, sedentary women and men, it was observed that whole genome methylation was decreased in skeletal muscle.

2. While exercise induced expression of PPARGC1A (PGC-1α), PDK4, and PPARD, the authors also noted reduced methylation at each of the promoters for these genes.

PPARGC1A is a key transcriptional regulator of OXPHOS (oxidative phosphorylation) genes. It is also an important type 2 diabetes gene.

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.

MicroRNAs and glucose metabolism

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

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

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

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

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

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

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

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

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

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

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

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

The Complexity of a Complex Disorder

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

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

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

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Reference

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

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