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.
Friday, February 24, 2012
Thursday, February 23, 2012
POTW: January, 2012 choices
Three papers published last month that I found to be of interest are listed here. These are:
The mystery of missing heritability: Genetic interactions create phantom heritability, by Zuk, et al. This addresses the missing heritability question, suggesting that "the total heritability may be much smaller and thus the proportion of heritability explained much larger."
Characterisation and discovery of novel miRNAs and moRNAs in JAK2V617F mutated SET2 cells, by Botoluzzi, et al. What interested me in this article was the generation of novel microRNAs that were induced by the cancerous state triggered by this JAK2 variant. This indicates to me that the microRNA realm is broad and rich with many as yet undiscovered relationships.
The PLoS One paper entitled "Genetic signatures of exceptional longevity in humans," by Sebastiani, et al. We here were very curious how this was different from the version retracted from Science and what findings are now reported. TOMM40 near APOE is indeed interesting.
The mystery of missing heritability: Genetic interactions create phantom heritability, by Zuk, et al. This addresses the missing heritability question, suggesting that "the total heritability may be much smaller and thus the proportion of heritability explained much larger."
Characterisation and discovery of novel miRNAs and moRNAs in JAK2V617F mutated SET2 cells, by Botoluzzi, et al. What interested me in this article was the generation of novel microRNAs that were induced by the cancerous state triggered by this JAK2 variant. This indicates to me that the microRNA realm is broad and rich with many as yet undiscovered relationships.
The PLoS One paper entitled "Genetic signatures of exceptional longevity in humans," by Sebastiani, et al. We here were very curious how this was different from the version retracted from Science and what findings are now reported. TOMM40 near APOE is indeed interesting.
Paper of the Week: cis-eQTL between normal and cancer tissue
My choice for Paper of the Week this week is "cis-Expression QTL Analysis of Established Colorectal Cancer Risk Variants in Colon Tumors and Adjacent Normal Tissue," by Loo, Cheng, Tiirikainen, Lum-Jones, Seifried, et al. (2012) appearing in PLoS One.
I find this article of interest because I feel that many GWAS hits for disease risk will serve to alter expression of a near or distant gene(s) in an allele-specific manner. This group looked at gene expression differences between tissues that were either colorectal tumors or their paired, adjacent normal tissue, and then associated those gene expression differences with allelic variation. Assaying 40 individuals was sufficient to identify 3 SNPs affecting expression of 4 genes: ATP5C1, DLGAP5, NOL3 and DDX28.
A link to this paper is here.
Citation:
Loo LWM, Cheng I, Tiirikainen M, Lum-Jones A, Seifried A, et al. (2012) cis-Expression QTL Analysis of Established Colorectal Cancer Risk Variants in Colon Tumors and Adjacent Normal Tissue. PLoS ONE 7(2): e30477.
I find this article of interest because I feel that many GWAS hits for disease risk will serve to alter expression of a near or distant gene(s) in an allele-specific manner. This group looked at gene expression differences between tissues that were either colorectal tumors or their paired, adjacent normal tissue, and then associated those gene expression differences with allelic variation. Assaying 40 individuals was sufficient to identify 3 SNPs affecting expression of 4 genes: ATP5C1, DLGAP5, NOL3 and DDX28.
A link to this paper is here.
Citation:
Loo LWM, Cheng I, Tiirikainen M, Lum-Jones A, Seifried A, et al. (2012) cis-Expression QTL Analysis of Established Colorectal Cancer Risk Variants in Colon Tumors and Adjacent Normal Tissue. PLoS ONE 7(2): e30477.
Monday, December 12, 2011
A bitter taste for the microbiome
Taste is a fine thing - nuanced by the presence of many compounds, their ratios to one another, and past experience. That is true of foods that enter the mouth. Could this also be true of the mix of food waste and bacteria in the colon? Although sampling a spoonful of Firmicutes or Bacteroidetes is admittedly something quite revolting, my thinking is leaning toward "yes." The brain likely has an idea of who is in residence in the colon and which metabolic byproducts are present.
A stir was created in 2007 when it was reported that two taste receptors and gustducin have a role in glucose-mediated responses, suggesting function as a previously undescribed glucose sensor in the gut lumen (Jang, Kokrashvili, et al. 2007 Proc Natl Acad Sci USA 104:15069–15074). One can find many news articles from that time highlighting this finding. It seems that the repertoire of taste receptors expressed in the gut, particularly in the colon, is much more extensive.
The ATLAS gene expression tool at the EBI is a semantically enriched database of meta-analysis based summary statistics over a curated subset of ArrayExpress gene expression data. ATLAS supports queries for condition-specific gene expression patterns as well as broader exploratory searches for biologically interesting genes. Using ATLAS, I found that the following ten taste receptors are expressed either in the small intestine or the colon:
TAS1R1
TAS2R13
TAS2R14
TAS2R16
TAS2R3
TAS2R38
TAS2R4
TAS2R7
TAS2R8
TAS2R9
Every one of these, except for one, are described as responding primarily to bitter tastants. TAS2R38 is sensitive to glucosinolate, a plant derived family of compounds, which do have a bitter-like taste. TAS1R1 is a type of sweet receptor, detecting primarily L-enantiomers of certain amino acids. It is highly likely that querying other gene expression databases will turn up other members of the taste receptor family as expressed in the lower gastrointestinal tract.
It makes sense to me that proteins that are designed to communicate what is in essence the composition of the external environment should be given such roles as sentry or monitor beyond the taste buds on the tongue. Taste receptors are designed to sense specific classes of chemicals and to relay a signal to the brain. Fecal fermentation of proanthocyanins, phytochemicals and other complex molecules is likely to be monitored in some way for the benefit of the human host. Perhaps taste receptors have a role in that process.
A stir was created in 2007 when it was reported that two taste receptors and gustducin have a role in glucose-mediated responses, suggesting function as a previously undescribed glucose sensor in the gut lumen (Jang, Kokrashvili, et al. 2007 Proc Natl Acad Sci USA 104:15069–15074). One can find many news articles from that time highlighting this finding. It seems that the repertoire of taste receptors expressed in the gut, particularly in the colon, is much more extensive.
The ATLAS gene expression tool at the EBI is a semantically enriched database of meta-analysis based summary statistics over a curated subset of ArrayExpress gene expression data. ATLAS supports queries for condition-specific gene expression patterns as well as broader exploratory searches for biologically interesting genes. Using ATLAS, I found that the following ten taste receptors are expressed either in the small intestine or the colon:
TAS1R1
TAS2R13
TAS2R14
TAS2R16
TAS2R3
TAS2R38
TAS2R4
TAS2R7
TAS2R8
TAS2R9
Every one of these, except for one, are described as responding primarily to bitter tastants. TAS2R38 is sensitive to glucosinolate, a plant derived family of compounds, which do have a bitter-like taste. TAS1R1 is a type of sweet receptor, detecting primarily L-enantiomers of certain amino acids. It is highly likely that querying other gene expression databases will turn up other members of the taste receptor family as expressed in the lower gastrointestinal tract.
It makes sense to me that proteins that are designed to communicate what is in essence the composition of the external environment should be given such roles as sentry or monitor beyond the taste buds on the tongue. Taste receptors are designed to sense specific classes of chemicals and to relay a signal to the brain. Fecal fermentation of proanthocyanins, phytochemicals and other complex molecules is likely to be monitored in some way for the benefit of the human host. Perhaps taste receptors have a role in that process.
Wednesday, October 26, 2011
The curious case of SNP rs2880301
"Wow! I've never seen anything like that before," my colleague Chao-Qiang Lai exclaimed when examining output from his analysis of genome-wide association (GWAS) data. He was looking for genetic markers influencing the level of triglyceride in serum as part of the GOLDN study. GOLDN is looking at the genetics of the response to lipid-lowering medication. The result of Chao's preliminary analysis indicated that SNP rs2880301 associated with TG levels with a p-value of 10-218. He showed me some data from the scan of the Affymetrix 6.0 genotyping chip and we postulated that we could be looking at some type of CNV (copy number variant) or deletion, but the lack of minor allele homozygotes troubled us.
What intrigued us right from the start was our colleagues at other institutions who are also analysis the GOLDN GWAS data did not report this SNP in their initial findings. The dbSNP entry for rs2880301 indicates a C to T variant with an allele frequency of 0.24 in the four primary HapMap populations from USA, Nigeria, China and Japan. No differences in allele frequency means no chance of positive (or negative) selection on this variant. No, none indeed as we were to learn later.
So, Chao dug deeper into his data and he and I shot ideas back and forth. After my suggestion to look at sex, he saw that when the SNP and sex are together in the same model, the analysis did not complete. Then, looking at the individual genotypes, he saw that all the men had genotype CT and all the women CC. This is from a total of just over 800 subjects.
OK, time for me to step in and see where this "SNP" maps in the genome. My first query was the flanking sequence supplied by Affymetrix. This 33-bp segment maps nearly perfectly to both chromosome 13, within intron 1 of the TPTE2 gene and agreeing with both dbSNP and Affyemtrix's annotation of the SNP, and curiously to a spot on the Y chromosome. (TPTE2 is a membrane-associated phosphatase which acts on the 3-position phosphate of inositol phospholipids and could be argues as relevant to TG biology.) The only residue not matching is the "polymorphic" base of the "SNP." A C is found on chr13 and a T is found on Y. Thus, the SNP becomes a marker of sex and Chao was right - it is a type of deletion (females carry no Y chromosome) - but a deletion he had not envisioned.
Is rs2880301 then a marker for gender? Not really. I compared the genomic regions where the homologous SNP sequences were found on both chromosomes, extending over 6 kbp in each direction. I saw a large region of sequence identity between 13 and Y - over 96% - for a ~5 kbp segment. Running RepeatMasker indicated that rs2880301 falls within an L1 LINE, a common repeat element. Thus, while it is intriguing that an array of repeats (70% of the 13-kbp segment of chr13 is masked by RepeatMasker) are conserved between chromosomes 13 and Y, and in order, SNP rs2880301 is not really a SNP. All subjects are C on chr13 and all Y chromosomes are T.
What we then had in our data were five genotypes: CC on chr13 for all women, CC on chr13 for all men and T on Y. Thus, the "allele frequencies" of C between 0.75 and 0.80 and T between 0.20 and 0.25 seen by us and others, including the HapMap data, roughly correspond to populations that are half to slightly more than half women.
What intrigued us right from the start was our colleagues at other institutions who are also analysis the GOLDN GWAS data did not report this SNP in their initial findings. The dbSNP entry for rs2880301 indicates a C to T variant with an allele frequency of 0.24 in the four primary HapMap populations from USA, Nigeria, China and Japan. No differences in allele frequency means no chance of positive (or negative) selection on this variant. No, none indeed as we were to learn later.
So, Chao dug deeper into his data and he and I shot ideas back and forth. After my suggestion to look at sex, he saw that when the SNP and sex are together in the same model, the analysis did not complete. Then, looking at the individual genotypes, he saw that all the men had genotype CT and all the women CC. This is from a total of just over 800 subjects.
OK, time for me to step in and see where this "SNP" maps in the genome. My first query was the flanking sequence supplied by Affymetrix. This 33-bp segment maps nearly perfectly to both chromosome 13, within intron 1 of the TPTE2 gene and agreeing with both dbSNP and Affyemtrix's annotation of the SNP, and curiously to a spot on the Y chromosome. (TPTE2 is a membrane-associated phosphatase which acts on the 3-position phosphate of inositol phospholipids and could be argues as relevant to TG biology.) The only residue not matching is the "polymorphic" base of the "SNP." A C is found on chr13 and a T is found on Y. Thus, the SNP becomes a marker of sex and Chao was right - it is a type of deletion (females carry no Y chromosome) - but a deletion he had not envisioned.
Is rs2880301 then a marker for gender? Not really. I compared the genomic regions where the homologous SNP sequences were found on both chromosomes, extending over 6 kbp in each direction. I saw a large region of sequence identity between 13 and Y - over 96% - for a ~5 kbp segment. Running RepeatMasker indicated that rs2880301 falls within an L1 LINE, a common repeat element. Thus, while it is intriguing that an array of repeats (70% of the 13-kbp segment of chr13 is masked by RepeatMasker) are conserved between chromosomes 13 and Y, and in order, SNP rs2880301 is not really a SNP. All subjects are C on chr13 and all Y chromosomes are T.
What we then had in our data were five genotypes: CC on chr13 for all women, CC on chr13 for all men and T on Y. Thus, the "allele frequencies" of C between 0.75 and 0.80 and T between 0.20 and 0.25 seen by us and others, including the HapMap data, roughly correspond to populations that are half to slightly more than half women.
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