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.
Thursday, February 23, 2012
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.
Tuesday, September 13, 2011
Genetics of hypertension
An important paper describing genetic factors involved in blood pressure and heart disease was published this week. 29 loci were identified - 16 of these novel. This is impressive, but not so surprising as both diastolic and systolic blood pressure are complex, heritable traits. Many, many factors are at play here. In fact, the authors describe a risk score based on genotypes at 29 genome-wide significant variants, which was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function.
Some time ago, I wrote about the genome of the SHR (spontaneous hypertensive rat). This rat and the FHH strain are models for hypertension. The genome of the SHR strain showed that 788 genes contained variants with respect to the reference rat genome. Whether any or all of those variants function in producing the hypertension phenotype is not clear, but reasoning went that these variants would be a logical place from which to build list of candidate genes.
Well, I thought it would be a fun and quick exercise to see how many of these 29 loci detected by genome-wide associations in nearly 70,000 individuals of European ancestry (with validation of top signals in up to 133,000 additional individuals of European descent) compare to those genes that possess genetic variants in either the FHH or SHR rat strains. This may give insight into the applicability of sequencing the genomes of specific model organism strains and into how many or how often genes are identified by both GWAS and whole-genome sequencing. To do this, I used the Rat Genome Browser hosted by the Medical College of Wisconsin.
Of the 29 human loci, 23 map in the rat genome within a QTL for blood pressure. This looks good, but consider that there are numerous blood pressure (BP) QTL mapped throughout the rat genome. In fact, the rat Adm gene maps within 27 different BP QTL and three other gene regions, Furin - Fes, Plekha7 and Mov10, map within more than 20 different BP QTL. Some of these QTL are large, spanning many genes, which means that fine-mapping is needed - such as a GWAS - to identify more precisely candidate loci.
Only 18 of the human BP genes identified in the paper contain SNPs in either the FHH or SHR strains. Often, the variants are shared in both strains. Both synonymous and nonsynonymous SNPs were noted, but synonymous far outnumbered those variants that altered the underlying amino acid sequence. No SNPs in gene control regions were noted, which may indeed be the case or a limitation of the data sources used here.
The human genes whose rat versions contain SNPs in the hypertensive-susceptible strains are:
SLC39A8
ATP2B1
GNAS - EDN3
MTHFR - NPPB
FGF5
CYP1A1 - ULK3
FURIN - FES
FLJ32810 - TMEM133
NPR3 - C5orf23
EBF1
PLCE1
BAT2-BAT5
ZNF652
TBX5 - TBX3
JAG1
GUCY1A3 - GUCY1B3
MECOM
ULK4
SNPs altering gene expression still need to be added to this analysis. Nonetheless, the numbers and types of genes that share genetic variation in hypertensive mammals (human, rat) is revealing. It is likely that the 788 identified genes with variation in the SHR rat are not all important for hypertension, but that strain does carry variants in 17 of these new BP genes. Or is that just 17?
Some time ago, I wrote about the genome of the SHR (spontaneous hypertensive rat). This rat and the FHH strain are models for hypertension. The genome of the SHR strain showed that 788 genes contained variants with respect to the reference rat genome. Whether any or all of those variants function in producing the hypertension phenotype is not clear, but reasoning went that these variants would be a logical place from which to build list of candidate genes.
Well, I thought it would be a fun and quick exercise to see how many of these 29 loci detected by genome-wide associations in nearly 70,000 individuals of European ancestry (with validation of top signals in up to 133,000 additional individuals of European descent) compare to those genes that possess genetic variants in either the FHH or SHR rat strains. This may give insight into the applicability of sequencing the genomes of specific model organism strains and into how many or how often genes are identified by both GWAS and whole-genome sequencing. To do this, I used the Rat Genome Browser hosted by the Medical College of Wisconsin.
Of the 29 human loci, 23 map in the rat genome within a QTL for blood pressure. This looks good, but consider that there are numerous blood pressure (BP) QTL mapped throughout the rat genome. In fact, the rat Adm gene maps within 27 different BP QTL and three other gene regions, Furin - Fes, Plekha7 and Mov10, map within more than 20 different BP QTL. Some of these QTL are large, spanning many genes, which means that fine-mapping is needed - such as a GWAS - to identify more precisely candidate loci.
Only 18 of the human BP genes identified in the paper contain SNPs in either the FHH or SHR strains. Often, the variants are shared in both strains. Both synonymous and nonsynonymous SNPs were noted, but synonymous far outnumbered those variants that altered the underlying amino acid sequence. No SNPs in gene control regions were noted, which may indeed be the case or a limitation of the data sources used here.
The human genes whose rat versions contain SNPs in the hypertensive-susceptible strains are:
SLC39A8
ATP2B1
GNAS - EDN3
MTHFR - NPPB
FGF5
CYP1A1 - ULK3
FURIN - FES
FLJ32810 - TMEM133
NPR3 - C5orf23
EBF1
PLCE1
BAT2-BAT5
ZNF652
TBX5 - TBX3
JAG1
GUCY1A3 - GUCY1B3
MECOM
ULK4
SNPs altering gene expression still need to be added to this analysis. Nonetheless, the numbers and types of genes that share genetic variation in hypertensive mammals (human, rat) is revealing. It is likely that the 788 identified genes with variation in the SHR rat are not all important for hypertension, but that strain does carry variants in 17 of these new BP genes. Or is that just 17?
Tuesday, May 24, 2011
More on microRNAs - a nutrition connection
The landscape at the intersection of microRNA (miR) expression and diet is sparse. This is even more so concerning the consequence of bioactive food components in affecting the physical aspects of the miR-mRNA interaction.
Nonetheless, evidence has been reported to suggest that miRs are key metabolic regulators. In adipose of mice, expression of miRs was shown to be sensitive to conjugated linoleic acid in the diet. In rats fed a diet of corn oil/fish oil with pectin/cellulose and in which colonic tumors were induced, a number of miRs, including miR-16, miR-19b, miR-21, miR-26b, miR-27b, miR-93 and miR-203, exhibited altered expression and were linked to oncogenic signaling pathways. Also in rats, downregulation in the liver of three miRs (miR-122, miR-451 and miR-27) and upregulation of miR-200a, miR-200b and miR-429 was noted after feeding of either a high-fat or high-fructose diet with consequences of diet-induced nonalcoholic fatty liver disease.
In mice, pregnant and lactating dams fed a high-fat diet displayed reduced expression of miR-26a, miR-122, miR-192, miR-194, miR-709 and the let-7 family with a common predicted target of methyl-CpG binding protein 2 (Mecp2).
A comparison of miR expression profiles in subcutaneous adipose of women highlighted eleven miRNAs as significantly deregulated in obese subjects with and without type 2 diabetes. Many of the same miRs also showed significant deregulation during adipocyte differentiation. The role of diet in regulating miR expression in prostate cancer has been reviewed. MiR-33, encoded in an intron of SERBF1/SREBF2, cooperatively regulates cholesterol homeostasis via targeting of ABCA1 and NPC1. The FXR/SHP signaling cascade regulates miR-34a and its target SIRT1, which likely functions as either a regulator of epigenetic gene silencing or an intracellular regulatory protein with mono-ADP-ribosyltransferase activity.
Using a mouse diet-induced obesity model, it was shown that hepatic expression of miR-107 decreases while its target FASN, encoding fatty acid synthase, increases.
In summary, there is a growing body of evidence to strongly implicate microRNAs as having significant functions in regulating the metabolic-based response of a number of cell types.
Nonetheless, evidence has been reported to suggest that miRs are key metabolic regulators. In adipose of mice, expression of miRs was shown to be sensitive to conjugated linoleic acid in the diet. In rats fed a diet of corn oil/fish oil with pectin/cellulose and in which colonic tumors were induced, a number of miRs, including miR-16, miR-19b, miR-21, miR-26b, miR-27b, miR-93 and miR-203, exhibited altered expression and were linked to oncogenic signaling pathways. Also in rats, downregulation in the liver of three miRs (miR-122, miR-451 and miR-27) and upregulation of miR-200a, miR-200b and miR-429 was noted after feeding of either a high-fat or high-fructose diet with consequences of diet-induced nonalcoholic fatty liver disease.
In mice, pregnant and lactating dams fed a high-fat diet displayed reduced expression of miR-26a, miR-122, miR-192, miR-194, miR-709 and the let-7 family with a common predicted target of methyl-CpG binding protein 2 (Mecp2).
A comparison of miR expression profiles in subcutaneous adipose of women highlighted eleven miRNAs as significantly deregulated in obese subjects with and without type 2 diabetes. Many of the same miRs also showed significant deregulation during adipocyte differentiation. The role of diet in regulating miR expression in prostate cancer has been reviewed. MiR-33, encoded in an intron of SERBF1/SREBF2, cooperatively regulates cholesterol homeostasis via targeting of ABCA1 and NPC1. The FXR/SHP signaling cascade regulates miR-34a and its target SIRT1, which likely functions as either a regulator of epigenetic gene silencing or an intracellular regulatory protein with mono-ADP-ribosyltransferase activity.
Using a mouse diet-induced obesity model, it was shown that hepatic expression of miR-107 decreases while its target FASN, encoding fatty acid synthase, increases.
In summary, there is a growing body of evidence to strongly implicate microRNAs as having significant functions in regulating the metabolic-based response of a number of cell types.
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