This week, my choice for Paper of the Week is one in which the mechanism by which a heart disease risk variant, initially observed in GWAS, has been determined. The paper is by Pu, Xiao et al. and published in the American Journal of Human Genetics 92:366-374.
ADAMTS7 encodes a metalloprotease which cleaves protein in order to activate them. One of its substrates is COMP, known as thrombospondin-5, which is known to be made by vascular smooth muscle cells (VSMC) and inhibit their migration. The authors noted that ADAMTS7 accumulated in smooth muscle cells in both carotid and coronary atherosclerotic plaques. The relevant genetic variant here is rs3825807, calling for a nonsynonymous A to G, leading to a substitution at amino acid 214, Ser to Pro, in the prodomain of the ADAMTS7 protease. VSMCs harboring the G/G genotype for rs3825807 had attenuated migratory ability, while conditioned media of VSMCs of the G/G genotype contained less of the cleaved form of COMP.
The authors write that "the results of our study indicate that rs3825807 has an effect on ADAMTS7 maturation, thrombospondin-5 cleavage, and VSMC migration, with the variant associated with protection from atherosclerosis and CAD (coronary artery disease) rendering a reduction in ADAMTS7 function." I find this a noteworthy study because it takes a GWAS hit for CAD risk and informs us as to how that allele can lead to plaque formation and atherosclerosis.
Showing posts with label POTW. Show all posts
Showing posts with label POTW. Show all posts
Friday, March 8, 2013
Friday, November 16, 2012
Paper of the week: Visualizing associations between paired data sets
This week's paper of the week is by González, et al., entitled "Visualising associations between paired `omics' data sets," and published in BioData Mining (vol 5:19). The pdf of this report can be found here.
The authors demonstrate that graphical outputs such as Correlation Circle plots, Relevance Networks and Clustered Image Maps are useful in the visualization and interpretation of output from integrative analysis tools. The goal is to facilitate an understanding of systems as a whole when complex data often force donning of blinders to not observe the whole forest.
The graphical tools described in the report are implemented in the freely available R package mixOmics and in its associated web application.
As an example of what the authors have built, consider their presentation of Nutrimouse data showing correlations (or not) between between large data sets, in case gene expression and metabolite levels in liver, as taken from their figure 5.The Nutrimouse data are from a nutrigenomic study in which 40 mice from two genotypes (wild-type and Ppara -/-) were fed five diets with different fatty acid compositions. Details are in the Methods section. Expression of 120 genes in liver cells was obtained with microarrays and concentrations of 21 hepatic fatty acids were measured by gas chromatography. Hence, the data matrices are of size (40 × 120) for the gene expression and (40 × 120) for the fatty acids measurements.
The Authors write: The Correlation Circle plot (above) displays all fatty acids and the genes selected on each component (100 in total in this plot). Highlighted are subsets of variables important in defining each component. For example, C18:2ω6, C20:2ω6 and C16:0 are fatty acids for which variation allows the definition of the sPLS component 2 (top and bottom of the y-axis). Similarly, genes such as Car1, Acoth, Siat4c, Scarb1 (SR.BI) and Slc10a1 (Ntcp, or Ntop [sic]) are positively correlated to each other, and to the fatty acid C16:1ω9 and their variation participate in defining the sPLS component 1 (left-hand side of the x-axis).
I find such analysis and depiction of results useful and look forward to trying this with our GWAS data.
The authors demonstrate that graphical outputs such as Correlation Circle plots, Relevance Networks and Clustered Image Maps are useful in the visualization and interpretation of output from integrative analysis tools. The goal is to facilitate an understanding of systems as a whole when complex data often force donning of blinders to not observe the whole forest.
The graphical tools described in the report are implemented in the freely available R package mixOmics and in its associated web application.
As an example of what the authors have built, consider their presentation of Nutrimouse data showing correlations (or not) between between large data sets, in case gene expression and metabolite levels in liver, as taken from their figure 5.The Nutrimouse data are from a nutrigenomic study in which 40 mice from two genotypes (wild-type and Ppara -/-) were fed five diets with different fatty acid compositions. Details are in the Methods section. Expression of 120 genes in liver cells was obtained with microarrays and concentrations of 21 hepatic fatty acids were measured by gas chromatography. Hence, the data matrices are of size (40 × 120) for the gene expression and (40 × 120) for the fatty acids measurements.
The Authors write: The Correlation Circle plot (above) displays all fatty acids and the genes selected on each component (100 in total in this plot). Highlighted are subsets of variables important in defining each component. For example, C18:2ω6, C20:2ω6 and C16:0 are fatty acids for which variation allows the definition of the sPLS component 2 (top and bottom of the y-axis). Similarly, genes such as Car1, Acoth, Siat4c, Scarb1 (SR.BI) and Slc10a1 (Ntcp, or Ntop [sic]) are positively correlated to each other, and to the fatty acid C16:1ω9 and their variation participate in defining the sPLS component 1 (left-hand side of the x-axis).
I find such analysis and depiction of results useful and look forward to trying this with our GWAS data.
Friday, May 4, 2012
POTW: Uncovering the function of an intergenic SNP
My choice for Paper Of The Week this week is a report from a few weeks back (digging through the pile...) in which a polymorphism conferring increased risk of renal cell carcinoma is investigated for allele-specific functions. The paper is "Common genetic variants at the 11q13.3 renal cancer susceptibility locus influence binding of HIF to an enhancer of cyclin D1 expression" by Schödel, et al. (Nature Genetics 44:420-425).
Although the authors had several clues that the risk SNPs would (likely) affect expression of CCND1 (cyclin D1) in a manner regulated by hypoxia-induced factors - namely, that HIFs were known to regulate CCND1 but from an unknown binding site and that CCND1 is an established oncogene, among others - they accumulated much new data to nail down the role of EPAS1 (HIF-2) in regulating CCND1 expression.
One nice aspect of this work is the authors' taking advantage of signals seen in a renal carcinoma cell line and not in a breast cancer cell line (serving then as control). For example, they looked at the epigenetic enhancer marks at the 11q13.3 susceptibility locus with FAIRE (ormaldehyde-assisted isolation of regulatory elements to identify regions of nucleosome occupancy), and EPAS1 binding as assessed by ChIP-qPCR. The use of pVHL-defective RCC cell lines verified the role of VHL (von Hippel–Lindau tumor suppressor) in this cancer and consequence of allele-specific expression of CCND1.
Taken together, the data presented show that the haplotype associating with reduced renal cell cancer risk hinders EPAS1 binding, "resulting in an allelic imbalance in cyclin D1 expression, thus affecting a link between hypoxia pathways and cell cycle control." This is nice work and a fine example of the approaches needed to develop a clear understanding of polymorphism and disease risk from a functional perspective.
Although the authors had several clues that the risk SNPs would (likely) affect expression of CCND1 (cyclin D1) in a manner regulated by hypoxia-induced factors - namely, that HIFs were known to regulate CCND1 but from an unknown binding site and that CCND1 is an established oncogene, among others - they accumulated much new data to nail down the role of EPAS1 (HIF-2) in regulating CCND1 expression.
One nice aspect of this work is the authors' taking advantage of signals seen in a renal carcinoma cell line and not in a breast cancer cell line (serving then as control). For example, they looked at the epigenetic enhancer marks at the 11q13.3 susceptibility locus with FAIRE (ormaldehyde-assisted isolation of regulatory elements to identify regions of nucleosome occupancy), and EPAS1 binding as assessed by ChIP-qPCR. The use of pVHL-defective RCC cell lines verified the role of VHL (von Hippel–Lindau tumor suppressor) in this cancer and consequence of allele-specific expression of CCND1.
Taken together, the data presented show that the haplotype associating with reduced renal cell cancer risk hinders EPAS1 binding, "resulting in an allelic imbalance in cyclin D1 expression, thus affecting a link between hypoxia pathways and cell cycle control." This is nice work and a fine example of the approaches needed to develop a clear understanding of polymorphism and disease risk from a functional perspective.
Friday, April 27, 2012
POTW: Bitter taste perception - a follow-up
Back in December, I posted an item on taste receptors expressed in the gut with mention of possible roles in sensing the microbiome. This week's Paper of the Week is entitled "Evolution of functionally diverse alleles associated with PTC bitter taste sensitivity in Africa" by the Tishkoff group and heightens those earlier, intriguing possibilities.
The publication dissects the long evolutionary history of the TAS2R38 gene encoding a bitter taste receptor. From RefSeq, we know that TAS2R38 encodes a seven-transmembrane G protein-coupled receptor that controls the ability to taste glucosinolates, a family of bitter-tasting compounds found in plants of the Brassica sp. Interestingly, TAS2R38 allows detection of bitter thiourea compounds, including 6-n-propylthiouracil (PROP) and phenylthiocarbamide (PTC). Humans who cannot taste these compounds tend to be poor at discriminating fat in foods, even though they prefer higher fat versions of these foods (Keller, KL 2012 J Food Science 77:S143). This would lead one to suppose, naturally, that the development of certain haplotypes of tasters and nontasters would arise as adaptation to the local diet. Tishkoff, et al show that is not likely to be the case.
First, the authors propose that the evolution of the three nonsynonymous mutations, which comprise the commonly observed haplotypes, likely represent an alternate path for building a diverse set of receptors in humans, which can then participate in various biological processes. They go on to suggest that a complex selection model, involving "ancient balancing and recent diversifying selection," has allowed both common and rare nonsynonymous variation, respectively, to persist in the coding exon of TAS2R38 in Africa. Importantly, different types of selection may have acted upon the noncoding regions compared to the TAS2R38 coding exon in all populations.
Second, diet is not the driver of haplotype frequencies. The authors propose that the three common haplotypes observed may appear at high frequencies due to selective pressures distinct from diet. Recent reports have demonstrated that bitter taste receptors are expressed in many cell types in the human gastrointestinal tract and lungs (second reference). Here this expression can affect glucose and insulin levels (Dotson et al. 2008), eliminate harmful inhaled substances, and promote relaxation of airways for better breathing. Thus, bitter taste loci, including TAS2R38, posses various functions and, as the authors write "raise[s] the possibility that common variants at TAS2R38 may be under selection due to their physiological roles in human health beyond oral gustatory function." The authors were not able to distinguish which selective forces - taste, gut microbiome organisms or biochemical production, or inhalants - are acting upon the TAS2R38 haplotypes.
Third, the genetic analysis and evolutionary history of TAS2R38 suggest that, in contrast to a common variant-common disease hypothesis, sensitivity to PTC bitter taste indicates that both rare and common variants together are able to significantly affect normal variation of phenotypes. This, of course, has implications, as genome-wide association studies tackle a wider range of phenotypes in a more diverse set of populations, and as genome sequencing (whole and exome) seek to identify and associate rare variants with disease risk and occurrence.
The publication dissects the long evolutionary history of the TAS2R38 gene encoding a bitter taste receptor. From RefSeq, we know that TAS2R38 encodes a seven-transmembrane G protein-coupled receptor that controls the ability to taste glucosinolates, a family of bitter-tasting compounds found in plants of the Brassica sp. Interestingly, TAS2R38 allows detection of bitter thiourea compounds, including 6-n-propylthiouracil (PROP) and phenylthiocarbamide (PTC). Humans who cannot taste these compounds tend to be poor at discriminating fat in foods, even though they prefer higher fat versions of these foods (Keller, KL 2012 J Food Science 77:S143). This would lead one to suppose, naturally, that the development of certain haplotypes of tasters and nontasters would arise as adaptation to the local diet. Tishkoff, et al show that is not likely to be the case.
First, the authors propose that the evolution of the three nonsynonymous mutations, which comprise the commonly observed haplotypes, likely represent an alternate path for building a diverse set of receptors in humans, which can then participate in various biological processes. They go on to suggest that a complex selection model, involving "ancient balancing and recent diversifying selection," has allowed both common and rare nonsynonymous variation, respectively, to persist in the coding exon of TAS2R38 in Africa. Importantly, different types of selection may have acted upon the noncoding regions compared to the TAS2R38 coding exon in all populations.
Second, diet is not the driver of haplotype frequencies. The authors propose that the three common haplotypes observed may appear at high frequencies due to selective pressures distinct from diet. Recent reports have demonstrated that bitter taste receptors are expressed in many cell types in the human gastrointestinal tract and lungs (second reference). Here this expression can affect glucose and insulin levels (Dotson et al. 2008), eliminate harmful inhaled substances, and promote relaxation of airways for better breathing. Thus, bitter taste loci, including TAS2R38, posses various functions and, as the authors write "raise[s] the possibility that common variants at TAS2R38 may be under selection due to their physiological roles in human health beyond oral gustatory function." The authors were not able to distinguish which selective forces - taste, gut microbiome organisms or biochemical production, or inhalants - are acting upon the TAS2R38 haplotypes.
Third, the genetic analysis and evolutionary history of TAS2R38 suggest that, in contrast to a common variant-common disease hypothesis, sensitivity to PTC bitter taste indicates that both rare and common variants together are able to significantly affect normal variation of phenotypes. This, of course, has implications, as genome-wide association studies tackle a wider range of phenotypes in a more diverse set of populations, and as genome sequencing (whole and exome) seek to identify and associate rare variants with disease risk and occurrence.
Friday, March 16, 2012
POTW: Evolutionary constraints and the discovery of disease markers
My selection for Paper of the Week for 16 March 2012 is by Joel Dudley, et al. and published as a letter in Molecular Biology & Evolution. Its title is "Evolutionary meta-analysis of association studies reveals ancient constraints affecting disease marker discovery."
The authors examined over 5800 disease-associating variants, comparing the genomic neighborhood across a panel of species. This covered 230 different disease and disease risk phenotypes. Importantly, the authors demonstrate that there is a propensity to discover such disease SNPs at "conserved genomic positions, because the effect size (odds ratio) and allelic P-value of genetic association of a SNP relates strongly to the evolutionary conservation of their genomic position." This then allowed them to develop a new means to rank such association SNPs in which a conservation score, based on the evolutionary analysis, is incorporated into the P-value of the genotype-phenotype association.
As many GWAS SNPs alter gene expression - either through altered transcription factor binding or microRNA-mRNA interaction, and as such evolutionary mechanisms most likely involve a sensing or monitoring of the environment with concomitant changes in gene expression, this makes sense. In fact, the role of such types of SNPs (those under selective pressure) and their role in heart disease, was a topic on which we published in 2010.
The article by Dudley, et al. is really nice work and one whose insight we will use to inform our GWAS analysis.
The authors examined over 5800 disease-associating variants, comparing the genomic neighborhood across a panel of species. This covered 230 different disease and disease risk phenotypes. Importantly, the authors demonstrate that there is a propensity to discover such disease SNPs at "conserved genomic positions, because the effect size (odds ratio) and allelic P-value of genetic association of a SNP relates strongly to the evolutionary conservation of their genomic position." This then allowed them to develop a new means to rank such association SNPs in which a conservation score, based on the evolutionary analysis, is incorporated into the P-value of the genotype-phenotype association.
As many GWAS SNPs alter gene expression - either through altered transcription factor binding or microRNA-mRNA interaction, and as such evolutionary mechanisms most likely involve a sensing or monitoring of the environment with concomitant changes in gene expression, this makes sense. In fact, the role of such types of SNPs (those under selective pressure) and their role in heart disease, was a topic on which we published in 2010.
The article by Dudley, et al. is really nice work and one whose insight we will use to inform our GWAS analysis.
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.
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.
Friday, March 2, 2012
POTW: Epigenetics and cognitive function
This weeks Paper of the Week adds some detail to connections between cognitive function and epigenetics as histone modifications. The paper is "An epigenetic blockade of cognitive functions in the neurodegenerating brain" by Gräff, et al. The paper was published in Nature on 29 Feb 2012.
What makes this a noteworthy paper, in my opinion, is the link between Alzheimer disease and lifestyle choices. The lifestyle choices of smoking, diet and physical activity (and likely others) have the ability to affect epigenetic patterns of either DNA methylation or histone acetylation. The authors demonstrate that cognitive abilities in a brain with developing neurodegeneration are held in check by an epigenetic-based restriction of gene transcription, and this is potentially reversible. This repression of mRNA synthesis is mediated by histone deacetylase 2 (or HDAC2). Furthermore, this repression is increased by Alzheimer’s-disease-related neurotoxic insults in vitro, in two mouse models of neurodegeneration and in patients with Alzheimer’s disease.
Imagine if something in the diet or something like exercise could reduce or repress the built-up activity of HDAC2 that occurs as a result of the neurotoxic insults described in the paper. That would be exciting. Thus, I see this work as important in showing, again, how environment and epigenetics can affect disease state. It is certainly likely that certain lifestyle choices would have greater or lesser impact on neurodegenerative processes and either augment or enhance the genetic risk of disease. Although not demonstrated in this article, it could be that an APOE epsilon 4 (E4) genotype, with its increased risk of Alzheimer disease could be partially ameliorated via those lifestyle choices that inhibit or curtail excessive HDAC2 activity. That woud indeed be quite exciting.
What makes this a noteworthy paper, in my opinion, is the link between Alzheimer disease and lifestyle choices. The lifestyle choices of smoking, diet and physical activity (and likely others) have the ability to affect epigenetic patterns of either DNA methylation or histone acetylation. The authors demonstrate that cognitive abilities in a brain with developing neurodegeneration are held in check by an epigenetic-based restriction of gene transcription, and this is potentially reversible. This repression of mRNA synthesis is mediated by histone deacetylase 2 (or HDAC2). Furthermore, this repression is increased by Alzheimer’s-disease-related neurotoxic insults in vitro, in two mouse models of neurodegeneration and in patients with Alzheimer’s disease.
Imagine if something in the diet or something like exercise could reduce or repress the built-up activity of HDAC2 that occurs as a result of the neurotoxic insults described in the paper. That would be exciting. Thus, I see this work as important in showing, again, how environment and epigenetics can affect disease state. It is certainly likely that certain lifestyle choices would have greater or lesser impact on neurodegenerative processes and either augment or enhance the genetic risk of disease. Although not demonstrated in this article, it could be that an APOE epsilon 4 (E4) genotype, with its increased risk of Alzheimer disease could be partially ameliorated via those lifestyle choices that inhibit or curtail excessive HDAC2 activity. That woud indeed be quite exciting.
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.
Subscribe to:
Posts (Atom)