Nearly three weeks have passed since my last entry. A big reason for that is the number of exciting papers and pertinent reviews recently published. In this regard, joining Twitter and being connected to the right people has been nothing short of amazing. Yes, I have learned much over the past three weeks, but have also been swimming in a stream of data, incorporating pathways, gene expression and proteomics data lists from supplemental files into my human genome database.
And all of that brings to mind what some elder statesmen in the nutrigenomics field have said - that one needs an army of data collectors and miners. This would be at the expense of some significant portion of the wet-lab work. Well, I agree. There are a lot of experiments that have already been done or two or three that when combined can give an insightful view into, say type 2 diabetes or obesity. This is essentially what Tiffin, Hyde et al. did a few years back. But with so much data, so many ideas to follow, too few partners (zero!) - swimming can easily become drowning. But it is really fun because our lab is looking into things and taking a view not often pursued.
Next I should write something about evolution of culture and chicken breeds.
Tuesday, March 23, 2010
Thursday, March 4, 2010
Drug side effects and genetic and lifestyle factors
I just found a neat database (SIDER) from Peer Bork's group at EMBL: http://sideeffects.embl.de. This is a database one can query for drug indications and side effects. Using the data here, I find that there are 301 drugs with side effect or indication to alter body weight. Of those, 126 (41%) show both weight gain and decrease. This makes one wonder about genetic and environmental effects of those in the drug development trials. Therefore, there is a need to control things beforehand by partitioning based on genetics, if not also based on some lifestyle parameters.
The group's publication describing SIDER can be accessed here. The citation is Kuhn, et al. (2010) Molecular Systems Biology 6:343.
The group's publication describing SIDER can be accessed here. The citation is Kuhn, et al. (2010) Molecular Systems Biology 6:343.
Wednesday, February 3, 2010
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!
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!
Saturday, January 30, 2010
Homeostasis
It's been a while since the last post - business travel and then the inevitable catching up.
I have been thinking about homeostasis lately and why GWAS results don't describe all that much regarding the total amount of variability. Perhaps it takes many hits ("disease" or "risk" alleles) in the same operational unit (e.g., a pathway) to see disease or biomarker thereof as a measurable phenotype. And so I wonder if the organism has a much stronger drive to maintain homeostasis than we realize. In other words, the body can absorb many small defects to a given pathway so long as the environmental conditions do not go awry such as might happen with years of poor nutrition. The body can even operate well within a certain range and that brings to mind a term I heard the other day - homeodynamics. It is true. Running a marathon won't make someone collapse, neither will a hot day and neither will a low intake of fluids. The combination, however, will greatly increase the risk of that taking place.
So, with respect to my research and examining GWAS data, I am more convinced than ever that we must look at an enrichment of pathways hit by the association data. I'm excited to give that a try.
I have been thinking about homeostasis lately and why GWAS results don't describe all that much regarding the total amount of variability. Perhaps it takes many hits ("disease" or "risk" alleles) in the same operational unit (e.g., a pathway) to see disease or biomarker thereof as a measurable phenotype. And so I wonder if the organism has a much stronger drive to maintain homeostasis than we realize. In other words, the body can absorb many small defects to a given pathway so long as the environmental conditions do not go awry such as might happen with years of poor nutrition. The body can even operate well within a certain range and that brings to mind a term I heard the other day - homeodynamics. It is true. Running a marathon won't make someone collapse, neither will a hot day and neither will a low intake of fluids. The combination, however, will greatly increase the risk of that taking place.
So, with respect to my research and examining GWAS data, I am more convinced than ever that we must look at an enrichment of pathways hit by the association data. I'm excited to give that a try.
Tuesday, January 19, 2010
Migraine and cardiovascular disease risk
A few recent papers report associations between migraine and cardiovascular disease (CVD) risk. This is particularly true for migraine with aura. Another study shows that this link is found in women. What is most exciting is the recent publication by Markus Schürks and colleagues reporting findings from a candidate gene study on the genetic basis of migraine. The data imply that polymorphisms of genes encoding constituents of inflammation pathways and migraine in women are associated. These genes include TNF, CCR2, TGFB1, NOS3, and IL9. We have looked at a few of these genes with respect to dyslipidemia, obesity and metabolic syndrome.
While it would be nice to look for direct connections between biomarkers of CVD and migraine, the phenotype collection of two of the largest and most useful populations with which we work do not contain information from the subjects on migraine or pain in general. These populations are GOLDN and BPRHS. This is where the proposed Nutrition Phenotype database project could come in handy. This group is calling for more extensive phenotyping of subjects. The project is in its infancy but an introductory paper has been accepted at Genes and Nutrition. The paper is titled "The Nutritional Phenotype database to store, share and evaluate nutritional systems biology studies" and was authored by B. van Ommen, J. Bouwman, L. Dragsted, C. A. Drevon, R. Elliott, P. de Groot, J. Kaput, J. C. Mathers, M. Müller, F. Pepping, J. Saito, A. Scalbert, M. Radonjic, P. Rocca-Serra, T. Travis , S. Wopereis and C. Evelo.
While it would be nice to look for direct connections between biomarkers of CVD and migraine, the phenotype collection of two of the largest and most useful populations with which we work do not contain information from the subjects on migraine or pain in general. These populations are GOLDN and BPRHS. This is where the proposed Nutrition Phenotype database project could come in handy. This group is calling for more extensive phenotyping of subjects. The project is in its infancy but an introductory paper has been accepted at Genes and Nutrition. The paper is titled "The Nutritional Phenotype database to store, share and evaluate nutritional systems biology studies" and was authored by B. van Ommen, J. Bouwman, L. Dragsted, C. A. Drevon, R. Elliott, P. de Groot, J. Kaput, J. C. Mathers, M. Müller, F. Pepping, J. Saito, A. Scalbert, M. Radonjic, P. Rocca-Serra, T. Travis , S. Wopereis and C. Evelo.
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