Although it is April 1st here, there is some serious business taking place on my desktop: Cleanup day. I'm reading through an electronic pile of papers and news items that have gathered over the last weeks.
Here's an interesting bit about human gene CDKN2A. This gene encodes cyclin-dependent kinase inhibitor 2A and is also known as p16INK4a. Suppression of CDKN2A by glucose restriction in human cells (fetal lung fibroblasts) was shown by Li & Tollefsbol to contribute to lifespan extension via epigenetic and genetic mechanisms that were mediated by SIRT1.
A year ago, we published a paper showing the effects on gene expression in mononuclear cells in metabolic syndrome subjects after intake of phenol-rich virgin olive oil. We noted repressed expression of several pro-inflammatory genes. Interestingly, CDKN2A was also significantly repressed. Thus, two dietary conditions - low glucose and phenol-rich olive oil - repress expression of this gene, albeit in different cell types and under different circumstances.
Friday, April 1, 2011
Monday, March 28, 2011
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)
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)
Thursday, March 10, 2011
Genetics of coronary heart disease
Note: This is a guest-post, authored by geneticist and molecular biologist Dr. Chao-Qiang Lai; with edits added by LP.
Last week, Nature Genetics published three letters reporting results from genome-wide association studies (GWAS) for coronary heart disease (CAD). The studies reported a number of markers that reached the threshold of statistical significance for association to CAD with concomitant association to traditional biomarkers of disease risk, such as elevated LDL-cholesterol (LDL-C), elevated total cholesterol, decreased HDL-cholesterol (HDL-C), hypertension, obesity (as measured by elevated body mass index), or type 2 diabetes. However, the two larger and more highly powered GWAS (C4D Genetics Consortium, Schunkert, et al.) also identified CAD-associated variants that are not associated with traditional biomarkers. The third study is of interest because it examines CAD in Chinese populations, but beginning with a discovery set of 130 cases and 130 controls leaves it a bit under-powered. They report a unique association between a SNP in C6orf105 and CAD, which is not found in European or south Asian populations. Curiously, this gene has also been implicated in non-syndromic oral cleft.
There are many sources of CAD. Blood lipids are most commonly thought of as the prime source, but blood pressure in the form of hypertension is also a source. Traditional biomarkers such as LDL-C, HDL-C, triglycerides, and hypertension have been used almost as the sole surrogates for measuring the devolvement and progression of CAD over the course of some 50 years. Meta-analyses of GWAS based on over 100,000 subjects (22,233 cases and 64,762 controls from 14 GWAS) thus far have identified 23 genetic variants associating with CAD. The eye-opening aspect to this is these variants account for about 10% of CAD cases with the shocking observation that 17 of 23 confirmed loci appear to have no association with traditional markers. This observation then suggests two possible explanations.
One possibility is when we assume that the remainder of the CAD cases (90%) contribute to risk associated with traditional markers, such genetic factors cannot be detected based on current GWAS methodology. This is likely to be true because of to the effect sizes of these variants are too small, or their effects are camouflaged by gene-gene (GxG) and gene-environment (GxE) interactions or by epigenetic mechanisms.
This second possibility rests on the fundamental premise that all markers associating with CAD have more or less equal chance to be detected. It then follows that a majority of genetic factors that contribute to CAD has nothing to do with traditional markers. If this is indeed the case, it opens a new avenue to identify the new mechanism(s) and new biomarkers that lead to CAD. In fact, this possibility is supported by many observations. For example, 50% of those individuals who have CAD have low LDL-C (Braunwald & Shattuck; Ridker).
These genes – for example, ADAMTS7, PDGFD, ABO and PPAP2B – point to new mechanisms. While GxG, GxE and epigenetic interactions remain as viable contributors to CAD risk, the path to better understanding of the other component(s) to CAD risk will likely transit through metabolic profiling to identify the compounds that distinguish elevated from nominal risk. Furthermore, research will need to be conducted in model organisms based on these newly discovered genes, perhaps in pig as this is a good model for heart function and disease in human.
Last week, Nature Genetics published three letters reporting results from genome-wide association studies (GWAS) for coronary heart disease (CAD). The studies reported a number of markers that reached the threshold of statistical significance for association to CAD with concomitant association to traditional biomarkers of disease risk, such as elevated LDL-cholesterol (LDL-C), elevated total cholesterol, decreased HDL-cholesterol (HDL-C), hypertension, obesity (as measured by elevated body mass index), or type 2 diabetes. However, the two larger and more highly powered GWAS (C4D Genetics Consortium, Schunkert, et al.) also identified CAD-associated variants that are not associated with traditional biomarkers. The third study is of interest because it examines CAD in Chinese populations, but beginning with a discovery set of 130 cases and 130 controls leaves it a bit under-powered. They report a unique association between a SNP in C6orf105 and CAD, which is not found in European or south Asian populations. Curiously, this gene has also been implicated in non-syndromic oral cleft.
There are many sources of CAD. Blood lipids are most commonly thought of as the prime source, but blood pressure in the form of hypertension is also a source. Traditional biomarkers such as LDL-C, HDL-C, triglycerides, and hypertension have been used almost as the sole surrogates for measuring the devolvement and progression of CAD over the course of some 50 years. Meta-analyses of GWAS based on over 100,000 subjects (22,233 cases and 64,762 controls from 14 GWAS) thus far have identified 23 genetic variants associating with CAD. The eye-opening aspect to this is these variants account for about 10% of CAD cases with the shocking observation that 17 of 23 confirmed loci appear to have no association with traditional markers. This observation then suggests two possible explanations.
One possibility is when we assume that the remainder of the CAD cases (90%) contribute to risk associated with traditional markers, such genetic factors cannot be detected based on current GWAS methodology. This is likely to be true because of to the effect sizes of these variants are too small, or their effects are camouflaged by gene-gene (GxG) and gene-environment (GxE) interactions or by epigenetic mechanisms.
This second possibility rests on the fundamental premise that all markers associating with CAD have more or less equal chance to be detected. It then follows that a majority of genetic factors that contribute to CAD has nothing to do with traditional markers. If this is indeed the case, it opens a new avenue to identify the new mechanism(s) and new biomarkers that lead to CAD. In fact, this possibility is supported by many observations. For example, 50% of those individuals who have CAD have low LDL-C (Braunwald & Shattuck; Ridker).
These genes – for example, ADAMTS7, PDGFD, ABO and PPAP2B – point to new mechanisms. While GxG, GxE and epigenetic interactions remain as viable contributors to CAD risk, the path to better understanding of the other component(s) to CAD risk will likely transit through metabolic profiling to identify the compounds that distinguish elevated from nominal risk. Furthermore, research will need to be conducted in model organisms based on these newly discovered genes, perhaps in pig as this is a good model for heart function and disease in human.
Friday, February 18, 2011
10 years with the human genome
This week marks the 10-year anniversary of the publications of a (nearly) completed human genome sequence. Much has been made already of this passage of time, as well as what we can look forward to in the next ten years.
What I thought I would do in this space is share a little personal story on my connection with this achievement. In late spring of 2001, I happened to search the Internet and PubMed for my name because I wanted to check to see if any presentations at conference or publications from previous laboratories in which I had worked had been released. To my surprise, I found a website in Japan with the title of something like "list of authors" which contained a collection of names of former colleagues from my days in the Genome Sequencing Center at Cold Spring Harbor Laboratory. That seemed strange and so investigating a bit I learned that we were included on the Nature paper describing the human genome - along with some 5000+ other authors (hence the special listing on this website, and no hits in PubMed). Well, needless to say but that was quite a thrill. I quickly updated my CV to include this landmark publication.
Back in 1997 to 1999, as the publicly funded project to sequence the human genome was ramping up and dollars were dangled in front of genome centers around the USA and the globe, we at CSHL were trying to deposit as much finished sequence into GenBank as possible. Monthly and quarterly totals of base pairs deposited were key to securing grant money. An introduction to all this came within my first two weeks as the Computational Fellow (post-doc) with Dick McCombie when I was told I would be leading the analysis segment of his Genome Sequencing course. I learned the ins and outs of a new computer system and new software tools (I came from a cell biology lab) just in time to teach the students. We worked hard during that 2-week course to sequence a 143-kbp BAC clone containing some critical HIV/AIDS-relevant genes: CCR2, CCR5 and CCR6. You can view the sequence entry I deposited to GenBank here, accession U95626.
From this initial BAC, we worked on many more to try to show that we could put high-quality sequence data together and to get as much sequence finished as possible. Of course, our main funding was to contribute to the Arabidopsis thaliana genome and so the human projects (BACs and cosmid/fosmid clones) took second priority. But we did contribute enough sequence to warrant inclusion on the paper and Dick was kind enough to remember everyone who had passed through his lab during those years.
What I thought I would do in this space is share a little personal story on my connection with this achievement. In late spring of 2001, I happened to search the Internet and PubMed for my name because I wanted to check to see if any presentations at conference or publications from previous laboratories in which I had worked had been released. To my surprise, I found a website in Japan with the title of something like "list of authors" which contained a collection of names of former colleagues from my days in the Genome Sequencing Center at Cold Spring Harbor Laboratory. That seemed strange and so investigating a bit I learned that we were included on the Nature paper describing the human genome - along with some 5000+ other authors (hence the special listing on this website, and no hits in PubMed). Well, needless to say but that was quite a thrill. I quickly updated my CV to include this landmark publication.
Back in 1997 to 1999, as the publicly funded project to sequence the human genome was ramping up and dollars were dangled in front of genome centers around the USA and the globe, we at CSHL were trying to deposit as much finished sequence into GenBank as possible. Monthly and quarterly totals of base pairs deposited were key to securing grant money. An introduction to all this came within my first two weeks as the Computational Fellow (post-doc) with Dick McCombie when I was told I would be leading the analysis segment of his Genome Sequencing course. I learned the ins and outs of a new computer system and new software tools (I came from a cell biology lab) just in time to teach the students. We worked hard during that 2-week course to sequence a 143-kbp BAC clone containing some critical HIV/AIDS-relevant genes: CCR2, CCR5 and CCR6. You can view the sequence entry I deposited to GenBank here, accession U95626.
From this initial BAC, we worked on many more to try to show that we could put high-quality sequence data together and to get as much sequence finished as possible. Of course, our main funding was to contribute to the Arabidopsis thaliana genome and so the human projects (BACs and cosmid/fosmid clones) took second priority. But we did contribute enough sequence to warrant inclusion on the paper and Dick was kind enough to remember everyone who had passed through his lab during those years.
Wednesday, February 16, 2011
PCSK, cholesterol homeostasis and osteoporosis
Today, I saw a news release on a series of articles concerning the PCSK gene family published by Dr. Nabil Seidah's group at the Institut de recherches cliniques de Montréal. The combined body of work suggests that the PCSK enzymes could influence health from cholesterol homeostasis to osteoporosis.
PCSK stands for proprotein convertase subtilisin/kexin. This means that it enzymatically converts a larger proprotein into a smaller functional entity. PCSK9 is certainly the most well publicized member of this family with much known about genetic variants associating with myocardial infarction, heart disease and plasma lipid levels, particularly LDL-cholesterol. PCSK9 interacts with the LDL-cholesterol receptor.
PCSK9 also shows decreased expression in a circadian rhythmic fashion in mouse liver depleted for Mir122. This comes from a report by Gatfield, Schibler, et al. 2009 Genes Dev. 23:1313-26.
Here are some other interesting bits about members of the PCSK gene family.
PCSK2 - homolog of nematode gene C51E3.7 which is involved in determination of adult lifespan. SNPs in PCSK2 may increase susceptibility to myocardial infarction and type 2 diabetes, which are both age-related afflictions. A QTL for HDL has been mapped to the vicinity of Pcsk2 in mouse: Hdlq19.
Interestingly, some of my own work on literature mining with Biomax BioLT tool indicated that both PCSK7 and PCSK1N have relationships with HDL-cholesterol. A QTL for HDL at the PCSK7 locus has been described.
Heterozygous knock-out mice for Pcsk1 show increased adipose mass. Transgenic expression in mice of Pcsk1n driven by an actin promoter yielded adult-onset obesity. This gene, in human, was recently proposed as a candidate obesity/type 2 diabetes (T2DM) genes by Chang Hsu (2011 Diabetes, in press) but did not pass their test for Fst measures of positive selection.
An interesting paper by Tiffin, Hide, et al. (2006) suggested that PCSK2 and PCSK7 are candidate obesity and T2DM genes.
Certainly interesting phenotypes here. Keep your eyes on these genes.
PCSK stands for proprotein convertase subtilisin/kexin. This means that it enzymatically converts a larger proprotein into a smaller functional entity. PCSK9 is certainly the most well publicized member of this family with much known about genetic variants associating with myocardial infarction, heart disease and plasma lipid levels, particularly LDL-cholesterol. PCSK9 interacts with the LDL-cholesterol receptor.
PCSK9 also shows decreased expression in a circadian rhythmic fashion in mouse liver depleted for Mir122. This comes from a report by Gatfield, Schibler, et al. 2009 Genes Dev. 23:1313-26.
Here are some other interesting bits about members of the PCSK gene family.
PCSK2 - homolog of nematode gene C51E3.7 which is involved in determination of adult lifespan. SNPs in PCSK2 may increase susceptibility to myocardial infarction and type 2 diabetes, which are both age-related afflictions. A QTL for HDL has been mapped to the vicinity of Pcsk2 in mouse: Hdlq19.
Interestingly, some of my own work on literature mining with Biomax BioLT tool indicated that both PCSK7 and PCSK1N have relationships with HDL-cholesterol. A QTL for HDL at the PCSK7 locus has been described.
Heterozygous knock-out mice for Pcsk1 show increased adipose mass. Transgenic expression in mice of Pcsk1n driven by an actin promoter yielded adult-onset obesity. This gene, in human, was recently proposed as a candidate obesity/type 2 diabetes (T2DM) genes by Chang Hsu (2011 Diabetes, in press) but did not pass their test for Fst measures of positive selection.
An interesting paper by Tiffin, Hide, et al. (2006) suggested that PCSK2 and PCSK7 are candidate obesity and T2DM genes.
Certainly interesting phenotypes here. Keep your eyes on these genes.
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