tag:blogger.com,1999:blog-82849458863079966072024-02-07T20:20:55.009-08:00Variable GenomeLarry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.comBlogger72125tag:blogger.com,1999:blog-8284945886307996607.post-25891955319313800752020-07-23T06:46:00.001-07:002020-07-23T06:46:32.539-07:00What's on your plate - nutrition, dark matter and healthGraham Lawton at <a href="https://www.newscientist.com/">New Scientist</a> magazine has written a nice <a href="https://www.newscientist.com/article/mg24732920-700-hidden-nutrition-we-dont-know-what-makes-up-99-per-cent-of-our-food/">feature</a> on the hidden elements of human nutrition. There is so much that we do not know about the effects of thousands of molecular compounds present in the foods we eat. He explores that topic fairly well in his article.<br />
<br />
That article mentions some software - <a href="https://github.com/seanharr11/phytebyte">PhyteByte</a> - colleagues and I designed and <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03582-7">published</a> in BMC Bioinformatics. The software is designed to identify food compounds with potential to have pharmacological properties. I'll share details on how all that came to be in another post. As that article was wending its way through the finals stages of editorial review, I was asked to contribute a blog to accompany the PhyteByte article. You can find what I wrote on <i>the “dark matter” of nutrition – just what are you eating</i> <a href="http://blogs.biomedcentral.com/bmcseriesblog/2020/06/16/the-dark-matter-of-nutrition-just-what-are-you-eating/">here</a>.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com1tag:blogger.com,1999:blog-8284945886307996607.post-81023927672152302932020-07-21T09:06:00.002-07:002020-07-21T09:26:04.477-07:00AMY1 copy number variation<div 16px="" font-family:="" font-size:="" font-stretch:="" line-height:="" normal="" times="">
<span style="font-kerning: none;">Amylase comes in two forms - salivary and pancreatic - encoded by the <i>AMY1</i> and <i>AMY2</i> gene clusters, respectively. These genes - <a href="https://www.ncbi.nlm.nih.gov/gene/280"><span style="-webkit-font-kerning: none;"><i>AMY2B</i></span></a>, <a href="https://www.ncbi.nlm.nih.gov/gene/279"><span style="-webkit-font-kerning: none;"><i>AMY2A</i></span></a>, <a href="https://www.ncbi.nlm.nih.gov/gene/276"><span style="-webkit-font-kerning: none;"><i>AMY1A</i></span></a>, <a href="https://www.ncbi.nlm.nih.gov/gene/277"><span style="-webkit-font-kerning: none;"><i>AMY1B</i></span></a>, <a href="https://www.ncbi.nlm.nih.gov/gene/278"><span style="-webkit-font-kerning: none;"><i>AMY1C</i></span></a> - map to human chromosome 1p21.1 and form a tight five-gene group of ~206 kbp. The genes, especially the <i>AMY1</i> cluster, have garnered added interest because of the findings that they show extensive copy number variation. In some studies, copy number variation (CNV) has been determined to be influenced at least in part by selection, or adaptation to available food sources.</span></div>
<div 16px="" 19px="" font-family:="" font-size:="" font-stretch:="" line-height:="" min-height:="" normal="" times="">
<span style="font-kerning: none;"></span><br /></div>
<div 16px="" font-family:="" font-size:="" font-stretch:="" line-height:="" normal="" times="">
<span style="font-kerning: none;">The first step in the digestion of dietary starch and glycogen is cleavage of the 1,4-alpha-glucoside bond. This cleavage is catalyzed by amylase. Hence, this is an important enzyme for the extraction of energy from food.</span></div>
<div font-family: Times; font-size: 16px; font-stretch: normal; line-height: normal;">
<span style="font-kerning: none;"><br />
Because the <i>AMY1</i> gene cluster CNV mediates salivary α-amylase levels and is linked to postprandial phenotypes relevant to type 2 diabetes, we initiated a study to examine if <i>AMY1</i>-CNV is associated with age-mediated change in insulin resistance. We noted positive associations of insulin resistance with age among participants of two cohorts with low <i>AMY1</i>-copy-numbers. Type 2 diabetes was correlated with age in those with low <i>AMY1</i>-copy-numbers but not with high <i>AMY1</i>-copy-numbers.</span></div>
<div 16px="" font-family:="" font-size:="" font-stretch:="" line-height:="" normal="" times="">
<span style="font-kerning: none;"><br /></span></div>
<div 16px="" font-family:="" font-size:="" font-stretch:="" line-height:="" normal="" times="">
<span style="font-kerning: none;">This work recently was published in <i>Clinical Chemistry</i> in an article titled "<a href="https://academic.oup.com/clinchem/article-abstract/66/5/718/5825409"><span style="-webkit-font-kerning: none;">Salivary <i>AMY1</i> copy number variation modifies age-related type 2 diabetes risk</span></a>, by Liu, <i>et al</i>."</span></div>
<span style="background-color: #ffe599; font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="background-color: white; caret-color: rgb(33, 33, 33);"><br /></span></span></span></span>Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-61364701812942395572014-12-30T10:28:00.000-08:002014-12-30T10:28:43.582-08:00FTO, cohort of birth and body mass index<span style="font-size: small;"><span style="font-family: inherit;">In a recently accepted article in <a href="http://www.pnas.org/" target="_blank"><i>PNAS</i></a>, entitled "</span></span><span style="font-size: small;"><span style="font-family: inherit;"><span style="font-weight: normal;"><a href="http://www.pnas.org/content/early/2014/12/25/1411893111.abstract" target="_blank">Cohort of birth modifies the association between <i>FTO</i> genotype and BMI</a>," the association of <a href="http://www.ncbi.nlm.nih.gov/gene/79068" target="_blank"><i>FTO</i> </a>variant rs993609 with body mass index is described as having essentially zero influ<span style="font-family: inherit;">ence for study participants born before 1942 and increasing in<span style="font-family: inherit;">fluence on this obesity phenotype <span style="font-family: inherit;">as participants were bor<span style="font-family: inherit;">n in increasing<span style="font-family: inherit;">ly</span> more recent<span style="font-family: inherit;"> years. <span style="font-family: inherit;">T</span>hat l<span style="font-family: inherit;">ong-range enhancers within the <i>FTO</i> region recapitulate aspects of <a href="http://www.ncbi.nlm.nih.gov/gene/79191" target="_blank"><i>IRX3</i></a> expression implies that the <a href="http://www.ncbi.nlm.nih.gov/pubmed/24646999" target="_blank">obesity-associated interval serves to regulate <i>IRX3</i></a>. Consistent with this, obesity-associated SNPs are associated with expression of <i>IRX3</i>, but not <i>FTO</i>, in human brains. Nonetheless, th<span style="font-family: inherit;">is is an important obesity locus, be <span style="font-family: inherit;">it FTO or IRX3 as the functional unit</span>.</span></span> </span></span></span></span></span></span></span></span><br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-weight: normal;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"> </span></span></span></span></span></span></span></span><br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-weight: normal;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;">The authors r<span style="font-family: inherit;">ightly suggest th<span style="font-family: inherit;">at gene-environment interactions (GxEs) coupled with changes to the environment <span style="font-family: inherit;">of the participants could alter the <i>FTO</i>-BMI association. </span></span></span></span></span></span></span></span></span></span></span><br />
<br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-weight: normal;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><i>FTO</i> is subject to exercise-induced change<span style="font-family: inherit;">s in DNA methylation. S<span style="font-family: inherit;">ee, for example, table 5 (and reference 3) of </span></span><a href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003572" target="_blank">Rönn, Volkov, et al</a>. We have <a href="http://www.biodatamining.org/content/7/1/21" target="_blank">cataloged </a>a large number of genetic variants that show the type of GxEs suggested by the re<span style="font-family: inherit;">cent PNAS artic<span style="font-family: inherit;">le. <span style="font-family: inherit;">That catalog shows that some nine different studies observed modulating effects of physical activity on the <i>FTO</i>-BMI association. (In most populations of European ancest<span style="font-family: inherit;">ry, for example, in which many of these studies were conducted, the variants analy<span style="font-family: inherit;">zed are in relatively strong to very strong linkage d<span style="font-family: inherit;">isequilibrium.) Other lifestyle choices also modulated the effects of <i>FTO </i>variants, including macronutrient intakes of carbohydrate, and fatty acids such as saturated fat, MUFA (mono-unsaturated fatty acid) and PUFAs (poly-unsaturated fatty acids). Whe<span style="font-family: inherit;">ther time spent engaged in physical activ<span style="font-family: inherit;">ity shrank as the birth cohorts became more recent, or diet changed, or some combination of this, is difficult to ascertain. But a list of known <i>FTO</i>-BMI GxEs would be a good place to begin such an analysis.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-weight: normal;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"><span style="font-family: inherit;"> </span></span></span></span></span></span></span></span></span></span></span> </span></span></span></span></span></span></span></span>Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com1tag:blogger.com,1999:blog-8284945886307996607.post-60152910213103379992014-12-18T09:36:00.000-08:002014-12-18T09:38:19.635-08:00CardioGxE analysis would not be so rich without the help of studentsIn late October we published a paper on a catalog of cardiometabolic gene-environment interactions pulled from over 380 publications. That paper is entitled "<a href="http://www.biodatamining.org/content/7/1/21/abstract">CardioGxE, a catalog of gene-environment interactions for cardiometabolic traits</a>" and represents, among many other aspects of my research, the benefit and satisfaction of giving first-year nutrition graduate students the opportunity to engage in research and contribute important results to a larger research effort.<br />
<br />
Lately, I have had several opportunities to guide students of the <a href="http://www.nutrition.tufts.edu/">Tufts Friedman School of Nutrition Science and Policy</a> during a practicum or directed study. I often try hard to find a project that will contribute directly to something we have ongoing that also has good potential to be published in the near future. That does not always come to be, but for our CardioGxE paper such was the case. Four of my co-authors were first-year grad students, and another three were more senior. Particularly for these four younger students, they each made unique and important contributions to the analyses we present in the paper. Our paper would not have the impact it is currently enjoying nor be as complete in showing the utility of gene-environment interactions without their work. Thank you to you all!<br />
<br />
Which brings me to my main point: Consider well the abilities that a group of students can bring to your project. Engaging them as equals, as true colleagues, could very well facilitate a project's completion and publication. And, if those students are now authors, say on their first paper, that makes it very nice all around.
Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-30262277159986659522014-09-09T01:22:00.002-07:002014-09-09T01:22:36.636-07:00NuGO Week: Mediterranean diet and the Nordic diet<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">Yesterday began the 11th NuGO Week conference on “Nutrigenomics of Food” with a debate on Mediterranean diet and the Nordic diet. The Mediterranean diet (MD) is well known if not precisely defined - little to no dairy in Spain, but feta and other cheeses in Greece. We heard of results from PREDIMED and the view from Spain. The Nordic diet (ND) is a contemporary adaptation of healthy and traditional food choices from Nordic countries.</span></div>
<div style="font-family: Helvetica; font-size: 11px; min-height: 13px;">
<span style="letter-spacing: 0.0px;"></span><br /></div>
<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">Both diets show health benefits in the respective examined populations. These benefits were described as mainly pertaining to cardiovascular disease, including glycemic measures, and to chronic inflammation.</span></div>
<div style="font-family: Helvetica; font-size: 11px; min-height: 13px;">
<span style="letter-spacing: 0.0px;"></span><br /></div>
<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">What we heard was much more compare and contrast (of data) than a debate. There was only very brief mention of conducting the same experiment for both diets (a metabolomics assessment of blood and urine from subjects taking the diet of the respective area (Spain and Norway, e.g.). But no one offered putting Spaniards on a Nordic diet and Norwegians on a Mediterranean diet.</span></div>
<div style="font-family: Helvetica; font-size: 11px; min-height: 13px;">
<span style="letter-spacing: 0.0px;"></span><br /></div>
<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">One thing I will like to see is an analysis of the response to the MD in PREDIMED based on an analysis of the genetic ancestry of the individuals. There are sufficient data to be able to classify the subjects by genetic ancestry along norther-southern European axes. Then, we can address if those persons with greater northern European ancestry show a weaker or equal beneficial response to the MD. </span></div>
<div style="font-family: Helvetica; font-size: 11px; min-height: 13px;">
<span style="letter-spacing: 0.0px;"></span><br /></div>
<br />
<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">Similarly, the ND projects would do well to engage more subjects - although I fully realize that the population of Spain is likely larger than that of all five Nordic countries combined - and incorporate genetics and other large data sets.</span></div>
<div>
<span style="letter-spacing: 0.0px;"><br /></span></div>
Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-29723913012328742092014-09-09T01:21:00.003-07:002014-09-09T01:21:38.649-07:00NuGO Week: Mediterranean diet and the Nordic diet<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">Yesterday began the 11th NuGO Week conference on “Nutrigenomics of Food” with a debate on Mediterranean diet and the Nordic diet. The Mediterranean diet (MD) is well known if not precisely defined - little to no dairy in Spain, but feta and other cheeses in Greece. We heard of results from PREDIMED and the view from Spain. The Nordic diet (ND) is a contemporary adaptation of healthy and traditional food choices from Nordic countries.</span></div>
<div style="font-family: Helvetica; font-size: 11px; min-height: 13px;">
<span style="letter-spacing: 0.0px;"></span><br /></div>
<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">Both diets show health benefits in the respective examined populations. These benefits were described as mainly pertaining to cardiovascular disease, including glycemic measures, and to chronic inflammation.</span></div>
<div style="font-family: Helvetica; font-size: 11px; min-height: 13px;">
<span style="letter-spacing: 0.0px;"></span><br /></div>
<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">What we heard was much more compare and contrast (of data) than a debate. There was only very brief mention of conducting the same experiment for both diets (a metabolomics assessment of blood and urine from subjects taking the diet of the respective area (Spain and Norway, e.g.). But no one offered putting Spaniards on a Nordic diet and Norwegians on a Mediterranean diet.</span></div>
<div style="font-family: Helvetica; font-size: 11px; min-height: 13px;">
<span style="letter-spacing: 0.0px;"></span><br /></div>
<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">One thing I will like to see is an analysis of the response to the MD in PREDIMED based on an analysis of the genetic ancestry of the individuals. There are sufficient data to be able to classify the subjects by genetic ancestry along norther-southern European axes. Then, we can address if those persons with greater northern European ancestry show a weaker or equal beneficial response to the MD. </span></div>
<div style="font-family: Helvetica; font-size: 11px; min-height: 13px;">
<span style="letter-spacing: 0.0px;"></span><br /></div>
<br />
<div style="font-family: Helvetica; font-size: 11px;">
<span style="letter-spacing: 0.0px;">Similarly, the ND projects would do well to engage more subjects - although I fully realize that the population of Spain is likely larger than that of all five Nordic countries combined - and incorporate genetics and other large data sets.</span></div>
<div>
<span style="letter-spacing: 0.0px;"><br /></span></div>
Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-26803209941944238102014-07-30T05:31:00.001-07:002014-07-30T05:32:39.556-07:00Genetic pedigree symbols and legendThe following has been making the rounds on Twitter the past few days. In order to make this useful information available to more viewers and for a longer time, I decided to post this here.<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4Gnuv2tDCzfmdd8_6qhk9-AjhyphenhyphenIvZGTtKyHOFvbJYeSmbbnpab3v1PGskzLS8KsEWiWqSahRCgEvYuHR3_AsmwxsRCY0dy84dboZyReh4WH_vJE9SUa8ycv_s5CuURhRuzd0OyoFTpT4f/s1600/pedigree_legend.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4Gnuv2tDCzfmdd8_6qhk9-AjhyphenhyphenIvZGTtKyHOFvbJYeSmbbnpab3v1PGskzLS8KsEWiWqSahRCgEvYuHR3_AsmwxsRCY0dy84dboZyReh4WH_vJE9SUa8ycv_s5CuURhRuzd0OyoFTpT4f/s1600/pedigree_legend.png" height="246" width="400" /></a></div>
<br />Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-72435084023225034352014-04-03T11:25:00.000-07:002014-04-03T11:25:02.738-07:00ARAP1 and type 2 diabetes - a circadian connection?<!--[if gte mso 9]><xml>
<w:WordDocument>
<w:View>Normal</w:View>
<w:Zoom>0</w:Zoom>
<w:TrackMoves/>
<w:TrackFormatting/>
<w:PunctuationKerning/>
<w:ValidateAgainstSchemas/>
<w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid>
<w:IgnoreMixedContent>false</w:IgnoreMixedContent>
<w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText>
<w:DoNotPromoteQF/>
<w:LidThemeOther>EN-US</w:LidThemeOther>
<w:LidThemeAsian>X-NONE</w:LidThemeAsian>
<w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript>
<w:Compatibility>
<w:BreakWrappedTables/>
<w:SnapToGridInCell/>
<w:WrapTextWithPunct/>
<w:UseAsianBreakRules/>
<w:DontGrowAutofit/>
<w:SplitPgBreakAndParaMark/>
<w:DontVertAlignCellWithSp/>
<w:DontBreakConstrainedForcedTables/>
<w:DontVertAlignInTxbx/>
<w:Word11KerningPairs/>
<w:CachedColBalance/>
</w:Compatibility>
<w:BrowserLevel>MicrosoftInternetExplorer4</w:BrowserLevel>
<m:mathPr>
<m:mathFont m:val="Cambria Math"/>
<m:brkBin m:val="before"/>
<m:brkBinSub m:val="--"/>
<m:smallFrac m:val="off"/>
<m:dispDef/>
<m:lMargin m:val="0"/>
<m:rMargin m:val="0"/>
<m:defJc m:val="centerGroup"/>
<m:wrapIndent m:val="1440"/>
<m:intLim m:val="subSup"/>
<m:naryLim m:val="undOvr"/>
</m:mathPr></w:WordDocument>
</xml><![endif]--><br />
<!--[if gte mso 9]><xml>
<w:LatentStyles DefLockedState="false" DefUnhideWhenUsed="true"
DefSemiHidden="true" DefQFormat="false" DefPriority="99"
LatentStyleCount="267">
<w:LsdException Locked="false" Priority="0" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Normal"/>
<w:LsdException Locked="false" Priority="9" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="heading 1"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 2"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 3"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 4"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 5"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 6"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 7"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 8"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 9"/>
<w:LsdException Locked="false" Priority="39" Name="toc 1"/>
<w:LsdException Locked="false" Priority="39" Name="toc 2"/>
<w:LsdException Locked="false" Priority="39" Name="toc 3"/>
<w:LsdException Locked="false" Priority="39" Name="toc 4"/>
<w:LsdException Locked="false" Priority="39" Name="toc 5"/>
<w:LsdException Locked="false" Priority="39" Name="toc 6"/>
<w:LsdException Locked="false" Priority="39" Name="toc 7"/>
<w:LsdException Locked="false" Priority="39" Name="toc 8"/>
<w:LsdException Locked="false" Priority="39" Name="toc 9"/>
<w:LsdException Locked="false" Priority="35" QFormat="true" Name="caption"/>
<w:LsdException Locked="false" Priority="10" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Title"/>
<w:LsdException Locked="false" Priority="1" Name="Default Paragraph Font"/>
<w:LsdException Locked="false" Priority="11" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Subtitle"/>
<w:LsdException Locked="false" Priority="22" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Strong"/>
<w:LsdException Locked="false" Priority="20" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Emphasis"/>
<w:LsdException Locked="false" Priority="59" SemiHidden="false"
UnhideWhenUsed="false" Name="Table Grid"/>
<w:LsdException Locked="false" UnhideWhenUsed="false" Name="Placeholder Text"/>
<w:LsdException Locked="false" Priority="1" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="No Spacing"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 1"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 1"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 1"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 1"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 1"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 1"/>
<w:LsdException Locked="false" UnhideWhenUsed="false" Name="Revision"/>
<w:LsdException Locked="false" Priority="34" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="List Paragraph"/>
<w:LsdException Locked="false" Priority="29" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Quote"/>
<w:LsdException Locked="false" Priority="30" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Intense Quote"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 1"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 1"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 1"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 1"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 1"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 1"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 1"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 1"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 2"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 2"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 2"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 2"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 2"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 2"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 2"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 2"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 2"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 2"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 2"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 2"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 2"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 2"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 3"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 3"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 3"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 3"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 3"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 3"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 3"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 3"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 3"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 3"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 3"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 3"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 3"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 3"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 4"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 4"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 4"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 4"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 4"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 4"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 4"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 4"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 4"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 4"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 4"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 4"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 4"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 4"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 5"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 5"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 5"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 5"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 5"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 5"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 5"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 5"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 5"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 5"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 5"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 5"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 5"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 5"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 6"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 6"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 6"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 6"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 6"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 6"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 6"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 6"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 6"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 6"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 6"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 6"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 6"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 6"/>
<w:LsdException Locked="false" Priority="19" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Subtle Emphasis"/>
<w:LsdException Locked="false" Priority="21" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Intense Emphasis"/>
<w:LsdException Locked="false" Priority="31" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Subtle Reference"/>
<w:LsdException Locked="false" Priority="32" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Intense Reference"/>
<w:LsdException Locked="false" Priority="33" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Book Title"/>
<w:LsdException Locked="false" Priority="37" Name="Bibliography"/>
<w:LsdException Locked="false" Priority="39" QFormat="true" Name="TOC Heading"/>
</w:LatentStyles>
</xml><![endif]--><!--[if gte mso 10]>
<style>
/* Style Definitions */
table.MsoNormalTable
{mso-style-name:"Table Normal";
mso-tstyle-rowband-size:0;
mso-tstyle-colband-size:0;
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-qformat:yes;
mso-style-parent:"";
mso-padding-alt:0in 5.4pt 0in 5.4pt;
mso-para-margin:0in;
mso-para-margin-bottom:.0001pt;
mso-pagination:widow-orphan;
font-size:10.0pt;
font-family:"Times New Roman","serif";}
</style>
<![endif]-->
<br />
<div class="MsoNormal">
A new <a href="http://www.ncbi.nlm.nih.gov/pubmed/24439111" target="_blank">report </a>in the American Journal of Human Genetics shows a variant with association to type 2 diabetes and insulin levels functions within an
allele-specific motif for islet cell transcription factors PAX4 and PAX6.
Specifically, "measurement of allele-specific mRNA levels in human pancreatic
islet samples heterozygous for rs11603334 showed that the T2D-risk and
proinsulin-decreasing allele (C) is associated with increased ARAP1 expression
(p < 0.02). We evaluated four candidate functional SNPs for allelic effects
on transcriptional activity by performing reporter assays in rodent pancreatic
beta cell lines. The C allele of rs11603334, located near one of the ARAP1
promoters, exhibited 2-fold higher transcriptional activity than did the T
allele (p < 0.0001); three other candidate SNPs showed no allelic
differences. Electrophoretic mobility shift assays demonstrated decreased
binding of pancreatic beta cell transcriptional regulators PAX6 and PAX4 to the
rs11603334 C allele."</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Interestingly, Pax4 is abundantly over-represented within cis-regulatory motifs in
mouse clock-controlled genes, in liver and muscle, per Table 1, and relative to
peak <a href="http://www.ncbi.nlm.nih.gov/gene/18627" target="_blank"><i>Per2</i></a> expression in mice, the Pax4 protein recognizes a motif
over-represented in the suprachiasmatic nucleus (SCN, the "master clock" in the brain) at phase 12, per Table 2. These data are from <a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0004882" target="_blank">Bozek, Relogio, et al 2009 PLoS One4:e4882</a>. That same study showed similar activity for Pax6: relative to peak <i>Per2</i> expression in mice, Pax6 protein also recognizes a motif over-represented in the SCN at phase 12,
per Table 2 (Bozek Relogio 2009 PLoS One 4:e4882, PMID 19287494).</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
This makes one wonder about the timing of the food intake that
might exacerbate glucose homeostasis in carriers of the risk allele.</div>
<div class="MsoNormal">
<br /></div>
Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-78600945807403782442014-03-14T06:04:00.000-07:002014-03-14T06:06:19.200-07:00APOE, memory impairment, diet and N-3 PUFAs<i>APOE</i> is a curious gene. It has roles in both lipid/cholesterol homeostasis and memory impairment with its associations with Alzheimer disease. For example, see this <a href="http://omim.org/entry/107741" target="_blank">entry in OMIM</a> and the section titled "Role of APOE in Abnormalities of Blood Lipids and in Cardiovascular Disease." If you read through that long section, over 2600 words, you'll learn that APOE is an important contributor to the management of low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL). If LDL and VLDL levels are not in homeostasis, triglyceride levels can become elevated, which increases risk of atherosclerosis.<br />
<br />
A recent report in Nature Medicine by Mapstone, <i>et al</i>. entitled "<a href="http://www.nature.com/nm/journal/vaop/ncurrent/full/nm.3466.html" target="_blank">Plasma phospholipids identify antecedent memory impairment in older adults</a>" identified a panel of ten blood-based lipid biomarkers for "detecting preclinical <br />
Alzheimer's disease in a group of cognitively normal older adults." Those ten lipids include two acylcarnitines and eight<br />
phosphatidylcholines (PC), specifically:<br />
<br />
propionylacylcarnitine<br />
3-OH-hexadecenoylcarnitine (C16:1-OH)<br />
phosphatidylcholine diacyl C36:6 (PC aa C36:6) <b>*</b><br />
lysophosphatidylcholine acyl C18:2 (lysoPC a C18:2)<br />
phosphatidylcholine diacyl C38:0 (PC aa C38:0) <b>*</b><br />
phosphatidylcholine diacyl C38:6 (PC aa C38:6) <b>*</b><br />
phosphatidylcholine diacyl C40:1 (PC aa C40:1)<br />
phosphatidylcholine diacyl C40:2 (PC aa C40:2)<br />
phosphatidylcholine diacyl C40:6 (PC aa C40:6) <b>*</b><br />
phosphatidylcholine acyl-alkyl C40:6 (PC ae C40:6) <b>*</b><br />
<br />
These were noted by the study to be lower in the group of cases compared to controls.<br />
<br />
Curiously, this group did not reference the findings from a 2013 study by Rudowska, <i>et al</i>., that <a href="http://www.ncbi.nlm.nih.gov/pubmed/?term=22748805" target="_blank">characterized the transcriptomic and metabolomic signatures of adding N-3 polyunsaturated fatty acid</a> (N-3 PUFA) to the diet in a Caucasian population. Of their findings, it is most notable that five of the eight above-listed PCs were increased after the six-week N-3 PUFA intervention. These are noted with an asterisk above.<br />
<br />
Whether a diet rich in N-3 PUFAs could decrease risk of memory impairment or Alzheimer disease (AD) is a matter for further investigation. Nonetheless, that five of these eight PCs show opposite changes when comparing an N-3 PUFA intervention with the group of cases in the Mapstone, <i>et al</i>. study is highly interesting. Consider also for the moment gene by diet or gene by environment (GxE) interactions. A GxE interaction is an association between a genetic marker and a phenotype that is modified by an environmental factor such as the diet, macronutrient (<i>ie</i>, fat, protein or carbohydrate) intake, physical activity or any of many other lifestyle choices. The risk allele will not show itself as risk until the environmental factor passes a given threshold, say too much saturated fat and now the risk is elevated. <br />
<br />
The overlap of the five PCs highlighted here, coupled with the large <a href="http://www.ncbi.nlm.nih.gov/pubmed/22328972" target="_blank">number of gene-environment interactions</a> we see for the common AD/blood lipid variants of <i>APOE</i> - SNPs rs429358 and rs7412 - strengthen my personal view that lifestyle has a significant role in cognitive decline.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com2tag:blogger.com,1999:blog-8284945886307996607.post-83529101199844828282013-10-18T13:07:00.000-07:002013-10-18T13:07:33.995-07:00Getting back in gear and tightening the focusAfter nearly three weeks of being idle as a result of the government shutdown, I find myself drawing a narrower focus on the activities in which I am engaged. This is a consequence of lost time and of the struggle to get fully re-engaged. The train of thought that is so vital to novel and creative research was severely wounded.<br />
<br />
A significant portion of the time in which to accomplish the milestones and achievements for fiscal year 2014, which coincidentally began on 1 Oct, the day the insanity began, is lost. This means that I will be less likely to accept invitations to review manuscripts and more likely to keep many projects on the back burner.<br />
<br />
We have a few very important ongoing projects that are at critical stages of either assembling the data into impactful manuscripts or for evaluation of our current and soon to end 5-year research plan. This is where my focus will be for the coming several weeks. This is where the push will be exerted. So many of those little things that add flavor and completeness to the research we do will simply have to wait, hopefully to be squeezed in later.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-1144748542977984732013-06-21T13:55:00.000-07:002013-06-21T13:55:17.004-07:00FTO and memory - connecting the dots<i>FTO</i> is a well known gene containing variants that have shown rather consistent association to obesity risk and BMI in many different populations. FTO is an efficient <a href="http://www.ncbi.nlm.nih.gov/pubmed/22002720">oxidative demethylase</a> targeting the abundant N6-methyladenosine (m6A) residues in RNA <i>in vitro</i> and mRNA in the nucleus. This implies that there is a heretofore undescribed, reversible regulatory process in mammalian cells.<br />
<br />
Late last year, it was <a href="http://www.ncbi.nlm.nih.gov/pubmed/23136261">reported </a>that <i>FTO</i> variants can raise susceptibility to decline in verbal memory as detected in middle-aged, community-dwelling adults. A <a href="http://www.ncbi.nlm.nih.gov/pubmed/23741617">new report</a> released this week has identified an inhibitor of PERK (also known as EIF2AK3) signaling that is rather potent at reversing the effects of EIF2S1 (eIF2alpha) phosphorylation. As EIF2AS1 phosphorylation is implicated in memory consolidation, it was notable that the mice treated with this inhibitor showed significant increases in spatial and fear-associated learning. Thus, the authors conclude, "memory consolidation is inherently limited by the integrated stress response (which involves PERK, PKR (EIF2AK2), GCN2 (EIF2AK4) and HRI (EIF2AK1)), and [the inhibitor] releases this brake."<br />
<br />
Here's where things get interesting and the FTO-EIF2AK3 connection tightens. Of 77 mRNAs whose levels are either up- or down-regulated in the liver, skeletal muscle or white adipose tissue of mice homozygous for a nonsynonymous <i>Fto</i> point mutation, as reported by <a href="http://www.ncbi.nlm.nih.gov/pubmed/19680540">Church, <i>et al</i>. 2009</a>, mRNAs from seven genes, which were all significantly up-regulated in <i>FTO</i> mutants, also contain methyl6A peaks: <i>Acaca</i>, <i>Atf6</i>, <i>Bip</i>, <i>Gcdh</i>, <i>Irs1</i>, <i>Perk</i>, and <i>Xbp1</i> (<a href="http://www.ncbi.nlm.nih.gov/pubmed/22608085">Meyer, et al. 2012</a>).<br />
<br />
<i>Data mining is fun!</i><br />
<br />Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-86682628576621337332013-06-19T11:51:00.003-07:002013-06-19T12:39:19.829-07:00GTEx Community Meeting - notesYesterday I attended the GTEx (<a href="http://www.ncbi.nlm.nih.gov/gtex/GTEX2/gtex.cgi">Genotype-Tissue Expression project</a>) Community Meeting held at the Broad Institute in Cambridge, MA, who hosts the <a href="http://www.broadinstitute.org/gtex/">GTEx portal</a>. This meeting offered opportunities to GTEx researchers and those scientists not part of this <a href="https://commonfund.nih.gov/GTEx/">NIH Common Fund program</a> to engage in dialog regarding new aspects of the GTEx project. An overview of the project is <a href="http://www.ncbi.nlm.nih.gov/pubmed/23715323">here</a>. A main impetus for GTEx is many GWAS signals linking genotype to disease phenotype have a role in regulation of gene expression. Thus, learning more about gene expression can assist in the interpretation of GWAS results. Below are my notes from this one-day meeting.<br />
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Simona Volpi</b> –
NIH. See commonfund.nih.gov/gtex for details. Samples are from biobanked
tissues. This may make it difficult to engage in challenge experiments. Goal is
to establish a database of genotype-gene expression relationships. Goal is to
collect from 900 donors. They use PAXgene, alcohol-based fixative for the
tissues in 0.2 – 0.5 gram aliquots. Tissue processing includes histopathologic
review, FPPE paraffin embedding, RNA extraction.<span style="mso-spacerun: yes;"> </span>A U01 RFA seeking application to propose ways
to enhance GTEx is being formed and will soon be announced. </div>
<ul>
<li><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span>BMI range for donors is greater than 18.5 and
less than 35.</li>
<li><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span>Cause of death of donors: 34% cerebrovascular,
13% cardiac, 22% respiratory, 21% from accidents (transportation and
non-transportation).</li>
<li><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span>RFA-RM-12-009 – eGTEx RFA from NIH – perhaps
this is closed. They are working on liberalizing the access policy.</li>
<li><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span>Data will be housed in dbGaP. Need to apply for
access to get a lot of info, but some basic info is available at the GTEx
portal at the Broad.</li>
</ul>
<br />
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Kristin Ardlie</b> – LDACC
– Laboratory, data analysis and coordinating center. There are 47 tissues: 35
PAXgene tissues + blood + 11 frozen brain sub-regions. Blood is collected and
processed pre-mortem. Goal by Jan 2014: 9534 RNA samples from 430+ donors. Goal
for RNA-seq is 50 million aligned reads and no less than 15 million. Input is
200 ng total RNA of RNAs w a RIN of 6.0 or higher. RIN = RNA integrity score. Skeletal
muscle and lung have high RINs, but pancreas, adipose and other enzyme-rich
tissues have lower RINs and quicker decay post-ischemic time (or post-mortem
interval). </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Manolis Dermitzakis</b>
uses a FDR of 5% based on Storey to identify eQTL SNPs. They use a 1 MB window around TSS but also a
100 kbp window. Using the established 15 PEER factors (to account for
population ancestry structure), about 800 or so eQTL for adipose are expected.
Overall, there are about 6200 eQTL genes for subcutaneous adipose.
Skeletal muscle gets toward 7800 eQTL genes. He urges caution when seeing overlap
between eQTL and GWAS hits because these are almost certain as data increase in
volume, so take into consideration the effect size (20% increase of mRNA and protein
is not the same in terms of biological consequences as a 2-fold increase). Can
they borrow power from a “related” tissue to look at a specific tissue? Estimates of tissue
sharing are high for adipose; nerve is best at 0.92, artery is 0.91. About 0.56
is the probability for a SNP to be active in all nine tissues. Probability of
being active in a single tissue is just 0.03. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Roderic Guigo.</b>
There are about 15000 to 20000 expressed genes in most tissues, with blood less
and testis more. Most tissues have 2000 to 3000 expressed lncRNAs, with testis
having many more. 3820 genes are expressed in only one tissue. Most genes express about half of annotated isoforms in a given tissue. With two isoforms, the major
isoform dominates with 90% of expression of that gene, ~40-50% of expression
comes from the dominant mRNA isoform when there are 5 or so isoforms. Splicing QTL,
SNPs affecting splicing pattern of the gene, but may or may not affect
expression. Their group had to develop software to detect these in the RNA-seq
data and also to account for the complex phenotype: isoforms and expression
counts.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Mike Weale.</b><i style="mso-bidi-font-style: normal;"> </i>Using arrays for eQTL studies can lead to generation of
false positives. See <a href="http://www.ncbi.nlm.nih.gov/pubmed/23435227">Ramasamy et al 2013 Nucl Acids Res</a> for an analysis of
probe-dropping with better reference data. Something like 5.5% of probes map to
ref genome SNPs but account for 90% of brain eQTL hits. See <a href="http://www.ncbi.nlm.nih.gov/pubmed/22723018">Trabzuni Hum MolGenet 21:4094</a> for the famous example of a false positive eQTL for <i>MAPT</i>. Their PiP finder tool is at
bitly.com/pipfinder.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Barbara Englehardt.</b>
Replication of cis- and trans-eQTL across cell types. Her goal is to predict
cis-eQTL as functional SNPs. This method is soon to be out in PLoS Genetics.
Study size and replicate arrays account for >95% of the variability in
fraction of genes showing an eQTL. They found no false positives when using
replicate expression arrays, but false negatives persisted. Replcation
strengthened cis-eQTL discovery. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Yaniv Erlich.</b>
STRs short tandem repeats of 2-6 bp. Sometimes these occur in promoter regions.
Using 1000G data, they saw about 80% of STRs are polymorphic with MAF >1%.
Many more STRs in introns than in exons and loss of heterozygozity with
populations not of African origin. Looking for effects of STR variation on gene
expression, they found 2673 eSTRs, but with replication in orthogonal data (use
arrays when original data came from RNA-seq, eg) they found 81% of eSTRs showed
the same direction of effect. Were they tagging SNPs? 77% of eSTR had same
slope as before when conditioned on most likely cis-eQTL SNP, meaning that they
were not tagging SNPs in most cases. They do not see any dose-dependence with
the STRs, meaning a length effect on the expression effect. He speculates that
the STRs create Z-DNA.<i style="mso-bidi-font-style: normal;"><br /></i></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">João Fadista.</b>
Prediction on individual level genotypes based on solely on GTEx gene
expression. Assuming each gene has at least one cis-eQTL and 20,000 genes,
there will be 3<sup>20,000</sup> combinations and this combination or pattern
could be used to predict a person’s genotype. His examples will come from the
Nordic Network for Islet Transplantation and includes other tissues/organs. 89
islet donors, 61% men, 5.8 ± 0.9% HbA1c levels. Found 136 eQTL. Only 22 of
these had genotype prediction data in all GTEx samples, but could be sufficient
to predict genotype: 3<sup>22</sup> is greater than current world population by
more than 4-fold. See also
work by Eric Schadt (<a href="http://www.ncbi.nlm.nih.gov/pubmed/22484626">Nat Genet, Bayesian method to predict individual SNP</a>…) on
their replication of liver and adipose eQTL. Why do this and jeopardize GTEx,
asks M. Dermitzakis, and JF replies that a blood gene expression test combined
with eQTL data can predict disease. M. Dermitzakis states that heritability of
gene expression is about 0.3 and so predictions of tissue-specific gene
expression will be limited.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Stephen Montgomery.</b>
He looks at allele-specific expression and GTEx data to discover eQTL. See Wei
Sun on TReCASE tool in Biometrics from 2012. Even with 15 million RNA-seq
reads, 52.2% of sites have depth less than 30 and so have lowered ability to
confidently label as ASE (allele specific expression). Allelic ratios of the
mRNAs are highly heritable, as seen by looking at a 3-generation, 17-member
family. Such is seen across low and moderately expressed genes. Can ASEs say
anything about deleterious variants? Looked at 10 tissues in one 25-yo Chinese
male, and looked at deleterious sties (50), loss-of-function (LoF, stop-gain) sites
(74) and <i>?</i> (very few). The LoF variant is lowly expressed across the
tissues, as reported by Dan MacArthur. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Tuuli Lappalainan.</b>
Uses December release of GTEx data to look at ASEs. ASEs can recover from
under-powered studies to identify eQTL. Master data to be released with
upcoming paper: ≥ 8 RNA-seq reads over a site. Most analyses sampling are done
to exactly 30 reads in order to avoid coverage issues. Note: only relatively
highly expressed, perhaps ubiquitously expressed, genes can be analyzed. She’s
examining distribution of allelic effects between individuals and between
tissues. She wants to quantify regulatory variation in each tissue by looking
across all tissues and samples. Thyroid has a large relative (to other tissues)
proportion of cis-eQTL and ASEs unlike other tissues. Her data are progressing
to descriptions of proxy tissues for eQTL analysis. She is asking, How likely
is a second tissue in the same individual to show ASE? eQTL work is done in
populations and now look at the individual and that person’s ASE effect.<span style="mso-spacerun: yes;"> </span>Because of wide variation in expression
levels and ASE effects across individuals, the variants are not great predictor
of individual phenotypes even at the cellular level.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Manuel Rivas.</b> Transcriptome
analysis of the functional impact of putative loss of function variants. Looks
to annotate exome resequencing data and rare variants to isoforms from RNA-seq.
<i style="mso-bidi-font-style: normal;">LMNA</i> provides a nice example of a
gene with muscle specific mRNA isoforms and thus only these two isoforms should
be used in explaining muscle disorders as the other isoforms are not expressed
in this tissue.<span style="mso-spacerun: yes;"> </span></div>
<div class="MsoNormal">
<br /></div>
<b style="mso-bidi-font-weight: normal;">Chris Fuller.</b> GWAS
variants as eQTL based on analysis of GTEx data. Sherlock is their tool, It
uses all GWAS SNPs even those below genome-wide significance. It uses both cis-
and trans-eQTL loci. Sherlock maps disease-SNP associations to disease-gene
groups. Linkage is very important in this work. The stronger results come from
cis-eQTL, as shown by looking at Crohn disease GWAS. He also implicates genes
through trans-eQTL data. See <a href="http://sherlock.ucsf.edu/">sherlock.ucsf.edu</a>. Many small GWAS may remain unpublished for lack of
strong single-SNP results. Aggregating SNPs boosts statistical power. They then
implicate a relevant leukemia gene (<i style="mso-bidi-font-style: normal;">FLI1</i>)
though multiple (n=6) trans-eQTL SNPs.<br />
<br />
<br />
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Eric Gamazon.</b> GTEx
– Expanding on GWAS. Uses the Wellcome Trust Case Control Consortium and their
7 diseases, including CAD. Adipose cis-eQTL are enriched for Crohn disease,
CAD, hypertension and rheumatoid arthritis variants. GTEx adipose eQTL improves HOMA-IR GWAS. What proportion
of variability in expression is captured by eQTL? He claims that he
can capture 30-50% of heritability from genome-wide markers SNPs (> 200,000)
for type 1 diabetes and Crohn disease when using a small number of informative
cis-eQTL GTEx SNPs (2883 SNPs for T1D, ~3000 for CD). He makes no claims about
saying anything about causal variants with this approach. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Jason Wright.</b> Chasing
causal loci: Genome engineering of a non-coding region of 9p21 to identify
mechanisms of diabetes predisposition. Enhancer assay of a standard type was
used to look at various 2-kbp sequences across the region. Little or no
enhancer activity seen until they transfected rodent islet cells. Promoter
regions of all three genes (<i>CDKN2A</i>, eg) physically interact with the 10-kbp
region containing the risk haplotype. TALEN (<a href="http://www.ncbi.nlm.nih.gov/pubmed/20660643">TAL effector nucleases</a>) genome
engineering gives near isogenic cell lines with or without specific alleles; he
did this to delete the entire risk region. It looks as though
the 9p21 region affects expression of <i style="mso-bidi-font-style: normal;">CDKN2A</i>
and not <i style="mso-bidi-font-style: normal;">CDKN2B</i> and <i style="mso-bidi-font-style: normal;">CDKN2AB-AS</i> by about 20% per risk allele
and in cis. He is now trying CRISPR genome engineering to fine map the 10-kbp
region.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Daniel MacArthur.</b>
Gene expression data and PPIs are used to inform the clinical exome sequencing.
IBAS protein-protein interaction score and way to score placement within a PPI
network.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Luke Ward.</b> Systematic
annotation of GTEx eQTL using ENCODE and Roadmap data. His slide on genetic
variant, tissue/cell type, molecular phenotypes (histone methylation, eg) and
organismal phenotypes (lipids, heart rate) slide is neat and while outlining potential for a
druggable path could also be used to outline a nutrition path to retain and
maintain health, as opposed to recovering health. HaploReg (<a href="http://nar.oxfordjournals.org/content/40/D1/D930.short">http://nar.oxfordjournals.org/content/40/D1/D930.short</a>)
is the portal where the HepG2 enhancer variants can be found – these are
relevant for blood lipids. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">GTEx & NIH panel.</b>
<i style="mso-bidi-font-style: normal;">Audience participation in terms of data types/fields to make available and discussion of other tissues to sample. </i></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Gad Getz.</b> He gave a recap of the day's talks…</div>
<br />
<br />Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-30987350031778336312013-03-08T13:44:00.000-08:002013-03-13T06:04:13.762-07:00Paper of the Week: Mechanistic dissection of a heart disease risk variantThis 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 <a href="http://www.ncbi.nlm.nih.gov/pubmed/23415669" target="_blank">paper</a> is by Pu, Xiao <i>et al</i>. and published in the American Journal of Human Genetics 92:366-374.<br />
<br />
<a href="http://www.ncbi.nlm.nih.gov/gene/11173"><i>ADAMTS7</i></a> encodes a metalloprotease which cleaves protein in order to activate them. One of its substrates is <a href="http://www.ncbi.nlm.nih.gov/gene/1311">COMP</a>, 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. <br />
<br />
<br />
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.<br />
<br />Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-47394921183460525202012-11-16T12:30:00.000-08:002012-11-16T13:47:55.746-08:00Paper of the week: Visualizing associations between paired data setsThis week's paper of the week is by González, <i>et al</i>., entitled "Visualising associations between paired `omics' data sets," and published in BioData Mining (vol <b>5:</b>19). The pdf of this report can be found <a href="http://www.biodatamining.org/content/pdf/1756-0381-5-19.pdf" target="_blank">here</a>.<br />
<br />
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.<br />
<br />
The graphical tools described in the report are implemented in the freely available R package mixOmics and in its associated web application.<br />
<br />
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 <a href="http://www.ncbi.nlm.nih.gov/pubmed/17326203">nutrigenomic study</a> in which 40 mice from two genotypes (wild-type and <i>Ppara</i> -/-) 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. <br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhzKtgJ5V3Zv-v_6hLphDqy-0fL9evJQiCfPMK185ybt9H_cnBclieKmTH5CusCpQPTsewkcfmg1eVjmZwb-QSpTdCe_VEbCwesZmkEEPmJyUs2kScyMSgFpl2d7_G7yww4cQni6GKltul0/s1600/Fig5.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhzKtgJ5V3Zv-v_6hLphDqy-0fL9evJQiCfPMK185ybt9H_cnBclieKmTH5CusCpQPTsewkcfmg1eVjmZwb-QSpTdCe_VEbCwesZmkEEPmJyUs2kScyMSgFpl2d7_G7yww4cQni6GKltul0/s400/Fig5.jpg" width="400" /></a></div>
<br />
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 <i>y</i>-axis). Similarly, genes such as <i>Car1</i>, <i>Acoth</i>, <i>Siat4c</i>, <i>Scarb1</i> (<i>SR.BI</i>) and <i>Slc10a1</i> (<i>Ntcp</i>, or <i>Ntop</i> [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 <i>x</i>-axis).<br />
<br />
I find such analysis and depiction of results useful and look forward to trying this with our GWAS data.<br />
<br />Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com1tag:blogger.com,1999:blog-8284945886307996607.post-3775114705772201962012-11-05T12:20:00.001-08:002012-11-05T12:20:47.529-08:00My agenda for ASHG 2012This week I will attend the annual conference of the American Society of Human Genetics, ASHG. It was suggested that we who will broadcast observations, comments, invites, critiques and musings on Twitter should also post an agenda of those sessions we feel are important to attend. I've done just that, listing below those sessions I plan to attend.<br />
<br />
Hashtag will be <b>#ASHG2012</b><br />
<br />
All talks are tweetable, opt-out, meaning if the speaker says nothing to the contrary, one can tweet<br />
<br />
<b>Wednesday, November 7</b><br />
<i>8:00 am - 10:00 am</i><br />
5. Gene Regulatory Change: The Engine of Human Evolution? <i>Room 135, Lower Level North</i><br />
9. Surveying Customer Responses to Personal Genetic Services <i>Room 132, Lower Level North</i><br />
<br />
<i>10:30 am - 12:45 pm</i><br />
15. New Loci for Obesity, Diabetes, and Related Traits <i>Gateway Ballroom 104, Lower Level South</i><br />
<br />
<i>2:15 pm - 4:15</i> pm<br />
Poster session 1<br />
<br />
<b>Thursday, November 8</b><br />
<i>8:00 am - 10:00 am</i><br />
22. Common and Rare CNVs: Genesis, Patterns of Variations and Human Diseases <i>Hall D, Lower Level North</i><br />
<br />
<i>10:30 am - 12:45</i> pm<br />
32. Cardiovascular Genetics: GWAS and Beyond <i>Room 134, Lower Level North</i><br />
37. Metabolic Disease Discoveries <i>Room 123, Lower Level North</i><br />
<br />
<i>2:15 pm - 4:15 pm</i><br />
Poster session 2<br />
<br />
<i>4:30 pm - 6:45 pm</i><br />
44. Tools for Phenotype Analysis <i>Room 132, Lower Level North</i><br />
<br />
<b>Friday, November 9</b><br />
<i>8:00 am - 10:30 am</i><br />
47. Structural and Regulatory Genomic Variation <i>Hall D, Lower Level North</i><br />
53. From SNP to Function in Complex Traits <i>Room 132, Lower Level North</i><br />
<br />
<i>2:15 pm - 4:15 pm</i><br />
Poster session 3<br />
<br />
<i>4:30 pm - 6:45 pm</i><br />
61. Missing Heritability, Interactions and Sequencing <i>Room 135, Lower Level North</i><br />
63. Transcriptional Regulation, Variation and Complexity <i>Gateway Ballroom 104, Lower Level South</i><br />
64. Epigenetics Room 124, <i>Lower Level North</i><br />
<br />
<b>Saturday, November 10</b><br />
<i>9:40 am - 11:40 am</i><br />
76. The Functional Consequences of microRNA Dysregulation in Human Disease <i>Room 134, Lower Level North</i><br />
<br />Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-29352381591908255662012-11-02T07:04:00.001-07:002012-11-05T11:43:33.042-08:00NuGO Week 2012<!--[if gte mso 9]><xml>
<o:OfficeDocumentSettings>
<o:RelyOnVML/>
<o:AllowPNG/>
</o:OfficeDocumentSettings>
</xml><![endif]--><br />
<!--[if gte mso 9]><xml>
<w:WordDocument>
<w:View>Normal</w:View>
<w:Zoom>0</w:Zoom>
<w:TrackMoves/>
<w:TrackFormatting/>
<w:PunctuationKerning/>
<w:ValidateAgainstSchemas/>
<w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid>
<w:IgnoreMixedContent>false</w:IgnoreMixedContent>
<w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText>
<w:DoNotPromoteQF/>
<w:LidThemeOther>EN-US</w:LidThemeOther>
<w:LidThemeAsian>X-NONE</w:LidThemeAsian>
<w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript>
<w:Compatibility>
<w:BreakWrappedTables/>
<w:SnapToGridInCell/>
<w:WrapTextWithPunct/>
<w:UseAsianBreakRules/>
<w:DontGrowAutofit/>
<w:SplitPgBreakAndParaMark/>
<w:DontVertAlignCellWithSp/>
<w:DontBreakConstrainedForcedTables/>
<w:DontVertAlignInTxbx/>
<w:Word11KerningPairs/>
<w:CachedColBalance/>
</w:Compatibility>
<m:mathPr>
<m:mathFont m:val="Cambria Math"/>
<m:brkBin m:val="before"/>
<m:brkBinSub m:val="--"/>
<m:smallFrac m:val="off"/>
<m:dispDef/>
<m:lMargin m:val="0"/>
<m:rMargin m:val="0"/>
<m:defJc m:val="centerGroup"/>
<m:wrapIndent m:val="1440"/>
<m:intLim m:val="subSup"/>
<m:naryLim m:val="undOvr"/>
</m:mathPr></w:WordDocument>
</xml><![endif]--><!--[if gte mso 9]><xml>
<w:LatentStyles DefLockedState="false" DefUnhideWhenUsed="true"
DefSemiHidden="true" DefQFormat="false" DefPriority="99"
LatentStyleCount="267">
<w:LsdException Locked="false" Priority="0" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Normal"/>
<w:LsdException Locked="false" Priority="9" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="heading 1"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 2"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 3"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 4"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 5"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 6"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 7"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 8"/>
<w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 9"/>
<w:LsdException Locked="false" Priority="39" Name="toc 1"/>
<w:LsdException Locked="false" Priority="39" Name="toc 2"/>
<w:LsdException Locked="false" Priority="39" Name="toc 3"/>
<w:LsdException Locked="false" Priority="39" Name="toc 4"/>
<w:LsdException Locked="false" Priority="39" Name="toc 5"/>
<w:LsdException Locked="false" Priority="39" Name="toc 6"/>
<w:LsdException Locked="false" Priority="39" Name="toc 7"/>
<w:LsdException Locked="false" Priority="39" Name="toc 8"/>
<w:LsdException Locked="false" Priority="39" Name="toc 9"/>
<w:LsdException Locked="false" Priority="35" QFormat="true" Name="caption"/>
<w:LsdException Locked="false" Priority="10" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Title"/>
<w:LsdException Locked="false" Priority="1" Name="Default Paragraph Font"/>
<w:LsdException Locked="false" Priority="11" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Subtitle"/>
<w:LsdException Locked="false" Priority="22" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Strong"/>
<w:LsdException Locked="false" Priority="20" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Emphasis"/>
<w:LsdException Locked="false" Priority="59" SemiHidden="false"
UnhideWhenUsed="false" Name="Table Grid"/>
<w:LsdException Locked="false" UnhideWhenUsed="false" Name="Placeholder Text"/>
<w:LsdException Locked="false" Priority="1" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="No Spacing"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 1"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 1"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 1"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 1"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 1"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 1"/>
<w:LsdException Locked="false" UnhideWhenUsed="false" Name="Revision"/>
<w:LsdException Locked="false" Priority="34" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="List Paragraph"/>
<w:LsdException Locked="false" Priority="29" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Quote"/>
<w:LsdException Locked="false" Priority="30" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Intense Quote"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 1"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 1"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 1"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 1"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 1"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 1"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 1"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 1"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 2"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 2"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 2"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 2"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 2"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 2"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 2"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 2"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 2"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 2"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 2"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 2"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 2"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 2"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 3"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 3"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 3"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 3"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 3"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 3"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 3"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 3"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 3"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 3"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 3"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 3"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 3"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 3"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 4"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 4"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 4"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 4"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 4"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 4"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 4"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 4"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 4"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 4"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 4"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 4"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 4"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 4"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 5"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 5"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 5"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 5"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 5"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 5"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 5"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 5"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 5"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 5"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 5"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 5"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 5"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 5"/>
<w:LsdException Locked="false" Priority="60" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Shading Accent 6"/>
<w:LsdException Locked="false" Priority="61" SemiHidden="false"
UnhideWhenUsed="false" Name="Light List Accent 6"/>
<w:LsdException Locked="false" Priority="62" SemiHidden="false"
UnhideWhenUsed="false" Name="Light Grid Accent 6"/>
<w:LsdException Locked="false" Priority="63" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 1 Accent 6"/>
<w:LsdException Locked="false" Priority="64" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Shading 2 Accent 6"/>
<w:LsdException Locked="false" Priority="65" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 1 Accent 6"/>
<w:LsdException Locked="false" Priority="66" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium List 2 Accent 6"/>
<w:LsdException Locked="false" Priority="67" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 1 Accent 6"/>
<w:LsdException Locked="false" Priority="68" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 2 Accent 6"/>
<w:LsdException Locked="false" Priority="69" SemiHidden="false"
UnhideWhenUsed="false" Name="Medium Grid 3 Accent 6"/>
<w:LsdException Locked="false" Priority="70" SemiHidden="false"
UnhideWhenUsed="false" Name="Dark List Accent 6"/>
<w:LsdException Locked="false" Priority="71" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Shading Accent 6"/>
<w:LsdException Locked="false" Priority="72" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful List Accent 6"/>
<w:LsdException Locked="false" Priority="73" SemiHidden="false"
UnhideWhenUsed="false" Name="Colorful Grid Accent 6"/>
<w:LsdException Locked="false" Priority="19" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Subtle Emphasis"/>
<w:LsdException Locked="false" Priority="21" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Intense Emphasis"/>
<w:LsdException Locked="false" Priority="31" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Subtle Reference"/>
<w:LsdException Locked="false" Priority="32" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Intense Reference"/>
<w:LsdException Locked="false" Priority="33" SemiHidden="false"
UnhideWhenUsed="false" QFormat="true" Name="Book Title"/>
<w:LsdException Locked="false" Priority="37" Name="Bibliography"/>
<w:LsdException Locked="false" Priority="39" QFormat="true" Name="TOC Heading"/>
</w:LatentStyles>
</xml><![endif]--><!--[if gte mso 10]>
<style>
/* Style Definitions */
table.MsoNormalTable
{mso-style-name:"Table Normal";
mso-tstyle-rowband-size:0;
mso-tstyle-colband-size:0;
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-qformat:yes;
mso-style-parent:"";
mso-padding-alt:0in 5.4pt 0in 5.4pt;
mso-para-margin-top:0in;
mso-para-margin-right:0in;
mso-para-margin-bottom:10.0pt;
mso-para-margin-left:0in;
line-height:115%;
mso-pagination:widow-orphan;
font-size:11.0pt;
font-family:"Calibri","sans-serif";
mso-ascii-font-family:Calibri;
mso-ascii-theme-font:minor-latin;
mso-fareast-font-family:"Times New Roman";
mso-fareast-theme-font:minor-fareast;
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;}
</style>
<![endif]-->
<br />
<div class="MsoNormal">
The following are what I took away as highlights from NuGO
Week 2012, held from 28 to 31 August 2012 in Helsinki, Finland. I felt that
this conference showed a marked maturity in research accomplishments of the
nutrigenomics community. In the past, this conference and others, as well as
personal communications, were quite often invoked intent to use omics platforms
without showing much in the way of data. That changed dramatically at this
conference – there were presentations with a lot of data.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Some of the top themes were: Networks and GxEs, metabolic
profiling done to quantify metabolites, either known or as discovery of a
metabolic process, or done to quantify adherence to a given diet/food type
intake, eg plant polyphenolics, aging and health.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Specific notes:</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
My take-away lessons from <b>Marju Orho-Melander</b>’s talk (Lund University): Your
genetic susceptibility is affected by what you eat/how you eat/what and how
much you exercise/etc. She uses the Malmö Diet and Cancer Study n=28499. 1750
have incident T2DM. Protein from animal sources increases risk of T2DM, while
whole grain/high-fiber intake decreases the risk. Using the epidemiology data
(eg, animal protein intake increases risk) to focus or inform the interaction
work may be something worth looking into. She’s ready to perform a GxE analysis
using the GWAS data from the Malmö Diet and Cancer Study. Instead of using a
genetic risk score for the disease, use a pathway approach to consider a marker
for the disease, prior to the endpoint of disease itself. So, look at
interactions for glucose and glucose homeostasis in place of interactions for
T2DM.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Aldons “Jake” Lusis</b>
of UCLA. It is difficult to go from a GWAS hit to a mechanism in humans. He
uses a systems genetics approach: integrate clinical traits and intermediate
phenotypes across a population using correlation and gene mapping. He uses mice
because this offers a controlled environment, tissue access, and deep
biochemical profiling. They use 100 classical inbred mouse strains, genome
sequenced and GWAS-like association mapping. His group is looking at genetics
of dietary response in 6 to 8 mice/strain. Some mice have no change in body fat
going from chow to high-fat/high-sugar diet, others have substantial change. They
also looked at food consumption. They always look at males and females
separately. Food intake may be more strongly related to lean body mass
according to the stronger correlation between food intake and body weight over
food intake with fat mass. They use a T-test on 135,000 SNPs in a GWAS.
Threshold is determined by permutation or simulation. Interactions will be
identified using association and correlation in his systems genetics strategy. Visit
<a href="http://systems.genetics.ucla.edu/">http://systems.genetics.ucla.edu</a>
to see the loci that control body fat or look gene by gene to see what traits
are associated with that gene. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Melissa Morine</b> of
University of Trento. Within a network, one can perform a modularity calculation – whereby
members are highly connected to each other and rather unconnected to nodes
outside the module. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Marjukka Kolehmainen</b>
of University of Eastern Finland<b style="mso-bidi-font-weight: normal;">.</b> Of 82 individuals
who were obese, only 34 donated abdominal subcutaneous tissue both before and
after very low-calorie diet. All 3 PPAR pathways were down-regulated in the
subcutaneous adipose during the very low calorie diet intervention. Energy
metabolism was also strongly down-regulated. Both pathways returned to near
normal levels during the maintenance period.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">ETHERPATHS.
Anne-Marja Aura</b> of VTT Technical Research Centre of Finland: There is a set of known and detectable
metabolites of given fatty acids that can be used as biomarkers of intake. It
seems important to assess both microbial metabolites and those found in the
serum. <b style="mso-bidi-font-weight: normal;">Robert Caesar</b>
(University of Gothenburg) examines diet-microbial regulation of liver and adipose
transcriptomics. Macrophages from germ-free (obese-resistant) mice have
decreased expression of Ccr2 chemokine receptor. Compare WAT and liver gene
expression in response to metabolites from the gut microflora. Liver should be
more responsive because of close link via vena cava. Liver pathways altered:
Up: lymphocyte mediated immunity, adaptive immune response, innate immune
response, immune effector process, cell activation, chemotaxis, positive
regulation in response to stimulus; Down: sterol metabolic process, cell
adhesion, lipid metabolic process, etc. Gut microbiota increases liver
inflammation during high-fat diet independent of dietary lipid quality. <b style="mso-bidi-font-weight: normal;">Tuulia Hyötyläinen</b> (VTT Technical Research Centre of Finland) looks
at lipoprotein lipids and polar lipids in the lipoprotein fractions. Some of
this work is published in Mol Biosyst in 2012. N-3 intervention caused TG
levels to go down in females, no change in males. There were also sex
differences for metabolites seen in lipoprotein fractions. </div>
<div class="MsoNormal">
<br />
<b style="mso-bidi-font-weight: normal;">Mark Boekschoten</b> of Wageningen
University. PLS-path model gives them 44 liver and 69 adipose genes important
in body weight gain. Variation in these genes in humans could manifest as GxEs
for total caloric intake or saturated fat intake on body weight. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Jessica Schwarz</b>
of Wageningen University. Her poster shows that a high-protein diet restores
VLDL production (which is lowered with a high-fat diet) and prevents fat
accumulation in the liver in mice. She got onto this project from the
observation that a high-protein diet showed lower oil-red staining in liver and
lower TG levels. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Hector Keun</b> of
Imperial College of London. He has two objectives: Testing for association within and
between data types; incorporating background knowledge to enhance our ability
to interpret associations. A multivariate model example is O2-PLS, which can be
used to compare two data blocks, reducing it to the simplest list of
associations. This was developed by Trygg & Wold. It is also of interest to
describe what variation is not common to the two data blocks. For example,
there could be variation that is specific to the metabolomic data that does not
show in the proteomic data from those same animals. Pathway significance is
calculated using the hypergeometric distribution test, when comparing a set of
up-regulated genes with genes in a given pathway to see if that pathway is
over-represented, by chance, in the set of up-regulated genes. It is also
possible to use this analysis approach on the FFQ data. See Kamburov and Cavill
for access to their webtools. His adjustment for background incorporates the
fact that the number of observations or tests really for genes is much higher
than for metabolites, for example. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Willem de Vos</b> of
Wageningen University. The Bacterioides/Firmicutes ratio found in the gut
microbiome is not helpful with regard to diet, interventions and obesity. This
group uses the log [CFU/g feces] on x-axis on a graph to look at correlation
with some factor (he used LPS binding proteins) with changes to the microbiota.
</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<b style="mso-bidi-font-weight: normal;">Jacqueline Monteiro</b>
of University of <span class="st">São Paulo</span>. There is a correlation or relationship between
calcium in the diet and adipocyte differentiation. Kids in the lowest quintile
for milk intake were in the highest quintile for BMI in their Delta Project.</div>
<div class="MsoNormal">
<br /></div>
Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-85526410846917352632012-07-24T14:42:00.000-07:002012-07-24T14:45:59.040-07:00Life aboard the International Space Station<p>As some of you who follow me on Twitter may already know, NASA astronaut <a href="http://www.jsc.nasa.gov/Bios/htmlbios/williams-s.html">Suni Williams</a> and I were childhood friends. We were on the same swim team together. She is aboard the <a href="http://www.nasa.gov/mission_pages/station/main/index.html">International Space Station</a> (ISS) at this moment on <a href="http://www.nasa.gov/mission_pages/station/expeditions/expedition32/index.html">Expedition 32</a>, beginning a 4-month stay about a week and a half ago. Since her first trip to the ISS in 2006, I've been in touch with her and that got me on the invite list to attend a special launch party for her current mission. At that event, there was a special presentation by Captain <a href="http://www.jsc.nasa.gov/Bios/htmlbios/burbank.html">Dan Burbank</a>. He was Commander of <a href="http://www.nasa.gov/mission_pages/station/expeditions/expedition30/index.html">Expedition 30</a> to the ISS and returned to Earth on 27 April 2012 after a five-month stay aboard the ISS.</p>
<p>I was curious to learn about the behavior of the astronauts on the ISS in terms of diet, physical activity (especially with regard to bone loss and muscle function) and sleep. Many of you know how our research group examines the role of environmental factors in modifying disease risk. These are GxE, or gene-by-environment, interactions. Diet, dietary components (eg, certain fatty acids, protein content, carbohydrates), exercise (or sedentary behavior) and sleep are key environmental factors for our work.</p>
<p>Dan told me that he would normally consume about 3500 calories per day on Earth but that increased by about 500 calories aboard the ISS. He could not say if it was more carbs or fat or protein or just a bit more of everything. He did not speak much about exercise other than to tell us all during his slide presentation that there is a new resistance machine on board that provides 400 pounds of resistance. The previous machine provided only 100 pounds of resistance and the 400 level is what is needed to stem bone loss. He told us that when one types on a keyboard, only a few strokes are needed to send the person across the room in microgravity. So, they "stand" with feet hooked under railings, like as bar rail. This gives them calluses on the tops of their feet, while those on the soles begin to fade. </p>
<p>What was perhaps the most interesting to me was Captain Dan's sleep habits. He said that on the ISS he needed only 4 to 7 hours of sleep per night. What's more, he did not strap himself in to provide a feeling of lying down, but could sleep anywhere, floating in his room. </p>
<p>All in all, it was a really cool experience to meet an astronaut, to learn about life aboard the ISS, and to see someone I know launch with a Soyuz rocket to begin her latest adventure.</p>
<p><b>Good luck and continued success with your mission, Suni!</b></p>Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-53183395292721048482012-05-22T06:27:00.002-07:002012-05-22T06:27:55.114-07:00The WHO's report on noncommunicable diseasesThe World Health Organization of the United Nations has released a report titled "Global status report on noncommunicable diseases." Access to the report and its individual chapters is at this <a href="http://www.who.int/nmh/publications/ncd_report2010/en/index.html">link</a>. I was particularly interested in Chapter 1 and the major contributing factors to noncommunicable diseases (NCD).<br />
<br />
According to the above report and others from the WHO, the four primary contributors to global increases in NCDs, such as type 2 diabetes, cancer, and cardiovascular diseases, are:<br />
<br />
<li>tobacco</li>
<li>harmful use of alcohol</li>
<li>unhealthy diet</li>
<li>physical inactivity</li>
<br />
<br />
While such a list is really not surprising, what I do take from this, with respect to my own research on the genetic basis for the differential response to the diet as it pertains to metabolic diseases, is these are our key environmental factors used to assess gene by environment, or GxE, interactions. In other words, while these factors are strong contributors to NCD onset and progression, genetic differences exert different influences on the disease risk, onset and progression in different individuals. That influence could be negative - increasing risk - or positive - being more protective.<br />
<br />
Thus, the importance of GxE identification cannot be overlooked, and ought really to be emphasized in genetic association studies.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-85777726456296382342012-05-04T10:18:00.000-07:002012-05-04T10:18:01.722-07:00POTW: Uncovering the function of an intergenic SNPMy 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 "<a href="http://www.nature.com/ng/journal/v44/n4/full/ng.2204.html">Common genetic variants at the 11q13.3 renal cancer susceptibility locus influence binding of HIF to an enhancer of cyclin D1 expression</a>" by Schödel, et al. (Nature Genetics <b>44:</b>420-425).<br />
<br />
Although the authors had several clues that the risk SNPs would (likely) affect expression of <i>CCND1</i> (cyclin D1) in a manner regulated by hypoxia-induced factors - namely, that HIFs were known to regulate <i>CCND1</i> but from an unknown binding site and that <i>CCND1</i> is an established oncogene, among others - they accumulated much new data to nail down the role of EPAS1 (HIF-2) in regulating <i>CCND1</i> expression.<br />
<br />
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 <i>CCND1</i>.<br />
<br />
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.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-83736309063229967932012-04-27T08:08:00.000-07:002012-04-27T08:08:29.758-07:00POTW: Bitter taste perception - a follow-upBack in December, I posted an item on <a href="http://varigenome.blogspot.com/2011/12/bitter-taste-for-microbiome.html">taste receptors expressed in the gut</a> with mention of possible roles in sensing the microbiome. This week's Paper of the Week is entitled "<a href="http://www.ncbi.nlm.nih.gov/pubmed/22130969">Evolution of functionally diverse alleles associated with PTC bitter taste sensitivity in Africa</a>" by the Tishkoff group and heightens those earlier, intriguing possibilities.<br />
<br />
The publication dissects the long evolutionary history of the <i>TAS2R38</i> gene encoding a bitter taste receptor. From RefSeq, we know that <i><a href="http://www.ncbi.nlm.nih.gov/gene/5726">TAS2R38</a></i> 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 <i>Brassica</i> 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 (<a href="http://www.ncbi.nlm.nih.gov/pubmed/22384968">Keller, KL 2012 J Food Science 77:S143</a>). 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, <i>et al</i> show that is not likely to be the case.<br />
<br />
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 <i>TAS2R38</i> in Africa. Importantly, different types of selection may have acted upon the noncoding regions compared to the <i>TAS2R38</i> coding exon in all populations.<br />
<br />
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 <a href="http://www.ncbi.nlm.nih.gov/pubmed/18024184">human gastrointestinal tract</a> and <a href="http://www.ncbi.nlm.nih.gov/pubmed?term=19628819">lungs</a> (<a href="http://www.ncbi.nlm.nih.gov/pubmed/20972434">second reference</a>). Here this expression can affect <a href="http://www.ncbi.nlm.nih.gov/pubmed/19092995">glucose and insulin levels</a> (Dotson et al. 2008), eliminate <a href="http://www.ncbi.nlm.nih.gov/pubmed?term=19628819">harmful inhaled substances</a>, and promote <a href="http://www.ncbi.nlm.nih.gov/pubmed/20972434">relaxation of airways for better breathing</a>. Thus, bitter taste loci, including <i>TAS2R38</i>, posses various functions and, as the authors write "raise[s] the possibility that common variants at <i>TAS2R38</i> 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 <i>TAS2R38</i> haplotypes.<br />
<br />
Third, the genetic analysis and evolutionary history of <i>TAS2R38</i> 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.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-58120261057470054892012-03-16T13:23:00.004-07:002012-03-16T13:41:00.069-07:00POTW: Evolutionary constraints and the discovery of disease markersMy selection for Paper of the Week for 16 March 2012 is by Joel Dudley, <span style="font-style:italic;">et al</span>. and published as a <a href="http://mbe.oxfordjournals.org/content/early/2012/03/01/molbev.mss079.full.pdf+html">letter in Molecular Biology & Evolution</a>. Its title is "Evolutionary meta-analysis of association studies reveals ancient constraints affecting disease marker discovery."<br /><br />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.<br /><br />As many GWAS SNPs alter gene expression - either through altered <a href="http://www.ncbi.nlm.nih.gov/pubmed/22300769">transcription factor binding</a> or <a href="http://www.ncbi.nlm.nih.gov/pubmed/21995669">microRNA-mRNA interaction</a>, 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 <a href="http://www.ncbi.nlm.nih.gov/pubmed/20154611">published</a> in 2010. <br /><br />The article by Dudley, <span style="font-style:italic;">et al</span>. is really nice work and one whose insight we will use to inform our GWAS analysis.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-7126775641721477112012-03-16T13:08:00.003-07:002012-03-16T13:23:06.192-07:00POTW: Exercise and gene methylationThe Paper of the Week for 9 March 2012 was entitled "<a href="http://www.ncbi.nlm.nih.gov/pubmed/22405075">Acute Exercise Remodels Promoter Methylation in Human Skeletal Muscle</a>" by Barres, <span style="font-style:italic;">et al</span>. It appeared in Cell Metabolism as a Short Article. <br /><br />The exercise test was performed on a stationary bicycle. One cohort of subjects were exercised until reaching either 40% or 80% of VO<sub>2</sub> peak. A second cohort was exercised until 1,674 kJ were expended. These were acute interventions, making the findings all the more remarkable.<br /><br />I found the following to be key points of this paper:<br /><br />1. In both healthy, sedentary women and men, it was observed that whole genome methylation was decreased in skeletal muscle.<br /><br />2. While exercise induced expression of <span style="font-style:italic;">PPARGC1A</span> (<span style="font-style:italic;">PGC-1α</span>), <span style="font-style:italic;">PDK4</span>, and <span style="font-style:italic;">PPARD</span>, the authors also noted reduced methylation at each of the promoters for these genes.<br /><br />PPARGC1A is a key transcriptional regulator of OXPHOS (oxidative phosphorylation) genes. It is also an important type 2 diabetes gene.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0tag:blogger.com,1999:blog-8284945886307996607.post-79207642334920871902012-03-02T11:49:00.002-08:002012-03-02T14:05:19.860-08:00POTW: Epigenetics and cognitive functionThis weeks Paper of the Week adds some detail to connections between cognitive function and epigenetics as histone modifications. The <a href="http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10849.html">paper</a> is "<span style="font-weight:bold;">An epigenetic blockade of cognitive functions in the neurodegenerating brain</span>" by Gräff, et al. The paper was published in <span style="font-style:italic;">Nature</span> on 29 Feb 2012.<br /><br />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 <span style="font-style:italic;">in vitro</span>, in two mouse models of neurodegeneration and in patients with Alzheimer’s disease.<br /><br />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 <span style="font-style:italic;">APOE</span> 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.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com1tag:blogger.com,1999:blog-8284945886307996607.post-6281386001553232192012-02-24T07:05:00.003-08:002012-02-24T10:06:55.837-08:00Proteomics of fastingIt was with keen interest that I read the <a href="http://www.biomedcentral.com/content/pdf/1755-8794-4-24.pdf">article</a> by Bouwman, et al. entitled “<span style="font-weight:bold;">2D-electrophoresis and multiplex immunoassay proteomic analysis of different body fluids and cellular components reveal known and novel markers for extended fasting</span>,” 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.<br /><br />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 (<span style="font-style:italic;">see figure 3</span>). 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.”<br /><br />This is not surprising given that of the 44 different proteins identified as responding to extended fasting (<span style="font-style:italic;">see tables 2 & 3, figure 4</span>), 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.<br /><br />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.<br /><br />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 <span style="font-style:italic;">IL10</span>, <span style="font-style:italic;">IL1B</span>, <span style="font-style:italic;">TNF</span>, <span style="font-style:italic;">SERPINE1</span>, <span style="font-style:italic;">INS</span> and <span style="font-style:italic;">CCL2</span> respond to extended fasting and are members of the Insulin resistance inflammation network (Olefsky, Glass (2010) in a review of <a href="http://www.ncbi.nlm.nih.gov/pubmed/20148674">Macrophages, Inflammation and Insulin Resistance</a> (Annu Rev Physiol 72:219-46)). Furthermore, <span style="font-style:italic;">VCAM1</span>, <span style="font-style:italic;">APCS</span>, <span style="font-style:italic;">CRP</span>, <span style="font-style:italic;">IL1B</span>, <span style="font-style:italic;">TNF</span>, <span style="font-style:italic;">IL18</span> and <span style="font-style:italic;">CCL2</span> are assigned an inflammation role within the set of <a href="http://www.ncbi.nlm.nih.gov/pubmed/20936127">PPARA target genes</a> (Rakhshandehroo, Kersten 2010 PPAR Research pii: 612089).<br /><br />A second comparison I undertook was to look at the number of genes responding to the fasting intervention and to an intervention termed AIDM: <a href="http://www.ncbi.nlm.nih.gov/pubmed/20181810">Anti-inflammatory dietary mix</a> (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 <span style="font-style:italic;">FABP3</span>, <span style="font-style:italic;">VCAM1</span>, <span style="font-style:italic;">IL12A</span>, <span style="font-style:italic;">AFP</span>, <span style="font-style:italic;">FTH1</span>, <span style="font-style:italic;">IL18</span>, <span style="font-style:italic;">APOA1</span> and <span style="font-style:italic;">F7</span>. 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.<br /><br />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 <a href="http://www.ncbi.nlm.nih.gov/pubmed/20681635">Rosenow, et al</a> 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.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com1tag:blogger.com,1999:blog-8284945886307996607.post-15229771344506755652012-02-23T13:24:00.005-08:002012-02-23T13:37:16.423-08:00POTW: January, 2012 choicesThree papers published last month that I found to be of interest are listed here. These are:<br /><br /><span style="font-weight:bold;">The mystery of missing heritability: Genetic interactions create phantom heritability</span>, by Zuk, et al. <a href="http:///">This</a> addresses the missing heritability question, suggesting that "the total heritability may be much smaller and thus the proportion of heritability explained much larger."<br /><br /><span style="font-weight:bold;">Characterisation and discovery of novel miRNAs and moRNAs in JAK2V617F mutated SET2 cells</span>, by Botoluzzi, et al. What interested me in <a href="http://www.ncbi.nlm.nih.gov/pubmed/22223824">this article</a> was the generation of novel microRNAs that were induced by the cancerous state triggered by this <span style="font-style:italic;">JAK2</span> variant. This indicates to me that the microRNA realm is broad and rich with many as yet undiscovered relationships.<br /><br />The PLoS One paper entitled "<span style="font-weight:bold;">Genetic signatures of exceptional longevity in humans</span>," by Sebastiani, et al. We here were very curious how <a href="http://www.ncbi.nlm.nih.gov/pubmed/22279548">this</a> was different from the version retracted from Science and what findings are now reported. <span style="font-style: italic;">TOMM40</span> near <span style="font-style: italic;">APOE</span> is indeed interesting.Larry_Parnellhttp://www.blogger.com/profile/12512295496896559084noreply@blogger.com0