Genomics 99 (2012) 144–151

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Genomics

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Gene transcripts associated with BMI in the motor cortex and caudate nucleus ofcalorie restricted rhesus monkeys

Amanda C. Mitchell a,b, Rehana K. Leak c,d, Michael J. Zigmond d, Judy L. Cameron e,f, Károly Mirnics a,b,⁎a Department of Psychiatry, Vanderbilt University, Nashville, TN, USAb Vanderbilt Kennedy Center for Research on Human Development, Vanderbilt University, Nashville, TN, USAc Division of Pharmaceutical Sciences, Mylan School of Pharmacy, Duquesne University, Pittsburgh, PA, USAd Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USAe Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USAf Oregon National Primate Research Center, Beaverton, OR, USA

⁎ Corresponding author at: Department of PsychiatryMRB III, 465 21st Avenue South, Nashville TN 37232, US

E-mail address: karoly.mirnics@vanderbilt.edu (K. M

0888-7543/$ – see front matter © 2011 Elsevier Inc. Alldoi:10.1016/j.ygeno.2011.12.006

a b s t r a c t

a r t i c l e i n f o

Article history:Received 7 November 2011Accepted 20 December 2011Available online 29 December 2011

Keywords:DNA microarrayRhesus monkeyBMIMotor cortexCaudate nucleusGene expressionERK pathway

Obesity affects over 500 million people worldwide, and has far reaching negative health effects. Given that highbody mass index (BMI) and insulin resistance are associated with alterations in many regions of brain and thatphysical activity can decrease obesity, we hypothesized that in Rhesus monkeys (Macaca mulatta) fed a highfat diet andwho subsequently received reduced calories BMIwould be associatedwith a unique gene expressionsignature in motor regions of the brain implicated in neurodegenerative disorders. In the motor cortex with in-creased BMI we saw the upregulation of genes involved in apoptosis, altered gene expression inmetabolic path-ways, and the downregulation of pERK1/2 (MAPK1), a protein involved in cellular survival. In the caudatenucleus with increased BMI we saw the upregulation of known obesity related genes (the insulin receptor(INSR) and the glucagon-like peptide-2 receptor (GLP2R)), apoptosis related genes, and altered expression ofgenes involved in various metabolic processes. These studies suggest that the effects of high BMI on the braintranscriptome persist regardless of two months of calorie restriction. We hypothesize that active lifestyleswith low BMIs together create a brain homeostasis more conducive to brain resiliency and neuronal survival.

© 2011 Elsevier Inc. All rights reserved.

1. Introduction

Since 1980 the number of people worldwide considered obese hasdoubled with over 1.5 billion people now considered overweight(body mass index, BMI, 25 or higher) and 500 million people consid-ered obese (BMI 30 or higher) [1]. The negative effects of obesity onhealth are far reaching. As weight increases to levels defined as over-weight and obese, the risks for the following conditions also increase:coronary heart disease, type 2 diabetes, cancers, hypertension, dysli-pidemia, stroke, liver and gallbladder disease, sleep apnea, osteoar-thritis, and gynecological problems [2–4]. According to theAmerican Medical Association the best ways to decrease the risk ofobesity are to eat a diet low in fat and to increase physical activitylevels [5]. Indeed, modern sedentary life is associated with increasedrisks for obesity, as well as cardiovascular disease, type II diabetes [6],and depression [7,8].

In the hypothalamus of the central nervous system (CNS) both in-sulin and leptin control body weight and feeding behavior. Leptin, ahormone derived from fat circulating in the blood, informs the

, Vanderbilt University, 8130AA.irnics).

rights reserved.

hypothalamus via JAK/STAT pathway about the energy status of pe-ripheral tissues [9], and reduces the expression of neuropeptides(neuropeptide Y (NPY), agouti-related peptide (AgRP), and melano-cyte stimulating hormone (MSH also known as POMC)) that stimu-late feeding [9–11]. Insulin, another circulating hormone, serves as afeedback signal from the periphery to reduce appetite and bodyweight in the hypothalamus [12] and serves to cause peripheral tis-sues to take glucose up from the blood and store it as glycogen [13].

Furthermore, insulin receptors (receptor tyrosine kinases) locatedthroughout the brain signal through both the PI3K/AKT and ERK1/2survival signaling pathways [14]. In obesity resistance to leptin andinsulin impairs proper signaling about energy status that results in in-creased body weight and feeding behaviors. The AKT and ERK1/2 sig-naling pathways are also downstream of many neurotrophic factorsignaling pathways and activation of these pathways leads to neuro-protection in several regions of the brain [15,16]. Thus, insulin resis-tance, may lead to decreased activation of AKT and ERK1/2,increased apoptosis, and decreased survival of neurons. This could ex-plain why type II diabetes (insulin resistance) is often linked to Alz-heimer's disease, a neurodegenerative disorder, and could be evenfurther detrimental to the total health of the brain [17].

Insulin resistance that results in impaired signaling also affectsother regions of the brain [18]. The effects of insulin on cortical

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activity are negatively related to the amount of body fat and the de-gree of peripheral insulin resistance [19]. Furthermore, decreasedbrain volumes have been seen in the frontal lobe, the temporal lobe,the anterior cingulate, the hippocampus, and the basal ganglia withhigh BMI [20]. Using correlations of BMI with brain volumes thesame group showed that overweight and obese individual's brainsage prematurely [21]. Other studies also link higher BMI with smallerregional brain volumes [21–24]. These findings provide evidence forcerebral insulin resistance in overweight humans.

Given that high BMI is associatedwith insulin resistance, that insulinresistance is associated with specific brain alterations, and that physicalactivity can decrease obesity, we hypothesized thatwe could find genesassociatedwith BMI inmotor regions of the brain.We investigated tran-scriptome changes associatedwith BMI in themotor cortex and caudatenucleus of 14 Rhesus monkeys with DNA microarrays. We found a dis-tinct BMI associated gene expression signature in both themotor cortexand the caudate nucleus, and decreased protein phosphorylation of asurvival kinase, pERK1/2, in the motor cortex. Our studies indicatethat obesity is also associated with molecular changes in motor regionsof the brain that these changes include alterations in metabolic and ap-optotic related genes and that increases in BMI are associated with de-creases in the phosphorylation of a survival kinase.

The same cohort ofmonkeyswas used in a previous calorie restrictionstudy, as numerous studies indicate that calorie restriction, like exercise,is neuroprotective. To mimic the typical American diet Rhesus monkeyswere fed a diet high in fat for the last 3 years of their life. They also under-went one month of a 30% calorie reduction followed by one month of a60% calorie reduction at the end of the study [25]. This is typical of a diet-ing person, and increases the production of BDNF and activates the AKTand ERK1/2 signaling and survival pathways in the brain [26]. While cal-orie restriction could theoretically represent a potential confound in ourexperiments as it may overcome some of the effects of a high fat diet andinsulin resistance [27], this was not the case: decreased pERK1/2, in-creased CASP9 & FOXO3mRNA, and an enrichment of apoptosis andme-tabolism related genes, all indicate decreased neuronal survival,suggesting that calorie restriction does not overcome the transcriptionalsignature of high BMIs in the brain.

2. Results

2.1. Microarray data analysis reveals a BMI-associated gene expressionpattern

We hypothesized that BMI would be associated with a uniquegene expression signature in the motor cortex and caudate nucleusthat may be of importance to neurodegenerative disorders given ob-served alterations in the brain in insulin resistance. To discoverother alterations we performed whole genome expression profilingof the motor cortex of 14 female, calorie restricted rhesus monkeyswith a wide range of BMIs using both Affymetrix Rhesus Macaque Ge-nome (RhG) and Human Genome (HG) microarrays (Fig. 1). The rhe-sus and human genomes share 93% sequence identity [28] and humanmicroarrays successfully have been used to query rhesus monkeygene expression changes in the past [29]. Whereas the AffymetrixRhG microarrays provided us with the most specific hybridization in-formation for rhesus monkeys, the Affymetrix HG microarrays con-tained more extensive and higher quality annotations.

Microarray data were log2 transformed, RMA-normalized [30,31]and linearly modeled with BMI. We focused on convergent findingsbetween the RhG and HG microarrays, which enabled us to identifya strong and distinct BMI-associated gene expression signature inthe motor cortex and caudate nucleus. Two types of data-mining ap-proaches were employed. First, we identified the transcripts thatwere associated with BMI, followed by a molecular pathway analysisthat revealed the most affected BMI-associated groups of interdepen-dent genes.

We identified 27 transcripts that were associated with BMI in themotor cortex (17 positively correlated and 10 negatively correlated)and 51 transcripts that were associated with BMI in the caudate nu-cleus (41 positively correlated and 10 negatively correlated) (Supple-mentary Table 1 and 2). All altered transcripts in the motor cortexand in the caudate nucleus were changed in the same direction onboth human and monkey microarrays, suggesting a high degree ofconcordance between the two data sets, and a very low false discov-ery rate in our experiment. Our Pearson Hierarchal Clustering [32]revealed a progression of gene expression levels from monkeys withhigh BMI to monkeys with to low BMI (Fig. 2). Furthermore, we vali-dated increased expression of caspase 9 (CASP9) in the motor cortexand increased expression of forkhead box O3 (FOXO3) in the caudatenucleus with increasing BMI by qPCR (Fig. 3).

Next, we performed Gene Ontology (GO) Biological Process analy-sis (Supplementary Table 3) using the Gene Ontology EnrichmentAnalysis Software Toolkit (GOEAST) [33,34] and GSEA assessmentusing the Broad Institute's Gene Pattern Software [35] (Tables 1 and2) to decipher which biological processes were involved with theBMI associated gene expression signature. GO Biological Process anal-ysis searched our differentially expressed gene datasets (Supplemen-tary Table 1 and 2) for significantly altered GO Biological Processes.While these analyses revealed no significant biological processes as-sociated with significant gene expression changes in the motor cor-tex, in the caudate nucleus we identified a large numbersignificantly altered biological processes that suggested alteration ofgene networks related to metabolism, cell death, and cellular devel-opment (Supplementary Table 3).

We also assessed our data for the enrichment of functional genepathways using Gene Set Enrichment Analysis (GSEA) [35] using thegene sets in the Molecular Signatures Database (MSigDBv.3.0) for thehuman (HG) arrays. We found 9 gene sets positively enriched withBMI in the motor cortex (Table 1), and 3 gene sets negatively enrichedwith BMI in the caudate nucleus (Table 2). Prominently, in the motorcortex the BMI-associated changes included VEGFA targets (genesupregulated in HUVEC endothelium cells at 30 min after VEGFA stimu-lation [36]) and AKT phosphorylation targets in the cytosol, while in thecaudate nucleus significant gene sets included HDAC targets (geneswhose transcription is altered by histone deacetylase inhibitors [37]).

2.2. The observed gene expression changes are putatively a result ofaltered ERK1/2 signaling

Several of the BMI-dependent genes attracted further attention.Our data identified inversely correlated mRNA levels of forkheadbox O3 (FOXO3) with BMI in the caudate nucleus. FOXO3 is activatedvia the growth factor pathway, and involved in apoptosis and neuro-nal survival [15]. Furthermore, as both AKT and ERK1/2 are effectorsignaling pathways of neurotrophic factors [15,16,26,38], we next in-vestigated the association of the AKT and ERK protein phosphoryla-tion state with BMI (Fig. 4) through Western analysis. While AKTlevels and its phosphorylation state was unchanged, we identified astrong negative correlation of both ERK1/2 and pERK1/2 levels withBMI (pERK1/2/tubulin r=−0.663 with p=0.010 and pERK1/2/ERK1/2 of −0.595 with p=0.025) (Fig. 4). As pERK1/2 phosphory-lates CREB, a transcription factor in the nucleus to reduce apoptosis[38] and activate transcription of neuronal survival genes [15], the de-creased phosphorylation of ERK1/2 with increasing BMIs suggeststhat this might be a critical signaling pathway mediating some ofthe detrimental effects of BMI on the gene expression in the brain.

3. Discussion

In summary, we discovered a distinct gene expression signatureassociated with BMI after calorie restriction in the motor cortex andcaudate nucleus. Our linear model allowed us to model BMI and

Fig. 1. Experimental design. Motor cortex and caudate nucleus tissue was harvested from 14 female rhesus macaques with wide ranges of BMI. The motor cortex and caudate nu-cleus transcriptomes were profiled separately using both Affymetrix Rhesus Macaque Genome microarrays (RhG) and Affymetrix Human Genome U133 2.0 Plus microarrays (HG).We used a linear model to define gene expression associated with BMI (gene value=α+β (BMI)) for both microarrays separately. Probes were cross matched based on sequencesimilarity and significance was defined if both microarray models (i) were significant with pb0.05, and (ii) had an interquartile range (IQR)>0.263 corresponding to a 20% geneexpression change. Significant genes are contained in Supplementary Tables 1 and 2, and clustered in Fig. 2. Pathway analysis was performed using both Gene Ontology (GO) con-tained in Supplementary Tables 3 and 4, and Gene Set Enrichment Analysis (GSEA) contained in Tables 1 and 2. Note that the human and monkey data are highly concordant for aset of genes both in the motor cortex and caudate nucleus.

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gene expression continuously, and to find a gene expression signa-ture with 100% concordance in directionality between significantprobes on RhG and HGmicroarrays. We saw increases in apoptotic re-lated genes and changes in metabolic related genes across both brainregions, suggesting that motor regions of the brain are affected at thetranscriptional level by obesity. Decreased ERK1/2 phosphorylationwas associated with increasing BMI. Activation of this prototypicalsurvival kinase provides a potential explanation for the enrichmentof pro-apoptotic genes in animals with high BMI and putatively

explains how obesity raises the risk for reduced brain volume[21–24]. Furthermore, leptin and insulin are both known to activateERK1/2 in addition to the AKT pathway [14,39].

Although we did not find evidence of BMI-dependent changes tothe AKT signaling pathway, we cannot rule out the possibility ofcross talk between the AKT and ERK1/2 pathways [40–43] in thenon-human primate brain. Furthermore, our previous data suggestthat physical activity is positively correlated with pAKT protein levelsin non-human primates [44], potentially leading to a double-risk to

Fig. 2. Hierarchal clustering of gene expression associated with BMI. Hierarchical clustering was performed on log2-transformed expression level of genes associated with BMI. Ver-tical column labels indicate BMI in decreasing order. Genes, denoted by Affymetrix Human Probes and NCBI gene symbols, were clustered horizontally. Each colored square rep-resents a normalized gene expression value with color intensity proportional to magnitude of change (blue indicates a decrease in expression and red an increase). (a) MotorCortex Gene Expression Signature for Rhesus Genome Microarrays. (b) Motor Cortex Gene Expression Signature for Human Genome Microarrays. (c) Caudate Nucleus Gene Expres-sion Signature for Rhesus Genome Microarrays. (d) Caudate Nucleus Gene Expression Signature for Human Genome Microarrays. Note that in both structures there is a progressionof gene expression values from monkeys with low BMI to monkeys with high BMI, and the data are concordant across the human and monkey array platforms.

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the brain: the sedentary lifestyle might decrease pAKT signaling, andincreased BMI can repress the activation of the neuronal survival ki-nase ERK1/2. These two events, in concert might significantly in-crease brain susceptibility to various insults. Although it is temptingto hypothesize that both of these events are the result of a changein a growth factor signaling pathway, we found relatively meager

evidence to support this theory at the level of the transcriptome.However, it is known that growth factor signaling is profoundly reg-ulated at the post-transcriptional level [16,45] and this should be atopic of further follow-up experiments.

The positive correlation between the expression of apoptosis-related genes and BMI is also noteworthy. Increased pro-apoptotic

Fig. 3. Validation of selected microarray-reported changes by qPCR. (a) Body Mass Index(BMI) was measured in kilograms per crown of rump length squared (kg/CRL2) whileCASP9 and FOXO3 relative expression levels were reported in threshold cycle differencerelative to β-actin and peptidylprolyl isomerase D respectively (ΔCT, dCT). CASP9 andFOXO3 gene expressions were significantly positively correlated with BMI. (b) Scatterplot showing qPCR expression (1/dCT) versus BMI (CRL2) for CASP9. (c) Scatter plot show-ing qPCR expression (1/dCT) versus BMI (CRL2) for FOXO3. Note that the microarray-reported expression changes were successfully validated by qPCR.

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gene expression can lead to increased vulnerability of neurons to var-ious environmental insults, and the decreased brain volume observedin persons with high BMI [21–24] might be closed linked to the geneexpression changes we uncovered.

Since our monkeys were placed on a calorie restriction diet imme-diately prior to transcriptome analyses some of the deleterious effectsassociated with high BMI may have been masked. A number of previ-ous studies have demonstrated many beneficial effects of calorie re-striction on health, the rate of aging, and longevity [46]. Thesefindings suggest important roles for the plasma membrane redox sys-tem in protecting brain cells against age-related increases in oxidativeand metabolic stress, and these findings might be directly related tothe BMI-correlated increases in pro-apoptotic gene expression [47].Furthermore, our results are in also in concordance with previous

transcriptome profiling studies performed on monkeys that under-went dietary restriction [26]. The neuroprotective effect of dietary re-striction reported increased production of neurotrophic factors andprotein chaperones, resulting in protection against oxyradical pro-duction, stabilization of cellular calcium homeostasis, and inhibitionof apoptosis.

Finally, based on our studies and previous literature we concludethat active lifestyles with low BMI create a brain homeostasis moreconducive to brain resiliency and neuronal survival. Brain health isclearly linked to body health, and, we now show the converse, thatmaintaining a health BMI is also essential for maintaining a healthybrain transcriptome.

4. Materials and methods

4.1. Subjects

Fourteen adult female ovariectomized rhesus monkeys (Macacamulatta) were allowed to eat a high fat diet freely for three yearswith care methods previously published [25,48,49]. Their activityand percent body fat were measured throughout the diet and werefound to be extremely stable. As part of a different study they werethen placed on a two month calorie reduction diet. Food compositionwas the same, but their calorie intake was reduced by 30% the firstmonth and 60% the second month from their normal intake. TheirBMI was measured in kilograms/crown of rump length squared (kg/CRL2) at the end of the study. Monkeys were deeply anesthetizedwith ketamine/pentobarbital and their entire brain was removed.The brain was then hemisected by a midline sagittal cut. The righthemisphere was cut into 5-mm thick coronal blocks, flash-frozen inisopentane over dry ice, and stored at −80 °C until use.

4.2. Sample preparation and hybridization

Motor cortex and caudate nucleus brain tissue was homogenizedand total RNA isolated using TRIzol® reagent (Invitrogen, Carlsbad,CA) with RNA quality assessed via analysis on an Agilent 2100 Bioa-nalyzer. Only samples with an RIN>7.0 were considered for furtheranalysis. The samples were prepared using the Enzo BioArray™Single-Round RNA Amplification and Biotin Labeling System. Thesamples were primed with a standard T7-oligo(dT) primer andcDNA synthesis was performed using 2 μg of total RNA according tothe Affymetrix® manufacturer's protocol. Amplified antisense RNA(aRNA) was produced using in vitro transcription directed by T7 po-lymerase. Fifteen micrograms of the purified and fragmented aRNAwere hybridized to GeneChip® Rhesus Macaque Genome (RhG) Ar-rays and GeneChip® Human Genome (HG) U133 Plus 2.0 Arrays.Image segmentation analysis and generation of DAT files was per-formed using Microarray Suite 5.0 (MAS5). All data is compliantwith the Minimum Information About a Microarray Experiment(MIAME) guidelines and raw data is available at ftp://mirnicslab.vanderbilt.edu/BMI microarrays/.

4.3. Microarray data analysis

Fourteen RhG arrays for the motor cortex and 14 RhG arrays forthe caudate nucleus were scanned and analyzed. Scaling factors ran-ged from 1.58 to 3.11 with backgrounds from 29.8 to 85 and percentpresent calls from 45 to 52%. Fourteen HG arrays for the motor cortexand 14 HG arrays for the caudate nucleus were scanned and analyzed.Scaling factors ranged from 6.26 to 15.28, noise was between 1.2 and1.8, and percent present calls were from 31.3% to 35.0%. Segmentedimages were normalized and log2 transformed using robust multi-array analysis (RMA) separately for Rhesus and Human arrays [50]with RMA normalized expression levels utilized for all of our subse-quent analyses.

Table 1Molecular Signatures Database (MSigDB) significant gene sets in the motor cortex using human arrays (HG).

Name Size FDR q-val Enriched genes

Kuninger IGF1 vs. PDGFB targets up 41 0.104 TPM2, CKM, MYBPH, MYOG, RYR1, USP2, MYL1, TNNT2, TNNI1, ENAH,PYGM, C1QTNF3, PPFIA4, NCAM1, NPNT, SGCA, PKIA, ATP2A1, SMTN

One carbon compound metabolic process 26 0.128 NSUN2, DMAP1, PRMT5, NSD1, CARM1, DNMT3A, DNMT3B, EHMT1, PRM17, FOS, PRMT2Module 440 18 0.173 CKM, OAT, ASL, GLUD1, BBOX1, CKB, ODC1, ACY1ABE VEGFA targets 30 min 19 0.179 HDC, DBC1, C2, EGR2, EGR3, CYR61, NR4A1, TRIB1Schuring STAT5A targets up 7 0.184 LIF, TRAF4, PIM1Module 199 57 0.197 EPHB6, TEK, IL4R, KIT, KDR, PTK7, EPHB1, TIE1, IL2RB, MERTK, EPHA4, CCL2, MYLK, AXL,

CDK5R1, ERBB2, EPHA7, MAP2K7, PDK4, FGFR3, IL13RA1, IL10RA, ROR2, EFNB3, PDGFRAReactome AKT phosphorylates targets in the cytosol 14 0.197 AKT2, CASP9, AKT1, TSC2S adenosylmethionine dependent methyltransferase activity 22 0.198 METTL1, NSUN2, NSD1, DNMT3A, DNMT3B, EHMT1, PEMT, PRMT7Methyltransferase activity 34 0.216 ATIC, METTL1, NSUN2, PRMT5, NSD1, CARM1, DNMT3A, DNMT3B, EHMT1,

PBMT, PRMT7, SUV39H1, GART

Table 2Molecular Signatures Database (MSigDB) significant gene sets in the caudate nucleuson the human arrays (HG). Gene Set Enrichment Analysis (GSEA) was performed onthe caudate nucleus data set. Three gene sets were positively correlated with BMI,had a nominal p-valueb0.001, and a false discovery rate (FDR)b0.25.

Name Size NOMp-val

FDRq-val

Correlationwith BMI

Enriched genes

Cellmaturation

15 0.000 0.137 – TP53, GADD45B, FAS, GSN, CASP3,GLRX, BAD, CCNE1, TXN, MCM3

Developmentalmaturation

17 0.000 0.202 – KCNIP2, MYH11, ACTA1, KRT19,EREG, IL21, PICK1, MYOZ1

MARKS HDACtargets up

18 0.000 0.238 – MYH11, ACTA1, KRT19, EREG,IL21, PICK1, MYOZ1

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We analyzed the microarrays for transcripts associated with BMI.We fit data from HG and RhG arrays using the model: log trans-formed gene expression value=α+β(BMI) [51]. RhG and HGprobe sets were matched on the basis of sequence similarity usingthe Affymetrix HG-U133 Plus 2.0 to Rhesus Best Match Spreadsheet.Genes were considered correlated BMI if (a) pb0.05, where H0:β=0 for RhG arrays; (b) pb0.05, where H0: β=0 dfor HG arrays;(c) if the interquartile range, IQR>0.263 on the RhG arrays, measur-ing the difference between the third and first quartiles was less than0.263 (representing a 20% change in gene expression values); and(d) if the interquartile range, IQR>0.263 on the HG arrays, measur-ing the difference between the third and first quartiles was less than0.263 (representing a 20% change in gene expression values). Thestrength of our model was tested for false discovery using a binomi-al probability test for the direction of change, where the H0:p=0.50. We tested our model using a binomial probability testwith a null hypothesis that p=0.50, which was rejected. Hierarchi-cal clustering of the replicating data was performed using GenePat-tern software [32]. Clustering was performed on log2 transformedGC-RMA normalized expression levels using row (gene) centeringand Pearson correlation.

4.4. Quantitative real-time PCR validation

As previously reported [44,49], we used the High Capacity cDNAArchive Kit® (Applied Biosystems, Foster City, CA) to synthesizecDNA from 500 ng of the same total RNA used for microarray analysisin 11 monkeys. Priming was performed with random hexamers. Foreach sample, amplified product differences were measured withthree independent replicates using SYBR Green chemistry-based de-tection [30]. β-actin was used as the endogenous reference gene forcaspase 9 (CASP9) and peptidylprolyl isomerase D was used as theendogenous reference gene for forkhead box O3 (FOXO3). Primer se-quences for β-actin were 5′ GAT GTG GAT CAG CAA GCA 3′ and 5′AGA AAG GCT GTA ACG CAA CTA 3′, for CASP9 were 5′ GAG TGGCTC CTG GTA CGT TG 3′ and 5′ CCT TTC ACC GAA ACA GCA TT 3′, forpeptidylprolyl isomerase D (cyclophilin D, CYC) were 5′ GCA GACAAG GTC CCA AAG 3′ and 5′ GAA GTC ACC ACC CTG ACA C 3′, andfor forkhead box O3 (FOXO3) were 5′ GCA TTG TGT GTG TTG CTTCC 3′ and 5′ CTG AAA TCA CAC CCT GAG CA 3′. The efficiency foreach primer set was assessed prior to qPCR measurements, and aprimer set was considered valid if its efficiency was >90%. The qPCRreactions were carried out on an ABI Prism 7300 thermal cycler (Ap-plied Biosystems Inc.), quantified using ABI Prism 7300 SDS software(with the auto baseline and auto threshold detection options select-ed) and statistically analyzed using Pearson correlations.

4.5. Gene ontology (GO) and gene Set enrichment analysis (GSEA)

We assessed our data for the enrichment of functional gene pathwaysusing Gene Ontology (GO) Biological Process terms [33,34] and Gene Set

Enrichment Analysis (GSEA) [35]. First, we analyzed the GO BiologicalProcess terms for all of our transcripts significantly associated with BMIin the motor cortex and caudate nucleus using the Gene Ontology En-richment Analysis Software Toolkit (GOEAST). We reported significantGO biological processes using the hypergeometric test statistic, with aminimum of 5 genes, and a Benjamini–Yekutieli FDRb0.1 [52]. Second,we performed GSEA [35] using all of the gene sets in the Molecular Sig-natures Database (MSigDBv.3.0) for the human (HG) arrays. We reportenriched gene sets with a nominal pvalueb0.01 and a false discoveryrate (FDR)b0.25. Module 199 is most strongly associated with leukemiaand Module 440 is most strongly with associated liver cancer [53].

4.6. Western blots

Infrared imaging of immunoblots was used for assessing phosphor-ylation and total protein state of ERK1/2 and Akt (Li-Cor Biosciences,Lincoln, NE). Samples were sonicated in a Triton-based lysis buffer(Cell Signaling, Danvers, MA) and subjected to standard SDS-page im-munoblotting procedures. Blots were then blocked in 5% bovineserum albumin in Tris-buffered saline (TBS) for 1 h prior to incubationovernight at 4 °C in primary antibody made in the same blocking solu-tion with 0.1% Tween. Antibodies used were: rabbit anti-phospho-ERK1/2 (Cell Signaling, Cat. no. 9101), mouse anti-total ERK1/2 (Milli-pore, Cat. no. 05–1152), rabbit anti-phospho-Akt (Cell Signaling, Cat.no. 9271), mouse anti-total Akt (Cell Signaling, Cat. no. 2920). Mouseα-tubulin (Sigma-Aldrich, Cat. no. T5168) was used concurrently tocontrol for differences in protein loading. After overnight incubationwith primary antibody, blots were washed in TBS-tween and incubatedwith goat secondary antibodies raised against the appropriate IgG fluo-rescing in the infrared range (700 and 800 nm, Li-Cor Biosciences).After further washing, immunostained protein bands on the blotswere visualized on an Odyssey Infrared Imager and quantifiedwith Od-yssey software (Li-Cor Biosciences).

Acknowledgments

This work was supported by National Institutes of Health (NIH)R01 MH067234 and MH079299. Training support for Amanda C.

Fig. 4. PhosphoERK1/2 levels are negatively correlated with BMI. Figure denotes a scat-terplot showing BMI (kg/CRL2) versus protein ratios for pERK1/2/tubulin (blue dia-mond) and for pERK1/2/ERK1/2 (red square). There is a significant negativecorrelation between BMI and pERK1/2/tubulin protein ratio (r=−0.663; p=0.010)and a significant negative correlation of BMI and protein ratio for phospho ERK1/2/total ERK1/2 (r=−0.595; p=0.025). Inset provides examples of ERK1/2 protein levelsin four monkeys with various BMI, with BMI levels denoted above the images.

150 A.C. Mitchell et al. / Genomics 99 (2012) 144–151

Mitchell was provided by National Institutes of Health (NIH) T32MH064913-07. Additionally, we are thankful to Lily Wang for Biosta-tistics advice.

Appendix A. Supplementary data

Supplementary data to this article can be found online at doi:10.1016/j.ygeno.2011.12.006.

References

[1] World Health Organization, Obesity and OverweightURL:, http://www.who.int/mediacentre/factsheets/fs311/en/index.html2008.

[2] NIH and NHLBI Obesity Education Initiative, Clinical Guidelines on the Identifica-tion, Evaluation, and Treatment of Overweight and Obesity in Adults, NIH Publi-cation No. 98–4083, 1998.

[3] H. Yatsuya, K. Yamagishi, K.E. North, F.L. Brancati, J. Stevens, A.R. Folsom, Associ-ations of obesity measures with subtypes of ischemic stroke in the ARIC Study, J.Epidemiol. 20 (5) (2010) 347–354.

[4] D. Falkstedt, T. Hemmingsson, F. Rasmussen, I. Lundberg, Body mass index in lateadolescence and its association with coronary heart disease and stroke in middleage among Swedish men, Int. J. Obes. (Lond) 31 (5) (2007) 777–783.

[5] R.F. Kushner, Roadmaps for Clinical Practice: Case Studies in Disease Preventionand Health Promotion—Assessment and Management of Adult Obesity: A Primerfor Physicians, American Medical Association, 2003.

[6] T.H. Marwick, M.D. Hordern, T. Miller, D.A. Chyun, A.G. Bertoni, R.S. Blumenthal,G. Philippides, A. Rocchini, Exercise training for type 2 diabetes mellitus: impacton cardiovascular risk: a scientific statement from the American Heart Associa-tion, Circulation 119 (25) (2009) 3244–3262.

[7] D.A. Lawlor, S.W. Hopker, The effectiveness of exercise as an intervention in themanagement of depression: systematic review and meta-regression analysis ofrandomised controlled trials, BMJ 322 (7289) (2001) 763–767.

[8] D.E. Vance, V.G. Wadley, K.K. Ball, D.L. Roenker, M. Rizzo, The effects of physicalactivity and sedentary behavior on cognitive health in older adults, J. AgingPhys. Activ. 13 (3) (2005) 294–313.

[9] J.M. Friedman, J.L. Halaas, Leptin and the regulation of body weight in mammals,Nature 395 (6704) (1998) 763–770.

[10] R.S. Ahima, S.M. Hileman, Postnatal regulation of hypothalamic neuropeptide ex-pression by leptin: implications for energy balance and body weight regulation,Regul. Pept. 92 (1–3) (2000) 1–7.

[11] J.S. Flier, E. Maratos-Flier, Lasker lauds leptin, Cell 143 (1) (2010) 9–12.[12] G.J. Morton, Hypothalamic leptin regulation of energy homeostasis and glucose

metabolism, J. Physiol. 583 (Pt 2) (2007) 437–443.[13] D. Hubble, Insulin resistance, Br. Med. J. 2 (4845) (1954) 1022–1024.[14] Y. Kido, J. Nakae, D. Accili, Clinical review 125: the insulin receptor and its cellular

targets, J. Clin. Endocrinol. Metab. 86 (3) (2001) 972–979.[15] A. Brunet, A. Bonni, M.J. Zigmond, M.Z. Lin, P. Juo, L.S. Hu, M.J. Anderson, K.C.

Arden, J. Blenis, M.E. Greenberg, Akt promotes cell survival by phosphorylatingand inhibiting a Forkhead transcription factor, Cell 96 (6) (1999) 857–868.

[16] N. Lindgren, R.K. Leak, K.M. Carlson, A.D. Smith, M.J. Zigmond, Activation of theextracellular signal-regulated kinases 1 and 2 by glial cell line-derived neuro-trophic factor and its relation to neuroprotection in a mouse model of Parkinson'sdisease, J. Neurosci. Res. 86 (9) (2008) 2039–2049.

[17] Z. Arvanitakis, R.S. Wilson, J.L. Bienias, D.A. Evans, D.A. Bennett, Diabetes mellitusand risk of Alzheimer disease and decline in cognitive function, Arch. Neurol. 61(5) (2004) 661–666.

[18] M. Hallschmid, B. Schultes, Central nervous insulin resistance: a promising targetin the treatment of metabolic and cognitive disorders? Diabetologia 52 (11)(2009) 2264–2269.

[19] O. Tschritter, H. Preissl, A.M. Hennige, M. Stumvoll, K. Porubska, R. Frost, H. Marx,B. Klosel, W. Lutzenberger, N. Birbaumer, et al., The cerebrocortical response tohyperinsulinemia is reduced in overweight humans: a magnetoencephalographicstudy, Proc. Natl. Acad. Sci. U. S. A. 103 (32) (2006) 12103–12108.

[20] A.J. Ho, C.A. Raji, J.T. Becker, O.L. Lopez, L.H. Kuller, X. Hua, S. Lee, D. Hibar, I.D.Dinov, J.L. Stein, et al., Obesity is linked with lower brain volume in 700 AD andMCI patients, Neurobiol. Aging 31 (8) (2010) 1326–1339.

[21] C.A. Raji, A.J. Ho, N.N. Parikshak, J.T. Becker, O.L. Lopez, L.H. Kuller, X. Hua, A.D.Leow, A.W. Toga, P.M. Thompson, Brain structure and obesity, Hum. BrainMapp. 31 (3) (2010) 353–364.

[22] D. Gustafson, L. Lissner, C. Bengtsson, C. Bjorkelund, I. Skoog, A 24-year follow-up of body mass index and cerebral atrophy, Neurology 63 (10) (2004)1876–1881.

[23] N. Pannacciulli, D.S. Le, K. Chen, E.M. Reiman, J. Krakoff, Relationships betweenplasma leptin concentrations and human brain structure: a voxel-based morpho-metric study, Neurosci. Lett. 412 (3) (2007) 248–253.

[24] Y. Taki, S. Kinomura, K. Sato, K. Inoue, R. Goto, K. Okada, S. Uchida, R. Kawashima,H. Fukuda, Relationship between body mass index and gray matter volume in1,428 healthy individuals, Obesity (Silver Spring) 16 (1) (2008) 119–124.

[25] E.L. Sullivan, F.H. Koegler, J.L. Cameron, Individual differences in physical activityare closely associated with changes in body weight in adult female rhesus mon-keys (Macaca mulatta), Am. J. Physiol. Regul. Integr. Comp. Physiol. 291 (3)(2006) R633–R642.

[26] T.A. Prolla, M.P. Mattson, Molecular mechanisms of brain aging and neurodegen-erative disorders: lessons from dietary restriction, Trends Neurosci. 24 (11Suppl.) (2001) S21–S31.

[27] G. Valdez, J.C. Tapia, H. Kang, G.D. Clemenson Jr., F.H. Gage, J.W. Lichtman, J.R.Sanes, Attenuation of age-related changes in mouse neuromuscular synapses bycaloric restriction and exercise, Proc. Natl. Acad. Sci. U. S. A. 107 (33) (2010)14863–14868.

[28] R.A. Gibbs, J. Rogers, M.G. Katze, R. Bumgarner, G.M. Weinstock, E.R. Mardis, K.A.Remington, R.L. Strausberg, J.C. Venter, R.K. Wilson, et al., Evolutionary and biomed-ical insights from the rhesusmacaque genome, Science 316 (5822) (2007) 222–234.

[29] M.J. Sabatini, P. Ebert, D.A. Lewis, P. Levitt, J.L. Cameron, K. Mirnics, Amygdalagene expression correlates of social behavior in monkeys experiencing maternalseparation, J. Neurosci. 27 (12) (2007) 3295–3304.

[30] D. Arion, T. Unger, D.A. Lewis, P. Levitt, K. Mirnics, Molecular evidence for in-creased expression of genes related to immune and chaperone function in theprefrontal cortex in schizophrenia, Biol. Psychiatry 62 (7) (2007) 711–721.

[31] D. Arion, T. Unger, D.A. Lewis, K. Mirnics, Molecular markers distinguishing supra-granular and infragranular layers in the human prefrontal cortex, Eur. J. Neurosci.25 (6) (2007) 1843–1854.

[32] A. Subramanian, P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A.Paulovich, S.L. Pomeroy, T.R. Golub, E.S. Lander, et al., Gene set enrichment anal-ysis: a knowledge-based approach for interpreting genome-wide expression pro-files, Proc. Natl. Acad. Sci. U. S. A. 102 (43) (2005) 15545–15550.

[33] M. Ashburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, A.P. Davis, K.Dolinski, S.S. Dwight, J.T. Eppig, et al., Gene ontology: tool for the unification of bi-ology. The Gene Ontology Consortium, Nat. Genet. 25 (1) (2000) 25–29.

[34] Q. Zheng, X.J. Wang, GOEAST: a web-based software toolkit for Gene Ontology en-richment analysis, Nucleic Acids Res. 36 (Web Server issue) (2008) W358–W363.

[35] A. Subramanian, H. Kuehn, J. Gould, P. Tamayo, J.P.Mesirov, GSEA-P: a desktop appli-cation for Gene Set Enrichment Analysis, Bioinformatics 23 (23) (2007) 3251–3253.

[36] M. Abe, Y. Sato, cDNA microarray analysis of the gene expression profile of VEGF-activated human umbilical vein endothelial cells, Angiogenesis 4 (4) (2001) 289–298.

[37] P.A. Marks, Discovery and development of SAHA as an anticancer agent, Onco-gene 26 (9) (2007) 1351–1356.

[38] E.M. Park, S. Cho, Enhanced ERK dependent CREB activation reduces apoptosis instaurosporine-treated human neuroblastoma SK-N-BE(2)C cells, Neurosci. Lett.402 (1–2) (2006) 190–194.

[39] A.S. Banks, S.M. Davis, S.H. Bates, M.G. Myers Jr., Activation of downstream signalsby the long form of the leptin receptor, J. Biol. Chem. 275 (19) (2000)14563–14572.

[40] M.K. Tang, H.Y. Zhou, J.W. Yam, A.S. Wong, c-Met overexpression contributes tothe acquired apoptotic resistance of nonadherent ovarian cancer cells through across talk mediated by phosphatidylinositol 3-kinase and extracellular signal-regulated kinase 1/2, Neoplasia 12 (2) (2010) 128–138.

[41] R. Dai, R. Chen, H. Li, Cross-talk between PI3K/Akt and MEK/ERK pathways medi-ates endoplasmic reticulum stress-induced cell cycle progression and cell deathin human hepatocellular carcinoma cells, Int. J. Oncol. 34 (6) (2009) 1749–1757.

[42] J.G. Shelton, W.L. Blalock, E.R. White, L.S. Steelman, J.A. McCubrey, Ability of theactivated PI3K/Akt oncoproteins to synergize with MEK1 and induce cell cycleprogression and abrogate the cytokine-dependence of hematopoietic cells, CellCycle 3 (4) (2004) 503–512.

[43] D.J. Levinthal, D.B. DeFranco, Transient phosphatidylinositol 3-kinase inhibitionprotects immature primary cortical neurons from oxidative toxicity via

151A.C. Mitchell et al. / Genomics 99 (2012) 144–151

suppression of extracellular signal-regulated kinase activation, J. Biol. Chem. 279(12) (2004) 11206–11213.

[44] A.C. Mitchell, R.K. Leak, K. Garbett, M.J. Zigmond, J.L. Cameron, K, Mirnics, Physicalactivity-associated gene expression signature in nonhuman primate motor cor-tex, Obesity (Silver Spring), 2011.

[45] M.J. Chen, A.A. Russo-Neustadt, Running exercise- and antidepressant-inducedincreases in growth and survival-associated signaling molecules are IGF-dependent, Growth Factors 25 (2) (2007) 118–131.

[46] J.W. Kemnitz, Calorie restriction and aging in nonhuman primates, ILAR J. 52 (1)(2011) 66–77.

[47] D.H. Hyun, S.S. Emerson, D.G. Jo, M.P. Mattson, R. de Cabo, Calorie restriction up-regulates the plasma membrane redox system in brain cells and suppresses oxi-dative stress during aging, Proc. Natl. Acad. Sci. U. S. A. 103 (52) (2006)19908–19912.

[48] E.L. Sullivan, J.L. Cameron, A rapidly occurring compensatory decrease in physicalactivity counteracts diet-induced weight loss in female monkeys, Am. J. Physiol.Regul. Integr. Comp. Physiol. 298 (4) (2010) R1068–R1074.

[49] A.C. Mitchell, G. Aldridge, S. Kohler, G. Stanton, E. Sullivan, K. Garbett, G. Faludi, K.Mirnics, J.L. Cameron, W. Greenough, Molecular correlates of spontaneous activi-ty in non-human primates, J. Neural Transm. 117 (12) (2010) 1353–1358.

[50] Z. Wu, R.A. Irizarry, Preprocessing of oligonucleotide array data, Nat. Biotechnol.22 (6) (2004) 656–658 author reply 658.

[51] E.J. Zabawski Jr., C.J. Cockerell, A middle aged female with a refractory comedoneof the upper lip, Dermatol. Online J. 4 (1) (1998) 4.

[52] Y. Benjamini, D. Drai, G. Elmer, N. Kafkafi, I. Golani, Controlling the false discoveryrate in behavior genetics research, Behav. Brain Res. 125 (1–2) (2001) 279–284.

[53] E. Segal, N. Friedman, D. Koller, A. Regev, A module map showing conditional ac-tivity of expression modules in cancer, Nat. Genet. 36 (10) (2004) 1090–1098.