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Variations in DNA elucidate molecular networks that cause disease
Author: Yanqing Chen
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"ARTICLES Variations in DNA elucidate molecular networks that cause disease Yanqing Chen 1 *, Jun Zhu 1 *, Pek Yee Lum 1 , Xia Yang 1 , Shirly Pinto 2 , Douglas J. MacNeil 2 , Chunsheng Zhang 1 , John Lamb 1 , Stephen Edwards 1 , Solveig K. Sieberts 1 , Amy Leonardson 1 , Lawrence W. Castellini 3 , Susanna Wang 3 , Marie-France Champy 6 , Bin Zhang 1 , Valur Emilsson 1 , Sudheer Doss 3 , Anatole Ghazalpour 3 , Steve Horvath 4 , Thomas A. Drake 5 , Aldons J. Lusis 3,4 & Eric E. Schadt 1 Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase b (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengtheningtheassociation between this network and metabolicdisease traits. Our analysis providesdirect experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors. A challenge in the post-genome era is deciphering the biological function of individual genes and gene networks that drive disease. Given the availability of low-cost, high-throughput technologies for genotyping hundreds of thousands of DNA markers, successes are being realized in identifying associations between DNA variants and diseases such as age-related macular degeneration 1?3 , diabetes 4 and obesity 5 . Although these and coming discoveries from a slew of genome-wideassociationstudiescurrently underwayprovideapeek into pathways that underlie disease, they are usually devoid of con- text, so elucidating the functional role of such genes in disease can linger for years, as has been the case for ApoE, an Alzheimer?s- susceptibility gene identified 15years ago 6 .Evenwhen an association to disease has been localized to a given region representing a single gene, in the absence of experimental support the gene cannot be definitively claimed to be the susceptibility gene. This problem is exacerbated in experimental crosses derived from inbred mouse strains,forwhichinadditiontotheproblemofinferringthefunction ofpositionallyclonedgenesfromthegeneticdataalone,theextentof linkage disequiliribum operating in such populations makes posi- tional cloning a difficult and time-consuming process. Analternativetotheforwardgeneticsapproachistheconstruction of molecular networks that define the molecular states of a system that underlie disease, where such networks are constructed from molecular phenotype data scored in populations that manifest dis- ease. The information that defines how variations in DNA lead to variations in complex traits flows through molecular networks. Characterizing molecularnetworksthatunderliecomplextraits such asdiseasecanprovideamorecomprehensiveview,whichinturncan lead to the direct identification of genes underlying disease processes andthe functionalrolesof thesegeneswithrespecttodisease.Recent studiescharacterizinggenenetworkshavedemonstratedhowgenetic loci associated with expression traits can be combined with clinical data to infer causal associations between expression and disease traits 7?12 . By leveraging DNA variations as a systematic source of perturbations on molecular networks and clinical traits, biological processes can be studied at the systems level, in addition to studying gene function at the level of individual pathways 13,14 . Here we report the development of an approach to uncover the components of co-expression networks that respond to variations in DNA associated with obesity-, diabetes- and atherosclerosis-related traits.Incontrasttoaforwardgeneticsapproach,weleveragequanti- tativetraitloci(QTL)associatedwithdiseasetoidentifycomponents of the co-expression network that are perturbed by the QTLand that in turn cause variations in disease traits. After constructing co- expression networks from liver and adipose tissues collected from a segregating mouse population, we identify sub-networks that are significantly associated with a complex of linked genetic loci related to obesity-, diabetes- and atherosclerosis-associated traits. A macro- phage-enriched metabolic sub-network was foundtobesignificantly enriched for expression traits supported as having a causal relation- ship with these metabolic traits. The connection to obesity and other metabolic syndrome traits is confirmed by validating three genes in this sub-network, Lpl, Lactb and Ppm1l, as previously unknown obesity genes. *These authors contributed equally to this work. 1 Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA. 2 Department of Metabolic Disorders, Merck & Co., Inc., 126 East Lincoln Avenue,Rahway,NewJersey07065,USA. 3 DepartmentofMicrobiology,MolecularGenetics,andImmunology, 4 DepartmentofHumanGenetics,and 5 DepartmentofPathologyand LaboratoryMedicine,UCLA,650YoungDriveSouth,LosAngeles,California90095,USA. 6 InstitutdeGenetiqueetdeBiologieMoleculaireetCellulaire,CNRS/INSERM/ULP,67404 Illkirch, France. Vol 452|27 March 2008|doi:10.1038/nature06757 429 Nature Publishing Group�2008 A complex linkage to metabolic traits A number of QTL mapping studies in experimental mouse cross populations have identified the distal half of chromosome 1 as a major contributor to metabolic traits such as weight, fat mass, and plasma glucose and cholesterol levels 15?18 . Much effort has been expendedtomapthequantitative traitgenes(QTGs)underlyingthis locus, and these efforts have met with some success. For example, apolipoprotein A-II (Apoa2) and tumour necrosis factor super- family, member 4 (Tnfsf4) have been mapped as QTGs for the cho- lesterol,fatmass,weight,insulinandatherosclerosis QTLmappedto the distal half of chromosome 1 (refs 19?23). However, it remains to be shown whether other genes in this chromosome 1 region contri- bute to these linkages beyond Apoa2 and Tnfsf4. Furthermore, how thechromosome1QTLaffectmolecularnetworksindifferenttissues thatinturnleadtopleiotropiceffectsonmetabolictraitshasnotbeen characterized. An alternative to mapping QTGs for QTL is to incor- porate molecular network data into these analyses to identify those network components that are perturbed by the QTL and that in turn leadtovariationsindiseasetraits.Aftercharacterizingthecomplexity of the chromosome 1 genomic region associated with metabolic traits, we implement a procedure to identify components of mole- cular networks that respond to genetic perturbations and in turn induce changes in metabolic traits. This procedure includes recon- structing co-expression networks and identifying highly inter- connected functional sub-networks constituting these networks supported as having a causal relationship with disease traits. In a previously described cross between C57BL6/J (B6) and C3H/HeJ (C3H) on an Apoe 2/2 background (referred to here as the B3H cross) 17 , the importance of distal chromosome 1 as a key driver of metabolic traits became apparent because every metabolic traitscoredintheB3Hcrosslinkstothisregionofthechromosome (Fig. 1a). Tnfsf4 and Apoa2 are located within 10megabases (Mb) of one another and are proximal to the peak log likelihood ratio (lod) score curves for the metabolic traits on chromosome 1. These two genes were positionally cloned from the B3H background and vali- dated using transgenic and knockout animals as having a causal relationship with plasma cholesterol and high-density lipoprotein (HDL) levels, fat mass, weight, insulin levels and atherosclerotic lesion size 19,21,22 . Apoa2 was specifically identified as having a muta- tioninC3HrelativetoB6thataffectedApoa2translationalefficiency, leading to lower liver transcript and protein levels in C3H relative to B6 (refs 22 and 24). Liver gene expression traits scored in the B3H cross provide a unique opportunity to confirm Apoa2 as a QTG and to assess its total contribution to the metabolic traits. Because the expression of Apoa2 and its association tothe chromosome 1 linkage region and metabolic traits can be considered simultaneously on the mixed genetic background in which the disease trait QTL were ori- ginally mapped, the gene can be validated in the exact context in which it was identified. Apoa2 liver gene expression in the B3H cross gave rise to a sig- nificant expression QTL (Fig. 2a) that was proximal to the Apoa2 structural gene, confirming that Apoa2 expression is significantly perturbed between B6 and C3H mice as previously reported 22 . However, of the eight metabolic traits tested (Fig. 1a), Apoa2 liver expression levels were only modestly correlated with glucose levels (expected P value50.014), and not at all correlated with obesity traits (Supplementary Fig. 1a). Interestingly, Apoa2 gene expression wasstronglysupportedasbeingindependentofeachofthemetabolic traits with respect to the chromosome 1 locus (see Fig. 2a, b for weight).ResultsforApoa2liverproteinexpressionintheB3Hcross were consistent with these gene expression results (Supplementary Results). Although the lack of association between Apoa2 expression andthemetabolictraitscannotexcludeApoa2asatleastoneofmany genes underlying the chromosome 1 metabolic trait QTL, it is con- sistent with genes other than Apoa2 having a more dominant role in this linkage region. Tnfsf4 was similarly examined in the B3H cross but was not found to be associated with any of the metabolic traits linkedtochromosome1intheB3Hcross(SupplementaryResults). However, because heart and aorta were demonstrated as the relevant tissues for Tnfsf4 activity associated with metabolic traits 21 , our failure to detect an association in this instance may be because we have not profiled the relevant tissue. Whereas the expression data in this specific B3H cross did not support Apoa2 and Tnfsf4 as having a causal relationship with the metabolictraits, we identified 112liverexpression traits correspond- ingtogeneslocatedinthechromosome1linkageregion(from90Mb to the end of the chromosome) that gave rise to expression QTL (eQTL) in this region supporting the metabolic trait QTL (Supple- mentary Table 1). Although none of these genes completely explains the linkage of the clinical traits to chromosome 1, the expression levels of 54 of these genes are statistically supported as at least par- tially explaining variation in the metabolic traits in a causal way 11 (Supplementary Table 1), suggesting that there may be many genes in this region that support the metabolic trait QTL. Figure 1b high- lights strong liver cis eQTL for 4 of these 54 genes that are physically located within 10Mb of Apoa2 as well as the peak lod scores for each of the metabolic traits. Upstream transcription factor 1 (Usf1) was identified as a susceptibility gene for familial combined hyperlipide- mia (FCH) 25 ; F11 receptor (F11r) is supported as being a susceptibi- lity gene for FCH and other inflammatory processes 26,27 ; serum amyloid P component (Apcs) is implicated in atherosclerotic lesion formation 28 ; and regulator of G-protein signalling 5 (Rgs5), a gene involved in vessel development and physiology, can distinguish the 0 20 40 60 80 100 120 140 160 180 200 0 2 4 6 8 10 12 14 16 Chromosome 1 position (Mb) Lod score Glucose Aortic lesions Free fatty acids Abdominal fat Total plasma cholesterol Plasma HDL cholesterol Weight Triglycerides a 0 20 40 60 80 100 120 140 160 180 200 0 2 4 6 8 10 12 14 16 Chromosome 1 position (Mb) Lod score (black curves) Lod score (coloured curves) Plasma HDL cholesterol Apoa2 Tnfs4 Usf1 F11r Apcs Rgs5 b 0 10 20 30 40 50 60 70 80 90 100 Figure 1 | The distal half of chromosome 1 strongly influences metabolic andgeneexpressiontraits. a,Lodscorecurvesformetabolictraitsscoredin theB3Hcrossdemonstrate thattheyarealldrivenbyoneormoreQTLon chromosome 1. b, Lod score curves for expression traits corresponding to genes mapped as QTGs for the metabolic traits in a (Apoa2 and Tnfs4)orto geneswithinten-millionbasepairsofApoa2thatgiverisetostrong,putative cis eQTL and that are significantly correlated with at least one of the metabolic traits depicted in a. ARTICLES NATURE|Vol 452|27 March 2008 430 Nature Publishing Group�2008 fibrous cap from other atherosclerotic plaque components 29 and has recently been associated with hypertension in humans 30 . Of these four expression traits, Rgs5 is the most strongly associated with the metabolic traits linked to the chromosome 1 genomic region (see Fig. 2 and Supplementary Fig. 1c for weight). Therefore, unlike Apoa2 and Tnfsf4, these expression traits are significantly correlated with the metabolic traits, are strongly linked to the chromosome 1 locus, are physically located near the chromosome 1 linkage peaks, and are strongly supported as having a causal relationship with the metabolic traits. TheextensivelinkagedisequilibriumoperatingintheB3Hcross, the number of possible QTGs in this region, the small-to-modest effects of each QTG and potential interactions among the QTGs make dissecting the individual contributions of the QTGs in the chromosome 1 region nearly impossible from the cross data alone. However, using gene expression data scored in the B3H cross, expression traits that capture the multiple genetic perturbations in this region and that in turn lead to variations in the metabolic traits 11,31 can be more readily identified. As an example, Fig. 2a high- lights transcript abundances for an uncharacterized gene (GenBank accession number, BB433460) that is positioned in an intron of intraflagellar transport 88 homologue (Ift88). The liver expression of this gene is highly correlated with metabolic traits such as obesity (Supplementary Fig. 1d), is significantly linked across the entire distal half of chromosome 1 (lod score.8 across most of the distal half of chromosome 1) and is supported as having a large contri- bution to the weight trait (Fig. 2a, b). Although BB433460 physically resides on chromosome 14, it captures more of the genetic variation driving the metabolic traits at the chromosome 1 locus than any of the genes physically located in this region, suggesting that networks of expression traits may be perturbed in trans by this complex of closely linked QTL and, as a result, lead to variation in the metabolic traits. Network changes induce phenotypic change Liver and adipose co-expression networks were reconstructed from the B3H data to identify components of these networks that, like BB433460, mediate the transfer of information from QTL in the chromosome 1 region to the metabolic traits. Supplementary Fig. 3adepictsthemosthighlyconnectedexpressiontraitsinthisnetwork as an ordered connectivity matrix. The pattern of distinct clusters or sub-networks that emerge among the highly connected nodes in liver and adipose (Supplementary Fig. 3) are notable and support a hierarchical structure in these networks (Supplementary Fig. 4). The different sub-networks highlighted are seen to be enriched for a number of biological processes (Supplementary Table 2), including insulin signalling (sub-network 1), inflammation (sub-network 5), muscle-related processes (sub-network 7) and cell cycle (sub- network 9). These sub-networks represent key functional units that make up the co-expression network and that underlie processes specific to the different cell types that constitute each tissue. For example, in the female liver co-expression network, sub-network 5 is enriched for genes involved in inflammatory processes, potentially reflecting activity in Kupffer cells. Sub-network 7 is enriched for muscle-related genes such as actin and myosin, potentially reflecting hepatic stellate cell activity, where these cells are known to control microvascular tone and, when activated, can turn into myofibro- blasts and express smooth muscle actin filaments and desmin. Thesub-networksrepresentdifferentsetsofoverlappingpathways and are readily seen to be enriched for genes that are perturbed by specific genetic loci. For example, 85% of the genes in liver sub- network 1 give rise to eQTL on chromosome 1 (Supplementary Fig. 5). To establish whether a given sub-network was supported as having a causal relationship with the metabolic traits linked to chro- mosome 1, we used a statistical procedure to test whether the gene expression traits in each sub-network supported a causal, reactive or independent relationship with each of the metabolic traits with Lod scores for the black and dashed coloured curves Lod scores for solid, coloured curves a 100 120 140 160 180 0 2 4 6 8 10 0 6 12 18 24 30 ApoA2 QTL ApoA2 Weight Rgs5 QTL Rgs5 Weight Novel gene Weight Chr. 1 QTL 1 Chr. 1 QTL 4 .. . (100%) (99%) (100%) b L 1 L 2 L 3 L 4 c Complex of closely linked QTL Genetic perturbation G 4 G 10 G 12 G 14 G 15 G 16 G 20 G 21 G 11 G 19 G 18 G 13 G 17 G 8 G 9 G 3 G 5 G 6 G 7 G 1 G 2 Gene network Environment Network perturbation Disease Figure 2 | Genetic loci perturb molecular phenotypes that in turn lead to variations in disease-associated traits. a, Lod score plots for weight (solid black line), Apoa2 liver expression (solid red), Rgs5 liver expression (solid blue) and BB433460 liver expression (solid green) traits in the B3H cross. The dashed curves represent the lod score curves for weight conditional on the Apoa2 (dashed red), Rgs5 (dashed blue) and BB433460 (dashed green) liver gene expression traits. Conditioning on Apoa2 expression does not significantly reduce the weight lod score (independent relationship), whereas conditioning on Rgs5 or BB433460 does (causal relationship). b, Relationships supported between the expression and weight traits described in a: Apoa2 (top), Rgs5 (middle) and BB433460 (bottom) are predictedtoberelatedtoweightinanindependent(Apoa2)andcausal(Rgs5 and BB433460) way. Percentages represent the number of times the model shown was inferred out of 1,000 random samples drawn from the B3H cross. c, Generalization of the relationship discovered between BB433460 and weight, in which genetic loci (L i ) and environment perturb molecular networks of genes (G i ) that in turn leads to disease. NATURE|Vol 452|27 March 2008 ARTICLES 431 Nature Publishing Group�2008 respecttothegeneticlocidrivingmetabolictraitsscoredintheB3H cross: abdominal fat mass, weight, plasma insulin levels, free fatty acids, total plasma cholesterol levels and aortic lesion sizes. We iden- tified a sub-network as having a causal relationship with a given metabolic trait if it was significantly enriched (P,0.01) for expres- sion traits that have been supported as having a causal association with that trait. For liver, only five sub-networks were identified as beingenrichedforatleastoneofthemetabolictraits(Supplementary Fig. 3c). Two of the sub-networks were weakly enriched for insulin, fat mass, weight or cholesterol candidate causal genes (sub-networks 6 and 14), whereas sub-networks 2 and 9 were strongly enriched for only cholesterol and weight candidate causal genes, respectively. However, one of the sub-networks (sub-network 5) was very signifi- cantly enriched for expression traits supported as having a causal relationship with every metabolic trait tested, directly implicating this sub-network as a key mediator of the genetic loci driving variation in the metabolic traits scored in the B3H cross (Supplementary Fig. 3c). This sub-network was also the most highly conserved between the sexes and tissues in the B3H cross. In fact, 90% of the genes in female liver sub-network 5 overlapped a corres- ponding male sub-network (P,10 2305 by the Fisher Exact Test), and 50% of these genes overlapped a corresponding adipose sub- network (P,6.47310 2147 by the Fisher Exact Test). Further- more, the adipose sub-network corresponding to liver sub-network 5 was the only adipose sub-network found to be significantly enriched for expression traits supported as having a causal relation- ship with all of the metabolic traits tested (Supplementary Fig. 3d). A macrophage sub-network causes disease To explore the strong pleiotropic effects of sub-network 5 on the metabolic traits in the B3H cross, we formed a supermodule by combining this sub-network with the corresponding sub-network identified in the adipose co-expression network (Supplementary Table 3). Compared to the individual sub-networks, this supermodule systematically increased the fold-change enrichments andcorrespondingsignificancescoresforexpressiontraitssupported as having a causal relationship with the metabolic traits (Table 1). In fact, the percentage of expression traits in this supermodule sup- ported as having a causal relationship with aortic lesions, weight or fat mass, plasma insulin or glucose levels, total cholesterol and HDL cholesterol were 75%, 50%, 45%, 50% and 47%, respectively (Sup- plementary Table 4). The probability that these overlaps occurred by chance are small. For example, the probability that 50% of the 762 expression traits supported as having a causal relationship with obesity fall in this single supermodule (out of the 23,574 transcripts represented on the array) is 2.30310 2262 . We also searched this supermodule comprised of 1,406 transcribed sequences against a body atlas of gene expression representing 60 distinct mouse tissues. For each tissue in the atlas, gene sets were formed on the basis of tissue-specific expression (Supplementary Methods) and these sets were intersected with the supermodule. Bone-marrow-derived macrophages and spleen were the two most enriched tissues (Table 1 and Supplementary Table 4), not liver and adipose as one might expect given the module origins. These enrichments, com- bined with the significant enrichment of genes in inflammatory pathways, suggest that this module reflects the significant macro- phage populations resident in liver and adipose tissues. This macrophage connection is further supported by a number of known macrophage markers represented in this supermodule, including Cd14, Cd68 and Emr1 (refs 32?34). Given the apparent macrophage-derived origins of this supermodule and its association with the metabolic traits in the B3H cross, we refer to it here as the macrophage-enriched metabolic network (MEMN) (Fig. 3a). The MEMN is comprised of a number of expression traits corres- pondingtogenesthatwerecentlyidentifiedandvalidatedashavinga causal relationship with obesity traits, including Zfp90 (ref. 11), Tgfbr2 (ref. 11), C3ar1 (ref. 11) and Alox5ap (arachidonate 5- lipoxygenase-activating protein) 31 . Because this network comprises a highly interconnected set of expression traits supported as having a causal relationship with the different metabolic traits, we hypothesized that perturbing single genes in the MEMN that had been previously validated as having a causal relationship with these traits would significantly perturb the entire MEMN. To test this, we constructed single gene perturbation signatures for two of the genes, Zfp90 and Alox5, recently validated as having a causal relationship withobesity-associatedtraits 11,31 .Inaddition,weconstructedasingle gene perturbation signature for Pparg, a gene that also resides in the MEMN and that has previously been validated as having a causal relationship with obesity and diabetes traits 35 . In all cases, the per- turbation signatures (Supplementary Table 4) were significantly enriched for expression traits in the MEMN (Table 1). For example, the Zfp90 transgenic signature comprised approximately 3,000 expression traits; 468 of these overlapped the MEMN, whereas only 179 would have been expected by chance?a greater than 2.5-fold enrichment (Fisher Exact P value54.83310 294 ). Furthermore, genes validated as having a causal relationship with obesity were observed in these different perturbation signatures. For example, Pparg falls in the Zfp90 signature, whereas Tgfbr2 and C3ar1 fall in the Pparg and Alox5 signatures, respectively. More generally, all sig- natures are enriched for expression traits supported as having a cau- salrelationship with the metabolictraits. Therefore, expression traits supported as having a causal relationship with the metabolic traits Table 1 | Gene sets significantly over-represented in the MEMN Gene set type Gene set description Gene set count* Overlap (fold enrichment){ Enrichment nominal P value (corrected P value){ GO biological process categories Immune response 1,503 246 (2.6) 4.26310 243 (1.94310 239 ) Defence response 1,565 251 (2.4) 1.97310 242 (8.98310 239 ) Inflammatory response 584 110 (2.8) 4.66310 224 (2.12310 220 ) Tissue-specific expression Bone-marrow-derived macrophage specific expression 289 65 (3.3) 1.10310 218 (1.04310 216 ) Spleen-specific expression 186 47 (3.8) 7.56310 215 (5.81310 214 ) Environmental perturbations Diet-induced obesity versus wild-type signature 1,108 415 (6.2) 5.17310 2232 Causal gene sets Genes supported as causal for atherosclerotic lesions 159 119 (12.4) 3.22310 2111 Genes supported as causal for obesity traits 762 375 (8.2) 2.30310 2262 Genes supported as causal for diabetes 589 272 (7.7) 4.76310 2176 Genes supported as causal for total cholesterol levels 245 131 (8.9) 1.01310 293 Genes supported as causal for HDL levels 77 36 (7.8) 7.98310 224 Single gene perturbation experiments Zfp90 transgenic signature 3,006 468 (2.6) 4.83310 294 5-LO knockout signature 5,264 605 (1.9) 5.95310 270 Rosiglitazone signature 837 118 (2.3) 3.03310 218 *The number of sequences in the MEMN used to compare to these gene sets is 1,406. {TheoverlapcountiscomputedbycountingthenumberofgenesintheintersectionbetweentheindicatedgenesetandtheMEMN.Thefoldenrichmentiscomputedastheobservedoverlapcount divided by the expected overlap count, estimated by multiplying the MEMN transcript count (1,406) by the fraction ?gene set count divided by total gene count (23,574)?. {Nominal P values represent the significance of the Fisher Exact Test statistic under the null hypothesis that the frequency of the indicated gene set is the same between a reference set of all transcripts represented on the array and the set of genes comprising the MEMN. The corrected P values represent the Bonferroni-corrected P values (nominal P value multiplied by the number of gene sets searched). ARTICLES NATURE|Vol 452|27 March 2008 432 Nature Publishing Group�2008 falling in the MEMN and moving this network when perturbed pro- videdirectsupportthatthemetabolictraitsareanemergentproperty of this network, with hundreds of expression traits supported as having a causal relationship with the metabolic traits. Lpl and Lactb validated as obesity genes In the MEMN, there were 375 expression traits supported as having a causal relationship with the obesity traits linked to the chromosome 1 locus. Although many of the genes corresponding to the expression traitsinthisnetworkhavebeenvalidatedashavingacausalrelationship with metabolic traits (Pparg, Alox5, Tgfbr2, C3ar1 and Zfp90,toname just a few), many others have not. We used replication over multiple studies as a way to prioritize genes for validation. Genes supported in multipleindependentexperimentsashavingacausalrelationshipwith disease are morelikely to be truly causal.Therefore, weintersected the MEMN with a set of genes we previously predicted to have a causal relationship with obesity in a completely independent experiment 11 . ThreeofthetengenespredictedinanindependentF 2 intercrosspopu- lation 11 wererepresentedintheMEMN:Zfp90,LplandLactb.Zfp90has already been validated as having a causal relationship with obesity, so we proceeded to validate the other two ?replicated? genes. Lpl has previously been supported as a susceptibility gene for atherosclerosis- and diabetes-associated traits 36 . However, an asso- ciation between Lpl and obesity has not been established. To our knowledge, Lactb has not ever been associated with any of the B3H metabolic traits. Given the prediction that Lpl and Lactb have a causal relationship with obesity, we recorded weight, fat mass and lean mass for Lpl 1/2 , Lactb transgenic mice and wild-type littermate controls every 2weeks starting at 11weeks of age using quantitative NMR. As predicted, the growth curves for the Lpl 1/2 and Lactb transgenic animalsweresignificantly different fromthoseofcontrols (Fig. 3b, c), with the fat-mass-to-lean-mass (FMLM) ratio difference generally increasing over time. At the final quantitative NMR mea- surement, the FMLM ratios in the Lpl 1/2 and Lactb transgenic mice were increased by 22% and 20%, respectively, over the wild-type controls (P51.09310 25 and P54.48310 25 , respectively). Lpl is the principal enzyme responsible for the hydrolysis of cir- culating triglycerides and is active in differentiated macrophages 37 , consistent with its presence in the MEMN. Although Lpl has not previously been functionally validated as a susceptibility gene for obesity, several studies have established an inverse relationship between Lpl activity and obesity-related traits, including a negative correlation observed between Lpl activity and percentage body fat in humans 38 . Lactb is a serine protease with high similarity to the bac- terial lactamase gene, but very little is known about its function in eukaryotes 39,40 . Lactamase metabolizes peptidoglycan in the bacterial cell wall but neither the substrate nor the function of Lactb in eukar- yotesisknown 41 . Lactb hasbeendetectedinthemitochondria aspart ofthemitochondrialribosomalcomplex 42?44 .Interestingly,astrainof ratthatexhibitslate-onsetobesitywasfoundtocontainamutationin the S26 subunit of the mitochondrial ribosome, at least partially explaining the obesity phenotype 45 . 1234567 0.10 0.12 0.14 0.16 0.18 0.20 Time (2-week intervals) Time (weeks) 1 2 4 6 8 10 12 14234567 Time (2-week intervals) FMLM rati o FMLM rati o b 22% increase Lpl knockout Wild type c 20% increase Lactb transgenic Wild type Wild type Weight (g) d 19.3% increase Ppm1l knockout 20 25 30 35 40 45 50 MEM network a Known disease and macrophage genes in MEMN Lpl Lactb Ppm1l C3ar1Zfp90Alox5apNrg1Cd68Emr1H2-AaH2-DMb1Pparg Novel obesity/metabolic trait genes in MEMN 0.16 0.17 0.18 0.19 0.20 0.21 0.22 Figure 3 | Genes in the MEM network validated as having a causal relationship with obesity traits. a, The MEMN is enriched for genes supported as having a causal relationship with disease traits in the B3H cross (red nodes). The black nodes represent genes in the MEMN not supported as causal for disease traits in the B3H cross. b, FMLM ratio curves for Lpl knockout (n525) and wild-type control (n523) mice (P51.09310 25 that the difference at the last time point is significant). c,FMLMratiocurvesfortheLactbtransgenic(n536)andwild-typecontrol (n527) mice (P54.48310 25 that the difference at the last time point is significant). d, Weight curves for the Ppm1l 2/2 (n518) and wild-type control (n518) mice (P51.93310 211 that the difference at the last time point is significant). Error bars in b?d represent61s.d. of the indicated measures based on replicates and signal-to-noise ratios derived from the model applied to the weight and fat mass differences. NATURE|Vol 452|27 March 2008 ARTICLES 433 Nature Publishing Group�2008 Ppm1l has a causal relationship with metabolic syndrome Given the causal association between the MEMN and many meta- bolic traits, we rank-ordered genes on the basis of the number of metabolic traits for which they were supported having a causal rela- tionship with (Supplementary Table 5) as an alternative to replica- tion as a way to prioritize genes for validation. Four genes ranked at thetopofthelist:Fgd6,Mmp27,BC032204 andPpm1l.However,not onlyisPpm1laclassically?druggable?gene,butaknockoutmousefor this gene was available from Deltagen, so we selected this gene for validation. Ppm1l is a newly discovered protein phosphatase, the function of which is not well characterized. Weight, fat mass, insulin and glucose levels, blood pressure and otherbiochemicalmeasuresinbloodwererecordedinPpm1l 2/2 and wild-type littermate controls. The growth curves for the knockout mice were significantly different from those of wild-type controls (Fig. 3d); at the final weight measurement, the knockout mice weighed 19.3% more than wild-type mice (Table 2). Ppm1l 2/2 mice also exhibited increased fat mass compared to wild-type controls, with an overall 46.7% increase in fat mass at 20weeks of age (Table 2). At 21weeks of age, an oral glucose tolerance test (OGTT) was performed on all mice. Baseline plasma glucose levels were observed to be 11.5% higher in Ppm1l 2/2 mice relative to wild- type mice. Male knockout mice demonstrated an improved glucose tolerance, with a 33.3% decrease in the area under the curve (AUC) relative to male wild-type mice (Table 2). In contrast, although glu- cose levels for females at the 60, 90 and 180min time points were significantly increased (P value50.0077, 0.050 and 0.0043, respec- tively), the difference in AUC was not statistically significant (P value50.11).Atthe30-minOGTTtimepoint,insulinlevelsinmale and female Ppm1l 2/2 mice were more than 100% increased com- pared to those of controls (Table 2). Blood was also collected in all mice at 29weeks of age, and total cholesterol, triglycerides and free fattyacidswererecorded.Asignificantdecreaseinfreefattyacidswas recorded in Ppm1l 2/2 mice relative to controls (Table 2), but no other major changes were observed for the other parameters (data not shown). Finally, given that the MEMN is supported as having a causal relationship with a number of traits associated with metabolic syndrome, and given the presence of genes such as ACE in this net- work, non-invasive blood pressure was monitored in all mice at 25weeks of age. Overall, the blood pressure in Ppm1l 2/2 mice was significantly increased compared to that of controls (Table 2). Discussion Byintegratingco-expressionnetworksandgenotypicdatafromanF 2 intercrosspopulation, weidentified aliverandadiposemacrophage- enriched sub-network that was associated with disease traits com- prising the metabolic syndrome and enriched for expression traits supported as having a causal relationship with these traits. Unlike classic genetics approaches that aim to identify genes underlying genetic loci associated with disease, the approach developed here seekstoidentifywholegenenetworksthatrespondintranstogenetic loci driving disease, and that in turn lead to variations in the disease traits. Ourresultsdemonstratethattheremay infactbethousandsof genes capable of increasing susceptibility to metabolic disease traits such as obesity, diabetes and atherosclerosis. Because the causal pre- dictions made in this study rely on conditional dependency argu- mentsthatarestatisticalinnature,experimentalvalidationiscritical. Towards that end, Lpl and Lactb wereidentified and validated in vivo as previously unknown obesity genes, whereas Ppm1l was identified and validated as a gene capable of modulating multiple obesity, dia- betes and hypertension traits. Network-based approaches for elucidating the complexity of dis- ease traits cast a broad net for genes that drive disease relative to classic genetic linkage or association studies that limit the search to genes that harbour DNA variations that associate with disease in the population under study. As a result, predictive networks provide the potential to identify hundreds of genes that drive disease and that could serve as points for therapeutic intervention. Our results sup- port the idea that common forms of disease may be emergent pro- perties of networks, where the networks associated with disease are highly interconnected, with many genes in the network potentially having a causal relationship with disease if perturbed strongly enough. With large-scale molecular profiling, genotypic and clinical data collected from large-scale populations, studying how a network of gene interactions affects disease will come to complement more strongly the classic focus of how a single protein or RNA affects disease. The integration of genetic, molecular profiling and clinical data has the potential to paint a more detailed picture of the particu- larnetworkstatesthatdrivedisease,andthisinturnhasthepotential toleadtomore progressive treatments of diseasethat may ultimately involve the targeting of whole networks as opposed to current thera- peutic strategies focused on targeting one or two genes 46 . METHODS SUMMARY Liver and adipose tissue were extracted from 334 F 2 animals in the B3H cross and profiled on an Agilent custom murine gene expression microarray 17 . All F 2 animals were genotyped at more than 1,300 single nucleotide polymorphism markers and clinically characterized with respect to obesity-, diabetes- and atherosclerosis-related traits 17 . The gene expression and genotype data were combined to construct co-expression networks comprised of the most highly connectednodesfromeachtissueandsexusingpreviouslydescribedmethods 47 . Highly interconnected sub-networks were then detected from each co- expression network using an iterative search algorithm 47,48 . QTL were detected for each of the expression and metabolic traits using a forward stepwise regres- sion procedure 17,49 . QTL with pleiotropic effects on expression and metabolic traits were identified using a multivariate likelihood test 11,50 . The B3H QTL, expression and metabolic trait data were then integrated to assess whether each expressiontraitineachtissuewassupportedashavingacausalrelationshipwith eachofthemetabolictraits,withrespecttoQTLdetectedwithpleiotropiceffects on the expression and metabolic traits 11 . To identify sub-networks as having a causal relationship with the metabolic traits, each sub-network was tested for enrichmentofexpressiontraitssupportedashavingacausalassociationwiththe metabolic traits using the Fisher Exact Test. Genes comprising the sub-network supported as having a causal relationship with all metabolic traits scored in the B3H cross were selected for validation on the basis of one of two criteria: the genewassupportedashavingacausalrelationshipwiththemetabolictraitsinan Table 2 | Comparison of metabolic traits between Ppm1l 2/2 and Ppm1l 1/1 mice Ppm1l 2/2 Ppm1l 1/1 Trait Age of mice (weeks) Mean trait value Sample size Mean trait value Sample Size Percentage change Difference P value* Weight (g) 21 49.69 17 41.65 18 19.31.93310 211 Total fat mass (g) 93.54 17 2.54 18 39.40.0037 Total fat mass (g) 20 22.10 17 15.06 18 46.70.00030 Baseline glucose (mgml 21 ) 21 1.55 17 1.39 18 11.50.0075 OGTT area under curve (male mice only) (min(mgml 21 )) 21 186 8 279 9 233.30.0069 OGTT insulin at 30min (mg/l) 21 5.17 17 2.44 18 111.90.017 Free fatty acids (mequiv.l 21 ) 29 0.4116 14{ 0.5457 17{ 224.60.00050 Non-invasive blood pressure (mm Hg) 25 90.13 17 86.07 18 4.70.027 *All P valuesreported,except weight and OGTTAUC, representthe significanceof the t statisticunderthe null hypothesisthat the differenceinmean, sex-adjustedtrait valuesbetweenthe Ppm1l knockout and Ppm1l wild-type groups is equal to 0. For OGTT AUC, this same null hypothesis was tested but for males only. See Supplementary Methods for calculation of the P value for weight. {By the 29week time point, 3 male knockout mice and 1 male wild-type mouse had died. ARTICLES NATURE|Vol 452|27 March 2008 434 Nature Publishing Group�2008 independent, previously published study, or the gene was supported as having a causalrelationshipwiththemostmetabolictraitsscoredintheB3Hcross.The threegeneschosenforvalidationusingthesecriteriawerevalidatedbyconstruct- ing gene-knockout mouse strains (Lpl and Pmp1l) or transgenic mouse strains overexpressing the gene of interest (Lactb). Full Methods are provided in the Supplementary Information. Received 5 October 2007; accepted 28 January 2008. Published online 16 March 2008. 1. Edwards, A. O. et al. Complement factor H polymorphism and age-related macular degeneration. Science 308, 421?424 (2005). 2. Haines, J. L. et al. 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Elucidating the murine brain transcriptional network in a segregatingmousepopulationtoidentifycorefunctionalmodulesforobesityand diabetes. J. Neurochem. 97 (suppl. 1), 50?62 (2006). 48. Ravasz,E.,Somera,A.L.,Mongru,D.A.,Oltvai,Z.N.&Barabasi,A.L.Hierarchical organization of modularity in metabolic networks. Science 297, 1551?1555 (2002). 49. Haley, C. S. & Knott, S. A. A simple regression method for mapping quantitative trait lociin line crosses using flanking markers. Heredity 69, 315?324 (1992). 50. Jiang, C. & Zeng, Z. B. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111?1127 (1995). Supplementary Information is linked to the online version of the paper at www.nature.com/nature. Acknowledgements This work was supported in part by grants from the NIH/ NIDDK and NIH/NHLBI to A.J.L. and T.A.D. Author Contributions S.P., D.J.M. and M.-F.C. constructed and characterized the Ppm1l knockout mouse. X.Y.,L.W.C., S.W.,S.D., A.G., T.A.D. andA.J.L.constructed and characterized the B3H cross, the Lpl knockout mouse and the Lactb transgenic mouse. S.H., A.G., S.D. and B.Z. assisted in the co-expression network analyses. S.E. and A.J.L. performed bioinformatic analyses. All authors discussed the results and commented on the manuscript. S.K.S. and C.Z. aided in the data analysis. P.Y.L. and J.L. aided in the study design and interpretation of the experimental results. Y.C., J.Z. and E.E.S. designed the study, developed methods, analysed the data and wrote the paper. AuthorInformationTheliverandadiposemicroarraydatafortheB3Hcrosshave been deposited into the GEO database under accession numbers GSE2814 and GSE3086, respectively. Expression data associated with the diet-induced obesity, Zfp90transgenic,Alox5 2/2 androziglitazone-treatedanimalshavebeenuploaded totheGEOdatabaseunderaccessionnumbersGSE7028,GSE7029,GSE7026and GSE7027, respectively. The authors declare competing financial interests: details accompany the full-text HTML version of the paper at www.nature.com/nature. Reprints and permissions information is available at www.nature.com/reprints. Correspondence and requests for materials should be addressed to E.E.S. (eric_schadt@merck.com). NATURE|Vol 452|27 March 2008 ARTICLES 435 Nature Publishing Group�2008 "
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