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Genetic regulation of liver lipids in a mouse model of insulin resistance and hepatic steatosis

2021; Springer Nature; Volume: 17; Issue: 1 Linguagem: Inglês

10.15252/msb.20209684

1744-4292

Frode Norheim, Karthickeyan Chella Krishnan, Thomas Bjellaas, Laurent Vergnes, Calvin Pan, Brian W. Parks, Yonghong Meng, Jennifer M. Lang, James A. Ward, Karen Reue, Margarete Mehrabian, Thomas E. Gundersen, Miklós Péterfy, Knut Tomas Dalen, Christian A. Drevon, Simon T. Hui, Aldons J. Lusis, Marcus M. Seldin,

Lipid metabolism and biosynthesis

Article8 January 2021Open Access Transparent process Genetic regulation of liver lipids in a mouse model of insulin resistance and hepatic steatosis Frode Norheim Frode Norheim Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway Search for more papers by this author Karthickeyan Chella Krishnan Karthickeyan Chella Krishnan Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Thomas Bjellaas Thomas Bjellaas Vitas AS, Oslo, Norway Search for more papers by this author Laurent Vergnes Laurent Vergnes Department of Human Genetics, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Calvin Pan Calvin Pan Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Brian W Parks Brian W Parks Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Yonghong Meng Yonghong Meng Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Jennifer Lang Jennifer Lang Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author James A Ward James A Ward Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Karen Reue Karen Reue Department of Human Genetics, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Margarete Mehrabian Margarete Mehrabian Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Thomas E Gundersen Thomas E Gundersen Vitas AS, Oslo, Norway Search for more papers by this author Miklós Péterfy Miklós Péterfy Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Depatrment of Basic Medical Sciences, Western University of Health Sciences, Pomona, CA, USA Search for more papers by this author Knut T Dalen Knut T Dalen Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway Search for more papers by this author Christian A Drevon Christian A Drevon Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway Vitas AS, Oslo, Norway Search for more papers by this author Simon T Hui Simon T Hui Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Aldons J Lusis Corresponding Author Aldons J Lusis [email protected] orcid.org/0000-0001-9013-0228 Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Department of Human Genetics, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Marcus M Seldin Corresponding Author Marcus M Seldin [email protected] orcid.org/0000-0001-8026-4759 Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, CA, USA Search for more papers by this author Frode Norheim Frode Norheim Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway Search for more papers by this author Karthickeyan Chella Krishnan Karthickeyan Chella Krishnan Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Thomas Bjellaas Thomas Bjellaas Vitas AS, Oslo, Norway Search for more papers by this author Laurent Vergnes Laurent Vergnes Department of Human Genetics, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Calvin Pan Calvin Pan Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Brian W Parks Brian W Parks Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Yonghong Meng Yonghong Meng Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Jennifer Lang Jennifer Lang Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author James A Ward James A Ward Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Karen Reue Karen Reue Department of Human Genetics, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Margarete Mehrabian Margarete Mehrabian Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Thomas E Gundersen Thomas E Gundersen Vitas AS, Oslo, Norway Search for more papers by this author Miklós Péterfy Miklós Péterfy Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Depatrment of Basic Medical Sciences, Western University of Health Sciences, Pomona, CA, USA Search for more papers by this author Knut T Dalen Knut T Dalen Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway Search for more papers by this author Christian A Drevon Christian A Drevon Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway Vitas AS, Oslo, Norway Search for more papers by this author Simon T Hui Simon T Hui Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Aldons J Lusis Corresponding Author Aldons J Lusis [email protected] orcid.org/0000-0001-9013-0228 Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Department of Human Genetics, University of California at Los Angeles, Los Angeles, CA, USA Search for more papers by this author Marcus M Seldin Corresponding Author Marcus M Seldin [email protected] orcid.org/0000-0001-8026-4759 Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, CA, USA Search for more papers by this author Author Information Frode Norheim1,2, Karthickeyan Chella Krishnan1, Thomas Bjellaas3, Laurent Vergnes4, Calvin Pan1, Brian W Parks5, Yonghong Meng1, Jennifer Lang1, James A Ward1, Karen Reue4, Margarete Mehrabian1, Thomas E Gundersen3, Miklós Péterfy1,6, Knut T Dalen2, Christian A Drevon2,3, Simon T Hui1, Aldons J Lusis *,1,4 and Marcus M Seldin *,1,7 1Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA 2Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway 3Vitas AS, Oslo, Norway 4Department of Human Genetics, University of California at Los Angeles, Los Angeles, CA, USA 5Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, USA 6Depatrment of Basic Medical Sciences, Western University of Health Sciences, Pomona, CA, USA 7Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, CA, USA *Corresponding author. Tel: +1 310 825 1359; E-mail: [email protected] *Corresponding author. Tel: +1 949 824 6765; E-mail: [email protected] Molecular Systems Biology (2021)17:e9684https://doi.org/10.15252/msb.20209684 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract To elucidate the contributions of specific lipid species to metabolic traits, we integrated global hepatic lipid data with other omics measures and genetic data from a cohort of about 100 diverse inbred strains of mice fed a high-fat/high-sucrose diet for 8 weeks. Association mapping, correlation, structure analyses, and network modeling revealed pathways and genes underlying these interactions. In particular, our studies lead to the identification of Ifi203 and Map2k6 as regulators of hepatic phosphatidylcholine homeostasis and triacylglycerol accumulation, respectively. Our analyses highlight mechanisms for how genetic variation in hepatic lipidome can be linked to physiological and molecular phenotypes, such as microbiota composition. Synopsis A survey of hepatic lipidome in a cohort of genetically diverse mice on high-fat/high-sucrose diet identifies novel modulators of liver lipid metabolism in the context of hepatic steatosis. The association between lipids, gene expression and metabolic traits is mapped. Network modeling identifies co-regulated modules of hepatic lipids. Association mapping prioritizes potential drivers of hepatic lipid metabolism. Identification and validation of Ifi203 and Map2k6 as regulators of specific hepatic lipids. Introduction Maintenance of hepatic lipid homeostasis is critical for many physiologic processes (Musso et al, 2018; Svegliati-Baroni et al, 2019). For example, lipid species such as ceramides and diacylglycerols appear to be key elements in non-alcoholic fatty liver disease (NAFLD), insulin resistance, and other metabolic diseases (Raichur et al, 2014; Ter Horst et al, 2017; Yang et al, 2018; Chaurasia et al, 2019). Recent advances in global lipidomics by mass spectrometry have allowed a more comprehensive view of the hepatic lipidome (Gorden et al, 2015; Yang et al, 2018). These analyses have highlighted the complexity of lipid species and generated correlative links to several chronic diseases (Gorden et al, 2015; Luukkonen et al, 2016; Peng et al, 2018). Although these studies have revealed intriguing relationships between individual lipid species and metabolic traits, it has proven difficult to translate findings to a population scale using traditional approaches, such as gain- and loss-of-function studies in mice. Systems genetics provides an alternative approach for unbiased hypothesis generation based on natural genetic variation, using DNA variation as a directional anchor. This is accomplished by monitoring clinical traits and molecular information (such as gene expression or lipidomics) in a genetically diverse population and analyzing the results using genome-wide association (GWA), correlation structure, and network modeling (Civelek & Lusis, 2014). Two recent studies have leveraged systems genetics approaches to understand how a number of hepatic lipids change across genetic backgrounds (Jha et al, 2018a; Parker et al, 2019). The first study surveyed hepatic lipids in parallel with clinical traits in a set of C57BL/6 x DBA/2J (BXD) recombinant inbred strains under two dietary conditions (Jha et al, 2018a). This study identified candidate genes that may modulate the abundance of a number of hepatic lipid species using GWA. They also proposed a role for cardiolipins (CL) in fatty liver progression (Jha et al, 2018a) and found plasma lipid signatures predicting hepatic lipid composition (Jha et al, 2018b). Another study utilized livers from the Hybrid Mouse Diversity Panel (HMDP) following an overnight fast. They performed liver lipidomics and proteomics and reported novel-specific proteins regulating global lipidome structure (Parker et al, 2019). This study also identified plasma lipid signatures predicting hepatic triglyceride composition with several biomarkers conserved in humans. Although these studies constitute valuable resources for future studies of genetic regulation of NAFLD (Seldin et al, 2019), limitations in these studies are the lack of power for association mapping (Jha et al, 2018a) and omics studies on livers after an overnight fast (Parker et al, 2019) which will likely not fully resemble lipids accumulating with NAFLD. We now report a new resource for investigation of genetic regulation of the hepatic lipidome and its relationship to hepatic steatosis (Hui et al, 2015), insulin resistance (Parks et al, 2015), obesity (Parks et al, 2013), plasma lipids, and gut bacteria (Parks et al, 2013) in mice fed a high-fat/high-sucrose (HF/HS) diet for 8 weeks. Initially, we examined a subset of mouse strains and observed overall dietary and genetic impacts on the hepatic lipidome. Next, we performed global hepatic lipidomics on 101 HMDP strains and integrated the data with genomic variation, microbiota composition, global gene expression, and other phenotypic traits. To our knowledge, this is the most comprehensive integration of such measures in a genetically diverse population. Using association mapping, correlation, and network analyses, we identified several novel pathways regulating hepatic lipid levels and provide experimental validation to define their roles in diet-induced NAFLD and insulin resistance. Results Dietary and genetic impacts on hepatic lipidome Initially, we evaluated the impact of a HF/HS diet on ~ 250 lipids from the hepatic lipidome in a small group of genetically diverse mice from the HMDP. We selected three strains (n = 3 mice/strain) responding differently to the HF/HS diet: the traditional C57BL/6J strain, DBA/2J, which becomes highly insulin resistant (Norheim et al, 2018) and C3H/HeJ, which carries a mutation in the Tlr4 gene regulating the lipopolysaccharide response locus (Heppner & Weiss, 1965). The hepatic lipids were measured in these strains fed a HF/HS or normal chow diet and compared using limma (Ritchie et al, 2015; Fig 1A). A large number of lipid species known to be involved in fatty liver development, such as ceramides (Chaurasia et al, 2019), were significantly changed in response to the HF/HS diet, regardless of genetic background; however, some lipids changed in a strain-specific manner, either across or between diets (Fig 1B). Particularly, the C3H/HeJ mice seemed to have a somewhat different response to a dietary perturbation for several of the phosphatidylcholine (PC) and phosphatidylethanolamine (PE) lipids than the other two strains (C57BL/6J and DBA/2J) suggesting gene-by-diet interactions. Free fatty acids (FFAs) and triacylglycerols (TAGs) with fewer carbon atoms were mostly increased after a HF/HS diet, several of the same species containing many carbon atoms decreased (Fig 1A–C). Another example showed that cholesterol esters (CE) were up- or down-regulated by HF/HS diet, dependent on the number of double bonds on their carbon backbone. Specifically, CE(C18:1) was increased and CE(C18:2) was decreased in responds to diet (Fig 1B and C). The full list of lipids impacted by diet in each strain is provided in Dataset EV1. These data indicate an interaction between genetics and diet to mediate changes in the hepatic lipidome and highlight consideration of genetic background when determining dietary effects on liver lipids. Figure 1. Dietary and genetic effects on the hepatic lipidome Volcano plot of the fold change (x-axis) plotted against significance (y-axis) of lipids changing upon HF/HS feeding. Lipids are colored according to fold change (log2, absolute) > 1 (orange), P-value < 0.05 (red), or both (green). P-values calculated from differential expression using limma. Heatmap of the fold change (log2) of each lipid in HF/HS compared to chow diet. Only lipid species detected in all mice are shown. Examples of different hepatic lipids within one class that are regulated in different directions in HF/HS fed as compared chow fed mice. Data information: Cer, ceramide; FFA, free fatty acids; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PG, phosphatidylglycerols; PI, phosphatidylinositols; PS, phosphatidylserines; SPM; sphingomyelins; TAG, triacylglycerols; CL, cardiolipins; CE, cholesterol esters. Specific comparison results are provided in Dataset EV1. N = 3 male mice per strain and diet group. Data represent mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001. P-values calculated using a Student t-test (two-tail) compared with chow group. Download figure Download PowerPoint We next expanded our survey to assay 256 hepatic lipids of 101 HMDP strains (279 mice) fed a HF/HS diet and to integrate lipidomics with other molecular layers (genome and liver transcriptome), as well as phenotypic outcomes such as HOMA-IR. We reasoned that these integrations might uncover new mechanisms by which genetic variation predisposes to metabolic alteration with involvement of liver lipids. A high degree of genetic variation was observed in the relative abundance of each lipid class compared with total lipid content (Fig 2A). For example, the most abundant lipid class (TAG) accounted from 44 to 79% of total lipids in liver and the content of PC varied > 3-fold (Fig 2A). The less abundant lipids generally exhibited greater variation across the strains. For example, ceramide-phosphatidylethanolamine (Cer-PE) and a phosphatidylinositol (PI) species varied 356-fold and 2,199-fold (Fig EV1) across the strains, respectively. Summary level statistics, such as mean abundance and variance across the 279 mice, are provided for each lipid class (Dataset EV2) and individual lipids (Dataset EV3). Not all lipid species varied substantially across strains. For example, Cer(34:2) and PC(34:1) showed minimal variation relative to the mean compared to other lipids (Dataset EV3). While analytical variation can clearly contribute to these observations, higher variation among lower abundances across genetic backgrounds has been widely appreciated for multiple omics measures and reviewed in detail (Liu et al, 2016). Figure 2. Genetic variation of hepatic lipidome in the HMDP The relative genetic variation of hepatic lipidome composition; all lipids were quantified in proportion to the total lipidome. Each lipid class is shown in a different color where differences can be observed across the strains. Heatmap showing correlations between different lipid species (x-axis) and the abundance of gut microbes (y-axis). Microbes were summarized at the levels of order (o_), genus (g_), or family (f_). *P < 0.05, **P < 0.01 P-values were calculated based on significance of regression (students test) and adjusted for multiple comparisons (FDR = 0.05). Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Highly variable lipids within the HMDP A, B. Ceramide-phosphatidylethanolamine 36:2 (Cer-PE(36:2)) and phosphatidylinositol 38:4 (PI(38:4)) are examples of hepatic lipid species with a substantial variation among the mice strains across the HMDP. Cer-PE(36:2) showed measured levels in 100 out of 101 HMDP strains. PI(38:4) showed measured levels in 81 out of 101 HMDP strains. Download figure Download PowerPoint Relationships between gut microbiota and hepatic lipids In this study, we provide several examples for how analyses can be performed on these data to infer new biologic mechanisms, where the most straightforward is correlation. While simple, analysis of correlation structure can be powerful. The intuition for assaying correlation structure is that natural genetic variation has produced a spread of complex interactions, where new relationships (either causal or reactive) can easily be inferred. For example, little is known about how individual hepatic lipid species may be affected by intestinal microbiota composition. Therefore, we performed correlation analyses to gauge genetic relationships between the hepatic lipidome and microbiota composition. Given that both of these traits appear to be highly heritable, we hypothesized that both known and new interactions could be identified (Parks et al, 2013; Org et al, 2015; Org et al, 2017). These analyses highlighted clusters of TAGs strongly correlated with the abundance of Ruminococcus, a relationship which has been observed with progression from NAFLD to non-alcoholic steatohepatitis (NASH) in humans (Boursier et al, 2016; Fig 2B). Additionally, Anaeroplasma, AF12, and Desulfovibrio showed negative correlations with many CL and lysophosphatidylcholine (LPC) species (Fig 2B). Anaeroplasma has been associated with unfavorable lipid profiles in humans (Granado-Serrano et al, 2019), but the underlying mechanisms are unclear. Desulfovibrio increases in the gut when C57BL/6J mice transition into hepatic steatosis and NASH after being treated with streptozotocin and fed a high-fat diet (Xie et al, 2016). To our knowledge, no previous study has observed an association between Anaeroplasma, AF12, and NAFLD. Our analyses suggest that the gut levels of Anaeroplasma, AF12, and Desulfovibrio might affect the hepatic levels of several hepatic lipids such as CL and LPC; however, these relationships require direct experimentation to prove directionality and causality. Because many lipids were strongly intercorrelated, we next aggregated lipid species into modules of correlated members using weighted gene co-expression network analysis (WGCNA) (Langfelder & Horvath, 2008). Lipid species clustered into 12 discrete modules, some were predominantly a single class, whereas others included lipids from multiple classes (Figs EV2, EV3, Dataset EV4). For example, a majority of the TAGs (36/47) and PCs (9/22) clustered into single modules (turquoise and magenta, respectively). Module membership for every lipid from this analysis is provided in Dataset EV4. We also assessed relationships between microbiome abundance profiles and these lipid modules (Fig EV2). This approach highlighted how intercorrelated lipid groups could better inform relationships with gut bacteria. For example, several lesser-abundant species such as Adlercreutzia and Desulfovibrio showed modest correlation with individual lipids species but were strongly correlated with a specific module (red, Fig EV2), which was composed exclusively of FFAs. While these genera have been observed to change in the context of inflammatory bowel disease (Bajer et al, 2017), little is known about their functional roles. Click here to expand this figure. Figure EV2. WGCNA analysis and correlations with microbiota WGCNA was performed to dissect which lipids segregated into modules and correlated with microbiota abundances. Samples were first arranged by hierarchical clustering to detect outliers, where a height of 2,100 (red line) was used as a cutoff for inclusion (top left). Scale-free topology (top middle) and mean connectivity (top right) are also provided for analysis. Microbes were summarized at the levels of order (o), genus (g), or family (f). Heatmap showing the lipid modules (y-axis) and correlation with type of microbiome abundances (x-axis). *P < 0.001. P-values were calculated based on significance of regression (students test) and adjusted for multiple comparisons (FDR = 0.05). Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Module membership by lipid class. Heatmap showing the number of lipids within a class per module WGCNA was performed to dissect which lipids segregated into modules and correlated with traits (body fat %, body weight, HDL, LDL, liver total cholesterol, liver mitochondria, liver total phosphatidyl cholesterol, plasma free fatty acids, plasma glucose, plasma insulin, and plasma triacylglycerol). The darker the color, the more lipids per module, with the highest membership shown in gray. CE, cholesterol esters; Cer, ceramides; CerPC, ceramide phosphatidylcholines; Cer-PE, ceramide-phosphatidylethanolamines; CL, cardiolipins; DiCer, dihydroceramides; FA, free fatty acids; GlcGP, glycosylglycerophospholipids; ISCerPC; IS-ceramide phosphatidylcholines; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; LPS, lysophosphatidylserines; PA, phosphatidic acid; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PI, phosphatidylinositols; PS, phosphatidylserines; SP, sphingolipids; TAG, triacylglycerols. Download figure Download PowerPoint Coregulated lipids are strongly correlated with phenotypic traits We next focused our WGCNA analysis of specific coregulated lipid modules on their relationships with clinical traits. As suggested above, lipids of the same class were generally correlated with each other across the HMDP strains (Fig 3A). This is consistent with previous observations and was especially apparent for TAGs (Jha et al, 2018a). There were also several examples of strong correlations between lipid classes, such as phosphatidylserines (PS) correlating with phosphatidylinositols (PI), as well as lysophosphatidylethanolamine (LPE) and LPC showing strong correlations with FFAs (Fig 3A). Because analysis of correlation structure between lipids is a key component of several analyses, we have provided the midweight bicorrelation coefficient and corresponding P-value for all lipid pairs in Dataset EV5. To examine further the relationships being driven by genetic architecture, we selected several relevant phenotypic traits and integrated these with separate lipid species (Fig 3B). Several key lipids showed strong correlation with traits consistent with previous studies. As examples, the levels of some hepatic ceramides and PEs correlated negatively with plasma glucose levels and body fat percentage, respectively (Fig 3B). These data show that genetic variation may drive hepatic lipids to cluster within or between classes and that pairwise relationships exist between individual lipid species and phenotypic traits. Figure 3. Genetic lipidome structure and correlation with phenotypic traits Heatmap showing correlations among hepatic lipids. Heatmap showing concordance between different lipid species (class listed on y-axis) and certain phenotypic traits on the x-axis. Results from WGCNA analyses, where lipids were separated in 12 modules and, labeled distinct colors, based on their internal correlations. Primary lipid classes, which comprise each module, are listed as primary module members, with the number of species in each module/total number of species detected. The correlations between each of these lipid modules and relevant phenotypic traits are shown as a heatmap, where bicor (top) and P-value (bottom) are listed. P-values were calculated based on significance of regression (students test) and adjusted for multiple comparisons (FDR = 0.05). Download figure Download PowerPoint To obtain a comprehensive picture of how lipid subgroups may relate to these traits, we adopted two network-based approaches. First, a correlation-based network map was constructed, where connections between components can be visualized through strength of correlation (Fig EV4). This cumulative network showed that metabolic syndrome traits such as body weight and HOMA-IR were strongly correlated with several lipid species like Cer-PE lipids. In contrast, plasma glucose concentration was more strongly correlated with several PC species (Fig EV4). Next, we asked if lipid modules identified from WGCNA (above) were correlated with the same traits. The turquoise and magenta modules both showed strong positive correlations with body weight and plasma insulin concentration (Fig 3C). All the CLs (13/13) clustered into a single module (blue) which showed a negative association with liver cholesterol, and plasma HDL, TAG, and glucose (Fig 3C). Other modules were more diverse in their membership, but still showed strong correlations with phenotypic traits. For example, the purple module contained lipid species from seven different classes (Fig 3C). When combined, this module showed significant correlations with body weight as well as plasma insulin and HDL (Fig 3C). Taken together, these data show that within a broad network, close connections can be observed between specific lipid species, global lipid classes, and traits. Click here to expand this figure. Figure EV4. Undirected network of interactions between hepatic lipids and indicated phenotypic traits in 101 strains of mice fed a HF/HS diet Nodes show either individual lipid species or phenotypic traits, where color indicators are given in the figure. Edges are connected between nodes with a significant correlation (P < 0.01), with the distance reflecting increasing significance of correlation. P-values were calculated based on significance of regression (Student’s test) and adjusted for multiple comparisons (FDR = 0.05) CE, cholesterol esters; Cer, ceramides; CerPC, ceramide phosphatidylcholines; Cer-PE, ceramide-phosphatidylethanolamines; CL, cardiolipins; DiCer, dihydroceramides; FA, free fatty acids; GlcGP, glycosylglycerophospholipids;