Tissue-Specific Pathways and Networks Underlying Sexual

Kurt et al. Biology of Sex Differences (2018) 9:46 https://doi.org/10.1186/s13293-018-0205-7

RESEARCH Open Access Tissue-specific pathways and networks underlying sexual dimorphism in nonalcoholic fatty liver disease Zeyneb Kurt1, Rio Barrere-Cain1, Jonnby LaGuardia1, Margarete Mehrabian2, Calvin Pan2, Simon T Hui2, Frode Norheim2, Zhiqiang Zhou2, Yehudit Hasin2, Aldons J Lusis2* and Xia Yang1*

Abstract Background: Non-alcoholic fatty liver disease (NAFLD) encompasses benign steatosis and more severe conditions such as non-alcoholic steatohepatitis (NASH), cirrhosis, and liver cancer. This chronic liver disease has a poorly understood etiology and demonstrates sexual dimorphisms. We aim to examine the molecular mechanisms underlying sexual dimorphisms in NAFLD pathogenesis through a comprehensive multi-omics study. We integrated genomics (DNA variations), transcriptomics of liver and adipose tissue, and phenotypic data of NAFLD derived from female mice of ~ 100 strains included in the hybrid mouse diversity panel (HMDP) and compared the NAFLD molecular pathways and gene networks between sexes. Results: We identified both shared and sex-specific biological processes for NAFLD. Adaptive immunity, branched chain amino acid metabolism, oxidative phosphorylation, and cell cycle/apoptosis were shared between sexes. Among the sex-specific pathways were vitamins and cofactors metabolism and ion channel transport for females, and phospholipid, lysophospholipid, and phosphatidylinositol metabolism and insulin signaling for males. Additionally, numerous lipid and insulin-related pathways and inflammatory processes in the adipose and liver tissue appeared to show more prominent association with NAFLD in male HMDP. Using data-driven network modeling, we identified plausible sex-specific and tissue-specific regulatory genes as well as those that are shared between sexes. These key regulators orchestrate the NAFLD pathways in a sex- and tissue-specific manner. Gonadectomy experiments support that sex hormones may partially underlie the sexually dimorphic genes and pathways involved in NAFLD. Conclusions: Our multi-omics integrative study reveals sex- and tissue-specific genes, processes, and networks underlying sexual dimorphism in NAFLD and may facilitate sex-specific precision medicine. Keywords: Non-alcoholic fatty liver disease (NAFLD), Sexual dimorphism, Multi-omics integration, Key regulator genes, Bayesian networks, Coexpression networks, Hybrid mouse diversity panel

Background insulin resistance, type II diabetes, and obesity [1, 4–6]. Nonalcoholicfattyliverdisease(NAFLD)coversawide Due to the lack of mechanistic understanding of NAFLD, spectrum of disorders spanning simple liver steatosis, non- there are no existing therapies directly targeting NAFLD. alcoholic steatohepatitis (NASH), cirrhosis, and hepatocel- Moreover, significant unexplained age-dependent sexual di- lular carcinoma [1–3]. NAFLD has rapidly become a morphisms have been observed in NAFLD. At younger significant health threat globally, affecting 25% of the world ages, NAFLD has a higher prevalence in males than fe- population on average, and is strongly associated with males, whereas at older ages, especially after menopause, the prevalence in females increases [7–10]. Males with * Correspondence: [email protected]; [email protected] NAFLD have more severe metabolic phenotypes than fe- 2 Department of Medicine/Division of Cardiology, David Geffen School of males, including higher glucose levels, higher systolic blood Medicine, University of California, Los Angeles, Los Angeles, CA, USA 1Department of Integrative Biology and Physiology, University of California, pressure, greater visceral adiposity, lower adiponectin levels, Los Angeles, Los Angeles, CA, USA

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Kurt et al. Biology of Sex Differences (2018) 9:46 Page 2 of 14

lower high-density lipoprotein cholesterol levels, and Methods greater liver injury as measured by alanine aminotransfer- Overall study design ase levels and aspartate aminotransferase levels [11]. Al- We aimed to understand the sexually dimorphic mecha- though endogenous estrogens, adipose distribution, and nisms underlying NAFLD using an integrative genomics other lifestyle factors have all been proposed as possible approach, Mergeomics [19, 20]. In our recent study [18], contributors to sex differences in NAFLD [10–13], the mo- we used this pipeline to integrate the multi-omics data lecular mechanisms are unclear. Revealing the underlying from the male mice of the hybrid mouse diversity panel biological mechanisms driving the sex differences in (HMDP) [17, 21] to identify causal NAFLD gene net- NAFLD can enable the identification of novel therapies and works and predict key regulator (driver) genes in these preventive strategies to ameliorate the heightening global networks. Our subsequent in vivo and ex vivo experi- health threat from NAFLD in a sex-specific and personal- ments supported the reliability of our multi-omics mod- ized manner. eling approach [18]. In our current study, we predicted Recently, human genome-wide association studies the NAFLD processes and their key driver genes using (GWAS) have revealed a handful of candidate causal the female HMDP mice [17, 21] and compared these genes such as PNPLA3, SAMM50, PARVB, GCKR, findings with those from the male-focused study [18]. As LCP1, LYPLAL1, PPP1R3B, TM6SF2, and TRIB1 for illustrated in Fig. 1, we first reconstructed tissue-specific NAFLD [14–16]. However, sex differences in genetic risks coexpression networks and identified modules (groups have not been investigated in these studies. In addition, of co-expressed genes) using liver and adipose tissue NAFLD is strongly influenced by environmental factors such as diet, which are difficult to control in human studies. Rodent models, on the other hand, allow for control of environmental factors and collection of molecular traits from the relevant tissues when examining a complex disease. To enable the study of sex-specific mechanisms of NAFLD, hepatic steatosis and its relevant clinical and molecular traits were recently examined in both male and female mice of more than 100 distinct inbred and recombinant inbred strains from the hybrid mouse diversity panel (HMDP) [17]. These mice were treated with a high fat and high sucrose diet to generate hepatic triglyceride accumulation or steatosis, a hallmark of NAFLD. A comprehensive NAFLD-associated and sex-specific multi- omics data resource has also been generated, encompass- ing genotyping of common genetic variants, transcriptome data from liver and adipose tissue, and tissue-specific expression quantitative trait loci (eQTLs, reflecting the genetic regulation of gene expression in individual tissues). These data sets enable tissue-specific and sex-specific investigations of NAFLD mechanisms. To fully leverage the multi-omic datasets from HMDP mice and incorporate disease-associated molecular signals with strong, moderate, and subtle effects, we have recently deployed an integrative approach to identify potential causal pathways, gene networks, and key regulators of NAFLD in males in a tissue-specific fashion, followed by experimental validation of the Fig. 1 Schematic representation of the methodology. Genotype, novel predictions [18]. In the current study, we apply liver and adipose tissue gene expression data, and hepatic this validated approach to compare the NAFLD biology triglyceride phenotypic data from both sexes of the hybrid mouse diversity panel (HMDP) mice were first integrated using Marker Set between sexes to uncover the shared, male-specific, and Enrichment Analysis in the Mergeomics pipeline to predict sex- and female-specific genes, processes, and networks poten- tissue-specific pathways perturbed in NAFLD. Then, potential tially driving NAFLD pathogenesis, thereby providing regulatory genes (key drivers) for male-specific, female-specific, and more comprehensive tissue-specific insight into the dif- shared pathways were identified using the Key Driver Analysis in ferential prevalence and manifestations of NAFLD be- Mergeomics. TG triglyceride, eQTL expression quantitative trait loci, GWAS genome-wide association studies tween sexes. Kurt et al. Biology of Sex Differences (2018) 9:46 Page 3 of 14

gene expression data from female mice across more than Statistical analysis 100 HMDP strains. Then, we integrated these modules Genome-wide association analysis of liver triglyceride and with GWAS of hepatic triglyceride levels together with tissue-specific eQTL analysis in females the eQTLs from liver and adipose tissue to identify gene Genotypes for 365 samples from 103 strains of female co-expression modules or biological pathways enriched mice were determined using the Mouse Diversity Array for NAFLD GWAS signals in females. The incorporation [25]. SNPs with poor quality, a minor allele frequency of of the genetic signals helped to infer causal modules and < 5% or a missing genotype rate of > 10%, were removed pathways that are perturbed by genetic risks. Lastly, these as previously described [17], resulting in about 200,000 NAFLD-associated gene sets in females were mapped on SNPs. Genome-wide association mapping of the liver tri- Bayesian networks that carry gene-gene regulatory infor- glyceride content and tissue-specific eQTLs were previ- mation to predict potential key driver genes of the ously generated using Factored Spectrally Transformed NAFLD processes. The findings from the female-specific Linear Mixed Models [26, 27]. We used cis-eQTLs that analysis were compared with those from the male-specific were defined within ±1 Mb region of the transcription study [18] to retrieve sex-specific and shared mechanisms. start and end sites of the genes. False discovery rate (FDR) estimated by the q value approach [28] was used HMDP study of NAFLD for correcting for multiple testing. In the adipose tissue, The HMDP strains and the NAFLD study design with a 216,296 cis-eQTL associations were used (76,451 unique high-fat high-sucrose diet were previously described in cis-eSNPs and 1954 cis-genes), and in the liver, 241,463 detail [17, 22]. Experimental procedures had been ap- cis-eQTL associations were used (81,076 unique cis- − proved by the UCLA animal research committee. Female eSNPs and 2168 cis-genes) at P <1E 6 (FDR < 0.01). mice from 103 mouse strains and male mice from 113 strains used in this study were purchased from the Jack- Reconstruction of tissue-specific co-expression networks son Laboratory and bred at University of California, Los from liver and adipose tissue transcriptome data from Angeles. These mice were fed an 8-week chow diet females followed by an 8-week high-fat high-sucrose diet with Tissue-specific coexpression networks from gene expres- 16.8% kcal protein, 51.4% kcal carbohydrate, and 31.8% sion data from 206 liver and 211 adipose tissue of female kcal fat [17]. They were sacrificed after a 4-h fasting. mice in HMDP were constructed using two complemen- tary network methods: Weighted Gene Co-expression Network Analysis (WGCNA) [29] and Multiscale Embed- Hepatic lipid content measurement ded Gene Co-expression Network Analysis (MEGENA) Liver lipids were extracted from 365 female mice and 465 [30]. WGCNA tends to cluster genes into large-sized male mice by following an established method [23]. About modules and assigns each gene into a single module, 60 mg of liver was used for lipid extraction, and the dried whereas MEGENA defines smaller, more coherent mod- organic extract was dissolved in 1.8% (wt/vol) Triton ules and can assign each gene into multiple modules. As X-100 [17]. Colorimetric assay from Sigma (St. Louis, shown in our recent study focusing on male NAFLD [18], MO) (triglyceride, total cholesterol, and unesterified chol- these two methods complement each other and can un- esterol) and Wako (Richmond, VA) (phospholipids) were cover hidden biology that could be missed by the other used to determine the amount of liver lipids in each ex- method. ’ tract according to the manufacturer sinstructions. Both network methods assign the co-regulated genes into the same coexpression module via hierarchical clus- RNA isolation and gene expression analyses of liver and tering based on correlation of gene pairs. WGCNA is adipose tissues based on agglomerative hierarchical clustering, whereas Flash-frozen liver and epididymal adipose samples from MEGENA uses a divisive method of clustering. Gene 256 female mice from 103 strains and 288 male mice clusters are merged (in agglomerative) or split (in div- from 113 mouse strains were weighed and homogenized, isive) according to a distance measure. The distance as detailed in [17]. RNA was isolated using RNeasy col- measure used in WGCNA is Topological Overlap Matrix umns and global gene expression was profiled using (TOM) subtracted from 1 (dissTOM = 1-TOM), which is Affymetrix HT_MG430A arrays for 206 liver and 211 based on the edge weight (correlation score) between two adipose tissues from females and 227 liver and 228 adi- nodes (genes) by considering the edge weights of their pose tissues from males (some mice had both liver and neighbors in the network. Distance between two clusters adipose tissue samples analyzed, whereas others had is calculated as the average dissTOM score of all the gene only one tissue passing quality control) [22]. ComBat pairs (one gene from each cluster in a pair-wise manner). provided in the SVA tool [24] was used to remove batch Divisive clustering of MEGENA is based on the shortest effects from the gene expression data. path distance measure and a nested k-medoids clustering Kurt et al. Biology of Sex Differences (2018) 9:46 Page 4 of 14

that finds k optimal clusters at each step by minimizing negative control gene sets were true negatives. There- the shortest path distance within each cluster to define fore, at P <1E−3, false positive rate = false positives/(false more compact modules. The nested clustering process positives + true negatives) = 0.1. continues until there is no more compact child cluster found. MEGENA clusters genes in a multi-scale manner, allowing the assignment of a gene into multiple modules, Mergeomics pipeline for multi-omics integration but at different scales. Similar to our previous study on male NAFLD [18], in the For each module from WGCNA or MEGENA, we an- current study, we integrated the genetic (liver triglyceride notated the putative biological functions using known GWAS) and functional genomics data (eQTLs, coexpres- biological pathways curated from MSigDB database [31], sion modules, and pathways) from female HMDP to de- which incorporates KEGG, Reactome, and Biocarta path- fine pathways and coexpression networks that are ways, using the one-tailed Fisher’s exact test. Bonferroni genetically associated with NAFLD using the Marker Set correction was used to correct the P values. Pathways that Enrichment Analysis (MSEA) in Mergeomics [19, 20]. have an adjusted P < 0.05 and shared ≥ 5 genes with a MSEA maps the genes within each pathway (from Bio- given module were deemed significant. Up to top five sig- carta, KEGG, and Reactome) or coexpression modules nificant pathways were used to annotate each module. For (from MEGENA and WGCNA) to the expression single modules without significant annotation terms, we used a nucleotide polymorphisms (eSNPs) through eQTLs of the less stringent cutoff at uncorrected P <5E−3and≥ 5 corresponding tissue in female mice. The cis-eQTLs shared genes to annotate them with suggestive pathways (within ±1 Mb of the transcription start and end sites) at − (indicated with an asterisk sign in figures and tables). P <1E 6 were used for mapping and eSNPs in linkage disequilibrium were trimmed to keep only one eSNP per Module preservation analysis of the sex- and tissue-specific linkage disequilibrium block based on the block informa- coexpression modules tion reported by the PLINK2 tool [32]. The mapped We evaluated the preservation of the female-specific coex- eSNPs for each module or pathway were queried for the pression modules within the male-specific modules and corresponding association P values from the hepatic tri- vice versa, in a tissue-specific manner, using the module- glyceride GWAS. Then, a modified chi-square statistics, Preservation procedure from the WGCNA R package. which summarizes enrichment assessment across a range This procedure reports a Z-summary score to determine of quantile-based cutoffs for the GWAS, is applied to each whether a module is preserved in another data set by eSNP set to assess the significance of enrichment for using both connectivity and density statistics of the nodes stronger disease-associated GWAS P values by comparing within a module. A Z-summary score > 2 means that there the GWAS P values of the given eSNP set against the is evidence for the preservation of a module in the second eSNPs of randomly generated gene sets [19]. Since this ap- condition/dataset tested [29]. The modulePreservation proach is not based on a single GWAS P value cutoff but procedure was applied in a tissue-matched manner be- uses a set of quantile-based cutoffs, it produces more stable enrichment scores and avoids artifacts. This modi- tween sexes to analyze the reciprocal preservation of the P − n pOffiffiffii Ei fied chi-square statistics is defined as χ ¼ ¼ , modules obtained from the female mice expression data i 1 Eiþκ with those from the male mice. where O and E are the observed and estimated number of positive findings (i.e., findings above the i-th quantile Filtering the coexpression modules based on their point), respectively; n is the number of quantile points (10 correlation with NAFLD quantile points were used ranging between the top 50% For downstream analysis, we only kept the coexpression and top 99.9% signals based on the rank of the GWAS P modules that are relevant to the phenotype of interest values), and κ = 1 denotes a stability parameter that re- (liver triglyceride levels), based on Pearson correlation duces the artifacts for small eSNP sets with low expected between liver triglyceride and eigen genes of MEGENA counts. Benjamini-Hochberg approach was used to cor- and WGCNA modules. To select NAFLD-correlated rect for multiple hypothesis testing and an FDR < 0.05 modules, correlation P <1E−3 was chosen as a cutoff, cut-off was used to define the significantly enriched gene which corresponds to a false positive rate of 0.1 based sets for a given GWAS. on a permutation test. We generated 1000 random gene If the significant gene sets (pathways or modules) for sets, with a member size ranging from 20 to 500 genes, NAFLD at FDR < 5% had significant sharing of member as our negative controls, followed by calculating the genes, defined as gene overlapping ratio > 0.33 and Bon- Pearson correlation between the trait and the eigen gene ferroni corrected Fisher’s exact test P value< 0.05, we of each negative control gene set. Among the 1000 nega- merged the overlapping gene sets into non-overlapping tive controls, 102 gene sets had a correlation P <1E−3 “supersets” to reduce the redundancy. Occasionally, we with the trait, representing false positives. The remaining observed that a canonical pathway and a coexpression Kurt et al. Biology of Sex Differences (2018) 9:46 Page 5 of 14

module, or two independent coexpression modules, enriched for estrogen target genes and whether the male which were annotated with the same biological term but networks are enriched for androgen target genes. Liver did not share significant numbers of genes. In such and adipose transcription factor regulatory networks were cases, they were kept as independent supersets despite obtained from the Functional Annotation of the Mamma- being annotated with the same (or similar) terms. lian genome (FANTOM) repository [44]. Tissue-specific To predict potential key regulators, termed key drivers, downstream genes of estrogen receptors and androgen re- within the NAFLD supersets, the Weighted Key Driver ceptors were extracted from the FANTOM networks and Analysis (wKDA) from Mergeomics pipeline was used. used to assess overlaps with the female and male NAFLD wKDA projects the tissue-specific NAFLD-associated gene networks using Fisher’s exact test in a sex and tissue sets onto a tissue-specific Bayesian network, which depicts matched manner (e.g., female liver NAFLD network genes putatively causal relationships between genes, to identify were overlapped with estrogen receptor target genes in network hub genes (i.e., key driver genes), whose network the FANTOM liver network). neighborhoods are significantly enriched for genes in the NAFLD-associated supersets compared to the neighbor- Curation of previously studied NAFLD-relevant genes hood of a random gene in the network. This analysis has As described in [18], 107 previously validated NAFLD-as- been previously shown to successfully derive meaningful sociated genes were taken from the DisGeNET database biological findings [19, 33, 34]. Here, we mapped the hep- [45], which manually curates the gene-disease associations atic triglyceride-associated supersets onto previously de- via text mining or from databases such as UniProt, Clin- fined liver and adipose tissue Bayesian networks, which Var, Comparative Toxicogenomics Database (CTD), and were curated from multiple human and rodent expression the GWAS Catalog. We compared these genes to the ones datasets of previous studies [35–41] and constructed using identified by the current study as an in silico validation of RIMBANET [42, 43] based on the gene expression pat- our findings. terns, genetic information, causal inference, and previously known regulatory relationships among the genes [18]. Since Gonadectomy and ovariectomy the Bayesian networks incorporate genetic data, they can The gonadectomy and ovariectomy experiments were reveal causal regulatory relationships between gene pairs performed as detailed previously [21, 46]. Male and fe- and enable the identification of potential regulators of dis- male C57BL/6 J mice were purchased from The Jackson ease genes and pathways. While combining the Bayesian Laboratory (Bar Harbor). Mice were maintained on a networks from these individual studies and defining a chow diet (Ralston Purina Company) till 8 weeks of age union network for each tissue, we did not consider the edge and then placed on a high-fat high-sucrose diet (Re- weights and the directions since some of the edge direc- search Diets D12266B) until 16 weeks of age. At 6 weeks tions included in these Bayesian networks might be con- of age, the mice were gonadectomized under isoflurane flicting while the edges included in each individual network anesthesia (n = 4/group). Scrotal regions of male mice were considered robust. Gene symbolsgiveninthenetwork were bilaterally incised, testes removed, and the incisions figures are illustrated in human orthologs since the curated closed with wound clips. Ovaries of female mice were re- networks were taken from both human and rodent studies. moved through an incision just below the rib cage. The Key drivers of a NAFLD superset were identified based on muscle layer was sutured, and the incision closed with a modified chi-square statistics, as described for the MSEA wound clips. In sham-operated control mice, incisions above, that evaluates the enrichment of the member genes were made and closed as described above. The gonads in the superset within the candidate key driver’s neighbor- were briefly manipulated but remained intact. hood in the Bayesian network compared to that of a random gene chosen from the same network. Benjamini- RNA library preparation and sequencing for liver and Hochberg FDR was calculated, and genes with an FDR < adipose tissues from gonadectomized or ovariectomized 0.05 were determined as significant key drivers of a given mice NAFLD superset. We identified the top key drivers in each Upon sacrifice, liver and gonadal adipose tissues were in- shared and sex-specific NAFLD-superset based on their stantly frozen in liquid nitrogen. Frozen tissues were ho- wKDA-FDR scores. Then, we extracted the subnetworks of mogenized in Qiazol, and following chloroform phase the top key drivers in each Bayesian network by gathering separation RNA was prepared from the pink phase using the network neighbors of these key drivers. Qiagen miRNAeasy kits as per original protocol. BioA- NAlyzer was used to validate total RNA quality (all sam- Assessing overlap between the sex-specific NAFLD networks ples had RIN > 8). RNA libraries were prepared by the and sex-hormone target genes sequencing facility at the UCLA Neurosciences Genom- To explore the origin of the sex-specific NAFLD net- ics Core using Illumina TruSeq Stranded kits v2, works, we investigated whether the female networks are followed by paired-end sequencing. Reads were aligned Kurt et al. Biology of Sex Differences (2018) 9:46 Page 6 of 14

using STAR 2.5.2b, mm10 genome, and GENCOD M11 percentages of adipose modules to be associated with transcript annotation. Reads-per-gene tables were gener- NAFLD [18], but a higher percentage of NAFLD-associated as part of STAR output, and DESeq2 was used for ated liver modules (32% vs 13% for MEGENA modules differential expression analysis (see below). and 35% vs 23% for WGCNA modules in males vs females). These percentages suggest less prominent Differentially expressed gene analysis between NAFLD-related liver gene network perturbations in fe- gonadectomized/ovariectomized mice and sham-operated males compared to males, agreeing with the milder mea- mice sures of liver injury in NAFLD females [11]. Functional DESeq2 R Bioconductor package [47] was used to iden- annotation of the NAFLD-associated modules in females tify the genes that were differentially expressed between revealed diverse pathways ranging from various metabolic the gonadectomized or ovariectomized mice and sham- pathways, immune pathways, to extracellular matrix operated mice. DESeq2 tool estimates the variance-mean organization (Additional file 2). These NAFLD-correlated dependence of the raw gene counts using a negative bino- modules were further integrated with GWAS signals from mial distribution and identifies the differentially expressed female HMDP to identify potential causal mechanisms, as genes between two groups (i.e., calculates the log2 fold detailed below. changes and corresponding P values for each gene) based on a generalized linear modeling. Benjamini-Hochberg adjusted P < 0.05 was used to identify the differentially Biological pathways and coexpression modules that expressed genes. Then, we analyzed enrichment of the dif- exhibit genetic association with NAFLD ferentially expressed genes for the sex-specific NAFLD To identify potential causal processes for NAFLD in fe- subnetworks in the liver and adipose tissues using Fisher’s male mice, genetic information (GWAS; which carries po- exact test. tential causal inference), tissue-specific eQTLs, NAFLD- correlated co-expression modules, and biochemical and Results signaling pathways were integrated to infer groups of Tissue-specific coexpression networks in females functionally connected genes that together demonstrate To retrieve functional gene-gene relationships, gene significant genetic association with NAFLD in females. coexpression networks were constructed based on tran- Out of the 1823 canonical pathways and the 75 hepatic scriptome data of 206 liver and 211 adipose tissue sam- triglyceride-correlated female modules (36 from liver and ples of female mice from 103 HMDP strains using 39 from adipose tissue), NAFLD GWAS signals from fe- WGCNA [29] and MEGENA [30] (see the “Methods” males were significantly enriched in 18 pathways and 5 section). For females, we identified 30 coexpression liver co-expression modules informed by liver eQTLs, and modules in the liver and 30 modules in adipose tissue 12 pathways and 6 adipose modules informed by adipose using WGCNA, whereas with MEGENA, we identified eQTLs, at FDR < 5% (Additional file 3). Some of the sig- 213 and 85 modules in the liver and adipose tissue, re- nificant modules or pathways share member genes and spectively. These findings are comparable with those represent similar biological processes. To reduce the re- from male mice [18]. Comparison of the modules be- dundancy, we derived “supersets” by merging overlapping tween females and males revealed high preservation, gene sets; thereby each superset was comprised of one or with ~ 95% and ~ 90% of the modules reciprocally pre- more overlapping NAFLD-associated gene sets (see the served between sexes in the liver and adipose tissues, re- “Methods” section). These supersets still carry enrichment spectively (Additional file 1). However, it is possible that for strong NAFLD genetic signals that their constituents different modules may be perturbed in NAFLD in each carried (Additional file 4). Some of the gene sets were an- sex by different genetic risk factors. notated with the same term but were not merged because the overlap in gene members was not sufficient to meet Identifying modules that are correlated with NAFLD our criteria as specified in the “Methods” section (e.g., in phenotypes in females female adipose tissue, 2 modules were annotated with Coexpression modules that are associated with the extracellular matrix and matrisome terms but were not NAFLD phenotype were identified based on the correl- merged). Comparison of supersets between tissues revealed ation between the expression patterns of the module 6 liver-specific supersets (e.g., innate immune system, oxi- eigengenes and hepatic triglyceride levels across all fe- dative phosphorylation, metabolism of vitamins and cofac- male samples (see the “Methods” section). We found 65 tors), 6 adipose-specific supersets (e.g., branched-chain MEGENA modules (29 from liver and 36 from adipose) amino acid metabolism, extracellular matrix glycoproteins, and 10 WGCNA modules (7 from liver and 3 from axon guidance), and 8 cross-tissue supersets (e.g., apop- adipose) to be significantly correlated with hepatic tri- tosis/cell cycle, gene expression regulation, growth factor glycerides. In the male HMDP data, we found similar receptor signaling) (Fig. 2). Kurt et al. Biology of Sex Differences (2018) 9:46 Page 7 of 14

Processes that were specific to one sex were also identified. For instance, insulin signaling-related terms were observed only in the adipose tissue of male mice. In addition, more metabolism-related pathways (e.g., lipid, lysophospholipid, fatty acid) were identified in males compared to their female counterparts. Another male-specific adipose pathway was Wnt signaling, which has diverse effects on cellular metabolism and inflammation. Perturbed Wnt signaling is known to contribute to increased inflammation and decreased adipogenesis, leading to triglyceride Fig. 2 Comparison between NAFLD processes perturbed in the liver accumulation and acceleration of NAFLD. Wnt signaling and adipose tissue for females. Putative causal pathways that are is known to crosstalk with Notch and TGFβ signaling, common to both tissues and unique to each tissue are listed. Co- expression modules are annotated with the most over-represented which were also found to be perturbed only in male liver gene ontology terms. “NA” indicates no over-represented terms and adipose tissues, respectively. Peroxisome, cytochrome were found for a given module. BCAA branched-chain amino acid, p450 drug metabolism, and G2-M DNA damage check- BCR B cell receptor, ECM extracellular matrix, TCR T cell receptor point are additional male-specific pathways in the liver (Additional file 4). These findings may help explain the Comparison of the NAFLD-associated supersets between more severe metabolic syndrome and liver injury, as well female and male mice as the higher ratio of DNA damage and inflammation Comparison of the female NAFLD supersets with those markers observed in males. from the males [18] revealed five supersets (lipid metab- For females, metabolism of vitamins and cofactors and olism, apoptosis/cell cycle, signal transduction, and tran- ion channel transport were uniquely identified in liver scription pathways) to be shared across tissues for both (Fig. 3b), whereas extracellular matrix glycoproteins, sexes (Fig. 3a; Additional file 4). In liver, oxidative phos- axon guidance, and TCR signaling were female-specific phorylation and transmembrane transport of small mol- in adipose tissue. Ion transport has been associated with ecules were shared between sexes, whereas branched- oxidative stress and plays an important role in the pro- chain amino acid metabolism was shared between sexes gression of liver steatosis to insulin insensitivity and in the adipose tissue (Fig. 3a). more severe conditions such as hepatocellular carcinoma Besides the consistent pathways between tissues and [46, 49–52]. Regarding the vitamins and cofactors path- sexes discussed above, we also found that certain pathway, adverse impacts of vitamin A, B12, D, and E defi- ways demonstrate differential tissue-specificity between ciency in NAFLD, fibrosis, and NASH have been studied males and females (Fig. 3a; Additional file 4); innate im- [53–57]. Vitamin A and D are involved in extracellular munity was specific to liver in females but was specific matrix remodeling during liver fibrosis, B12 deficiency to adipose tissue in males, whereas the adaptive immune impairs fatty acid oxidation [54], and vitamin E reduces system and B cell receptor (BCR) signaling superset was endoplasmic reticulum stress and prevents liver inflam- shared by both tissues in males but was adipose-specific mation and apoptosis. Some of these terms have been in females. Therefore, the two arms of the immune sys- associated with NAFLD before, but here, we provide tem appear to act in different tissues during NAFLD in novel evidence that they may play a causal role in females, whereas both the innate and adaptive immune NAFLD based on the fact that we incorporated genetic systems were found to be perturbed in adipose tissue in signals in our analysis. males. Although the adaptive immune system is implicated in both female and male adipose tissue, it still has Predicting key driver genes of the NAFLD-associated a sex-specific pattern since males have transforming gene supersets in females growth factor-beta (TGFβ) and BCR signaling pathways, To predict the potential regulators of the NAFLD genes while females have a T cell receptor (TCR) pathway. and pathways, we used liver and adipose tissue-specific TGFβ is a cytokine that can induce profibrogenic gene Bayesian networks [42, 43] that involve causal or regula- expression and may promote the progression from stea- tory relationships between gene pairs and were independ- tosis to steatohepatitis in males [48]. Additionally, males ently constructed from previous human and mouse showed perturbations in fatty acid, triacylglycerol, and studies [35–41]. Numerous key drivers were predicted for ketone body metabolism in both tissues, but this path- each of the female NAFLD-associated superset at FDR < way was liver-specific in females. On the other hand, 5% (top key drivers for each superset in Additional file 4; growth factor receptor signaling and cancer pathways full list in Additional file 5). We found that 23 of all were adipose-specific in males but identified in both tis- significant key drivers were among the 107 previously sues in females. reported NAFLD genes curated in DisGeNET Kurt et al. Biology of Sex Differences (2018) 9:46 Page 8 of 14

Fig. 3 Comparison between NAFLD processes perturbed in the liver and adipose tissue of both sexes. Putative causal pathways that are a shared between sexes in one or both tissues and b unique to each sex and each tissue are listed. TCA the citric acid

(Additional file 6), including AHSG, FASN, RBP4,and various aspects of metabolism (e.g., fatty acids—FASN and SREBF1, which were found to be key drivers in both liver CD36, mitochondria—CPT2 and CHCHD6), insulin sig- and adipose tissue in our analysis. naling (e.g., FASN, GYS1), immune system (e.g., INPP5D, We compared the top key drivers predicted for the fe- FCER1G, NCKAP1L), and cell growth and apoptosis male NAFLD subnetworks with those predicted for (ANXA2, CIDEC)(Fig.4e for liver, Fig. 4f for adipose). males in a tissue-specific manner. As elucidated in Fig. 4 Notably, both the shared and sex-specific key driver sub- and detailed in Additional file 4, the shared liver or adi- networks contain numerous known human GWAS genes pose key drivers between sexes included genes with di- such as PNPLA3 and TRIB1, but the GWAS genes are less verse functions involved in fatty acid and cholesterol likely to be key drivers. The combined subnetworks of metabolism (such as ACOT2, DECR1, DHCR7, SQLE, NAFLD processes that are female-specific or shared be- INSIG1, and ACSS2), branched-chain amino acid catab- tween sexes are given in Additional file 7 A and B for liver olism (such as BCKDHA, MCCC1, and ECHS1), cell and adipose tissue, respectively, whereas the male-specific cycle (such as CDCA8, MKI67,andCCNA2), extracellu- or shared networks were previously presented in [18]. lar matrix (such as FBN1, COL1A2,andCCDC80), and immune system and inflammation (such as RELB, IFNG, Potential regulation of the NAFLD networks by sex and CXCL10). The key drivers and the pathways they hormones regulate intimately interconnect in gene networks (Fig. 4a To explore the potential role of sex hormones in regulat- for liver and Fig. 4b for adipose tissue). ing the sexually dimorphic NAFLD genes and pathways We also identified key drivers (Additional file 4)and identified, we tested the sex-specific liver and adipose tis- their associated subnetworks (Fig. 4c–f) for the sue key driver subnetworks for enrichment of known tar- sex-specific mechanisms in a tissue-specific manner. get genes of estrogen receptors (ESR1, ESRRA, ESRRB, Many top female-specific key drivers are immune-related and ESRRG) and androgen receptor (AR) based on FAN- genes such as PTPRO, SH3BP2, TYROBP,andC8B (Fig. 4c TOM5 transcription factor regulatory networks [44]from for liver, Fig. 4d for adipose). In contrast, male-specific key matching tissues. We found that female-specific liver and drivers show broader functional diversity including adipose NAFLD networks were strongly enriched with Kurt et al. Biology of Sex Differences (2018) 9:46 Page 9 of 14

CENPK MLF1IP BUB1B KIF20A

A DLAT B DCK PCYT2 CROT RETSAT ESCO2 HMMR TPMT BCL3 Key Driver genes CA5A FCGR1A HJURP Metabolism of lipids CCL2 CH25H ACACB NFS1 ACSL5 PKMYT1 DHCR24 XCL1 E2F2 GAMT NUF2 GSTM3 KIF2C SREBF1 CD37 STAT1 CENPN NPC1L1 CXCL11 and lipoproteins RRM2 RACGAP1 CDC20 Signal transduction SLC19A2 SLC25A1 CYB5R3 MCM2 Human NAFLD GWAS genes SC5DL CCL7 CXCL10 TPH2 DTL POLE2 MAT2A ECHDC2 CCL5 CXCL9 NEIL3 CASC5 GPC1 ACLY PAOX STAB1 LMNB1 ACSS2 LCK TOP2A PNPLA3 VSTM2B GCKR PDIA5 SH3BP2 PLK1 KNTC1 GFPT2 GCK QDPR ZNF324 COL6A3 OMD H2AFX CENPF CCND1 NUDT19 CENPM CCNA2 SPC24 SPAG5 CALM1 LDLR GZMB FSTL1 INSIG1 ITGAD AURKB CRLS1 UBE2C FASN L06463 CXCR3 Network genes AQP8 EBP IL2RG FBN1 UBB CCNB2 IFNG MCM5 CENPI BUB1CDCA5 NCAPG INCENP PLAT GNGT1 APOE ELOVL5 CDT1 ITGAL QPRT MCM6 PLTP CYP2R1 MMT00033809 ZNF565 CLSPNBIRC5 AURKA CD248 AADAT ERCC6L MVD CCNB1 DNM1 GPAM ACSL3 ZFP14 HSD17B1 LSS RHOV RAB11FIP5 TBCB CXCR6 TRAT1 TK1 BRCA1 Non-member network genes ZNF566 RAB8B CDCA8 WNT2 NIT2 GMNN PGRMC2 HMGCR NSDHL FDPS CCL4 DEPDC1B FADS2 PMVK ZFP30 CDC25C EPHX2 CENPE UGT2B28 LCP1 SLC22A15 SUSD4 ZNF569 TPX2 MVK FXYD3 ZNF790 FADS1 FDX1 Gene TIMP1 ARHGAP25 H2AFZ FRMD4B PDK2 TNFRSF17 MEF2C Transmembrane transport of small molecules MMP3 SREBF2 FTH1 CENPA GALNT7 SDHD CPT2 CYP2C18 DHCR7 GPR4 CCDC99 ITPR2 ACADM SLC27A2 MMAB NDC80 ADIPOR2 HSD17B4 FDFT1 expression VCAM1 HDAC1 DECR2 ZNF226 DNA2 GCLC SLC25A13 MSMO1 ASF1B RGS2 SESN1 CHRNA2 CENPM ZNF383 CAPZA2 ARHGEF6 PPP1R3B TEC FABP7 MAD2L1 CCL19 MRC1 SLC2A12 SLC25A10 MGST3 UGT1A6 ABCD3 SGK2 DBF4 HAO1 STARD4 SGOL2 OLFML3 GALC SLC25A20 TM7SF2 GPD1 CA4 HADHB NDUFS2 PPARG TYMS CXCL12 ST8SIA4 PCCB MMT00079923 NDUFA10 SPC25 SUMF1 CCR2 SLC25A24 ACAT1 ITPR2 SLC47A1 SQLE RHBDD2 MAOA GINS1 NM_009040 ALDOC SDHB XM_357345 IFI30 PTPRO AGPAT6 DUSP4 PAK1 ZNF23 GPR34 AKR1D1 MACROD1 S100A4 SLC6A6 SLC2A9 SPC25 PPARGC1B CYCS ADCY5 EID1 NT5E ABCC3 HSD17B7 RUNX2 PROS1 CS FCER1G GPI ATP5D CD84 ESRRA ADCY1 SLC22A3 CYSLTR1 BCKDHB BHMT CYP4F8 IDI1 FGFR1 IRS2 DIO3 MDH2 COX5A TES MAN1C1 PMM1 DECR1 ABCG5 ACOT4 HSD17B8 POR KDR EVI2A RFC3 HMGCS1 COX4I1 DRD1 RASA1 CD200R1 ALDH3A2 CYP51A1 COX5B KCNB1 UBASH3B LRIG1 RAB2A STUB1 ATP5B NDUFS7 CSAD RP2 CGNL1 CYP2C9 HADHA PILRA NOS3 GMFG CMBL TAT LYPLAL1 DLAT ARHGEF4 ADCY7 RGS18 ACOT1 HADH ETNK2 A2M TRIB1 ACTR2 FAP CASP4 MAN1A1 IVD SREBF1 IRF8 PRODH ACAD10 ENPP1 DTX2 SLC25A13 HTR2B HSD17B4 XRCC5 ARHGAP15 SLC16A7 LCP1 PSTPIP2 SUCLG1 EHHADH KLB SYNGR2 CADM1 HMGCS2 FH FMO3 NDUFV1 CABLES1 HPGD FMO2 MGLL TMEM56 COQ6 MLYCD ENTPD5 AGPAT3 REEP6 MORC4 PLTP CYBA ACOT4 PDE3B IDH3B UCK1 CORO1A PRKCD PIK3R1 NDUFA1 PMM1 CRAT ADH1C SLC22A5 CPS1 NUF2 ACADM CSF1R STK24 TWF2 ACMSD ITPKA C16orf74 ARMC7 Cell cycle ACSS2 NTRK2 HPD CYC1 PTPN18 UQCRC1TALDO1 CYP1B1 PTPRC NDUFB8 FMO1 FXYD5 ABHD6 GSTT2 GPD2 CDO1 OPTN GRN LAMB3 VNN3 CHKB ACO2 SLC25A20 GYS1 PKIB FYB ARHGDIB SNX20 METRNL ASPA SELENBP1 KCNE3 IL15RA IRS1 ACSM3 C1QC PSMB8 G0S2 ASL CYP39A1 KIF2C BCKDHA PRKCE MCM4 LPCAT2 Adaptive PTPN6 COQ5 SH2B2 SRGAP1 UTS2 AASS TLR2 GPSM3 CXCL16 CRAT ETFDH DBF4 OXCT1 ATP5A1 ACADVL SAMM50 PPARA LGALS4 AGPAT6 GYG1 PNPLA3 IDH3G ETFDH MMP8 ATF3 ARG1 OIP5 TRIT1 ACACB AIF1 RAC2 LPXN IMPA2 SHMT1 CD86 CPT1A VNN1 ACOX1 PKMYT1 PLK1 UQCRC2 ABHD6 SLC39A6 ACOT2 SMC2 TOP2A CCDC109B RGS14 RAB30 NEK2 HADH LY86 LCP2 SAP30 immune PHYH GLS2 IDH3A ACADL HINT2 TPMT LST1 ACTR3 CYP4F12 KLF6 MIS18A PDHB IL1RL1 CYP4A11 ETHE1 GOT1 CYP2B6 CDCA5 MAD2L1 SDHA BAG1 IRF5 SDF2L1 CA2 SLC3A1 HEXA PSMD14 CPT2 ORMDL3 TST MAT1A HADHB ST3GAL6 SLC2A4 PTPLAD2 WAS PYGL SMS SFXN1 PIK3CG FERMT3 ELOVL6 PIK3CD SASH3 NDUFB9 CENPA NDC80 AGPAT9 DBT GRHPR ARRB2 CSK system, UROS NDUFB9 GAL3ST1 GK FASN DECR1 METAP2 ARHGAP9 PDZRN3 ACP5 CCNB2 ETFA S100A1 OR4K5 PEX11A CYP17A1 ATP6V0E2 FST ACADS RENBP DDT BLCAP PTPLA CPT1B AHCY CDCA8 CCNB1 INCENP POLE2 GSTM2 ETFB ACAT1 KDSR PDE4B DERL1 NRG4 PCCB ALDH2 LYPLAL1 FBP1 HSDL2 ERCC6L C15orf23 CDKN2C MAOB BCR EHHADH ECHS1 ALDH6A1 IDH2 AKAP13 ATP5C1 CKLF IGSF8 GNAI1 OSBPL1A ENSMUST00000066988 APOC2 AURKA CYP2E1 ARHGAP30 SLC4A4 RAB34 NDUFB5 CFL1 CCBL1 ANGPTL4 CHPT1 ADHFE1 NCKAP1L PDGFRB PFKFB1 ACADSB SLC6A9 RALGDS RACGAP1 CYP27B1 HIBADH ZEB1 FN3K PADI2 PTPRJ ACOT8 PSAT1 PGM1 IDH1 signaling CENPE GPX4 ATP6V0A2 GCKR LUM ESCO2 DLD SLAMF9 MYC ATP8B2 CHPT1 HIBCH RETSAT CCNA2 MCCC1 MCCC2 CD36 UROC1 ACSL1 HACL1 PLA2G6 ASNS IL10RA HMHA1 DOK3 ALDOC Metabolism RCSD1 AMPD3 CIDEC HMMR MUT CCBL2 CA3 B4GALT5 CSGALNACT1 PDHA1 PRKAR2B PRODH CKS1B CDC20 VAV1 RGS19 PLA2G4C DOK1 RELB RRM2 CHCHD6 DENND4B AATK RBP4 PCCA FABP4 CDO1 GSK3B SYK MCM2 MLF1IP RHBG GCAT MKI67 FDX1 TLN1 of lipids and FLT3 AURKB TAGLN2 TM6SF2 BUB1 AUH ASS1 CAT CYP2F1 MID1IP1 PCLO NCAPG ACACA CSF2RA BIRC3 COL4A2 PDGFC GCLC NUDT18 CSNK1A1 BLNK RUNX1 FBN1 BIRC5 COX15 BCKDHB PLXND1 PON1 LIG1 GPT CRLS1 lipoproteins Fatty acid, triacylglycerol, CCND1 NFKB2 FSHR ALB PCK1 GPAM TAOK3 LAMB1 MCM6 MCM7 RGS7 HSD17B10 AMY1A COL4A1 LAMA2 MCM5 STAT5B BCAA metabolism ketone body metabolism NM_053200 KDSR PDS5B MDH1 PPP1R3B CDT1 PRIM1 IL18 Pa ncer MCM3 POLE CEP192 VRK1 RAD51 SEMA3B THRA PDPN EPS8L2 HOXD4 PFKM PNPLA3 MMP23B C1QC HLA-DQA1 SNX5 SLC11A1 PLTP PADI4 TNFSF13B Lysosome, DENND4B DTL SLC39A4 FMN2 PTPN13 NBL1 DUSP14 ADAM33 PDLIM3 FBLN1 EMR1 TNIP1 AK5 TNFRSF1B RIPK3 IGFBP6 GPSM3 C PYCARD D COL4A5 GAS1 C1R SCARA3 TCR MGAT1 LGALS3 CD36 ACTR3 TEKT2 UNC93B1 VAV1 SLC9A3R1 CLEC11A C8A RBP1 CTSS APOM CAPN6 CSF1R IL10RA signaling LYVE1 MMP17 LCP1 GPR137B RSPO1 HEXA Innate immune system RAC2 FCGR1A C3 SCN3B SLC22A7 IGFBP5 FES C1QC FGF9 PTGIS SLIT3 MAN2B1 IGF1 FMOD OSR1 ATP13A2 ITGB2 MOGAT1 CD86 IL13RA1 LCP2 PERP AHCY TMEM35 SERPINA5 IL33 PLA2G4A PLD4 SDC3 HSD17B2 WT1 PTGS2 C2orf40 PTGS1 CYBRD1 CYBB VSIG4 IGSF6 F11 ALOX15 SMPD3 AIM2 PLD4 GJB5 PKHD1L1 PDGFB CA1 C8B C9 WASF1 ELN HCK SLC17A2 TYROBP LYPLAL1 COL4A6 SEMA3A NCAM1 EBI3 TLR2 CFB RHPN2 C2 LIPC MSLN ABCG1 GPC3 CSRP2 FGF1 TUFT1 PQLC1 DOK1 TLR1 AEBP1 TMEM119 FCER1G GPR34 IKBKE C19orf47 FCER1G PRG4 CRYM KCNAB1 Post-translational C4BPA ANPEP IGFALS MMT00060752 SLPI FCGR2A EGFR SPOCK2 RPP25 SYK FERMT3 GPR98 LGMN PPL APOBEC1 NCF4 C6orf132 P2RY6 PLEKHO2 NCKAP1L CTNNBL1 CACNA1D NAB2 protein modification PIK3AP1 TMEM98 BNC1 MEIS2 METRNL C2 SRC LY96 GCKR CFI GPM6A GREM2 PTPN6 NDRG3 DPP4 SOAT1 SLC15A3 DOK3 TLR6 MAD2L2 WFDC1 FCGR3A MMT00049791 TPCN2 EFEMP1 MYH11 SLC23A3 SEMA5A KRT7 TBXAS1 CD86 IRF5 SIRPA TSPYL4 ADCY7 NLRC4 NFE2L3 PTPRE CD53 TM6SF2 SLC39A8 ZFPM2 TREM2 C1QB EVL LY86 KRT19 COTL1 MANBAL MST4 F5 ST8SIA4 PTPRO SYK RALGPS2 AQP5 SLC16A4 GATA6 CYBA MAPRE1 FMO1 PRSS23 NPL MRPS14 CFH OMD NCKAP1L SH3BP2 FERMT3 PLCG2 COBL MAFB FMO4 NPNT BAIAP2L1 LCP1 VAV1 NKAIN4 RGS18 FCGR2A CDK5RAP1 ZHX3 SPOCK3 HCLS1 CST3 WAS SNX20 OLR1 SAMM50 P2RY12 LRMP HHEX HCK MMT00037110 DMKN SLAMF8 LAT2 C1QA QSOX1 EZR CD72 ADRBK1 ITPR3 GALNT7 MMT00023637 LRRC1 CCDC109B ARHGAP9 PPP1R3B CTSB LRP2 CXADR CLDN10 SAMSN1 C9orf3 INADL EVI2A EPS8 BLNK MOCOS TMEM106A CYP2S1 TAOK3

SOD2 Matrisome, ECM glycoproteins, axon guidance BCAR3MAP1LC3A LIPF CRLF1 RAB38 SPC25

DEGS1 SLC2A3 RNF138 LYPLAL1 PRPS1 S100A8 IL13RA2 mRNA processing, HIV life cycle STRN3 OPN3 IFI30 RBM7 Cytochrome p450, complement& OLFM1 HP FYTTD1 CDKN1A E F NCAN RANBP2 ECM receptor HPCAL1 CCDC132 ACSS2 HR LRG1 GPR27 UBD coagulation, biological oxidations AK005014 CPA2 INCENP CD93 CA3 CD274 TPH2 PSMD14 COL5A3 LTBP3 SAA2 CRISP2interactions GBE1 ARHGAP24 PRKCD ITPR3 PPIG ZNF644 TBC1D15 WWTR1 NUDT7 DDIT4 PTPN6 ADHFE1 LBP MRC2 ESF1 DNTTIP2 DUSP22 LAP3 SREBF1 RASL12 ITGA7 SERPINE1 S100A10 COL4A2 VNN1 ACLY TLN1 CNP FKBP1A PPIC TAX1BP3 DUSP9 PVALB NFKBIE RIF1 CCNG1 SPRR1A GCAT KLF15 NUPL1 KTN1 ABCB1 SYK KLHL2 TPST1 PNPLA3 LPAR3 IRF5 ME1 LAMB3 ITIH5 CFB ELOVL3 RTN4 HK2 SH3KBP1 PDGFA DAP SORBS1 IGFBP2 HSDL2 PMEPA1 NUP210 RB1 INPP5D P2RY14 COMTD1 FADS1 ENTPD5 CYP4F12 NM_176994 DGAT2 PAK1 PACRG SULF2 ELOVL6 MORC4 CFD GPD1 PKP2 AK004012 AGPAT2 VAV1 PLCG2 TMEM98 CPT1A ELOVL5 SYNPO ACAT2 HSD17B12 RCAN1 C14orf132 MST1R CYBA APOC2 TKT TP53INP2 TNFRSF21 ACOT4 F7 NOL3 CCDC91 NET1 FIGN ADCY7 ACSM3 CYP1A2 VNN3 CD36 SRXN1 TMEM43 GPAM SYT12 TWF2 OSBPL5 LRRC16A MEST KISS1R KCNJ14 CLSTN3 SMPD3 SCNN1A FASN PPARG LIMCH1 LCP1 TRIM24 NCKAP1L NCF4 RIPK4 CDH1 ACYP2 SMC2 HMGCS1 CYS1 PIGP CDKN2C AACS ST3GAL4 SYTL4 ACADS FCER1G ANXA2 LY6D ACSL1 CYP2D6 MUSTN1 LZTS2 SMARCA2 CABP4 ACOT1 FMO2 SLC5A6 ERMP1 AGPAT9 PALMD TMEM159 SLC1A3 AVPR1A C1orf86 BSCL2 SLC16A5 PPARG VEGFA IRS1 TYROBP CNNM2 COG4 HK3 TCEAL8 LGALS1 RAB34 FCGRT SLC22A3 TREH ACACB TNK2 ADRB3 LCP2 SLC25A1 LPIN1 CD86 DENND2D KCNK1 DERL1 DGAT1 CRIM1 NCF2 SLC16A7 ANXA5 MESP1 DUSP8 GPR146 FERMT2 PINK1 TUFT1 CIDEC SUSD4 PANK1 PYGL TMEM143 PNPLA2 SH3D21 SFXN1 ABCD2 MOGAT1 UNC119 PCYT2 PC PIM3 HSD17B2 SLC35F5 FABP2 ECHS1 ST6GALNAC5 AADAT CYBB FCGR1A FGL2 AQP1 TPRKB PCCB PEX11A RBPMS2 SLC2A4 CA1 FGFRL1 HSD3B7 CHCHD6 C10orf35 MKNK2 MFNG LIFR F11 CD1D CYP27A1 CA2 CYP4F3 LIMS2 TGF beta signaling, MRC1 C8B CTSS HCK PDIA5 KLRD1 NAGK SAMM50 ACAT1 HADHB CGREF1 MOCS1 TSPAN7 PCK1 DAPP1 JAK2 VLDLR CYP7B1 CIDEA DHRS7B PCCA GYS1 CHST1 ATOH8 DKK4 AOC3 CCND1 RAI2 LAMA3 C2 CCND1 CPT2 IGFBP5 SLCO2A1 GPC1 PPP1R3B RNF149 HIF2, IGF1 pathways IDH2 CPEB1 CYC1 DAB2IP SLC22A3 APOM HADH VEGFB ATP9A IL13RA1 LAMB3 IL17RC ID1 CRYBB3 UGT2B15 SLC22A7 ZYG11B MYADM ACADL CKLF SMAD7 ETFDH POR MCM4 WFDC2 GPX4 REEP5 GNPAT POLE2 ARL6 DEF8 SERPINA10 MGST3 AGPAT6 MCM2 Adaptive immune LIPE ATPIF1 GCKR DECR1 C6 MMAA SOX7 HEXA ACADM FN3K CARD10 ESCO2 FGL1 PPARA MCM3 ANXA4 FMO3 STK39 ACADVL BCL9L TJP1 RRM2 FMO1 Cell cycle TSPAN2 H6PD AK004012 system, C8A COASY MMT00033656 CCNA2 ITIH3 OXCT1 SLC25A20 IL15RA ALDH18A1 STAT3 GAS6 ZNF467 S phase MCAM PRKCA SFRP5 ENPP1 CDCA5 BCR signaling MMT00060752 CDKN1C NUDT7 mitotic, ATP11A NANP MCM6 QSOX1 PNPLA3 TPD52 EHD2 SERPINF2 BLCAP EIF4EBP1 BAG4 HSPB8 MCM5 LRG1 EGFR POU2F1 SKP2 ALDH3A2 RXRG NFIA AGT HEATR1 MMT00049791 FOXM1 insulin signaling C1R INPP5A Peroxisome, SLC3A1 NM_009040 TUSC3 C16orf74 NM_020568 KCNJ2 SERPINA3 HNMT OLFML2B AMOTL2 GADD45B TMEM134 MTHFD2 SERPINA4 CDT1 MAD2L1 pathway, TMEFF1 ST14 G0S2 AK008993 RNF128 lysophospholipid LYPLAL1 HOXC8 GNAI1 CLIC1 SGPP1 GLIPR1 TPX2 Phosphatidylinositol STAP1 TM6SF2 G2-M AIG1 SERPINA5 FHDC1 pathway SERPINA12 Fatty acid, triacylglycerol, LCP1 HAL TMEM144 NPL signaling checkpoints SLC25A13 CADM1 ketone body metabolism TES Fig. 4 Bayesian gene network representations of NAFLD pathways and their key driver genes. a Liver Bayesian subnetwork comprised of shared liver NAFLD supersets between sexes and their top key drivers. b Adipose tissue Bayesian subnetwork comprised of shared adipose NAFLD supersets between sexes and their top key drivers. Female-specific c liver Bayesian subnetworks and d adipose tissue Bayesian subnetworks and their corresponding top key drivers. Male-specific e liver Bayesian subnetworks and f adipose tissue Bayesian subnetworks and their corresponding top key drivers. Key driver genes are shown with larger node sizes, human GWAS candidate genes are represented in hexagon shapes, and the rest of the genes are represented by smaller node sizes. Each NAFLD-associated superset is indicated with a distinct color in each network. Network genes that are not members of the NAFLD supersets are represented in gray. See also Additional file 4 target genes of estrogen receptors and male-specific key is a target gene of the androgen receptor. These results sug- driver subnetworks showed a strong enrichment for the gest that sex hormones may regulate the sex-specific key target genes of AR in both tissues (Additional file 8). We drivers and pathways and partially explain the sexual di- also investigated whether individual key drivers in the morphism in NAFLD. sex-specific networks are targets of sex hormones or show interactions with sex hormones. Among the female-specific Gonadectomy and ovariectomy experiments support the key drivers, NCKAP1L is a target of estrogen receptor regulatory roles of sex hormones in NAFLD sexual ESRRA; C8B isatargetofmultipleestrogenreceptors dimorphism ESRRA, ESRRB, and ESRRG; and SH3BP2 is a target of To more directly test the role of sex hormones, we com- ESR1 [44]. Among the male-specific key drivers, CHCHD6 pared our NAFLD subnetworks with the transcriptome Kurt et al. Biology of Sex Differences (2018) 9:46 Page 10 of 14

of both liver and adipose tissues from gonadectomized molecular insights into the sex differences in NAFLD. and ovariectomized mice. At FDR < 5%, we identified Furthermore, both our in silico FANTOM5 transcription 2435 and 196 differentially expressed genes in liver tis- factor analysis and in vivo hormone modulation experi- sue of males and females, respectively, and 1804 and ments support a strong role of sex hormones in the regu- 1491 differentially expressed genes in adipose tissue of lation of sex-specific NAFLD pathways. males and females, respectively (Table 1). These results We conducted our analysis in a tissue and sex-specific suggest a significant impact of testosterone on both male manner, identifying and comparing causal NAFLD pro- tissues and estrogen on the female adipose tissue. In cesses between sexes in the liver and adipose tissue since contrast, female liver tissue was less affected by estrogen those are the most relevant and implicated tissues in deficiency. We analyzed the enrichment of each differen- NAFLD pathogenesis [48]. We observed numerous pro- tially expressed gene list in our sex-specific NAFLD sub- cesses that are shared by both sexes, such as the cell networks (Fig. 4c–f) for the corresponding sex and cycle, apoptosis, and lipid metabolism in both tissues, tissue. Consistent with the numbers of differentially fatty acid metabolism, oxidative phosphorylation, and expressed genes from the gonadectomy/ovariectomy ex- growth factor receptor signaling in liver tissue, and periments, the female liver NAFLD subnetworks (Fig. 4c) branched-chain amino acid metabolism, adaptive im- had the smallest number of genes (86 compared to hun- mune system, and post-translational protein modifica- dreds in the other networks). Only two genes in this tion in adipose tissue (Additional file 4). Many of these subnetwork were found to be affected by estrogen defi- pathways have been previously associated with NAFLD ciency in the female liver. In stark contrast, about half of [48, 58, 59], confirming the reliability of our approach. the male liver NAFLD subnetwork genes (100 of 218 The consistency of these shared terms between sexes genes) were found to be affected by testosterone defi- highlights them as the core processes that can be tar- ciency in male liver tissue. Similarly, significant overlaps geted generally for NAFLD. between the adipose NAFLD subnetwork genes and the To facilitate the identification of potential targets for adipose genes affected by sex hormone deficiency were the above-shared pathways, we used a network modeling observed for both sexes. Besides, some of our top key approach to define candidate regulator genes or key driver genes were among the differentially expressed drivers. The predicted key drivers were found to orches- genes for the corresponding tissue and sex as listed in trate genes in the NAFLD processes shared by both Table 1 (see also Fig. 4c–f). These results support that sexes, forming highly connected subnetworks containing sex hormones are involved in the regulation of the numerous previously known NAFLD genes (such as male-specific NAFLD processes in both tissues but only AHSG, FDFT1, FASN, ACADVL, PNPLA3, GCKR, adipose pathways in females. LYPLAL1, and LCP1) (Fig. 4a, b). In liver tissue, many key drivers are involved in fatty acid and cholesterol me- Discussion tabolism, such as ACOT2 and DHCR7. In adipose tissue, To understand the sexual dimorphism observed in notable key drivers such as BCKDHA, MCCC1, and NAFLD, here, we compared the genetically perturbed ECHS1 are key enzymes involved in branched-chain mechanisms in NAFLD between sexes via integration of amino acid catabolism. Branched-chain amino acids are multi-omics data including GWAS, tissue-specific tran- known to be involved in numerous physiological activ- scriptomic and eQTL data, and NAFLD phenotypic data ities related to nutrition, metabolism, and immune sys- from > 100 inbred and recombinant inbred mouse strains tem processes and metabolic disorders such as insulin for females and males. This comprehensive data-driven resistance and NAFLD. Impairments in branched-chain analysis revealed both shared and sex-specific pathways, amino acid degradation deteriorate the TCA cycle, which regulatory genes, and networks, thereby providing may lead to mitochondrial dysfunction in NAFLD [60].

Table 1 Overlap between sex-specific NAFLD subnetworks and DEGs affected by sex hormones Tissue Sex Network size DEG size Overlap gene size Overlap KD list Fold change P value (overlap KD size) Liver M 218 2435 100 (14) C8B, CYP7B1, SLC16A7, SLC16A5, CD36, 6.13 8.28E−71 MGST3, NCKAP1L, INPP5D, ANXA2, HCK, FCER1G, FGL2, CIDEC, TBC1D15 F 86 196 2 (0) – 3.86 8.68E−02 Adipose M 261 1804 71 (8) FASN, AACS, ETFDH, GYS1, ECHS1, 4.39 9.34E−35 SH3D21, SLC2A3, PMEPA1 F 206 1491 34 (5) MSLN, GPM6A, PTGIS, RSPO1, BNC1 3.22 4.57E−13 One-sided Fisher’s exact test was used to calculate enrichment P values Kurt et al. Biology of Sex Differences (2018) 9:46 Page 11 of 14

Identification of the key drivers that regulate the shared It is noted that estrogen deficiency in mice fed a pathogenic processes between sexes may facilitate the high-fat diet leads to accelerated NAFLD progression prioritization of targets to treat the disease in the general [13], and after menopause, the rate and severity of population. NAFLD increases in females [10, 12]. Testosterone has In addition to the shared pathways, we observed also been associated with a protective role in NAFLD in female-specific processes in the liver and adipose tissue males [46, 63 , 64]. Since both male and female sex hor- (Additional file 4) and predicted their potential regula- mones might have a role in slowing the NAFLD progres- tors. NCK-associated protein 1-like (NCKAP1L) and sion, we also investigated the potential effects of the sex TYRO protein tyrosine kinase binding protein (TYR- hormones on the sexual dimorphism in NAFLD using OBP) were found to be the top key drivers of the innate both an in silico transcription factor analysis and in vivo immune system in the liver. The same key drivers have gonadectomy/ovariectomy experiments. Both analyses been previously found to regulate an inflammation net- support significant overlaps between the sex-specific work involved in a large number of diseases including liver and adipose tissue NAFLD networks and sex hor- diabetes, obesity, cardiovascular disease, and cancers mone target genes. The significant overlaps also include [61]. Another top key driver regulating a female-specific our top key driver genes for the corresponding tissue process in the liver is complement C8 beta chain (C8B) and sex (Table 1, Fig. 4c–f). These results support a role in the complement pathway. It has not been previously of sex hormones in the regulation of the sex-specific associated with NAFLD; however, it is connected to NAFLD networks and pathways. However, hormones three human NAFLD GWAS genes (GCKR, LYPLAL1, only partially explain the sex-specific networks. In the and TM6SF2) in our liver network, making it a strong case of females, ovariectomy had limited effects on liver novel candidate target for NAFLD. SH3 domain-binding gene expression, suggesting minor contribution of estro- protein 2 (SH3BP2), is a female-specific adipose key gen deficiency to the female-specific liver networks. driver for the TCR signaling and lysosome module. Since the ovariectomy experiment was carried out in SH3BP2 has not been previously associated with NAFLD adulthood, it is possible that developmental estrogen ex- to the best of our knowledge, but it is a target of estro- posure plays a stronger role in shaping female liver net- gen receptor 1 according to FANTOM5 adipose tissue work. It is also plausible that sex chromosomes play an gene regulatory network [44], which may support our important role, particularly in the female liver. Further sex and tissue-specific finding on SH3BP2. investigations are required to explore the role of sex Notably, lipid-related processes (e.g., phospholipid, lyso- chromosomes in regulating sexually dimorphic networks phospholipid) are prominent in males (Additional file 4) for NAFLD. [18] but not in females. Another male-specific process, A major strength and novelty of our study is the Notch signaling, was previously found to be correlated utilization of a multi-omics integrative approach to iden- with insulin resistance, hepatic steatosis, alanine amino- tify genetically causal or perturbed sex- and tissue-specific transferase, and NAFLD activity score in liver biopsies processes and key regulatory genes in NAFLD based on [62]. Our results support a causal role of this pathway in genetics, functional genomics, and gene regulatory net- NAFLD development, particularly in males. We also ob- works. The integration of multidimensional datasets en- served male-specific adipose tissue pathways such as Wnt abled us to derive one of the most comprehensive views of and insulin signaling, which agrees with the observations the genes and pathways that are likely perturbed by gen- that insulin resistance is strongly correlated with NAFLD etic risks of NAFLD in both males and females, thereby mainly in males and that males with NAFLD have higher significantly enhancing our understanding of sexual di- glucose and lower adiponectin levels than females [11]. morphisms in NAFLD. Compared to a previous study ex- Differences in insulin sensitivity may partially explain sex ploring sex differences in the same animal cohort, which differences in NAFLD. Male-specific top key drivers in- focused on a few genome-wide significant loci and clude CHCHD6 and CD36 in liver and FASN and CPT2 in pair-wise correlations between phenotypes and individual adipose tissue, which primarily regulate mitochondrial genes [46], our study uniquely leveraged the full spectrum function and fatty acid metabolism. Coiled-coil-helix-coi- of molecular associations across multi-omics dimensions led-coil-helix domain containing 6 (CHCHD6) was experi- and benefited from network representations of regulatory mentally validated in our recent study as a novel key relationships. In addition to confirming mitochondrial regulator of NAFLD by impairing mitochondrial functions function, oxidative phosphorylation, and transmembrane in the liver [18]. These male-specific pathways and regula- transport as important NAFLD processes [46], we identi- tors along with the female-specific ones discussed earlier fied numerous sex-specific genes and pathways that were can facilitate future efforts to investigate the sex-specific missed in the previous study. mechanisms in NAFLD and may serve as potential thera- A principle limitation of our study is that our analyt- peutic targets for sex-dependent treatments. ical framework only considers the effect sizes but not Kurt et al. Biology of Sex Differences (2018) 9:46 Page 12 of 14

the directionality of NAFLD GWAS associations and Abbreviations gene expression changes. As such, it is difficult to infer eQTL: Expression quantitative trait loci; GWAS: Genome-wide association studies; HMDP: Hybrid mouse diversity panel; KDA: Key Driver Analysis; if the sex-specific pathways are protective or pathogenic MSEA: Marker Set Enrichment Analysis; NAFLD: Non-alcoholic fatty liver for NAFLD. For example, it is possible that some of the disease; NASH: Nonalcoholic steatohepatitis sex-specific pathways reduce instead of promote disease Funding risks. Building on our recent successes in experimentally This work was supported by NIH-HL28481 and 30568 (AJL), NIH-DK104363 validating predictions from the integrative genomics ap- (XY), AHA fellowship 17POST33670739 (ZK), The Research Council of Norway proach described here, future studies that perturb indi- 240405/F20 (FN), and Iris Cantor-UCLA Women’s Health Center/CTSI fellowship UL1TR001881 (ZK). The funders had no role in study design, data collec- vidual sex-specific pathways and key drivers identified tion and interpretation, or the decision to submit the work for publication. here will help confirm the causality of the predicted genes and pathways and determine whether they should Availability of data and materials be activated or inhibited to ameliorate NAFLD. The NCBI GEO accession number of the gene expression data from > 100 mouse strains that was used in this study is GSE64770. The NCBI GEO Our sex-specific findings help understand the long-ob- accession number of the liver and adipose gene expression datasets from served sexual dimorphism in NAFLD and provide in- the gonadectomy and ovariectomy experiments is GSE112947. sights into the potential differential pathogenic pathways Authors’ contributions between males and females, which can guide future de- ZK conducted the analysis, interpreted the results, and wrote the manuscript. velopment of sex-specific therapeutics in translational RBC contributed to the interpretation of the results and writing of the paper. studies. Although our study is carried out in mice, there JLG contributed to the computational analysis. AJL, STH, and CP contributed to the generation of the HMDP datasets. MM, FN, ZZ, and YH conducted the is strong evidence for replication of our findings in hu- gonadectomy and ovariectomy experiments. XY and AJL designed the study. man studies. For instance, results from our previous ZK, RBC, and XY drafted the manuscript, and all authors read, revised, and male-focused study [18] are consistent with an inde- approved the final manuscript. pendent human study demonstrating the functional as- Ethics approval and consent to participate sociations of FASN, PKLR, and THRSP with NAFLD All data utilized in the current study were generated and published [65]. Additionally, our NAFLD gene subnetworks (Fig. 4) previously. Ethical issues have been addressed appropriately by the initial contained human NAFLD GWAS genes such as studies. PNPLA3, LCP1, and TMSF2. Furthermore, our recent Consent for publication study examining liver fibrosis identified significant over- Since our manuscript does not contain any data from any individual person, laps in the genes and pathways between mouse and hu- consent for publication is not applicable for this article. man [66]. These observations support the translatability Competing interests of our findings from mouse studies to human NAFLD or The authors declare that they have no competing interests. NASH pathogenesis. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in Conclusions published maps and institutional affiliations. The National Institute of Health and the Food and Received: 28 June 2018 Accepted: 3 October 2018 Drug Administration have prioritized personalized medicine as a key to developing effective therapies [67]. Sex is one of the important factors to consider Additional files when developing personalized medicine as it can Additional file 1: Z-summary scores for the module affect drug dose level, adverse reactions, and re- preservation of the female and male coexpression mod- sponses. There are numerous reported sex differ- ules for both MEGENA and WGCNA methods in each ences in liver disease drugs with regard to the rate tissue. Preservation of the female MEGENA modules in of response and adverse reactions, but few of these male MEGENA modules for (A) Liver and (B) Adipose sex differences are understood [12]. Our multi-omics tissues. Preservation of female WGCNA modules in integrative approach combining genetic, gene expres- male WGCNA modules for (C) Liver and (D) Adipose sion, functional genomics, and network modeling tissues. Preservation of the male MEGENA modules in delineated genes, pathways, and tissue-specific net- female MEGENA modules for (E) Liver and (F) Adipose works that contribute to the sexual dimorphism of tissues. Preservation of male WGCNA modules in fe- NAFLD. Our findings support the existence of core male WGCNA modules for (G) Liver and (H) Adipose pathogenic processes that can be targeted in both tissues. (I) Mutual preservation ratios of female and male sexes and pinpoint sex-specific mechanisms to guide coexpression modules for both methods and both tis- differential therapeutic options for males and females sues. (PDF 345 kb)Additional file 2: GO annotation of to more effectively mitigate NAFLD in a personal- the hepatic triglyceride-related modules (based on eigen ized manner. gene and trait correlation) (XLSX 32 kb)Additional file Kurt et al. Biology of Sex Differences (2018) 9:46 Page 13 of 14

3: Raw MSEA results. Left Panel shows the liver tissue adipocytokines influence adolescent nonalcoholic fatty liver disease. results, while right panel shows the adipose tissue re- Hepatology. 2011;53:800–9. 12. Buzzetti E, Parikh PM, Gerussi A, Tsochatzis E. Gender differences in liver sults. (XLSX 14 kb)Additional file 4: MSEA and KDA disease and the drug-dose gender gap. Pharmacol Res. 2017;120:97–108. results of the sex-specific, tissue-specific, and shared 13. Ballestri S, Nascimbeni F, Baldelli E, Marrazzo A, Romagnoli D, Lonardo A. supersets between sexes in each tissue. A superset is a NAFLD as a sexual dimorphic disease: role of gender and reproductive status in the development and progression of nonalcoholic fatty liver gene set merging a group of highly overlapping pathways disease and inherent cardiovascular risk. Adv Ther. 2017;34:1291–326. or coexperssion modules to reduce redundancy. Coex- 14. Kozlitina J, Smagris E, Stender S, Nordestgaard BG, Zhou HH, Tybjærg- pression modules are labeled with IDs from WGCNA or Hansen A, et al. Exome-wide association study identifies a TM6SF2 variant that confers susceptibility to nonalcoholic fatty liver disease. Nat Genet. MEGENA network and annotated with GO terms. (XLSX 2014;46:352–6. 14 kb)Additional file 5: KDA results. Left Panel shows 15. Kitamoto T, Kitamoto A, Yoneda M, Hyogo H, Ochi H, Nakamura T, et al. the liver tissue results, while right panel shows the adipose Genome-wide scan revealed that polymorphisms in the PNPLA3, SAMM50, and PARVB genes are associated with development and progression of tissue results. (XLSX 71 kb)Additional file 6: Previously nonalcoholic fatty liver disease in Japan. Hum Genet. 2013;132:783–92. annotated NAFLD-associated genes from the literature. 16. Chambers JC, Zhang W, Sehmi J, Li X, Wass MN, Van der Harst P, et al. Genes that were identified as a key driver in our analysis Genome-wide association study identifies loci influencing concentrations of – are highlighted in particular colors as denoted below. liver enzymes in plasma. Nat Genet. 2011;43:1131 8. 17. Hui ST, Parks BW, Org E, Norheim F, Che N, Pan C, et al. The genetic (XLSX 18 kb)Additional file 7: Combined subnetworks of architecture of NAFLD among inbred strains of mice. Elife. 2015;4:e05607. NAFLD processes that are shared between sexes or 18. Krishnan KC, Kurt Z, Barrere-Cain R, Sabir S, Das A, Floyd R, et al. Integration female-specific. (A) Liver Bayesian subnetwork comprised of multi-omics data from mouse diversity panel highlights mitochondrial dysfunction in non-alcoholic fatty liver disease. Cell Syst. 2018;6:103–115.e7. of NAFLD supersets that are perturbed in liver tissue of fe- 19. Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, et al. Mergeomics: males (as a combination of female-specific processes or multidimensional data integration to identify pathogenic perturbations to shared processes between sexes) and the top key drivers of biological systems. BMC Genomics. 2016;17:874. 20. Arneson D, Bhattacharya A, Shu L, Mäkinen V-P, Yang X, Civelek M, et al. each superset. (B) Adipose Bayesian subnetwork comprised Mergeomics: a web server for identifying pathological pathways, networks, of NAFLD supersets that are perturbed in adipose tissue of and key regulators via multidimensional data integration. BMC Genomics. females (as a combination of female-specific processes or 2016;17:722. 21. Parks BW, Sallam T, Mehrabian M, Psychogios N, Hui ST, Norheim F, et al. Genetic shared processes between sexes) and the top key drivers of architecture of insulin resistance in the mouse. Cell Metab. 2015;21:334–46. each superset. (PDF 4683 kb)Additional file 8: Enrichment 22. 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