An alternative splicing program promotes adipose tissue thermogenesis

Abstract

Alternative pre-mRNA splicing expands the complexity of the transcriptome and controls isoform-specific gene expression. Whether alternative splicing contributes to metabolic regulation is largely unknown. Here we investigated the contribution of alternative splicing to the development of diet-induced obesity. We found that obesity-induced changes in adipocyte gene expression include alternative pre-mRNA splicing. Bioinformatics analysis associated part of this alternative splicing program with sequence specific NOVA splicing factors. This conclusion was confirmed by studies of mice with NOVA deficiency in adipocytes. Phenotypic analysis of the NOVA-deficient mice demonstrated increased adipose tissue thermogenesis and improved glycemia. We show that NOVA proteins mediate a splicing program that suppresses adipose tissue thermogenesis. Together, these data provide quantitative analysis of gene expression at exon-level resolution in obesity and identify a novel mechanism that contributes to the regulation of adipose tissue function and the maintenance of normal glycemia.

DOI: http://dx.doi.org/10.7554/eLife.17672.001

Introduction

Alternative pre-mRNA splicing is an important mechanism that increases the complexity of the transcriptome (Pan et al., 2008; Wang et al., 2008) and expands the diversity and function of the proteome (Nilsen and Graveley, 2010; Yang et al., 2016). Indeed, differences in pre-mRNA splicing can contribute to the specialization of different cell types within the body (Vaquero-Garcia et al., 2016). The regulation of alternative pre-mRNA splicing is therefore an important aspect of cellular differentiation. Indeed, processes that regulate pre-mRNA splicing represent potential mechanisms that can control cell function. Recent studies have identified changes in pre-mRNA splicing associated with autism (Irimia et al., 2014; Weyn-Vanhentenryck et al., 2014), cardiac hypertrophy (Mirtschink et al., 2015), embryonic stem cell re-programming (Han et al., 2013a), tumorigenesis (Oltean and Bates, 2014; Hsu et al., 2015; Koh et al., 2015), and the regulation of signal transduction pathways (Gupta et al., 1996; Tournier et al., 1999; Maimon et al., 2014; Martinez et al., 2015). Moreover, alternative pre-mRNA splicing associated with pathogenesis represents a tractable target for the development of new therapies (Daguenet et al., 2015).

The role of alternative pre-mRNA splicing in metabolism is unclear. We studied pre-mRNA splicing in adipocytes to investigate whether adipocyte function may be regulated by changes in pre-mRNA splicing. Adipose tissue is critically important for whole body metabolic regulation because it acts as both an endocrine organ and as a storage depot for triglyceride (Rosen and Spiegelman, 2014). Interestingly, both adipose tissue deficiency (lipodystrophy) and adipose tissue accumulation (obesity) are associated with the development of metabolic syndrome and pre-diabetes (Grundy, 2015). Moreover, the widespread incidence of human obesity represents a major risk factor for the development of diabetes and mortality (Flegal et al., 2013). It is therefore important that we obtain an understanding of the molecular mechanisms that control adipocyte function.

The purpose of this study was to examine alternative pre-mRNA splicing in adipocytes. We report that the consumption of a high fat diet causes differential exon inclusion/exclusion in the transcriptome. Bioinformatics analysis implicated a role for NOVA pre-mRNA splicing factors and this was confirmed by studies of mice with adipocyte-specific NOVA deficiency. Functional studies demonstrated that NOVA acts to suppress adipose tissue thermogenesis. Together, these data demonstrate that alternative pre-mRNA splicing contributes to the regulation of adipocyte biology.

Results

Diet-induced obesity induces a program of alternative pre-mRNA splicing

We examined gene expression in white epididymal adipocytes by RNA sequencing (Illumina NextSeq 500 machine, 150 bp paired-end format, approximately 400 million mean reads/sample, n=3) (Table 1). Comparison of adipocyte mRNA isolated from mice fed (16 wk) a chow diet (CD) or a high fat diet (HFD) demonstrated differential expression of 4941 genes (q<0.05; absolute log2-fold change >0.75) and differential inclusion/exclusion of 1631 exons (FDR<0.05; absolute change in exon inclusion (absolute ∆Inc level) >0.1) (corresponding to 1249 genes) in the transcriptome (Figure 1A and Figure 1—figure supplements 1 and 2). This differential exon inclusion/exclusion in mRNA most likely represents alternative pre-mRNA splicing in adipocytes. However, it is possible that some of the detected changes in pre-mRNA splicing reflect the differential presence of stromal vascular cells (Figure 1—figure supplement 3). Only 6.4% of the differentially expressed genes were alternatively spliced, but 25% of the alternatively spliced genes were differentially expressed (Figure 1B). These data indicate that the genomic response to the consumption of a HFD causes quantitative changes in both gene expression and alternative pre-mRNA splicing in adipocytes. Analysis of the co-regulated genes demonstrated enrichment for pathways including mRNA processing and multiple signaling pathways (e.g. insulin signaling and MAPK signaling) that are known to regulate adipose tissue biology (Figure 1—figure supplement 4).10.7554/eLife.17672.003

Figure 1.

Diet-induced obesity causes changes in alternative pre-mRNA splicing in adipose tissue.

(A) RNA-seq analysis demonstrates that the consumption (16 wk) of a HFD, compared with a CD, causes significant changes in mRNA expression genes (q<0.05; absolute log2-fold change >0.75) and differential exon inclusion/exclusion (FDR<0.05; absolute ∆Inc level >0.1) in the epididymal adipocyte transcriptome. (B) Genes that are significantly differentially expressed and genes that are subjected to alternative pre-mRNA splicing are depicted using a Venn diagram. (C,D) Classification of HFD-induced alternative splicing events (C) and selected examples (D) are presented. (E) Enrichment of NOVA CLIP-seq tags with significantly (FDR<0.05; absolute ∆Inc level >0.1) alternatively spliced exons (± 500 bp of intron/exon junctions) compared with non-alternatively spliced exons (FDR≥0.05) and random genomic sequences. (F) Immunoblot analysis of NOVA proteins in lysates prepared from hepatocytes and epididymal adipocytes. (G) Nova1 and Nova2 mRNA expression by epididymal adipocytes of wild-type and obese ob/ob mice (6 wk old) was examined by quantitative RT-PCR (mean ± SEM; n=5; *p<0.05; ***p<0.001). The source data are included as Figure 1—source data 1. (H) Wild-type mice were fed a CD or an HFD (16 wk). Nova1 and Nova2 mRNA expression by epididymal adipocytes was measured by quantitative RT-PCR analysis (mean ± SEM; n=7; *p<0.05). The source data are included as Figure 1—source data 2.

DOI: http://dx.doi.org/10.7554/eLife.17672.003

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Figure 1—figure supplement 1.

Alternative pre-mRNA splicing in adipocytes.

(A–D) Alternatively spliced exons identified by comparison of adipocytes of CD and HFD mice (Figure 1) and comparison of adipocytes of F^WT and F^∆N1,2 mice (Figure 2) are presented. The number of significantly alternatively spliced exons (∆Inc level >0.01, >0.05, >0.1, >0.25, >0.5, and >0.75) is presented with FDR<0.05 (A), FDR<0.01 (B), FDR<0.001 (C), and FDR<0.0001 (D).

DOI: http://dx.doi.org/10.7554/eLife.17672.006

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Figure 1—figure supplement 2.

Alternative pre-mRNA splicing in adipocytes.

(A) Semi-quantitative RT-PCR analysis of epididymal adipocyte mRNA isolated from CD-fed mice, HFD-fed mice, and F^∆N1,2 mice (NOVA DKO) was performed to detect alternative splicing of Adam15 mRNA (skipping exon 20) and Yap1 mRNA (skipping exon 6). (B) Mutually exclusive inclusion of exons 7a and 7b in Fyn mRNA by adipocytes of CD-fed and HFD-fed mice is presented as a sashimi plot.

DOI: http://dx.doi.org/10.7554/eLife.17672.007

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Figure 1—figure supplement 3.

Expression of adipocyte and stromal vascular fraction marker genes.

Epididymal fat pads of CD-fed and HFD-fed (16 wk) wild-type mice were used to isolate adipocytes and the stromal vascular fraction (SVF). The expression of adipocyte and SVF marker genes were examined by measurement of mRNA by quantitative RT-PCR (mean ± SEM; n = 4~6; *p<0.05; ***p<0.001). These data demonstrate that adipocyte marker genes Adipoq and Leptin were not expressed by the SVF, but low level expression of the SVF marker genes Emr1 (F4/80), Itgam (Cd11b), and Cd68 was detected the adipocyte fraction. This SVF contamination of the adipocyte fraction may be mediated by lipid-loaded macrophages (Xu et al., 2013). Control studies were performed using a gene (Fabp4) that is expressed by both adipocytes and SVF.

DOI: http://dx.doi.org/10.7554/eLife.17672.008

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Figure 1—figure supplement 4.

Biological pathway enrichment analysis of differential gene expression and alternative pre-mRNA splicing caused by feeding a HFD.

Genes that are differentially expressed and/or alternatively spliced in response to the consumption of a HFD (Figure 1B) were examined by pathway analysis to detect significant enrichment for specific biological processes.

DOI: http://dx.doi.org/10.7554/eLife.17672.009

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Figure 1—figure supplement 5.

NOVA expression in human subcutaneous adipose tissue.

NOVA1 and NOVA2 mRNA expression in abdominal subcutaneous adipose of obese (BMI>30 kg/m²) and non-obese (BMI<30 kg/m²) matched humans was examined (mean ± SEM; n = 30 obese, n = 26 non-obese; *p<0.05).

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Table 1.

Summary of RNA-seq data.

DOI: http://dx.doi.org/10.7554/eLife.17672.011

GEO Accession Subseries/ Superseries	Biological groups	Sample number	Platform	Mean read number / sample (after trimming, if applicable)	Read length (after trimming, if applicable)	Mean read alignment rate
GSE76294/ GSE76134	FWT (3) F∆N1 (3) F∆N2 (3)	9	Illumina HiSeq 2000/ 2 x 100 bp	135,500,000	100 bp	89.4%
GSE76133/ GSE76134	CD (3) HFD (3)	6	Illumina NextSeq 500/ 2 x 150 bp	406,200,000	90 bp	74.9%
GSE76317/ GSE76134	FWT (4) F∆N1,2 (4)	8	Illumina NextSeq 500/ 2 x 150 bp	319,700,000	130 bp	92.5%

The most common form of HFD-induced alternative pre-mRNA splicing was exon skipping (1052 exons), but we also detected 144 mutually exclusive exon inclusions, 160 retained introns, 123 alternative 5’ splice sites, and 152 alternative 3’ splice sites (FDR<0.05; absolute ∆Inc level >0.1) (Figure 1C,D).

In contrast to the extensive changes in alternative pre-mRNA splicing in adipocytes caused by diet-induced obesity (Figure 1A–C) few HFD-regulated alternative pre-mRNA splicing events were detected in liver, although we did find 2 skipped exons, 0 mutually exclusive exon inclusions, 13 retained introns, 1 alternative 5’ splice site, and 1 alternative 3’ splice site (FDR<0.05; absolute ∆Inc level >0.1). This comparative analysis of gene expression indicates that widespread changes in pre-mRNA splicing are not a general response to diet-induced obesity, but a selective response of white adipocytes to the consumption of a HFD.

One example of altered splicing is the mutually exclusive inclusion of exons 7a or 7b in the tyrosine kinase FYN that changes the strength of SH3 domain-mediated autoinhibition (Brignatz et al., 2009). Increased inclusion of Fyn exon 7b, compared with exon 7a, in response to the consumption of an HFD (Figure 1—figure supplement 2B) is anticipated to increase FYN tyrosine kinase activity (Brignatz et al., 2009) leading to suppression of fatty acid oxidation and promotion of insulin resistance (Bastie et al., 2007). Together, these data indicate that alternative pre-mRNA splicing may contribute to the adipocyte response to obesity.

Obesity suppresses expression of the NOVA group of pre-mRNA splicing factors

To gain insight into the mechanism of HFD-induced pre-mRNA splicing, we examined exons (plus 500 bp of flanking intron sequence) to identify potential motifs that were significantly enriched for alternatively spliced exons. This analysis led to the identification of potential binding sites (YCAY) for NOVA alternative pre-mRNA splicing factors (Darnell, 2013). Indeed, we found significant (p<2.2 × 10⁻¹⁶) enrichment of UV-mediated cross-linking and immunoprecipitation sequencing (CLIP-seq) tags for NOVA proteins identified in brain tissue (Licatalosi et al., 2008) within the HFD-induced group of alternatively spliced exons in adipocytes (Figure 1E). The NOVA CLIP-seq tags intersected with 56% of the HFD-induced alternatively spliced exons (FDR<0.05; absolute ∆Inc level >0.1) (Figure 1E) and were associated with both HFD-induced exon inclusion (53% intersection) and HFD-induced exon exclusion (62% intersection), consistent with the known role of NOVA proteins to cause context-dependent exon inclusion/exclusion (Ule et al., 2006). This analysis implicates a role for NOVA proteins in a sub-set of HFD-induced alternative splicing events.

NOVA proteins are expressed in several tissues, including neurons (Darnell, 2013), vascular endothelial cells (Giampietro et al., 2015), and pancreatic β cells (Villate et al., 2014). Whether NOVA proteins are expressed in peripheral metabolic tissues is unclear. Indeed, NOVA proteins were not detected in liver, but both NOVA1 and NOVA2 proteins were found in white adipocytes (Figure 1F). Interestingly, Nova gene expression in white adipocytes was partially reduced in obese humans and mice (Figure 1G and Figure 1—figure supplement 5) and in mice fed a HFD (Figure 1H). This decrease in NOVA expression may be relevant to obesity-regulated changes in alternative pre-mRNA splicing in adipocytes. Moreover, the absence of NOVA expression in liver (Figure 1F) may contribute to the minimal effect of HFD consumption on alternative splicing of pre-mRNA in the liver.

NOVA contributes to the obesity-induced program of alternative pre-mRNA splicing

Reduced expression of Nova1 or Nova2 in mice causes developmental defects and neonatal lethality (Jensen et al., 2000; Ruggiu et al., 2009). We therefore established Nova1^LoxP/LoxP and Nova2^LoxP/LoxP mice to study the role of NOVA proteins in adult mice with tissue-specific NOVA deficiency (Figure 2—figure supplement 1). Adipoq-Cre^−/+ mice were used to selectively ablate Nova1 and Nova2 genes in mature adipocytes. We initially compared control F^WT mice (Adipoq-Cre^−/+) with compound mutant F^∆N1,2 mice (Adipoq-Cre^−/+ Nova1^LoxP/LoxP Nova2^LoxP/LoxP).

RNA-seq analysis of white adipocytes from HFD-fed F^WT and F^∆N1,2 mice (150 bp paired-end format, approximately 320 million mean reads/sample, n = 4) was performed (Table 1). Compound NOVA1/2 deficiency caused only a small change in gene expression (55 genes; q<0.05; absolute log2-fold change >0.75), but NOVA1/2 deficiency caused a large change in differential exon inclusion/exclusion (1169 exons; FDR<0.05; absolute ∆Inc level >0.1) in the adipocyte transcriptome (Figure 2A and Figure 1—figure supplement 1). These data indicate that NOVA deficiency primarily changes pre-mRNA splicing. The most common form of alternative pre-mRNA splicing caused by NOVA deficiency was exon skipping (768 exons), but we also detected 128 mutually exclusive exon inclusions, 99 intron retentions, 64 alternative 5’ splice sites, and 110 alternative 3’ splice sites (Figure 2D,E). Analysis of white adipocytes with single gene ablations of Nova1 or Nova2 identified fewer changes in alternative pre-mRNA splicing, including 10 & 7 skipped exons, 1 & 4 mutually exclusive exon inclusions, 12 & 16 intron retentions, 1 & 0 alternative 5’ splice sites, and 2 & 4 alternative 3’ splice sites, respectively (FDR<0.05; absolute ∆Inc level >0.1). These data indicate that NOVA1 and NOVA2 can cause isoform-specific changes in adipocyte pre-mRNA alternative splicing. However, the effect of compound NOVA1 plus NOVA2 deficiency to cause widespread changes in alternative pre-mRNA splicing (Figure 2) indicates that these NOVA proteins exhibit some functional redundancy in adipocytes.10.7554/eLife.17672.012

Figure 2.

NOVA proteins contribute to alternative pre-mRNA splicing associated with diet-induced obesity.

(A) RNA-seq analysis of F^WT and F^∆N1,2 mice fed an HFD (16 wk) identifies significant changes in gene expression (q<0.05; absolute log2-fold change >0.75) and differential exon inclusion/exclusion (FDR<0.05; absolute ∆Inc level >0.1) in the epididymal adipocyte transcriptome. (B) The number of genes with significant differential alternative splicing (CD-fed vs HFD-fed WT mice and HFD-fed F^WT mice vs HFD-fed F^∆N1,2 mice) are depicted using a Venn diagram. (C) Biological pathway enrichment analysis of the 323 genes co-regulated by alternative pre-mRNA splicing following HFD consumption and NOVA deficiency. (D,E) Classification of alternative splicing events caused by NOVA deficiency (D) and selected examples (E) are presented.

DOI: http://dx.doi.org/10.7554/eLife.17672.012

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Figure 2—figure supplement 1.

Establishment of Nova1^LoxP/LoxP and Nova2^LoxP/LoxP mice.

(A,B) The methods used to create the floxed Nova1 and floxed Nova2 alleles by homologous recombination are illustrated schematically. (C,D) PCR analysis of genomic DNA isolated epididymal white adipose tissue (eWAT) and interscapular brown adipose tissue (BAT) of Adipoq-cre⁺ (F^WT) mice, Adipoq-cre⁺ Nova1^LoxP/LoxP(F^∆N1) mice, Adipoq-cre⁺ Nova2^LoxP/LoxP (F^∆N2) mice, and Adipoq-cre⁺ Nova1^LoxP/LoxP Nova2^LoxP/LoxP (F^∆N1,2) mice.

DOI: http://dx.doi.org/10.7554/eLife.17672.013

Comparison of white adipocyte genes with differential alternative splicing (FDR<0.05; absolute ∆Inc level >0.1) caused by the consumption of a HFD (Figure 1A) or NOVA1/2 deficiency (Figure 2B) demonstrated 323 co-regulated genes (Figure 2B). These co-regulated genes represent 26% of the 1249 HFD-regulated genes and 34% of the 950 NOVA-regulated genes. Analysis of the co-regulated genes demonstrated enrichment for pathways including mRNA processing and multiple signaling pathways (e.g. NF-κB signaling, MAPK signaling) that contribute to the physiological regulation of adipose tissue (Figure 2C).

NOVA-mediated alternative pre-mRNA splicing suppresses JNK signaling in adipose tissue

To confirm the observation that NOVA proteins contribute to signaling mechanisms that can mediate metabolic regulation, we examined the expression of the JNK group of MAPK in cells that express NOVA proteins (white adipocytes) and cells that do not express NOVA proteins (hepatocytes) (Figure 3A). The genes Mapk8 (encodes the JNK1 protein kinase) and Mapk9 (encodes the JNK2 protein kinase) express pre-mRNA are alternatively spliced by the mutually exclusive inclusion of either exon 7a or 7b to yield the α and β isoforms of the JNK1 and JNK2 protein kinases (Gupta et al., 1996). The sequences surrounding exons 7a and 7b contain consensus sites for NOVA binding (YCAY) that are established to be NOVA binding sites by CLIP-seq analysis (Licatalosi et al., 2008). We designed and validated a Taqman assay to quantitate the inclusion of exon 7a or 7b sequences in Mapk8 mRNA (Mapk8α and Mapk8β) and Mapk9 mRNA (Mapk9α and Mapk9β) (Figure 3—figure supplement 1A,B). This analysis demonstrated that adipocytes and hepatocytes express different alternatively spliced JNK isoforms (Figure 3—figure supplement 1C). These tissue-specific differences in Mapk8/9 pre-mRNA splicing may be influenced by the selective expression of NOVA proteins in adipocytes, but not hepatocytes (Figures 1F and 3A), although NOVA-independent mechanisms likely also contribute to the observed cell type-specific pattern of JNK isoform expression.10.7554/eLife.17672.014

Figure 3.

NOVA promotes signal transduction by JNK in adipose tissue.

(A) The expression of Nova mRNA in adipocytes and hepatocytes was measured by quantitative RT-PCR (mean ± SEM; n=7~8; **p<0.01; ***p<0.001). The source data are included as Figure 3—source data 1. (B) The α / β expression ratios of Mapk8 and Mapk9 mRNA by hepatocytes was measured by quantitative RT-PCR (mean ± SEM; n=10; *p<0.05; **p<0.01). The effect of hepatic expression of GFP (Control) or NOVA2 using adenoviral vectors was examined. The source data are included as Figure 3—source data 2. (C) The expression ratio of the α and β isoforms of Mapk8 and Mapk9 mRNA by F^WT and F^∆N1,2 adipocytes was measured by quantitative RT-PCR (mean ± SEM; n=5~8; *p<0.05; **p<0.01). The source data are included as Figure 3—source data 3. (D) The mutually exclusive inclusion of exons 7a or 7b in Mapk8 and Mapk9 mRNA is illustrated. NOVA CLIP-seq tags are highlighted in red. (E) Mapk8^∆/∆ Mapk9^-/- MEF transduced with retroviruses expressing JNK1α, JNK1β, JNK2α, JNK2β or empty vector (-) were exposed without and with 60 J/m² UV (60 min) and lysates were examined by immunoblot analysis. (F) Adipocytes prepared from F^WT and F^∆N1,2 mice were treated without and with 1 µg/ml anisomycin (10 min) and lysates were examined by immunoblot analysis.

DOI: http://dx.doi.org/10.7554/eLife.17672.014

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Figure 3—figure supplement 1.

Design and validation of Taqman assays to detect inclusion of the mutually exclusive exons 7a and 7b in Mapk8 and Mapk9 mRNA.

(A) Schematic illustration of the design of Taqman assays to detect expression of Mapk8α, Mapk8β, Mapk9α, and Mapk9β mRNA. (B) Taqman assays were performed using 1 ng of cDNA template (Mapk8α, Mapk8β, Mapk9α, and Mapk9β cDNA) and probes designed to detect Mapk8α, Mapk8β, Mapk9α, and Mapk9β. The data presented are the mean ± SEM (n = 3). (C) The relative expression of Mapk8α, Mapk8β, Mapk9α, and Mapk9β mRNA in epididymal adipocytes and hepatocytes was measured by RT-PCR analysis (mean ± SEM; n = 3). The numbers above the bars represents the α/β ratio.

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Figure 3—figure supplement 2.

Comparison of JNKα and JNKβ protein kinase activity in vitro.

(A) Flag-tagged JNK1α1 and JNK1β1 expressed in 293T cells were isolated by immunoprecipitation using anti-Flag (M2) agarose. Protein kinase activity was measured with an in vitro assay using GST-cJun and [γ-³²P] ATP as substrates. The amount of JNK in each assay was examined by immunoblot analysis. The amount of phosphorylated cJun was examined by Phosphorimager analysis. (B) In vitro kinase assays were performed using Flag-tagged JNK2α2 and JNK2β2.

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Figure 3—figure supplement 3.

Effect of adipocyte-specific JNK-deficiency on thermogenic gene expression.

(A) Core body temperature of F^WT and F^∆J1,2 mice fed a CD or n HFD (16 wk) was measured by telemetry using an implanted probe (mean ± SEM; n = 7~10). (B) F^WT and F^∆J1,2 mice were fed a CD or an HFD (16 wk). Gene expression by sub-cutaneous inguinal adipocytes was examined by measurement of mRNA by RT-PCR (mean ± SEM; n=5~8). Statistically significant differences between F^WT and F^∆J1,2 mice are indicated: *p<0.05; **p<0.01; ***p<0.001.

DOI: http://dx.doi.org/10.7554/eLife.17672.020

To test the role of NOVA proteins on JNK isoform expression, we examined the effect of increased and decreased NOVA expression. We found that hepatic expression of NOVA2 caused a decrease in the Mapk8α/β ratio and an increase in the Mapk9α/β ratio, indicating that NOVA can promote the expression of the Mapk8β and Mapk9α alternatively spliced isoforms (Figure 3B). In contrast, adipocyte-specific deficiency of NOVA1 plus NOVA2 caused an increase in the Mapk8α/β ratio and a decrease in the Mapk9α/β ratio, indicating that NOVA-deficiency promotes the expression of the Mapk8α and Mapk9β alternatively spliced isoforms (Figure 3C). These observations are consistent with the finding that HFD consumption causes both decreased NOVA expression (Figure 1H) and significant changes in the mutually exclusive inclusion of Mapk8 exons 7a and 7b (FDR=0.0066). Together, these data demonstrate that NOVA proteins can promote the expression of the Mapk8β and Mapk9α isoforms.

The sequence differences between the α and β isoforms of JNK1 and JNK2 are located in the substrate binding site (Figure 3D) and influence the interaction of JNK with protein substrates (Davis, 2000). Studies using the substrate cJun demonstrate low activity (JNK1α and JNK2β) and high activity (JNK1β and JNK2α) groups of JNK protein kinases in vitro (Figure 3—figure supplement 2) and in vivo (Figure 3E). Since NOVA can promote the expression of Mapk8β and Mapk9α mRNA that encode the high activity forms of JNK1 and JNK2, NOVA-deficiency can be predicted to cause expression of the low activity forms JNK1α and JNK2β in adipocytes. To test this conclusion, we prepared primary adipocytes from F^WT and F^∆N1,2 mice and examined stress-induced JNK activation. No differences in JNK expression or stress-induced activating phosphorylation (pThr¹⁸⁰-Pro-pTyr¹⁸²) of JNK between F^WT and F^∆N1,2 adipocytes were detected by immunoblot analysis (Figure 3F). However, stress-induced phosphorylation of the JNK substrate pSer⁶³ cJun was markedly suppressed in F^∆N1,2 adipocytes compared with F^WT adipocytes (Figure 3F). Together, these data confirm that NOVA proteins can regulate signaling mechanisms in adipocytes.

To examine the consequences of reduced JNK signaling in adipocytes, we established F^∆J1,2 mice with adipocyte-specific JNK-deficiency (Adipoq-Cre^−/+ Mapk8^LoxP/LoxP Mapk9^LoxP/LoxP). These mice exhibited increased core body temperature and increased expression of genes that mediate sympathetic activation (Adr3b) and thermogenesis (Ucp1) in adipocytes (Figure 3—figure supplement 3). We therefore considered the possibility that NOVA-deficiency in adipocytes might also cause increased adipose tissue thermogenesis.

NOVA-mediated alternative pre-mRNA splicing in adipose tissue regulates energy expenditure

To test the physiological role of NOVA proteins in adipocytes, we examined the effect of feeding a CD or a HFD to control F^WT and F^∆N1,2 mice. We found that CD-fed F^WT and F^∆N1,2 mice gained similar body mass, but HFD-fed F^∆N1,2 mice gained significantly less mass than HFD-fed F^WT mice (Figure 4A and Figure 4—figure supplement 1A). Proton magnetic resonance imaging demonstrated that the decreased body mass was caused by reduced fat mass (Figure 4B). Analysis of organs at necropsy demonstrated a reduction in mass of the liver and adipose tissue in HFD-fed F^∆N1,2 mice compared with HFD-fed F^WT mice (Figure 4—figure supplement 1B) and analysis of tissue sections demonstrated reduced adipocyte hypertrophy, reduced hepatic steatosis, and reduced hypertrophy of pancreatic islets in the NOVA-deficient mice (Figure 4—figure supplement 2).10.7554/eLife.17672.021

Figure 4.

NOVA promotes the development of diet-induced obesity.

(A) The change in body mass of F^WT and F^∆N1,2 mice fed a CD or a HFD is presented (mean ± SEM; n=8~30; *p<0.05). (B) Body composition was examined by proton magnetic resonance spectroscopy (mean ± SEM; n=8~22; *p<0.05; **p<0.01). The source data are included as Figure 4—source data 1. (C) Blood insulin, leptin, resistin, and glucose in overnight starved CD-fed and HFD-fed (12 wks) F^WT and F^∆N1,2 mice were measured (mean ± SEM; n=8~16; *p<0.05; ***p<0.001). The source data are included as Figure 4—source data 2. (D) CD-fed and HFD-fed (12 wks) F^WT and F^∆N1,2 mice were examined by glucose tolerance tests (mean ± SEM; n=8~26; *p<0.05; ***p<0.001). The source data are included as Figure 4—source data 3. (E) Energy expenditure (EE) by HFD-fed (4 wks) F^WT mice (n = 9) and F^∆N1,2 mice (n = 8) was examined using metabolic cages over 3 days (12 hr light; 12 hr dark). The source data are included as Figure 4—source data 4.

DOI: http://dx.doi.org/10.7554/eLife.17672.021

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Figure 4—figure supplement 1.

Effect of adipose tissue-specific NOVA1/2-deficiency on organ mass.

(A) The change in% body mass of F^WT and F^∆N1,2 mice fed a CD or ais presented (mean ± SEM; n=8~30; *p<0.05). (B) The mass of the liver, epididymal white adipose tissue (eWAT), interscapular brown adipose tissue (BAT), and gastrocnemius muscle (Gastroc.) of F^WT and F^∆N1,2 mice fed a CD or a(16 wk) is presented (mean ± SEM; n=21). Statistically significant differences between F^WT and F^∆N1,2 mice are indicated: *p<0.05; **p<0.01.

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Figure 4—figure supplement 2.

Comparison of adipose tissue, liver, and pancreas in F^WT and F^∆N1,2 mice.

(A) Analysis of hematoxylin & eosin-stained tissue sections prepared from epididymal adipose tissue of CD-fed and HFD-fed (16 wk) F^WT and F^∆N1,2 mice. The relative area of adipocytes is presented (mean ± SEM; n=6 mice). (B) Representative hematoxylin & eosin-stained liver sections are presented (n=6). (C) Sections of the pancreas were stained with an antibody to insulin (green). DNA was stained with DAPI. The relative area of islets is presented (mean ± SEM; n=6 mice). Statistically significant differences between F^WT and F^∆N1,2 mice are indicated: **p<0.01; ***p<0.001.

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Figure 4—figure supplement 3.

Effect of adipose tissue-specific ablation of the Nova1 or Nova2 genes.

(A) The change in body mass of F^WT, F^∆N1 and F^∆N2 mice fed a CD or ais presented (mean ± SEM; n=8~21). (B) Body composition was examined by proton magnetic resonance spectroscopy (mean ± SEM; n=8~21). (C) Blood glucose concentration in overnight starved CD-fed and HFD-fed (16 wks) mice (mean ± SEM; n=8~21). (D) The mass of the liver, epididymal white adipose tissue (eWAT), interscapular brown adipose tissue (BAT), and gastrocnemius muscle (Musc.) of F^WT and F^∆N1,2 mice fed a CD or a(16 wk) is presented (mean ± SEM; n=8~21). (E) CD-fed and HFD-fed (16 wks) mice were examined by glucose tolerance tests (mean ± SEM; n=8~21). (F) Sections of the liver, eWAT, and BAT were stained with hematoxylin and eosin (n=6). Scale bar, 100 µm. (G) Blood insulin and leptin in overnight starved mice were measured (mean ± SEM; n=8~21). Statistically significant differences between Control and NOVA-deficient mice are indicated: *p<0.05.

DOI: http://dx.doi.org/10.7554/eLife.17672.028

10.7554/eLife.17672.029

Figure 4—figure supplement 4.

Metabolic cage analysis of physical activity and the consumption of food and water.

(A) Food consumption (B) water consumption, and (C) physical activity were measured using metabolic cages (3 days; 12 hr light and 12 hr dark) using F^WT and F^∆N1,2 mice fed a CD or a(4 wk). The data presented are the mean ± SEM (n=9 mice). No statistically significant differences between F^WT and F^∆N1,2 mice were detected (p>0.05). The source data are included as Figure 4—figure supplement 4—source data 1.

DOI: http://dx.doi.org/10.7554/eLife.17672.029

10.7554/eLife.17672.031

Figure 4—figure supplement 5.

Metabolic cage analysis of gas exchange and energy expenditure.

(A) Energy expenditure (EE), (B) VO₂, (C) VCO₂, and (D) respiratory exchange ratio (RER) were measured using metabolic cages (3 days; 12 hr light and 12 hr dark) using F^WT and F^∆N1,2 mice fed a CD or a(4 wk). The data presented are the mean ± SEM (n=7~9 mice). Statistically significant differences between F^WT and F^∆N1,2 mice are indicated: *p<0.05; **p<0.01; ***, 0.001. The source data are included as Figure 4—figure supplement 5—source data 1.

DOI: http://dx.doi.org/10.7554/eLife.17672.031

The reduced obesity of HFD-fed F^∆N1,2 mice compared with F^WT mice suggested that NOVA-deficiency may suppress HFD-induced metabolic syndrome. Indeed, hyperinsulinemia and hyperleptinemia were reduced in F^∆N1,2 mice compared with F^WT mice (Figure 4C). Moreover, HFD-fed F^WT mice were found to be more glucose intolerant than F^∆N1,2 mice (Figure 4D) and the HFD-induced hyperglycemia observed in F^WT mice was suppressed in F^∆N1,2 mice (Figure 4C). Similar (although smaller) phenotypes were detected in mice with adipocyte-specific single gene ablation of Nova1 or Nova2 (Figure 4—figure supplement 3).

Metabolic cage analysis demonstrated that food/water consumption and physical activity of F^WT and F^∆N1,2 mice were similar (Figure 4—figure supplement 4), but F^∆N1,2 mice exhibited greatly increased energy expenditure compared with F^WT mice (Figure 4E and Figure 4—figure supplement 5). This increase in energy expenditure may account for the suppression of HFD-induced obesity caused by NOVA deficiency in adipocytes. These data demonstrate that NOVA proteins in adipocytes suppress energy expenditure and promote obesity-associated metabolic syndrome.

NOVA-mediated alternative pre-mRNA splicing regulates adipose tissue thermogenesis

We measured core body temperature by telemetry using an implanted probe in mice housed at the ambient temperature of the vivarium (21°C) and following a cold challenge (4°C). This analysis demonstrated that the core body temperature of F^∆N1,2 mice was significantly higher than F^WT mice during the course of this study (Figure 5A). This was associated with increased expression of genes in sub-cutaneous adipocytes of F^∆N1,2 mice that are associated with a 'browning' (beige/brite) phenotype (Cidea, Dio2, Ppargc1a, Ppargc1b, and Ucp1) compared with F^WT mice (Figure 5B). Together, these data indicate that NOVA proteins in adipocytes can suppress adipose tissue thermogenesis.10.7554/eLife.17672.033

Figure 5.

NOVA regulates a thermogenic program in adipose tissue.

(A) F^WT and F^∆N1,2 mice were subject to cold challenge (4°C). Core body temperature was measured by telemetry using an implanted probe (mean ± SEM; n=8; *p<0.05; **p<0.01). The source data are included as Figure 5—source data 1. (B) The effect of cold challenge (4°C, 6 hr) on gene expression by inguinal adipocytes (iWAT) of F^WT and F^∆N1,2 mice was examined by quantitative RT-PCR (mean ± SEM; n=6~8; *p<0.05; **p<0.01). The source data are included as Figure 5—source data 2. (C) F^WT and F^∆N1,2 mice were housed under thermal neutral conditions (30°C). The change in body mass of CD and HFD-fed mice is presented (mean ± SEM; n=7~18). (D) Glucose tolerance tests were performed on F^WT and F^∆N1,2 mice housed under thermoneutral conditions (30°C). The effect of feeding a CD or a HFD (16 wk) is presented (mean ± SEM; n=8). The source data are included as Figure 5—source data 3. (E) Sections of brown adipose tissue (BAT) and iWAT of HFD-fed (16 wk) mice housed at 21°C and 30°C were stained with hematoxylin & eosin. The data shown are representative of 6 mice per group. (F) F^WT and F^∆N1,2 mice housed at 21°C and 30°C were fed a HFD (16 wk). Gene expression by adipocytes of iWAT and retroperitoneal adipose tissue (rWAT) was examined by quantitative RT-PCR (mean, n=8). The data are presented as a heat map.

DOI: http://dx.doi.org/10.7554/eLife.17672.033

10.7554/eLife.17672.037

Figure 5—figure supplement 1.

Thermogenic gene expression by primary adipocytes.

(A) Primary pre-adipocytes were treated with 10 µM forskolin (4 hr). Gene expression was examined by measurement of mRNA by RT-PCR (mean ± SEM; n=6). Statistically significant differences between Control and Forskolin-treated adipocytes are indicated: *p<0.05; **p<0.01; ***, 0.001. (B) Primary adipocytes were cultured from F^WT and F^∆N1,2 mice and treated with forskolin (4 hr). Gene expression was examined by measurement of mRNA by RT-PCR (mean ± SEM; n=6). Statistically significant differences between F^WT and F^∆N1,2 adipocytes are indicated: *p<0.05.

DOI: http://dx.doi.org/10.7554/eLife.17672.037

10.7554/eLife.17672.038

Figure 5—figure supplement 2.

Thermogenic gene expression is increased by shRNA-mediated suppression of Nova1 or Mapk8β/Mapk9α.

(A) An shNova1 lentiviral vector was used to suppress Nova1 gene expression in 3T3 L1 adipocytes. Nova2 expression was not detected in these cells. Gene expression was examined by measurement of mRNA by RT-PCR (mean ± SEM; n=6). (B) Lentiviral vectors expressing shMapk8β and shMapk9α were used to suppress Mapk8β and Mapk9α mRNA expression in 3T3 L1 adipocytes. Gene expression was examined by measurement of mRNA by RT-PCR (mean ± SEM; n=6). Statistically significant differences between Control and shRNA knock-down adipocytes are indicated: *p<0.05; **p<0.01; ***p<0.001.

DOI: http://dx.doi.org/10.7554/eLife.17672.038

To test whether NOVA proteins act by a cell autonomous mechanism to regulate the beige/brite phenotype, we established primary adipocytes in culture. Sympathetic stimulation of adipose tissue increases cAMP and promotes the browning of white adipocytes (Rosen and Spiegelman, 2014). This process can be studied in vitro by treating adipocytes with drugs that raise intracellular cAMP. Indeed, treatment of primary adipocytes with the drug forskolin increases cAMP concentration and promotes the beige/brite phenotype (Rosen and Spiegelman, 2014), and causes increased expression of the Adrb3, Dio2, Fgf21, Ppargc1a, and Ucp1 genes (Figure 5—figure supplement 1A). This treatment also causes decreased Nova gene expression and promotes expression of the alternatively spliced isoform Mapk9β (Figure 5—figure supplement 1A). To examine whether NOVA proteins contribute to the beige/brite phenotype, we compared the effects of increased cAMP on F^WT and F^∆N1,2 primary adipocytes. This analysis demonstrated that NOVA-deficiency caused increased expression of genes in adipocytes that mediate sympathetic activation (Adrb3) and thermogenesis (Ucp1) (Figure 5—figure supplement 1B).

To further test the cell autonomous role of NOVA proteins on the beige/brite phenotype, we examined 3T3L1 adipocytes that are normally resistant to the effects of cAMP to promote browning. These cells express NOVA1, but NOVA2 was not detected. Nova1 knock-down using shRNA was associated with increased expression of the beige/brite phenotype genes Prdm16 and Ucp1 (Figure 5—figure supplement 2A). Similarly, knock-down of Mapk8β mRNA plus Mapk9α mRNA using shRNA caused increased expression of the Prdm16 and Ucp1 genes (Figure 5—figure supplement 2B). Together, these data confirm that NOVA-mediated alternative splicing in adipocytes contributes to adipose tissue thermogenesis.

NOVA-mediated adipose tissue thermogenesis contributes to the development of metabolic syndrome

To test the role of adipose tissue thermogenesis in the metabolic phenotype of F^∆N1,2 mice (Figure 4), we examined the effect of housing mice under thermoneutral conditions (30°C). We found that F^∆N1,2 mice and F^WT mice gained equal body mass at thermoneutrality (Figure 5C). Moreover, the increased glucose tolerance of F^∆N1,2 mice compared with F^WT mice at 21°C was not observed at 30°C (Figure 5D). These data indicate that the improved glycemic regulation exhibited by F^∆N1,2 mice compared with F^WT mice at 21°C was caused by increased thermogenesis. Consistent with this conclusion, we found that the reduced adipocyte hypertrophy observed in HFD-fed F^∆N1,2 mice compared with F^WT mice at 21°C was not observed at 30°C (Figure 5E). Similarly, the increased expression of thermogenesis-related genes (Adrb3, Cidea, Dio2, Ppargc1a/b Prdm16, and Ucp1) by adipocytes in HFD-fed F^∆N1,2 mice compared with F^WT mice at 21°C was not found at 30°C (Figure 5F). Together, these data demonstrate that adipose tissue thermogenesis contributes to the improved glycemia of mice with NOVA-deficiency in adipocytes.

Discussion

The browning of white adipose tissue is associated with the appearance of beige/brite adipocytes, increased energy expenditure, and improved obesity-induced metabolic syndrome (Rosen and Spiegelman, 2014). An understanding of molecular mechanisms that account for browning is therefore important for the development of potential therapies for the treatment of metabolic syndrome based on increasing adipose tissue energy expenditure. It is established that beige/brite adipocytes in white adipose tissue depots can arise from specialized progenitor cells (Wang et al., 2013b). Beige/brite adipocytes may also arise from inactive cells present within white adipose tissue (Rosenwald et al., 2013). These two mechanisms may contribute to functional beige/brite cell development (Rosen and Spiegelman, 2014).

The increased adipose tissue browning detected in mice with adipocyte-specific NOVA deficiency was observed using floxed Nova alleles and a Cre driver (Adipoq-Cre) that is expressed in mature adipocytes. It is therefore likely that the increased white adipose tissue browning caused by NOVA deficiency does not reflect a change in adipocyte differentiation from specialized progenitor cells, but rather a change in mature adipocyte function. Whether these mature adipocytes represent white adipocytes or inactive beige/brite adipocytes is unclear. However, the observation that housing mice at thermoneutrality prevents the effect of NOVA deficiency to cause 'browning' suggests that NOVA deficiency may act by promoting the activation of inactive beige/brite adipocytes.

Our analysis demonstrates that NOVA expression in white adipocytes is reduced in obese humans and mice (Figure 1). These changes in NOVA expression may cause changes in adipose tissue physiology. Gene knockout studies demonstrate that compound NOVA-deficiency in adipocytes caused increased adipose tissue thermogenesis and improved whole body glycemia in HFD-fed mice (Figure 4). However, partial NOVA-deficiency in adipocytes (potentially modeling the changes in NOVA expression caused by feeding a HFD) caused modest changes in adipocyte physiology (Figure 4—figure supplement 3). The significance of the decrease in adipocyte NOVA expression in HFD-fed mice is therefore unclear. Nevertheless, our data establish that NOVA proteins in adipocytes function to suppress adipose tissue thermogenesis. The mechanism appears to be mediated by NOVA-regulated changes in pre-mRNA splicing that promote adipocyte thermogenic gene expression (Figure 5), but we cannot exclude additional contributions caused by other biochemical actions of NOVA proteins, including alternative mRNA polyadenylation (Licatalosi et al., 2008) and functional regulation of Argonaute/microRNA complexes (Storchel et al., 2015). Further studies will be required to examine the relative contributions of NOVA-regulated pre-mRNA splicing, alternative polyadenylation, and microRNA function. However, our analysis demonstrates that NOVA proteins do function as alternative pre-mRNA splicing factors in adipocytes (Figure 2).

The changes in pre-mRNA splicing caused by NOVA proteins include both increased and decreased exon inclusion, consistent with the known context-dependent role of NOVA proteins to promote both exon inclusion and exclusion (Ule et al., 2006). Importantly, there are significant gaps in our knowledge concerning mechanisms of alternative pre-mRNA splicing in adipocytes. First, NOVA proteins may act in a combinatorial manner with other splicing factors (Zhang et al., 2010), but roles for such factors in adipocytes have not been established. Second, our analysis of mice with NOVA-deficiency in adipocytes implicates a role for NOVA proteins in the alternative splicing of 26% of the genes that are regulated by HFD consumption (Figure 2B). Consequently, NOVA proteins do not contribute to 74% of the genes that exhibit HFD-regulated pre-mRNA splicing and mechanisms that contribute to the regulation of these genes have not been established. Further studies will be required to achieve an understanding of these processes.

The regulation of adipose tissue thermogenesis by NOVA proteins may not be entirely dependent upon the classical UCP1 pathway (Kozak, 2010) because of the existence of alternative thermogenic mechanisms (Kazak et al., 2015). Nevertheless, our data demonstrate that NOVA proteins can suppress adipocyte thermogenesis. Reduced NOVA expression in adipocytes causes thermogenesis (Figure 5) and may potentially be achieved by drugs targeting NOVA-mediated pre-mRNA splicing. This role of NOVA proteins is most likely mediated by a network response to a pre-mRNA splicing program that collectively regulates adipocyte thermogenesis.

In conclusion, we describe a NOVA-dependent alternative pre-mRNA splicing program in white adipocytes that regulates browning of white adipose tissue. These data identify alternative pre-mRNA splicing as a biological process that may be targeted by drugs designed to increase adipocyte thermogenesis and improve metabolic syndrome caused by obesity.

Materials and methods

Mice

C57BL/6J mice (RRID:IMSR_JAX:000664), B6.Cg-Lep^ob/J (RRID:IMSR_JAX:000632), B6;FVB-Tg(Adipoq-Cre)1Evdr (RRID:IMSR_JAX:010803) (Eguchi et al., 2011), B6;129-Gt(ROSA)26Sor^{tm1(cre/ERT)Nat}/J mice (RRID:IMSR_JAX:004847) (Badea et al., 2003), and B6.129S4-Gt(ROSA)26Sor^tm1(FLP1)Dym/RainJ mice (RRID:IMSR_JAX:009086) (Farley et al., 2000) were obtained from The Jackson Laboratory. We have previously reported Mapk8^LoxP/LoxP (Das et al., 2007), Mapk9^LoxP/LoxP mice (Han et al., 2013b), and Mapk9^-/- mice (RRID:IMSR_JAX:004321) (Yang et al., 1998).

We generated Nova1 and Nova2 conditional mice using ES cells targeted by homologous recombination (Nova1^{tm1a(EUCOMM)Hmgu} and Nova2^{tm1a(KOMP)Wtsi}), the preparation of chimeric mice, and breeding to obtain germ-line transmission of the disrupted Nova1 and Nova2 genes using standard methods. The Frt-Neo^R-Frt cassette was excised by crossing with FLPeR mice to obtain mice with the Nova1^LoxP and Nova2^LoxP alleles. Mice were genotyped by PCR analysis of genomic DNA using the primers 10F (5’-GTCCGTAAGGCATGTC-3’) and 2R (5’-AGCAAAAAGCCATCCATG-3’) to detect the Nova1⁺ (894 bp), Nova1^LoxP (1,101 bp), and Nova1^∆ (281 bp) alleles, the primers 1F (5’-CAGAAGAACTGGAGAC-3’) and N2-2R (5’- GGTTGGGCTGTCAGTG-3’) to detect the Nova2⁺ (149 bp) and Nova2^LoxP (127 bp) alleles, or with the primers N2delF1 (5’-CAGGCTGGCGCCGGAAC-3’) and N2-2R (5’-GGTTGGGCTGTCAGTG-3’) to detect the Nova2^LoxP (970 bp) and Nova2^∆ (153 bp) alleles.

All mice were studied on the C57BL/6J strain background. Male mice (8 wks old) were fed a chow diet (Iso Pro 3000, Purina) or a HFD (S3282, Bioserve). Body weight was measured with a scale. Whole body fat and lean mass were non-invasively measured using ¹H-MRS (Echo Medical Systems).

The mice were housed at 21°C (alternatively at 4°C or 30°C, where indicated) in a specific pathogen-free facility accredited by the American Association for Laboratory Animal Care (AALAC). The Institutional Animal Care and Use Committee (IACUC) of the University of Massachusetts Medical School approved all studies using animals.

Metabolic cages

The analysis was performed by the Mouse Metabolic Phenotyping Center at the University of Massachusetts Medical School. The mice were housed under controlled temperature and lighting with free access to food and water. The food/water intake, energy expenditure, respiratory exchange ratio, and physical activity were measured using metabolic cages (TSE Systems).

Body temperature

Biocompatible and sterile microchip transponders (IPTT-300 Extended Accuracy Calibration; Bio Medic Data Systems) were implanted subcutaneously. Cold tolerance tests (4°C) were performed using mice fed a chow diet ad-libitum.

Blood analysis

Blood glucose was measured with an Ascensia Breeze 2 glucometer (Bayer). Adipokines and insulin in plasma were measured by multiplexed ELISA using a Luminex 200 machine (Millipore).

Glucose and insulin tolerance tests

Glucose and insulin tolerance tests were performed by intraperitoneal injection of mice with glucose (1 g/kg) or insulin (1.5 U/kg) using methods described previously (Sabio et al., 2008).

Hepatic expression of NOVA

Mice (8 wks) were fed a HFD. At 12 wks, the mice treated with 5 × 10⁹ pfu/mouse Adenovirus-NOVA2 or Adenovirus-GFP (Applied Biological Materials Inc.) by tail vein injection. The mice were euthanized at 2 wks post-injection.

Plasmids

Human Flag-tagged Mapk8α1, Mapk8β1, Mapk9α2, and Mapk9β2 cDNA cloned in the expression vector pCDNA3 have been described previously (Gupta et al., 1996). Murine Mapk8α1, Mapk8β1, Mapk9α2, and Mapk9β2 cDNA were isolated by RT-PCR and cloned by blunt-end ligation in the SnaB1 site of the retroviral expression vector pBABE-puro (Addgene plasmid #1764) (Morgenstern and Land, 1990). Retroviral stocks were prepared by transfection of Phoenix-ECO cells (American Type-Culture Collection, ATCC CRL-3214) (Lamb et al., 2003).

The DNA sequences used to generate shRNA vectors were Nova1 (5’-CCGGGCTGCTCAGTATTTAATTACTCGAGTAATTAAATACTGAGCAGCTTTTTG-3’), Mapk8β (5’-CCGGTCATGGGAGAAATGATCAAAGCTCGAGCTTTGATCATTTCTCCCATGATTTTTG-3’), Mapk9α (5’-CCGGGTGAAAGGTTGTGTGATATTCCTCGAGGAATATCACACAACCTTTCACTTTTTG-3’). These sequences were cloned in the Age1/EcoR1 sites of the lentiviral vector pLKO.1-puro (Addgene #8453; [Stewart et al., 2003]). Lentiviral stocks were prepared by transfection of 293T cells with the indicated replication-incompetent lentiviral vector (pLKO1-shRNA) together with the packaging plasmid psPAX2 and the envelope plasmid pMD2.G (Addgene #12259 and #12260; [Naldini et al., 1996]).

Adipocyte tissue culture

Primary inguinal adipocytes were prepared from male mice (8 wk old). The fat pads were minced with a razor blade and incubated (40 min, 37°C) in 12.5 mM Hepes pH 7.4, 120 mM NaCl, 6 mM KCl, 1.2 mM MgSO₄, 1 mM CaCl₂, 2%BSA, 2.5 mM glucose, 1 mg/ml collagenase II (Sigma) and 0.2 mg/ml DNAse I (Sigma). The digested tissue was filtered through a 100 µm nylon strainer and centrifuged (8 min at 250 g). The pellet was suspended in Dulbecco’s modified Eagle’s medium (DMEM):Ham’s F12 (1:1) medium with 8%FBS, 1x Antibiotic-Antimycotic, 2 mM glutamine (Life Technologies). 100,000 cells/well were seeded in 12 well plates. The medium was refreshed every 2 days. On day 6, differentiation was induced using medium further supplemented with 0.5 mM IBMX (Sigma), 5 µg/ml insulin (Sigma), 1 µM Troglitazone (TZD; Calbiochem), and 2.5 µM dexamethasone (Sigma). On day 8, the medium was refreshed using medium supplemented with insulin and troglitazone only. On day 11, the medium was refreshed using medium supplemented with insulin only. Mature adipocytes were observed on day 14.

3T3-L1 MBX cells (American Type-Culture Collection, ATCC CRL-3242) were cultured in high glucose DMEM supplemented with 10% fetal bovine serum (FBS), sodium pyruvate (1 mM), glutamine (2 mM), and 100 units/ml penicillin, and 100 µg/ml streptomycin (Life Technologies). Transduction assays were performed using pLKO1 lentiviruses and selection with 2 µg/ml puromycin. The cells were differentiated to adipocytes by growing to confluence for 48–72 hr. On day 0, the media were changed to media supplemented with 0.5 mM IBMX (Sigma), 5 µg/ml insulin (Sigma), 1 µM Troglitazone (TZD; Calbiochem), and 1 µM dexamethasone (Sigma)). This medium was refreshed every 2 days. On day 4, the medium was refreshed using medium supplemented with insulin and TZD only. On day 6, the medium was refreshed using medium supplemented with 0.5 µg/ml insulin only. Mature adipocytes were observed on day 8.

Murine embryo fibroblasts

E13.5 primary murine fibroblasts (MEF) obtained from mice that express 4-hydroxytamoxifen-inducible Cre were established in culture (Das et al., 2007). Cre^ERT2-/+ Mapk8^+/+ Mapk9^+/+ MEF and Cre^ERT2-/+ Mapk8^LoxP/LoxP Mapk9^-/- MEF (Das et al., 2007) were cultured in DMEM supplemented with 10% fetal bovine serum, 100 units/ml penicillin, 100 µg/ml streptomycin, 0.1 mM 2-mercaptoethanol, and 2 mM L-glutamine (Life Technologies). Transduction assays were performed using pBABE-puro retroviruses and selection with 2 µg/ml puromycin (Lamb et al., 2003). Cells were treated with 1 µM 4-hydroxytamoxifen (24h) and subsequently cultured (5 days). The cells were exposed without or with 60 J/m² UV-C and incubated (60 mins) prior to harvesting.

Protein kinase assays

Lysates were prepared from TNFα-treated (10 ng/ml, 10 mins) and non-treated transfected HEK 293T/17 cells (American Type-Culture Collection, ATCC CRL-11268) expressing Flag-tagged JNK proteins (Gupta et al., 1996). JNK proteins were isolated by immunoprecipitation using agarose-bound Flag M2 antibody (Sigma-Aldrich Cat# A2220, RRID:AB_10063035) (Gupta et al., 1996). JNK activity was measured using an in vitro protein kinase assay with the substrates cJun and [γ-³²P]ATP as substrates (Whitmarsh and Davis, 2001).

Immunoblot analysis

Tissue extracts were prepared using Triton lysis buffer (20 mM Tris-pH 7.4, 1% Triton-X100, 10% glycerol, 137 mM NaCl, 2 mM EDTA, 25 mM β-glycerophosphate, 1 mM sodium orthovanadate, 1 mM PMSF and 10 µg/mL leupeptin plus aprotinin). Extracts (30–50 µg of protein) were examined by immunoblot analysis by probing with antibodies to cJUN (Cell Signaling Technology Cat# 9165L, RRID:AB_2129578), pSer⁶³-cJUN (Cell Signaling Technology Cat# 9261S, RRID:AB_2130162), Flag M2 (Sigma-Aldrich Cat# F1804, RRID:AB_262044), GAPDH (Santa Cruz Biotechnology Cat# sc-365062, RRID:AB_10847862), JNK1/2 (BD Biosciences Cat# 554285, RRID:AB_395344), pThr¹⁸⁰-Pro-pTyr¹⁸²-JNK (pJNK) (Cell Signaling Technology Cat# 4668P, RRID:AB_10831195), NOVA1 (Abcam Cat# ab97368, RRID:AB_10680798), NOVA2 (Sigma-Aldrich Cat# AV40400, RRID:AB_1854572), and βTubulin (BioLegend Cat# 903401, RRID:AB_2565030). Immunocomplexes were detected by fluorescence using anti-mouse and anti-rabbit secondary IRDye antibodies (Li-Cor) and quantitated using the Li-Cor Imaging system

Analysis of tissue sections

Histology was performed using tissue fixed in 10% formalin for 24 hr, dehydrated, and embedded in paraffin. Sections (7 µm) were cut and stained using hematoxylin & eosin (American Master Tech Scientific). Paraffin sections were stained with an antibody to insulin (Dako Cat# A0564, RRID:AB_10013624) that was detected by incubation with anti-Ig conjugated to Alexa Fluor 488 (Life Technologies). DNA was detected by staining with DAPI (Life Technologies). Fluorescence was visualized using a Leica TCS SP2 confocal microscope equipped with a 405-nm diode laser.

Adipocyte isolation

Inguinal, retroperitoneal, and epididymal fat pads were surgically removed at necropsy. Adipocytes were isolated after incubation (40 min at 37°C with shaking) of adipose tissue in 12.5 mM Hepes pH 7.4, 120 mM NaCl, 6 mM KCl, 1.2 mM MgSO₄, 1 mM CaCl₂, 2% BSA, 2.5 mM glucose, 1 mg/ml collagenase II (Sigma #C6885) and 0.2 mg/ml DNAse I (Sigma #DN25)). Larger particles were removed using a 100 µm nylon sieve and the filtrates were centrifuged at 1000 rpm (3 min). Floating adipocytes were washed twice with 1x PBS and subsequently centrifuged at 1000 rpm (3 min) prior to RNA isolation. The stromal vascular fraction (SVF) was collected after centrifugation at 3000 rpm (5 min) prior to RNA isolation. The expression of adipocyte marker genes (Adipoq & Leptin), SVF marker genes (Emr1 (F4/80), Itgam (Cd11b), Cd68) in adipocytes and the SVF was measured by quantitative RT-PCR analysis (Figure 1—figure supplement 3). The expression of a gene (Fabp4) that is expressed by both adipocytes and SVF was also examined.

RNA sequencing

RNA was isolated using the RNeasy kit (Qiagen). RNA quality (RIN > 9) was verified using a Bioanalyzer 2100 System (Agilent Technologies). Total RNA (10 µg) was used for the preparation of each RNA-seq library by following the manufacturer’s instructions (Illumina). Three or four independent libraries prepared from different mice were sequenced (Illumina) for each condition. Table 1 presents a summary of the adipocyte RNA-seq data and associated GEO accession numbers. The liver RNA-seq data (CD vs HFD (n=3)) were previously reported (Vernia et al., 2014) (GEO accession number GSE55190).

Quantitative RT-PCR analysis of gene expression

The expression of mRNA was examined by quantitative PCR analysis using a Quantstudio PCR system (Life Technologies). Taqman assays were used to quantitate Adipoq (Mm00456425_m1), Adrb3 (Mm02601819_g1), Cd68 (Mm03047340_m1), Dio2 (Mm00515664_m1), Emr1 (F4/80) (Mm00802530_m1), Fabp4 (Mm00445880_m1), Itgam (Cd11b) (Mm00434455_m1), Leptin (Mm00434759_m1), Mapk8 (Mm00489514_m1), Mapk9 (Mm00444231_m1), Nova1 (Mm01289097_m1), Nova2 (Mm01324153_m1), Ppargc1a (Mm00447183_m1), Ppargc1b (Mm00504720_m1), Prdm16 (Mm00712556_m1), Ucp1 (Mm01244861_m1) mRNA and 18S RNA (4308329) (Applied Biosystems). Standard curves were constructed using the threshold cycle (Ct) values for each template dilution plotted as a function of the logarithm of the amount of input template. The number of mRNA copies for each gene-sample combination was calculated using the slope of the standard curve. To obtain a normalized abundance, copy numbers were corrected for the amount of 18S RNA in each sample.

Quantitative RT-PCR analysis of alternative splicing

The inclusion of exons 7a and 7b in Mapk8 and Mapk9 mRNA was examined by quantitative PCR using the Quantifast probe PCR kit (Qiagen) and the following combination of primers and Taqman probes (Applied Biosystems): Mapk8α: Fwd, GGAGAACGTGGACTTATGGTCTGT; Probe: 6FAM-TGCCACAAAATCCT-MGBNFQ; Rev, TGATCAATATAGTCCCTTCCTGGAA. Mapk8β: Fwd, GAACGTTGACATTTGGTCAGTTG; Probe, 6FAM-AGAAATGATCAAAGGTGGTGTT-MGBNFQ; Rev, TCAATATGATCTGTACCTGGGAACA. Mapk9α: Fwd, GGTCAGTGGGTTGCATCATG; Probe, 6FAM-AGCTGGTGAAAGGTT-MGBNFQ; Rev, TGATCAATATGGTCAGTACCTTGGA. Mapk9β: Fwd, ATCTGGTCTGTCGGGTGCAT; Probe: 6FAM-AAATGGTCCTCCATAAAG-MGBNFQ; Rev, GATCAATATAGTCTCTTCCTGGGAACA. Mapk8 and Mapk9 spliced variants were quantitated using the relative quantification method. The alternative splicing is represented as the ratio Mapk8α/Mapk8β and Mapk9α/Mapk9β.

Semi-quantitative RT-PCR analysis of alternative splicing

RT-PCR analysis was performed using amplimers based on the sequences of Adam15 exons 19 and 21 (svADAM15F1: 5’-GCGGGCACAGCAGATGAC-3’ and svADAM15R1: 5’- GGGTTGGCAGGCAGTGGC-3’) and Yap1 exons 5 and 7 (svYap1F1: 5-GGAGAAGGAGAGACTG-3’ and svYap1R2: 5’-GTCCCTCCATCCTGCTC-3’). The PCR products were examined by agarose gel electrophoresis and staining with ethidium bromide. Adam15 mRNA and exon 20-skipped Adam15 mRNA were detected as 277 bp and 78 bp DNA fragments. Yap1 mRNA and exon 6-skipped Yap1 mRNA were detected as 144 bp and 96 bp DNA fragments.

Bioinformatics analysis

Fastqc v0.10.1 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and cutadapt (v1.7) (https://pypi.python.org/pypi/cutadapt/1.7.1) were used to generate sequence quality reports and trim/filter the sequences respectively. Reads below a minimum quality PHRED score of 30 at the 3’ end were trimmed (Table 1). The filtered reads were aligned to the mouse reference genome (Ensembl GRCm38). Alignments were carried out using Bowtie2 (v2.2.1.0) (Langmead and Salzberg, 2012) and Tophat2 (v2.0.9) (Kim et al., 2013). Samtools (v0.0.19) (Li et al., 2009) and IGV (v2.3.60) (Thorvaldsdottir et al., 2013) were used for indexing the alignment files and viewing the aligned reads respectively. Gene expression was quantitated as fragments per kilobase of exon model per million mapped fragments (FPKM) using Cufflinks (v2.2.0) (Trapnell et al., 2010). Differentially expressed genes were identified using Cufflinks tools (Cuffmerge and Cuffdiff). CummeRbund (v2.4.1) (Trapnell et al., 2012) was used to assess biological replicate concordance. Significant changes in gene expression were defined as q<0.05 and absolute log2 fold change >0.75. Gene sets were examined using Wikipathways in Webgestalt (Wang et al., 2013a). Alternative splicing was examined using rMATS software (v3.0.9) (Shen et al., 2014). rMATS was run using default settings to compute p-values and FDRs of splicing events. Significant changes in alternative splicing were defined as FDR<0.05 and absolute ∆Inc level >0.1. Rmats2sashimiplot (https://github.com/Xinglab/rmats2sashimiplot) and Sashimiplot (Katz et al., 2015) were used for quantitative visualization of alternative exon expression from rMATS.

The microarray analysis of human adipose tissue mRNA expression data (GEO GSE25402) (Arner et al., 2012) was performed using Genespring. The summarization method was based on RMA16, the normalization method was based on the median approach on log2 scale, and the fold change was computed from average signal intensity values.

NOVA interactions with pre-mRNA were examined using CLIP-seq data obtained from murine brain (Licatalosi et al., 2008) provided (in BED format) by Dr. R. B. Darnell (Rockefeller University). The coordinates were based on the mm9 assembly. The UCSC liftOver utility was used to convert the NOVA CLIP-seq tag coordinates from the mm9-based assembly to the mm10/GRCm38 assembly. The coordinates for the NOVA CLIP-seq tags were sorted based on chromosome and start position. Custom PERL scripts (Source code 1 and 2) were used to extract three groups of genomic regions: (1) statistically significant differentially expressed alternatively spliced exons (FDR<0.05 and the absolute inclusion level difference >0.1) (n = 1631); (2) exons not meeting statistical significance (FDR≥0.05) (n = 11,490); and (3) randomly selected regions of the mouse genome comprising 100 bp (n = 10,000). The coordinates for each genomic region were expanded to include an additional 500 bp of sequence flanking the 5’ and 3’ ends of the genomic region. If an exon belongs to a gene comprising one or more alternatively spliced exons with FDR<0.5 and one or more alternatively spliced exons with FDR≥0.05, the exon was excluded from the second group. In each group, the regions were sorted based on the chromosome and the start position. Duplicated regions were removed. The BEDtools (2.22.0) intersect command (Quinlan, 2014) was used to determine the intersection between the regions in each group and the NOVA-CLIP-seq tags. The number of intersecting regions between a group and the NOVA binding sites were tallied. For each group, the number and the percentage of regions with and without NOVA binding sites were calculated (Figure 1E). Thus, NOVA CLIP-tags intersecting with the following groups were examined: (1) statistically significant differentially spliced exons plus 500 bp of flanking sequence both upstream and downstream; (2) non-alternatively spliced exons plus 500 bp of flanking sequence both upstream and downstream; (3) random genomic sequences of similar fragment size (Figure 1E). Statistical significance between two groups was determined by the Pearson’s Chi-squared test.

Statistical analysis of experimental data

Values are given as means ± SEM of at least three independent experiments. Sample sizes were determined by prior experimentation. Differences between groups were examined for statistical significance using the Student´s test or analysis of variance (ANOVA) with the Fisher’s test (*p<0.05, **p<0.01 and ***p<0.001).