ABSTRACT
Background and Aims Liver is exposed to changing metabolic and inflammatory environments. It must sense and adapt to metabolic need while balancing resources required to protect itself from insult. PGC-1α is a transcriptional coactivator that both coordinates metabolic adaptation to diverse stimuli and protects against inflammation in several tissues. However, it is not known how PGC-1α integrates extracellular signals to balance metabolic and anti-inflammatory outcomes. PGC-1α exists as multiple, alternatively spliced variants expressed from different promoters.
Methods Tissue samples from transgenic mice and humans with fatty liver disease were analyzed for expression of PGC-1α isoforms and apoptosis caused by inflammatory damage. Primary mouse hepatocytes were used to identify effectors of PGC-1α activity downstream of TNFα.
Results We show in human liver that non-alcoholic fatty liver disease (NAFLD) and steatohepatitis (NASH) preferentially activated the alternative PPARGC1A promoter. Gene expression analysis in primary mouse hepatocytes identified shared and isoform-specific roles for PGC-1α variants in response to TNFα. PGC-1α1 primarily impacted gene programs of nutrient and mitochondrial metabolism, while TNFα signaling revealed that PGC-1α4 influenced several pathways related to innate immunity and cell death. Gain- and loss-of-function models showed that PGC-1α4 specifically enhanced expression of anti-apoptotic gene programs and attenuated hepatocyte apoptosis in response to TNFα or LPS. This was in contrast to PGC-1α1, which reduced expression of a wide inflammatory gene network, but did not prevent liver cell death.
Conclusions We conclude that PGC-1α variants have distinct, yet complimentary roles in hepatic responses to inflammation and identify PGC-1α4 as an important mitigator of apoptosis.
INTRODUCTION
The unique anatomical architecture of the liver allows it to perform a broad range of metabolic functions, but at the same time it exerts powerful immunocompetence, surveilling portal blood and acting as a protective barrier 1. The liver must adapt quickly to various metabolic and inflammatory signals from the digestive tract or systemic circulation, concurrently responding to changing glucose and lipid homeostasis. Importantly, hepatic metabolism can be reprogramed by an inflammatory response 2, allowing a trade-off between energy destined for nutrient metabolism versus tolerance to infection. However, mechanisms helping to balance metabolic demand with inflammatory response are not clear.
The peroxisome proliferator activated receptor gamma coactivator-1 alpha (PGC-1α) regulates many transcriptional programs related to nutrient metabolism, energy homeostasis and mitochondrial respiration 3 by binding to nuclear receptors and other transcription factors to enhance their activity 4. PGC-1α also activates expression of gene programs within a broader set of biological functions in muscle 5-8 and liver 9-14.
Evidence suggests that PGC-1α is also an essential component of the inflammatory response, but mechanisms for this are unclear. Over-expression in muscle protects mice from disease, exercise, and age-related inflammatory damage 15-18 and preservation of PGC-1α activity blunts lipopolysaccharide (LPS)-induced inflammatory damage to heart and kidney 19, 20. On the other hand, reduced PGC-1α increases pro-inflammatory cytokine expression and increases inflammation damage to muscle and liver tissue in response to stresses 17, 21, 22. Over-expression of PGC-1α decreases expression of pro-inflammatory cytokines, while simultaneously inducing expression of secreted anti-inflammatory factors 17, 23. How PGC-1α regulates inflammatory responses and effects of this within cells is not yet understood.
Although PGC-1α is a coactivator, data suggest that PGC-1α may indirectly represses NF-κB target gene transcription though coactivation of anti-inflammatory transcriptional networks linked to PPARs 18. It may also bind to the p65 subunit of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) 24. Thus, mechanistic understanding of links between inflammatory signaling and PGC-1α activity remain limited. Data support PGC-1α as an important regulator of both mitochondria metabolism and inflammation, yet it is not known how PGC-1α integrates multiple extra-cellular signals to coordinate and balance each cellular response. In this study, we show that differentially spliced variants of the PGC-1α protein have unique functions in regulating hepatocyte responses to concurrently integrate metabolic and inflammatory signals.
MATERIALS AND METHODS
Mice
Hepatocyte-specific PGC-1α knockout mice (LKO: Ppargc1afl/fl, Alb-cre) were generated as previously described 10, 11, 22. Age-matched, male mice on a C57BL/6J background were used. Tissue-specific PGC-1α4 over-expressing mouse line (LSLPGC-1α4) was generated by inserting PGC-1α4 cDNA downstream of a Lox-stop-Lox cassette at the ROSA26 locus (Supplementary Fig. 1). Ppargc1a Alternative Promoter Knock-out (AltPromKO) mice were generated by inserting LoxP sites flanking exon 1b and 1b’ of the alternative Ppargc1a promoter (Supplementary Fig. 1). Experiments were performed in accordance with IRCM institutional animal care and use committee regulations.
Mouse housing, diets, and lipopolysaccharide treatment
Mice were maintained on ad libitum chow (Tekland #2918) at 22°C (12h light/dark cycle). For in vivo model of steatohepatitis, mice were fed a methionine-choline deficient (MCD) diet (A02082002B, Research Diets) or matched control diet (A02082003B) starting at 8 weeks of age for up to 42 days. For LPS treatment, livers of 10-week-old male or female mice were harvested 6 hours after tail-vein injection of LPS (2 mg/kg, Invivogen) or vehicle (PBS).
Primary hepatocyte isolation and treatment
Primary mouse hepatocytes from 12-week-old mice were isolated and cultured as previously described 22. One day after isolation, hepatocytes were infected with adenovirus (5 MOI) overnight and starved of insulin and dexamethasone for 24 hours prior to treatment with tumor necrosis factor alpha (TNFα) (Fitzgerald) at 2 ng/mL for 2 hours for signaling/gene expression, or 20 ng/mL for 8 hours for apoptosis. Apoptosis was measured by Cell Death Detection ELISA (Roche). For reporter assays, cells were transfected (Lipofectamine) with a construct expressing firefly luciferase downstream of 3x NF-κB response elements. Activity was normalized to total protein following quantification using the Dual Luciferase Reporter Assay System (Promega).
Protein isolation, immunoprecipitation and immunoblotting
Proteins were solubilized in radioimmunoprecipitation assay buffer containing protease and phosphatase inhibitors. Hepatic PGC-1α was immunoprecipitated from liver using anti-PGC-1α (Millipore, ST1202) in 1% Triton/TBS. Elutes and total proteins were resolved by SDS-PAGE, blotted, and probed with antibodies (Supplementary Table 1).
Immunofluorescence
H2.35 cells cultured in DMEM, supplemented with 10% Fetal Bovine Serum (FBS, Wisent), 1% penicillin/streptomycin, 0.2 μM dexamethasone were incubated on poly-L-lysine coated coverslips and transfected with V5-tagged PGC-1α variants for 24 hours (Lipofectamine). Cells were starved overnight of dexamethasone prior to TNFα treatment (50 ng/ml) for 3 hours and fixation with 4% paraformaldehyde. Triton-permeabilized cells were incubated with anti-V5 antibody overnight, followed by Alexa 488-conjugated secondary antibody to visualize proteins.
Cell fractionation
H2.35 cells transduced with adenovirus expressing control vector, PGC-1α1 or PGC-1α4 were starved overnight of dexamethasone prior to TNFα treatment (50 ng/ml) for 3 hours. Cell pellets were washed in PBS and resuspended in Lysis Buffer (10 mM Hepes (pH 7.5), 10 mM KCl, 3 mM MgCl2, 0.35 M sucrose, 0.1% NP40, 3 mM 2-mercaptoethanol, 0.4 mM PMSF, 1 uM pepstatin A, 1 uM leupeptin and 5 ug/ml aprotinin). After centrifugation, supernatants were kept as cytoplasmic fraction. The pellet (nuclear fraction) was washed twice with lysis buffer, resuspended in Buffer A (3 mM EDTA, 0.2 mM EGTA, 1 mM dithiothreitol, 100 mM NaCl and 0.8% NP40) and sonicated for 10 minutes (cycles of 30 seconds ON and 30 seconds OFF). Equal amount of proteins were resolved by SDS-PAGE.
Microarray and Gene set enrichment analysis
mRNA was isolated from primary mouse hepatocytes infected with adenovirus expressing PGC-1α1, PGC-1α4 or vector control treated with 2 ng/mL TNFα or vehicle (PBS) for 2 hours (n = 3) and gene expression profiles generated using Affymetrix Mouse Genome 430 2.0 Arrays. Raw CEL files were normalized using RMA [PMID: 12925520] and annotated using biomaRt [PMID: 16082012]. Raw data and sample annotation are available on GEO (GSE132458).
Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) term enrichment were performed on chip data. See Supplementary Methods for details. Full GSEA results are provided in Supplementary File 1. Full GO processes are provided in Supplementary Files 2 (untreated samples) and 3 (TNFα–treated samples). Gene lists were evaluated for enrichment of transcription factor signatures and binding sites in the proximal promoters and distant regulatory elements using iRegulon and DiRE (http://dire.dcode.org) with default analysis settings.
RNA isolation, PCR, and quantitative RT-PCR
RNA was isolated from frozen tissue or cells using TRIzol (Invitrogen). 1 μg of RNA treated with DNase I was reverse-transcribed using the High Capacity Reverse Transcription Kit (Applied Biosystems). cDNA was quantified using SYBR Green PCR master mix (Bioline) and normalized to Hypoxanthine-guanine phosphoribosyltransferase (Hprt) mRNA using the ΔΔCt threshold cycle method. Presence or absence of PGC-1α variants was confirmed using isoform-specific primers by conventional PCR and sequencing (Supplementary Tables 2 and 3).
Patients and liver samples
Human liver samples were collected from 38 subjects age 33-81 years (Low 3 M: 4 W, NAFLD 10 M: 4 W, NASH 6 M: 3 W, Cirrhotic 4 M: 4 W) undergoing hepatic resection at the McGill University Health Centre after informed consent obtained. Samples were snap-frozen and stored at −80°C. Specimens were scored by a pathologist and classified based on NAFLD Activity Score (NAS: Low =<2, NAFLD =3-5, NASH =6-9, Cirrhotic =7-9) and fibrosis staging from 1A to 3. Study protocol was approved by the Research Ethics Boards of McGill and the Institut de recherches cliniques de Montréal (IRCM). M= men W= women.
Statistical analysis
Sample sizes were based on previous experience with assays and knowledge of expected variance. Normal distribution and homoscedasticity of data were tested by Shapiro-Wilks and Bartlett tests, respectively. Parametric tests were used if distributions normal and variances equal. One-way or Two-way analysis of variance were followed by Tukey’s (one-way) or Dunnett’s multiple comparisons (two-way) post-hoc test using GraphPad Prism software. Data are expressed as mean ± SEM unless otherwise indicated.
RESULTS
PGC-1α1 and PGC-1α4 are expressed in inflamed liver
PGC-1α mRNA expression is controlled by different promoters (proximal and alternative) induced in a stimulus- and context-dependent manner 25-31 and multiple splice variants of PGC-1α have been identified 32-36. Using liver tissue from human subjects with biopsy-confirmed inflammatory liver disease (i.e. NAFLD, NASH or cirrhosis), we investigated whether the PPARGC1A gene is regulated in fatty liver disease. Splice variants cannot be fully distinguished by qPCR 37. Using variant-specific non-quantitative PCR, we detected transcripts for NT-PGC-1α-a and PGC-1α1 of the proximal promoter in livers of all conditions, while PGC-1α-b and PGC-1α4 of the alternative promoter were amplified only from livers in subjects with NAFLD and NASH (Fig. 1A). Using quantitative PCR (qPCR), we found only transcripts from the alternative promoter (containing exons 1b and 1b’) were increased compared to livers of subjects with simple steatosis (low) (Fig. 1B). Since PGC-1α-b is believed to play a similar role to canonical PGC-1α1 38, we focused on PGC-1α4, a truncated PGC-1α protein found in muscle to regulate myocyte hypertrophy, but with no known function in liver 34. By qPCR, transcripts truncated at exon 6 (including PGC-1α4) increased proportionally with the severity of inflammatory liver disease (Fig 1C). We also observed similar increases in PGC-1α proteins in livers of mice subjected to a methionine-choline-deficient (MCD) diet, a murine model of inflammatory steatohepatitis (Fig 1D). Taken together, our data suggest that chronic inflammation differentially regulates PGC-1α variant expression and that the induction of the alternative PPARGC1A promoter points toward PGC-1α4 being the predominant isoform in inflammatory liver disease.
PGC-1α1 and PGC-1α4 have distinct roles in the hepatic response to TNFα
Since PGC-1α isoforms can have overlapping and distinct biological activity 37, we sought to determine whether PGC-1α1 and PGC-1α4 influence inflammatory signaling pathways. We first compared the transcriptome of primary mouse hepatocytes by microarray following over-expression of PGC-1α1, PGC-1α4, or vector alone in the absence or presence of the inflammatory cytokine TNFα, an inflammatory cytokine associated with NAFLD/NASH (Supplementary Fig. 2). More than 1000 genes changed by ≥2-fold following PGC-1α1 over-expression compared to vector alone (adj. p-value < 0.01), while only 24 were changed by PGC-1α4 and only 4 genes overlapped between the two lists (Fig 2A, Supplementary File 1). Following TNFα treatment, >4500 genes were changed ≥2-fold in hepatocytes over-expressing PGC-1α1 and >3000 for PGC-1α4, with 36% of the genes shared between isoforms (Fig 2A, Supplementary File 2). Clustering of PGC-1α4-modulated genes and comparison to levels in vector- or PGC-1α1-expressing hepatocytes suggested that the activity of PGC-1α4 relied heavily on the presence of TNFα (Fig 2B). Furthermore, within this inflammatory context, PGC-1α4 had both over-lapping and distinct activity from PGC-1α1. Of the 2051 genes shared by the variants in TNFα-treated cells, the majority (91.5%) were regulated in the same manner (positively or negatively, Supplementary File 2).
To gain a global impression of biological process regulated by the PGC-1α variants in hepatocytes, we performed gene set enrichment analysis (GSEA). Gene sets relating to mitochondrial respiration and substrate metabolism were statistically enriched by both PGC-1α1 and PGC-1α4. PGC-1α1 predominantly regulated mitochondrial respiration, and glucose, amino acid and fatty acid metabolism, regardless of TNFα treatment (Supplementary File 3). This is consistent with known roles of PGC-1α1 on mitochondrial metabolism and supported by qPCR (Supplementary Fig. 3). Although we saw no effect of PGC-1α4 on these PGC-1α1 target genes, PGC-1α1 and PGC-1α4 shared many overlapping gene sets (Supplementary File 3). GSEA for PGC-1α4 in untreated hepatocytes centered on lipid metabolism (fatty acids and triglycerides), sterol metabolism and mitochondrial respiration, but individual gene effects were mild and most did not reach the 2-fold cut-off. However, when TNFα was present, PGC-1α4-enriched pathways included regulation of transcription factor transport to the nucleus, innate immunity, responses to interferon/PAMP, TLR signaling, acute inflammation, and apoptosis. Overall, TNFα signaling revealed isoform-specific responses and highlighted processes related to the innate immune response and cell death unique to PGC-1α4.
To explore differential effects of the isoforms on inflammation, we performed gene ontology (GO) analysis on gene changes occurring only in the presence of TNFα. Top 10 GO pathways unique to each variant, or shared, are shown in Fig 2C. All of the top PGC-1α1-regulated processes focused on energy metabolism and were shared with PGC-1α4. However, 6 of the top pathways for PGC-1α4 were unique to this variant, including 6-carbon metabolism, proteolysis, immune signaling in response to pathogens, and regulation of cell death (Fig 2C). Interestingly, GO terms associated with the 175 shared genes regulated in an opposite manner by the variants (Supplementary File 4) centered mainly on cell death and apoptosis (Fig 2D). These data suggest that apoptosis is an important effector pathway differentially regulated by these two PGC-1α protein variants.
TNFα signaling influences localization of PGC-1α4 within liver cells
TNFα treatment substantially increased the number of PGC-1α4 gene targets, revealing that external signals such as inflammation might be necessary for PGC-1α4 activity. We first tested whether TNFα regulates transcription from the Ppargc1a promoters using primary mouse hepatocytes. Unlike glucagon treatment, which acutely induces both promoters, TNFα treatment did not change transcript levels from proximal or alternative promoters over a 24-hour period (Fig 3A-B), prompting us to investigate other mechanisms. Over-expressed PGC-1α4 localized primarily to the cytoplasm of liver cells; therefore, nuclear exclusion might explain why increased PGC-1α4 has little effect on basal gene expression in untreated hepatocytes (Fig 2A). Following addition of TNFα to media, a significant proportion of PGC-1α4 was observed in the perinuclear and nuclear compartments (Fig 3C). Cell fractionation confirmed that PGC-1α4 protein was only detected in the nuclear pellet following TNFα treatment (Fig 3C). In contrast, PGC-1α1 localized exclusively to the nucleus of liver cells regardless of treatment condition (Fig 3D).
Increased PGC-1α4 prevents hepatocyte apoptosis in response to inflammatory signaling
Data so far suggested that different PGC-1α isoforms influence inflammatory and anti-apoptotic signals in liver cells. Using gain- and loss-of-function models, we investigated whether PGC-1α1 or PGC-1α4 impacted cell death downstream of inflammatory signals in vitro and in vivo. Primary mouse hepatocytes over-expressing PGC-1α1 had increased cleaved caspase 3 (Fig 4A) and nucleosome fragmentation (Fig 4B) in response to TNFα treatment compared to vector, while over-expression of PGC-1α4 almost completely blocked apoptosis. To test this in vivo and avoid potentially confounding effects of inflammatory and immune responses caused by viral vectors, we created a transgenic mouse model permitting tissue-specific over-expression of PGC-1α4 (Fig 4C). Recombination at LoxP sites using Albumin promoter-driven Cre-recombinase drove PGC-1α4 expression only in hepatocytes (PGC-1α4HepTg, Fig 4D,E). A small increase in PGC-1α4 transcripts in the absence of Cre-recombinase (LSLPGC-1α4, Fig 4C) indicated a low level of leaky transgene expression, but an increase of ∼50-fold expression was observed in livers of PGC-1α4HepTg mice. Supporting an anti-apoptotic role for hepatic PGC-1α4, there were reduced levels of cleaved caspase 3 in livers of both male and female PGC-1α4HepTg mice following injection of LPS (Fig 4F).
Consistent with gain-of-function studies, mice lacking PGC-1α in liver had increased cleaved caspase 3 levels when exposed to LPS (Fig 5A). However, this knockout model ablates all Ppargc1a transcripts, making it impossible to discern roles for any specific isoform. Thus, we created a mouse model where only the alternative promoter of Ppargc1a was disrupted in a tissue-specific manner (AltPromFL/FL), blunting expression of transcripts containing exon 1b and 1b’ (including PGC-1α4), but not PGC-1α1 (Fig 5B). To assess efficiency of the promoter knockout, primary hepatocytes from control and KO mice were treated with glucagon, which significantly induced expression of multiple PGC-1α transcripts (Fig 5C) and proteins (Fig 5D) from both the proximal and alternative promoter in control AltPromFL/FL cells. In contrast, ablation of the alternative promoter by crossing floxed mice with Alb-CreTg mice (AlbPromKO) blunted induction of alternative transcripts in response to glucagon, yet increases in proximal transcripts were similar to (or even higher than) control cells (Fig 5C). The 37kD PGC-1α protein induced by glucagon was almost completely ablated by knockout of the alternative promoter, identifying PGC-1α4 as the predominant truncated PGC-1α variant responsive to glucagon in liver cells (Fig 5D). Consistent with PGC-1α4 being involved in prevention of apoptosis, hepatocytes from AlbPromKO mice had higher basal and TNFα-induced cleaved caspase 3 levels (Fig 5E) and increased fragmented nucleosomes in response to inflammatory signaling (Fig 5F) compared to cells from littermate controls. Taken together, PGC-1α4 appears to have the unique ability to prevent inflammatory-mediated apoptosis in liver cells.
PGC-1α isoforms differentially regulate pathways involved in inflammation and cell survival
In an attempt to identify transcription factors with links to apoptosis and cell death downstream of PGC-1α variants, we probed array data for transcription factor motifs enriched in genes changed by PGC-1α1 or PGC-1α4 alone, oppositely regulated by the two variants, or shared when TNFα was present using iRegulon (Supplementary Table S4) and DiRE (Supplementary Fig. 4). Many motifs not previously associated with PGC-1α were identified including: ETV6 (only PGC-1α1); SP4, the NFY complex, IRF6, GM7148, TGIF2, HSF4, and E2F1DP1 (only PGC-1α4); and IRF4, LK4, NR1H2 (LXRβ), ZBTB33 (KAISO), ZFP143, and PITX2 (shared). Among the 175 genes oppositely regulated by the variants, binding sites for STAT, SPIB, NFATC2, and KLF4 were identified. ST18 (also known as MYT3 or NZF3), was found using the gene list implicated in cell survival (Fig 2D). Narrowing analysis to transcription factors implicated in cell death and inflammation, we evaluated whether over-expression of the PGC-1α variants modulated expression of their target genes. PGC-1α4 had no significant effects on mRNA expression, while PGC-1α1 repressed SP4, NF-Y, and STAT targets (Supplementary Fig. 5A,B,C), and increased IRF4 targets Tnfrsf17 and Nip3 (Supplementary Fig. 5D).
Our data illustrated that PGC-1α1 and PGC-1α4 had a variety of effects on expression of multiple mediators of inflammation and apoptosis. However, these could not explain opposing effects on cell death observed for the variants in our in vitro models. Searching the microarray for candidate anti-apoptotic genes downstream of PGC-1α4, we identified Birc2 (Ciap1) and Tnfaip3 (also known as A20) (Fig. 2A), two anti-apoptotic proteins that prevent cell death downstream of inflammatory signalling. In a separate experiment, we confirmed that their transcript levels were significantly higher in mouse primary hepatocytes over-expressing PGC-1α4 only in the presence of TNFα (Fig 6A). Related Birc3 (Ciap2) was also increased by TNFα/PGC-1α4, while Birc5 expression did not change. In addition, transcripts for apoptosis inhibitors Naip and Xiap were significantly increased by PGC-1α4, regardless of TNFα treatment. In contrast, over-expression of PGC-1α1 decreased expression of Birc3, Birc5, and Tnfαip3 (Fig 6A) and had no effect on Naip and Xiap.
Since these genes are all regulated by NF-κB, we hypothesized that PGC-1α4 might enhance NF-κB activity, contrasting with reported repressive effects of PGC-1α1 on this pro-inflammatory transcription factor. Basal expression of a 3x NF-κB response element reporter was increased when PGC-1α1 was co-expressed in primary hepatocytes (Fig 6B); yet consistent with previous findings 18, 24, induction of the reporter by TNFα was significantly blunted by high PGC-1α1. PGC-1α4 had no effect on basal or TNFα-induced NF-κB reporter activity. Protein levels of p50 were decreased by both PGC-1α1 and PGC-1α4 in the presence of TNFα, and p65 remained unchanged in all conditions. Over-expression of PGC-1α1 modestly decreased IKKβ and IκBα proteins, which could relieve inhibition on NF-κB and possibly explain increased basal activity. PGC-1α4 over-expression had no effect on these proteins (Fig 6C). However, consistent with previous reports, increased PGC-1α1 significantly decreased basal and/or TNFα-induced levels of pro-inflammatory genes Mcp-1, Tnfα, Iκbα and Ccl5 in primary hepatocytes (Fig 6D), demonstrating a strong inhibitory role on NF-κB target genes. In contrast, PGC-1α4 had little impact on these genes, except to potentiate the Tnfα response similar to the pattern seen on the anti-apoptotic targets (Fig 6A).
In summary, PGC-1α1 had generally repressive effects on transcription of genes involved in inflammation and cell death that were mostly independent of TNFα. In contrast, PGC-1α4 differentially enhanced a select program of anti-apoptotic factors in hepatocytes only in the presence of inflammatory signaling.
DISCUSSION
In the current study, we found that various non-canonical PGC-1α protein variants are expressed in human and mouse inflamed liver and differentially regulate hepatic inflammatory signaling. Gene set enrichment analysis revealed that in the presence of the inflammatory cytokine TNFα, PGC-1α4 influences innate immunity and cell death, while PGC-1α1 remains primarily associated with mitochondrial function and metabolic processes. Gene ontology (GO) analysis illustrated that genes implicated in cell death and apoptosis appear to be oppositely regulated by these two variants. In primary liver cells, PGC-1α4 significantly blunted apoptosis in response to TNFα, a function that may be controlled by shuttling of PGC-1α4 between cytoplasm and nucleus. We conclude that alternative forms of PGC-1α are induced in inflammatory environments, giving rise to increased levels of the truncated PGC-1α4 isoform that attenuates apoptosis downstream of inflammatory signaling. These findings give mechanistic insight into how PGC-1α, as a family of proteins, facilitate parallel adaptation to metabolic demand and mitigation of inflammatory damage in cells.
Immune responses to danger signals are metabolically challenging and can lead to a trade-off between maintaining highly energy demanding processes of nutrient metabolism versus adaptation to inflammatory stimuli 2. Inflammation itself may also inhibit metabolism and impede mitochondrial function. Here, we show that signaling via TNFα or LPS leads to a shift in the PGC-1α gene program downstream of PGC-1α1 and PGC-1α4, ensuring that concurrent inflammatory signaling does not impede the ability to respond to metabolic need. This mechanism represents an additional layer by which the family of PGC-1α proteins help balance an integrated metabolic response modulated by the inflammatory status of the liver.
It is now well established that PGC-1α is a family of proteins created by alternative splicing of the PPARGC1A gene in many tissues including skeletal muscle 34, 35, 37, brown adipose tissue 36, 39, and liver 32. However, a functional role for many of the alternative isoforms remains unknown. While some PGC-1α variants share overlapping functions with canonical PGC-1α1 29, 30, 33, 38, 40, PGC-1α4 has distinct effector pathways in muscle and brown adipose tissue 34, 41. We show here that PGC-1α1 and PGC-1α4 also have differential effects on cell death downstream of inflammatory signals. PGC-1α4 almost completely blocks apoptosis in vitro and in vivo, while PGC-1α1 decreases expression of a broad program of inflammatory genes, but does not inhibit cell death in response to TNFα. PGC-1α1 can induce apoptosis through PPARγ, TFAM, generation of reactive oxygen species, or Ca2+ signaling 42-46 or attenuate cell death through a p38/GSK3B/Nrf-2 axis or activation of p53 47, 48. Several splice variants coming from differentially regulated promoters adds a layer of complexity, but also may explain existing and often conflicting previous reports.
An obvious candidate effector in inflammation-mediated apoptosis is NF-κB. Consistent with previous studies 49, 50, we show that PGC-1α1 represses NF-κB activity. However, unlike PGC-1α1, our evidence suggests no impact of PGC-1α4 on this transcription factor. Although PGC-1α4 shares the complete activation domain of PGC-1α1, its alternative exon 1 and significant C-terminal truncation may lead PGC-1α4 to regulate a different set of DNA-binding proteins. Our microarray identifies multiple pathways differentially regulated by the two variants, including those targeted by NF-κB, SP4, NF-Y, STAT and IRF4. However, in our model system, PGC-1α4 did not appear to act as a traditional transcriptional coregulator for many of their gene targets. One possible explanation could be that PGC-1α4 instead promotes novel splicing events to create alternative gene products, similar to the function of related PGC-1α2 and PGC-1α3 variants 51. Aberrant alternative splicing can substantially affect cellular function and is associated with disease. For example, alternative splicing of TNFα-regulated genes (such as Tnfaip3) produces protein variants with distinct roles in cell death and cell survival 52.
While the exclusive nuclear localization of PGC-1α1 supports its function as a transcriptional coactivator, the ability of PGC-1α4 to shuttle between compartments suggests that it might interact with transcription factors in the cytoplasm and/or regulate their entry into the nucleus, a possibility also supported by our GSEA analysis. Interferon (INF) regulatory factors (IRFs) are well-known transcription factors that shuttle in response to inflammatory stimuli 53 and our data suggest that both PGC-1α1 and PGC-1α4 converge on interferon signaling in liver cells. Canonical PGC-1α has been associated with interferon response in the contexts of HCV infection and thermogenesis 54, 55. Interestingly, three interferon regulatory factors (IRF1, IRF4, IRF6) were identified in our motif enrichment analysis and numerous studies implicate interferons as critical regulators of apoptosis 56, 57. Although we focused on TNF signaling, our data suggest that PGC-1α1 and PGC-1α4 might also regulate the interferon response; however, further studies are necessary to confirm this hypothesis.
PGC-1α4 shares many similarities to another isoform, NT-PGC-1α, which is transcribed from the proximal promoter. Both have two N-terminal nuclear exclusion signals and three putative phosphorylation (S190, S237, and T252) sites, which regulate nuclear shuttling of NT-PGC-1α 39. Our data are consistent with reports describing cytoplasmic to nuclear movement of other truncated variants of PGC-1α 36, 39. Given similarities between these two proteins, it is possible that NT-PGC-1α localization is also regulated by inflammation similar to PGC-1α4, and while likely, it remains to be seen whether PGC-1α4 and NT-PGC-1α have overlapping functions.
We note that only transcripts from the alternative promoter were increased in human NASH and cirrhotic livers. This would suggest that inflammatory signals shift preference from the proximal to the alternative PGC-1α promoter and imply that PGC-1α4 (from the alternative promoter) could be the predominant truncated isoform influencing apoptosis in inflamed human liver. This shift in PPARGC1A promoter usage is consistent with previous studies showing a shift to the proximal promoter upon cold exposure in brown adipose tissue and to the alternative promoter upon exercise in skeletal muscle 25-31, 58. Our data also imply boosting expression of multiple PGC-1α isoforms could allow liver cells to more efficiently respond to energy demand when faced with both high metabolic and inflammatory challenges associated with metabolic disease.
Mechanisms underlying the anti-apoptotic role of hepatic PGC-1α4 appear complex, possibly involving interaction with cytoplasmic proteins, dominant-negative effects on other PGC-1α variants, or regulation of alternative splicing of genes implicated in apoptosis. In conclusion, coordinated activity of these PGC-1α isoforms allows fine-tuning of metabolic and inflammatory networks, supporting efficient adaptation to energy demand within the highly dynamic and inflammatory environment of the liver. Offsetting this balance could result in inefficient nutrient metabolism and/or inappropriate responses to inflammatory stimuli, which may play a role in the pathogenesis of NAFLD or NASH.
Declaration of interests
The authors have none to declare.
Author contributions
ML, ABP, NJ, SJ, JLR and JLE designed concept and experiments. ML, ABP, NJ, SJ, NPK, SS, CB, AD, JC, JB and PJ performed and analyzed experiments. SKP, AL, and PM created the human liver biobank, characterized samples, and contributed to analysis design. ML, ABP, NJ, SJ, JLR and JLE wrote the manuscript. All authors reviewed the manuscript.
Grant Support
Research was supported by grants from the CIHR (PJT-148771) and IDRC (108591-001) to JLE, and the Swedish Research Council and Karolinska Institutet to JLR. ML received a doctoral scholarship and JLE a Chercheur-boursier from the FRQS. SJ and NJ are supported by post-doctoral fellowships from Diabetes Canada and the Montreal Diabetes Research Centre, respectively.
Common Abbreviations
- PGC-1α
- Peroxisome proliferator activated receptor gamma coactivator-1 alpha
- NF-κB
- nuclear factor kappa-light-chain-enhancer of activated B cells
- TNFα
- tumor necrosis factor alpha
- NAFLD
- non-alcoholic fatty liver disease
- NASH
- non-alcoholic steatohepatitis
Transcript Profiling
GEO accession number GSE132458
Supplementary Methods
Gene set enrichment analysis was performed using the javaGSEA software [PMID: 16199517] (version 3.0 – build: 01600) on chip data using the Gene Ontology processes [PMID: 30395331] gene set (number of permutations = 1000, Permutation type = gene_set, Chip platform = Affy_430_2.0_mgi (version 2011) from the Mouse Genome Database [PMID: 29092072]. The Ppargc1a probe (1434099_at) was removed prior to analysis to eliminate over-expression bias. GO processes displaying a FDR < 0.1 (more stringent than the usual FDR < 0.25 threshold [PMID: 16199517]) has been considered to be statistically significant.
Clustering based on PGC-1α4-regulated genes was performed using dChip software. Over-representation analysis (ORA) of Gene Ontology processes was performed with regulated genes displaying an adj. p-value < 0.01 and 2 fold changes (Log10 fold changes ≥ 0.3 or ≤ −0.3), by using ClusterProfiler [PMID: 22455463] and the mouse genome-wide annotation in R (www.r-project.org). The top 10 statistically over-represented GO processes (adj. p-value < 0.05, correction method = Bonferroni) were determined for each condition and represented as a dot plot. For 175 genes regulated oppositely by the variants, ORA of GO biological processes was performed using g:Profiler [PMID: 27098042] using the following parameters: adj. p-value < 0.05, correction method = g:SCS threshold.
ACKNOWLEDGEMENTS
We thank Dr Bruce Spiegelman for generating the AlbPromFL/FL mouse line and members of the IRCM animal, microscopy, and molecular biology core facilities for invaluable technical assistance.
Footnotes
New data added New author added Manuscript reformatted for journal