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. 2014 Aug 22;28(10):1707–1718. doi: 10.1210/me.2014-1083

Hepatic SRC-1 Activity Orchestrates Transcriptional Circuitries of Amino Acid Pathways with Potential Relevance for Human Metabolic Pathogenesis

Mounia Tannour-Louet 1,*, Brian York 1,*, Ke Tang 1, Erin Stashi 1, Hichem Bouguerra 1, Suoling Zhou 1, Hui Yu 1, Lee-Jun C Wong 1, Robert D Stevens 1, Jianming Xu 1, Christopher B Newgard 1, Bert W O'Malley 1,#, Jean-Francois Louet 1,#,✉
PMCID: PMC4179626  PMID: 25148457

Abstract

Disturbances in amino acid metabolism are increasingly recognized as being associated with, and serving as prognostic markers for chronic human diseases, such as cancer or type 2 diabetes. In the current study, a quantitative metabolomics profiling strategy revealed global impairment in amino acid metabolism in mice deleted for the transcriptional coactivator steroid receptor coactivator (SRC)-1. Aberrations were hepatic in origin, because selective reexpression of SRC-1 in the liver of SRC-1 null mice largely restored amino acids concentrations to normal levels. Cistromic analysis of SRC-1 binding sites in hepatic tissues confirmed a prominent influence of this coregulator on transcriptional programs regulating amino acid metabolism. More specifically, SRC-1 markedly impacted tyrosine levels and was found to regulate the transcriptional activity of the tyrosine aminotransferase (TAT) gene, which encodes the rate-limiting enzyme of tyrosine catabolism. Consequently, SRC-1 null mice displayed low TAT expression and presented with hypertyrosinemia and corneal alterations, 2 clinical features observed in the human syndrome of TAT deficiency. A heterozygous missense variant of SRC-1 (p.P1272S) that is known to alter its coactivation potential, was found in patients harboring idiopathic tyrosinemia-like disorders and may therefore represent one risk factor for their clinical symptoms. Hence, we reinforce the concept that SRC-1 is a central factor in the fine orchestration of multiple pathways of intermediary metabolism, suggesting it as a potential therapeutic target that may be exploitable in human metabolic diseases and cancer.

Recent observations have highlighted the importance of amino acid metabolism in human diseases, such as cancer, obesity, and type 2 diabetes (1–3). Indeed, the glutamine pathway is now appreciated as a key element of the metabolic reprogramming that occurs in cancer cells (4, 5). The “glutamine addiction” of tumors impacts their metabolism to maintain a high activation of mammalian target of rapamycin kinase and supports nicotinamide adenine dinucleotide hydrogen production needed for macromolecular synthesis and redox control (5, 6). Reprogramming this aberrant amino acid metabolism highlights new avenues for combating cancer progression (7, 8). Additionally, metabolomics profiling of obese vs lean humans revealed a distinctive metabolic “signature” for branched-chain amino acids that correlates with obesity-associated insulin resistance and precedes development of type 2 diabetes (2, 9, 10). Also, caloric restriction, a regimen proven to positively impact pathologies, such as cancer, the metabolic syndrome, and life-span, appears to correlate with amino acid balance (11).

During the last 2 decades, significant strides have been made in identifying the transcriptional actors that regulate metabolic pathways. peroxisome proliferator-activated receptor gamma coactivator-1 (PGC-1)α is certainly one of the most studied transcriptional coregulators in the field of energy homeostasis because of its key responses to a variety of environmental cues, from nutritional status to thermogenic control, and its coordinated regulatory actions of major metabolic pathways, such as maintenance of lipid, glucose, and energy homeostasis (12). It appears, however, that additional coregulators act along with this coactivator to influence energy balance (13).

The steroid receptor coactivators (SRCs), including SRC-1, SRC-2, and SRC-3, were among the first transcriptional coactivators identified that interact with nuclear receptors and enhance their transactivation in a ligand-dependent manner (14). All 3 members of SRC family are overexpressed in various types of human cancer, including steroid hormone-promoted breast and prostate cancers (for review, see Ref. 15). In addition to their involvement in cell growth and proliferation, recent work has clearly defined the role of this family of coregulators in metabolic processes ranging from whole-body glucose homeostasis to tissue-specific energy regulation and adipogenesis (16–20). However, the selective impact of this family of coactivators on intermediary metabolism is still not fully understood. Here, we combined the use of 2 powerful unbiased profiling technologies, metabolomics and chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq), to unveil an unexpected role for SRC-1 in hepatic amino acid metabolism and homeostasis.

Materials and Methods

Mice

SRC-1 mice were maintained on a pure C57BL/6J background. SRC-2 knockout (KO) mice were maintained on a mixed background (C57BL/6J and SV129). We used 8- to 16-week-old male mice and sex-matched wild type (WT) mice for all in vivo studies. For the liver-selective SRC-1 reexpression, 16-week-old male mice were exposed to adenoviral-mediated transgenesis (1011 virus particles per mouse), via tail-vein injections as previously described (16, 21). Mice were killed 4 days after viral injection, and metabolite profiling experiments were performed. The Baylor College of Medicine Institutional Animal Care and Utilization Committee approved all experiments.

Metabolite profiling studies

Amino acids and acylcarnitines were analyzed in plasma, liver, brain, and skeletal muscles of fed mice. Methods of tissue handling and extraction have been described previously (22, 23). Amino acids and acylcarnitines were analyzed by tandem mass spectrometry (MS/MS) as described (9, 22–24). All MS analyses employed stable-isotope-dilution with internal standards from Isotec, Cambridge Isotope Laboratories, and CDN Isotopes. A list of all internal standards used in these studies has been published previously (9, 22).

RNA analysis

We used standard RNA extraction procedures (RNeasy Mini kit from QIAGEN). Reverse transcription was carried out using the Superscript III kit (Invitrogen). For gene expression analysis, quantitative PCR (qPCR) was performed using sequence-specific primers and probes from Roche (Universal Probe Library). Glyceraldehyde 3-phosphate dehydrogenase was used as an internal control for all gene-expression assays. All qPCR primer sequences are available upon request.

ChIP-Seq data analysis

ChIP-Seq was performed by Active Motif, Inc on liver lysates from fed C57BL/6J WT mice using SRC-1 antibody. Approximately 42 million reads identified by Illumina's Genome Analyzer 2 were mapped to the mouse genome (NCBI Build 37.1/mm9) using the ELAND software. Of those reads, 33,344,354 unique alignments were binned into 3,499,277 fragments for peak calling using MACS 1.3.7.1. A stringent MACS cut-off of P value = 10−7 produced 7778 intervals over input control. MACS distance between the modes was 144. The average SRC-1 ChIP-Seq-interval length is 840 bp.

Galaxy/Cistrome's SeqPos motif tool (v1.0.0) (http://cistrome.dfci.harvard.edu/ap/) was used on the entire SRC-1 ChIP-Seq interval set (7778 intervals) for de novo (MDscan algorithm) and reference-based (Transfac, JASPAR, pbm and hPDI databases) sequence motif analyses, and their implementation of the cis-regulatory element annotation system (CEAS) tools for enrichment analyses on chromosomes and genomic annotations. The Genomic Regions Enrichment of Annotations Tool (GREAT) Bioinformatics Resource v2.0.2 (http://bejerano.stanford.edu/great/public/html/) was used on protein coding genes called using their basal plus extension (settings: proximal, 7.5 kb; upstream, 2.5 kb; downstream plus distal, up to 1 kb) from the SRC-1 ChIP-Seq intervals to identify enriched biological processes (25). All analyses were related to the mouse reference gene annotations from NCBI Build 37.1/mm9.

Cell culture, adenoviral transduction, and transient transfection experiments

Primary mouse hepatocytes were isolated from 8- to 12-week-old SRC-1 WT mice as described previously (16). Cells were incubated overnight in Williams E media (Invitrogen) containing 10% fetal bovine serum and dexamethasone 10−8M before each experiment for attachment. For adenoviral transduction experiments, SRC-1 and PGC-1α adenoviruses were used as described (16). For knockdown experiments, small interfering RNA against SRC-1 or/and hepatic nuclear factor 4α (HNF4α) were purchased from Dharmacon. RNA extractions were carried out 48 hours after treatment. For transfection experiments, HeLa cells were transfected with promoter gene plasmids containing a direct repeat 1 (DR1)-responsive elements driven by a luciferase gene reporter. These constructs were cotransfected as indicated in each experiment by different expression vectors encoding SRC-1 and HNF4α. Reporter-gene levels were determined 48 hours after transfection using the manufacturer's instructions (Promega).

ChIP and Western blotting

In vivo ChIP was performed by using liver tissue from fed mice. Formaldehyde (1%) was added to produce cross-linking during 10 minutes at room temperature. The ChIP procedure was performed using the EZ-ChIP kit (Millipore) following the manufacturer's protocol. SRC-1 antibody was from Millipore. qPCR for ChIP was performed using the SYBR-Green technology (Applied Biosystems) using sequence specific primers. Results were normalized to input in each case. Primer sequences are available upon request.

SRC-1 gene sequence analysis

Genomic DNA obtained from 16 patients presenting with clinical features suggestive of genetic tyrosine deficiency were subjected to sequence analysis of the SRC-1 gene. Coding exons and 50 base pairs of the flanking intronic sequences of the SRC-1 gene were PCR amplified using sequence-specific oligonucleotide primers linked to the M13 universal primers at the 5′-ends, followed by sequencing as previously described (20). Nucleotide 1 corresponds to the A of the initiation codon AUG. All data are presented in accordance with Baylor College of Medicine procedures on protection of patient identity.

Statistical analysis

One-way ANOVA was used to determine the significance between samples. Significance threshold was set at P = 5.0 × 10−2. Age-grouped mice were compared across genotypes using ANOVA, and differences within groups were determined using the Bonferroni post hoc tests at a threshold of P < .05.

Results

SRC-1 KO mice have profound defects in amino acid metabolism

Recent improvements in MS instrumentation and methods for detecting metabolites of intermediary metabolism rendered these tools among the most sensitive for uncovering disease phenotypes (26, 27). We therefore employed this “metabolomics” technology to conduct an unbiased screen of SRC-1 and SRC-2 function in mice. Our analysis reveals that under fed conditions, SRC-1 KO mice presented with significant defects in the metabolism of 7 amino acids (of 15 tested), with the plasma levels of all of these amino acids being increased in SRC-1 null animals compared with wild-type littermates (Figure 1, A and B). The fact that the genetic ablation of SRC-1 affects the total levels of all the amino acids tested (Figure 1C) strongly suggests a key role of this coactivator for controlling global amino acid metabolism. However, not all classes of metabolites were affected by the absence of SRC-1 in vivo, because the levels of acylcarnitines, which reflect β-oxidation, were not altered in SRC-1 null animals (Figure 1D and Supplemental Figure 1). In contrast, mice ablated for SRC-2, which had large alterations in lipid metabolism (19, 21, 28), failed to show any perturbations in amino acid levels (Supplemental Figure 2, A and B). Taken together, these observations highlight a direct impact of the SRC-1 coactivator on amino acid homeostasis.

Figure 1.

Figure 1.

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Amino acid metabolism is markedly defective in SRC-1 KO mice. A and B, Increase in amino acid levels in the plasma of SRC-1 KO mice compared with WT littermates as determined by metabolomics. Amino acid levels were determined in the plasma of SRC-1 KO and WT mice during ad libitum feeding (n = 5 mice per group). C, Plasma of SRC-1 KO mice shows an increase in the total levels of all amino acids (All AA) tested by metabolomics. Amino acid levels were determined in the plasma of SRC-1 KO and WT mice during ad libitum feeding (n = 5 mice per group). D, No differences in the levels of medium and long chain acylcarnitine metabolites were observed by metabolomics between SRC-1 KO and WT mice. Acylcarnitine levels were determined in the plasma of SRC-1 KO and WT mice during ad libitum feeding (n = 5 mice per group). Data are represented as mean ± SEM. *, P < .05; **, P < .01; ***, P < .005. Ala, alanine; Arg, arginine; Asx, asparagine/aspartic acid; Cit, citrulline; Glx, glutamine/glutamic acid; Gly, glycine; His, histidine; Leu/Ile, leucine/isoleucine; Met, methionine; Orn, ornithine; Phe, phenylalanine; Pro, proline; Ser, serine; Tyr, tyrosine; Val: valine.

We then analyzed the potential perturbations of amino acid homeostasis with respect to 4 key metabolic tissues: liver, skeletal muscle, brain, and white adipose tissue (WAT). We examined only amino acids that showed significant differences in the plasma levels of SRC-1 KO vs WT mice. In the liver, the concentration of methionine, phenylalanine, tyrosine, aspartic acid, and glutamic acid was significantly increased in SRC-1 KO compared with WT mice (Figure 2A). Tyrosine levels were also elevated in skeletal muscle, along with ornithine and arginine (Figure 2B). Interestingly, no changes were observed for any of the amino acids tested in the brain and WAT (Figure 2, C and D). The examination of the levels of combined amino acids in each analyzed tissue of SRC-1 KO mice and their wild type littermates revealed significant differences only in the liver (Supplemental Figure 3), therefore supporting a key hepatic impact of SRC-1 function on amino acid metabolism.

Figure 2.

Figure 2.

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Tissue-selective impact of SRC-1 on amino acid metabolism. A, Increase in levels of methionine, phenylalanine, tyrosine, asparagin/aspartic acid, and glutamine/glutamic acid in the liver of fed SRC-1 KO mice compared with WT controls (n = 5 mice per group). Each homogenate contained 50 mg wet tissue/mL. B, Increase in tyrosine, ornithine, and arginine levels in skeletal muscles (quadriceps) of fed SRC-1 KO mice compared with WT controls (n = 5 mice per group). Each homogenate contained 50 mg wet tissue/mL. C and D, No differences in amino acid levels were found in brain and white adipose tissue (WAT). Amino acid levels were determined using SRC-1 KO and WT mice during ad libitum feeding (n = 5 mice per group). Each homogenate contained 50 mg wet tissue/mL. Data are represented as mean ± SEM. *, P < .05; **, P < .01; ***, P < .005. Ala, alanine; Arg, arginine; Asx, asparagine/aspartic acid; Cit, citrulline; Glx, glutamine/glutamic acid; Gly, glycine; His, histidine; Leu/Ile, leucine/isoleucine; Met, methionine; Orn, ornithine; Phe, phenylalanine; Pro, proline; Ser, serine; Tyr, tyrosine; Val, valine.

Selective reexpression of SRC-1 in the liver of SRC-1 null mice restored normal levels of amino acids

To substantiate the importance of SRC-1 in the observed phenotype, we studied the effect of an acute hepatic reexpression of SRC-1 using adenovirus delivery. Selective reintroduction of SRC-1 in the liver efficiently normalized the circulating levels of most amino acids, including methionine, phenylalanine, tyrosine, citrulline, arginine, and glutamic acid in SRC-1 KO mice when compared with serum profiles of littermates receiving green fluorescent protein control adenovirus (Figure 3A). Similar observations were made for liver where all levels of altered amino acids were returned to those of WT (Figure 3B). These findings strongly support a central hepatic role of SRC-1 in the regulation of amino acid homeostasis.

Figure 3.

Figure 3.

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Selective reexpression of SRC-1 in the liver of SRC-1 KO mice impacts amino acid metabolism. A and B, Correction of amino acid deficiency in SRC-1 KO mice after adenovirus-mediated reexpression of SRC-1. Plasma and liver amino acid levels were measured by mass spectrometry as described in Figure 1 (n = 4 per group). Each homogenate contained 50-mg wet tissue/mL. The WT and the KO groups were treated with a control green fluorescent protein adenovirus, and the KO group was treated with an adenovirus expressing SRC-1 (KO + SRC-1). Data are represented as mean ± SEM. *, P < .05; **, P < .01; ***, P < .005. Arg, arginine; Asx, asparagine/aspartic acid; Cit, citrulline; Glx, glutamine/glutamic acid; His, histidine; Met, methionine; Orn, ornithine; Phe, phenylalanine; Pro, proline; Tyr, tyrosine.

Cistromic analysis of hepatic SRC-1 identifies a genetic program for control of amino acid metabolism

To better understand the functions of SRC-1 in coordinating hepatic metabolism, we analyzed its genomic occupancy in the mouse liver, using ChIP-Seq. We found SRC-1 binding sites to be strongly enriched in gene-proximal regions (Figure 4A). In fact, a whole-genome comparison of SRC-1 cistromic occupancy revealed a significant enrichment of binding within 1000 bp of the transcriptional start site as well as the 5′ untranslated region, which supports the known action of this coactivator on gene transcription (Figure 4B). Gene annotation enrichment analyses of potential SRC-1 targets revealed the amino acid metabolism pathways among the top classified functions for annotated genes (Figure 4C). Further supporting these findings, DNA sequence analyses of SRC-1 binding sites identified the expected enrichments for nuclear receptor binding motifs but also enriched for master transcriptional coordinators of hepatic amino acid metabolism, including HNF4α, CCAAT-enhancer-bidning protiens alpha and beta, and PPARs (Figure 4D) (29). Finally, consistent with our MS data showing an effect of SRC-1 on the levels of tyrosine, glutamine, methionine, and arginine (Figures 1, A and B, and 3, A and B), binding sites of SRC-1 on the native chromatin context in the liver were found in the proximal promoters of major genes involved in the catabolism of tyrosine (tyrosine aminotransferase [TAT]; fumaryl acetoacetate hydrolase) and glutamine (glutaminases 1 and 2 [GLS1 and GLS2]), and also in the metabolism of methionine (methionine adenosyl transferase 1 and 2) and arginine (liver-specific arginase [ARG1]) (Figure 4E). However, no SRC-1 binding sites were identified in the first 25 kb of promoter regions of other amino acid-related genes, such as nonhepatic arginase type 2 (ARG2), glutamine synthase, also known as glutamate-ammonia ligase (GLUL), and the nitric oxide synthase genes (NOS1, NOS2, and NOS3) (Figure 4E). Collectively, these results substantiate our previous data and confirm the selective binding activity of SRC-1 on a specific cassette of genes required for amino acid metabolism. Hence, SRC-1 appears to major player required for fine-tuning the genetic program for maintenance of amino acid levels in the liver.

Figure 4.

Figure 4.

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Analysis of the hepatic SRC-1 cistrome. A, Histogram analysis of hepatic SRC-1 binding sites identified by ChIP-Seq. Whole cistrome SRC-1 ChIP-Seq dataset was used to analyze −10 to +10 kb of the transcriptional start site (TSS). Occurrences of binding sites within that region were counted and binned (200 bp/bin) to determine the number of peaks per bin. B, Cis-regulatory element annotation system (CEAS) genomic enrichment distributions of SRC-1 binding sites in the mouse hepatic genome. Data are log2 transformed relative to the genomic proportions of each group. C, Binomial fold enrichments for ranked functional annotation processes of hepatic genes identified from the SRC-1 ChIP-Seq. Associated P values for each process are highlighted within each bar. Biological processes related to amino acid metabolism are highlighted in blue. D, Transcription factor (TF) binding enrichment from the SRC-1 ChIP-Seq. The top ten most enriched motifs identified using the SeqPos motif tool from Galaxy Cistrome (http://cistrome.org/ap/) are shown. E, Snapshots of selected amino acid metabolism target genes demonstrating the binding site(s) for SRC-1 (green boxes) taken from the UCSC genome browser (http://genome.ucsc.edu/cgi-bin/hgGateway).

SRC-1 is central to the hepatic tyrosine catabolism by directly controlling the gene expression of the rate-limiting enzyme, TAT

We decided to focus on tyrosine metabolism to finely dissect the molecular events underlying the action of SRC-1, because the tyrosine pathway presented the most profound and consistent phenotypic defect in SRC-1 KO mice as compared with WT (Figure 1, A–C); 2) was the only one exhibiting altered levels in all the biological samples tested (ie, plasma, liver, and skeletal muscle) (Figure 1, A–C); and 3) had SRC-1 binding sites on the promoter of the gene encoding its rate-limiting enzyme, TAT (Figure 4E). The TAT gene presents an excellent model to mechanistically investigate the impact of SRC-1 as the architecture of its regulatory sequences in the liver is well characterized (30). Furthermore, tyrosine is a main precursor of important hormones influencing liver metabolism, such as catecholamines and thyroid hormones. Finally, inborn errors of tyrosine metabolism (tyrosinemia) arising from dysfunction of the TAT gene product have been found in humans with important health consequences (31).

We quantified the in vivo expression of TAT and found in SRC-1 KO mice compared with WT littermates, a clear decrease of TAT mRNA (Figure 5A) and protein (Figure 5B and Supplemental Figure 4) levels in the liver. To confirm the in vivo physical association of SRC-1 with the TAT promoter as previously identified by the hepatic SRC-1 ChIP-Seq, we assessed the occupancy of SRC-1 on 2 distinct enhancers known to be involved in hepatic regulation of the TAT gene (32, 33). Using a classical ChIP-qPCR assay on hepatic mouse tissues, we found SRC-1 to be present within the −3.6-kb enhancer of the TAT promoter but not in the sequence located up to −2.5 kb from the start site (Figure 5C). These data validate the ChIP-Seq findings and further suggest that SRC-1 directly regulates transcription of the TAT gene in vivo, via a specific upstream regulatory sequence that also has been previously reported to modulate the effects of glucagon (32–34). Interestingly, the −3.6-kb enhancer of the TAT promoter binds the liver-specific nuclear receptor HNF4α via a DR1 sequence (33), which was identified as the top candidate transcription factor from our motif analysis of the SRC-1 cistrome (Figure 4D). We then tested the functional action of SRC-1 on the DR1 motif using HNF4α. As shown in Figure 5D, SRC-1 strongly enhanced the transcriptional activity of HNF4α. These data reveal the key role of the SRC-1 coactivator in the coordination of transcriptional control of TAT gene and substantiate our conclusions derived from in vivo metabolomic and ChIP-Seq observations.

Figure 5.

Figure 5.

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SRC-1 coactivator directly controls the gene expression of the rate-limiting enzyme TAT. A, SRC-1 regulation of the TAT gene in the liver. Levels of mRNA for the indicated gene were measured by qPCR in the liver of SRC-1 WT and KO animals under fed conditions (n = 5 mice per group). B, The hepatic protein levels of TAT decrease in SRC-1 KO mice. Levels of protein for the indicated genes were measured by Western blotting in the liver of SRC-1 WT and KO animals under fed conditions. HSP90, heat shock protein 90. C, In mouse liver, SRC-1 binds to the TAT promoter. ChIP assays were performed with 100-bp amplicons flanking the region containing 2 different enhancers (−3.6 and −2.5 kb) of the TAT promoter. qPCR (normalized to input) was used to assess SRC-1 binding on unique regions of the TAT promoter after ChIP with an SRC-1-specific antibody. D, SRC-1 strongly coactivates HNF4α on a DR1-responsive element. Transient transfections were performed in HeLa cells with luciferase driven promoters. E and F, Overexpression of SRC-1 activates TAT mRNA levels in murine and human hepatoma cells. The SRC-1 mRNA levels were overexpressed by adenovirus infection (48 h) of Hepa1.6 cells (D) and HepG2 cells (E). Expression levels of SRC-1 and TAT genes were evaluated by qPCR. G, Specific knock-down of SRC-1 and HNF4α affects TAT mRNA levels. The gene expression levels of SRC-1, HNF4α, and TAT genes were evaluated by qPCR after treatment with SRC-1 or/and HNFα small interfering RNA in Hepa1.6 cells. H, Impact of overexpression of SRC-1 and/or PGC-1α on TAT gene expression in primary hepatocytes. Mouse primary hepatocytes were treated with SRC-1 and/or PGC-1α adenoviruses and harvested 48 hours later for measurements of TAT mRNA by qPCR. Data are represented as mean ± SEM. *, P < .05; **, P < .01; ***, P < .005.

To determine the cell autonomous effect of SRC-1 on TAT gene expression, we overexpressed SRC-1 using an adenovirus approach in mouse hepatoma cells (Hepa1.6). In this context, we observed by qPCR, a marked activation of TAT gene expression (Figure 5E). A similar result was found in the human hepatoma cell line (HepG2), suggesting that the impact of SRC-1 on TAT gene expression could be extended to a human cell context (Figure 5F).

To further dissect the molecular mode of action of SRC-1 and HNF4α in the control of TAT gene expression, genetic invalidation of these 2 transcriptional effectors, independently and in combination, was performed. We found a significant decrease of TAT mRNA levels when SRC-1 or HNF4α were knocked down distinctly, confirming the importance of these 2 protagonists in TAT gene regulation (Figure 5G). The fact that no additive effect in TAT expression levels was observed when SRC-1 and HNF4α were both simultaneously knocked down (Figure 5G) suggests a common molecular action of these 2 transcriptional actors that clearly substantiates our previous findings.

Together, our results present substantial evidence for a major role of the SRC-1 coactivator in the regulation of the hepatic TAT gene expression. Because SRC-1 has been shown to occasionally modulate gene expression in conjunction with another important hepatic coactivator, PGC-1α (35), we tested whether this mechanism was operative in the context of TAT gene regulation. Overexpression of SRC-1, PGC-1α, or both coactivators in primary hepatocytes revealed that PGC-1α, in contrast to SRC-1, is dispensable for TAT gene expression (Figure 5H). These findings confirm that SRC-1 impacts TAT gene expression in primary liver cells and establish a critical role for hepatic SRC-1 in the regulation of tyrosine metabolism.

Potential implication of SRC-1 in the clinical pathogenesis of hypertyrosinemia

Because one of the main clinical features associated with hypertyrosinemia in human TAT deficiency (tyrosinemia type 2) is altered bilateral cornea (31), we investigated this phenotypic trait in the SRC-1 KO mice and found a clear difference in the basal epithelium of the central cornea between SRC-1 KO and WT mice (Figure 6A). In fact, central epithelial cells of SRC-1 KO mice presented a “limbus-like” architecture, suggesting an alteration in the turnover of these cells (36). This assumption was confirmed by Ki67 immunohistochemistry, which showed a marked decrease of the signal in the central cornea of SRC-1 KO animals compared with WT (Figure 6B). Chronic hypertyrosinemia observed in SRC-1 KO mice may be the cause of such corneal alteration, because bilateral central cornea defects in humans with tyrosinemia type 2 have been shown to result from elevated levels of tyrosine (37).

Figure 6.

Figure 6.

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SRC-1 KO mice present with a bilateral central cornea defect. A, Hematoxylin and eosin staining of SRC-1 KO and WT subepithelial section of the cornea. B, Ki67 immunofluorescence staining showing a marked decrease of the signal in the central cornea of SRC-1 KO animals compared with WT. C, Schematic representation of the impact of SRC-1 on amino acid metabolism in vivo and its molecular regulation of TAT gene expression in the liver.

To further investigate potential ramifications of SRC-1 dysfunction in the etiology of human tyrosine metabolism disorders, we screened for point mutations of the coding sequence of SRC-1 in 16 unrelated patients presenting with hypertyrosinemia-like symptoms, with no known pathogenic mutations in the TAT gene or in the type 1 tyrosinemia-associated gene, fumaryl acetoacetate hydrolase (31). Of note, the small size of our cohort is strictly linked to the extremely low frequency of idiopathic cases of human tyrosine disorders (31). As described in Table 1, 2 patients (numbers 011 and 812) harbored a heterozygous missense variant, c.3814C>T in exon 18, resulting in a proline to serine change at amino acid position 1272 (p.P1272S). Reported as a low frequency single nucleotide polymorphism (rs1804645), the p.P1272S substitution is localized in the activation domain 2 of SRC-1 protein. Importantly, we previously have published that this mutation in SRC-1 disrupts an adjacent glycogen synthase kinase 3β phosphorylation site in SRC-1 and alters its coactivation potential (38). Because the allele frequency of the p.P1272S substitution in the general population is 1.1% (http://www.ncbi.nlm.nih.gov/projects/SNP/), the chances of identifying 2 cases in a random cohort of 16 patients is 1.2% (calculated by binomial probability), which is significantly different from the 12.5% that we found in our cohort of patients (modified Wald method, 95% confidence interval). Therefore, we conclude that genetic modifications in the coding sequence of SRC-1 may represent one risk factor explaining the clinical features observed in the analyzed patients with unknown etiological reasons for their hypertyrosinemia, although screening larger cohorts of this rare human metabolic disease is necessary in order to further support this observation.

Table 1.

Mutation Analysis of SRC-1 Gene in Patients Presenting with Idiopathic Hypertyrosinemia

Sample Exon/Intron Zygosity Results Codon rs Number and Frequency
204 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Intron 15 Homozygous c.3202-49G>A rs3820933G, A = 0.125
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
252 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
396 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Intron 7 Heterozygous c.532 + 38T>C rs139452950, C = 0.003
Intron 12 Heterozygous c.2600-19C>A rs142840721, A = 0.003
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
011 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Intron 15 Homozygous c.3202-49G>A rs3820933, A = 0.125
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
Exon 18 Heterozygous c.3814C>T p.P1272S rs1804645, T = 0.011
188 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 11 Heterozygous c.1512A>T p.P504P rs3731628, T = 0.107
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
812 Intron 16 Heterozygous c.3304-25A>G
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
Exon 18 Heterozygous c.3814C>T p.P1272S rs1804645, T = 0.011
985 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
769 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
062 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 18 Homozygous c.3801T>G pL1267L rs11125763, T = 0.136
141 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 6 Heterozygous c.477A>C p.I159I
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
142 Intron 3 Heterozygous c.89 + 31C>A rs17046446, A = 0.055
Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 11 Heterozygous c.1512A>T p.P504P rs3731628, T = 0.107
Intron 13 Heterozygous c.2717 + 27A>C rs41281517, C = 0.055
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
378 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Intron 15 Homozygous c.3202-49G>A rs3820933, A = 0.125
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
733 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 11 Heterozygous c.1635T>C p.N545N
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
161 Intron 3 Heterozygous c.89 + 31C>A rs17046446, A = 0.055
Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 11 Heterozygous c.1512A>T p.P504P rs3731628, T = 0.107
Intron 13 Heterozygous c.2717 + 27A>C rs41281517, C = 0.055
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
620 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Intron 15 Homozygous c.3202-49G>A rs3820933, A = 0.125
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
046 Exon 6 Homozygous c.462G>C p.T154T rs11125744, G = 0.162
Exon 18 Homozygous c.3801T>G p.L1267L rs11125763, T = 0.136
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Individual sequencing results from 16 patients presenting with symptoms of hypertyrosinemia but without known pathogenic mutations. Data are reported by exon/intron locations, zygosity, loci of mutation, and codon alterations. All data are presented in accordance with Baylor College of Medicine procedure on protection of patient identity. Bold face is the mutation of interest.

Discussion

Metabolic homeostasis is governed by a delicate interplay between different tissues and is maintained by a finely tuned regulatory circuitry controlled largely by mechanisms involving myriad transcriptional factors and coregulators. Several studies have established the importance of transcriptional coregulators as key genes coordinating complex physiological processes (12, 39, 40). Coactivators exert their control at virtually every step of transcription, including chromatin modification and remodeling, initiation of transcription, elongation, alternative RNA splicing, and termination of transcription (14, 18, 41). They also act as sensors for extracellular stimuli by receiving signal-induced posttranslational modifications, such as acetylation, phosphorylation, and ubiquitination (18, 42, 43).

The present study unmasked a previously undefined role for the coactivator SRC-1 in the liver (Figure 6C). By orchestrating complex interdependent metabolic axes ranging from the mobilization of amino acids during postabsorptive situations to the control of key gluconeogenic genes in fasting conditions (16), SRC-1 appears as a central molecular coordinator capable of fine-tuning metabolic cues and transcriptional signals in hepatic tissues. These integrative functions allow a precise and rapid adjustment of energy availability during the fed to fasting adaptation. Here, an in-depth analysis of the role of SRC-1 in the hepatic tyrosine metabolism revealed the rate-limiting enzyme TAT as a previously unknown target gene of SRC-1 coactivator (Figure 6C). Interestingly, the transcriptional regulation of the TAT gene is predominantly controlled in the liver, by the same key biological input signals (ie, insulin, glucagon, and glucocorticoids), and with comparable amplitudes as those observed in master genes involved in glucose homeostasis, such as phosphoenolpyruvate carboxykinase and glucose-6-phosphatase (29, 33, 44).

In addition to its metabolic involvement, the TAT enzyme has recently been shown to be a tumor suppressor in human liver cancers (45) and a key player involved in developmental programming and longevity (46). Thus, the discovery of a key coactivator for the TAT gene in vivo is an important element improving our knowledge on the fine transcriptional coordination of the complex molecular circuitry involved in the control of intermediary metabolism and other important processes.

Our clinical investigation strengthens the potential implication of SRC-1 in human tyrosine metabolism and provides substantial elements for the relevance of this transcriptional coregulator to genetic medicine. The nonsynonymous single nucleotide polymorphism (rs1804645), found enriched in idiopathic cases of hypertyrosinemia, results in a proline to serine substitution in exon 18 of the SRC-1 gene (p.P1272S). The tamoxifen agonist effects on the estrogen receptor transcriptional action were abolished when using the (p.P1272S) SRC-1 mutant in HepG2 cells, clearly providing experimental evidence for the negative impact of this variant on SRC-1 transcriptional activity in a hepatic context (38). The p.P1272S substitution maps to a functional domain (activation domain 2) critical for the activation function of SRC-1 (47) and affects its turnover, which is reminiscent with previous reports for the SRC-3 phospho-ubiquitin clock (48). The present results suggest SRC-1 as one potential candidate gene for idiopathic cases of human tyrosinemia. The fact that coactivator functions are affected through multiprotein complexes argues that combinations of even weakly penetrant alleles may coalesce into deleterious effects that mimic polygenic diseases (49).

Our findings prove an important effect of SRC-1 on amino acid metabolism, mainly in the liver. The net accumulation observed by MS, of several types of amino acids in the plasma and the liver results from imbalances of dynamic fluxes between anabolic and catabolic pathways of amino acid metabolism (50). Our ChIP-Seq data strongly suggest that SRC-1 would preferentially impact amino acid catabolic processes in the liver, because SRC-1 binds promoters of major enzymes involved in the catabolism of arginine (ARG1) and glutamine (GLS1 and GLS2) but not the promoter of the main enzyme involved in the production of glutamine (GLUL). Additionally, SRC-1 is induced during fasting, a metabolic situation favoring amino acid catabolism in the liver (16, 51, 52). Although beyond the scope of this study, a fine dissection of the molecular implication of SRC-1 in the extreme complexity of the interconnected pathways involving amino acid catabolic and anabolic processes is of great interest and definitely merits further investigation.

Because derangements in amino acid and glucose metabolism exist in patients with type 2 diabetes and during cancer cell metabolic reprogramming, our study, which defines the SRC-1 coregulator as a molecular transcriptional determinant at the crossroads of various intermediary metabolic pathways, opens avenues for a better understanding of how coactivators could be involved in the fine-tuned control of gene expression of key metabolic actors and the development of complex human diseases.

Acknowledgments

We thank Pere Puigserver and Nikolai and Ramiro Jover, respectively, for the PGC-1a and the SRC-1 adenovirus constructs. Adenoviral vectors and primary hepatocytes were produced by the Gene Vector Core and the Mouse Metabolism Core, respectively, in the Diabetes and Endocrinology Research Center at Baylor College of Medicine.

This work was supported by funding from both Société Francophone du Diabète and Ypsomed (to J.-F.L.), and by Nuclear Receptor Signaling Atlas Grant DK62434 (to J.-F.L., C.B.N., and B.W.O.). This study was also supported by the grant HD-8188 from the National Institutes of Health (to B.W.O.).

Disclosure Summary: The authors have nothing to disclose.

Footnotes

Abbreviations:
ARG
arginase
ChIP-Seq
chromatin immunoprecipitation followed by deep sequencing
DR1
direct repeat 1
GLS
glutaminase
HNF4α
hepatic nuclear factor 4α
KO
knockout
MS/MS
tandem mass spectrometry
NOS
nitric oxide synthase
PGC-1
peroxisome proliferator-activated receptor gamma coactivator-1
qPCR
quantitative PCR
SRC
steroid receptor coactivator
TAT
tyrosine aminotransferase
WAT
white adipose tissue
WT
wild type.

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