Histone macroH2A1.2 promotes metabolic health and leanness by inhibiting adipogenesis
- Valerio Pazienza1Email author,
- Concetta Panebianco1,
- Francesca Rappa2, 3, 4,
- Domenico Memoli5,
- Michela Borghesan1, 6,
- Sara Cannito1,
- Asami Oji7,
- Giuseppe Mazza6,
- Domenico Tamburrino8,
- Giuseppe Fusai8,
- Rosario Barone2, 4,
- Giulia Bolasco9,
- Francesc Villarroya10, 11,
- Joan Villarroya10, 11,
- Kiyotaka Hatsuzawa12,
- Francesco Cappello2, 4,
- Roberta Tarallo5,
- Tomoko Nakanishi12, 13 and
- Manlio Vinciguerra1, 4, 6, 14Email author
© The Author(s) 2016
Received: 22 March 2016
Accepted: 17 October 2016
Published: 25 October 2016
Obesity has tremendous impact on the health systems. Its epigenetic bases are unclear. MacroH2A1 is a variant of histone H2A, present in two alternatively exon-spliced isoforms macroH2A1.1 and macroH2A1.2, regulating cell plasticity and proliferation, during pluripotency and tumorigenesis. Their role in adipose tissue plasticity is unknown.
Here, we show evidence that macroH2A1.1 protein levels in the visceral adipose tissue of obese humans positively correlate with BMI, while macroH2A1.2 is nearly absent. We thus introduced a constitutive GFP-tagged transgene for macroH2A1.2 in mice, and we characterized their metabolic health upon being fed a standard chow diet or a high fat diet. Despite unchanged food intake, these mice exhibit lower adipose mass and improved glucose metabolism both under a chow and an obesogenic diet. In the latter regimen, transgenic mice display smaller pancreatic islets and significantly less inflammation. MacroH2A1.2 overexpression in the mouse adipose tissue induced dramatic changes in the transcript levels of key adipogenic genes; genomic analyses comparing pre-adipocytes to mature adipocytes uncovered only minor changes in macroH2A1.2 genomic distribution upon adipogenic differentiation and suggested differential cooperation with transcription factors. MacroH2A1.2 overexpression markedly inhibited adipogenesis, while overexpression of macroH2A1.1 had opposite effects.
MacroH2A1.2 is an unprecedented chromatin component powerfully promoting metabolic health by modulating anti-adipogenic transcriptional networks in the differentiating adipose tissue. Strategies aiming at enhancing macroH2A1.2 expression might counteract excessive adiposity in humans.
KeywordHistone variants macroh2a1.2 Adipose tissue Obesity
The current pandemic in obesity/metabolic syndrome (with 30–50% of the overall population affected in the Western world) is a risk factor for many types of diseases, including cardiovascular diseases and cancer. Epigenetic mechanisms of nuclear chromatin remodeling are increasingly recognized as crucial factors in the pathophysiology of obesity and related complications . In fact, metabolic alterations in peripheral tissues are triggered at the cellular level by changes in gene transcriptional patterns dependent on the degree of nuclear chromatin compaction. The latter is regulated at several levels, allowing transcriptional plasticity. For instance, epigenetic marks such as DNA methylation are intensely investigated for their causal and associative role in the determination of body mass index (BMI) [2–4]. A recently emerged alternative mechanism of transcriptional plasticity is the replacement of canonical histones, around which DNA is wrapped (H2A, H2B, H3 and H4), with the incorporation of histone variants, mostly of histones H2A or H3 [5–7]. The histone variant of H2A, known as macroH2A1, is believed to act as a strong transcriptional modulator that can either repress transcription or activate it in response to as yet undefined nutrients or growth signals [8–13]. The impact of macroH2A1 on transcriptional processes has now come to take a center stage in the plasticity of stem cell differentiation and in the pathogenesis of a growing number of cancer types [14–17]. MacroH2A1 is composed of a domain displaying 66% homology with histone H2A, and a domain called macro that is conserved in multiple functionally unrelated proteins throughout the animal kingdom and that can bind in vitro with tight affinity ADP-ribose-like metabolites, providing a direct molecular interaction between intermediate metabolism and the chromatin, whereby a metabolite can tweak gene expression in vitro . MacroH2A1 is in turn present in two alternatively exon-spliced isoforms, macroH2A1.1 and macroH2A1.2, which differ for a few amino acids . Whether these two isoforms play different roles in cell plasticity is debated and context dependent; however, most reports support a pro-differentiation role for macroH2A1.1 and an anti-differentiation and pro-proliferative role for macroH2A1.2 [14–17, 19, 20]. Mice models, knockout (KO) for the whole macroH2A1 gene, have been reported. In KO mice generated in the pure C57BL/6 J background, modest developmental changes in macroH2A1-mediated gene regulation under a standard diet, and a very mild systemic protection against obesity upon a high fat regimen, were observed [21, 22]. By contrast, in KO mice for macroH2A1 generated in a mixed background a variable hepatic lipid accumulation in 50% of the females has been described, without changes in body weight . Therefore, despite compelling in vitro evidence that macroH2A1 modulates gene expression programs involved in cell metabolism, proliferation and differentiation, the existing evidence for its role at the organism level upon nutritional stress, especially during fat accumulation obesity, is controversial. Moreover, data deriving from KO approaches might be often influenced by functional redundancy or compensatory effects between the isoforms. Under a standard diet, in SWR/J mice, featuring a higher metabolic health and a better triglyceride metabolism compared to common BALB/cByJ and C57BL/6J strains, a >threefold increase in hepatic basal mRNA levels of macroH2A1.2, among the top 15 upregulated genes, was found . Conversely, in genetic or dietary mice models of non-alcoholic fatty liver disease (NAFLD), a disorder that is present in 90% of obese subjects, the hepatic content of macroH2A1.2, but not of macroH2A1.1, is augmented .
The in vivo role of macroH2A1 isoforms in lipid metabolism and obesity is thus unclear. Here, we challenged newly generated macroH2A1.2–EGFP transgenic (Tg) mice  with an obesogenic high fat diet (60% energy from lard): Our findings identify macroH2A1.2 as a new and potent epigenetic inhibitor of adipogenesis. Its systemic overexpression leads to a spectacular protection from obesity and its related complications. Mechanistically, macroH2A1.2 strongly impaired adipogenesis, both in vitro and in vivo.
Differential association between macroH2A1.1 and macroH2A1.2 protein levels and BMI in human adipose tissue.
MacroH2A1.2 transgenic (Tg) mice are leaner independently of food intake and energy expenditure
MacroH2A1.2 Tg mice are more glucose tolerant and insulin sensitive, and display smaller pancreatic islets
MacroH2A1.2 Tg mice have decreased hepatic and pancreatic fat content and inflammation upon a HF diet
macroH2A1.2 counteracts adipogenesis in vivo and in vitro
Genome occupancy of macroH2A1.2 display minor changes upon adipocyte differentiation
Obesity and obesity-related disorders are attaining pandemic proportions with more than a third of the population living in Western countries, with profound repercussions on the health systems. Epigenetic mechanisms are key players in the pathophysiology of obesity and related complications. Among these mechanisms, canonical histones can be replaced with histone variants that alter chromatin structure and DNA accessibility.
MacroH2A1 is a histone variant of histone H2A present in two spicing isoforms, macroH2A1.1 and macroH2A1.2, playing diverse roles in cell differentiation and plasticity [8–11, 14–18]. Previous conflicting evidence showed that histone variant macroH2A1 whole gene transcriptional activity either favors lipid accumulation  or it may be protective against it [21, 23], in either cases with a mild phenotype. This is the first study showing that macroH2A1.2 isoform strongly protects against HF-induced obesity. Mice bearing a chimeric macroH2A1.2-GFP transgene displayed drastically reduced total and visceral adiposity compared to their wild-type littermates both upon a chow or a HF diet. Obesity-induced liver and pancreatic damages in terms of fat accumulation and inflammation were completely wiped out by macroH2A1.2 transgene in mice which looked healthy as the wild type fed a chow diet, without differences in food intake. Tg mice typically relied on a mix carbohydrate/fat burn for energy consumption, while wild-type animals relied rather on fat consumption, which could be explained by the very low levels of basal body adiposity in Tg mice. MacroH2A1.2 Tg mice were also slightly shorter than controls; however, we did not find differences in IGF-1 levels, which are implicated in growth and adiposity extent in mice . Tg mice were also, to a large extent, more insulin sensitive and glucose tolerant than wild-type mice. The reduced size of pancreatic islets in Tg mice is reminiscent of a caloric restriction regimen . Glucagon levels are often disrupted in obese individuals: accordingly, the number of glucagon-producing α-cells was perturbed in wild-type but not in Tg mice when fed a HF diet. Altogether, these strong evidences of metabolic health unrelated to alterations of food intake or GH/IGF-1 axis led us to hypothesize that the leanness of the Tg mice might be intrinsic to the adipose tissue and due to reduced adipogenesis at the molecular level. Reduced adipogenesis could translate into less circulating fat and triglycerides that might damage other peripheral organs such as the liver and the pancreas, without directly impinging on energy expenditure and hunger. Expression analysis of mRNA transcripts implicated in adipogenesis uncovered downregulation of pro-adipogenic genes ACACB, AGT, FASN, RETN and SLC2A4 and upregulation of anti-adipogenic genes E2F1, EGR2, JUN, LMNA, anti-inflammatory genes SIRT1, SIRT2, thermogenic gene UCP1 and pro-proliferative genes ANGPT2, CCND1, CDKN1A, CDKN1B in macroH2A1.2 Tg mice, which is consistent with a reduction of adipocyte mass and with a potential expansion of the undifferentiated pre-adipocyte cellular pool. Consistently, macroH2A1.2-GFP-overexpressing 3T3-L1 pre-adipocytes showed strongly reduced lipid content upon differentiation into adipocytes, as observed in Tg mice. It is has been shown that 3T3-L1 cell differentiation associates with genome-wide epigenetic dynamic changes in the DNA demethylation/methylation ratio in a time- and stage-dependent manner ; DNA hypomethylating agent decitabine blocked the 3T3-L1 adipogenic process . In the context of cancer cells, macroH2A1 incorporation antagonizes the anti-proliferative effects of decitabine [12, 13]. We postulate that macroH2A1.2 might interfere with DNA methylation events during the adipogenic gene expression program.
Surprisingly, stable overexpression of macroH2A1.1, the second splicing variant of macroH2A1 gene, yielded opposite effects, with large lipid accumulation upon differentiation. Our data in mice and humans suggest also that adipose tissue display endogenous low or absent expression of macroH2A1.2, while macroH2A1.1 is present, and its levels increase upon a HF diet-induced obesity in mice, and in obese compared to mildly overweight individuals; however, these patients were enrolled for other pathologies and this could have an impact on the expression of macroH2A1 isoforms. Since depletion of the whole macroH2A1 gene  has also, although milder, anti-adipogenic effects in mice fed a HF diet, we argue that macroH2A1.1 has a stronger pro-adipogenic role than the protective one of macroH2A1.2. Generation of macroH2A1.1 transgenic murine models will prove its mechanistic role and tissue-specific interaction with macroH2A1.2 during the development of obesity in vivo. Genome-wide distribution of macroH2A1 histone variants in mouse liver chromatin indicate that macroH2A1 functions primarily as a repressor in adult liver .
Variations in macroH2A1 transcriptional activities to a large extent independent of genome occupancy in response to nutritional and DNA methylation status have been reported in cancer cells [9, 11, 12]. Consistent with these studies, our ChIP-Seq analyses comparing macroH2A1.2 Tg genome binding between 3T3-L1 pre-adipocytes and 3T3 post-differentiated adipocytes revealed high degree of similarity in genome binding patterns, including within the bodies of the subset of genes involved in the adipogenic process that we analyzed. Variations in the expression and sequential binding patterns of key TF (GATA, C/EBPβ and −δ (C/EBPδ), PPARγ and C/EBPα) function in the adipogenic differentiation program. Our in silico analysis of TF-binding sites in proximity of macroH2A1.2 genome binding revealed that GATA-binding sites were overrepresented in the proximity of macroH2A1.2 binding in 3T3-L1 pre-adipocytes compared to differentiated cells, consistent with their role in adipocyte precursors and with their downregulation setting the stage for terminal differentiation. In mature adipocytes, RXRA::VDR and TP53-binding sites were enriched, which might reflect their anti-adipogenic transcriptional effects [33, 34]. The enrichment of consensus sequences for TF controlling homeobox genes, including the overrepresented PAX4, in macroH2A1.2 associates chromatin reads in the context of adipocyte differentiation deserves further investigation. In addition to potential dynamic interactions with TFs, it is unclear how posttranscriptional modifications might affect histone variants function without changes in occupancy. Adipose tissue is at the nexus of processes involved in health span and metabolic dysfunction: Progression of age-related fat tissue dysfunction follows different trajectories across different fat depots, with fat becoming redistributed from subcutaneous to intraperitoneal depots and ultimately ectopic sites. This is associated with metabolic disturbances. The pre-adipocytes from which new fat cells develop switch into a pro-inflammatory, tissue-remodeling state in old age, instead of differentiating into fat cells . In cellular senescence, proliferation becomes arrested and cells acquire a pro-inflammatory senescent secretory phenotype (SASP), with release of chemokines and cytokines . We and others have shown that macroH2A1.1 ectopic expression sustains SASP in fibroblasts and hepatoma cells [12, 36]; a similar signaling loop might take place in the adipose tissue. How to boost macroH2A1.2 expression/activity, to the detriment of macroH2A1.1, with an anti-obesity therapeutic purpose? Some of the switch factors controlling macroH2A1 splicing into macroH2A1.1 or macroH2A1.2 isoforms have been identified in cancer cells: QKI and RNA helicases Ddx17/Ddx5 [37, 38]. Epigenetic splicing regulatory strategies are already being explored for cancer therapy and might hold also a potential against obesity and its related complications.
Histone variant macroH2A1.2 is an unprecedented chromatin component that, if overexpressed, inhibits pro-adipogenic transcriptional programs, fat deposition and massively protects mice from high fat diet-induced obesity. Given that this histone is not expressed in human differentiated adipose tissue, it is possible to envisage that strategies aiming at its reintroduction in fat tissues could pave the way for new anti-obesity therapies.
Seven patients with BMI from 25 to 40 were enrolled (Additional file 1: Supplementary Table I, Supplementary Material). Patient underwent surgical procedures (Whipple’s, right hepatectomy, wedge resections of the liver) according to conditions including pancreatic adenocarcinoma and colorectal liver metastases. Informed consent was obtained from each patient. Procedures to extract adipose tissue biopsies were performed through incision that depended on the type of operation, transverse abdominal for pancreas, midline or inverted L shape for liver. The incision of the skin was performed with the blade knife until the subcutaneous tissue was visualized. Around the umbilicus, a small part of the subcutaneous fat (0.5 cm3) was resected with the scissors. Specimens were frozen at −80 °C before further characterization by immunoblotting. The study was approved by the UCL Royal Free Biobank Ethical Review Committee (NRES Rec Reference: 11/WA/0077). Tissues were processed in accordance with the UCL Royal Free Biobank protocols under the Research Tissue Bank Human Tissue Act licence, prior to use in research .
The MacroH2A1.2–EGFP transgenic mouse line established by Soma et al.  was backcrossed to C57BL/6 J for 5–6 generations. Tg mice positive for the transgene were identified by PCR amplification from tail tissue genomic DNA, using the primers 5′-TGACAGAAAGCTGAAATCCATCGC-3′ and 5′-TCCAGCAGGACCATGTGATCGC-3′, and by observing whole-body EGFP fluorescence using a UVL-56 handheld UV lamp (UVP, Cambridge, UK or Upland, CA, USA). All experiments were approved by Tottori University Ethical Committee, were performed using heterozygous transgenic mice and were carried out according to the Guide for the Care and Use of Laboratory Animals of Tottori University. Six-week old wild-type (WT) and transgenic (Tg) mice were assigned into 4 groups (8–9 mice/group): WT fed with chow diet, Tg fed with chow diet, WT fed with high fat (HF) diet and Tg fed with HF diet for 12 weeks. Obesogenic diet consist 60% energy from lard .
EchoMRI quantitative magnetic resonance and CT scan
EchoMRI™ quantitative magnetic resonance (QMR) technology was used to measure the body composition of live mice in terms of whole-body fat, and lean masses, according to manufacturer’s instructions: Measurements were made by placing live free moving mice into a thin wall plastic cylinder (4.7 cm, inside diameter; 0.15 cm thick) with freedom to turn about but limited to ~4 cm vertical movements by a plastic insert. After 2 min, when the measurement was completed, conscious mice returned to their home cage. Alternatively, CT scanning was performed in isofluorane-anesthetized animals at 2-mm intervals from head to tail to determine body length, or from the diaphragm to the bottom of the abdominal cavity to determine visceral fat and liver fat content, using a LaTheta™ LCT 200 in vivo micro-CT scanner, according to manufacturer’s instructions (Hitachi, Aloka Medical, Japan).
Metabolic cages, glucose tolerance test (GTT) and insulin tolerance test (ITT)
In order to monitor in real time the metabolic gas exchange, groups of 8–9 mice per genotype on HFD were placed in indirect calorimetric cages where energy expenditure, food intake and activity were evaluated : The whole-body energy metabolism in mice was calculated in vivo as previously described . GTT and ITT were performed as previously described .
Histology and immunofluorescence
Histological analyses and immunofluorescence staining are described in detail in the Additional file 1: Supplementary Materials and methods.
Quantification of circulating cytokines
Insulin, leptin and IGF-1 levels were assessed in the sera of wild-type and macroH2A1.2 Tg animals, using a customized mouse MILLIPLEX® MAP (multi-analyte panels) Luminex system (Merck Millipore) , according to the manufacturer’s instructions.
A commercially available adipogenesis array (mouse RT-Profiler array, Qiagen, Italy) was used to measure genes involved in adipogenesis by qRT‐PCR in mice visceral adipose tissue; expression data were normalized to the geometric mean of three house keeping genes (Actb, GAPDH and GusB). Briefly, after homogenization, tissue RNAs were isolated using Trizol (1 ml per 100 mg), according to manufacturer’s protocol. RNA samples were purified using mini spin columns (Qiagen), quantified using Nanodrop Spectrophotometer (Thermo scientific, UK). As for 3T3-L1 cell differentiation experiments, the following QuantiTect primers (Qiagen) were used: FASN (QT00149240), RETN (QT00093450), EGR2 (QT00160125) and E2F1 (QT01079106). qRT-PCR for determining gene expression in 3T3-L1 cells was performed on 50 ng of purified RNA using the one-step QuantiFast SYBR Green RT-PCR kit (Qiagen) and the Mouse SYBR Green QuantiTect primer assay. All reactions were set up in 96-well plates using a 7700HT Real-Time PCR System (Applied Biosystems, Foster City, CA).
Total and cytoplasmic/nuclear/histone proteins extraction and immunoblotting analyses were performed as previously described . Primary antibodies were as follows: anti-GFP (Abcam, ab13970) anti-macroH2A1.1 (Cell Signaling, Cat 12455), anti-macroH2A1.2 (Cell Signaling, Cat. 4827), anti-AKT (Cell Signaling, Cat. 9272), anti-phosphoAKT-Ser473 (Cell Signaling, Cat. 9271), anti-β-actin (Cell Signaling, Cat. 4967) and anti-H3 (Cell Signaling, Cat. 9715).
Generation of stable clones of 3T3-L1 cells: differentiation and lipid staining
Stable expression of macroH2A1.2 variant in 3T3-L1 pre-adipocytes was achieved by lentiviral transduction as previously described . Stable 3T3-L1 pre-adipocytes were cultured to differentiate into mature adipocytes according to an established 15-day protocol . 3T3-L1 cells at the 15th day of differentiation seeded on coverslips were washed with PBS and fixed with 4% paraformaldehyde for 10 min at room temperature. After fixation and further washings with PBS, cells were stained with an Oil Red O solution in 40% isopropanol. Coverslips were then mounted on microscope slides with Vectashield mounting medium with DAPI, and images were collected using a Nikon Eclipse E600 microscope.
Chromatin immunoprecipitation, sequencing and data analysis
Chromatin immunoprecipitation was performed in mice adipose tissue with a modified protocol as described previously [12, 41], using an anti-GFP antibody (ab290, Abcam). 10 ng of purified ChIP DNA was used as starting material for sequencing libraries preparation. Indexed libraries were prepared with TruSeq ChIP Sample Prep Kit (Illumina Inc.). Size distribution of each ChIP library sample was assessed by running a 1 µl aliquot on Agilent High Sensitivity DNA chip using an Agilent Technologies 2100 Bioanalyzer (Agilent Technologies). The concentration of each sample was determined by using a Qubit Fluorometer (Life Technologies). Libraries were sequenced (single read, 1 × 50 cycles) at a concentration of 10 pM/lane on HiSeq 2500 (Illumina Inc.). The raw sequence files generated (fastq) underwent quality control analysis using FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were aligned to the mus musculus genome (assembly mm10) using Bowtie software, allowing up to 2 mismatch and considering uniquely mappable reads. Duplicate sequences were removed before peaks enrichment calculation using MarkDuplicates (Picard Tools; https://broadinstitute.github.io/picard/). Violin plots representing the density of reads were generated with the vioplot package . The enriched ChIP-Seq regions were identified using Spatial Clustering for Identification of ChIP-Enriched Regions (SICER)  setting standard parameters and a false discovery rate of 1%. For each analyzed condition, only regions in common between the two biological replicates were considered for further analysis. The annotation of peaks to the nearest gene was performed using the annotatePeaks.pl function from HOMER . Overrepresented transcription factor binding sites analysis was performed using PscanChip , while annotation plots were generated using ChIPseek . Circos plot was generated with Circos .
Results are expressed as mean ± S.E.M. Comparisons between groups were performed with the parametric Student’s t test or the nonparametric Mann–Whitney U test, as appropriate, using GraphPad Prism software (version 5.00 for Windows, San Diego, CA, USA): A P value ≤0.05 was considered significant.
body mass index
quantitative echography and magnetic resonance imaging
glucose tolerance test
insulin tolerance test
Oil Red O
non-alcoholic fatty liver disease
non-alcoholic fatty pancreas disease
VP, TN and MV conceived the study; VP and MV analyzed the data and wrote the manuscript; CP and MB performed cell culture experiments; VP, AO, KH, TN and MV performed animal experiments; MB, MV, SC, CP, DM, and RT performed ChIP-Seq experiments and analyses; MB performed qRT-PCR experiments and immunoblotting; GB, FR, RB and FC performed histological analyses and immunofluorescence experiments; FV and JV performed ELISA experiments; GM, DT and GF provided human specimens; VP, CP and MV performed the statistical analyses; VP, TN and MV critically revised the manuscript. All authors read and approved the final manuscript.
We thank Hiroshi Shigeta for assistance with CT scan imaging and Masaru Tanaka for help with animal experiments; Illar Pata and Pille Pata for generating transgenic 3T3-L1 cell lines.
M.V. is supported by a My First Associazione Italiana Ricerca sul Cancro (AIRC) Grant-AIRC Grant No. 13419, by University College London and by grants No. LQ1605 from the National Program of Sustainability II (MEYS CR) and FNUSA-ICRC No. CZ.1.05/1.1.00/02.0123 (OP VaVpI). P.V. and M.V. are supported by Italian Ministry of Health, Bando GR-2010-2311017. Research was also supported by the Ministry of Education, University and Research (MIUR grants RBFR12W5V5_003 and PON03PE_00146_1) and National Research Council (CNR, Flagship Project InterOmics) of Italy and Genomix4Life Srl. D.M. is a PhD student of the Research Doctorate “Molecular Medicine and Medical Biotechnology,” University of Naples ‘Federico II’.
The authors declare that they have no competing interests.
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- Podrini C, Borghesan M, Greco A, Pazienza V, Mazzoccoli G, Vinciguerra M. Redox homeostasis and epigenetics in non-alcoholic fatty liver disease (NAFLD). Curr Pharm Des. 2013;19(15):2737–46.View ArticlePubMedGoogle Scholar
- Goni L, Milagro FI, Cuervo M, Martinez JA. Single-nucleotide polymorphisms and DNA methylation markers associated with central obesity and regulation of body weight. Nutr Rev. 2014;72(11):673–90.View ArticlePubMedGoogle Scholar
- Martinez JA, Milagro FI, Claycombe KJ, Schalinske KL. Epigenetics in adipose tissue, obesity, weight loss, and diabetes. Adv Nutr. 2014;5(1):71–81.View ArticlePubMedPubMed CentralGoogle Scholar
- Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aissi D, Wahl S, Meduri E, Morange PE, Gagnon F, Grallert H, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014;383(9933):1990–8.View ArticlePubMedGoogle Scholar
- Henikoff S, Smith MM. Histone variants and epigenetics. Cold Spring Harb Perspect Biol. 2015;7(1):a019364.View ArticlePubMedGoogle Scholar
- Skene PJ, Henikoff S. Histone variants in pluripotency and disease. Development. 2013;140(12):2513–24.View ArticlePubMedGoogle Scholar
- Talbert PB, Henikoff S. Histone variants–ancient wrap artists of the epigenome. Nat Rev Mol Cell Biol. 2010;11(4):264–75.View ArticlePubMedGoogle Scholar
- Doyen CM, An W, Angelov D, Bondarenko V, Mietton F, Studitsky VM, Hamiche A, Roeder RG, Bouvet P, Dimitrov S. Mechanism of polymerase II transcription repression by the histone variant macroH2A. Mol Cell Biol. 2006;26(3):1156–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Borghesan M, Mazzoccoli G, Sheedfar F, Oben J, Pazienza V, Vinciguerra M. Histone variants and lipid metabolism. Biochem Soc Trans. 2014;42(5):1409–13.View ArticlePubMedGoogle Scholar
- Pazienza V, Borghesan M, Mazza T, Sheedfar F, Panebianco C, Williams R, Mazzoccoli G, Andriulli A, Nakanishi T, Vinciguerra M. SIRT1-metabolite binding histone macroH2A1.1 protects hepatocytes against lipid accumulation. Aging. 2014;6(1):35–47.View ArticlePubMedPubMed CentralGoogle Scholar
- Gamble MJ, Frizzell KM, Yang C, Krishnakumar R, Kraus WL. The histone variant macroH2A1 marks repressed autosomal chromatin, but protects a subset of its target genes from silencing. Genes Dev. 2010;24(1):21–32.View ArticlePubMedPubMed CentralGoogle Scholar
- Borghesan M, Fusilli C, Rappa F, Panebianco C, Rizzo G, Oben JA, Mazzoccoli G, Faulkes C, Pata I, Agodi A, Rezaee F, Minogue S, Warren A, Peterson A, Sedivy JM, Douet J, Buschbeck M, Cappello F, Mazza T, Pazienza V, Vinciguerra M. DNA hypomethylation and histone variant macroH2A1 synergistically attenuate chemotherapy-induced senescence to promote hepatocellular carcinoma progression. Cancer Res. 2016;76(3):594–606.View ArticlePubMedGoogle Scholar
- Jueliger S, Lyons J, Cannito S, Pata I, Pata P, Shkolnaya M, Lo Re O, Peyrou M, Villarroya F, Pazienza V, Rappa F, Cappello F, Azab M, Taverna P, Vinciguerra M: Efficacy and epigenetic interactions of novel DNA hypomethylating agent guadecitabine (SGI-110) in preclinical models of hepatocellular carcinoma. Epigenetics Published online: 11 Aug 2016. doi: 10.1080/15592294.2016.1214781.
- Creppe C, Janich P, Cantarino N, Noguera M, Valero V, Musulen E, Douet J, Posavec M, Martin-Caballero J, Sumoy L, et al. MacroH2A1 regulates the balance between self-renewal and differentiation commitment in embryonic and adult stem cells. Mol Cell Biol. 2012;32(8):1442–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Cantarino N, Douet J, Buschbeck M. MacroH2A–an epigenetic regulator of cancer. Cancer Lett. 2013;336(2):247–52.View ArticlePubMedGoogle Scholar
- Barrero MJ, Sese B, Kuebler B, Bilic J, Boue S, Marti M, Izpisua Belmonte JC. Macrohistone variants preserve cell identity by preventing the gain of H3K4me2 during reprogramming to pluripotency. Cell Rep. 2013;3(4):1005–11.View ArticlePubMedGoogle Scholar
- Gaspar-Maia A, Qadeer ZA, Hasson D, Ratnakumar K, Leu NA, Leroy G, Liu S, Costanzi C, Valle-Garcia D, Schaniel C, et al. MacroH2A histone variants act as a barrier upon reprogramming towards pluripotency. Nat Commun. 2013;4:1565.View ArticlePubMedPubMed CentralGoogle Scholar
- Kustatscher G, Hothorn M, Pugieux C, Scheffzek K, Ladurner AG. Splicing regulates NAD metabolite binding to histone macroH2A. Nat Struct Mol Biol. 2005;12(7):624–5.View ArticlePubMedGoogle Scholar
- Sporn JC, Jung B. Differential regulation and predictive potential of MacroH2A1 isoforms in colon cancer. Am J Pathol. 2012;180(6):2516–26.View ArticlePubMedPubMed CentralGoogle Scholar
- Sporn JC, Kustatscher G, Hothorn T, Collado M, Serrano M, Muley T, Schnabel P, Ladurner AG. Histone macroH2A isoforms predict the risk of lung cancer recurrence. Oncogene. 2009;28(38):3423–8.View ArticlePubMedGoogle Scholar
- Changolkar LN, Costanzi C, Leu NA, Chen D, McLaughlin KJ, Pehrson JR. Developmental changes in histone macroH2A1-mediated gene regulation. Mol Cell Biol. 2007;27(7):2758–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Sheedfar F, Vermeer M, Pazienza V, Villarroya J, Rappa F, Cappello F, Mazzoccoli G, Villarroya F, van der Molen H, Hofker MH, et al. Genetic ablation of macrohistone H2A1 leads to increased leanness, glucose tolerance and energy expenditure in mice fed a high-fat diet. Int J Obes (Lond). 2015;39(2):331–8.View ArticleGoogle Scholar
- Boulard M, Storck S, Cong R, Pinto R, Delage H, Bouvet P. Histone variant macroH2A1 deletion in mice causes female-specific steatosis. Epigenetics Chromatin. 2010;3(1):8.View ArticlePubMedPubMed CentralGoogle Scholar
- Lin X, Yue P, Chen Z, Schonfeld G. Hepatic triglyceride contents are genetically determined in mice: results of a strain survey. Am J Physiol Gastrointest Liver Physiol. 2005;288(6):G1179–89.View ArticlePubMedGoogle Scholar
- Rappa F, Greco A, Podrini C, Cappello F, Foti M, Bourgoin L, Peyrou M, Marino A, Scibetta N, Williams R, et al. Immunopositivity for histone macroH2A1 isoforms marks steatosis-associated hepatocellular carcinoma. PLoS ONE. 2013;8(1):e54458.View ArticlePubMedPubMed CentralGoogle Scholar
- Soma A, Sato K, Nakanishi T. Visualization of inactive X chromosome in preimplantation embryos utilizing MacroH2A-EGFP transgenic mouse. Genesis. 2013;51(4):259–67.View ArticlePubMedGoogle Scholar
- Carter R, Mouralidarane A, Soeda J, Ray S, Pombo J, Saraswati R, Novelli M, Fusai G, Rappa F, Saracino C, et al. Non-alcoholic fatty pancreas disease pathogenesis: a role for developmental programming and altered circadian rhythms. PLoS ONE. 2014;9(3):e89505.View ArticlePubMedPubMed CentralGoogle Scholar
- Arsenijevic T, Gregoire F, Delforge V, Delporte C, Perret J. Murine 3T3-L1 adipocyte cell differentiation model: validated reference genes for qPCR gene expression analysis. PLoS ONE. 2012;7(5):e37517.View ArticlePubMedPubMed CentralGoogle Scholar
- Fontana L, Vinciguerra M, Longo VD. Growth factors, nutrient signaling, and cardiovascular aging. Circ Res. 2012;110(8):1139–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Gao X, Yan D, Zhao Y, Tao H, Zhou Y. Moderate calorie restriction to achieve normal weight reverses beta-cell dysfunction in diet-induced obese mice: involvement of autophagy. Nutr Metab (Lond). 2015;12:34.View ArticleGoogle Scholar
- Sakamoto H, Kogo Y, Ohgane J, Hattori N, Yagi S, Tanaka S, Shiota K. Sequential changes in genome-wide DNA methylation status during adipocyte differentiation. Biochem Biophys Res Commun. 2008;366(2):360–6.View ArticlePubMedGoogle Scholar
- Changolkar LN, Singh G, Cui K, Berletch JB, Zhao K, Disteche CM, Pehrson JR. Genome-wide distribution of macroH2A1 histone variants in mouse liver chromatin. Mol Cell Biol. 2010;30(23):5473–83.View ArticlePubMedPubMed CentralGoogle Scholar
- Molchadsky A, Ezra O, Amendola PG, Krantz D, Kogan-Sakin I, Buganim Y, Rivlin N, Goldfinger N, Folgiero V, Falcioni R, et al. p53 is required for brown adipogenic differentiation and has a protective role against diet-induced obesity. Cell Death Differ. 2013;20(5):774–83.View ArticlePubMedPubMed CentralGoogle Scholar
- Ji S, Doumit ME, Hill RA. Regulation of adipogenesis and key adipogenic gene expression by 1, 25-dihydroxyvitamin D in 3T3-L1 cells. PLoS ONE. 2015;10(6):e0126142.View ArticlePubMedPubMed CentralGoogle Scholar
- Tchkonia T, Morbeck DE, Von Zglinicki T, Van Deursen J, Lustgarten J, Scrable H, Khosla S, Jensen MD, Kirkland JL. Fat tissue, aging, and cellular senescence. Aging Cell. 2010;9(5):667–84.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen H, Ruiz PD, McKimpson WM, Novikov L, Kitsis RN, Gamble MJ. MacroH2A1 and ATM play opposing roles in paracrine senescence and the senescence-associated secretory phenotype. Mol Cell. 2015;59(5):719–31.View ArticlePubMedPubMed CentralGoogle Scholar
- Novikov L, Park JW, Chen H, Klerman H, Jalloh AS, Gamble MJ. QKI-mediated alternative splicing of the histone variant MacroH2A1 regulates cancer cell proliferation. Mol Cell Biol. 2011;31(20):4244–55.View ArticlePubMedPubMed CentralGoogle Scholar
- Dardenne E, Pierredon S, Driouch K, Gratadou L, Lacroix-Triki M, Espinoza MP, Zonta E, Germann S, Mortada H, Villemin JP, et al. Splicing switch of an epigenetic regulator by RNA helicases promotes tumor-cell invasiveness. Nat Struct Mol Biol. 2012;19(11):1139–46.View ArticlePubMedGoogle Scholar
- Mazza G, Rombouts K, Rennie Hall A, Urbani L, Vinh Luong T, Al-Akkad W, Longato L, Brown D, Maghsoudlou P, Dhillon AP, et al. Decellularized human liver as a natural 3D-scaffold for liver bioengineering and transplantation. Sci Rep. 2015;5:13079.View ArticlePubMedPubMed CentralGoogle Scholar
- Veyrat-Durebex C, Montet X, Vinciguerra M, Gjinovci A, Meda P, Foti M, Rohner-Jeanrenaud F. The Lou/C rat: a model of spontaneous food restriction associated with improved insulin sensitivity and decreased lipid storage in adipose tissue. Am J Physiol Endocrinol Metab. 2009;296(5):E1120–32.View ArticlePubMedGoogle Scholar
- Bolasco G, Calogero R, Carrara M, Banchaabouchi MA, Bilbao D, Mazzoccoli G, Vinciguerra M. Cardioprotective mIGF-1/SIRT1 signaling induces hypertension, leukocytosis and fear response in mice. Aging (Albany NY). 2012;4(6):402–16.View ArticleGoogle Scholar
- Hintze JL, Nelson RD. Violin plots: a box plot-density trace synergism. Am Stat. 1998;52(2):181–4.Google Scholar
- Xu S, Grullon S, Ge K, Peng W. Spatial clustering for identification of ChIP-enriched regions (SICER) to map regions of histone methylation patterns in embryonic stem cells. Methods Mol Biol. 2014;1150:97–111.View ArticlePubMedPubMed CentralGoogle Scholar
- Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38(4):576–89.View ArticlePubMedPubMed CentralGoogle Scholar
- Zambelli F, Pesole G, Pavesi G: PscanChIP: finding over-represented transcription factor-binding site motifs and their correlations in sequences from ChIP-Seq experiments. Nucleic Acids Res 2013, 41(Web Server issue):W535–543.
- Chen TW, Li HP, Lee CC, Gan RC, Huang PJ, Wu TH, Lee CY, Chang YF, Tang P. ChIPseek, a web-based analysis tool for ChIP data. BMC Genom. 2014;15:539.View ArticleGoogle Scholar
- Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19(9):1639–45.View ArticlePubMedPubMed CentralGoogle Scholar
- Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, Ferrell LD, Liu YC, Torbenson MS, Unalp-Arida A, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005;41(6):1313–21.View ArticlePubMedGoogle Scholar
- Mathur A, Marine M, Lu D, Swartz-Basile DA, Saxena R, Zyromski NJ, Pitt HA. Nonalcoholic fatty pancreas disease. HPB (Oxford). 2007;9(4):312–8.View ArticleGoogle Scholar