MCB Accepted Manuscript Posted Online 26 January 2015 Mol. Cell. Biol. doi:10.1128/MCB.01404-14 Copyright © 2015, American Society for Microbiology. All Rights Reserved.
(Article, Molecular and Cellular Biology)
Lysine-specific demethylase LSD2 suppresses lipid influx and
metabolism in hepatic cells
Katsuya Nagaoka1,2, Shinjiro Hino1*, Akihisa Sakamoto1, Kotaro Anan1, Ryuta Takase1,
Takashi Umehara3, Shigeyuki Yokoyama3, Yutaka Sasaki2 & Mitsuyoshi Nakao1,4*
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Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, 2 Department of Gastroenterology and Hepatology, Graduate School of Medical
Sciences, Kumamoto University, Kumamoto, 860-0811, Japan, RIKEN Systems and Structural Biology Center, Yokohama, Japan, 4 Core Research for Evolutional Science and Technology (CREST), Japan Science and
Technology Agency, Tokyo, Japan.
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*Address correspondence to:
Mitsuyoshi Nakao M.D., Ph.D. Shinjiro Hino Ph.D.
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Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University 2-2-1 Honjo, chuo-ku, Kumamoto 860-0811, Japan. Phone: +81-96-373-6800; Fax: +81-96-373-6804 E-mail: [email protected]
or [email protected]
Cells link environmental fluctuations such as nutrition to metabolic remodeling.
Epigenetic factors are thought to be involved in such cellular process, but the molecular
basis remains unclear. Here we report that the lysine-specific demethylase LSD2
suppresses the flux and metabolism of lipids to maintain the energy balance in hepatic
cells. Using transcriptome and chromatin immunoprecipitation-sequencing analyses, we
reveal that LSD2 represses the genes involved in lipid influx and metabolism, through
demethylation of histone H3K4. Selective recruitment of LSD2 at lipid metabolism
gene loci was mediated in part by a stress-responsive transcription factor c-Jun.
Intriguingly, LSD2 depletion increased the intracellular levels of many lipid metabolites,
which was accompanied by an increased susceptibility to toxic cell damage in response
to fatty acid exposure. Our data demonstrate that LSD2 maintains metabolic plasticity
under fluctuating environment in hepatocytes, by mediating the crosstalk between the
epigenome and metabolism.
Organisms and cells must adjust their energy strategy to fluctuating nutrient
availability and other environmental conditions. Epigenetic mechanisms have been
implicated in the phenotypic plasticity in response to environmental changes, as well as
in consistent execution of the developmental program (1). It has been shown that
nutrients and dietary composition potently influence epigenetic marks including DNA
methylation and histone methylation/acetylation in both humans and animal models (2).
Because chromatin-modifying enzymes utilize nutrient-derived metabolites as
substrates and coenzymes, epigenome formation is, by nature, influenced by nutritional
and metabolic conditions (3-6). Lysine-specific demethylase 1 and 2 (LSD1 and LSD2),
also respectively known as KDM1A and KDM1B, comprise the flavin-dependent amine
oxidase family of histone demethylases (7). These enzymes require flavin adenine
dinucleotide (FAD) as a coenzyme for the removal of methyl groups from the lysine
residue of histone H3 and other proteins (8, 9). FAD is a vitamin B2-derived metabolite
that serves as a redox cofactor in key metabolic processes such as fatty acid oxidation
and succinate dehydrogenation in the tricarboxylic acid cycle (10). Thus, the cellular
metabolic state may influence the demethylase activity of these proteins. Indeed, we and
others have previously demonstrated that LSD1 controls energy metabolism genes in
response to extracellular conditions (11, 12), suggesting that FAD-dependent epigenetic
factors may link environmental information to metabolic programming. LSD2 was
identified as a second flavin-dependent histone demethylase that targets methylated
lysines 4 and 9 of histone H3 (H3K4 and H3K9, respectively) (8, 13-15). Although
LSD2 has been implicated in the establishment of maternal genomic imprinting in
oocytes (16), little is known about its biological functions, particularly in relation to
In the liver, hepatocytes play a crucial role in the homeostatic control of lipid
metabolism (17). Hepatocytes incorporate adipose- and diet-derived fatty acids, which
are either stored by themselves as neutral lipids or redistributed to other tissues in the
form of very low-density lipoproteins (18). When hepatocytes are exposed to an
intolerably high amount of fatty acids, for example due to over-feeding, excessive fatty
acids and their toxic metabolites accumulate in the cells, often leading to the lipotoxic
liver injury known as nonalcoholic fatty liver disease (NAFLD) (19, 20). Epigenetic
alterations in the liver have been linked to insulin resistance and NAFLD in humans
(21), and diet-induced steatosis in mice (22). A recent report by Ahrens et al. examined
the DNA methylation profiles of liver biopsies from patients with NAFLD and
non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD (23). Of particular
note, some disease-state dependent methylation patterns could be reversed after
improvement of the disease condition by bariatric surgery (23), suggesting that hepatic
lipid homeostasis is associated with epigenetic plasticity. However, we still lack
knowledge of whether a specific epigenetic factor could be involved in the homeostatic
control of hepatic lipid metabolism.
In the present study, we provide direct evidence that LSD2 plays an essential
role in the homeostatic control of lipid metabolism in hepatocytes. Our integrative
immunoprecipitation-sequencing (ChIP-seq) analyses reveal that LSD2 suppresses lipid
transport and metabolism by repressing key metabolic genes through the regulation of
methylated H3K4 (H3K4me). We further show that LSD2 depletion leads to enhanced
lipotoxic cell damage under fatty acid exposure. We propose an epigenetic mechanism
for ensuring metabolic plasticity in response to lipid overload, in which LSD2 maintains
the proper expression of lipid metabolism genes in hepatocytes.
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MATERIALS and METHODS
HepG2 cells were cultured in high glucose (25 mM D-glucose) Dulbecco’s
modified Eagle’s medium (Sigma) supplemented with 10% (v/v) heat-inactivated fetal
bovine serum and penicillin/streptomycin. For the knockdown experiments, specific
siRNAs were introduced to the cells using RNAiMAX reagent (Invitrogen) when they
were approximately 50% confluent. After being cultured for 3 to 4 days, semiconfluent
cells were harvested for subsequent analyses. Target sequences used for siRNA design
are listed in Table S1. siRNA against firefly luciferase gene was used as a control. For
the knockdown of c-Jun gene, siGENOME Human JUN siRNA SMARTpool
(Dharmacon) was used, while siGENOME Non-Targeting siRNA SMARTpool #1
(Dharmacon) was used as a control.
Anti-LSD2 polyclonal antibody was raised in rabbits by administering
recombinant human LSD2 protein (region 26 – 822), which was prepared using a
baculovirus expression system. Anti-LSD2 antisera were purified by affinity
chromatography of the IgG fraction, and were titrated prior to use. Anti-histone H3 5
(ab1791), anti-H3K4me1 (ab8895) and anti-H3K27ac (ab4729) antibodies were
purchased from Abcam. The following antibodies were also used: anti-histone
H3K4me2 (07-030, Millipore), anti-normal rabbit IgG (sc-2027, Santa Cruz), anti-Flag
M2 (F1804, Sigma-Aldrich).
To construct pcDNA3-Flag-hLSD2, an LSD2 expression vector, a fragment
(from +1 to +2,469) of the human LSD2 gene was PCR-amplified using cDNAs from
HepG2 cells, and cloned into EcoRV and XbaI sites of the pcDNA3-Flag-mock vector.
Gene expression analysis
Total RNA from tissues and cells was extracted using Trizol reagent
(Invitrogen). cDNAs were produced using a ReverTra Ace qPCR RT-Kit (Toyobo).
Quantitative RT-PCR was performed by the SYBR green method using Thunderbird
reagents (Toyobo) and an ABI 7300 Sequence Detector (Applied Biosciences). Data are
presented as mean ± SD. Statistical analyses were performed using two-tailed Student’s
t-test. Primers used in this study are listed in Table S1.
Genome-wide expression analysis was performed using a GeneChip Human
Genome Array U133 Plus 2.0 in combination with a GeneChip Hybridization, Wash and
Stain Kit (Affymetrix). We prepared three HepG2 samples, by introducing each of three
different siRNAs against LSD2. We also prepared two control-KD samples. Total RNA
from cells was extracted, and the sample integrity was confirmed using a Bioanalyzer 6
RNA 6000 Nano Assay (Agilent). Data annotation analysis was performed using
GeneSpring GX software (Agilent). GSEA was done using GSEA ver. 2.0 software
In the ChIP experiments for detecting modified histones, cells were
cross-linked with 1% formaldehyde. Following cell lysis, isolated nuclei were subjected
to sonication for chromatin fragmentation. Chromatin fragments were incubated at 4 °C
overnight with appropriate antibodies, followed by a pull-down assay using protein
A/G-conjugated agarose beads. Purified DNAs were subjected to quantitative PCR
(qPCR) using the primer sets listed in Table S1. To detect LSD2 enrichment on
genomic DNA, we employed a protocol for detecting indirect associations between
protein and DNA (24). Briefly, enhanced cross-linking of chromatin using
3′-dithiobispropionimidate 2HCl (Sigma) was performed to increase the stability of
protein-DNA complexes. Chromatin fragmentation was done by sonication in regular
RIPA buffer containing 0.1% SDS, followed by immunoprecipitation, as described
For ChIP-seq analysis to detect LSD2 or H3K4me1/DNA interactions, 1×107
HepG2 cells were collected for a ChIP experiment. After crosslinking, chromatin DNA
was fragmented using a Covaris S220 sonicator (Covaris Inc.), followed by 7
immunoprecipitation with either an anti-LSD2 or an anti-H3K4me1 polyclonal antibody.
The protein-bound chromatin fraction was collected using Dynabeads Protein A/G (Life
Technologies), and the DNA was purified. A DNA fragment library for sequencing was
constructed using an Ion Fragment Library Kit (Life Technologies). Adopter-ligated
DNA fragments were purified using Agencourt AMPure XP (Beckman Coulter Inc.).
High throughput sequencing was performed using Ion PGM and Ion Proton
semiconductor sequencers (Life Technologies), according to the manufacturer’s
instruction. All sequence data obtained from Ion PGM and Proton were merged before
alignment onto the human reference genome hg19 using the BWA algorithm (CLC
Genomics Workbench Software). With the same software, the duplicate reads and low
read/mapping quality reads were trimmed out. For LSD2 ChIP-seq, final numbers of
mapped reads were 36,409,704 reads for LSD2, and 27,141,872 reads for Input. For
H3K4me1 ChIP-seq, final numbers of mapped reads were 33,087,078 reads for the
control siRNA sample, and 53,725,212 reads for input of the control siRNA sample;
49,410,448 reads for the LSD2-KD sample, and 58,829,288 reads for input of the
Peak detection was done using the MACS algorithm in Avadis NGS software.
LSD2 binding sites were detected based on the LSD2 peaks significantly enriched over
input peaks at a cutoff value of p=10-5. H3K4me1 peaks enhanced by LSD2-KD were
detected based on the H3K4me1 peaks in LSD2-KD samples significantly enriched over
H3K4me1 peaks in control siRNA sample peaks at a cutoff value of p=10-5.
For identification of the direct regulatory target genes of LSD2, we first
detected 414,095 LSD2 peaks using the MACS algorithm (25) and selected 15,532
robust peaks that met the criteria of >25 reads and >5-fold enrichment. Based on this 8
peak detection, 6,079 neighboring genes were identified within 5,000 bases of the LSD2
peaks using Avadis NGS software with the gene annotation provided by Ensembl.
Among these genes, we identified 226 genes with >1.5-fold expression change in
response to LSD2-KD.
For identifying genes with both increased H3K4me1 and expression change in
response to LSD2-KD, we first detected 180,505 exclusive H3K4me1 peaks under
LSD2-KD using the MACS algorithm, and selected 12,735 robust peaks that met the
criterion of >30 reads and >5-fold enrichment. Based on this peak detection, 5,768
neighboring genes were identified within 5,000 bases of the H3K4me1 peaks using
Avadis NGS with the gene annotation provided by Ensembl. Of these genes, we
identified 207 with >1.5-fold expression change in response to LSD2-KD.
Visualization of ChIP-Seq data with smoothing (smoothing window size = 2
bp) was done using Avadis NGS. ChIP-seq data for chromatin modifications in HepG2
cells were obtained from the ENCODE/Broad Institute via the UCSC Genome Browser
website (http://genome.ucsc.edu/). The accession numbers of the files are as follows:
(GSM733743) for H3K27ac. ChIP-seq data for p300 enrichment in HepG2 cells was
from ENCODE/Stanford/Yale/USC/Harvard (wgEncodeEH001862), FAIRE data was
from ENCODE/OpenChrom (UNC Chapel Hill) (wgEncodeEH000546 (GSM864354)),
c-Jun data was from ENCODE/Stanford/Yale/USC/Harvard (wgEncodeEH001794) and
c-Myc data was from ENCODE/Open Chrom (UT Austin) (wgEncodeEH000542
Correlation analyses of ChIP-seq peaks 9
Correlations of LSD2 peaks with histone modifications were analyzed using
reference data sets, histone modification data of HepG2 cells were obtained from the
ENCODE/Broad Institute via the UCSC Genome Browser website. The accession
numbers of the files used in the analyses are as follows: wgEncodeEH001749
(GSM798321) for H3K4me1, wgEncodeEH001023 (GSM733754) for H3K27me3.
LSD2 peaks were selected using the criteria of >28 reads and >5-fold enrichment from
the peaks detected using the MACS algorithm. Peaks for H3K4me1 and H3K27me3
were detected using NGS analyzer (Genomatix) or the MACS algorithm in Avadis
Metabolomic analyses were performed using CE-TOFMS and LC-TOFMS at
Human Metabolome Technologies (HMT, Japan, http://humanmetabolome.com).
Triplicate samples of control and LSD2-KD2 cells were subjected to both CE-TOFMS
and LC-TOFMS, while LSD2-KD1 was analyzed by LC-TOFMS. Cells were washed
with PBS and collected by trypsinization, then washed twice with 5% (w/w) mannitol
solution at room temperature. Cells were re-dispersed in LC/MS grade methanol
(Wako) for CE-TOFMS or LC/MS grade ethanol (Wako) for LC-TOFMS, both
containing HMT’s Internal Standard Solution 1. Peaks were extracted as previously
described (26). After quantification, metabolite concentrations were normalized by cell
number. Of ~900 (for CE-TOFMS) and ~500 (for LC-MS) metabolites from the HMT
databases, 275 metabolites were detected above the signal-to noise threshold (listed in 10
Table S4). The databases include glycolysis and TCA cycle intermediates, amino acids,
nucleic acids, nucleotides, nucleosides, Coenzyme A, organic acids, nicotinamide
coenzymes, fatty acids, bile acids, lipids, steroid derivatives and polyphenols.
Clustering of metabolites according to changes in concentration was
performed using Cluster 3.0, which was obtained from the Laboratory of DNA
Information Analysis of the Human Genome Center in the Institute of Medical Science,
clustering was calculated using Pearson correlation (centered correlation) and complete
Linkage. Clustering results were visualized using Java TreeView software (27).
Fatty acid uptake assay
Cellular fatty acid uptake was tested using QBT Fatty Acid Uptake Assay Kit
(Molecular Devices), which uses dodecanoic acid conjugated with BODIPY, a
fluorescent dye (BODIPY-DA). Fatty acid uptake assay stock solutions were dissolved
completely by adding 10 ml of 1× HBSS buffer (1× Hank’s balanced salt solution with
20 mM HEPES and 0.2% fatty acid-free bovine serum albumin). HepG2 cells were
cultured in the medium containing the assay reagent for 10 min at 37 °C. Cells were
either subjected to microscopic analysis, or trypsinized for fluorescence-activated cell
sorting analysis, using a FACS Canto cytometer (Becton Dickinson).
To analyze cellular lipotoxicity, siRNA-introduced HepG2 cells were treated
with bovine serum albumin (BSA)-conjugated oleic acid for 48 hours. Following 11
trypsinization, cells were counted using an automatic cell imaging counter, Cytorecon
(GE Healthcare). For oleic acid conjugation to BSA, a sodium oleate (≥99% (capillary
GC), Sigma) solution in 150 mM NaCl was added to a fatty acid-free BSA solution
(Wako). The final molar ratio of oleic acid to BSA was 6:1.
Animal experiments were conducted in accordance with the guidelines of the
Animal Care and Use Committee of Kumamoto University. For NAFLD induction,
7-week-old male C57BL/6J mice were fed a high fat diet (HFD) containing 21% kcal
fat (A02082003BP: Research Diets Inc.) or a methionine/choline-deficient diet (MCD,
A02082002BG: Research Diets Inc.), which also contains 21% kcal fat for four weeks.
Body weight was monitored weekly throughout the test period. After 16-hour fasting,
liver tissues were dissected, and the sections were either snap-frozen in liquid nitrogen
for RNA analyses or fixed with formalin for histological analyses.
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Statistical analyses All statistical analyses between two groups were done by Student’s t-test unless otherwise stated. Equality of variance was examined using F-test.
The accession number of microarray and ChIP-seq data in GEO is GSE59695.
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LSD2 selectively represses lipid metabolism genes in hepatocytes
Because LSD1 regulates the metabolic genes under diverse cellular contexts
(11, 12, 28), we tested, in this study, whether another FAD-dependent demethylase
LSD2 could be involved in the metabolic programming. During our initial examinations
in mouse tissues, we found that LSD2 was expressed more highly in the liver than in
other metabolic tissues (Fig. 1A). To gain insight into the role of LSD2 function in
hepatocytes, we depleted LSD2 in HepG2 human hepatic cells using three different
siRNAs (Fig. 1B), then carried out an expression microarray experiment. We detected
1,362 probe sets with more than 1.5-fold difference between the control and
LSD2-knockdown (KD) cells (Fig. 1C). Of these, 906 probe sets were up-regulated,
while 456 were down-regulated. Using gene set enrichment analysis (GSEA) (29, 30),
we established that genes associated with “metabolism of lipids and lipoproteins” were
significantly enriched in the probe sets that were up-regulated by LSD2-KD (statistical