CpG Array Analysis of Histone H3 Lysine 4 Trimethylation in Peripheral Blood Mononuclear Cells of Uremia Patients

Abstract

Studies of the epigenome have attracted little interest in nephrology, especially in uremia. Several lines of evidence have suggested that there are links between genomic DNA hypomethylation and cardiovascular complications in uremia patients. However, to date, our knowledge about the alterations in histone methylation in uremia is unknown. H3K4me3 variations were analyzed in peripheral blood mononuclear cells from 20 uremia patients and 20 healthy subjects, using chromatin immunoprecipitation microarray (ChIP-chip) approach. ChIP–real-time polymerase chain reaction (PCR) was used to validate the microarray results. mRNA expression and DNA methylation status can be further analyzed by quantitative (q) reverse transcription (RT)-PCR and methyl-DNA immunoprecipitation (MeDIP)-qPCR, respectively. Seven hundred twenty-six increased and 218 decreased H3K4me3 genes displaying significant H3K4me3 differences were found in uremia patients compared with healthy subjects. The results of ChIP–real-time PCR coincided well with microarray results. Expression analysis by qRT-PCR revealed positive correlations between mRNA and H3K4me3 levels. Aberrant DNA methylation can also be found on selected positive genes (CNOT1 PLTP EDG1 TCF3 KIR3DL2). In addition, we even found that there is an inverse relationship between H3K4me3 and promoter DNA methylation in uremia patients. Our studies indicate that there are significant alterations of H3K4me3 in uremia patients; these significant H3K4me3 candidates may help to explain the immunological disturbance and high cardiovascular complications in uremia patients. Such novel findings show the significance of H3K4me3 as a potential biomarker or promising target for epigenetic-based uremia therapies.

Introduction

U remia refers to the condition that occurs when kidney function regresses during chronic kidney disease. It is the terminal stage of chronic renal failure and requires replacement therapy, such as hemodialysis. Patients with uremia present a many-fold increased mortality rate for cardiovascular disease than the general population. Despite intensive research, the pathologic mechanisms of uremia phenotype are still not completely understood and are probably multifactorial. Both genetic and environmental factors have been associated with uremia phenotype, but these factors cannot entirely explain the onset of uremia phenotype. Recently, the associations between the unphysiological uremic environment and the epigenotype have attracted little interest among nephrologists (Stenvinkel and Ekström, 2008a, 2008b, 2009; Ekström and Stenvinkel, 2009). This association study may uncover new mechanisms to explain the uremia phenotype that characterizes these patients, such as the high incidence of cardiovascular disease.

Histone lysine methylation, which is one of the important epigenetic modifications, is believed to be part of a histone code and has been implicated in multiple biological processes including gene activation, silencing, X-chromosome inactivation, DNA repair, cell cycle control, and DNA methylation (Martin and Zhang, 2005; Ruthenburg et al., 2007). Lysine methylation displays the highest degree of complexity among known covalent histone modifications, with each site of methylation regulating the association of different effector molecules. Lysines can accept three methyl groups and, therefore, can be monomethylated, dimethylated, or trimethylated. Among the various histone lysine methylation patterns, recently, much attention has been focused on methylation at lysine 4 of histone H3 (H3K4), owing to its association with active chromatin and gene expression (Santos-Rosa et al., 2002; Schneider et al., 2004) and H3K4 can be mono-, di-, or tri-methylated, Trimethylated H3K4 (H3K4me3) is preferentially detected at active genes and has been proposed to promote gene expression through recognition by transcription-activating effector molecules (Bannister and Kouzarides, 2004). Aberrant alterations in histone lysine methylation patterns that change chromatin structure could lead to dysregulated gene transcription and disease progression. Therefore, it is of significant interest to investigate whether there are aberrant H3K4me3 in the unphysiological uremic environment. This may provide important clues to assist in the development of new treatments for uremia as well as to give a deeper understanding of the etiology of uremia phonotype.

In the postgenomic era, systematic and high-throughput technologies allow us to enumerate biological components on a large scale. As one of the approaches, chromatin immunoprecipitation coupled with microarray (ChIP-chip), which is an unbiased, high-throughput microarray, has been used to explore the genome-wide information on histone modifications (Wu et al., 2006; Bataille and Robert, 2009; Pillai and Chellappan, 2009). Ren et al. (2000) identified novel targets of the yeast transcription factors Gal4 and Ste12 first with ChIP-chip on a yeast intergenic DNA array. In humans, one of the first ChIP-chip experiments adopted was the use of a CpG island array for screening novel E2F4 targets (Weinmann et al., 2002). Recently, this approach has been applied successfully to delineate the profile of H3K9, H3K4, and H3K27 methylation in the human disease (Miao et al., 2007; Dai et al., 2010; Zhang et al., 2009).

Some investigations have recently indicated that aberrant DNA methylation is associated with cardiovascular events commonly observed in uremia, and folate treatment can reduce hyperhomocysteinemia, which is an independent risk factor for atherosclerosis, and correct DNA hypomethylation (Ingrosso et al., 2003; Stenvinkel et al., 2007). However, to date, there are no reports of uremia research from the aspect of histone modification. Hence, in this study, we adopted ChIP-chip technology to profile and compare the variations in H3K4me3 at the genome-wide level in peripheral blood mononuclear cells (PBMCs) from uremia patients and healthy controls to gain a better understanding of the pathogenic mechanisms in uremia. We also explored the relevance between H3K4me3 and DNA methylation under this disease condition.

Materials and Methods

Human subjects

Forty subjects were enrolled in the study (Table 1). They included 20 uremia patients on dialysis and 20 healthy volunteers. All uremia patients were recruited from the inpatient unit in the Department of Nephrology in the Shenzhen People's Hospital and were free of active infections, diabetes mellitus, and autoimmune diseases. Age-, race-, and sex-matched healthy controls were recruited by advertising. None of the patients received immunosuppressive treatment, lipid-lowering agents, or nonsteroidal anti-inflammatory drugs at the time of the study. The cause of renal failure was chronic glomerulonephritis. The Ethics Committee of the Jinan University approved the study and peripheral blood samples were obtained with informed consent from all participating individuals.

Table 1.

Demographic and Clinical Characteristics of the Patients and Controls

	Healthy controls	Uremia patients
Males/females (n)	12/8	12/8
Ages (years)	40.86 ± 12.24	43.22 ± 11.57
Creatinine (mg/dL)	0.94 ± 0.04	11.68 ± 1.51
BUN (mg/dL)	11.82 ± 1.55	68.87 ± 6.72
Hemoglobin (g/L)	131.30 ± 10.15	68.53 ± 8.99
Total protein (g/L)	70.60 ± 8.35	54.60 ± 6.53
Albumin (g/L)	45.80 ± 7.86	30.15 ± 5.05
C-reactive protein (μg/mL)	0.65 ± 0.03	4.78 ± 1.03
Urinary protein (g/day)	Negative	1.82 ± 0.36
Parathyroid hormone (pg/mL)	45.38 ± 7.57	989.58 ± 78.03
Ccr (mL/min)	108.50 ± 12.82	8.25 ± 1.80

Values are expressed as mean ± SD.

BUN, blood urea nitrogen; Ccr, creatinine clearance rate; SD, standard deviation.

Isolation of PBMCs

Blood samples were obtained from uremia patients (n = 20) and normal healthy donors (n = 20). The blood (10 mL per subject) was diluted with equal volumes of phosphate-buffered saline. An equal volume of diluted blood was overlaid on Ficoll-Paque Plus in a 1:1 ratio and centrifuged at 800 g for 25 min at 22°C. PBMC layer was harvested and washed with phosphate-buffered saline two times to remove plasma and Ficoll. Then, these samples were stored at −80°C until assay.

Chromatin immunoprecipitation microarray

The ChIP-chip was performed according to described protocols (Huebert et al., 2006) with some modifications. Briefly, PBMCs were crosslinked with 1% formaldehyde (final concentration) for 10 min at 37°C, and then glycine (0.125 M) was added for 5 min at 37°C to stop the reaction. After washing twice with 10 mL of ice-cold 1 × PBS, the cell pellets were resuspended with 300 μL lysis buffer (10 mM Tris-HCl [pH 8.0], 100 mM NaCl, 1 mM ethylenediaminetetraacetic acid [EDTA, pH 8.0], 0.1% Na-deoxycholate, and protease inhibitors) and incubated on ice for 30 min. Then the cell suspension was sonicated for 4 min total time (30 s “ON” and 30 s “OFF”) to reduce DNA lengths to between 200 and 1000 bp. Then, 555 μL of dilution buffer containing protease inhibitor cocktail was added to each ChIP sample. The lysate was then divided into three fractions. The first lysate was incubated with anti-K4 trimethylated histone H3 antibody (Upstate Biotechnology) at 4°C overnight. The second lysate was used as input control and the third lysate as negative control. To collect the immunoprecipitated complexes, 50 μL of magnetic beads (Bangs Laboratories) was added and incubated for 1 h at 4°C. Pellet beads were subjected to magnetic separation racking for 2 min at 4°C, and then the magnetic beads were sequentially washed in low salt, high salt, LiCl salt, and TE buffers. The protein/DNA complexes were eluted and formaldehyde crosslinks were reversed by heating the sample at 65°C for 5 h. Samples were treated with RNase for 20 min at 37°C and then proteinase K overnight. DNA was extracted by the phenol/chloroform method, ethanol precipitated, and resuspended in water. Polymerase chain reaction (PCR) amplification of DNA was carried out with diluted DNA aliquots, according to the whole-genome amplification kit (Sigma) instructions. After amplification, DNA was purified by QIAquick PCR purification kit (Qiagen), Cy5™-dUTP and Cy3™-dUTP (Invitrogen)–labeled methylated K4 precipitated DNA and input DNA, respectively, and DNA was cohybridized to the human 12K CpG-island array (UNH Microarray Centre, Toronto, Canada). The sequences of CpG islands on the array and alignment data are available at http://data.microarrays.ca/.

The hybridized microarray slides were then scanned using a GenePix 4000B scanner (Axon Instruments). GenePix pro V6.0 was used to read the raw intensity of the image. The resulting text files were imported into the Agilent GeneSpring GX software for further analysis. The two microarray datasets were normalized in GeneSpring GX using the Agilent two-color scenario (mainly LOWESS normalization), and then CpGs marked present (“All Targets Value”) were chosen for further analysis. The differences between test and control sample were identified by a twofold change (Kondo et al., 2004).

Chromatin immunoprecipitation–quantitative PCR

ChIP was conducted the same way as in ChIP-chip. DNA pools from ChIP, input control, and negative control were used for quantitative PCR (qPCR). PCR amplification was performed on an ABI 7700 Realtime PCR (Applied Biosystems). The PCR conditions were an initial step of 4 min at 95°C, followed by 40 cycles of 15 s at 95°C, 20 s at 59°C, and 20 s at 72°C. Primers were designed according to the selected genes for evaluating ChIP on chip data (Table 2). To generate a standard curve for each amplicon, Ct values of serially diluted input DNA, which was extracted in the ChIP experiment, were determined. The H3K4me3 changes were determined using the 2^−ΔΔCT method (Livak and Schmittgen, 2001). Melting curve analysis was performed for each reaction to ensure a single peak. Each experiment was performed in triplicate, and the values were averaged to obtain one datum per sample.

Table 2.

ChIP-Quantitative Polymerase Chain Reaction Primers Used for Validation of the CpG Array Data

Gene	Primer sequences (5′–3′)	Annealing (°C)	Size (bp)
CNOT1	F: GAAGCCGATGCCCACTGC R: CCCTCTGAGACCTCCACGAC	59	77
PLTP	F: CGCCTATGATGACTGGCTTGG R: TCGGGAGACTGGAGGCTGA	59	109
EDG1	F: AGATGCGAAGCGAGCCGTA3 R: CCATAAATCACAAGTGCCCAAG	59	266
KIR3DL2	F: CCCTCCATGCCGTGTCTACT R: TTCTCCCATTTCCGCTTTCT	59	206

F, forward primer; R, reverse primer.

RNA extraction and real-time quantitative reverse transcription–PCR

Total RNA was extracted from PBMCs with Trizol reagent (Invitrogen) following the manufacturer's instructions. The concentration and quality of RNA were measured by ultraviolet absorbance at 260 and 280 nm (A260/A280 ratio) and checked by agarose gel electrophoresis individually. Two micrograms of total RNA was reverse transcribed into cDNA with M-MLV reverse transcriptase and oligo-dT as a primer. Real-time PCR involved SYBR-green dye and Taq polymerase. One-tenth of the resulting cDNA template was used for DNA amplification on a 7700 Real-Time PCR System apparatus. PCR amplification using the real-time PCR was performed as described above. A standard curve for each gene was generated by serial dilution of the amplified product standard of known starting concentration. Glyceraldehyde-3-phosphate dehydrogenase mRNA, which yielded a amplicon of 203 bp, was used as a control (primers 5′-AAGAAGGTGGTGAAGCAGGC-3′ and 5′-TCCACCACCCTGTTGCTGTA-3′) for data normalization. The PCR primers used for each gene in this analysis are given in Table 3. Expression was assessed by evaluating threshold cycle (CT) values. The relative amount of expressed mRNA was calculated by the 2^−ΔΔCT method (Livak and Schmittgen, 2001).

Table 3.

Primers Used in Reverse Transcription–Quantitative Polymerase Chain Reaction

Gene	Primer sequences (5′–3′)	Annealing (°C)	Size (bp)
CNOT1	F: GAAGTGTTCCCAGTAGACCTCAT R: CATCCTCTGGTGGTGCTTTC	59	160
PLTP	F: CATGCGGGATTCCTCACC R: GAGGGGGCACTACAGGCTAT	59	220
EDG1	F: GGAAGGGAGTATGTTTGTGGC R: GGAAGTTATTGCTCCCGTTG	59	116
KIR3DL2	F: GTGCGTTGGGTTCTTCTTG R: CGTGGCTTCTGTCTTCTTTGT	59	176
TCF3	F: ACCCTGTCCTTCACTCCTTG R: TGTCCTGTGCTGGCTCCT	59	89

Methyl-DNA immunoprecipitation–qPCR

Genomic DNA from 20 uremia patients and 20 healthy controls was prepared by overnight proteinase K treatment, phenol–chloroform extraction, ethanol precipitation, and RNase digestion. Before carrying out methyl-DNA immunoprecipitation (MeDIP), genomic DNA was sonicated to produce random fragments ranging in size from 200 to 1000 bp. Six micrograms of fragmented DNA was used for a standard MeDIP assay. Following denaturation (95°C 10 min), DNA was incubated overnight at 4°C with 8 μg of 5-methylcytidine monoclonal antibody (Eurogentec). Fifty microliters of rabbit anti-IgG magnetic beads (BioLabs S1430S) was added and incubated for 2 h at 4°C. Magnetic beads–monoclonal antibody–DNA complexes were sequentially washed by gentle mixing at 4°C for 4 min with 1 mL of wash buffer 1 (2 mM EDTA, 20 mM Tris [pH = 8.0], 1% Triton X-100, 0.1% sodium dodecyl sulfate [SDS], 150 mM NaCl), wash buffer 2 (2 mM EDTA, 20 mM Tris [pH = 8.0], 1% Triton X-100, 0.1% SDS, 500 mM NaCl), and wash buffer 3 (1 mM EDTA, 10 mM Tris [pH = 8.0]). After washing, the complexes were subjected to magnetic separation rack for 10 min at 4°C, and then elution was performed with 400 μL elution buffer (50 mM Tris-HCl [pH 8.0], 10 mM EDTA [pH 8.0], 1% SDS). The elution fraction was subjected to by phenol–chloroform extraction and ethanol precipitation. The quantity of immunoprecipitated DNA was checked with a Nanodrop spectrophotometer (Agilent). PCR amplification using the real-time PCR was performed as described above. Relative enrichment of DNA methylation for each gene was determined by the same method described above. The PCR primers used for each gene in this analysis are given in Table 4.

Table 4.

Primers Used in Methyl-DNA Immunoprecipitation-Quantitative Polymerase Chain Reaction

Gene	Primer sequences (5′–3′)	Annealing (°C)	Size (bp)
CNOT1	F: GAAGCCGATGCCCACTGC R: CCCTCTGAGACCTCCACGAC	59	77
PLTP	F: CGCCTATGATGACTGGCTTGG R: TCGGGAGACTGGAGGCTGA	59	109
EDG1	F: AGATGCGAAGCGAGCCGTA R: CCATAAATCACAAGTGCCCAAG	59	266
KIR3DL2	F: CCCTCCATGCCGTGTCTACT R: TTCTCCCATTTCCGCTTTCT	59	206
TCF3	F: GTGAGGGACTTGGGCTTTC R: CATGGCGTGAGGTAGGGAT	59	95

Statistical analysis

Quantitative data are shown as mean values ± standard deviation. Statistical analyses were performed using independent-samples t-test. p < 0.05 was regarded as statistically significant.

Results

To obtain a global overview of H3K4me3 profiles of uremia patients and healthy controls, the ChIP-chip data were first corrected for background and normalized to remove systematic bias. Methylation profiles were then determined by the ratios between normalized Cy5 and Cy3 intensities. The ratios >2 was used as a cutoff for scoring positive for H3K4me3. Hence, we selected the ratios >2 as H3K4me3 targets and identified 858,489 targets using CpG arrays. Detailed lists of candidate genes are provided in Supplementary Tables S1 and S2 (Supplementary Data are available online at www.liebertonline.com/dna). Next, we analyzed H3K4me3 candidate gene variations between uremia patients and healthy subjects. Our last aim was to identify genes that underwent H3K4me3 changes in uremia patients compared with healthy subjects. For this, we adopted two steps to analyze these data. First, genes that show significant differences (the ratio >2 or <0.5) between uremia and control groups are H3K4me3 candidates; second, genes that show significant methylation differences between uremia and control groups, but are not identified as H3K4me3 targets, were excluded in the target list. These stringent restrictions might have caused some false negatives (i.e., genes that underwent changes in methylation but not identified by our analysis), but it reduced the possibility of introducing too many false positives.

Comparison of H3K4me3 status between uremia patients and healthy subjects

By applying the above analysis procedure to the CpG array, we found that 944 probes displayed significant H3K4me3 differences in uremia patients compared with healthy subjects. Among these probes, 726 probes displayed increased H3K4me3 and 218 probes decreased H3K4me3. The H3K4me3 alterations of selected 20 genes are presented in Table 5. Detailed lists of genes with H3K4me3 alterations are provided in Supplementary Table S3.

Table 5.

The Selected 20 Probes with H3K4me3 Alterations Between Uremia Patients and Healthy Subjects, Identified by ChIP-CpG Microarray Analysis

Probe ID	Fold change	Within	Genome location	Description
UHNhscpg0009553	6.58	WARS	chr14:99911464-99912659	Tryptophanyl-tRNA synthetase isoform a
UHNhscpg0007040	4.51	PLTP	chr20:43973065-43973549	Phospholipid transfer protein isoform a
UHNhscpg0009740	3.75	MAP3K6	chr1:27564764-27565219	Mitogen-activated protein kinase kinase kinase
UHNhscpg0005536	3.59	EDG1	chr1:101474992-101475322	Endothelial differentiation gene encoded receptor-1
UHNhscpg0005994	3.21	JMJD2D	chr11:94345677-94346588	Jumonji domain containing 2D
UHNhscpg0008069	3.07	CHPD1	chr12:100616320-100616556	Cholinephosphotransferase
UHNhscpg0007675	3.04	CPT1A	chr11:68363111-68363332	Carnitine palmitoyltransferase 1A isoform 2
UHNhscpg0005260	2.94	TCF3	chr19:1602017-1602493	Transcription factor 3
UHNhscpg0007184	2.69	CACNA1D	chr3:53684940-53685411	Calcium channel, voltage-dependent, L type, alpha
UHNhscpg0002426	2.32	LTBR	chr12:6365416-6365921	Lymphotoxin beta receptor
UHNhscpg0006652	2.25	IL4R	chr16:27237495-27237759	Interleukin 4 receptor alpha chain isoform a
UHNhscpg0009183	2.17	FABP3	chr1:31618283-31618497	Fatty acid binding protein 3
UHNhscpg0010844	2.13	CNOT1	chr16:57220747-57221210	CCR4-NOT transcription complex, subunit 1
UHNhscpg0005908	2.02	CR2	chr1:205694276-205694997	Complement component (3d/Epstein Barr virus)
UHNhscpg0008961	0.49	KIR3DL2	chr19:60055042-60055358	NK receptor
UHNhscpg0007755	0.40	IREB2	chr15:76517964-76518338	Iron-responsive element binding protein 2
UHNhscpg0005776	0.38	ABL1	chr9:132728826-132729034	v-abl Abelson murine leukemia viral oncogene
UHNhscpg0001229	0.26	HIGD2BP	chr15:70765263-70765993	HIG1 domain family, member 2B pseudogene
UHNhscpg0008692	0.30	LACE1	chr6:108722592-108724012	Lactation elevated 1
UHNhscpg0006307	0.15	HPSE2	chr10:100983740-100984257	Heparanase 2

Validation for CpG microarray data

To validate the microarray results, selected genes that displayed increased H3K4me3 (CNOT1 PLTP EDG1) and decreased H3K4me3 (KIR3DL2) in uremia patients were then verified by ChIP-qPCR. As shown in Table 6, the qPCR results of these chosen K4me3 candidates are consistent with the ChIP array analyses. Taken together, these ChIP validations support the accuracy of the array data.

Table 6.

Quantitative Real-Time Polymerase Chain Reaction Verification of ChIP-chip Data

		Quantitative real-time PCR		Microarray change
Probe ID	Gene	P	C	P
UHNhscpg0010844	CNOT1	1.00	2.58 ± 0.03^a	U
UHNhscpg0007040	PLTP	1.00	2.51 ± 0.03^a	U
UHNhscpg0005536	EDG1	1.00	1.77 ± 0.03^a	U
UHNhscpg0008961	KIR3DL2	1.00	0.45 ± 0.02^a	D

Microarray changes (H3K4me3) are presented as upregulated (U) and downregulated (D) compared with the healthy group. Similarly, real-time PCR values are expressed as mean ± SD compared with the healthy group. Quantitative data were calculated by 2^−ΔΔCT.

p < 0.05, which is considered statistically significant (independent-samples t-test). The assays were done in triplicate.

C, healthy subjects; P, uremia patients; PCR, polymerase chain reaction.

H3K4me3 alterations and gene expression

To confirm correlations between H3K4me3 and gene expression, we next performed mRNA expression analysis by real-time quantitative reverse transcription–PCR for the five randomly selected H3K4me3 candidates (CNOT1, PLTP, EDG1, TCF3, KIR3DL2). As shown in Table 7, there are mRNA expression changes on H3K4me3 candidates in uremia patients compared with healthy subjects.

Table 7.

H3K4me3 Variations and mRNA Expression Levels by Real-Time Quantitative Reverse Transcription–Polymerase Chain Reaction Analysis

		qRT-PCR		Microarray change
Probe ID	Gene	P	C	P
UHNhscpg0010844	CNOT1	1.00	2.20 ± 0.03^a	U
UHNhscpg0007040	PLTP	1.00	1.84 ± 0.04^a	U
UHNhscpg0005536	EDG1	1.00	2.18 ± 0.03^a	U
UHNhscpg0005260	TCF3	1.00	1.78 ± 0.03^a	U
UHNhscpg0008961	KIR3DL2	1.00	0.56 ± 0.02^a	D

Microarray changes (H3K4me3) are presented as U and D compared with the healthy group. Similarly, relative mRNA values are expressed as mean ± SD compared with the healthy group. Quantitative data were calculated by 2^−ΔΔCT.

p < 0.05, which is considered statistically significant (independent-samples t-test). The experiments were done in triplicate.

qRT, quantitative reverse transcription.

The relationship between H3K4me3 and DNA methylation

To further study the mechanisms and the relationship between H3K4me3 and DNA methylation, we examined the methylation status of the selected five positive genes, CNOT1, PLTP, EDG1, TCF3, and KIR3DL2, in uremia patients compared with healthy subjects. The results are presented in Table 8.

Table 8.

The Relationship Between H3K4me3 Alterations and DNA Methylation Levels by Real-Time Polymerase Chain Reaction Analysis

		MeDIP-qPCR		Microarray change
Probe ID	Gene	P	C	P
UHNhscpg0010844	CNOT1	1.00	0.76 ± 0.02^a	U
UHNhscpg0007040	PLTP	1.00	0.54 ± 0.03^a	U
UHNhscpg0005536	EDG1	1.00	0.63 ± 0.03^a	U
UHNhscpg0005260	TCF3	1.00	0.58 ± 0.02^a	U
UHNhscpg0008961	KIR3DL2	1.00	1.56 ± 0.02^a	D

Microarray changes (H3K4me3) are presented as U and D compared with the healthy group. Similarly, real-time PCR values (DNA methylation levels) were expressed as mean ± SD compared with the healthy group. Quantitative data were calculated by 2^−ΔΔCT.

p < 0.05, which is considered statistically significant (independent-samples t-test). The experiments were done in triplicate.

MeDIP, methyl-DNA immunoprecipitation.

Discussion

Modifications of histone tails are thought to specify a code that regulates the expression of genes (Jenuwein and Allis, 2001). The emerging consensus is that high levels of H3K4me3 are associated with active genes. Analyses of the H3K4me3 distribution indicate that this histone modification occurs primarily in the vicinity of the transcription start site (Liang et al., 2004; Roh et al., 2006). To cover these regions, in this study, we used human 12K CpG island arrays, which contain a significant percentage of the CpG islands found in the human genome, with ∼68% located near a transcription start site, although not fully representative of promoter regions (Heisler et al., 2005). H3K4me3 has been observed several years back, but still only little is known about its subtle interrelationships with other epigenetic modifications and potential functional significance in human disease. Based on these, in this study, we selected H3K4me3 as the target, performed investigation by ChIP-chip strategy, and explored the hypothesis that H3K4me3 are associated with the pathogenesis of uremia.

In the present study, we mainly analyzed the trimethylation status of H3K4 in uremia patients and healthy subjects. The identified candidate genes with significant methylation differences are available from Supplementary Table S3. All in all, these genes included genes associated with immunity, cell signal transduction, protein transcription and synthesis, ion channel and transporters, DNA and RNA processing, and extracellular matrix etc. Using ChIP-qPCR, we were able to confirm the validity of the microarray data, and the result consistency ultimately proves the value of this approach.

Among the candidates identified in the CpG array, interestingly, we found that CNOT1 displayed increased H3K4me3 between uremia patients and healthy controls. CNOT1 is a large subunit of CCR4-NOT transcription complex. The Ccr4-Not complex is a highly conserved regulator of mRNA metabolism (Denis and Chen, 2003; Collart and Timmers, 2004). It also associates with the proteasome and regulates histone methylation (Laribee et al., 2007). Recently, some data show that the CCR4/NOT complex selectively regulates H3K4me3 levels by acting parallel to or downstream of the Bur1/2 kinase to facilitate PAF complex recruitment and subsequent H2B ubiquitylation (Mulder et al., 2007). These results provide a connection between the role of the Ccr4-Not complex in the regulation of transcription and histone methylation marks. Phospholipid transfer protein (PLTP) is one of at least two lipid transfer proteins found in human plasma. PLTP participates in key processes in lipoprotein metabolism, including interparticle phospholipid transfer, remodeling of high-density lipoprotein (HDL), cholesterol and phospholipid efflux from peripheral tissues, and the production of hepatic very low-density lipoprotein (van Tol, 2002; Samyn et al., 2008). Mice with overexpression of human PLTP have an increased ability to generate prebeta-HDL, reduced total HDL levels, and increased susceptibility to atherosclerosis (Samyn et al., 2009). Our study demonstrated that the mRNA expression of PLTP was upregulated; this might provide a novel potential explanation for increased atherosclerosis in uremia patients. Endothelial differentiation gene encoded receptor-1 (EDG1), renamed Sphingosine-1-phosphate (S1P) receptor subtype 1 (S1P1), is structurally similar to G protein-coupled receptors and is highly expressed in endothelial cells. It binds the ligand S1P with high affinity and high specificity and has been suggested to be involved in the processes that regulate the differentiation of endothelial cells (Watterson et al., 2002). Chi and Flavell (2005) demonstrated that S1P1 signaling affects systemic trafficking of peripheral T cells and immune responses and highlighted that levels of S1P1 expression represent an important mechanism of immune regulation. In this study, we showed that the mRNA expression EDG1 was upregulated, which will provide a better explanation for immune abnormalities observed in uremia patients.

We also observed that the other two candidate genes KIR3DL2 and TCF3 showed a significant decrease in histone H3K4me3 in uremia patients compared with healthy subjects. KIR3DL2 is one member of killer cell immunoglobulin-like receptors (KIRs), which are transmembrane glycoproteins expressed by natural killer cells and subsets of T cells. The ligands for several KIR proteins are subsets of HLA class I molecules (Becker et al., 2003); thus, KIR proteins are thought to play an important role in regulation of the immune response. Recent studies suggest that aberrant KIR3DL2 expression is involved in some human diseases (Chan et al., 2005; Obama et al., 2007). In this study, we demonstrated that the mRNA expression of KIR3DL2 was downregulated. This may be associated with immune abnormalities involved in uremia. For the other gene, the transcription factor 3, our results showed that the mRNA expression of TCF3 was downregulated in uremia patients compared with healthy subjects. TCF3 (renamed E2A) protein is thought to play a critical role in the regulation of cell commitment, growth, and differentiation in a range of cell types including lymphocytes, muscle cells, and neurons (Slattery et al., 2008). Emerging evidence suggests that E2A proteins also play key roles in the process of epithelial mesenchymal transition, a mechanism that contributes significantly to kidney disease progression and tumor metastasis (Lan, 2003).

DNA methylation is a postsynthetic modification that is responsible for epigenetic modulation of gene expression. A large body of work has demonstrated that cytosine methylation of the regulatory sequences of DNA is associated with transcriptional inactivation of genes, whereas hypomethylation contributes to the activation of transcription (Delcuve et al., 2009; Law and Jacobsen, 2009). DNA methylation and histone modification may act synergistically or antagonistically on gene expression. Recent studies have suggested that acetylated histones and dimethylated histone H3 at lysine 4 (H3K4me2) are inversely correlated with DNA methylation (Irvine et al., 2002; Okitsu and Hsieh, 2007); however, whether increased or decreased H3K4me3 is also accompanied by local changes in DNA methylation in uremia patients remains less clear. Thus, here we again selected five positive genes (CNOT1, PLTP, EDG1, TCF3, KIR3DL2) from this microarray to initially explore whether these modifications coincide in the context of this disease. We found that there is an inverse relationship between H3K4me3 and promoter DNA methylation in uremia patients. This may indicate that DNA methylation and H3K4me3 are cooperatively involved in the pathogenesis of uremia patients, at least in these genes.

Taken together, here, for the first time, we systematically evaluated the status of H3K4me3 in PBMCs of uremia patients and gained new insights into the links between key genes and histone methylation in the context of uremia. Our results indicate that H3K4 trimethylation is involved in unphysiological uremic environment and these novel candidate genes may become potential biomarkers or future therapeutic targets. Further investigations are needed to clarify the roles of identified H3K4me3 candidate genes in the pathogenesis of uremia.

Footnotes

Acknowledgments

The authors are deeply grateful to all the volunteers who donated blood. This work was supported by grants from the Key Project for Science and Technology of Shenzhen (no. 200801013) and the National Natural Science Foundation of China (no. 30972741).

Disclosure Statement

The authors confirm that neither the text nor the data reported have been published previously. None of the authors has any potential financial conflict of interest related to this manuscript.

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