Changes in DNA methylation in naïve T helper cells regulate the pathophysiological state in minimal-change nephrotic syndrome
© The Author(s) 2017
Received: 16 September 2016
Accepted: 29 July 2017
Published: 15 September 2017
DNA methylation plays a crucial role in regulating transcription, and changes in DNA methylation affect gene expression and disease development. Minimal change nephrotic syndrome (MCNS) has been reported to involve immunological disturbances. Since the characteristic features of the disease include recurrent relapse and sex and age preference, the disease pathogenesis may be partly related to epigenetic changes. However, little is known about these changes.
We analyzed genome-wide DNA methylation using the microarray-based integrated analysis of methylation by isoschizomers method. This method was used to evaluate methylation in monocytes (patient number; n = 6) and naïve T helper cells (n = 4) from the peripheral blood of MCNS patients both in relapse and following remission and that of healthy controls (n = 5).
In total, 85 co-occurring genes were identified in naïve T helper cells, while 4 such genes were identified in monocytes, which were common among the 3 following comparisons for changes in DNA methylation using sample pairs: (1) relapse versus remission, (2) relapse versus controls, and (3) remission versus controls. In 82 of 85 co-occurring genes (96.5%) in naïve T helper cells, the level of DNA methylation was altered according to disease activity, but was not related to disease activity in the 4 genes detected in monocytes.
Therefore, in 82 co-occurring genes in naïve T helper cells, the regulation of DNA methylation was well correlated with the clinical and pathophysiological state. Our genome-wide approach to analyze DNA methylation provides further insight into the pathogenesis of MCNS and indicates potential prediction and diagnostic tool for the disease.
Minimal-change nephrotic syndrome (MCNS) is the most common cause of nephrotic syndrome in children. It is characterized by massive proteinuria and hypoalbuminemia in a relapse/remission course without histological evidence of immune-mediated inflammatory damage. These manifestations are typically reversible with corticosteroid therapy. Although the pathogenesis of MCNS remains unknown, immunological disruption has been implicated in this disease , and T cell-derived vascular permeability factors have been shown to be responsible for alterations in glomerular permeability [2–4]. During childhood, the incidence of MCNS has been reported to be twofold higher in male children, with a prevalence that is inversely proportional to age. Recurrent relapse tends to decrease after adolescence [5, 6]. The characteristic features of MCNS include a recurrent relapse/remission course, gender preference, age preference of onset and relapse, and steroid response in most patients, therefore, a single genetic defect is likely not responsible for the disease. However, epigenetic alterations may occur without a direct change in the genetic sequence and regulate the phenotype in relapsed MCNS patients.
Epigenetics is the study of mitotically heritable changes, which alter gene expression without direct DNA sequence alterations. DNA methylation, one of the principal epigenetic mechanisms in mammals, involves the covalent addition of a methyl group to a cytosine residue followed by a guanine residue . DNA methylation regulates gene expression and is essential for differentiation, embryonic development , genomic imprinting , and X-chromosome inactivation . DNA methylation within the promoter region of a gene is commonly associated with transcriptional inactivation, whereas demethylation contributes to transcriptional activation. Changes in the DNA methylation profile can also lead to differences in gene expression programs and thus influence disease development .
Audard et al. reported that nuclear factor related to kappa B binding protein (NFRKB) is highly expressed in the nuclear compartment during relapse and that NFRKB promotes the hypomethylation of genomic DNA, suggesting epigenetic involvement in the pathogenesis of MCNS . Elie et al. suggested that alterations in epigenetic modifications induced by external cues such as viral infection affect MCNS progression; this is supported by the fact that MCNS relapse is frequently triggered by external or internal environmental factors. Such environmental changes affect the epigenotype and alter gene expression . We previously reported that the DNA methylation states of 3 genes, GATA binding protein 2 (GATA2), pre-B cell leukemia homeobox 4 (PBX4), and nyctalopin (NYX) in naïve T helper cells (Th0s), but not in monocytes, significantly differed between relapse and remission in affected patients . Additionally, epigenetic regulation in Th0s underlies the pathogenesis of MCNS, the disturbance of which has been implicated in MCNS development .
In this study, we examined whether DNA methylation states change according to the clinical states of MCNS. We identified candidate genes whose DNA methylation states change during relapse and remission and compared them to those in healthy controls to gain insight into MCNS pathogenesis.
Study population characteristics
MCNS patient characteristics at sampling
Age at onset
Total follow-up perioda, months
Total relapse timea
Sampling age at rel
Relapse times at sampling
PSL at relc mg/day
PLS at remc mg/d
IS at relc mg/day
IS at remc mg/day
CD14+, n = 6
RO−, n = 4
10 years, 4 months
13 years, 0 months
5 years, 4 months
6 years, 0 months
5 years, 11 months
18 years, 6 months
7 years, 3 months
19 years, 10 months
9 years, 9 months
11 years, 7 months
11 years, 11 months
12 years, 5 months
8 years, 4 months
13 years, 5 months
Microarray-based integrated analysis of methylation by isoschizomers (MIAMI)
Number of probes in which DNA methylation changed significantly
Relapse versus remission
Relapse versus control
Remission versus control
Genes co-occurring among three comparisons in monocytes and Th0s derived from patients in relapse and remission, and healthy controls
List of the 82 genes co-occurring among the three sets of comparisons in Th0 cells
Less methylated in relapse
More methylated in relapse
Common gene name
Common gene name
Ingenuity pathway analysis (IPA) of 85 co-occurring genes in Th0s
Ingenuity pathway analysis of the 85 co-occurring genes
(a) Top molecules
SAC3 domain containing 1
Eukaryotic translation initiation factor 3, subunit B
Peptide deformylase (mitochondrial)
FK506 binding protein 1A, 12 kDa
TIMP metallopeptidase inhibitor 2
Ryanodine receptor 3
U2 small nuclear RNA auxillary factor 2
UDP-Gal:betaGal beta 1, 3-galactosyltransferase polypeptide 6
Chromobox homolog 7
Cyclin-dependent kinase 19
(b) Top canonical pathways
Insulin receptor signaling
Protein kinase A signaling
Clarin-mediated endocytosis signalling
Co-occurring genes between Th0s isolated from patients in relapse and remission, and healthy controls
We analyzed 206 genes that were common in the 2 comparisons, including in relapse versus remission and relapse versus controls (Additional file 2: Table S2), and 58 genes in relapse remission versus controls (Additional file 3: Table S3) in Th0 cells, shown in the Venn diagram in Fig. 3b. The former 206 genes are proteinuria-related genes, while the latter 58 genes are disease-related genes. Among the 206 co-occurring genes, 186 genes were less methylated in the relapse (proteinuric) state than in the remission and control groups (non-proteinuric state), while the remaining 20 were more methylated in the relapse (proteinuric) state than in other samples (non-proteinuric) (Additional file 2: Table S2). Furthermore, among the latter 58 co-occurring genes, 20 were less methylated in both relapse and remission (disease) than in controls (non-disease), while the remaining 37 genes were more methylated in relapse and remission (disease) than in controls (non-disease) (Additional file 3: Table S3). The consistency of the results from the 2 different experiments using each probe indicates the accuracy of the experiment and the high probability of detection. We further examined these genes using DAVID bioinformatics resources 6.7 . The 5 KEGG pathways identified the 206 proteinuria-associated genes, cytokine-cytokine receptor interaction (hsa04060), and hematopoietic cell lineage (hsa04640), glycine, serine, and threonine metabolism (hsa00260), and 56 disease-associated genes, including 1 KEGG pathway of focal adhesion (hsa04510) (Additional file 4: Table S4, Table S5).
We previously identified 3 genes (GATA2, PBX4, and NYX) showing significantly lower methylation in Th0s in the relapse state than in the remission state by using regression analysis , which was originally used in the MIAMI method [19, 20]. In the current study, we used the Mahalanobis distance rather than regression analysis and determined that probes showing 99% CI were significant. Mahalanobis distance is the distance of the sample points from the center of the mass. Therefore, this value shows greater correlation with the differences in DNA methylation ratios between 2 samples in paired comparisons compared to regression analysis. The distributions of distance for all probes from the center of the mass were significantly different (P < 0.0001) between monocytes and Th0s, even though they were from the same individuals, and were significantly greater in Th0s than in monocytes in all 3 comparisons (Fig. 2a–c). This indicates that the steady-state regulation of DNA methylation differs between monocytes and Th0s and that more significant changes in DNA methylation were observed in Th0s than in monocytes in each comparison.
MIAMI is used for semi-quantitative and comparative analysis of DNA methylation. In 82 (95.6%) of the 85 co-occurring genes in 3 permutations of 3 Th0s samples, DNA methylation changes occurred in the same direction and was consistent in all 3 comparisons, and these changes were in accordance with the level of disease activity. This finding indicates that DNA methylation of the 82 genes is very relevant to MCNS development. Furthermore, 2 associated network functions were identified by IPA, involving 34 of the 85 co-occurring genes. The network functions contained nuclear factor kappa B (NF-κB) (Fig. 5a) and tumor necrosis factor alpha (TNF-α) (Fig. 5b) networks, both of which have been previously implicated in MCNS pathogenesis [21–25]. Particularly, TNF was less methylated in relapse than in remission, which is consistent with previous reports of elevated TNF-α levels in relapse [25, 26]. Moreover, the proteinuric state was associated with 206 co-occurring genes, while the disease was associated with 58 co-occurring genes, demonstrating that the direction of changes in DNA methylation in Th0s was consistent with disease activity (Fig. 3b) (Additional file 2: Table S2, Additional file 3: Table S3).
In this study, we focused on immunocytes and their epigenetic states. We purified and used 2 different cell types from PBMCs isolated from the participants: monocytes, which are precursors of antigen-presenting cells derived from the myeloid cell series, and Th0s, derived from the lymphoid system, because different types of blood cells have different methylation patterns that reflect differences in gene expression during cell differentiation into specific lineages . An increasing body of evidence suggests that epigenetic regulation is essential for the differentiation of blood cells such as T helper type 2 (Th2) cells [27, 28]. Therefore, we isolated and studied monocytes and Th0s rather than total PBMCs, which contain a mixture of various cell types in different developmental stages and in differing epigenetic states. This restricted the analysis to epigenetic changes related to disease development, avoiding the inclusion of changes caused by differences in cell composition in the analysis with isolated cell types. Although Th2 activation has been shown to be involved in MCNS [21, 29–32] we hypothesized that epigenetic changes occur in Th0s before differentiation into Th2 cells, predisposing individuals to the active state of the disease. Examination of Th0s revealed that relevant genes showed modified DNA methylation states associated with disease activity. This is consistent with the results of a previous study showing that immature lymphocytes play a role in MCNS pathogenesis .
We found that variations in DNA methylation are associated with disease activity, proteinuria, and disease development in Th0s rather than in monocytes. Furthermore, the functions of the 82 genes associated with disease activity were found to be related to the TNF-α or NF-κB pathways, which have been reported to be related to MCNS pathogenesis. These results may suggest that the DNA methylation changes in Th0s were not only concomitant with disease activity, but also functionally relevant.
The limitation of this study is that limited number of patients and heterogeneity in treatment states were included. Genes that are sensitive to prednisolone and cyclosporine exposure in DNA methylation may be included in the detected genes. Further studies that include a larger number of MCNS patients without any immunotherapies both at relapse and remission are required for the identification of candidate genes for MCNS.
Based on our data though it is preliminary, regulation of DNA methylation in Th0s may underlie the pathogenesis and pathophysiological states of MCNS. This suggests that DNA methylation analysis could be potentially a useful diagnostic tool that can be used to distinguish different disease states, particularly between remission and cured states.
Samples for MIAMI analysis were obtained from 6 male patients with MCNS (Table 1) at relapse and following complete remission. All patients had developed nephrotic syndrome before 16 years of age, and were diagnosed according to the criteria of the International Study of Kidney Disease in Children. In brief, the diagnostic criteria for MCNS are (1) heavy proteinuria of ≥0.96 g/day m2 BSA, determined using a urine sample collected overnight; (2) hypoalbuminemia of ≤2.5 g/dL; (3) no evidence of underlying systemic diseases nor exposure to agents that are known to be associated with NS at its onset; (4) response during 8 week of the initial treatment with 60 mg/24 h m2 prednisone in three divided daily doses for 4 week, followed by administration of 40 mg/24 h m2 in divided doses on 3 consecutive days per week for 4 week. Response was defined as the reduction in the rate of urinary excretion of protein to <4 mg/h m2 (Albustix, 0 to trace) for 3 consecutive days. Relapse involved the reappearance of proteinuria of 40 mg/h m2 (Albustix, + +or greater) for 3 consecutive days . A frequent relapser is a patient who experiences 2 or more relapses during a 6-month period subsequent to treatment .
Informed consent was obtained from all parents for the children and from the patients who were older/adolescents. The healthy controls included five male voluntary participants who were 23 years old. This study was approved by the Ethics Committee of Gunma University Graduate School of Medicine, Japan (Receipt Number 89).
PBMCs were isolated from 20 mL samples of anti-coagulated blood obtained by gradient separation using the Lymphprep™ Tube (Axis-Shield PoC AS, Oslo, Norway). Monocytes and Th0s were separated using an autoMACS Pro Separator (Miltenyi Biotec, Bergisch Gladbach, Germany) from PBMCs that had been magnetically labeled with CD14, CD4, and CD45RO MicroBeads (Miltenyi Biotec). To obtain monocytes, the CD14-positive (CD14+) fraction was collected as monocytes, whereas CD14-negative, CD4-positive, and CD45RO-negative (CD14−CD4+CD45RO−) fractions were collected as Th0s. Flow cytometric analysis using a MACSQuant® Analyzer (Miltenyi Biotec) showed that the precision of cell separation was 96.2% for CD14+ cells and 94.46% for CD4-positive and CD45RA-positive cells as a CD45RO-negative fraction .
CD14+ monocytes were obtained from all 6 patients, while CD14−CD4+CD45RO− Th0 cells were obtained from 4 of these patients (Table 1a) for MIAMI analysis, both at relapse and following complete remission. Genomic DNA (gDNA) was extracted from these cells as previously described . Consistent amounts of extracted gDNA (300 ng from monocytes and 250 ng from Th0s) from each selected cell type were pooled and used for subsequent DNA methylation analysis using MIAMI. This was done to exclude the influence of epigenetic differences among cell types, identify specific changes resulting from the clinical courses of MCNS, and reduce noise caused by individual differences.
To determine methylation states, we used the MIAMI method, which provides high-throughput global analysis of DNA methylation [19, 20]. Since this method is based on the comparison of the digestion efficiency between 2 different samples by methyl-sensitive HpaII restriction enzymes, it indicates the differences in DNA methylation between the samples. The method was performed as described previously using 1.8 and 1.0 µg of pooled gDNA from the monocytes of 6 patients and Th0s of 4 patients, respectively [14, 19, 20]. Briefly, isoschizomers (HpaII and MspI) that recognize the same recognition site (CCGG) were used. Pooled gDNA was digested with HpaII, a methylation-sensitive restriction enzyme that cleaves only unmethylated DNA, and then the samples were adapter-ligated and amplified by PCR using primers designed against the adapter sequences. The samples were further digested with MspI, a methylation-insensitive enzyme that digests CCGG sites irrespective of their methylation status, and were amplified again with the same primer set (HpaII–MspI treatment). The second MspI treatment yielded amplicons from unmethylated DNA fragments only. Hence, only HpaII-cleavable unmethylated DNA fragments were amplified and were quantified based on their fluorescence intensities by microarray analysis. The amplified products were then labeled with Cy3 (remission or control samples) or Cy5 (relapse or remission samples) and co-hybridized (i.e. remission vs relapse, control vs relapse, or control vs remission) to a microarray spotted with 38,172 sixty-mer oligonucleotides covering the vicinity of the transcription start sites of 14,978 genes. Following hybridization, the microarray was scanned and the fluorescence intensities obtained were quantified and normalized. The same pooled gDNA samples were treated first with MspI rather than HpaII (MspI–MspI treatment) and analyzed on a duplicate array to correct for false-positives caused by single-nucleotide polymorphisms or incomplete digestion.
Gene networks and canonical pathways representing key genes were identified using the curated IPA database (http://www.ingenuity.com/). The data set containing gene identifiers and corresponding fold-changes was uploaded into the web-delivered application, and each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. IPA generates hypothetical protein–protein interactions, which are not limited to “binding”, but also include “activation”, “inhibition”, “expression”, and other interactions described in the literature. Functional analysis identified the biological functions and/or diseases that were most significant to the data sets. Fisher’s exact test was performed to calculate a P value to indicate the probability that each biological function and/or disease assigned to the data set was because of chance alone. The data set was mined for significant pathways using the IPA library of canonical pathways, using either (1) a ratio of the number of genes from the data set that mapped to the pathway divided by the total number of genes that mapped to the canonical pathway or (2) a Fisher’s exact test to calculate a P value determining the probability that the association between the genes in the data set and the canonical pathway was explained by chance alone [19, 20].
DAVID bioinformatics resources
DAVID bioinformatics resources (http://david.abcc.ncifcrf.gov) consist of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. DAVID is a high-throughput and integrated data-mining environment that can analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers, followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering, and a functional annotation table. The major statistical methods have been described previously , and Additional file 2: Table S2 shows the associated parameters used in DAVID.
Mahalanobis distance was calculated using R version 2.0. The threshold values were determined based on 99% confidence intervals (Fig. 1). Statistical analyses of the distance distributions from the center of mass (Mahalanobis distance) for each probe used in the assays were performed using a non-parametric Mann–Whitney U test (Fig. 2). Statistical analyses were performed using GraphPad PRISM5 software (GraphPad Software, La Jolla, CA, USA) with the significance level set at P < 0.05.
YK, AA and TT designed the research; YK and AA performed the research; YK, AA and KI analyzed the data; YK and AA wrote the manuscript; TT, IH and HA gave comments on the manuscript and revised it. All authors read and approved the final manuscript.
The authors thank Ms. Tomoko Endo, Ms. Chinori Iijima, Ms. Sachiko Hayashi, and Ms. Kiyoe Ishii for their technical assistance, Dr. Chikage Yoshizawa, Dr. Hiromi Horiguchi, Dr. Yuka Ikeuchi, Dr. Satoko Kakegawa and Dr. Toshio Watanabe for their contribution in collecting patients’ samples.
The authors declare that they have no competing interests.
Availability of data and materials
All data generated or analysed during this study are included in this published article and its Additional files.
Consent for publication
Consent for publication has been obtained from all the participants/parents or legal guardian.
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Gunma University Graduate School of Medicine, Japan (Receipt Number 89). Consent and permission to participate have been obtained from all the participants. Parental consent for participation of children under 16 years of age have been obtained. Participants and parents of children provided written consent.
This study was supported by JSPS KAKENHI Grant Numbers 19591238, 25430177, and 25670265; Kawano Masanori Memorial Foundation for Promotion of Pediatrics; the Joint Research Program of the Institute for Molecular and Cellular Regulation, Gunma University (Research Project Number: 11027); and Takeda Science Foundation.
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