DNA methylation and single-nucleotide polymorphisms in DDX58 are associated with hand, foot and mouth disease caused by enterovirus 71

Ya-Ping Li; Chen-Rui Liu; Hui-Ling Deng; Mu-Qi Wang; Yan Tian; Yuan Chen; Yu-Feng Zhang; Shuang-Suo Dang; Song Zhai

doi:10.1371/journal.pntd.0010090

. 2022 Jan 18;16(1):e0010090. doi: 10.1371/journal.pntd.0010090

DNA methylation and single-nucleotide polymorphisms in DDX58 are associated with hand, foot and mouth disease caused by enterovirus 71

Ya-Ping Li ¹, Chen-Rui Liu ¹, Hui-Ling Deng ^2,³, Mu-Qi Wang ¹, Yan Tian ¹, Yuan Chen ², Yu-Feng Zhang ², Shuang-Suo Dang ^1,^*, Song Zhai ^1,^*

Editor: Johan Van Weyenbergh⁴

PMCID: PMC8765647 PMID: 35041675

Abstract

Background

This research aimed to explore the association between the RIG-I-like receptor (RIG-I and MDA5 encoded by DDX58 and IFIH1, respectively) pathways and the risk or severity of hand, foot, and mouth disease caused by enterovirus 71 (EV71-HFMD). In this context, we explored the influence of gene methylation and polymorphism on EV71-HFMD.

Methodology/Principal findings

60 healthy controls and 120 EV71-HFMD patients, including 60 mild EV71-HFMD and 60 severe EV71-HFMD patients, were enrolled. First, MiSeq was performed to explore the methylation of CpG islands in the DDX58 and IFIH1 promoter regions. Then, DDX58 and IFIH1 expression were detected in PBMCs using RT-qPCR. Finally, imLDR was used to detect DDX58 and IFIH1 single-nucleotide polymorphism (SNP) genotypes. Severe EV71-HFMD patients exhibited higher DDX58 promoter methylation levels than healthy controls and mild EV71-HFMD patients. DDX58 promoter methylation was significantly associated with severe HFMD, sex, vomiting, high fever, neutrophil abundance, and lymphocyte abundance. DDX58 expression levels were significantly lower in mild patients than in healthy controls and lower in severe patients than in mild patients. Binary logistic regression analysis revealed statistically significant differences in the genotype frequencies of DDX58 rs3739674 between the mild and severe groups. GeneMANIA revealed that 19 proteins displayed correlations with DDX58, including DHX58, HERC5, MAVS, RAI14, WRNIP1 and ISG15, and 19 proteins displayed correlations with IFIH1, including TKFC, IDE, MAVS, DHX58, NLRC5, TSPAN6, USP3 and DDX58.

Conclusions/Significance

DDX58 expression and promoter methylation were associated with EV71 infection progression, especially in severe EV71-HFMD patients. The effect of DDX58 in EV71-HFMD is worth further attention.

Author summary

EV71-HFMD is now prevalent in many Asia countries, including China, which caused a lot of death over the last few years. Therefore, it’s important to distinguish patients with a severe tendency. In this research, we investigated the association between the epigenetic mechanisms of RIG-I-like pattern recognition receptors (DDX58 and IFIH1) and EV71-HFMD. It showed that children with higher level of DDX58 promoter methylation were related to severe EV71-HFMD and other clinical indicators. The DDX58 mRNA expression level was significantly lower in severe patients. Besides, DDX58 polymorphisms played an important role in the severity of EV71-HFMD. Therefore, various epigenetic biomarkers of DDX58 may be used as indicators to help clinicians identify EV71-HFMD patients with a tendency towards severity.

Introduction

In recent years, hand, foot and mouth disease (HFMD), a highly infectious digestive tract disease that is mainly caused by enterovirus 71 (EV71) and coxsackievirus A16 (CA16) infection among children and infants, has been prevalent in China [1–3]. Although most patients with HFMD can recover, it was reported that during 2008~2015, there were more than 120,000 cases of severe HFMD in mainland China, of which more than 3300 died [4,5]. In the Asia-Pacific region, HFMD has also been prevalent in many other countries, such as Japan, South Korea, and Singapore [6–8]. Epidemiological investigations have shown that EV71 is still the main pathogen of severe HFMD, although other enteroviruses causing HFMD have increased in prevalence in recent years [9,10].

Retinoic acid-inducible gene I (RIG-I, encoded by the DDX58 gene) and melanoma differentiation-associated protein 5 (MDA5, encoded by the IFIH1 gene) are both RIG-I-like pattern recognition receptors (PRRs) that can be activated by double-stranded RNA (dsRNA) and interact with mitochondrial antiviral signaling (MAVS) proteins in cells, resulting in the activation and expression of type I interferon genes [9,10,11]. In other words, they are important receptors of the early immune response after virus infections. Recent studies have shown that hantavirus, rotavirus and filovirus as well as others can be specifically recognized by RIG-I and MDA5 [12–14], which cause antiviral effects. EV71 is an RNA virus, and dsRNA is produced during the process of its replication. It can be speculated that RIG-I and MDA5 may also be activated by EV71 to produce antiviral effects. A recent study showed that EV71 can activate MDA5 in human neural cells and be inhibited by inducing the expression of interferon (IFN)-β though the MDA5 signaling pathway [15]. However, some other studies showed that EV71 may inhibit the production of type I IFN by affecting the signaling pathway mediated by RIG-I and MDA5 [16,17]. Regardless of the circumstances, RIG-I and MDA5 are closely related to the prognosis of HFMD caused by EV71 (EV71-HFMD).

Base methylation and demethylation in the DNA promoter region are often considered important ways to regulate gene expression levels [18]. The degree of methylation is different in different regions of DNA. The CpG context is highly methylated, while most CpG islands are not methylated, which means that CpG islands in the promoter region can be methylated, resulting in the inhibition of gene expression [19–22]. The gene expression of RIG-I and MDA5 is also regulated by the methylation level of CpG islands in the promoter region of the coding genes [23,24]. The methylation level of CpG islands can reflect the expression of RIG-I and MDA5 and indirectly reflect the antiviral effect [23]. Zhang et al found that the methylation level of the chicken genome was significantly upregulated after infection with H5N1 avian influenza virus [25]. Zheng Zhu et al. showed that EV71 infection may change the methylation status of CpG islands in host DNA and regulate gene expression [26]. However, the antiviral effect is not only associated with regulated gene expression levels but also related to many other parameters, among which single-nucleotide polymorphisms (SNPs) and gene mutations are important research focuses. Our previous study indicated that RIG-1 (rs3739674 and rs9695310) polymorphisms were associated with an increased risk of EV71-HFMD in Chinese children, whereas the RIG-1 rs3739674 and TLR3 rs5743305 polymorphisms were associated with disease severity [27]. In this study, we investigated the association among the RIG-I and MDA5 promoter region methylation, nucleic acid sequence mutation and EV71-HFMD severity.

Methods

Ethics statements

This research was approved by the Medical Ethics Committee of the study hospital. Written informed consent was obtained from the parent/guardian of each participant under 18 years of age.

Study population

Clinical specimens used in this research were collected from hospitalized HFMD patients during the 2017–2020 peak season (April-July and October-November) of HFMD in Xi’an at Xi’an Jiaotong University Second Affiliated Hospital and Xi’an Children’s Hospital. HFMD was diagnosed based on criteria in the Hand, Foot and Mouth Disease Clinical Guidelines (2018 edition), which was issued by the National Health and Family Planning Commission of the People’s Republic of China. Children with mild HFMD had rashes on their hands, feet, mouths, and buttocks, with or without fever. Severe case had one of the following complications involving nervous system, respiratory system and circulatory system: aseptic meningitis, encephalitis, acute flaccid paralysis, pulmonary edema or hemorrhage, and cardiopulmonary collapse. EV71 RNA detection in peripheral blood of all participants were positive. HFMD cases, which were positive for other enteroviruses or co-detected with other viral infections, were excluded.

Another group of 60 normal children served as the control group to collect blood samples with the informed consent of the children’s guardians, which were enrolled from the healthy infants of same age for physical examination at the same hospital and the same time as well as the patients group. The details of the control group were displayed in S1 Table.

Detection of EV71 infection in the clinical laboratory

EV71 detection in stool/throat specimens in the study hospital was primarily performed using commercially available panenterovirus, EV71, and CA16 diagnostic kits (Da An Gene Co., Ltd., Guangzhou, China) according to the manufacturer’s instructions. If the quick diagnosis was EV71 positive, viral RNA was extracted from serum using an RNA extraction kit (Qiagen, Germany). EV71 infection in patients was confirmed by positive EV71 identification through reverse transcription-PCR (RT-PCR) detection of the virus nucleic acid in serum.

Collection and preservation of specimens

For children with EV71-HFMD, a total of 120 Chinese patients, including 60 mild and 60 severe patients, were included in this study. Venous blood samples of all participants were collected in the morning after overnight fasting, placed in a sterile, enzyme-free EDTA (1 ml) anticoagulation tube and a PAXgene Blood RNA Tube (2 ml), and stored at −30°C and −80°C for extraction of genomic DNA and RNA, respectively.

DNA extraction and methylation analysis

Genomic DNA was extracted from enzyme-free EDTA-blood samples using a Gentra Puregene Blood Kit (Germany) according to the manufacturer’s instructions. CpG islands located in the DDX58 and IFIH1 promoter regions were selected for analysis. Bisulfite conversion of 1 μg of genomic DNA was performed with an EZ DNA Methylation-GOLD Kit (Zymo Research, CA, USA). After PCR amplification (HotStarTaq Polymerase Kit, TAKARA, Tokyo, Japan) of target CpG regions and library construction, the products were sequenced on an Illumina MiSeq Benchtop Sequencer (CA, USA) with a mean coverage of >600X. The methylation level at each CpG site, named based on the relative distance to the transcriptional start site (TSS) in base pairs (bp), was calculated as the percentage of methylated cytosines divided by the total tested cytosines.

Quantitative real-time PCR (qrt-PCR)

Total RNA was extracted from the white blood cells of 120 EV71-HFMD patients and 60 normal children. Using SYBR Advantage qPCR Premix (Takara), qRT-PCR to investigate the expression of DDX58 and IFIH1 was carried out on an ABI 7300 Real-Time PCR System (Applied Biosystems). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as the housekeeping gene for standardization. GAPDH expression were same in the groups. All assays were performed at least in triplicate, and the relative gene expression is presented as 2^-ΔΔCt. The primer pairs utilized in this study were as follows: DDX58-Forward (AGACCCTGGACCCTACCTACA) and DDX58-Reverse (CTCCATTGGGCCCTTGTTGT) and IFIH1-Forward (ATTCAGGCACCATGGGAAGTG) and IFIH1-Reverse (TTTGGTAAGGCCTGAGCTGGA).

SNP genotyping

Genomic DNA was extracted using a Gentra Puregene Blood Kit (Germany) from enzyme-free EDTA-blood samples according to standard protocols. Based on our previous research, positive SNP sites associated with EV71 infection were selected. DDX58 SNP (rs3739674/5’-UTR exon 1) and IFIH1 SNP (rs1990760/nonsynonymous exon 15) genotyping was performed using an improved multitemperature ligase detection reaction (imLDR) technique (Genesky Biotechnologies, Shanghai, China). Specifically, imLDR determined genotypes by PCR amplification of genomic DNA and sequencing. PCR primers were separately designed using Primer 6.0 software and synthesized by Shanghai Sangon Biotech Company. The primer sequences were as follows: rs3739674F: CCTAAACCAGGGGGCCATGTAG, rs3739674R: TCCTCGGAAAATCCCTGCTTTC; rs1990760F: GGCCCACAGCAATTTACTCACC, and rs1990760R: GCAATAACAAGCCTGGGAAGCA.

DDX58 and IFIH1 network analysis

To derive DDX58 and IFIH1 functions, GeneMANIA (http://www.genemania.org/) was used to obtain genes with functions similar to those of DDX58 and IFIH1 and generate interactive functional association networks (Warde-Farley, 2010). Finally, the DDX58 and IFIH1 networks was built, including colocalization, coexpression, physical interactions, genetic interaction, and predictions, as well as their functions.

Statistics

Statistical analyses were performed with SPSS 22.0 software (SPSS Inc., Chicago, IL). The methylation and expression levels among the three groups were not normally distributed, so these variables were summarized as medians (interquartile ranges, IQRs). Then, a nonparametric test was used for comparisons of continuous variables, and significance of differences between groups was analyzed by the Mann-Whitney test or Kruskal-Wallis test. The heatmap (“heatmap.2” R package) was constructed on the average methylation expression after standardization in order to construct separate methylation correlation matrices for disease status. The correlation between the average methylation level and expression of each gene was assessed using Spearman rank correlation coefficient. The receiver operating characteristic (ROC) curves, areas under the ROC curve (AUCs), or partial AUCs as well as their 95% confidence intervals were calculated using the R package pROC. A Kruskal-Wallis test was applied to investigate significant changes in methylation levels depending on disease condition. False Discovery Rate (FDR) analysis were adjusted P-value. Genotype frequencies were analyzed by the Hardy-Weinberg test. The various genotype and allele frequencies were compared using binary logistic regression analysis between the EV71-HFMD and normal groups and between the mild case and severe case groups to calculate the χ2, P, and odds ratio (OR) values and 95% CI (adjusted by age and sex). For statistical analysis, GraphPad Prism version 7 was used. The significance level was set at P < 0.05.

Results

Clinicopathological characteristics

A total of 120 EV71-HFMD patients were recruited, with a median age of 35(15–61) months (range: 6–96 months). 80 patients were male, and 40 were female. Age- and sex-matched healthy controls (60 children) were also recruited in the study, with a median age of 35(27–66) months (range: 24–79 months) (S1 Table).

DDX58 and IFIH1 methylation levels

To determine whether the DDX58 and IFIH1 promoters experience aberrant methylation patterns in EV71-HFMD patients, three groups, including 60 mild and 60 severe cases and 60 healthy controls, were selected for MethylTarget assays. Twenty-one CpG islands adjacent to the DDX58 promoter regions were sequenced (Table 1), and twenty-three CpG islands adjacent to the IFIH1 promoter regions were sequenced (Table 2). The results demonstrate that severe EV71-HFMD patients exhibit higher levels of DDX58 promoter methylation than healthy controls and mild EV71-HFMD patients (Fig 1A). To better characterize the methylation of DDX58, we also assessed the methylation level of each CpG site. We observed that the 29, 134, 154, 168, 232, 246 and 248 CpG sites of DDX58 had a significantly higher methylation pattern in severe HFMD patients than in the corresponding healthy controls and mild HFMD patients, and 179 CpG site had a significantly higher methylation pattern in healthy controls than in the severe and mild HFMD patients(Table 1, Fig 2A). After adjust for p value by FDR, 29, 134, 154, 232, 246 and 248 CpG sites of DDX58 are still positive (Table 1, Fig 2A).

Table 1. Details of the CpG regions in the CpG islands of DDX58.

Position	Chr	Genome position	Distance 2TSS	Healthy	Mild	Severe	P	FDR
29	9	32526145	177	0.006(0.005–0.008)	0.007(0.006–0.010)	0.009(0.008–0.010)	<0.001^***	<0.001^***
32	9	32526148	174	0.007(0.006–0.010)	0.008(0.007–0.009)	0.007(0.006–0.009)	0.327	0.450
39	9	32526155	167	0.006(0.005–0.008)	0.006(0.005–0.009)	0.007(0.005–0.009)	0.220	0.304
51	9	32526167	155	0.008(0.006–0.009)	0.007(0.006–0.009)	0.008(0.006–0.010)	0.299	0.619
74	9	32526190	132	0.005(0.004–0.006)	0.005(0.004–0.007)	0.005(0.003–0.006)	0.070	0.177
81	9	32526197	125	0.009(0.007–0.011)	0.009(0.007–0.010)	0.008(0.007–0.010)	0.629	0.688
110	9	32526226	96	0.008(0.007–0.010)	0.007(0.005–0.010)	0.008(0.006–0.010)	0.144	0.216
125	9	32526241	81	0.006(0.005–0.008)	0.006(0.005–0.008)	0.007(0.005–0.008)	0.633	0.688
130	9	32526246	76	0.006(0.005–0.008)	0.007(0.005–0.009)	0.008(0.005–0.009)	0.095	0.153
134	9	32526250	72	0.005(0.004–0.007)	0.006(0.004–0.008)	0.007(0.006–0.009)	<0.001^***	<0.001^***
154	9	32526270	52	0.009(0.007–0.010)	0.009(0.007–0.010)	0.012(0.010–0.017)	<0.001^***	<0.001^***
168	9	32526284	38	0.010(0.009–0.011)	0.008(0.007–0.010)	0.010(0.009–0.012)	0.003^**	0.055
172	9	32526288	34	0.008(0.007–0.010)	0.008(0.006–0.010)	0.007(0.005–0.009)	0.051	0.161
174	9	32526290	32	0.008(0.006–0.010)	0.007(0.006–0.010)	0.008(0.007–0.010)	0.456	0.688
179	9	32526295	27	0.008(0.006–0.010)	0.006(0.005–0.008)	0.007(0.006–0.009)	0.033^*	0.143
217	9	32526333	-11	0.007(0.005–0.008)	0.008(0.006–0.009)	0.007(0.006–0.008)	0.087	0.153
232	9	32526348	-26	0.006(0.005–0.007)	0.007(0.006–0.009)	0.010(0.009–0.011)	<0.001^***	<0.001^***
235	9	32526351	-29	0.006(0.005–0.007)	0.006(0.004–0.007)	0.006(0.005–0.007)	0.864	0.888
239	9	32526355	-33	0.006(0.005–0.007)	0.006(0.005–0.008)	0.006(0.005–0.008)	0.181	0.277
246	9	32526362	-40	0.009(0.007–0.010)	0.009(0.007–0.011)	0.012(0.009–0.014)	<0.001^***	<0.001^***
248	9	32526364	-42	0.007(0.005–0.009)	0.008(0.006–0.009)	0.011(0.009–0.014)	<0.001^***	<0.001^***
Average
Mean±SD				0.007±0.001	0.008±0.001	0.009±0.001	<0.001^***	/
Median (IQR)				0.007(0.007~0.008)	0.008(0.007~0.008)	0.008(0.008~0.009)	<0.001^***	<0.001^***

Open in a new tab

Chr, chromosome; Genome position, the position of the product on the reference genome; TSS, the mRNA transcription initiation site; Distance2TSS, the distance from the product to the TSS; Target strand, the product orientation. Specific CpG site methylation levels of the healthy, mild and severe groups are shown as the median (IQR, interquartile range). P: Kruskal-Wallis test. False Discovery Rate (FDR) analysis were adjusted P-value.

* P<0.05

**P<0.01

***P<0.001.

Table 2. Details of the CpG regions in the CpG islands of IFIH1.

Position	Chr	Genome position	Distance 2TSS	Healthy	Mild	Severe	P	FDR
27	2	163174873	345	0.007(0.006–0.009)	0.007(0.005–0.010)	0.006(0.005–0.008)	0.051	0.318
45	2	163174891	327	0.006(0.005–0.008)	0.006(0.005–0.008)	0.006(0.005–0.009)	0.913	0.567
50	2	163174896	322	0.010(0.008–0.012)	0.0100.007–0.012)	0.011(0.009–0.012)	0.010^*	0.037^*
57	2	163174903	315	0.010(0.008–0.013)	0.010(0.009–0.013)	0.010(0.009–0.011)	0.341	0.318
86	2	163174932	286	0.011(0.009–0.012)	0.011(0.009–0.015)	0.009(0.008–0.011)	0.002^**	0.037^*
106	2	163174952	266	0.005(0.004–0.006)	0.006(0.004–0.007)	0.005(0.004–0.007)	0.795	0.865
132	2	163174978	240	0.009(0.007–0.011)	0.009(0.006–0.010)	0.010(0.006–0.013)	0.700	0.566
135	2	163174981	237	0.008(0.006–0.009)	0.006(0.005–0.008)	0.007(0.006–0.010)	0.072	0.174
137	2	163174983	235	0.010(0.009–0.013)	0.010(0.008–0.012)	0.009(0.008–0.011)	0.146	0.318
139	2	163174985	233	0.007(0.005–0.010)	0.007(0.006–0.010)	0.008(0.006–0.009)	0.846	0.865
151	2	163174997	221	0.010(0.008–0.012)	0.009(0.008–0.012)	0.009(0.008–0.011)	0.340	0.531
173	2	163175019	199	0.006(0.004–0.008)	0.007(0.005–0.009)	0.007(0.005–0.009)	0.118	0.318
188	2	163175034	184	0.014(0.012–0.016)	0.013(0.011–0.018)	0.012(0.010–0.016)	0.698	0.567
193	2	163175039	179	0.010(0.007–0.012)	0.009(0.007–0.011)	0.009(0.007–0.010)	0.102	0.309
196	2	163175042	176	0.007(0.005–0.009)	0.007(0.005–0.009)	0.007(0.005–0.007)	0.221	0.316
198	2	163175044	174	0.044(0.036–0.047)	0.044(0.036–0.051)	0.046(0.042–0.052)	0.066	0.118
200	2	163175046	172	0.012(0.011–0.015)	0.013(0.010–0.014)	0.013(0.011–0.015)	0.559	0.863
206	2	163175052	166	0.013(0.010–0.016)	0.013(0.011–0.016)	0.013(0.012–0.015)	0.857	0.905
208	2	163175054	164	0.007(0.006–0.010)	0.008(0.007–0.010)	0.008(0.006–0.010)	0.357	0.863
221	2	163175067	151	0.007(0.005–0.009)	0.006(0.005–0.007)	0.006(0.005–0.007)	0.053	0.118
225	2	163175071	147	0.010(0.008–0.012)	0.011(0.008–0.013)	0.011(0.008–0.013)	0.212	0.319
232	2	163175078	140	0.008(0.007–0.011)	0.008(0.007–0.011)	0.008(0.007–0.011)	0.989	0.990
234	2	163175080	138	0.006(0.006–0.008)	0.007(0.006–0.010)	0.007(0.006–0.009)	0.439	0.865
Average
Mean±SD				0.010±0.001	0.010±0.001	0.011±0.001	0.536	/
Median (IQR)				0.011(0.010–0.011)	0.011(0.010–0.011)	0.011(0.010–0.012)	0.898	0.908

Open in a new tab

* P<0.05

**P<0.01

***P<0.001.

Fig 2 — DDX58 methylation levels of each positive CpG site (the 29, 134, 154, 232, 246 and 248 CpG sites) in all samples, including healthy subjects and EV71-HFMD patients (A). IFIH1 methylation levels of each positive CpG site (the 50 and 86 CpG sites) in all samples, including those of healthy subjects and EV71-HFMD patients. (B) *p < 0.05, **p < 0.01, ***p < 0.001.

The average methylation levels of IFIH1 among the three groups were not significantly different (Fig 1C and 1D), but we also assessed the methylation level of each CpG site. We observed that the 50, 86 CpG sites, and the average methylation level of 50 CpG sites was significantly higher, while 86 CpG sites was significantly lower in severe HFMD patients than that in the corresponding healthy controls and mild HFMD patients (Table 2, Fig 2B).

The association between DDX58 and IFIH1 methylation patterns and clinical indicators of EV71-HFMD

A ROC curve was used to assess the clinical application of DNA methylation for the prediction of EV71-HFMD severity. DDX58 methylation status was able to differentiate between mild and severe EV71-HFMD (AUC = 0.806 on average, AUC = 0.699 at the 29 CpG site, 0.660 at the 134 CpG site, 0.798 at the 154 CpG site, 0.903 at the 232 CpG site, 0.779 at the 246 CpG site and 0.860 at the 248 CpG site) (Fig 3A). The results suggest that DDX58 promoter methylation may be a useful marker to identify EV71-HFMD severity. Then, the relationship between DDX58 methylation and clinical features was analyzed. All EV71-HFMD samples were divided into two groups according to their median DDX58 methylation value (Table 3). Next, nine clinical indicators were analyzed by a χ2 test. We found that altered DDX58 promoter methylation was significantly associated with severe HFMD, sex, vomiting, high fever, neutrophil abundance, and lymphocyte abundance (Table 3). Specifically, a higher level of DDX58 promoter methylation was related to severe EV71-HFMD, increased neutrophil abundance and glucose levels, and decreased lymphocyte abundance.

Fig 3 — (A). IFIH1 methylation status and levels of each positive CpG site (the 50 and 86 CpG sites) between mild and severe EV71-HFMD patients. (B) *p < 0.05, **p < 0.01, ***p < 0.001.

Table 3. The relationship between the DDX58/IFIH1 methylation levels and clinicopathological factors in EV71-HFMD.

Variable	DDX58 Average methylation (median as cutoff)			IFIH1 Average methylation (median as cutoff)
Variable	Low	High	P	Low	High	P
Mild/severe	45/15	15/45	<0.001	32/33	28/27	0.855
Male/female	33/27	47/13	0.007	37/28	43/12	0.014
Age (≤3/>3 years)	34/26	45/15	0.034	48/17	31/24	0.044
Vomiting (No/Yes)	48/12	24/36	<0.001	39/26	33/22	0.990
Fever course (≤3/>3 days)	19/41	14/46	0.307	18/47	15/40	0.959
High fever (≤39/>39°C)	30/30	11/49	<0.001	21/44	20/35	0.641
Neutrophil%	45.74 (29.20–61.00)	70.90 (48.70–75.65)	<0.001	57.10 (34.95–71.00)	57.10 (41.30–75.40)	0.176
Glucose (mmol/L)	6.00 (4.06–7.10)	5.50 (4.41–6.80)	0.990	5.50 (3.98–6.68)	6.00(4.90–7.30)	0.049
Lymphocyte%	53.40 (41.40–61.60)	32.00 (19.60–37.60)	<0.001	35.40 (24.60–55.65)	44.50 (20.30–55.80)	0.918
mRNA expression /GAPDH	0.120 (0.099–0.136)	0.100 (0.088–0.116)	<0.001	0.039 (0.003–0.056)	0.037(0.026–0.054)	0.246

Open in a new tab

Neutrophil%, Glucose, Lymphocyte% and mRNA expression were presented as Median (interquartile range, IQR); P: Mann-Whitney test.

The IFIH1 average methylation level was not significantly different among the three groups (Table 2), but there were three positive sites that were significantly different between mild and severe EV71-HFMD (AUC = 0.644 at the 50 CpG site, and 0.670 at the 86 CpG site) (Fig 3B). The clinical indicator statistical findings showed that average IFIH1 promoter methylation was not associated with disease severity, neutrophil abundance, glucose levels or lymphocyte abundance (Table 3).

Determination of DDX58 and IFIH1 mrna expression

GAPDH is used as an internal control. DDX58 mRNA expression levels were significantly lower in mild patients (median 0.123, IQR 0.101–0.134) than in healthy controls (median 0.184, IQR 0.149–0.206, P<0.001; Fig 4A) and lower in severe patients (median 0.098, IQR 0.088–0.112) than in mild patients (median 0.123, IQR 0.101–0.134, P<0.001; Fig 4C). Additionally, DDX58 mRNA levels were able to differentiate between mild and severe EV71-HFMD (AUC = 0.814 on average; Fig 4B). IFIH1 mRNA expression levels were significantly lower in severe patients (median 0.035, IQR 0.027–0.040) than in mild patients (median 0.043, IQR 0.036–0.060, P = 0.001; Fig 4F), and IFIH1 mRNA levels were able to differentiate between mild and severe EV71-HFMD (AUC = 0.669 on average; Fig 4E).

Fig 4 — Determination of DDX58 mRNA expression in all samples (A). AUC of DDX58 mRNA levels between mild and severe EV71-HFMD patients (B). Determination of DDX58 mRNA expression between mild and severe EV71-HFMD patients (C). Determination of IFIH1 mRNA expression in all samples (D). AUC of IFIH1 mRNA levels between mild and severe EV71-HFMD patients (E). Determination of IFIH1 mRNA expression between mild and severe EV71-HFMD patients (F). ***p < 0.001.

Promoter methylation is associated with DDX58 expression

The relationship between the level of DNA methylation in the promoter region of DDX58 and the expression of DDX58 mRNA was analyzed. Spearman’s correlation analysis showed that the average level of CpG methylation in the DDX58 promoter region was negatively correlated with the level of DDX58 mRNA expression among the three groups. Furthermore, the 29, 130, 134, 154, 232, 246, 248 CpG sites and the average level of CpG methylation were negatively correlated with DDX58 mRNA expression in all three groups (Fig 5A). Between the mild and severe groups, the 154, 179, 232, 248 CpG sites and the average level of CpG methylation in the DDX58 promoter region were negatively correlated with the level of DDX58 mRNA expression (Fig 5B). In addition, there was no correlation between IFIH1 expression and the average methylation level (Fig 5C and 5D).

Fig 5 — Spearman rank correlation coefficient analysis was performed between DDX58 and IFIH1 methylation and mRNA expression. All samples for DDX58 (A). EV71-HFMD patients for DDX58 (B). All samples for IFIH1 (C). EV71-HFMD patients for IFIH1 (D).

Simple linear regression analysis were performed to show the variance in DDX58 expression accounted by HFMD severity (R square = 0.293, p<0.001), by DDX58 methylation (R square = 0.058, p = 0.008), and the variance in IFIH1 expression accounted by HFMD severity (R square = 0.033, p = 0.046), by IFIH1 methylation (R square = 0.007, p = 0.347).

Relationship between DDX58 polymorphisms and severity of EV71-HFMD

After adjusting for sex and age, there were significant differences in genotype frequencies of DDX58 rs3739674 identified through binary logistic regression analysis between the mild and severe groups according to an allele model for the C allele (G vs. C: OR 1.421, 95% CI 1.077–1.873, P = 0.013) (Table 4). DDX58 mRNA expression and methylation levels were determined for each genotype at polymorphic sites (Fig 6A and 6B). At DDX58 SNP rs3739674, genotype GG (median 0.112, IQR 0.094–0.133) was associated with significantly lower DDX58 mRNA expression levels than both the CC (median 0.146, IQR 0.010–0.185) and GC genotypes (median 0.132, IQR 0.106–0.161, Fig 6A). At DDX58 SNP rs3739674, genotype GG (median 0.009, IQR 0.008–0.009) was associated with significantly higher DDX58 methylation levels than both the CC (median 0.007, IQR 0.006–0.007) and GC genotypes (median 0.008, IQR 0.007–0.008, Fig 6B).

Table 4. DDX58 rs3739674 and IFIH1 rs1990760 genotype distribution and allele frequencies in the mild and severe HFMD groups.

Polymorphism	Genotype	Mild cases (%) (n = 60)	Severe cases (%) (n = 60)	P	OR(95% CI)
rs3739674	Codominant model
	CC	19 (31.7)	6 (10.0)	1.000 (ref.)
	GC	20 (33.3)	22 (36.7)	0.028	3.906 (1.163–13.122)
	GG	21(35.0)	32(53.3)	0.010	2.083 (1.193–3.635)
	Dominant model
	CC	19 (31.7)	6 (10.0)	1.000 (ref.)
	GC+GG	41 (68.3)	54 (90.0)	0.007	3.670 (1.313–10.255)
	Allele model
	C	58 (48.3)	34 (28.3)	1.000 (ref.)
	G	62 (51.7)	86 (71.7)	0.013	1.421 (1.077–1.873)
H-W		0.010	0.452
rs1990760	Codominant model
	CC	37 (61.7)	37 (61.7)	1.000 (ref.)
	CT	17 (28.3)	22 (36.7)	0.548	1.289 (0.563–2.952)
	TT	6 (10.0)	1 (1.6)	0.107	0.398 (0.129–1.222)
	Dominant model
	CC	37 (61.7)	37 (61.7)	1.000 (ref.)
	CT+TT	23 (38.3)	23 (38.3)	0.947	1.027 (0.472–2.232)
	Allele model
	C	91 (75.8)	96 (80.0)	1.000 (ref.)
	T	29 (24.2)	24 (20.0)	0.501	0.897 (0.653–1.231)
H-W		0.078	0.259

Open in a new tab

Data are presented as n (%). OR, odds ratio; 95% CI, 95% confidence interval. Bold values emphasize the positive locus determined by statistical analysis.

Fig 6 — DDX58 *expression* levels in each genotype at polymorphic sites (A). *DDX58* methylation levels in each genotype at polymorphic sites (B). IFIH1 *expression* levels in each genotype at polymorphic sites (C). IFIH1 methylation levels in each genotype at polymorphic sites (D) **p < 0.01, ***p < 0.001.

After adjusting for sex and age, there were no significant differences in genotype frequencies of IFIH1 rs1990760 among the mild and severe groups (Table 4). IFIH1 mRNA expression and methylation levels were determined for each genotype at polymorphic sites (Fig 6C and 6D). At IFIH1 SNP rs1990760, genotype TT (median 0.012, IQR 0.011–0.013) was associated with significantly higher IFIH1 methylation levels than both the CC (median 0.010, IQR 0.009–0.011) and CT genotypes (median 0.011, IQR 0.010–0.012, Fig 6D). In addition, there was no correlation between IFIH1 expression and the genotypes of IFIH1 SNP rs1990760 (Fig 6C).

DDX58 and IFIH1 network analysis

A gene–gene interaction network for DDX58 was constructed, and its function was analyzed using the GeneMANIA database (Fig 7A). GeneMANIA revealed that 19 proteins displayed correlations with DDX58, including DHX58, HERC5, MAVS, RAI14, WRNIP1, and ISG15. DDX58 was correlated with RAI14 in terms of physical interactions. Further functional analysis showed that these 20 proteins displayed the greatest correlation with negative regulation of type I IFN production (FDR = 1.08E-24), regulation of type I IFN production (FDR = 4.83E-19), type I IFN production (FDR = 4.83E-19), negative regulation of cytokine production (FDR = 8.24E-19), negative regulation of multicellular organismal process (FDR = 5.43E-15), and response to virus (FDR = 1.33E-13). Additionally, these proteins were correlated with positive regulation of IFN-β production, regulation of multiorganism process, positive regulation of type I IFN production, regulation of IFN-β production, and defense response to virus. (S2 Table).

A gene–gene interaction network for IFIH1 was constructed, and its function was analyzed using the GeneMANIA database (Fig 7B). GeneMANIA revealed that 19 proteins displayed correlations with IFIH1, including TKFC, IDE, MAVS, DHX58, NLRC5, TSPAN6, USP3, and DDX58. IFIH1 was correlated with IDE in terms of physical interactions. Further functional analysis showed that these 20 proteins displayed the greatest correlation with negative regulation of type I IFN production (FDR = 2.22E-13), regulation of type I IFN production (FDR = 6.04E-10), type I IFN production (FDR = 6.04E-10), negative regulation of cytokine production (FDR = 7.74E-10), negative regulation of multicellular organismal process (FDR = 2.74E-07), response to virus (FDR = 7.44E-08), and regulation of innate immune response (FDR = 1.05E-06). Additionally, these proteins were correlated with the cytoplasmic PRR signaling pathway in response to virus, cellular response to virus, defense response to virus, defense response to other organisms, and single-stranded RNA binding. (S3 Table).

Discussion

EV71 causes HFMD, neurological complications or even fatal diseases in young children and infants. It is still a major challenge due to some children experiencing serious consequences [28]. PRRs are the first line of defense against EV71 infection. DDX58 and IFIH1 are the main antiviral molecules among host innate immune PRRs. DDX58 and IFIH1 can distinguish different RNA viruses and play a key role in the host antiviral response [29]. Lack of both DDX58 and IFIH1 decreased innate immune signaling in the target cells of West Nile virus (WNV)-infected mice, and the pathogenesis was serious in vivo [30]. In addition, virus-infected cells can also be recognized by the molecular pattern recognition of DDX58 and IFIH1 [31]. DDX58 is essential in the signal transduction of influenza A virus, influenza B virus and human respiratory syncytial virus [32]. In contrast, IFIH1 uniquely triggers innate immune defenses during picornavirus infections [29]. Our previous study confirmed the potential association between genetic polymorphisms and the risk and severity of EV71 infection, while the DDX58 rs9695310 GC/CC genotype increased the risk of EV71 infection [27]. Overexpression of the MDA5 protein reverses the suppression of IRF3 activation caused by EV71 infection, indicating that IFIH1 is an important factor for EV71 RNA-activated type I IFN expression [33]. The association between the MDA5 rs1990760 polymorphism and an increased risk of severe EV71 infection was indicated in Chinese children [34]. In view of these findings, we further analyzed the relationship between PRR gene methylation and SNPs and EV71 infection and tried to explore the reasons for different clinical outcomes.

An increasing number of studies have focused on the association between aberrant methylation and various diseases in the field of life science, such as cancer [35,36], diabetes [37] and cardiovascular diseases [38]. At the same time, many studies have shown that viral infection can affect the DNA methylation status of host cells, leading to changes in epigenetic patterns [39,40]. Through epigenetic mechanisms and host cell fusion, both Epstein-Barr virus and cytomegalovirus can demethylate the host syncytial protein 1 and 2 genes, increasing gene transcription and leading to the formation of syncytial bodies in tissues where those genes are normally hypermethylated and silenced [41,42]. In recent studies, epigenetic mechanisms seem to be an important part of the pathophysiology and disease severity of COVID-19 [43]. There are also many studies on the relationship between EV71 infection and methylation. Acute enterovirus infections, such as those caused by EV71 and CA16, induce significant changes in host cellular DNA methylation status. Differentially methylated CpG sites are widely distributed throughout the genome, which may affect the expression of many target genes and therefore contribute to disease occurrence [26]. Other studies have identified that EV71 infection increased methylation of the miR-17-92 promoter, while expression of the hsa-miR-17-92 cluster was significantly downregulated [44]. Our previous studies have shown that vitamin D receptor expression and promoter methylation are associated with the progression of EV71 infection [45]. The DNA methylation level of the VDR promoter in children with severe EV71- HFMD was lower than that in healthy controls and mild HFMD patients, and the average CG methylation level in the VDR promoter region was negatively correlated with the mRNA expression level of VDR in both EV71-HFMD patients and healthy controls.

In this study, low DDX58 expression is significantly associated with severe HFMD compared with mild HFMD or healthy controls. We found that hypermethylation levels may decrease the expression of DDX58 and increase susceptibility to EV71 infection. Further research also confirmed this hypothesis, which showed that the mRNA expression levels of DDX58 and IFIH1 in severe patients were significantly lower than those in mild patients and healthy controls. These results suggest that low expression of DDX58 may be associated with high methylation level and SNP typing. High DDX58 promotor methylation might be a reason for low DDX58 expression. Simple linear regression analysis shows that the variance in DDX58 expression could be 29.3% explained by the severity of HFMD, while IFIH1 expression 3.3%, suggesting that low expression of DDX58 can increase the susceptibility of children to EV71, accelerate the progress of HFMD, and lead to severe disease easily. Therefore, DDX58 expression plays a bigger role in HFMD severity. Correlation analysis and simple linear regression analysis between DDX58/IFIH1 expressions and their promotor CpG sites methylation suggest that promotor methylation might have a minimal effect on DDX58 expressions. These findings indicate that DDX58 and IFIH1 methylation are early events in EV71-HFMD. We selected EV71-HFMD patients as the research objects and only collected samples after infection. We selected healthy children as controls instead of the patients themselves before infection. In addition, there are many factors affecting the expression of DDX58. Therefore, we could not compare the data before and after the onset, nor can we find out the causal relationship between DDX58 and severe HFMD, which is one of the limitations of our study. Maybe follow-up animal experiment could confirm the changes of DDX58 expression before and after infection and its impact on the severity of the disease.

In addition, some studies have shown that SNPs may affect the DNA methylation level of the CpG island region or regulate gene expression, thus participating in the occurrence and development of diseases [46]. SNP sites are significantly correlated with gene expression and methylation levels, and they can affect the methylation level of multiple CpG sites [47,48]. Our previous study showed that DDX58 rs3739674 and rs9695310 were associated with an increased risk of EV71-HFMD in Chinese children, while DDX58 rs3739674 and TLR3 rs5743305 were associated with the severity of the disease. These findings supported the important role of the innate immune mechanism in EV71 infection [27]. On this basis, we further collected another group of samples to confirm the correlation between SNPs, mRNA expression and methylation of DDX58 and IFIH1. We found that there was a significant difference in the DDX58 rs3739674 genotype frequency between the mild and severe EV71-HFMD groups. At DDX58 SNP rs3739674, the expression level of DDX58 mRNA associated with the GG genotype was significantly lower than that associated with the CC and GC genotypes, while the DDX58 methylation levels associated with the GG genotype were significantly higher than those associated with the other two genotypes. These results indicated that the DDX58 SNP rs3739674 GG genotype was associated with the severity of EV71 infection, which is consistent with our previous results. Many viruses interfere with and inhibit the RIG-I (DDX58) signaling pathway through their own proteins and nucleic acids, thereby inhibiting the cellular antiviral response. DDX58 rs3739674 is located in the 5′UTR, which may affect the transcription level and further regulate the protein expression level through mutation.

Similarly, the IFIH1 gene is located on chromosome 2q24.2 and encodes a DEAD box protein involved in immune regulation. Previous studies have shown that the IFIH1 rs1990760 polymorphism is associated with a variety of autoimmune diseases, including type 1 diabetes, systemic lupus erythematosus, multiple scleral hyperplasia and rheumatoid arthritis [49]. There are also correlations with viral infections, such as COVID-19, WNV and EV71. IFIH1 rs9596310 is a risk factor for chronic hepatitis C virus (HCV) infection [50]. Both EV71 and HCV are positive single-stranded RNA viruses that may cause similar immune responses in the host. Our results showed that there was no significant difference in the frequency of the IFIH1 rs1990760 genotype among the mild, severe and healthy groups. At IFIH1 SNP rs1990760, the methylation level of IFIH1 in the TT genotype was significantly higher than that in the CC and CT genotypes. There was no correlation between the expression of IFIH1 and the genotype of IFIH1 rs1990760. The differences between our results and other studies may be related to the differences in case populations and grouping. Notably, we need to further expand the sample size to verify the correlation. A significant combined effect was observed between these two candidate SNPs, which showed that the risk of EV71 infection was increased with an increasing number of unfavorable DDX58 SNP rs3739674 GG and IFIH1 rs9596310 TT genotypes. Of course, we must discuss the potential limitations of our research. Little is known about the biological mechanism of the association between important SNPs and EV71 infection, and other factors need to be further considered. This aspect indicates that it is urgent to study the functional characteristics of these SNPs to verify our results.

In conclusion, this is the first report, to our knowledge, that elucidates DDX58 methylation patterns in EV71-HFMD. DDX58 expression and promoter methylation were associated with EV71 infection progression, especially in the severe EV71-HFMD group. Although future studies should expand the sample size and clarify the mechanism of how DDX58 influences EV71-HFMD, we herein provide a new perspective on the involvement of DDX58 in EV71-HFMD.

Supporting information

S1 STROBE Checklist. Checklist of items that should be included in reports of observational studies.

(DOCX)

Click here for additional data file.^{(29KB, docx)}

S1 Table. Clinical data in 120 EV71-HFMD cases and 60 healthy control cases.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S2 Table. DDX58 network functional analysis.

(XLSX)

Click here for additional data file.^{(12KB, xlsx)}

S3 Table. IFIH1 network functional analysis.

(XLSX)

Click here for additional data file.^{(12KB, xlsx)}

S1 Data. Clinical and Experimental Data in DDX58.

(XLSX)

Click here for additional data file.^{(67.5KB, xlsx)}

S2 Data. Clinical and Experimental Data in IFIH1.

(XLSX)

Click here for additional data file.^{(63.3KB, xlsx)}

Acknowledgments

The authors thank the doctors of the Department of Infectious Diseases of Xi’an Children’s Hospital and Xi’an Jiaotong University Second Affiliated Hospital for their help with sample collection. The authors also thank the Shanghai Genesky Bio-Tech Center for Human Genetics Research for technical assistance with genotyping.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was supported by the National Natural Science Foundation of China (grant no. 81701632, received by YPL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Ventarola D, Bordone L, Silverberg N. Update on hand-foot-and-mouth disease. Clin Dermatol. 2015;33:340–346. doi: 10.1016/j.clindermatol.2014.12.011 [DOI] [PubMed] [Google Scholar]
2.Yip CC, Lau SK, Woo PC, Yuen KY. Human enterovirus 71 epidemics: What’s next? Emerg Health Threats J. 2013;6:19780. doi: 10.3402/ehtj.v6i0.19780 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Mao Q, Wang Y, Yao X, Bian L, Wu X, Xu M, et al. Coxsackievirus A16: epidemiology, diagnosis, and vaccine. Hum Vaccin Immunother. 2014;10:360–367. doi: 10.4161/hv.27087 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Huang J, Liao Q, Ooi MH, Cowling BJ, Chang Z, Wu P, et al. Epidemiology of recurrent hand, foot and mouth disease, China, 2008–2015. Emerg Infect Dis. 2018;24:432–442. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Zou R, Zhang G, Li S, Wang W, Yuan J, Li J, et al. A functional polymorphism in IFNAR1 gene is associated with susceptibility and severity of HFMD with EV71 infection. Sci Rep. 2015;5:18541. doi: 10.1038/srep18541 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Ang LW, Koh BK, Chan KP, Chua LT, James L, Goh KT. Epidemiology and control of hand, foot and mouth disease in Singapore, 2001–2007. Ann Acad Med Singap. 2009;38:106–112. [PubMed] [Google Scholar]
7.Takahashi S, Metcalf CJE, Arima Y, Fujimoto T, Shimizu H, Rogier van Doorn H, et al. Epidemic dynamics, interactions and predictability of enteroviruses associated with hand, foot and mouth disease in Japan. J R Soc Interface. 2018;15:20180507. doi: 10.1098/rsif.2018.0507 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Hyeon JY, Hwang S, Kim H, Song J, Ahn J, Kang B, et al. Accuracy of diagnostic methods and surveillance sensitivity for human enterovirus, South Korea, 1999–2011. Emerg Infect Dis. 2013;19:1268–1275. doi: 10.3201/eid.1908.130496 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Mao Q, Wang Y, Bian L, Xu M, Liang Z. EV-A71 vaccine licensure: a first step for multivalent enterovirus vaccine to control HFMD and other severe diseases. Emerg Microbes Infect. 2016;5:e75. doi: 10.1038/emi.2016.73 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Ni H, Yi B, Yin J, Fang T, He T, Du Y, et al. Epidemiological and etiological characteristics of hand, foot, and mouth disease in Ningbo, China, 2008–2011. J Clin Virol. 2012;54:342–348. doi: 10.1016/j.jcv.2012.04.021 [DOI] [PubMed] [Google Scholar]
11.Brisse M, Ly H. Comparative structure and function analysis of the RIG-I-Like receptors: RIG-I and MDA5. Front Immunol. 2019;10:1586. doi: 10.3389/fimmu.2019.01586 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Sen A, Pruijssers AJ, Dermody TS, Garcia-Sastre A, Greenberg HB. The early interferon response to rotavirus is regulated by PKR and depends on MAVS/IPS-1, RIG-I, MDA-5, and IRF3. J Virol. 2011;85:3717–3732. doi: 10.1128/JVI.02634-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Kell AM, Hemann EA, Turnbull JB, Gale M Jr. RIG-I-like receptor activation drives type I IFN and antiviral signaling to limit Hantaan orthohantavirus replication. PLoS Pathog. 2020;16:e1008483. doi: 10.1371/journal.ppat.1008483 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Uzri D, Greenberg HB. Characterization of rotavirus RNAs that activate innate immune signaling through the RIG-I-like receptors. PLoS One. 2013;8:e69825. doi: 10.1371/journal.pone.0069825 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Huang HI, Lin JY, Chen SH. EV71 infection induces IFNbeta expression in neural cells. Viruses. 2019;11:1121. doi: 10.3390/v11121121 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Lei X, Liu X, Ma Y, Sun Z, Yang Y, Jin Q, et al. The 3C protein of enterovirus 71 inhibits retinoid acid-inducible gene I-mediated interferon regulatory factor 3 activation and type I interferon responses. J Virol. 2010;84:8051–8061. doi: 10.1128/JVI.02491-09 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Lei X, Sun Z, Liu X, Jin Q, He B, Wang J. Cleavage of the adaptor protein TRIF by enterovirus 71 3C inhibits antiviral responses mediated by Toll-like receptor 3. J Virol. 2011;85:8811–8818. doi: 10.1128/JVI.00447-11 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Traube FR, Carell T. The chemistries and consequences of DNA and RNA methylation and demethylation. RNA Biol. 2017;14:1099–1107. doi: 10.1080/15476286.2017.1318241 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Siegfried Z, Eden S, Mendelsohn M, Feng X, Tsuberi Bz, Cedar H. DNA methylation represses transcription in vivo. Nat Genet. 1999;22:203–206. doi: 10.1038/9727 [DOI] [PubMed] [Google Scholar]
20.Boyes J, Bird A. DNA methylation inhibits transcription indirectly via a methyl-CpG binding protein. Cell. 1991;64:1123–1134. doi: 10.1016/0092-8674(91)90267-3 [DOI] [PubMed] [Google Scholar]
21.Bird AP. CpG-rich islands and the function of DNA methylation. Nature. 1986;321:209–213. doi: 10.1038/321209a0 [DOI] [PubMed] [Google Scholar]
22.Bird A, Taggart M, Frommer M, Miller OJ, Macleod D. A fraction of the mouse genome that is derived from islands of nonmethylated, CpG-rich DNA. Cell. 1985;40:91–99. doi: 10.1016/0092-8674(85)90312-5 [DOI] [PubMed] [Google Scholar]
23.Zhang Y, Chen Y, Xu Q, Liu R, Wu N, Chen G. Identification of DNA methylation and transcriptional regulatory regions in the promoter of duck retinoic acid inducible gene I (RIG-I). Br Poult Sci. 2018;59:40–44. doi: 10.1080/00071668.2017.1390213 [DOI] [PubMed] [Google Scholar]
24.Rhead B, Shao X, Quach H, Ghai P, Barcellos LF, Bowcock AM. Global expression and CpG methylation analysis of primary endothelial cells before and after TNFa stimulation reveals gene modules enriched in inflammatory and infectious diseases and associated DMRs. PLoS One. 2020;15:e230884. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Zhang YH, Meng JL, Gao Y, Zhang JY, Niu SL, Yu XZ, et al. Changes in methylation of genomic DNA from chicken immune organs in response to H5N1 influenza virus infection. Genet Mol Res. 2016;15. doi: 10.4238/gmr.15037382 [DOI] [PubMed] [Google Scholar]
26.Zhu Z, Li Y, Zhou J, Zhang Y, Cao RY. Acute enterovirus infections significantly alter host cellular DNA methylation status. Infect Genet Evol. 2020;80:104190. doi: 10.1016/j.meegid.2020.104190 [DOI] [PubMed] [Google Scholar]
27.Li YP, Li M, Jia XL, Deng HL, Wang WJ, Wu FP, et al. Association of gene polymorphisms of pattern-recognition receptor signaling pathway with the risk and severity of hand, foot, and mouth disease caused by enterovirus 71 in Chinese Han population. J Med Virol. 2018;90:692–698. doi: 10.1002/jmv.25000 [DOI] [PubMed] [Google Scholar]
28.Li M, Li YP, Deng HL, Wang MQ, Wang WJ, Wang J, et al. Association of gene polymorphisms of CD55 with susceptibility to and severity of hand, foot, and mouth disease caused by enterovirus 71 in the Han Chinese population. J Med Virol. 2020. Epub ahead of print. [DOI] [PubMed] [Google Scholar]
29.Kato H, Takeuchi O, Sato S, Yoneyama M, Yamamoto M, Matsui K, et al. Differential roles of MDA5 and RIG-I helicases in the recognition of RNA viruses. Nature. 2006;441:101–105. doi: 10.1038/nature04734 [DOI] [PubMed] [Google Scholar]
30.Errett JS, Suthar MS, McMillan A, Diamond MS, Gale M Jr. The essential, nonredundant roles of RIG-I and MDA5 in detecting and controlling West Nile virus infection. J Virol. 2013;87:11416–11425. doi: 10.1128/JVI.01488-13 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Chen S, Cheng A, Wang M. Innate sensing of viruses by pattern recognition receptors in birds. Vet Res. 2013;44:82. doi: 10.1186/1297-9716-44-82 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Loo YM, Fornek J, Crochet N, Bajwa G, Perwitasari O, Martinez-Sobrido L, et al. Distinct RIG-I and MDA5 signaling by RNA viruses in innate immunity. J Virol. 2008;82:335–345. doi: 10.1128/JVI.01080-07 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Kuo RL, Kao LT, Lin SJ, Wang RY, Shih Sr. MDA5 plays a crucial role in enterovirus 71 RNA-mediated IRF3 activation. PLoS One. 2013;8:e63431. doi: 10.1371/journal.pone.0063431 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Pang L, Gong X, Liu N, Xie G, Gao W, Kong G, et al. A polymorphism in melanoma differentiation-associated gene 5 may be a risk factor for enterovirus 71 infection. Clin Microbiol Infect. 2014;20:O711–O717. doi: 10.1111/1469-0691.12618 [DOI] [PubMed] [Google Scholar]
35.Ding W, Chen G, Shi T. Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis. Epigenetics. 2019;14:67–80. doi: 10.1080/15592294.2019.1568178 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Pan Y, Liu G, Zhou F, Su B, Li Y. DNA methylation profiles in cancer diagnosis and therapeutics. Clin Exp Med. 2018;18:1–14. doi: 10.1007/s10238-017-0467-0 [DOI] [PubMed] [Google Scholar]
37.De Jesus DF, Zhang Z, Kahraman S, Brown NK, Chen M, Hu J, et al. M⁶A mRNA methylation regulates human β-cell biology in physiological states and in type 2 diabetes. Nat Metab. 2019;1:765–774. doi: 10.1038/s42255-019-0089-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Liu G, Bin P, Wang T, Ren W, Zhong J, Liang J, et al. DNA methylation and the potential role of methyl-containing nutrients in cardiovascular diseases. Oxid Med Cell Longev. 2017;2017:1670815. doi: 10.1155/2017/1670815 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Brocard M, Ruggieri A, Locker N. M6A RNA methylation, a new hallmark in virus-host interactions. J Gen Virol 2017;98:2207–2214. doi: 10.1099/jgv.0.000910 [DOI] [PubMed] [Google Scholar]
40.Chlamydas S, Papavassiliou AG, Piperi C. Epigenetic mechanisms regulating COVID-19 infection. Epigenetics. 2021;16:263–270. doi: 10.1080/15592294.2020.1796896 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Niller HH, Tarnai Z, Decsi G, Zsedenyi A, Banati F, Minarovits J. Role of epigenetics in EBV regulation and pathogenesis. Future Microbiol. 2014;9:747–756. doi: 10.2217/fmb.14.41 [DOI] [PubMed] [Google Scholar]
42.Esteki-Zadeh A, Karimi M, Strååt K, Ammerpohl O, Zeitelhofer M, Jagodic M, et al. Human cytomegalovirus infection is sensitive to the host cell DNA methylation state and alters global DNA methylation capacity. Epigenetics. 2012;7:585–593. doi: 10.4161/epi.20075 [DOI] [PubMed] [Google Scholar]
43.Pruimboom L. Methylation pathways and SARS-CoV-2 lung infiltration and cell membrane-virus fusion are both subject to epigenetics. Front Cell Infect Microbiol. 2020;10:290. doi: 10.3389/fcimb.2020.00290 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Fu Y, Zhang L, Zhang R, Xu S, Wang H, Jin Y, et al. Enterovirus 71 suppresses miR-17-92 cluster through up-regulating methylation of the miRNA promoter. Front Microbiol. 2019;10:625. doi: 10.3389/fmicb.2019.00625 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Li YP, Deng HL, Wang WJ, Wang MQ, Li M, Zhang YF, et al. Vitamin D receptor gene methylation in patients with hand, foot, and mouth disease caused by enterovirus 71. Arch Virol. 2020;165:1979–1985. doi: 10.1007/s00705-020-04701-8 [DOI] [PubMed] [Google Scholar]
46.Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6:e1000952. doi: 10.1371/journal.pgen.1000952 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Cheung WA, Shao X, Morin A, Siroux V, Kwan T, Ge B, et al. Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome. Genome Biol. 2017;18:50. doi: 10.1186/s13059-017-1173-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Pierce BL, Tong L, Argos M, Demanelis K, Jasmine F, Rakibuz-Zaman M, et al. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun. 2018;9:804. doi: 10.1038/s41467-018-03209-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Cen H, Wang W, Leng RX, Wang TY, Pan HF, Fan YG, et al. Association of IFIH1 rs1990760 polymorphism with susceptibility to autoimmune diseases: a meta-analysis. Autoimmunity. 2013;46:455–462. doi: 10.3109/08916934.2013.796937 [DOI] [PubMed] [Google Scholar]
50.Zhu XB, Zhuo LY, Yue M, Liu M, Zang F, Fan HZ, et al. Genetic variants in IFIH1 and DDX58 influence hepatitis C virus clearance in Chinese Han population. J Med Virol. 2019;91:1097–1103. doi: 10.1002/jmv.25398 [DOI] [PubMed] [Google Scholar]

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0010090.r001

Decision Letter 0

Jen-Ren Wang, Johan Van Weyenbergh

20 Jul 2021

Dear Dr. Zhai,

Thank you very much for submitting your manuscript "DNA methylation and single-nucleotide polymorphisms in DDX58 are associated with hand, foot and mouth disease caused by enterovirus 71" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Johan Van Weyenbergh

Associate Editor

PLOS Neglected Tropical Diseases

Jen-Ren Wang

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: in this study the authors are trying to find how DNA methylation at promoter, gene expression and SNPs at genes DDX58 and IFIH1 are regulated in to determine the the risk and severity of hand, foot, and mouth disease caused by enterovirus 71

(EV71-HFMD). They test 60 non patients and 60 mild and severe patients to test out the levels of DNA methylation at promoter and gene expression. The authors don't mention how they classify the patients as mild vs severe. More information in this regard would be helpful.

Reviewer #2: 1. Bonferroni corrections for multiple testing are needed, which should include any testing that was done on data that was not presented in the paper. In order to preserve the statistical significance of the main findings it may be necessary to clearly state a small number of hypotheses to be prospectively tested and present fewer minor findings. Maybe move them to supplementary data.

2. How were the 60 control children recruited?

3. Were the 3 groups of children recruited simultaneously? What were the dates for the first and last recruited child in each group?

4. A table comparing the age/sex details of the children in each group would be helpful as well as first and last dates of recruitment.

5. Give more details of the patients who were excluded. E.g. How many? What were all the reasons?

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: They show that DNA methylation on the promoter of DDX58 at some of the locations were higher in the severe dataset wrt to the mild and control. While some of the places showed a clear distinction between control and severe datasets with mild being in between the two, some of the regions like CpG 248, 168 and 179 had no difference between control and severe (figure 2). It would be more helpful if the authors provide individual data points in each of the 3 subsets to better understand the range of values ( in Figure 2,3,8). They also checked the promoter of IFIH1 but don't see a general effect though at very few points DNA methylation was higher in severe category as compared to control. here again authors claim that CpG86 was significantly different but the severe looks same in not lower than the control. (Both in Figure 3 and Figure 5).

They compare the correlation between DNA methylation and gene expression for both DDX58 and IFIH1, and find an inverse correlation for many CpG methylation status with DDX58 expression. They show correlation in ALL people tested vs Patient population with not much change in the correlation values. It would make more sense to show correlation values in pateints and non patients(and not ALL) (in figure7). There was no significant correlation for IFIH1.

The authors also checked the SNPs in DDX58 gene and find that DNA methylation and gene expression were correlated with the associated base in SNPs, but for IFIH1 gene the DNA methylation correlated well with the SNPs but not the gene expression. Again providing individual data points for this figure would be helpful in understanding the data better.

Reviewer #2: 1. The units of measurement should be stated on the y axis of figures and also in the figure legends.

2. How were the heat maps made? It would help to explain the meaning of the +1 and -1 values.

3. The word ‘compound’ in figure 7 is inaccurate.

4. I assume that the mean/median levels of GAPDH expression were the same in the groups?

5. The SNPs were genotyped by two different techniques. Which technique was used for the results in the publication? Did the two techniques give the same results?

6. What were the results of the Hardy-Weinberg tests for each group?

7. What is the blue rectangle in the upper left of figure 1?

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: In conclusion the authors claim that DDX58 expression and promoter methylation are associated with EV71 infection progression, which is collaborated by their data. Although their claim that the levels can be used to distinguish patients with severe disease may not hold true as the difference between control and severe patients in though significantly different in many cases was not hugely separated.

Reviewer #2: 1. In the conclusion of the Discussion the authors assume that the DDX58 parameters influence severe infection. It seems more likely that the infection influences the changes in methylation and expression. If DDX58 methylation was the cause of the severe infection (ie it preceded the infection) then the variance in the control group should be the same as the variance in the combined infected groups? In this data, high levels of DDX58 methylation are only seen after infection.

2. How biologically significant are these findings? It would be good to discuss this. How much of the variance (r squared) in DDX58 methylation is accounted for by clinical outcome? How much of the variance in DDX58 expression is accounted for by DDX58 methylation. How much of the variance in clinical outcome is accounted for by DDX58 expression?

3. How clinically significant are the findings? Are they likely to have any influence on patient management?

4. Etc is not a scientific expression

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Line 121: uppercase W in "written"

Reviewer #2: The standard of English is generally very good, except for the Author Summary which needs re-writing.

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: (No Response)

Reviewer #2: The data is of interest and is a logical extension of previous work from the author's laboratory. There is too much data presented. Only present data that you are actually going to discuss.

-------------------

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

PLoS Negl Trop Dis. 2022 Jan 18;16(1):e0010090. doi: 10.1371/journal.pntd.0010090.r002

Author response to Decision Letter 0

4 Sep 2021

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(24.8KB, docx)}

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0010090.r003

Decision Letter 1

Jen-Ren Wang, Johan Van Weyenbergh

8 Oct 2021

Dear Dr. Zhai,

When you are ready to resubmit, please upload the following:

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Sincerely,

Johan Van Weyenbergh

Associate Editor

PLOS Neglected Tropical Diseases

Jen-Ren Wang

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: (No Response)

Reviewer #2: I have asked for more information about the study subjects - see comments 1, 2 and 3. Otherwise OK.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: (No Response)

Reviewer #2: The editor should decide whether the text of the figures is large enough to be legible in the published article. it looks a bit small to me.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: (No Response)

Reviewer #2: They address the public health relevance with ROC analyses, but probably overstate it. To be useful the analyses would need to be done prospectively at an early stage of infection before severe sequelae develop. It is possible that the changes in parameters they measure are a consequence of severe infection rather than the cause of it. They need to perform ROC analyses on lymphocyte and neutrophil data, which may have more predictive value and be far easier to measure. I see the value of this work, which I think has been carefully performed, as offering clues to disease pathophysiology that could be explored in further studies. For example I would be interested in knowing which subsets of blood leucocytes these changes occurred in and whether previous subclinical dengue virus infections or toxoplasmosis exposure influenced the chance of developing severe disease (unlikely but interesting if true).

They have a tendency to re-state the results in the discussion rather than discussing the results in the discussion. This makes it a bit laborious to read.

--------------------

Editorial and Data Presentation Modifications?

Reviewer #1: (No Response)

Reviewer #2: Comments 4, 5 and 7 require data modifications. Comments 13 and 14 require small changes to statistics.

--------------------

Summary and General Comments

Reviewer #1: The authors have addressed all the major comments and the revised manuscript is stronger and more clear.

Reviewer #2: This manuscript contains interesting data exploring the relationships between HFMD infection severity and DDX58 and IFIH1 promoter methylation, gene expression and SNP genotypes. There are still some improvements to be made.

1. Table S1 shows there were two windows for recruiting patients. Was the number of severe and mild cases the same in each window?

2. The timing of the blood sample taking needs to be stated more clearly. Was the blood taken before or after the patients had ben classified into mild and severe groups? It would also be interesting to know the percentage of hospitalised patients that would normally be expected to develop severe disease.

3. Dengue virus infections have recently been reported in Xi’an and the peak of dengue infections overlaps HFMD. Is it possible that there was a different frequency of subclinical dengue infections between the mild and severe groups that might have influenced the study parameters?

4. Lines 226/227 say the 86 CpG has the highest methylation level in the severe group, but the data in the heat map (1C) and Table 2 show the opposite. This needs explanation.

5. The average methylation rectangles in heat map 1C clearly show that the mild group (brown) has higher methylation than the other groups (blue). However the data in figure 1D and in Table 2 do not show this. Why not?

6. Line 188. Say ‘the significance of differences between the groups was analysed ……’

7. CpG 179 had the highest methylation in the healthy group, not the severe group.

8. It is hard to imagine how a difference of between 7 and 9 methylated cytosines per 1000 cytosines (Table 1) can be of biological significance, although the data do show this difference is statistically significant. This needs to be discussed.

9. Is the text in the figures large enough to be legible in the published article?

10. Does the low lymphocyte count in the severe group account for the methylation and mRNA transcription differences? That is, do lymphocytes have lower methylation levels and higher mRNA levels than non-lymphocytes and so when the lymphocyte count falls it appears as if methylation has increased and mRNA has decreased?

11. Do methylation levels or mRNA levels make better predictions of disease severity than the lymphocyte count? If you were looking for a clinically useful prediction assay the lymphocyte count might be more convenient.

12. Are blood leucocytes, a complex mixture of many cell types, a good choice for these analyses? Surely it should be the cells infected by the virus. Maybe in future work you could determine which leucocytes these changes were occuring in. This would be relevant to understanding pathophysiology.

13. Since the distributions of the methylation and gene expression data were not normal, a Spearman rank order correlation coefficient is more appropriate.

14. Were the results of the Students t test adjusted for non-normal distributions?

15. Line 337. What is a virus cell?

16. Use the word ‘children’ instead of ‘kids’.

--------------------

Figure Files:

Data Requirements:

Reproducibility:

PLoS Negl Trop Dis. 2022 Jan 18;16(1):e0010090. doi: 10.1371/journal.pntd.0010090.r004

Author response to Decision Letter 1

16 Nov 2021

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(20.5KB, docx)}

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0010090.r005

Decision Letter 2

Jen-Ren Wang, Johan Van Weyenbergh

14 Dec 2021

Dear Dr. Zhai,

We are pleased to inform you that your manuscript 'DNA methylation and single-nucleotide polymorphisms in DDX58 are associated with hand, foot and mouth disease caused by enterovirus 71' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Johan Van Weyenbergh

Associate Editor

PLOS Neglected Tropical Diseases

Jen-Ren Wang

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

Minor changes needed: Line 95: parameters instead of reasons Line 219: remove 179 Line 267: Spearman's Line 273: Remove 246

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #2: No problems

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #2: No Problems

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #2: No problems

**********

Editorial and Data Presentation Modifications?

Reviewer #2: Minor changes needed:

Line 95: parameters instead of reasons

Line 219: remove 179

Line 267: Spearmans

Line 273: Remove 246

**********

Summary and General Comments

Reviewer #2: No comments

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0010090.r006

Acceptance letter

Jen-Ren Wang, Johan Van Weyenbergh

4 Jan 2022

Dear Dr. Zhai,

We are delighted to inform you that your manuscript, "DNA methylation and single-nucleotide polymorphisms in DDX58 are associated with hand, foot and mouth disease caused by enterovirus 71," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 STROBE Checklist. Checklist of items that should be included in reports of observational studies.

(DOCX)

Click here for additional data file.^{(29KB, docx)}

S1 Table. Clinical data in 120 EV71-HFMD cases and 60 healthy control cases.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S2 Table. DDX58 network functional analysis.

(XLSX)

Click here for additional data file.^{(12KB, xlsx)}

S3 Table. IFIH1 network functional analysis.

(XLSX)

Click here for additional data file.^{(12KB, xlsx)}

S1 Data. Clinical and Experimental Data in DDX58.

(XLSX)

Click here for additional data file.^{(67.5KB, xlsx)}

S2 Data. Clinical and Experimental Data in IFIH1.

(XLSX)

Click here for additional data file.^{(63.3KB, xlsx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(24.8KB, docx)}

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(20.5KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

[pntd.0010090.ref001] 1.Ventarola D, Bordone L, Silverberg N. Update on hand-foot-and-mouth disease. Clin Dermatol. 2015;33:340–346. doi: 10.1016/j.clindermatol.2014.12.011 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref002] 2.Yip CC, Lau SK, Woo PC, Yuen KY. Human enterovirus 71 epidemics: What’s next? Emerg Health Threats J. 2013;6:19780. doi: 10.3402/ehtj.v6i0.19780 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref003] 3.Mao Q, Wang Y, Yao X, Bian L, Wu X, Xu M, et al. Coxsackievirus A16: epidemiology, diagnosis, and vaccine. Hum Vaccin Immunother. 2014;10:360–367. doi: 10.4161/hv.27087 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref004] 4.Huang J, Liao Q, Ooi MH, Cowling BJ, Chang Z, Wu P, et al. Epidemiology of recurrent hand, foot and mouth disease, China, 2008–2015. Emerg Infect Dis. 2018;24:432–442. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref005] 5.Zou R, Zhang G, Li S, Wang W, Yuan J, Li J, et al. A functional polymorphism in IFNAR1 gene is associated with susceptibility and severity of HFMD with EV71 infection. Sci Rep. 2015;5:18541. doi: 10.1038/srep18541 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref006] 6.Ang LW, Koh BK, Chan KP, Chua LT, James L, Goh KT. Epidemiology and control of hand, foot and mouth disease in Singapore, 2001–2007. Ann Acad Med Singap. 2009;38:106–112. [PubMed] [Google Scholar]

[pntd.0010090.ref007] 7.Takahashi S, Metcalf CJE, Arima Y, Fujimoto T, Shimizu H, Rogier van Doorn H, et al. Epidemic dynamics, interactions and predictability of enteroviruses associated with hand, foot and mouth disease in Japan. J R Soc Interface. 2018;15:20180507. doi: 10.1098/rsif.2018.0507 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref008] 8.Hyeon JY, Hwang S, Kim H, Song J, Ahn J, Kang B, et al. Accuracy of diagnostic methods and surveillance sensitivity for human enterovirus, South Korea, 1999–2011. Emerg Infect Dis. 2013;19:1268–1275. doi: 10.3201/eid.1908.130496 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref009] 9.Mao Q, Wang Y, Bian L, Xu M, Liang Z. EV-A71 vaccine licensure: a first step for multivalent enterovirus vaccine to control HFMD and other severe diseases. Emerg Microbes Infect. 2016;5:e75. doi: 10.1038/emi.2016.73 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref010] 10.Ni H, Yi B, Yin J, Fang T, He T, Du Y, et al. Epidemiological and etiological characteristics of hand, foot, and mouth disease in Ningbo, China, 2008–2011. J Clin Virol. 2012;54:342–348. doi: 10.1016/j.jcv.2012.04.021 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref011] 11.Brisse M, Ly H. Comparative structure and function analysis of the RIG-I-Like receptors: RIG-I and MDA5. Front Immunol. 2019;10:1586. doi: 10.3389/fimmu.2019.01586 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref012] 12.Sen A, Pruijssers AJ, Dermody TS, Garcia-Sastre A, Greenberg HB. The early interferon response to rotavirus is regulated by PKR and depends on MAVS/IPS-1, RIG-I, MDA-5, and IRF3. J Virol. 2011;85:3717–3732. doi: 10.1128/JVI.02634-10 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref013] 13.Kell AM, Hemann EA, Turnbull JB, Gale M Jr. RIG-I-like receptor activation drives type I IFN and antiviral signaling to limit Hantaan orthohantavirus replication. PLoS Pathog. 2020;16:e1008483. doi: 10.1371/journal.ppat.1008483 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref014] 14.Uzri D, Greenberg HB. Characterization of rotavirus RNAs that activate innate immune signaling through the RIG-I-like receptors. PLoS One. 2013;8:e69825. doi: 10.1371/journal.pone.0069825 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref015] 15.Huang HI, Lin JY, Chen SH. EV71 infection induces IFNbeta expression in neural cells. Viruses. 2019;11:1121. doi: 10.3390/v11121121 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref016] 16.Lei X, Liu X, Ma Y, Sun Z, Yang Y, Jin Q, et al. The 3C protein of enterovirus 71 inhibits retinoid acid-inducible gene I-mediated interferon regulatory factor 3 activation and type I interferon responses. J Virol. 2010;84:8051–8061. doi: 10.1128/JVI.02491-09 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref017] 17.Lei X, Sun Z, Liu X, Jin Q, He B, Wang J. Cleavage of the adaptor protein TRIF by enterovirus 71 3C inhibits antiviral responses mediated by Toll-like receptor 3. J Virol. 2011;85:8811–8818. doi: 10.1128/JVI.00447-11 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref018] 18.Traube FR, Carell T. The chemistries and consequences of DNA and RNA methylation and demethylation. RNA Biol. 2017;14:1099–1107. doi: 10.1080/15476286.2017.1318241 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref019] 19.Siegfried Z, Eden S, Mendelsohn M, Feng X, Tsuberi Bz, Cedar H. DNA methylation represses transcription in vivo. Nat Genet. 1999;22:203–206. doi: 10.1038/9727 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref020] 20.Boyes J, Bird A. DNA methylation inhibits transcription indirectly via a methyl-CpG binding protein. Cell. 1991;64:1123–1134. doi: 10.1016/0092-8674(91)90267-3 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref021] 21.Bird AP. CpG-rich islands and the function of DNA methylation. Nature. 1986;321:209–213. doi: 10.1038/321209a0 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref022] 22.Bird A, Taggart M, Frommer M, Miller OJ, Macleod D. A fraction of the mouse genome that is derived from islands of nonmethylated, CpG-rich DNA. Cell. 1985;40:91–99. doi: 10.1016/0092-8674(85)90312-5 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref023] 23.Zhang Y, Chen Y, Xu Q, Liu R, Wu N, Chen G. Identification of DNA methylation and transcriptional regulatory regions in the promoter of duck retinoic acid inducible gene I (RIG-I). Br Poult Sci. 2018;59:40–44. doi: 10.1080/00071668.2017.1390213 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref024] 24.Rhead B, Shao X, Quach H, Ghai P, Barcellos LF, Bowcock AM. Global expression and CpG methylation analysis of primary endothelial cells before and after TNFa stimulation reveals gene modules enriched in inflammatory and infectious diseases and associated DMRs. PLoS One. 2020;15:e230884. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref025] 25.Zhang YH, Meng JL, Gao Y, Zhang JY, Niu SL, Yu XZ, et al. Changes in methylation of genomic DNA from chicken immune organs in response to H5N1 influenza virus infection. Genet Mol Res. 2016;15. doi: 10.4238/gmr.15037382 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref026] 26.Zhu Z, Li Y, Zhou J, Zhang Y, Cao RY. Acute enterovirus infections significantly alter host cellular DNA methylation status. Infect Genet Evol. 2020;80:104190. doi: 10.1016/j.meegid.2020.104190 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref027] 27.Li YP, Li M, Jia XL, Deng HL, Wang WJ, Wu FP, et al. Association of gene polymorphisms of pattern-recognition receptor signaling pathway with the risk and severity of hand, foot, and mouth disease caused by enterovirus 71 in Chinese Han population. J Med Virol. 2018;90:692–698. doi: 10.1002/jmv.25000 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref028] 28.Li M, Li YP, Deng HL, Wang MQ, Wang WJ, Wang J, et al. Association of gene polymorphisms of CD55 with susceptibility to and severity of hand, foot, and mouth disease caused by enterovirus 71 in the Han Chinese population. J Med Virol. 2020. Epub ahead of print. [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref029] 29.Kato H, Takeuchi O, Sato S, Yoneyama M, Yamamoto M, Matsui K, et al. Differential roles of MDA5 and RIG-I helicases in the recognition of RNA viruses. Nature. 2006;441:101–105. doi: 10.1038/nature04734 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref030] 30.Errett JS, Suthar MS, McMillan A, Diamond MS, Gale M Jr. The essential, nonredundant roles of RIG-I and MDA5 in detecting and controlling West Nile virus infection. J Virol. 2013;87:11416–11425. doi: 10.1128/JVI.01488-13 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref031] 31.Chen S, Cheng A, Wang M. Innate sensing of viruses by pattern recognition receptors in birds. Vet Res. 2013;44:82. doi: 10.1186/1297-9716-44-82 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref032] 32.Loo YM, Fornek J, Crochet N, Bajwa G, Perwitasari O, Martinez-Sobrido L, et al. Distinct RIG-I and MDA5 signaling by RNA viruses in innate immunity. J Virol. 2008;82:335–345. doi: 10.1128/JVI.01080-07 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref033] 33.Kuo RL, Kao LT, Lin SJ, Wang RY, Shih Sr. MDA5 plays a crucial role in enterovirus 71 RNA-mediated IRF3 activation. PLoS One. 2013;8:e63431. doi: 10.1371/journal.pone.0063431 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref034] 34.Pang L, Gong X, Liu N, Xie G, Gao W, Kong G, et al. A polymorphism in melanoma differentiation-associated gene 5 may be a risk factor for enterovirus 71 infection. Clin Microbiol Infect. 2014;20:O711–O717. doi: 10.1111/1469-0691.12618 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref035] 35.Ding W, Chen G, Shi T. Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis. Epigenetics. 2019;14:67–80. doi: 10.1080/15592294.2019.1568178 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref036] 36.Pan Y, Liu G, Zhou F, Su B, Li Y. DNA methylation profiles in cancer diagnosis and therapeutics. Clin Exp Med. 2018;18:1–14. doi: 10.1007/s10238-017-0467-0 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref037] 37.De Jesus DF, Zhang Z, Kahraman S, Brown NK, Chen M, Hu J, et al. M⁶A mRNA methylation regulates human β-cell biology in physiological states and in type 2 diabetes. Nat Metab. 2019;1:765–774. doi: 10.1038/s42255-019-0089-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref038] 38.Liu G, Bin P, Wang T, Ren W, Zhong J, Liang J, et al. DNA methylation and the potential role of methyl-containing nutrients in cardiovascular diseases. Oxid Med Cell Longev. 2017;2017:1670815. doi: 10.1155/2017/1670815 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref039] 39.Brocard M, Ruggieri A, Locker N. M6A RNA methylation, a new hallmark in virus-host interactions. J Gen Virol 2017;98:2207–2214. doi: 10.1099/jgv.0.000910 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref040] 40.Chlamydas S, Papavassiliou AG, Piperi C. Epigenetic mechanisms regulating COVID-19 infection. Epigenetics. 2021;16:263–270. doi: 10.1080/15592294.2020.1796896 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref041] 41.Niller HH, Tarnai Z, Decsi G, Zsedenyi A, Banati F, Minarovits J. Role of epigenetics in EBV regulation and pathogenesis. Future Microbiol. 2014;9:747–756. doi: 10.2217/fmb.14.41 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref042] 42.Esteki-Zadeh A, Karimi M, Strååt K, Ammerpohl O, Zeitelhofer M, Jagodic M, et al. Human cytomegalovirus infection is sensitive to the host cell DNA methylation state and alters global DNA methylation capacity. Epigenetics. 2012;7:585–593. doi: 10.4161/epi.20075 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref043] 43.Pruimboom L. Methylation pathways and SARS-CoV-2 lung infiltration and cell membrane-virus fusion are both subject to epigenetics. Front Cell Infect Microbiol. 2020;10:290. doi: 10.3389/fcimb.2020.00290 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref044] 44.Fu Y, Zhang L, Zhang R, Xu S, Wang H, Jin Y, et al. Enterovirus 71 suppresses miR-17-92 cluster through up-regulating methylation of the miRNA promoter. Front Microbiol. 2019;10:625. doi: 10.3389/fmicb.2019.00625 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref045] 45.Li YP, Deng HL, Wang WJ, Wang MQ, Li M, Zhang YF, et al. Vitamin D receptor gene methylation in patients with hand, foot, and mouth disease caused by enterovirus 71. Arch Virol. 2020;165:1979–1985. doi: 10.1007/s00705-020-04701-8 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref046] 46.Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6:e1000952. doi: 10.1371/journal.pgen.1000952 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref047] 47.Cheung WA, Shao X, Morin A, Siroux V, Kwan T, Ge B, et al. Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome. Genome Biol. 2017;18:50. doi: 10.1186/s13059-017-1173-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref048] 48.Pierce BL, Tong L, Argos M, Demanelis K, Jasmine F, Rakibuz-Zaman M, et al. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun. 2018;9:804. doi: 10.1038/s41467-018-03209-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pntd.0010090.ref049] 49.Cen H, Wang W, Leng RX, Wang TY, Pan HF, Fan YG, et al. Association of IFIH1 rs1990760 polymorphism with susceptibility to autoimmune diseases: a meta-analysis. Autoimmunity. 2013;46:455–462. doi: 10.3109/08916934.2013.796937 [DOI] [PubMed] [Google Scholar]

[pntd.0010090.ref050] 50.Zhu XB, Zhuo LY, Yue M, Liu M, Zang F, Fan HZ, et al. Genetic variants in IFIH1 and DDX58 influence hepatitis C virus clearance in Chinese Han population. J Med Virol. 2019;91:1097–1103. doi: 10.1002/jmv.25398 [DOI] [PubMed] [Google Scholar]

PERMALINK

DNA methylation and single-nucleotide polymorphisms in DDX58 are associated with hand, foot and mouth disease caused by enterovirus 71

Ya-Ping Li

Chen-Rui Liu

Hui-Ling Deng

Mu-Qi Wang

Yan Tian

Yuan Chen

Yu-Feng Zhang

Shuang-Suo Dang

Song Zhai

Roles

Abstract

Background

Methodology/Principal findings

Conclusions/Significance

Author summary

Introduction

Methods

Ethics statements

Study population

Detection of EV71 infection in the clinical laboratory

Collection and preservation of specimens

DNA extraction and methylation analysis

Quantitative real-time PCR (qrt-PCR)

SNP genotyping

DDX58 and IFIH1 network analysis

Statistics

Results

Clinicopathological characteristics

DDX58 and IFIH1 methylation levels

Table 1. Details of the CpG regions in the CpG islands of DDX58.

Table 2. Details of the CpG regions in the CpG islands of IFIH1.

Fig 1. The methylation patterns of DDX58 and IFIH1 in EV71-HFMD patients and healthy controls.

Fig 2.

The association between DDX58 and IFIH1 methylation patterns and clinical indicators of EV71-HFMD

Fig 3. DDX58 methylation status and levels of each positive CpG site (the 29, 134, 154, 232, 246 and 248 CpG sites) between mild and severe EV71-HFMD patients.

Table 3. The relationship between the DDX58/IFIH1 methylation levels and clinicopathological factors in EV71-HFMD.

Determination of DDX58 and IFIH1 mrna expression

Fig 4.

Promoter methylation is associated with DDX58 expression

Fig 5.

Relationship between DDX58 polymorphisms and severity of EV71-HFMD

Table 4. DDX58 rs3739674 and IFIH1 rs1990760 genotype distribution and allele frequencies in the mild and severe HFMD groups.

Fig 6.

DDX58 and IFIH1 network analysis

Fig 7.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jen-Ren Wang

Johan Van Weyenbergh

Roles

Author response to Decision Letter 0

Decision Letter 1

Jen-Ren Wang

Johan Van Weyenbergh

Roles

Author response to Decision Letter 1

Decision Letter 2

Jen-Ren Wang

Johan Van Weyenbergh

Roles

Acceptance letter

Jen-Ren Wang

Johan Van Weyenbergh

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases