Literature DB >> 28821749

DNA methylation in demyelinated multiple sclerosis hippocampus.

Anthony M Chomyk1, Christina Volsko1, Ajai Tripathi1, Sadie A Deckard1, Bruce D Trapp1, Robert J Fox2, Ranjan Dutta3.   

Abstract

Multiple Sclerosis (MS) is an immune-mediated demyelinating disease of the human central nervous system (CNS). Memory impairments and hippocampal demyelination are common features in MS patients. Our previous data have shown that demyelination alters neuronal gene expression in the hippocampus. DNA methylation is a common epigenetic modifier of gene expression. In this study, we investigated whether DNA methylation is altered in MS hippocampus following demyelination. Our results show that mRNA levels of DNA methyltransferase were increased in demyelinated MS hippocampus, while de-methylation enzymes were decreased. Comparative methylation profiling identify hypo-methylation within upstream sequences of 6 genes and hyper-methylation of 10 genes in demyelinated MS hippocampus. Genes identified in the current study were also validated in an independent microarray dataset generated from MS hippocampus. Independent validation using RT-PCR revealed that DNA methylation inversely correlated with mRNA levels of the candidate genes. Queries across cell-specific databases revealed that a majority of the candidate genes are expressed by astrocytes and neurons in mouse and human CNS. Taken together, our results expands the list of genes previously identified in MS hippocampus and establish DNA methylation as a mechanism of altered gene expression in MS hippocampus.

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Year:  2017        PMID: 28821749      PMCID: PMC5562763          DOI: 10.1038/s41598-017-08623-5

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


Introduction

Multiple sclerosis (MS) is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system (CNS) that affects more than two million people worldwide[1, 2]. Among the spectrum of cognitive impairments, memory dysfunction is most common among MS patients[3, 4]. Hippocampal demyelination is extensive in individuals with MS and modulates expression of neuronal genes involved in synaptic plasticity and memory function[5, 6]. Large-scale genome-wide association studies (GWAS) have identified MS susceptibility loci, including human leukocyte antigen loci and other immune-function related genes[7-10]; however, their functional significance related to MS pathogenesis is still unknown. The relatively low concordance rate of single-nucleotide polymorphisms in monozygotic twins[11], the presence of a strong gender bias, and the influence of migration on disease onset collectively suggest that the pathogenesis of MS likely results from a combination of both genetic and epigenetic factors[12, 13]. Epigenetic modifications, including DNA methylation, histone modification, chromatin remodeling, and noncoding RNA regulation have been reported to regulate gene expression and to participate in the etiology of MS[14, 15]. DNA methylation occurs in special genomic regions called CpG islands, which contain greater than 50% cytosine and guanine nucleotides. It plays a major role in aberrant expression of genes that are important in several neurological diseases[16, 17] as well as in memory formation and maintenance[18, 19]. We previously compared and identified several genes and microRNAs in MS hippocampus that correlate with synaptic changes, memory dysfunction, and hippocampal demyelination[5, 20]. In this study, we investigated additional epigenetic mechanisms that alter gene expression in MS hippocampus. We found significant increases in mRNA levels of key DNA methyltransferase enzymes (DNMTs). Interestingly, the mRNA levels of the three DNA de-methylation enzymes (ten-eleven translocation methylcytosine dioxygenase 1–3; TET1-TET3), which catalyze hydroxy-methylation as well as the total level of hydroxy-methylated residues, were significantly decreased in MS demyelinated hippocampus. Several differentially methylated positions (DMPs) were also identified by comparing MS myelinated to demyelinated hippocampus. The methylation status of DMPs inversely correlated with mRNA levels of target genes that have been associated with neuronal survival and memory function. As methylation patterns in different cell types may contribute to overall methylation patterns[21], human and mouse cell-specific databases indicated that a majority of the target genes were localized to neurons and astrocytes. Collectively, our results provide evidence that DNA methylation could play a major role in controlling gene expression in MS hippocampus.

Results

Demyelination correlates with changes in DNA methylation and de-methylation enzymes in MS hippocampus

In order to study DNA methylation, we measured levels of DNMT enzymes responsible for inserting and maintaining DNA methylation (DNMT1, DNMT3A, and DNMT3B) in myelinated and demyelinated MS hippocampi. Demyelination led to significant increases in mRNA levels of all three DNMTs in MS hippocampus (Fig. 1A). Immunohistochemical analysis showed that DNMT1 (Fig. 1B,C), DNMT3A (Fig. 1D,E) and DNMT3B (Fig. 1F,G) were primarily associated with hippocampal neurons in MS myelinated (Fig. 1B,D,F) and MS demyelinated (Fig. 1C,E,G) hippocampus.
Figure 1

DNA methyltransferase (DNMT) expression in hippocampi from multiple sclerosis (MS) brains. RT-PCR analysis shows significant increases in mRNA levels of DNMT 1, DNMT3A, and DNMT3B in demyelinated hippocampus (n = 4) compared to myelinated hippocampus (n = 4) (A). Immunohistochemistry showing cellular expression of DNMT1, DNMT3A, and DNMT3B in myelinated (B,D,F) and demyelinated hippocampus (C,E,G), with predominant expression in hippocampal neurons. Scale Bars: B–G: 30 μm; Error bars indicate + S.E.M.; *p < 0.05.

DNA methyltransferase (DNMT) expression in hippocampi from multiple sclerosis (MS) brains. RT-PCR analysis shows significant increases in mRNA levels of DNMT 1, DNMT3A, and DNMT3B in demyelinated hippocampus (n = 4) compared to myelinated hippocampus (n = 4) (A). Immunohistochemistry showing cellular expression of DNMT1, DNMT3A, and DNMT3B in myelinated (B,D,F) and demyelinated hippocampus (C,E,G), with predominant expression in hippocampal neurons. Scale Bars: B–G: 30 μm; Error bars indicate + S.E.M.; *p < 0.05. In concert with DNMT enzymes inserting and maintaining methylation patterns, the TET family of de-methylation enzymes oxidizes methylcytosine (5 mC) to hydroxymethylcytosine (5 hmC) to remove methylation[22]. Three TET proteins (TET 1–3) have been identified in humans[23], which all convert 5 mC to 5 hmC. Given the relationship between methylation and TET enzymes, we also quantified mRNA levels of TET1–3 in MS hippocampus (Fig. 2A). The results showed a significant decrease in mRNA levels of all three TET genes in demyelinated hippocampi from individuals with MS. In order to test whether the lower mRNA levels of TET enzymes also correlated with levels of 5hmC residues, we measured the global levels of 5hmC within DNA isolated from MS myelinated and demyelinated hippocampi. The results showed that there was a significant decrease in the overall level of 5hmC within demyelinated hippocampus that correlated with the decreased mRNA levels of TET1–3 (Fig. 2B). We then conducted histochemical analysis to determine the cellular expression of 5hmC and we determined that neurons were the major cell type containing 5hmC in MS myelinated (Fig. 2C) and demyelinated hippocampus (Fig. 2D).
Figure 2

Demyelination in MS brains leads to lower expression of TET enzyme mRNA and hydroxymethyl residues. RT-PCR analysis shows significant decreases in mRNA levels of TET 1, TET 2, and TET 3 in demyelinated hippocampus (n = 4) compared to myelinated hippocampus (n = 4) (A). Compared to myelinated hippocampus (n = 8), demyelination (n = 7) led to a significant decrease in levels of hydroxymethyl content (hmC) (B). Immunohistochemistry analysis using a 5hmC antibody shows neurons as the major cell type with 5 hmC expression in myelinated (C) and demyelinated hippocampus (D). Scale Bars: C–D: 30 μm; Error bars indicate + S.E.M.; *p < 0.05.

Demyelination in MS brains leads to lower expression of TET enzyme mRNA and hydroxymethyl residues. RT-PCR analysis shows significant decreases in mRNA levels of TET 1, TET 2, and TET 3 in demyelinated hippocampus (n = 4) compared to myelinated hippocampus (n = 4) (A). Compared to myelinated hippocampus (n = 8), demyelination (n = 7) led to a significant decrease in levels of hydroxymethyl content (hmC) (B). Immunohistochemistry analysis using a 5hmC antibody shows neurons as the major cell type with 5 hmC expression in myelinated (C) and demyelinated hippocampus (D). Scale Bars: C–D: 30 μm; Error bars indicate + S.E.M.; *p < 0.05.

Loss of myelin correlates with DNA methylation changes within transcription start sites

Imprinted gene expression is regulated by epigenetic mechanisms, particularly DNA methylation at DMPs. We did not detect any significant changes in overall 5mC levels in DNA from myelinated or demyelinated MS hippocampus (data not shown). We therefore used a more sensitive global array-based approach to identify DMPs by comparing myelinated (n = 8) and demyelinated (n = 7) MS hippocampus (Fig. 3A) using an Infinium HumanMethylation 450 K array (Illumina Inc, USA). Patient details are in Table 1. The 450 k array contains 485,512 probes covering 99% of RefSeq genes. The probes interrogate 19,755 unique CpG islands with additional coverage in shore regions as well as 3091 probes at non-CpG sites[24]. Resultant methylation assays and global clustering showed that the methylation pattern could reliably differentiate between myelinated and demyelinated MS hippocampus (Fig. 3A). Demyelination resulted in identification of 144 DMPs (62 hypermethylated, 82 hypomethylated) within the assessed CpG sites (adjusted p < 0.05, delta β 20%; Fig. 3B). Localization of these DMPs in the context of genomic location was classified into different categories (Fig. 3C): CpG islands, flanking CpG Islands (CGIs) (shores and shelves; 2–4 kb from CGIs), and open sea (not related to CpGs). We found 29 out of 62 (46.7%) DMPs detected within the CpG islands had increased methylation, whereas 26.8% (22 out of 82) of the DMPs within the CpG islands showed decreased methylation. We next asked whether these DMPs fall within the genomic location of well-characterized genes (UCSD genome browser hg119). The DMPs were mapped to either the ‘1st exon’, 3′ and 5′ UTRs, or the ‘body’ (non-exonic) of the gene, or within 1500 bp of the transcription start site (TSS). The results showed that 92 out of 144 altered DMPs (65%) were mapped to 75 annotated genes (UCSD genome browser hg119). Several of the identified genes had multiple DMPs associated with their sequences (Table 2). To further validate our findings, we compared these identified genes with results obtained from an independent gene microarray database using MS hippocampus[5]. The results showed that a significant number of genes (43 out of 75) identified in the current study were also altered at the mRNA expression level in the previous study (gene symbols in bold letters, Tables 2 and 3).
Figure 3

Global methylation profiles are different between myelinated and demyelinated MS hippocampus. Global DNA methylation was assessed using the Illumina 450 K array. Resultant clustering analysis shows that the MS demyelinated samples (shown in red) are separated from MS myelinated samples (A). Compared to myelinated MS hippocampus, 144 DMPs (62 hyper-methylated, 82 hypo-methylated) were identified in MS demyelinated hippocampus (B). Localization of these DMPs based on CpG islands, flanking CpG Islands (CGIs) (shores and shelves; 2–4 kb from CGIs), and open sea (non-related to CpGs) are shown (C). DMPs mapped onto genomic features (‘1st exon’, 3′ and 5′ UTRs, ‘body’ of the gene, within 1500 bp of the transcription start site (TSS) of well characterized genes (UCSD genome browser hg19) show differences between hypo- and hyper-methylated targets.

Table 1

Demographics of MS patients.

Type of MSHippocampal Myelin StatusDisease Duration (yrs)Age(yrs)/SexEDSSPMI (hrs)
MS 01SPMSDemyelinated3859/F9.05
MS 02SPMSMyelinated1065/F3.09
MS 03SPMSDemyelinated4570/F9.54
MS 04RRMSDemyelinated1049/M9.018
MS 05SPMSDemyelinated1462/M8.05
MS 06SPMSDemyelinated2949/F9.04
MS 07SPMSDemyelinated946/F9.07
MS 08PPMSMyelinated1364/M8.04
MS 09SPMSDemyelinated4673/F6.57
MS 10SPMSMyelinated1455/M7.57
MS 11PPMSMyelinated3961/F8.012
MS 12SPMSMyelinated3075/F8.08
MS 13SPMSMyelinated2771/F9.55
MS 14SPMSMyelinated1661/F6.08
MS 15SPMSMyelinated3763/M7.57
Average25.161.37.87.3

SPMS: Secondary Progressive Multiple Sclerosis; PPMS: Primary Progressive Multiple Sclerosis; RRMS: Relapsing Remitting Multiple Sclerosis. EDSS: Expanded Disability Status Scale; PMI: Postmortem Interval.

Table 2

Cellular identity of hypo- and hyper-methylated CpG target genes identified in MS hippocampus following demyelination.

Hypomethylated Mouse Human
3'UTR probeID Gene Name A N O M A N O M
MLLT4 cg06738063 Myeloid/Lymphoid or Mixed-Lineage Leukemia XXXX
PPIF cg26584456 Peptidylprolyl Isomerase F XX
SCRT2cg24595510Scratch Family Zinc Finger 2X
cg07482223
SNRNP40 cg22802014 Small Nuclear Ribonucleoprotein 40kDA XXXXXX
5'UTR probeID Gene Name
ISLR2cg19470379Immunoglobulin Containing Leucine-Rich Repeat 2X
cg25666233
MEF2A cg00062736 Myocyte Enhancer Factor 2A XXXX
PMEPA1 cg26681770Prostate Transmembrane Protein, Androgen Induced 1XXX
cg04628369
Body probeID Gene Name
ABCA4 cg04350215 ATP-Binding Cassette, Subfamily A, Member 4 X
ADAMTS12 cg10594543 ADAM Metallopeptidase TS12 X
AHRR cg23576855 Aryl-Hydrocarbon Receptor Repressor XXX
BEST3cg03196364Bestrophin 3X
CASP7 cg01128042 Caspase 7, Apoptosis-Related Cysteine Peptidase XXX
CCL4L2cg04850148Chemokine (C-C Motif) Ligand 4-Like 2X
CPXM2 cg01512466 Carboxypeptidase X (M14 Family), Member 2 X
FBXW8cg02017074F-Box and WD Repeat Domain Containing 8XXXXX
HLA-B cg23427945 Human Leukocyte Antigen, Class I, B X
LOC145845cg00216138Uncharacterized LOC 145845
cg13020870
cg25718467
cg24956533
cg21375869
MEIS1 cg06833110 Meis Homeobox 1 XXX
MGMT cg07638938 O-6-Methylguanine-DNA Methyltransferase XXX
MYO7A cg17355865 Myosin VIIA XX
NXNcg19669385NucleordeoxinX
cg08190450
PKP2 cg03762760 Plakophilin 2 XX
PQLC1 cg20218571 PQ Loop Repeat Containing 1 XX
PSD3 cg10695549 Pleckstrin and Sec7 Domain Containing 3 XXX
SCN4Bcg22251955Sodium Channel, Voltage Gated, Type IV BXXX
SDK2 cg05787106 Sidekick Cell Adhesion Molecule 2 XXXX
SMYD3 cg06999043 SET and MYND Domain Containing 3 XXXX
TGFBI cg17386240 Transforming Growth Factor, Beta-Induced XX
TMEM165 cg00532122 Transmembrane Protein 165 XX
Hypomethylated Mouse Human
3′UTR probeID Gene Name A N O M A N O M
PON1 cg09416203 Paraoxonase 1 X
5′UTR probeID Gene Name
HDLBP cg17240976 High Density Lipoprotein Binding Protein XXXX
MKKS cg08331829 McKusick-Kaufman Syndrome XXX
TRIM26 cg10985055 Tripartite Motif Containing 26 XX
TRPS1 cg04613734 Trichorhinophalangeal Syndrome 1 XX
1 st Exon probeID Gene Name
KRTAP27-1cg05809586Keratin Associated Protein 27-1
MGPcg06601891Matrix Gla ProteinX
Body probeID Gene Name
AJAP1 cg00345083 Adherens Junctions Associated Protein 1 XX
C1orf106 cg10092377 Chromosome 1 open reading frame 106 X
C2orf62cg13215060Ciliogenesis Associated TTC17 Interacting Protein
DSE cg24407607 Dermatan Sulfate Epimerase XXX
EIF2C2cg14708514Eukaryotic Translation Initiation Factor 2C, S 2XXXX
GATA5cg00286102GATA Binding Protein 5
HLA-B cg19493134 Human Leukocyte Antigen, Class I, B X
IGSF9B cg25790212 Immunoglobulin Superfamily, Member 9B X
INSCcg24136292Inscuteable Homolog (Drosophila)X
KIAA1026 cg25307521 Kazrin, Periplakin Interacting Protein
KIF25 cg24246628 Kinesin Family Member 25
cg14316629
LOC100292680cg24730756Uncharacterized LOC100292680
NFASC cg23564471 Neurofascin XXX
RASA3 cg27086157 RAS P21 Protein Activator 3 XXX
SDK1cg24441899Sidekick Cell Adhesion Molecule 1XX
SHISA2cg05918715Shisa Family Member 2XX
SOLH cg26722972 Calpain 15 or Small Optic Lobes Homolog XX
SORBS2 cg09120722 Sorbin and SH3 Domain Containing 2 XX
TAGLN3 cg08522473 Transgelin 3 XXX
TBX5 cg06725552T-Box 5
cg11841394
TM9SF1 cg02898977 Transmembrane 9 Superfamily Member 1 XXX
TOP1MTcg00033213Topoisomerase (DNA)I, MitochondrialX
ZSCAN1cg27002247Zinc Finger and SCAN Domain Containing 1

Identified genes were mapped to astrocytes (A), neurons (N), oligodendrocytes (O), or microglia/macrophage lineage (M) cells in mouse and human CNS cell-specific database[25, 26]. To increase confidence in cellular identity, matching genes showing expression levels above the 50th percentile were selected. Genes significantly altered in a comparison of MS hippocampus using previously published microarray-based analsis[5] are shown in bold.

Table 3

Cellular Identity of hypo- and hyper-methylated CpGs near TSS in human and mouse.

HypomethylatedMouseHuman
TSS1500probeIDGene NameANOMANOM
AKNA cg13910813 AT-Hook Transcription Factor XX
EBPL cg04663916 Emopamil Binding Protein-like XXXXX
FLJ42709cg15444648Uncharacterized LOC441094XX
cg05977002
cg16600634
cg07546139
HERC6 cg08684066 HECT Domain Containing E3 UBL Member 6 XX
OR52M1cg17040924Olfactory Receptor, Family 52, Subfamily M1
SFRP1 cg03575666 Secreted Frizzled-Related Protein 1 XXXX
cg14824386
cg23359714
cg01074584
cg00930833
cg06166767
Hypermethylated Mouse Human
TSS1500 probeID Gene Name A N O M A N O M
C22orf43cg11466708Aspartate-Rich 1 or Chromosome 22 ORF43
LOC285830cg12035144Uncharacterized LOC 288530
NAPEPLDcg11692070N-Acyl Phosphatidylethanolamine DXXXX
NHLH2 cg22427279 Nascent Helix Loop Helix 2 X
PLCH1 cg02344477 Phospholipase C, Eta 1 XXX
SERPINA9cg13251750Serpin Peptidase Inhibitor, Member 9
SLFN13cg00364956Schlafen Family Member 13
TMEM132B cg01012836 Transmembrane Protein 132B XXXX
TTLL3 cg06870118 Tubulin Tyrosine Ligase-Like 3 XXXXX
WDR81 cg03854564 WD Repeat Domain X

Genes where altered methylation was detected within 1500 bp were matched to corresponding cell types. Genes significantly altered in a comparison of MS hippocampus using previously published microarray-based analysis[5] are shown in bold. mRNA levels of genes measured using RT-PCR and presented in Figure 4 are italicized.

Global methylation profiles are different between myelinated and demyelinated MS hippocampus. Global DNA methylation was assessed using the Illumina 450 K array. Resultant clustering analysis shows that the MS demyelinated samples (shown in red) are separated from MS myelinated samples (A). Compared to myelinated MS hippocampus, 144 DMPs (62 hyper-methylated, 82 hypo-methylated) were identified in MS demyelinated hippocampus (B). Localization of these DMPs based on CpG islands, flanking CpG Islands (CGIs) (shores and shelves; 2–4 kb from CGIs), and open sea (non-related to CpGs) are shown (C). DMPs mapped onto genomic features (‘1st exon’, 3′ and 5′ UTRs, ‘body’ of the gene, within 1500 bp of the transcription start site (TSS) of well characterized genes (UCSD genome browser hg19) show differences between hypo- and hyper-methylated targets. Demographics of MS patients. SPMS: Secondary Progressive Multiple Sclerosis; PPMS: Primary Progressive Multiple Sclerosis; RRMS: Relapsing Remitting Multiple Sclerosis. EDSS: Expanded Disability Status Scale; PMI: Postmortem Interval. Cellular identity of hypo- and hyper-methylated CpG target genes identified in MS hippocampus following demyelination. Identified genes were mapped to astrocytes (A), neurons (N), oligodendrocytes (O), or microglia/macrophage lineage (M) cells in mouse and human CNS cell-specific database[25, 26]. To increase confidence in cellular identity, matching genes showing expression levels above the 50th percentile were selected. Genes significantly altered in a comparison of MS hippocampus using previously published microarray-based analsis[5] are shown in bold. Cellular Identity of hypo- and hyper-methylated CpGs near TSS in human and mouse. Genes where altered methylation was detected within 1500 bp were matched to corresponding cell types. Genes significantly altered in a comparison of MS hippocampus using previously published microarray-based analysis[5] are shown in bold. mRNA levels of genes measured using RT-PCR and presented in Figure 4 are italicized.
Figure 4

Inverse correlation between DMP and mRNA levels of target genes. RT-PCR analysis shows significant increases in mRNA levels of AT-hook transcription factor (AKNA), Emopamil Binding Protein Like (EBPL), HECT domain and RCC1-like domain containing protein 6 (HERC6), and Secreted frizzled related protein 1 (SFRP1) in demyelinated MS hippocampus (n = 4) compared to myelinated MS hippocampus (n = 4). Hyper-methylation within Nescient helix-loop-helix 2 (NHLH2), Phospholipase C eta 1 (PLCH1), Transmembrane protein 132B (TMEM132B), and WD repeat domain 81 (WDR81) following demyelination led to significant decreases in mRNA levels. Immunohistochemistry showing cellular expression of SFRP1 and PLCH1 in myelinated (B,D) and demyelinated hippocampus (C,E), with predominant expression in hippocampal neurons. Scale Bars: B–E: 30 μm; Error bars indicate + S.E.M.; * p < 0.05, **p < 0.005, ***p < 0.0005, *****p < 0.000005.

Identification of cell types expressing transcripts containing DMPs in mouse CNS

After we identified altered DMPs and their target genes in MS hippocampus, we compared the cellular expression of these target genes in mouse CNS using published mouse and human RNA-seq databases[25, 26]. This comparison revealed that DMPs are associated with genes expressed by the four major cell types (astrocytes, neurons, oligodendrocytes, and microglia) in mouse and human CNS (Table 2). Among identified hypomethylated targets (within UTR, gene body, and exon), 41% were specific to astrocytes, whereas 44% (13 out of 29) of the target genes were expressed by neurons in mouse CNS. The results also showed that 34% of the target genes were expressed by microglia/macrophage lineage cells in mouse (Table 2). Interestingly, hypomethylation identified 1 gene in mouse oligodendrocytes (F Box and WD repeat domain 8) and 4 in human oligodendrocytes (Small Nuclear Ribonucleoprotein 40 kDA; Bestrophin 3; Meis Homeobox 1; and Transmembrane Protein 165). None of the genes were expressed by both mouse and human oligodendrocytes. Among the hypermethylated transcripts, (within UTR, gene body, and exon), 26% of the genes were expressed by astrocytes and neurons (8 out of 30), while 20% were also expressed by microglia/macrophage cells in mouse brain (Table 2). Four hypermethylated genes were oligodendrocyte-specific in mouse brain (McKusick-Kaufmna Syndrome; Inscuteable Homolog (Drosophila); Neurofascin; and Topoisomerase (DNA) I, Mitochondrial) and three (Chromosome 1 open reading frame 106; Neurofascin; and Shisa Family Member 2) were localized to human oligodendrocytes. Cellular analysis revealed that the hypermethylated, oligodendrocyte-specific Neurofascin 155, which is involved in maintenance of axoglial junctions[27], is expressed by both mouse and human oligodendrocytes. As the presence of DMPs within the promoter and TSS has the greatest possibility of affecting mRNA expression[14], we identified the genes where a DMP was identified within 1500 bp of the TSS (Table 2). Our results showed that there were 14 DMPs (targeting 6 genes) that showed decreased methylation, while 10 DMPs (identified with 10 genes) showed increased methylation. Due to the presence of DMPs within 1500 bp of the TSS, we determined the cellular identification of the targets in both mouse and human RNA databases[25, 26]. The comparative results identified several genes that are differentially expressed by mouse and human CNS cells (Table 3). As brain tissue is a heterogeneous mixture of several cell types, these results not only provide clues as to the cellular specificity of each of the target genes, but they also provide options for further genetic manipulations in mouse models.

DNA methylation changes within transcription start sites of genes inversely correlate with mRNA levels of target genes

DNA methylation at the proximity of the TSS is generally considered to be a potent epigenetic modification that prohibits transcription factor (TF) recruitment, resulting in transcription suppression[28, 29]. We therefore validated whether the presence of DMPs within 1500bp of the TSS affects mRNA levels in 4 hyper-methylated and 4 hypo-methylated target genes. The results showed (Fig. 4) that hypomethylation within the TSS1500 of AT-hook transcription factor (AKNA), Emopamil Binding Protein Like (EBPL), HECT domain, RCC1-like domain containing protein 6 (HERC6), and Secreted frizzled related protein 1 (SFRP1) led to significant increases in mRNA levels of these genes following demyelination. In contrast, hyper-methylation identified within the TSS1500 of Nescient helix-loop-helix 2 (NHLH2), Phospholipase C eta 1 (PLCH1), Transmembrane protein 132B (TMEM132B), and WD repeat domain 81 (WDR81) correlated with the decrease in mRNA levels of these genes following demyelination in MS hippocampus. Immunohistochemical analysis to confirm cellular localization, showed that SFRP1 (Fig. 4B,C), and PLCH1 (Fig. 4D,E) were primarily associated with hippocampal neurons in MS myelinated (Fig. 4B,D) and MS demyelinated (Fig. 4C,E) hippocampus. Inverse correlation between DMP and mRNA levels of target genes. RT-PCR analysis shows significant increases in mRNA levels of AT-hook transcription factor (AKNA), Emopamil Binding Protein Like (EBPL), HECT domain and RCC1-like domain containing protein 6 (HERC6), and Secreted frizzled related protein 1 (SFRP1) in demyelinated MS hippocampus (n = 4) compared to myelinated MS hippocampus (n = 4). Hyper-methylation within Nescient helix-loop-helix 2 (NHLH2), Phospholipase C eta 1 (PLCH1), Transmembrane protein 132B (TMEM132B), and WD repeat domain 81 (WDR81) following demyelination led to significant decreases in mRNA levels. Immunohistochemistry showing cellular expression of SFRP1 and PLCH1 in myelinated (B,D) and demyelinated hippocampus (C,E), with predominant expression in hippocampal neurons. Scale Bars: B–E: 30 μm; Error bars indicate + S.E.M.; * p < 0.05, **p < 0.005, ***p < 0.0005, *****p < 0.000005.

Discussion

Epigenetic targets of gene regulation provide interesting targets for therapeutics because they are modifiable. The present study identifies DNA methylation as a correlate of demyelination and gene expression in MS hippocampus. Demyelination coincides with increases in mRNA levels of DNA methylation enzymes, with concomitant decreases in levels of DNA demethylation enzymes. Global methylation analysis identified 144 DMPs in MS hippocampus targeting several genes expressed by the major cell types in the human brain. As human tissue is not amenable to experimental manipulations, we also performed a comparative database search to determine the cellular specificity of the candidate genes in mouse brain. These data were further screened to identify the 25 DMPs localized within 1500 bp of TSS of 16 genes. Independent validation revealed that mRNA levels of the identified genes inversely correlated with DNA methylation status. Previous studies have shown that hippocampal demyelination is common in MS patients, which leads to loss of synaptic density and can influence expression of synaptic and neuronal genes as well as regulate the expression of neuronal miRNAs[5, 20]. In this study, we identified candidate genes that are altered by methylation changes following demyelination in MS hippocampus and that may play a role in altering synaptic plasticity, memory performance, and neuronal survival in MS brains. The presence/loss of methylation within the TSS alters mRNA levels of the target genes. We identified 6 genes where demyelination led to decreased methylation. Among these, mRNA levels of AKNA were significantly increased following demyelination. AKNA is a major regulator of CD40 and CD40 ligand[30] and is reported in RNA seq databases to be expressed by microglia/macrophage cells (Table 3) in both mouse and human. Increases in microglial CD40 expression and interactions with its ligand CD40L have been shown to induce/stimulate the expression of tumor necrosis factor-alpha (TNF-α) and to initiate neuronal death[31]. Following demyelination, we detected a significant increase in methylation within the TSS of WDR81, a gene involved in neuronal survival[32]. Increased levels of AKNA in conjunction with decreased WDR81 following demyelination could therefore lead to increased levels of TNF-α and neuronal injury. In addition, we also identified hypomethylation and a significant increase in mRNA levels of SFRP1 following demyelination. SFRP1 is an inhibitor of the WNT signaling proteins, which regulate learning and memory as well as synaptic plasticity at central synapses[33, 34]. Our comparative methylation analysis also identified hypermethylation (near the TSS) and decreased mRNA levels of NHLH2 and PLCH1 (Fig. 4). NHLH2 is a positive regulator of melanocortin receptors and modulates memory and learning[35, 36], while PLCH1 loss is generally associated with impaired working memory[37]. Differential methylation and concomitant increased levels of SFRP1 as well as decreased NHLH2 and PLCH1 could therefore lead to the decreased synaptic density and memory performance that have been previously reported following demyelination[5, 20]. The ongoing discovery of epigenetic factors underlying pathogenesis in MS is substantial[14]. These factors are relatively easily modifiable, in many cases utilizing readily available pharmacological agents targeting epigenetic modifiers. The study of DNA methylation in MS pathogenesis has been largely limited to comparisons of peripheral blood cells[14, 15, 38]. Using the same platform used in this study, 74 methylation sites (29 associated with either the MHC or the human leukocyte antigen (HLA-DRB1 region) were identified in a comparison between CD4+ T cells from patients with MS and healthy individuals[39]. In addition, significant differences in the methylation patterns associated with CD4 and CD8 T cells were detected in MS patients compared to healthy controls[40]. A genome-wide study of methylation in normal appearing white matter from MS patients identified 319 significantly hypermethylated and 220 significantly hypomethylated regions, which mapped to genes involved in oligodendrocyte survival and immune responses[41]. Our study is the first to provide insight into the landscape of methylation changes following demyelination in MS hippocampus. In addition, none of the genes identified in the current study was common to the candidates described in our previous collaborative study using MS normal appearing white matter[41]. This supports the need to develop datasets of gene changes from different gray matter regions in MS brains. Of great interest is the fact that the identified genes are expressed by different cell types in human and mouse brain. While similar DNA methylation studies in mouse are not currently possible due to the lack of a global mouse methylation profile chip, our species-specific cell identity provides the option to query, validate, and manipulate candidate genes for their possible role in memory function using animal models. Future tissue-, region-, and cell-specific analyses of epigenetic changes in MS brains need to be conducted in order to gain further insight into the pathogenesis of MS.

Materials and Methods

Human subjects and regulatory compliance

All brains were collected as part of the tissue procurement program approved by the Cleveland Clinic Institutional Review Board. Patient anonymity was strictly maintained and all tissue samples were handled in a coded fashion. All donors or their surrogates gave informed consent for their brains to be used for research studies. All experiments were carried out in accordance with the relevant Cleveland Clinic Institutional regulations and guidelines.

Tissue collection and characterization

Brains were removed according to a rapid autopsy protocol, sliced (1 cm thick), and then either fixed in 4% paraformaldehyde for morphological studies or rapidly frozen for biochemical analysis. Patient demographics are listed in Table 1. All hippocampi were characterized for demyelination by immunostaining using proteolipid protein (PLP) as described previously[5, 20]. Briefly, frozen 30 µm sections were cut for characterization by immunohistochemistry and for assessment of demyelination. This was followed by collection of 3–4 subsequent sections for DNA isolation. No significant differences in disease duration (27.2 yrs vs 23.2 yrs; p = 0.60) or postmortem interval (7.1 hr vs 7.5 hr; p = 0.87) were detected between the myelinated and demyelinated MS patients.

Methylation profiling

Genomic DNA was isolated from frozen tissue sections corresponding to regions of myelinated (n = 8) or demyelinated MS hippocampus (n = 7). Genomic DNA was isolated using a QIAamp DNA mini kit (Qiagen Inc, USA) following the manufacturer’s instructions. Purified genomic DNA was processed for bisulfide conversion and subsequent methylation assays. DNA samples were delivered to the Case Western Reserve University Genomics Core Facility, where 1.5 µg of DNA was bisulphite-treated (Zymo EZ DNA methylation gold) per manufacturer’s instructions. Genome-wide methylation profiles were generated using Illumina 450 K methylation arrays. Isolated DNA (100 ng) was used to measure DNA hydroxymethylation with MethylFlash™ Global DNA Hydroxymethylation (5hmC) ELISAs (Epigentek Inc, USA) using a 5hmC mAb-based detection complex.

Data analysis and cell type identification

Raw data (idat format) were generated through Illumina’s Genome Studio software. Raw data were preprocessed, normalized, and analyses was carried out in the R environment using the ChAMP package, which integrates currently available 450 k analysis methods and also offers its own novel functionality[42]. After running basic quality control metrics, we performed a beta mixture quantile normalization method to adjust for bias introduced by the Infinium type 2 probe design[42]. All DNA methylation data files will be deposited at the GEO (https://www.ncbi.nlm.nih.gov/GEO) and can be accessed through the accession number GSE101658. As part of routine analysis of DNA methylation datasets in our study, the probes on the X chromosome were normalized for males and females separately and independently of autosomal probes because X chromosome inactivation causes significant gender differences in methylation patterns. In addition, to account for age-related DNA methylation changes, we employed the methods described by Horvath et al.[43]. Briefly, this multi-tissue predictor of age allows for the estimation of the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. Using these methods, we did not detect any differences in DNA methylation for predicted and actual age between MS samples. To determine the cellular identity of the methylation target, identified genes were queried against mouse and human cell-specific RNA sequencing databases[25, 26]. To further validate the results and cellular specificity, we only selected genes that were above the 50th percentile of expression level across all cell types.

Immunohistochemistry

Sections (30 μm thick) from corresponding (to frozen sections) fixed blocks of the hippocampus were cut on a sliding microtome, microwaved in 10 mM citric acid buffer (pH 6.0) for 5 minutes, incubated in 3% hydrogen peroxide and 1% Triton X-100 in phosphate-buffered saline for 30 minutes, and immunostained by the avidin-biotin complex procedure with diaminobenzidine (DAB) for myelin PLP, MHC Class II, HuR, or other antibodies as described previously[5, 20]. The extent of demyelination and neuronal status were determined from PLP and HuR staining. Using the same protocol, sections were stained using antibodies specific to DNMT1 (1:250; HPA002694; Sigma Aldrich, St. Louis, MO), DNMT3A (1:250; HPA02588; Sigma Aldrich, St. Louis, MO), DNMT3B (1:250; HPA001595; Sigma Aldrich, St. Louis, MO), and α-5-hydroxymethylcytosine (1:500; ab106918; Abcam Inc. Cambridge MA), PLCH1 (1:250; GTX108612; GeneTex Inc. Irvine CA), SFRP1 (1:250; ab94942; Abcam Inc. Cambridge MA).

Real-time PCR

RT-PCR was performed using a standard TaqMan PCR kit protocol on an Applied Biosystems 7500HT Sequence Detection System using 0.2 μM TaqMan probes. Probes specific to DNMT1 (Hs00154749_m1), DNMT3A Hs01027166_m1) DNMT3B (Hs00171876_m1), TET1 (Hs00286756_m1), TET2 (Hs00325999_m1), TET3 (Hs00379125_m1), PLP1(Hs00166914_m1), AKNA (Hs00363936_m1), WDR81 (Hs00912091_m1), TMEM132B (Hs00287113_m1), EBPL (Hs00831100_s1), HERC6 (Hs00215555_m1), SFRP1 (Hs00610060_m1), NHLH2 (Hs00271585_s1), and PLCH1 (Hs00324566_m1) were used in triplicate reactions. Total RNA was reverse transcribed using a high capacity cDNA reverse transcription kit (Applied Biosystems, Carlsbad, CA). Reverse transcription reactions contained hippocampal RNA, 1X RT random primers (Applied Biosystems, Carlsbad, CA), 0.25 mM dNTP mix, 50 U MultiScripe reverse transcriptase (Applied Biosystems, Carlsbad, CA), and 1 U/μL RNase inhibitor (Applied Biosystems, Carlsbad, CA). TaqMan gene expression assays for multiplex reactions were performed following the manufacturer’s protocol. Each RT-PCR reaction contained 100ng of cDNA product, 1X TaqMan gene expression mix, 100nM GAPDH primers (Applied Biosystems, ID# 4310884E), and 100 nM FAM-labeled target probes for methylated target genes (Applied Biosystems, Carlsbad, CA). Resultant Ct values were normalized and quantitative data are expressed as mean ± S.E.M. The statistical significance of differences between groups in RT-PCR were determined using previously published methods[20].
  43 in total

1.  Decoding multiple sclerosis.

Authors:  Jorge R Oksenberg; Stephen L Hauser
Journal:  Ann Neurol       Date:  2011-12       Impact factor: 10.422

2.  Activity-dependent Wnt 7 dendritic targeting in hippocampal neurons: plasticity- and tagging-related retrograde signaling mechanism?

Authors:  Nino Tabatadze; Rhona McGonigal; Rachel L Neve; Aryeh Routtenberg
Journal:  Hippocampus       Date:  2014-01-09       Impact factor: 3.899

Review 3.  Environmental factors in multiple sclerosis.

Authors:  Alberto Ascherio
Journal:  Expert Rev Neurother       Date:  2013-12       Impact factor: 4.618

Review 4.  Epigenetic regulation of memory formation and maintenance.

Authors:  Iva B Zovkic; Mikael C Guzman-Karlsson; J David Sweatt
Journal:  Learn Mem       Date:  2013-01-15       Impact factor: 2.460

Review 5.  Mechanisms and functions of Tet protein-mediated 5-methylcytosine oxidation.

Authors:  Hao Wu; Yi Zhang
Journal:  Genes Dev       Date:  2011-12-01       Impact factor: 11.361

Review 6.  Environmental factors and their timing in adult-onset multiple sclerosis.

Authors:  Adam E Handel; Gavin Giovannoni; George C Ebers; Sreeram V Ramagopalan
Journal:  Nat Rev Neurol       Date:  2010-02-16       Impact factor: 42.937

7.  Hippocampal demyelination and memory dysfunction are associated with increased levels of the neuronal microRNA miR-124 and reduced AMPA receptors.

Authors:  Ranjan Dutta; Anthony M Chomyk; Ansi Chang; Michael V Ribaudo; Sadie A Deckard; Mary K Doud; Dale D Edberg; Brian Bai; Michael Li; Sergio E Baranzini; Robert J Fox; Susan M Staugaitis; Wendy B Macklin; Bruce D Trapp
Journal:  Ann Neurol       Date:  2013-04-17       Impact factor: 10.422

Review 8.  Epigenetics and Human Disease.

Authors:  Huda Y Zoghbi; Arthur L Beaudet
Journal:  Cold Spring Harb Perspect Biol       Date:  2016-02-01       Impact factor: 10.005

9.  Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Authors:  Stephen Sawcer; Garrett Hellenthal; Matti Pirinen; Chris C A Spencer; Nikolaos A Patsopoulos; Loukas Moutsianas; Alexander Dilthey; Zhan Su; Colin Freeman; Sarah E Hunt; Sarah Edkins; Emma Gray; David R Booth; Simon C Potter; An Goris; Gavin Band; Annette Bang Oturai; Amy Strange; Janna Saarela; Céline Bellenguez; Bertrand Fontaine; Matthew Gillman; Bernhard Hemmer; Rhian Gwilliam; Frauke Zipp; Alagurevathi Jayakumar; Roland Martin; Stephen Leslie; Stanley Hawkins; Eleni Giannoulatou; Sandra D'alfonso; Hannah Blackburn; Filippo Martinelli Boneschi; Jennifer Liddle; Hanne F Harbo; Marc L Perez; Anne Spurkland; Matthew J Waller; Marcin P Mycko; Michelle Ricketts; Manuel Comabella; Naomi Hammond; Ingrid Kockum; Owen T McCann; Maria Ban; Pamela Whittaker; Anu Kemppinen; Paul Weston; Clive Hawkins; Sara Widaa; John Zajicek; Serge Dronov; Neil Robertson; Suzannah J Bumpstead; Lisa F Barcellos; Rathi Ravindrarajah; Roby Abraham; Lars Alfredsson; Kristin Ardlie; Cristin Aubin; Amie Baker; Katharine Baker; Sergio E Baranzini; Laura Bergamaschi; Roberto Bergamaschi; Allan Bernstein; Achim Berthele; Mike Boggild; Jonathan P Bradfield; David Brassat; Simon A Broadley; Dorothea Buck; Helmut Butzkueven; Ruggero Capra; William M Carroll; Paola Cavalla; Elisabeth G Celius; Sabine Cepok; Rosetta Chiavacci; Françoise Clerget-Darpoux; Katleen Clysters; Giancarlo Comi; Mark Cossburn; Isabelle Cournu-Rebeix; Mathew B Cox; Wendy Cozen; Bruce A C Cree; Anne H Cross; Daniele Cusi; Mark J Daly; Emma Davis; Paul I W de Bakker; Marc Debouverie; Marie Beatrice D'hooghe; Katherine Dixon; Rita Dobosi; Bénédicte Dubois; David Ellinghaus; Irina Elovaara; Federica Esposito; Claire Fontenille; Simon Foote; Andre Franke; Daniela Galimberti; Angelo Ghezzi; Joseph Glessner; Refujia Gomez; Olivier Gout; Colin Graham; Struan F A Grant; Franca Rosa Guerini; Hakon Hakonarson; Per Hall; Anders Hamsten; Hans-Peter Hartung; Rob N Heard; Simon Heath; Jeremy Hobart; Muna Hoshi; Carmen Infante-Duarte; Gillian Ingram; Wendy Ingram; Talat Islam; Maja Jagodic; Michael Kabesch; Allan G Kermode; Trevor J Kilpatrick; Cecilia Kim; Norman Klopp; Keijo Koivisto; Malin Larsson; Mark Lathrop; Jeannette S Lechner-Scott; Maurizio A Leone; Virpi Leppä; Ulrika Liljedahl; Izaura Lima Bomfim; Robin R Lincoln; Jenny Link; Jianjun Liu; Aslaug R Lorentzen; Sara Lupoli; Fabio Macciardi; Thomas Mack; Mark Marriott; Vittorio Martinelli; Deborah Mason; Jacob L McCauley; Frank Mentch; Inger-Lise Mero; Tania Mihalova; Xavier Montalban; John Mottershead; Kjell-Morten Myhr; Paola Naldi; William Ollier; Alison Page; Aarno Palotie; Jean Pelletier; Laura Piccio; Trevor Pickersgill; Fredrik Piehl; Susan Pobywajlo; Hong L Quach; Patricia P Ramsay; Mauri Reunanen; Richard Reynolds; John D Rioux; Mariaemma Rodegher; Sabine Roesner; Justin P Rubio; Ina-Maria Rückert; Marco Salvetti; Erika Salvi; Adam Santaniello; Catherine A Schaefer; Stefan Schreiber; Christian Schulze; Rodney J Scott; Finn Sellebjerg; Krzysztof W Selmaj; David Sexton; Ling Shen; Brigid Simms-Acuna; Sheila Skidmore; Patrick M A Sleiman; Cathrine Smestad; Per Soelberg Sørensen; Helle Bach Søndergaard; Jim Stankovich; Richard C Strange; Anna-Maija Sulonen; Emilie Sundqvist; Ann-Christine Syvänen; Francesca Taddeo; Bruce Taylor; Jenefer M Blackwell; Pentti Tienari; Elvira Bramon; Ayman Tourbah; Matthew A Brown; Ewa Tronczynska; Juan P Casas; Niall Tubridy; Aiden Corvin; Jane Vickery; Janusz Jankowski; Pablo Villoslada; Hugh S Markus; Kai Wang; Christopher G Mathew; James Wason; Colin N A Palmer; H-Erich Wichmann; Robert Plomin; Ernest Willoughby; Anna Rautanen; Juliane Winkelmann; Michael Wittig; Richard C Trembath; Jacqueline Yaouanq; Ananth C Viswanathan; Haitao Zhang; Nicholas W Wood; Rebecca Zuvich; Panos Deloukas; Cordelia Langford; Audrey Duncanson; Jorge R Oksenberg; Margaret A Pericak-Vance; Jonathan L Haines; Tomas Olsson; Jan Hillert; Adrian J Ivinson; Philip L De Jager; Leena Peltonen; Graeme J Stewart; David A Hafler; Stephen L Hauser; Gil McVean; Peter Donnelly; Alastair Compston
Journal:  Nature       Date:  2011-08-10       Impact factor: 49.962

10.  The effects of cytosine methylation on general transcription factors.

Authors:  Jianshi Jin; Tengfei Lian; Chan Gu; Kai Yu; Yi Qin Gao; Xiao-Dong Su
Journal:  Sci Rep       Date:  2016-07-07       Impact factor: 4.379

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  23 in total

Review 1.  Proteomic Approaches to Decipher Mechanisms Underlying Pathogenesis in Multiple Sclerosis Patients.

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Journal:  Proteomics       Date:  2019-06-21       Impact factor: 3.984

Review 2.  Risk Factors from Pregnancy to Adulthood in Multiple Sclerosis Outcome.

Authors:  Enrique González-Madrid; Ma Andreina Rangel-Ramírez; María José Mendoza-León; Oscar Álvarez-Mardones; Pablo A González; Alexis M Kalergis; Ma Cecilia Opazo; Claudia A Riedel
Journal:  Int J Mol Sci       Date:  2022-06-25       Impact factor: 6.208

Review 3.  Newly Identified Deficiencies in the Multiple Sclerosis Central Nervous System and Their Impact on the Remyelination Failure.

Authors:  Giuseppe Scalabrino
Journal:  Biomedicines       Date:  2022-03-30

4.  DNA methylation changes in glial cells of the normal-appearing white matter in Multiple Sclerosis patients.

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Journal:  Epigenetics       Date:  2022-01-30       Impact factor: 4.861

5.  DNA methylation dynamic of bone marrow hematopoietic stem cells after allogeneic transplantation.

Authors:  Stefania Trino; Pietro Zoppoli; Angelo Michele Carella; Ilaria Laurenzana; Alessandro Weisz; Domenico Memoli; Giovanni Calice; Francesco La Rocca; Vittorio Simeon; Lucia Savino; Luigi Del Vecchio; Pellegrino Musto; Antonella Caivano; Luciana De Luca
Journal:  Stem Cell Res Ther       Date:  2019-05-20       Impact factor: 6.832

Review 6.  Epigenetic Contribution and Genomic Imprinting Dlk1-Dio3 miRNAs in Systemic Lupus Erythematosus.

Authors:  Rujuan Dai; Zhuang Wang; S Ansar Ahmed
Journal:  Genes (Basel)       Date:  2021-05-01       Impact factor: 4.096

7.  N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis.

Authors:  Fei Ye; Tianzhu Wang; Xiaoxin Wu; Jie Liang; Jiaoxing Li; Wenli Sheng
Journal:  J Transl Med       Date:  2021-07-22       Impact factor: 5.531

Review 8.  The role of TET proteins in stress-induced neuroepigenetic and behavioural adaptations.

Authors:  Alec Dick; Alon Chen
Journal:  Neurobiol Stress       Date:  2021-06-11

Review 9.  DNA methylation in human diseases.

Authors:  Zelin Jin; Yun Liu
Journal:  Genes Dis       Date:  2018-01-31

10.  DNMT3B-579G>T (rs1569686G>T) polymorphism and the risk of multiple sclerosis in a subset of Iranian population.

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Journal:  Iran J Neurol       Date:  2019-04-04
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