Regulation of Gene Expression through Gut Microbiota ... · commensal bacteria on gene expression in colonic epithelial cells (CoECs) were investigated with focus on regulation of

CellsEpithelialMicrobiota-Dependent DNA Methylation in Colonic

Regulation of Gene Expression through Gut

Tsuda, Akira Hosono and Shuichi KaminogawaKyoko Takahashi, Yutaka Sugi, Kou Nakano, Tetsuro Kobayakawa, Yusuke Nakanishi, Masato

http://www.immunohorizons.org/content/4/4/178https://doi.org/10.4049/immunohorizons.1900086doi:

2020, 4 (4) 178-190ImmunoHorizons

This information is current as of July 15, 2020.

MaterialSupplementary

lementalhttp://www.immunohorizons.org/content/suppl/2020/04/14/4.4.178.DCSupp

Referenceshttp://www.immunohorizons.org/content/4/4/178.full#ref-list-1

, 11 of which you can access for free at: cites 40 articlesThis article

Email Alertshttp://www.immunohorizons.org/alertsReceive free email-alerts when new articles cite this article. Sign up at:

ISSN 2573-7732.All rights reserved.1451 Rockville Pike, Suite 650, Rockville, MD 20852The American Association of Immunologists, Inc.,

is an open access journal published byImmunoHorizons

by guest on July 15, 2020http://w

ww

.imm

unohorizons.org/D

ownloaded from

by guest on July 15, 2020

http://ww

w.im

munohorizons.org/

Dow

nloaded from

https://doi.org/10.4049/immunohorizons.1900086

http://www.immunohorizons.org/content/4/4/178

http://www.immunohorizons.org/content/suppl/2020/04/14/4.4.178.DCSupplemental

http://www.immunohorizons.org/content/suppl/2020/04/14/4.4.178.DCSupplemental

http://www.immunohorizons.org/content/4/4/178.full#ref-list-1

http://www.immunohorizons.org/alerts

http://www.immunohorizons.org/


Regulation of Gene Expression through Gut Microbiota-Dependent DNA Methylation in Colonic Epithelial Cells

Kyoko Takahashi, Yutaka Sugi, Kou Nakano, Tetsuro Kobayakawa, Yusuke Nakanishi, Masato Tsuda, Akira Hosono,and Shuichi KaminogawaCollege of Bioresource Sciences, Nihon University, Fujisawa-shi, Kanagawa 252-0880, Japan

ABSTRACT

A huge number of commensal bacteria inhabit the intestine, which is equipped with the largest immune system in the body. Recently,

the regulation of various physiological functions of the host by these bacteria has attracted attention. In this study, the effects of

commensal bacteria on gene expression in colonic epithelial cells (CoECs) were investigated with focus on regulation of DNA

methylation. RNA sequencing analyses of CoECs from conventional, germ-free, and MyD882/2 mice indicated that, out of the genes

affected by commensal bacteria, those downregulated in a MyD88-independent manner were most frequently observed.

Furthermore, when the 59 regions of genes downregulated by commensal bacteria in CoECs were captured using a customized array

and immunoprecipitated with the anti-methyl cytosine Ab, a certain population of these genes was found to be highly methylated.

Comprehensive analysis of DNA methylation in the 59 regions of genes in CoECs from conventional and germ-free mice upon pull-

down assay with methyl-CpG–binding domain protein 2 directly demonstrated that DNA methylation in these regions was influenced

by commensal bacteria. Actually, commensal bacteria were shown to control expression of Aldh1a1, which encodes a retinoic

acid–producing enzyme and plays an important role in the maintenance of intestinal homeostasis via DNA methylation in the

overlapping 59 region of Tmem267 and 3110070M22Rik genes in CoECs. Collectively, it can be concluded that regulation of DNA

methylation in the 59 regions of a specific population of genes in CoECs acts as a mechanism by which commensal bacteria have

physiological effects on the host. ImmunoHorizons, 2020, 4: 178–190.

INTRODUCTION

The significance of gut microbiota in the maintenance of healthhas recently attracted considerable attention, as increasing evidencedemonstrates that the gut microbiota regulates various physiolog-ical functions of the host (1–4). In accordancewith this, it has beenshown that dysbiosis of the intestinal ecosystem is correlatedwitha wide variety of diseases, including inflammatory bowel disease,allergy, cancer, autism, andmetabolic syndrome (3, 5–9). Although

it is difficult to determine whether such differences in gutmicrobiota are the cause or the result of these diseases, studieshave demonstrated that change in microbiota or specific bacteriais actually involved in the onset, pathogenesis, and prevention ofthe diseases (10–12). Therefore, many efforts have been made toprevent the onset or alleviate the symptoms of the diseases byintervention to the gut microbiota (13–17).

As the intestine is equipped with the largest immune systemin the body, it is considered that commensal bacteria affect the

Received for publication October 11, 2019. Accepted for publication March 17, 2020.

Address correspondence and reprint requests to: Dr. Kyoko Takahashi, Department of Applied Biological Science, College of Bioresource Sciences, Nihon University,1866 Kameino, Fujisawa-shi, Kanagawa 252-0880, Japan. E-mail address: [email protected]

The sequences presented in this article have been submitted to DNA Data Bank of Japan Sequence Read Archive (https://www.ddbj.nig.ac.jp/dra) under accessionnumbers DRA008905, DRA008906, and DRA008907.

This study was supported in part by grants from the Japan Society for the Promotion of Science (KAKENHI 17K07801) and Nagase Science and Technology Foundation(to K.T.).

Abbreviations used in this article: CoEC, colonic epithelial cell; CV, conventional; DDBJ, DNA Data Bank of Japan; GF, germ-free; IEC, intestinal epithelial cell; IP,immunoprecipitation; LMD, laser microdissection; MBD2, methyl-CpG–binding domain protein 2; NGS, next-generation sequencing; qRT-PCR, quantitative RT-PCR;Reg3, regenerating islet-derived 3; RNAi, RNA interference; RNA-seq, RNA sequencing; SCFA, short chain fatty acid; SIEC, small IEC; TE, Tris–EDTA; WT, wild-type.

The online version of this article contains supplemental material.

This article is distributed under the terms of the CC BY 4.0 Unported license.

Copyright © 2020 The Authors

178 https://doi.org/10.4049/immunohorizons.1900086

RESEARCH ARTICLE

Innate Immunity

ImmunoHorizons is published by The American Association of Immunologists, Inc.


ww

.imm

unohorizons.org/D

ownloaded from

mailto:[email protected]

https://www.ddbj.nig.ac.jp/dra

http://www.immunohorizons.org/lookup/suppl/doi:10.4049/immunohorizons.1900086/-/DCSupplemental

https://creativecommons.org/licenses/by/4.0/



development and function of the intestinal immune system, par-ticularly in the period immediately after birth when the immunesystem is in the process of maturation (18–20). Moreover, specificmechanismsmightberequired toestablish andmaintainsymbiosiswith commensal bacteria in the intestine considering that com-mensal bacteria are immunologically recognizedas foreignAgsbutare not excluded completely. Although induction of immune reac-tions involves inflammation, inflammatory reactions are strictlycontrolledat lowphysiological levels in the intestinedespite such alarge amount of bacterial Ags. Actually, excessive inflammation isoften observed in diseases associated with dysbiosis. Interestingly,intestinal bacteria themselves contribute to this regulation byaffecting host cells; however, the precise molecular mechanismsremain to be elucidated.

Basedon this context, elucidationof thephysiological effects ofcommensal bacteria on host cells becomes key evidence to clarifythe role of commensal bacteria in intestinal homeostasis. For thispurpose, the effects of commensal bacteria on gene expression inintestinal epithelial cells (IECs) covering the intestinalmucosa andthe underlying regulatory mechanisms were investigated in thisstudy. IECs forma front line of defense by separating the intestinaltract from the internalmilieu and are usually exposed to commen-sal bacteria inhabiting the intestinal tract. Therefore, commensalbacteria have a great impact on gene expression in IECs. Inaddition, it has been shown that commensal bacteria conferepigenetic effects to specific genes in IECs (21–23). DNA meth-ylation, as well as posttranslational histone modifications andnoncoding RNA, are important mechanisms mediating epigeneticregulation of gene expression. We previously reported that DNAmethylation of the gene encoding TLR4, an innate immunereceptor that recognizes LPS ofGram-negative bacteria, in colonicepithelial cells (CoECs) is induced by commensal bacteria (23). Asstimulation with LPS through TLR4 induces strong inflammatoryreactions, stringent control of TLR4 gene expression is required toprevent excessive inflammationand therebymaintainhomeostasisin the intestinal ecosystem; this means that commensal bacteriathemselves contribute to establish the symbiotic relationship withthe host in the intestine. Although it has been shown that com-mensal bacteria have potential effects on gene expression andmodulate DNA methylation of specific genes, it remains to beclarified formajority of genes how the change inDNAmethylationby commensal bacteria is involved in transcriptional regulation. Inthis study, the effects of commensal bacteria on gene expressionand DNAmethylation in IECs were analyzed comprehensively todetermine their relationship.

MATERIALS AND METHODS

MiceWild-type (WT) and MyD882/2 BALB/c mice were purchasedfromCLEA Japan (Tokyo, Japan) andOriental BioService (Kyoto,Japan), respectively. Mice were bred under conventional (CV) orgerm-free (GF) conditions as described previously (24). Femalemice were used at 9–13 wk of age. All experiments were approved

by the Nihon University Animal Care and Use Committee andconducted in accordance with their guidelines.

IEC preparationSmall IECs (SIECs) and CoECs were prepared from the wholesmall intestineand thewhole colonofmice, respectively.Epithelialcell preparationwas performed as described previously (25). Afterremoving Peyer patches, the tissues were cut into 2–3-mm piecesand washed in HBSS supplemented with 1 mMDTT and 0.5 mMEDTA accompanied by shaking. The tissues were then treatedwith dispase (BD Biosciences, Franklin Lakes, NJ) to collectsingle-cell suspensions. Lymphocytes were depleted by MACSusing Dynabeads M-450 Streptavidin (Invitrogen, Thermo FisherScientific) and biotin-conjugated anti-CD45 Ab (eBioscience, SanDiego, CA).

Cell cultureThe mouse IEC line CMT-93 developed from rectal carcinomawas purchased from DS Pharma Biomedical (Osaka, Japan) andcultured in DMEM supplemented with 10% (v/v) FBS (Biowest,Nuaillé, France), 100 U/ml penicillin, 100 mg/ml streptomycin,and 5 3 1025 M 2-ME at 37°C in a humidified incubator with5% CO2.

RNA sequencingTotal RNA was prepared from SIECs and CoECs of WT-CV,MyD882/2-CV, andWT-GFmicebypooling fromsix toeightmiceper group using the High Pure RNA Isolation Kit (Roche, Basel,Switzerland). RNA sequencing (RNA-seq) was performed byINFOBIO (Tokyo, Japan). Briefly, mRNA was extracted, reversetranscribed, treated with restriction enzyme NlaIII, and ligatedwith adaptor sequences. The library was prepared by processingthe tag with EcoP15I and analyzed using Genome Analyzer IIx(Illumina, San Diego, CA). DNA sequences originated from thelibrary were extracted from the resulting reads, and CATGsequence was added to the 59 ends. The acquired tags (26 bp fromthe 59 end) were then mapped toMus musculusmRNA referencesequences (National Center for Biotechnology InformationRefSeq; ftp://ftp.ncbi.nlm.nih.gov/refseq/M_musculus/mRNA_Prot/) using Maq 0.7.1 (Wellcome Trust Sanger Institute). Tocompare mRNA expression among CV, MyD88, and GF mice, tagnumbers obtained for each gene were corrected to the totalnumbers of tags obtained (22,577,586, 21,832,965, and 24,226,181for CV, GF, and MyD882/2, respectively). If these correctedvalues differed by more than 3-fold between CV and GF mice,except for genes with unquantifiable or low expression towhichonly one digit tags were mapped in both CV and GF mice, theexpression of the corresponding gene was considered to beaffected by commensal bacteria. MyD88 dependency for regu-lation of these genes by commensal bacteria was determined ifthe values from results ofMyD882/2micewere close to those ofCV or GF mice. Next-generation sequencing (NGS) data havebeen deposited in the DNA Data Bank of Japan (DDBJ)Sequence Read Archive (https://www.ddbj.nig.ac.jp/dra) underaccession number DRA008906.


ImmunoHorizons EPIGENETIC EFFECT OF COMMENSALS ON COLONIC EPITHELIAL CELLS 179


ww

.imm

unohorizons.org/D

ownloaded from

ftp://ftp.ncbi.nlm.nih.gov/refseq/M_musculus/mRNA_Prot/

ftp://ftp.ncbi.nlm.nih.gov/refseq/M_musculus/mRNA_Prot/




Quantitative RT-PCRFor the quantitative analysis of mRNA expression, total RNA wasprepared from cells using the High Pure RNA Isolation Kit(Roche). Total RNA was then reverse-transcribed using Super-Script IV Reverse Transcriptase (Invitrogen, Thermo FisherScientific) and oligo(dT)20 primers (Invitrogen, Thermo FisherScientific). SYBR Green I Master reagent (Roche) was used toquantify cDNA by real-time PCR using LightCycler 480 (Roche)with the cycling condition of 95°C for 10 s, 58°C for 10 s, and 72°Cfor 12 s. The relative expression of each gene was calculated fromthe calibration curve created using a dilution series of standardsamples followed by normalization to that of Gapdh. Specificamplification of each gene was confirmed by melting curveanalysis. Information on primers is provided in SupplementalTable I.

Methyl-CpG–binding domain protein 2 pull-down assayGenomic DNA was prepared from CoECs of CV or GF mice bypooling from two independent experiments (six to sevenmice perexperiment) and fragmented by nebulization (N2 0.23MPa, 6min)in shearing buffer (Tris–EDTA [TE; pH 8], 10% glycerol). ShearedDNAwas endrepaired, followedbydA-tailing andadaptor ligationusing the NEBNext DNA Sample Prep Master Mix Set (NewEngland Biolabs, Ipswich, MA) according to the manufacturer’sinstructions. DNA fragments obtained were electrophoresed onagarose gel, cut out from the gel for purification using a GelExtraction Kit (QIAGEN, Hilden, Germany). Then, methylatedDNA fragments were precipitated with methyl-CpG–bindingdomain protein 2 (MBD2)–coupled beads using theMethylMinerMethylated DNA Enrichment Kit (Applied Biosystems, ThermoFisher Scientific), as described in the protocol supplied by themanufacturer. DNA fragments collected were amplified by PCRusing adapter sequencesasprimersaccording to theuser’s guideofthe NimbleGen SeqCap EZ Library SR (Roche). The library thusprepared was analyzed using HiSeq 2000 (Illumina) to acquire101-bp single-end reads at INFOBIO. Identified reads weremapped to the mouse genome (mm10) using the Burrows–Wheeler Aligner (version 0.6.2-r126) and then annotated to theregions near the transcription start site of each gene (fromnt21000to +501 on both the same and the opposite strands; nucleotidenumbers are counted from the transcription start site as +1).Extracted reads were compared quantitatively between CV andGF mice. NGS data have been deposited in the DDBJ SequenceRead Archive (https://www.ddbj.nig.ac.jp/dra) under accessionnumber DRA008905.

Bisulfite sequencingGenomicDNAwas prepared fromcells using a PureLinkGenomicDNA Mini Kit (Invitrogen, Thermo Fisher Scientific), and meth-ylation status of CpG motifs was analyzed as follows. GenomicDNA (1 mg) was denatured with 6 N NaOH and modified by thebisulfite conversion reaction using a BisulFast DNA modificationkit (TOYOBO,Osaka, Japan). The 388-bp 59 region (nt2102/+286;nucleotidenumbers are counted fromthe transcriptionstart site as+1.) of theTmem267 genewas amplified by PCR from themodified

DNA. Sequences of the synthetic oligonucleotides used as PCRprimers are as follows: 59-GGTGGAGAGTTGAGGTTTTTTGTG-39 (forward) and 59-CCCCTAACCCAAACTACCTTCATC-39 (re-verse). PCR products were cloned into the pCR2.1 vector, andnucleotide sequences of 16–17 independent clones were analyzed.

RNA interferenceTo construct plasmids for RNA interference (RNAi) againstTmem267 and 3110070M22Rik RNAi, synthetic oligo DNA listedbelow was respectively annealed by denaturing at 95°C for 4 minand subsequently cooling: Tmem267 forward, 59-TGCTGTACATCAGGAATGAGCACAGGGTTTTGGCCACTGACTGACCCTGTGCTTTCCTGATGTA-39; Tmem267 reverse, 59-CCTGTACATCAGGAAAGCACAGGGTCAGTCAGTGGCCAAAACCCTGTGCTCATTCCTGATGTAC-39; 3110070M22Rik RNAiforward, 59-TGCTGTCATGATCCAGCCTTGAACTTGTTTTGGCCACTGACTGACAAGTTCAACTGGATCATGA-39; and3110070M22Rik reverse, 59-CCTGTCATGATCCAGTTGAACTTGTCAGTCAGTGGCCAAAACAAGTTCAAGGCTGGATCATGAC-39

Double-strandedoligoDNA thus obtainedwas introduced intothe pCDNA6.2-GW/EmGFP-miR vector, respectively, using theBLOCK-iT PolII miR RNAi Expression Vector Kit (Invitrogen,ThermoFisher Scientific). Resulting clones or thenegative controlplasmid provided with the kit were introduced into CMT-93 cellsusing the X-tremeGene HP Reagent (Roche). After selectingtransfected cells with 4 mg/ml blasticidin for 10–14 d, total RNAwas prepared for quantitative RT-PCR (qRT-PCR) analysis.

Western blottingCells were washed with ice-cold PBS and incubated on ice for30 min in the lysis buffer (20 mM Tris [pH 7.5], 150 mM NaCl,1mMEDTA, 60mM n-octyl-b-D-glucoside, and 1%Nonidet P-40)supplemented with protease inhibitor mixture (Nacalai Tesque,Kyoto, Japan). After centrifugation at 4°C, 20,0003 g, for 10 min,the supernatantswere collected. Theprotein content of the lysateswas measured using the BCA Protein Assay Kit (Pierce, ThermoFisher Scientific), and equal amounts of protein were analyzed byimmunoblotting using anti-ALDH1A1 (ab52492; Abcam, Cam-bridge, U.K.) and anti–b-Actin (ab49900; Abcam) Abs.

Laser microdissectionSmall intestine and colonwere surgically excised frommice. Smallintestinesweredivided intofivepiecesofequal length, and thefirst(proximal), the third (medial), and thefifth (distal) partswereusedfor further experiments. Luminal content was gently washed outwith Ca2+- and Mg2+-free Hanks’ balanced salt solution (Sigma-Aldrich, St. Louis, MO) containing 5% FBS and was replaced withSCEM-L1 embedding medium (Section-lab, Hiroshima, Japan).Tissues were then embedded in SCEM-L1 and cut into 16-mmsections using the HM550 cryostat (Thermo Fisher Scientific).Tissue sections were fixed with ethanol and stained withtoluidine blue. Lasermicrodissection (LMD)was performed usingLMD7000 (Leica Microsystems) to capture segments includingIECs at upper (apical) and bottom (basolateral) portions of villi (as


180 EPIGENETIC EFFECT OF COMMENSALS ON COLONIC EPITHELIAL CELLS ImmunoHorizons


ww

.imm

unohorizons.org/D

ownloaded from






shown in Supplemental Fig. 1). Total RNA was prepared fromcollected tissue segments of approximately equal total area usingRNeasy Micro Kit (QIAGEN) for quantification of Aldh1a1expression by qRT-PCR.

Sequence capture assayA customized array with DNA sequences covering the nt21000/+501 regions of genes showed ,1/3 expression in CoECs of CVmice than that seen in those of GFmice by RNA-seqwas designedand manufactured by Roche (relevant genes are listed inSupplemental Table II). GenomicDNAwas prepared fromCoECsof CV mice by pooling from two independent experiments (fourmice per experiment). Shearing, end repairing, dA tailing, adaptorligation, andpurificationwerecarriedoutasdescribedabove.DNAfragments were hybridized onto the customized array. Hybridiza-tion, washing, and collection of hybridized DNA fragments wereperformed using the SeqCap EZ Reagent (Roche) according tothe manufacturer’s instructions. Collected DNA fragments weredenatured at 95°C for 5 min and then mixed with the anti–5-methylcytosine Ab (clone 33D3; Abcam) at 4°C overnight withrotation in immunoprecipitation (IP) buffer (10mMTris-HCl [pH8], 150 mM NaCl, 1 mM EDTA, and 0.05% Triton X-100) in thepresenceof random20-merDNA.Ab-boundDNAwas thenmixedwith salmon sperm DNA–treated protein G Dynabeads (Invitro-gen, Thermo Fisher Scientific) at 4°C for 2 h with rotation. Afterfive washes with the IP buffer and a subsequent wash with TE,immunoprecipitated DNAwas eluted in TE (pH 8) supplementedwith 0.25% SDS and 500 mg/ml proteinase K at 55°C for 2 h andwas purified using theMinElute ReactionCleanupKit (QIAGEN).The eluents and input control before IP were amplified by PCRusing adapter sequences as primers and were purified with theMinElute ReactionCleanupKit in the presence of random20-merDNAas described in theNimbleGen SeqCapEZLibrary SRUser’sGuide. Libraries obtainedwere subjected toNGS to acquire 50-bp,single-end reads using HiSeq 2000 (Illumina) at INFOBIO.Identified reads were mapped to the mouse genome (mm9) usingMaq (version 0.7.1) and then annotated to nt21000/+501 regionsof genes whose expression was,1/3 in CoECs of CVmice than inthose of GF mice. NGS data have been deposited in the DDBJSequence Read Archive (https://www.ddbj.nig.ac.jp/dra) underaccession number DRA008907.

StatisticsDifferences between two or more groups were analyzed by two-tailed Student t test or one-way ANOVA followed by Dunnett testor Tukey test, respectively. A p value ,0.05 was consideredstatistically significant.

RESULTS

Commensal bacteria regulate gene expression in IECs inboth MyD88-dependent and -independent mannersAs IECs receive the stimulation from commensal bacteria at thefront line andmore than 99% of the intestinal bacteria inhabit the

colon, the effect of commensal bacteria on gene expression inCoECs was first examined comprehensively. For this purpose,mRNA expression in CoECs was compared amongWTmice bredunder CV and GF conditions andMyD882/2mice bred under CVcondition (described as CV, GF, and MyD882/2, respectively) byRNA-seq. The numbers of genes showing more than three-folddifference in expressionbetweenCVandGFmice are summarizedin Table I. MyD88 dependency for up- and downregulation bycommensal bacteria is also specified in Table I based on whethergene expression inMyD882/2mice is similar to that in CV or GFmice. We found that the number of genes downregulated bycommensals was larger than those upregulated by commensals(644and388, respectively). Interestingly, of theupregulatedgenes,MyD88-dependent regulationwaspredominant (261/388),whereas,of the downregulated genes, MyD88-independent regulation waspredominant (517/644).

Genes encoding antimicrobial peptides, cytokines, andchemokines are differently regulated by commensal bacteriain SIECs and CoECsTables II and III respectively show representative genes thatwereupregulated and downregulated by commensals as seen in theRNA-seq results from CoECs. For example, expression of genesencodingantimicrobial peptides suchas regenerating islet-derived3 (Reg3) and a-defensin were markedly affected by commensalbacteria. Notably, expression of Reg3b and Reg3g was inducedby commensals in a MyD88-dependent manner. In contrast,severala-defensins, including 5, 20, 1, and24,weredownregulatedby commensals; the former two were suppressed in a MyD88-independent manner, and in contrast, the latter two were sup-pressed in a MyD88-dependent manner.

Next, theexpressionofa-Defensin 1,a-Defensin5, andReg3b inSIECs andCoECs ofCV,GF, andMyD882/2micewere quantifiedby qRT-PCR (Fig. 1A). Results from CoECs were similar to thoseobtained by RNA-seq. However, expression patterns weredifferent between SIECs and CoECs; a-Defensin 1 and 5 showedlower expression in CoECs of CV mice than in those of GF mice,but theywere expressed at higher levels in SIECs of CVmice thanin those ofGFmice. In addition, the suppressionofa-Defensin 1 bycommensal bacteria in CoECs was MyD88 dependent, whereas

TABLE I. Up- and downregulated genes in CoECs by commensals

Upregulation(CV . GF)

Downregulation(CV , GF)

MyD88

independent

119 517

Intermediate 8 11MyD88 dependent 261 116Total 388 644

Gene expression was systematically analyzed by RNA-seq using RNA preparedfrom CoECs of CV, GF, and MyD882/2 mice. Numbers of genes expressedmore than 3.0-fold (upregulation by commensal bacteria) or ,1/3 (down-regulation by commensal bacteria) in CV mice than in GF mice are shown.MyD88 dependency was considered based on whether the gene expression inMyD882/2 mice is closer to that in CV mice (MyD88 independent) or GF mice(MyD88 dependent) or just middle of these (intermediate).




ww

.imm

unohorizons.org/D

ownloaded from






that of a-Defensin 5 was MyD88 independent. As a-defensin ismainly produced in the epithelium of the small intestine, whichcontains Paneth cells, the expression levels of a-Defensin 1 and5 were much higher in SIECs than in CoECs, whereas theexpression of Reg3b was almost comparable in these cells.Expression of genes encoding some chemokines such as CCL25,CCL11, and CXCL1 and cytokines such as TGF-b1, TNF, and IL-17C were also affected by commensals (Tables II, III). When theexpression of Ccl25 andCxcl1 in SIECs and CoECs was quantifiedby qRT-PCR (Fig. 1B), the effect of commensal bacteria on theexpression of these chemokine genes was not found to be assignificant in SIECs as compared with that seen in CoECs.

Commensal bacteria affect DNA methylation in the59 regions of specific genes in CoECsWe previously reported that commensal bacteria induce DNAmethylation of the gene encodingTLR4,which acts as a sensor forGram-negative bacteria, in CoECs (23). Thus, we focused on DNAmethylation as a mechanism underlying the regulation of geneexpression by commensal bacteria. Genomic DNA fragments

obtained fromCoECs of CV and GFmice were pulled down usingMBD2-coupled beads and analyzed by NGS to compare CpGmethylation in the 59 regions of genes between these mice. Thereads mapped to the region from nt 21000 to nt +501 wereextracted and compared between CV and GF mice for each gene(Fig. 2). Out of 30,395 transcription start sites, 23 and 453 sitesshowed more than 2.0-fold and 1.5-fold change in the annotationfrequency between CoECs from CV and GF mice, respectively(Table IV). These included both types of genes methylated athigher and lower levels in CoECs of CV mice than in those of GFmice. The results directly demonstrated that commensal bacteriaaffect DNAmethylation in the 59 regions of specific genes.

Aldh1a1 expression is regulated by commensal bacteria inCoECs via DNA methylation of Tmem267 and3110070M22Rik genesThe 59 regions of Tmem267 and 3110070M22Rik encoding atransmembrane protein 267 and a noncoding RNA, respectively,showed highermethylation in CVmice than in GFmice (Table V).These genes are closely located on different DNA strands ofchromosome 13 inmice as shownFig. 3A.Methylation frequenciesofCpGmotifs present in theoverlapping59 regionof these genes inCoECs from CV and GF mice were further analyzed by bisulfitesequencing. Similar to the results of the MBD2 pull-down assay,methylation frequencywas significantly higher inCVmice than inGF mice (Fig. 3B, 3C). In accordance with these results, gene

TABLE II. Genes induced by commensals in CoECs

Gene CV GF MyD882/2

MyD88-independentDelta-like non-canonical Notch

ligand 1 (Dlk1)

126 4 83

ATP/GTP binding protein-like 2

(Agbl2)

107 8 78

Angiogenin, RNase A family,

member 4 (Ang4)

2773 269 43,569

Gasdermin C4 (Gsdmc4) 3240 408 4102Phospholipase A2, group IIA (Pla2g2a) 285 49 544Phospholipase A2, group III (Pla2g3) 148 34 116TGF, b 1 (Tgfb1) 68 14 56NO synthase 2, inducible (Nos2) 46 12 89Solute carrier family 30, member 10

(Slc30a10)

1080 287 940

MyD88-dependentRegenerating islet-derived 3 b

(Reg3b)

25,426 2111 1908

Regenerating islet-derived 3 g

(Reg3g)

71 2 0

Chemokine (C-X-C motif) ligand 1

(Cxcl1)

908 252 239

TNF (Tnf) 707 145 164IL-17C (Il17c) 39 3 12Fucosyltransferase 2 (Fut2) 1199 340 569Cytochrome P450, family 3, subfamily

a, polypeptide 44 (Cyp3a44)

290 11 64

Cytochrome P450, family 2, subfamily

c, polypeptide 55 (Cyp2c55)

33,206 4752 10,046

Aquaporin 4 (Aqp4) 5745 862 1128Carbonyl reductase 2 (Cbr2) 533 87 232Chloride intracellular channel 6

(Clic6)

98 13 30

Representatives of genes induced by commensals in CoECs in MyD88-independent (upper) and -dependent (lower) manner. Numbers of readsmapped to each corresponding gene in CV, GF, and MyD882/2 are shownaccordingly. Values are corrected by the total reads obtained for each sample.

TABLE III. Genes suppressed by commensals in CoECs

Gene CV GF MyD882/2

MyD88-independentDefensin a5 (Defa5) 0 143 0Defensin a20 (Defa20) 0 47 0C-C chemokine ligand 25 (Ccl25) 125 934 150C-C chemokine ligand 11 (Ccl11) 7 45 20Amylase 2a (Amy2a2, 3, 4) 93 1025 9Sucrase-isomaltase (Sis)

(a-glucosidase)

25 868 36

Solute carrier family 2 member 5

(Slc2a5) (fructose transporter)

47 158 15

Apolipoprotein A-I (Apoa1) 2 146 4Fatty acid binding protein 4 (Fabp4) 17 129 3Carboxypeptidase A1 (Cpa1) 4 92 0Cytochrome P450 (Cyp4b1) 724 2322 670Adenylate cyclase 9 (Adcy9) 34 132 65IL-22R (Il22ra1) 10 38 19Calcium/calmodulin-dependent

protein kinase II a (Camk2a)

0 12 0

MyD88-dependentDefensin a1 (Defa1) 8 1321 873Defensin a24 (Defa24) 8 1341 873Adenosine A1R (Adora1) 7 39 80Inositol 1,4,5-trisphosphate 3-kinase

A (Itpka)

13 47 31

Folate receptor 1 (Folr1) 1 12 9H+-K+-ATPase (Atp12a) 793 2479 2373

Representatives of genes suppressed by commensals in CoECs in MyD88-independent (upper) and -dependent (lower) manner. Numbers of readsmapped to each corresponding gene in CV, GF, and MyD882/2 are shownaccordingly. Values are corrected by the total reads obtained for each sample.




ww

.imm

unohorizons.org/D

ownloaded from



FIGURE 1. Gut microbiota differently regulates antimicrobial peptide and chemokine expression in SIECs and CoECs.

Total RNA was prepared from SIECs and CoECs of CV, GF, and MyD882/2 mice to quantify mRNA expression of antimicrobial peptides (A) and

chemokines (B) by qRT-PCR. Values are normalized using GAPDHmRNA levels and expressed as percentages relative to their expression in SIECs of

CV mice. Results are expressed as mean 6 SD of four to five independent experiments. Five or six mice were used for each experiment. *p , 0.05,

**p , 0.01, ***p , 0.005, ****p , 0.001, *****p , 0.0005 by Tukey test.




ww

.imm

unohorizons.org/D

ownloaded from



expression of both Tmem267 and 3110070M22Rikwas found to belower in CV mice than in GF mice (Fig. 3D).

As the function of Tmem267 is unknown, the effect ofTmem267 knockdown by RNAi in a CoEC line CMT-93 wasanalyzed. As shown in Fig. 4A, it was confirmed that expression ofTmem267 and 3110070M22Rik was significantly suppressed byeachcorrespondingRNAi. Inaddition, unexpectedly,RNAiagainst3110070M22Rik also caused decreased expression of Tmem267.The effect of RNAi against Tmem267 or 3110070M22Rik onexpression of various genes in IECs was then analyzed.Suppression of Tmem267 markedly decreased the expression ofAldh1a1 encoding RALDH1, an enzyme mediating the conversionof retinal, derived from vitamin A, to retinoic acid, whereas it didnot affect tight junction-related proteins (Occuldin and Zo-1),pattern recognition receptors (Tlr2 and Tlr4), and negativeregulator of TLR signaling (Tollip) (Fig. 4B, Supplemental Fig. 2).In addition, suppression of 3110070M22Rik similarly caused adecrease in Aldh1a1 expression. RALDH1 protein expression wasalso significantly decreased by knockdown of Tmem267 and3110070M22Rik (Fig. 4C).

The expression of Aldh1a1 in the epithelium from variousintestinal regions including proximal, medial, and distal smallintestine and colon of CV and GF mice was further analyzed byLMD, followed by RNA extraction and qRT-PCR (Fig. 4D).Aldh1a1 expression was lower in the epithelial tissues of CV micethan in those of GF mice in all intestinal portions. These resultsindicated that commensal bacteria suppressAldh1a1 expression inthe intestinal epithelium invivo, probably through the suppression

ofTmem267 and 3110070M22Rik expression via DNAmethylationof 59 regions of these genes. Furthermore, Aldh1a1 expressiontendedtobe lower in thedistal small intestineandcolon,whichareinhabited by a large number of commensal bacteria, than in theproximal and medial small intestine. However, because thistendencywas observed in bothCV andGFmice, additional factorsother than the commensal bacteria, such as food components inthe upper small intestine, could also influence the expression ofAldh1a1. In addition,Aldh1a1 expressionwas tended tobehigher inapical portions than inbasolateral portionsof thevillus epithelium,particularly in the proximal small intestine.

A part of the 59 regions of genes suppressed by commensalbacteria is highly methylatedAs specific gene expression seemed to be regulated by DNAmethylation in the 59 region in CoECs, we examined the methyl-ation status in the 59 regions of the 644 genes downregulated to,1/3 by commensals, as shown in Table I. For this analysis,genomicDNAfragments fromCoECsofCVmicewerehybridizedusing an array with sequences in the 59 regions (nt 21000/+501) of 562 out of 644 geneswhose transcription start sites couldbe identified. The captured DNA fragments were then immuno-precipitated with the anti–5-methylcytosine Ab and analyzedbyNGS. Figure 5 shows frequencies of the readsmapped to the nt21000/+501regionofeach target generelative to the totalnumberof reads before (input) and after IP. Reads that were mapped tothe nt21000/+501 region were detected for 510 out of 562 genesafter IP, indicating that these genes were possibly methylated.Moreover, reads were mapped with high frequency to some spe-cific genes in IP sample, whereas remarkable difference in theirfrequency was not observed among the genes in input sample,showing that specific genes among those downregulated bycommensals in CoECs were highly methylated. In contrast, asreads that were mapped to 35 genes were obtained from the inputsample, but not from the IP sample, these geneswere judged to beunmethylated. Reads mapped to the remaining 17 genes were notdetected in input sample.

Representative genes showing relatively high levels of meth-ylation are indicated in Table VI. These include genes related tothe cytoskeleton (Fgd1,Gas7, andMark4) and apoptosis (Tnfrsf25).In addition, Table VII summarizes themethylation status in the 59region of the 20 genes listed in Table III. Out of these 20 genes, sixgenes showed relatively higher levels of methylation, nine werepossibly methylated, and one was not methylated detectably.Among the six highly methylated genes, Ccl11 encoding the C-C

ratio

of a

nnot

atio

n fre

quen

cy (

GF

vs. C

V)

0.1

1

10

1692

1383

2074

2765

3456

4147

4838

5529

6220

6911

7602

8293

8984

9675

10366

11057

11748

12439

13130

13821

14512

15203

15894

16585

17276

17967

18658

19349

20040

20731

21422

22113

22804

23495

24186

24877

25568

26259

26950

27641

28332

29023

29714

Genes1 30395

FIGURE 2. Gut microbiota alters DNA methylation frequencies in the

59 regions of genes.

To compare the DNA methylation patterns, genomic DNA was pre-

pared from CoECs of CV and GF mice by pooling from two in-

dependent experiments (six to seven mice per experiment). Genomic

DNA fragments were pulled down using MBD2-coupled beads and

analyzed by NGS. Identified reads were annotated to 1.5-kb regions

near the transcription start site (nt21000/+501) of each gene. The ratio

of annotated read number of GF mice relative to that of CV mice is

shown for each gene.

TABLE IV. Effect of commensals on DNA methylation in the 59 regionsof genes in CoECs

No. of Genes

.1.5-Fold change 453

.2.0-Fold change 23

Genomic DNA fragments of CoECs from CV and GF mice were pulled downusing MBD2-coupled beads and analyzed by NGS. Values represent numbers ofgenes with indicated quantitative differences in the reads mapped to their 59region between CV and GF mice.




ww

.imm

unohorizons.org/D

ownloaded from




chemokine ligand 11, which recruits eosinophils; Folr1 encodingfolate receptor a, which is known to be overexpressed in variousepithelial-derived cancers including colorectal cancer; andSlc2a5 encoding a fructose transporter were included. TheDNA fragments corresponding to the 59 regions of the remainingfour genes were not detected in the input sample and weretherefore considered not efficiently collected by the sequencecapture array.

Further,methylation levels in the59 regionof thegenesanalyzedin this assaywerecomparedbetweenGFandCVmiceby linking thedata of MBD2 pull-down assay. The average ratio of methylationlevels in GF mice to that in CV mice was 1.01 for the entire genepopulation analyzed in this assay. Thus, it can be inferred that nodifferencewas found in themethylation levels between thesemice.

Next, we focused on the genes categorized as unmethylated andmethylated by this assay. Out of the 35 unmethylated genes, data ofmethylation in GF and CV mice were obtained for 25 genes byMBD2 pull-down assay. When the GF versus CV ratio of methyl-ation levels were compared between these 25 genes and the top 25methylated genes selected in descending order of IP versus inputratios of read count percentages, they were significantly different(p, 0.05). The average ratios of GF to CV were 1.06 for the genescategorized as unmethylated and 0.964 for the genes categorized asmethylated. Taken together, these results indicate that for a part ofthe gene population analyzed, DNA methylation is induced bycommensal bacteria in the 59 regions, showing that induction ofDNAmethylation is one mechanism by which commensal bacteriamediate the downregulation of genes in CoECs.

FIGURE 3. Gut microbiota suppresses expression of Tmem267 and 3110070M22Rik genes through induction of DNA methylation in their

overlapping 59 region.

(A) Positional relationship of Tmem267 and 3110070M22Rik genes on chromosome 13. (B and C) Genomic DNA was prepared from CoECs of CV

and GF mice by pooling from two independent experiments (five to six mice per experiment), respectively. Methylation of 27 CpG motifs in the 59

region of Tmem267 and 3110070M22Rik genes was analyzed by the bisulfite reaction. Analysis was performed for 16–17 independent clones. In (B),

filled and open circles present methylated and unmethylated motifs, respectively, and clones are arranged in order of their methylation frequency.

Data are summarized in (C) as mean6 SD of methylated CpG motifs for the total 27 motifs in the region. (D) Total RNA was prepared from CoECs of

CV and GF mice to analyze expression of Tmem267 and 3110070M22Rik by qRT-PCR. Results are expressed as mean 6 SD of seven independent

experiments. Each experiment was conducted using five to six mice per group. *p , 0.05, **p , 0. 005 by two-tailed Student t test.

TABLE V. Comparison of DNA methylation in the 59 region of Tmem267 and 3110070M22Rik genes in CoECs between CV and GF mice

CV Strand GF Strand

Same Opposite Total Same Opposite Total

Tmem267 301 240 541 97 88 1853110070M22Rik 240 298 538 86 96 182

Results for the MBD2 pull-down analysis showing DNA methylation in the 59 region (nt21000/+501) of Tmem267 and 3110070M22Rik in CoECs from CV and GF miceare summarized. The numbers of reads mapped respectively to the same and the opposite strand as the gene, and those of the total are shown.




ww

.imm

unohorizons.org/D

ownloaded from



FIGURE 4. Tmem267 induces expression of Aldh1a1, which produces retinoic acid, an immunomodulator in the intestine, in CoECs.

The mouse IEC line CMT-93 was transfected with Tmem267 or 3110070MRik small interfering RNA (siRNA) expression vector. After selection of

transfected cells with blasticidin for 10–14 d, cells were collected for qRT-PCR and Western blotting analyses. (A) To confirm the effect of RNAi,

expression of Tmem267 or 3110070MRik in transfected cells was quantified. Results are expressed as mean6 SD of three independent experiments.

(B) The effect of RNAi for Tmem267 and 3110070MRik on Aldh1a1mRNA expression was analyzed by qRT-PCR. Results are expressed as mean6 SD

of three independent experiments. (C) The effect of RNAi for Tmem267 and 3110070MRik on RALDH1 protein expression was analyzed by

Western blotting. Representative blots of an experiment performed in duplicate (left) and the mean 6 SD of the relative band (Continued)




ww

.imm

unohorizons.org/D

ownloaded from



DISCUSSION

Comprehensive analysis of mRNA expression in CoECs bycomparison of results fromCV andGFmice showed that a greaternumber of genes was downregulated by commensal bacteria thanthose upregulated by them. In addition, a large proportion of thedownregulated geneswas suppressedby commensals in aMyD88-independent manner (Table I). These results suggest that theexpression of a large number of genes in CoECs is suppressed bycommensal bacteria through their metabolites rather than theircellular constituents. In fact, the expression of a-Defensin 5 inCoECs was suppressed by commensal bacteria almost completelyin aMyD88-independentmanner (Fig. 1), indicating that bacterialmetabolites suppress expression of this gene in CoECs. In-terestingly, it was shown that the expression of a-Defensin 5 genewas upregulated by commensals through their constituents inSIECs (Fig. 1). Given that commensal bacteria are found in greaterabundance in the colon as compared with the small intestine andthe composition of microbiota differs between these intestinalportions, CoECs and SIECs might have different regulatorymechanisms for genes that encode key molecules involved in hostdefense and immune responses.

In addition to immune-related genes, those encoding enzymesof the digestive system including amylase, glucosidase, and car-boxypeptidase, fructose transporter, and lipid metabolism-related

molecules such as apolipoprotein and fatty acid binding proteinwere also found tobedownregulated inCoECsbycommensals in aMyD88-independent manner (Table III), indicating that com-mensal bacteriamight affect immune and inflammatory responsesand also metabolism of the host through the regulation of geneexpression in IECs. As digestion and absorption mainly occur inthe small intestine rather than the colon, regulation of these genesin the small intestine needs to be analyzed. Other types of regu-lation by commensal bacteria than MyD88-independent down-regulationof genesmight also contribute to intestinal homeostasis.For example,MyD88-dependent induction of fucosyltransferase 2(Fut2) in CoECs was observed (Table II) as previously reported(26). Fut2 fucosylates glycans on the surface of the intestinalepithelium and is known to serve as an attachment receptor anda nutrient source for specific bacteria, suggesting its role inestablishing intestinal symbiosis and preventing diseases (27–30).

The DNA methylation status of a specific population of geneswas shown to be altered by gutmicrobiota. For instance, commen-sal bacteria-inducedmethylation of CpGmotifs in the overlapping59 region of Tmem267 and 3110070M22Rik suppressed theexpression of these genes (Fig. 3). Furthermore, these genes werefound to regulate the expressionofRALDH1; this is thefirst report,to our knowledge, showing the function of Tmem267 in IECs.The mechanism by which this transmembrane protein regu-lates Aldh1a1 expression is yet to be elucidated. Suppression of

intensities normalized to b-actin from three independent experiments (right) are shown. (D) Tissue sections of apical (a) and basolateral (b)

portions of villus epithelia of proximal (Pro), medial (Med), and distal (Dis) small intestine and colon (Co) were collected from CV (n = 4, open

bars) and GF (n = 4, filled bars) mice by LMD and pooled so that the total area was almost equal. Total RNA was extracted from the pooled tissue

sections of each intestinal portion to analyze Aldh1a1 mRNA expression by qRT-PCR. *p , 0.05, **p , 0.01 by Dunnett test.

FIGURE 5. A part of gene population downregulated by commensals is methylated at relatively high levels in their 59 regions.

Genomic DNA was prepared from CoECs of CV mice by pooling from two independent experiments (four mice per experiment). The genomic DNA

fragments corresponding to the sequences in nt 21000/+501 regions of 562 genes that were downregulated by commensals were captured using

a customized array, immunoprecipitated with the anti-methylcytosine Ab, and analyzed by NGS. Frequencies of the reads mapped to each target

gene relative to the total number of reads before (input, red) and after (IP, blue) IP are shown.




ww

.imm

unohorizons.org/D

ownloaded from



3110070M22Rik upon RNAi decreased not only the expression of3110070M22Rik but also, surprisingly, that of Tmem267 (Fig. 4).Although it isnot clearwhysuppressionof3110070M22Rik leads tothe decrease in Tmem267 expression, the effect of RNAi for3110070M22Rik on Aldh1a1 expression is probably caused by thesuppression of Tmem267 expression. Many studies have focusedon RALDH2 expressed in dendritic cells and found that retinoicacid produced by this enzyme plays a crucial key role in IgAproduction, T cell homing, and regulatory T cell induction in theintestine (31–33). In contrast, however, it has been also suggestedthat RALDH1, which is highly expressed in IECs, also affects theintestinal immune system because retinoic acid produced fromIECs was shown to be important for dendritic cell conditioning(34, 35). It is considered that gutmicrobiota epigenetically controlsspecific host genes, Tmem267 and 3110070M22Rik, and therebyregulate RALDH1 expression in CoECs to control the concentra-tion of retinoic acid, a key immunomodulator in the intestine.Interestingly, dietary fiber and short chain fatty acids (SCFAs)were shown to increase the expression ofAldh1a1 in SIECs (36). Inparticular, the effect was observed specifically in the proximalSIECs, not inCoECs, althoughgutmicrobiota is known toproduceSCFAs by metabolizing dietary fiber mainly in the colon. Aldh1a1expression in IECs was lower in CV mice than in GF mice in ourstudy, indicating that gut microbiota might affect Aldh1a1expression throughbothSCFA- andTmem267-mediatedmannersdifferently in SIEC and CoECs. Recently, Grizotte-Lake et al. (37)reported that commensal bacteria suppress retinoic acid synthesisby IECs to control IL-22 activity and prevent dysbiosis. Theyshowed that expression of Rdh7, another enzyme involved in theretinoic acid synthesis, is suppressed by commensals, suggestingthat commensal bacteria regulate expression of multiple enzymesinvolved in vitamin A metabolism in IECs.

Focusing on the population of genes, whose expression wasdownregulated to,1/3-fold by commensals, a part of these geneswas shown to be actually methylated (Fig. 5, Tables VI, VII). Theremaining genes might be suppressed by commensals throughmechanisms other than DNA methylation, such as the binding of

some suppressive transcription factors. These findings suggest thatcommensal bacteria contribute to intestinal homeostasis via DNAmethylation of specific genes in CoECs of the host. Thus, commen-sal bacteria maintain intestinal symbiosis by themselves throughepigenetic control of a specific population of genes in CoECs,whichmight act as a mechanism supporting the intestinal ecosystem.

Onlya fewstudieswithin therecent 1–2yhavecomprehensivelyanalyzed the status of DNA methylation in IECs in combinationwithmRNAexpression. Howell et al. (38) evaluated differences inDNA methylation and transcription patterns in IECs betweeninflammatoryboweldiseasepatients andcontrolsbygenome-wideDNA methylation and transcriptome analyses. They found thatIECs from inflammatory bowel disease children before treatmentshowed alternations in DNA methylation and transcription andsuggested that it might explain variations in disease outcomes andthus might be used as prognostic biomarkers. The correlationbetween clinical outcome and DNAmethylation, or transcription,was analyzed not in combination, but separately in their study.In contrast, methylome and transcriptome analyses of SIECs fromCV and GFmice were performed to examine changes during post-natal development (39). The results showed the presence of bothmicrobiota-dependent and -independent processes and furtherhelped to identify 126 genomic loci at whichmicrobiota-dependent

TABLE VI. Representative genes with relatively high levels of DNAmethylation in their 59 region

Gene Input (%) IP (%)

FYVE, RhoGEF, and PH domain

containing 1 (Fgd1)

0.117 1.448

TNFR superfamily, member 25

(Tnfrsf25)

0.325 3.325

Growth arrest specific 7 (Gas7) 0.358 2.378MAP/microtubule affinity-regulating

kinase 4 (Mark4)

0.183 1.021

Secreted and transmembrane 1A

(Sectm1a)

0.316 1.476

Src-like adaptor 2 (Sla2) 0.291 1.270

Genomic DNA fragments corresponding to the 59 regions of genes down-regulated by commensals were captured using a customized array and thenimmunoprecipitated with anti-methylcytosine Ab. Representative genes withrelatively high levels of DNA methylation are listed with percentage of readsobtained relative to the total reads for the input and IP samples.

TABLE VII. DNA methylation status in the 59 regions of genesdownregulated by commensal bacteria

Gene Input (%) IP (%)

Relatively high methylationAdenylate cyclase 9 (Adcy9) 0.2331 0.6520Carboxypeptidase A1 (Cpa1) 0.2081 0.5687C-C chemokine ligand 11 (Ccl11) 0.4162 0.9115Folate receptor 1 (Folr1) 0.2081 0.4215Solute carrier family 2 member 5

(Slc2a5)

0.1748 0.2822

Calcium/calmodulin-dependent

protein kinase II a (Camk2a)

0.3912 0.6104

Possible methylationIL-22R (Il22ra1) 0.2913 0.1760C-C chemokine ligand 25 (Ccl25) 0.1665 0.0856Inositol 1,4,5-trisphosphate 3-kinase

A (Itpka)

0.1665 0.0353

Apolipoprotein A-I (Apoa1) 0.2913 0.0463Defensin a5 (Defa5) 0.0250 0.0035Cytochrome P450 (Cyp4b1) 0.3246 0.0401Fatty acid binding protein 4 (Fabp4) 0.1332 0.0057H+-K+-ATPase (Atp12a) 0.3163 0.0054Defensin a24 (Defa24) 0.0666 0.0005

Methylation Not DetectedAdenosine A1R (Adora1) 0.2580 —

Results obtained from sequence capture NGS analysis for the status ofmethylation in the 59 region of the genes downregulated by commensals(listed in Table III) are summarized. Percentage of reads obtained relative to thetotal reads for the IP sample [IP (%)] and the input sample [Input (%)] is shown.Relatively high methylation indicates that the proportion of reads relative to thetotal reads for the IP sample was higher than that for the input sample; possiblemethylation indicates that reads were obtained for the IP sample, but theproportion of the reads relative to the total reads for the IP sample was nothigher than that for the input sample; and methylation not detected indicatesthat reads were obtained for the input sample but were not obtained for the IPsample. —, not detected.




ww

.imm

unohorizons.org/D

ownloaded from



alternation of DNA methylation and transcription were bothdetected. In our study, integrated analysis of DNA methylationand transcription by focusing on DNAmethylation profiles aroundthe transcription start sites of genes in CoECs, several genesencoding key molecules for the maintenance of intestinal homeo-stasis such as cytoskeleton-relatedmolecules, chemokine, receptor,and transporter were identified as those regulated by microbiotathrough DNA methylation (Tables VI, VII). Interestingly, it wasreported that genomic DNA in CoECs of GF mice was hyper-methylated as compared with that of CV mice (40). Althoughsignificant difference in methylation frequencies was not observedin regions around the transcription start sites (Fig. 2), and a greaternumber of genes were expressed at lower levels in CoECs of CVmice than in those of GF mice (Table I), how the genome-widemethylation status, including that of the regions apart from thetranscription start sites, affects the function of IECs is yet to beelucidated. Our experiments focusing on DNA methylation in the59 regionsofgenesandmRNAexpression inCoECs fromCVandGFmice show thatmodulation of DNAmethylation in the 59 regions ofhost genes by commensal bacteria, as well as their metabolites, actas one mechanism for the regulation of gene expression in IECs.However, we cannot completely exclude the possibility that thedifference of irradiated and nonirradiated diet between the GFand CVmice, respectively, affects the status of DNAmethylation orgene expression. Further analyses will clarify detailed molecularmechanisms that explain how gut microbiota affects physiologicalfunctions of the host by regulating gene expression in IECs viaDNA methylation, which leads to the establishment of targets andstrategies for preventing diseases by interference with intestinalecosystems.

DISCLOSURE

The authors have no financial conflicts of interest.

ACKNOWLEDGMENTS

We are very grateful to Dr. Ametani and Dr. Yurino (INFOBIO Co., Ltd.)for kind discussion of the NGS data analysis.

REFERENCES

1. Parker, A., S. Fonseca, and S. R. Carding. 2020. Gut microbes andmetabolites as modulators of blood-brain barrier integrity and brainhealth. Gut Microbes 11: 135–157.

2. Zaiss, M. M., R. M. Jones, G. Schett, and R. Pacifici. 2019. The gut-bone axis: how bacterial metabolites bridge the distance. J. Clin. In-vest. 129: 3018–3028.

3. Stephen-Victor, E., and T. A. Chatila. 2019. Regulation of oral immunetolerance by the microbiome in food allergy. Curr. Opin. Immunol. 60:141–147.

4. Ducarmon, Q. R., R. D. Zwittink, B. V. H. Hornung, W. van Schaik,V. B. Young, and E. J. Kuijper. 2019. Gut microbiota and colonizationresistance against bacterial enteric infection. Microbiol. Mol. Biol. Rev.83: e00007-19.

5. Lo Presti, A., F. Zorzi, F. Del Chierico, A. Altomare, S. Cocca, A. Avola,F. De Biasio, A. Russo, E. Cella, S. Reddel, et al. 2019. Fecal andmucosal microbiota profiling in irritable bowel syndrome and in-flammatory bowel disease. Front. Microbiol. 10: 1655.

6. Herrera, S., J. Martınez-Sanz, and S. Serrano-Villar. 2019. HIV, can-cer, and the microbiota: common pathways influencing differentdiseases. Front. Immunol. 10: 1466.

7. Sudo, N. 2019. Role of gut microbiota in brain function and stress-related pathology. Biosci. Microbiota Food Health 38: 75–80.

8. Tilg, H., N. Zmora, T. E. Adolph, and E. Elinav. 2020. The intestinalmicrobiota fuelling metabolic inflammation. Nat. Rev. Immunol. 20:40–54.

9. Vatanen, T., E. A. Franzosa, R. Schwager, S. Tripathi, T. D. Arthur,K. Vehik, A. Lernmark, W. A. Hagopian, M. J. Rewers, J. X. She, et al.2018. The human gut microbiome in early-onset type 1 diabetes fromthe TEDDY study. Nature 562: 589–594.

10. Tanoue, T., S. Morita, D. R. Plichta, A. N. Skelly, W. Suda, Y. Sugiura,S. Narushima, H. Vlamakis, I. Motoo, K. Sugita, et al. 2019. A definedcommensal consortium elicits CD8 T cells and anti-cancer immunity.Nature 565: 600–605.

11. Torres, J., J. Hu, A. Seki, C. Eisele, N. Nair, R. Huang, L. Tarassishin,B. Jharap, J. Cote-Daigneault, Q. Mao, et al. 2020. Infants born tomothers with IBD present with altered gut microbiome that transfersabnormalities of the adaptive immune system to germ-free mice. Gut69: 42–51.

12. Liao, L., K. M. Schneider, E. J. C. Galvez, M. Frissen, H. U. Marschall,H. Su, M. Hatting, A. Wahlstrom, J. Haybaeck, P. Puchas, et al. 2019.Intestinal dysbiosis augments liver disease progression via NLRP3 in amurine model of primary sclerosing cholangitis. Gut 68: 1477–1492.

13. Allegretti, J. R., B. H. Mullish, C. Kelly, and M. Fischer. 2019. Theevolution of the use of faecal microbiota transplantation and emergingtherapeutic indications. Lancet 394: 420–431.

14. Sanders, M. E., D. J. Merenstein, G. Reid, G. R. Gibson, andR. A. Rastall. 2019. Probiotics and prebiotics in intestinal health anddisease: from biology to the clinic. [Published erratum appears in 2019Nat. Rev. Gastroenterol. Hepatol. 16: 642] Nat. Rev. Gastroenterol.Hepatol. 16: 605–616.

15. Zhu, W., M. G. Winter, M. X. Byndloss, L. Spiga, B. A. Duerkop,E. R. Hughes, L. Buttner, E. de Lima Romão, C. L. Behrendt,C. A. Lopez, et al. 2018. Precision editing of the gut microbiotaameliorates colitis. Nature 553: 208–211.

16. Depommier, C., A. Everard, C. Druart, H. Plovier, M. Van Hul,S. Vieira-Silva, G. Falony, J. Raes, D. Maiter, N. M. Delzenne, et al.2019. Supplementation with Akkermansia muciniphila in overweightand obese human volunteers: a proof-of-concept exploratory study.Nat. Med. 25: 1096–1103.

17. Zhu, W., N. Miyata, M. G. Winter, A. Arenales, E. R. Hughes, L. Spiga,J. Kim, L. Sifuentes-Dominguez, P. Starokadomskyy, P. Gopal, et al.2019. Editing of the gut microbiota reduces carcinogenesis in mousemodels of colitis-associated colorectal cancer. J. Exp. Med. 216:2378–2393.

18. Selma-Royo, M., M. Tarrazo, I. Garcıa-Mantrana, C. Gomez-Gallego,S. Salminen, and M. C. Collado. 2019. Shaping microbiota during thefirst 1000 days of life. Adv. Exp. Med. Biol. 1125: 3–24.

19. Pronovost, G. N., and E. Y. Hsiao. 2019. Perinatal interactions betweenthe microbiome, immunity, and neurodevelopment. Immunity 50:18–36.

20. Cerdo, T., E. Diéguez, and C. Campoy. 2019. Early nutrition and gutmicrobiome: interrelationship between bacterial metabolism, immunesystem, brain structure, and neurodevelopment. Am. J. Physiol.Endocrinol. Metab. 317: E617–E630.

21. Woo, V., E. M. Eshleman, T. Rice, J. Whitt, B. A. Vallance, andT. Alenghat. 2019. Microbiota inhibit epithelial pathogen adherenceby epigenetically regulating C-type lectin expression. Front. Immunol.10: 928.




ww

.imm

unohorizons.org/D

ownloaded from



22. Martin-Gallausiaux, C., P. Larraufie, A. Jarry, F. Béguet-Crespel,L. Marinelli, F. Ledue, F. Reimann, H. M. Blottiere, and N. Lapaque.2018. Butyrate produced by commensal bacteria down-regulatesindolamine 2,3-dioxygenase 1 (IDO-1) expression via a dual mecha-nism in human intestinal epithelial cells. Front. Immunol. 9: 2838.

23. Takahashi, K., Y. Sugi, K. Nakano, M. Tsuda, K. Kurihara, A. Hosono,and S. Kaminogawa. 2011. Epigenetic control of the host gene bycommensal bacteria in large intestinal epithelial cells. J. Biol. Chem.286: 35755–35762.

24. Nakata, K., Y. Sugi, H. Narabayashi, T. Kobayakawa, Y. Nakanishi,M. Tsuda, A. Hosono, S. Kaminogawa, S. Hanazawa, and K. Takahashi.2017. Commensal microbiota-induced microRNA modulates intestinalepithelial permeability through the small GTPase ARF4. J. Biol. Chem.292: 15426–15433.

25. Sugi, Y., K. Takahashi, K. Kurihara, K. Nakata, H. Narabayashi,Y. Hamamoto, M. Suzuki, M. Tsuda, S. Hanazawa, A. Hosono, and S.Kaminogawa. 2016. Post-transcriptional regulation of Toll-interactingprotein in the intestinal epithelium. PLoS One 11: e0164858.

26. Meng, D., D. S. Newburg, C. Young, A. Baker, S. L. Tonkonogy,R. B. Sartor, W. A. Walker, and N. N. Nanthakumar. 2007. Bacterialsymbionts induce a FUT2-dependent fucosylated niche on colonicepithelium via ERK and JNK signaling. Am. J. Physiol. Gastrointest.Liver Physiol. 293: G780–G787.

27. Nanthakumar, N. N., D. Meng, and D. S. Newburg. 2013. Glucocorti-coids and microbiota regulate ontogeny of intestinal fucosyltransfer-ase 2 requisite for gut homeostasis. Glycobiology 23: 1131–1141.

28. Goto, Y., T. Obata, J. Kunisawa, S. Sato, I. I. Ivanov, A. Lamichhane,N. Takeyama, M. Kamioka, M. Sakamoto, T. Matsuki, et al. 2014.Innate lymphoid cells regulate intestinal epithelial cell glycosylation.Science 345: 1254009.

29. Pickard, J. M., C. F. Maurice, M. A. Kinnebrew, M. C. Abt,D. Schenten, T. V. Golovkina, S. R. Bogatyrev, R. F. Ismagilov,E. G. Pamer, P. J. Turnbaugh, and A. V. Chervonsky. 2014. Rapidfucosylation of intestinal epithelium sustains host-commensal sym-biosis in sickness. Nature 514: 638–641.

30. Omata, Y., R. Aoki, A. Aoki-Yoshida, K. Hiemori, A. Toyoda,H. Tateno, C. Suzuki, and Y. Takayama. 2018. Reduced fucosylation inthe distal intestinal epithelium of mice subjected to chronic socialdefeat stress. Sci. Rep. 8: 13199.

31. Mora, J. R., M. Iwata, B. Eksteen, S. Y. Song, T. Junt, B. Senman,K. L. Otipoby, A. Yokota, H. Takeuchi, P. Ricciardi-Castagnoli, et al.2006. Generation of gut-homing IgA-secreting B cells by intestinaldendritic cells. Science 314: 1157–1160.

32. Iwata, M., A. Hirakiyama, Y. Eshima, H. Kagechika, C. Kato, andS. Y. Song. 2004. Retinoic acid imprints gut-homing specificity onT cells. Immunity 21: 527–538.

33. Coombes, J. L., K. R. Siddiqui, C. V. Arancibia-Carcamo, J. Hall,C. M. Sun, Y. Belkaid, and F. Powrie. 2007. A functionally specializedpopulation of mucosal CD103+ DCs induces Foxp3+ regulatory T cellsvia a TGF-beta and retinoic acid-dependent mechanism. J. Exp. Med.204: 1757–1764.

34. Edele, F., R. Molenaar, D. Gutle, J. C. Dudda, T. Jakob, B. Homey,R. Mebius, M. Hornef, and S. F. Martin. 2008. Cutting edge: in-structive role of peripheral tissue cells in the imprinting of T cellhoming receptor patterns. J. Immunol. 181: 3745–3749.

35. Iliev, I. D., E. Mileti, G. Matteoli, M. Chieppa, and M. Rescigno. 2009.Intestinal epithelial cells promote colitis-protective regulatory T-celldifferentiation through dendritic cell conditioning. Mucosal Immunol.2: 340–350.

36. Goverse, G., R. Molenaar, L. Macia, J. Tan, M. N. Erkelens, T. Konijn,M. Knippenberg, E. C. Cook, D. Hanekamp, M. Veldhoen, et al. 2017.Diet-derived short chain fatty acids stimulate intestinal epithelial cellsto induce mucosal tolerogenic dendritic cells. J. Immunol. 198: 2172–2181.

37. Grizotte-Lake, M., G. Zhong, K. Duncan, J. Kirkwood, N. Iyer,I. Smolenski, N. Isoherranen, and S. Vaishnava. 2018. Commensalssuppress intestinal epithelial cell retinoic acid synthesis to regulateinterleukin-22 activity and prevent microbial dysbiosis. Immunity 49:1103–1115.e6.

38. Howell, K. J., J. Kraiczy, K. M. Nayak, M. Gasparetto, A. Ross, C. Lee,T. N. Mak, B. K. Koo, N. Kumar, T. Lawley, et al. 2018. DNA meth-ylation and transcription patterns in intestinal epithelial cells frompediatric patients with inflammatory bowel diseases differentiatedisease subtypes and associate with outcome. Gastroenterology 154:585–598.

39. Pan, W. H., F. Sommer, M. Falk-Paulsen, T. Ulas, P. Best, A. Fazio,P. Kachroo, A. Luzius, M. Jentzsch, A. Rehman, et al. 2018. Exposureto the gut microbiota drives distinct methylome and transcriptomechanges in intestinal epithelial cells during postnatal development.Genome Med. 10: 27.

40. Poupeau, A., C. Garde, K. Sulek, K. Citirikkaya, J. T. Treebak,M. Arumugam, D. Simar, L. E. Olofsson, F. Backhed, and R. Barres. 2019.Genes controlling the activation of natural killer lymphocytes areepigenetically remodeled in intestinal cells from germ-free mice.FASEB J. 33: 2719–2731.




ww

.imm

unohorizons.org/D

ownloaded from



Documents

Regulation of Gene Expression through Gut Microbiota ... · commensal bacteria on gene expression in colonic epithelial cells (CoECs) were investigated with focus on regulation of