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Cited by 157Gut microbiota and healthEpigenetics and DNA MethylationDigestive system and related health

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Exposure to the gut microbiota drives distinct methylome and transcriptome changes in intestinal epithelial cells during postnatal development

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Pan, Wei-Hung, Sommer, Felix, Falk-Paulsen, Maren, Ulas, Thomas, Best, Lena, Fazio, Antonella, Kachroo, Priyadarshini, Luzius, Anne, Jentzsch, Marlene, Rehman, Ateequr, Müller, Fabian, Lengauer, Thomas, Walter, Jörn, Künzel, Sven, Baines, John F., Schreiber, Stefan, Franke, Andre, Schultze, Joachim L., Bäckhed, Fredrik, Rosenstiel, Philip·2018·Genome Medicine
DOI 10.1186/s13073-018-0534-5PMC PMC5899322
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Abstract

Background: The interplay of epigenetic processes and the intestinal microbiota may play an important role in intestinal development and homeostasis. Previous studies have established that the microbiota regulates a large proportion of the intestinal epithelial transcriptome in the adult host, but microbial effects on DNA methylation and gene expression during early postnatal development are still poorly understood. Here, we sought to investigate the microbial effects on DNA methylation and the transcriptome of intestinal epithelial cells (IECs) during postnatal development. Methods: We collected IECs from the small intestine of each of five 1-, 4- and 12 to 16-week-old mice representing the infant, juvenile, and adult states, raised either in the presence or absence of a microbiota. The DNA methylation profile was determined using reduced representation bisulfite sequencing (RRBS) and the epithelial transcriptome by RNA sequencing using paired samples from each individual mouse to analyze the link between microbiota, gene expression, and DNA methylation. Results: We found that microbiota-dependent and -independent processes act together to shape the postnatal development of the transcriptome and DNA methylation signatures of IECs. The bacterial effect on the transcriptome increased over time, whereas most microbiota-dependent DNA methylation differences were detected already early after birth. Microbiota-responsive transcripts could be attributed to stage-specific cellular programs during postnatal development and regulated gene sets involved primarily immune pathways and metabolic processes. Integrated analysis of the methylome and transcriptome data identified 126 genomic loci at which coupled differential DNA methylation and RNA transcription were associated with the presence of intestinal microbiota. We validated a subset of differentially expressed and methylated genes in an independent mouse cohort, indicating the existence of microbiota-dependent “functional” methylation sites which may impact on long-term gene expression signatures in IECs. Conclusions: Our study represents the first genome-wide analysis of microbiota-mediated effects on maturation of DNA methylation signatures and the transcriptional program of IECs after birth. It indicates that the gut microbiota dynamically modulates large portions of the epithelial transcriptome during postnatal development, but targets only a subset of microbially responsive genes through their DNA methylation status.

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Fig. 1 Experimental study design. a Mice that were raised conventionally (CONV-R) or germ-free (GF) were sacrificed at three developmental stages: 1 week, 4 weeks, and between 12 and 16 weeks of age. b Intestinal epithelial cells (IECs) from the distal small intestine were collected. DNA and RNA were isolated and gene expression and DNA methylation analyzed by RNA-seq and RRBS, respectively
Fig. 1 Experimental study design. a Mice that were raised conventionally (CONV-R) or germ-free (GF) were sacrificed at three developmental stages: 1 week, 4 weeks, and between 12 and 16 weeks of age. b Intestinal epithelial cells (IECs) from the distal small intestine were collected. DNA and RNA were isolated and gene expression and DNA methylation analyzed by RNA-seq and RRBS, respectivelyCC BY · PubMed Central
Fig. 2 Microbial effects on the host epithelial transcriptome during postnatal development. a Principal component analysis displaying overall gene expression profiles across all samples. The first dimension explained 63% variation and separated W1 and the other two stages. The second dimension explained 8% variation and separated both W4 versus W12/16 and samples of a stage for their microbiota status. b Transcription factor binding sites enriched among microbially regulated genes (differentially expressed in CONV-R vs GF) for each of the three developmental stages. The bar plot depicts the 15 most significantly enriched transcription factors of either up- or downregulated genes. All p values were corrected for multiple testing using the Benjamini–Hochberg method. c Hierarchical clustering of microbially regulated genes identified 12 groups with specific expression profiles, e.g., group 3 genes that were repressed by the presence of the microbiota at W4 and W12/16 or conversely group 8 genes induced by the microbiota
Fig. 2 Microbial effects on the host epithelial transcriptome during postnatal development. a Principal component analysis displaying overall gene expression profiles across all samples. The first dimension explained 63% variation and separated W1 and the other two stages. The second dimension explained 8% variation and separated both W4 versus W12/16 and samples of a stage for their microbiota status. b Transcription factor binding sites enriched among microbially regulated genes (differentially expressed in CONV-R vs GF) for each of the three developmental stages. The bar plot depicts the 15 most significantly enriched transcription factors of either up- or downregulated genes. All p values were corrected for multiple testing using the Benjamini–Hochberg method. c Hierarchical clustering of microbially regulated genes identified 12 groups with specific expression profiles, e.g., group 3 genes that were repressed by the presence of the microbiota at W4 and W12/16 or conversely group 8 genes induced by the microbiotaCC BY · PubMed Central
Fig. 3 The microbiota modulates distinct functional expression nodes during postnatal development. Co-expression network analysis (CENA) was performed based on 970 co-expressed genes (correlation factor greater than 0.8 across all conditions). Each dot represents a gene and the color indicates its expression compared to the average gene expression level (red = up, blue = down). Note that ellipsoids represent only estimated visualization of transcript groups (for details see the “Methods” section). Exemplary GO terms enriched among the groups of co-regulated genes are listed, representing the main biological function of that gene group (for full list of GO terms see Additional file 10)
Fig. 3 The microbiota modulates distinct functional expression nodes during postnatal development. Co-expression network analysis (CENA) was performed based on 970 co-expressed genes (correlation factor greater than 0.8 across all conditions). Each dot represents a gene and the color indicates its expression compared to the average gene expression level (red = up, blue = down). Note that ellipsoids represent only estimated visualization of transcript groups (for details see the “Methods” section). Exemplary GO terms enriched among the groups of co-regulated genes are listed, representing the main biological function of that gene group (for full list of GO terms see Additional file 10)CC BY · PubMed Central
Fig. 4 Postnatal development and the microbiota affect the DNA methylation profile. a Multidimensional scaling analysis plot displaying the overall methylation profiles. b Venn plots showing the number of differentially methylated sites between CONV-R and GF at the three developmental stages. Note the high number of differentially methylated sites at W1. c Number of hypo- and hypomethylated sites among all DMPs (CONV-R vs GF) for each developmental stage. d Expression of Dnmt3a and Tet3 genes, which function in de novo methylation and demethylation, respectively. e Hierarchical clustering of differentially methylated sites between CONV-R and GF in the three developmental stages. Each row indicates a CpG site and the color scale represents the methylation level
Fig. 4 Postnatal development and the microbiota affect the DNA methylation profile. a Multidimensional scaling analysis plot displaying the overall methylation profiles. b Venn plots showing the number of differentially methylated sites between CONV-R and GF at the three developmental stages. Note the high number of differentially methylated sites at W1. c Number of hypo- and hypomethylated sites among all DMPs (CONV-R vs GF) for each developmental stage. d Expression of Dnmt3a and Tet3 genes, which function in de novo methylation and demethylation, respectively. e Hierarchical clustering of differentially methylated sites between CONV-R and GF in the three developmental stages. Each row indicates a CpG site and the color scale represents the methylation levelCC BY · PubMed Central
Fig. 5 The microbiota may modulate host gene expression through DNA methylation. a Schematic analysis workflow. A 5-kb window up- and downstream of each microbially regulated gene was screened for CpG positions. Next, CpG regions were defined and tested for differential methylation (CONV-R vs GF) and p values of all differentially methylated CpG sites were corrected for multiple testing. It is noteworthy that any sequential analysis reflects a certain bias by the individual order of filter steps. b Microbial effects on gene expression and DNA methylation of Camk2b during postnatal development. c Genomic map of all methylation–transcription interactions dependent on the microbiota and postnatal development. The boxes in the outer circle depict the mouse chromosomes and their banding indicates the staining properties within the genomic locations (black = heterochromatin region, white = euchromatin region, gray = intermediate). The boxes in the inner circle represent genes that were both differentially expressed and methylated. The gene name is colored according to the expression difference in CONV-R vs GF comparison (red = upregulated, blue = downregulated). Box coloring corresponds to the developmental stage, in which a significant difference was detected (red = W1, green = W4, blue = W12/16). Width of the boxes indicates gene length, while methylation differences in CONV-R vs GF comparison are scaled along the height of the boxes. Red and blue dots within the gene boxes represent hyper- and hypomethylated CpG sites, respectively
Fig. 5 The microbiota may modulate host gene expression through DNA methylation. a Schematic analysis workflow. A 5-kb window up- and downstream of each microbially regulated gene was screened for CpG positions. Next, CpG regions were defined and tested for differential methylation (CONV-R vs GF) and p values of all differentially methylated CpG sites were corrected for multiple testing. It is noteworthy that any sequential analysis reflects a certain bias by the individual order of filter steps. b Microbial effects on gene expression and DNA methylation of Camk2b during postnatal development. c Genomic map of all methylation–transcription interactions dependent on the microbiota and postnatal development. The boxes in the outer circle depict the mouse chromosomes and their banding indicates the staining properties within the genomic locations (black = heterochromatin region, white = euchromatin region, gray = intermediate). The boxes in the inner circle represent genes that were both differentially expressed and methylated. The gene name is colored according to the expression difference in CONV-R vs GF comparison (red = upregulated, blue = downregulated). Box coloring corresponds to the developmental stage, in which a significant difference was detected (red = W1, green = W4, blue = W12/16). Width of the boxes indicates gene length, while methylation differences in CONV-R vs GF comparison are scaled along the height of the boxes. Red and blue dots within the gene boxes represent hyper- and hypomethylated CpG sites, respectivelyCC BY · PubMed Central
Fig. 6 Integrated analysis identifies genomic loci with coupled differential DNA methylation and RNA transcription associated with the presence of intestinal microbiota. Network analysis based on differentially methylated and differentially expressed genes (CONV-R vs GF) across the three developmental stages with a relationship confidence greater than 0.6. Larger blue circles indicate candidate genes identified from our analysis and smaller black circles denote imputed interacting genes
Fig. 6 Integrated analysis identifies genomic loci with coupled differential DNA methylation and RNA transcription associated with the presence of intestinal microbiota. Network analysis based on differentially methylated and differentially expressed genes (CONV-R vs GF) across the three developmental stages with a relationship confidence greater than 0.6. Larger blue circles indicate candidate genes identified from our analysis and smaller black circles denote imputed interacting genesCC BY · PubMed Central

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  1. Legendre P, Legendre L. Numerical ecology. 3rd ed. Cambridge: Elsevier; 2012.

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Metadata

Authors
Pan, W.-H., Sommer, F., Falk-Paulsen, M., Ulas, T., Best, L., Fazio, A., Kachroo, P., Luzius, A., Jentzsch, M., Rehman, A., Müller, F., Lengauer, T., Walter, J., Künzel, S., Baines, J. F., Schreiber, S., Franke, A., Schultze, J. L., Bäckhed, F., & Rosenstiel, P.
Affiliations
  • Christian-Albrechts-Universität zu Kiel (DE)
  • University of Bonn (DE)
  • Max Planck Institute for Informatics (DE)
  • Saarland University (DE)
  • University of Copenhagen (DK)
Corresponding author
Rosenstiel, Philip
ORCID
Venue
Genome Medicine
Publisher
Springer Science and Business Media LLC
Published
2018-04-13
Volume
10
Issue
1
Pages
27-27
ISSN
1756-994X (ISSN-L)
DOI
10.1186/s13073-018-0534-5
PMC
PMC5899322
OpenAlex
W2802827720
Cited by
157
References
77
Citations by year
2026: 11 · 2025: 15 · 2024: 16 · 2023: 24 · 2022: 21 · 2021: 20 · 2020: 29 · 2019: 15 · 2018: 6
FWCI
6.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Max Planck Digital Library (submittedVersion, other-oa)
  • DZNE Pub (submittedVersion, other-oa)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
Funders
  • Deutsche Forschungsgemeinschaft (RTG 1743/1, CRC877 B9, ExC 306 Inflammation at Interfaces, ExC 1023 ImmunoSensation, CRC1182 C2)
  • BMBF (DEEP IHEC network grant (TP5.2 and 2.3))
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Health Sciences › Medicine › Gastroenterology › Digestive system and related health
Keywords
gut microbiota, DNA methylation, intestinal epithelial cells, postnatal development, transcriptome, immune pathways, metabolism, intestinal development
MeSH terms
Gastrointestinal Microbiome, Animals, Epithelial Cells, Female, Intestines, Mice, Inbred C57BL, RNA, Transcription, Genetic, Gene Expression Regulation, Developmental, DNA Methylation, Growth and Development, Genetic Loci, Transcriptome

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