Supplementary Information for

0 downloads 0 Views 1MB Size Report
counts calculated using the python tool HTSeq based on UCSC RefSeq gene .... the number of permutations with an overlap larger or equal than the observed ...
Supplementary Information for

Paternal chronic colitis causes epigenetic inheritance of susceptibility to colitis

Markus Tschurtschenthaler, Priyadarshini Kachroo, Femke-Anouska Heinsen, Timon Erik Adolph, Malte Christoph Rühlemann, Johanna Klughammer, Felix Albert Offner, Ole Ammerpohl, Felix Krueger, Sébastien Smallwood, Silke Szymczak, Arthur Kaser, Andre Franke

Inventory Supplementary Materials and Methods Supplementary Figure legends Supplementary Dataset legends Supplementary References

!

Supplementary Materials and Methods

Flow cytometry (FACS). Single cell suspensions were incubated with the Fc-Block CD16/CD32 (BD Bioscience) for 10 min on ice. Antibody staining using antibodies against CD45 (FITC conjugated, Biolegend) and CD326 (EpCAM; PE conjugated, eBioscience) was performed according to manufacturer’s instruction. DAPI (eBioscience) was used as a live vs. dead discriminator. Samples were analyzed on a BD LSR Fortessa cell analyzer or with a FACSAria flow cytometer (BD Biosciences) and EpCAM+ CD45– epithelial cells were sorted and isolated.

DNA and RNA isolation. Genomic DNA and total RNA were extracted from freshly isolated and unfixed EpCAM+ CD45– intestinal epithelial cells (IECs) and sperm cells using the AllPrep DNA/RNA Mini or Micro Kit (Qiagen) according to manufacturer’s instructions. Quality and quantity of DNA and RNA was assessed with a Qubit fluorometer (Invitrogen) using Quant-iT PicoGreen dsDNA Kit and Quant-iT RNA Assay Kit, respectively.

RNA-Seq data pre-processing and analysis. Removal of bad quality reads was carried out using Illumina’s CASAVA filter version 0.1 and quality checks were performed with FastQC v0.10.1. Sequence reads were aligned to the UCSC mm10 reference genome from UCSC, using Tophat2 1 and Bowtie2 version 2.0.0-beta6 2. Gene expression was quantified as read counts calculated using the python tool HTSeq based on UCSC RefSeq gene annotations 3. Genes with at least 1 count per million (cpm) in at least five samples were retained for further analysis. To account for the different sequencing depths between samples, TMM normalization was applied and differential expression analysis was performed as implemented in the Bioconductor package edgeR 4. Benjamini–Hochberg correction was used to adjust for

!

multiple testing. Overrepresentation of gene ontology categories regarding biological processes was analyzed using the Bioconductor package goseq. The hyper-geometric test was applied since no gene length biases were detected with all analyzed genes as reference. The list of significantly expressed genes was further filtered to genes with an absolute logtransformed fold change >1. Significant categories and corresponding genes were visualized using the Bioconductor package GeneAnswers 5.

Reduced Representation Bisulfite Sequencing (RRBS). 100-250 ng gDNA were subjected to MspI digest (37°C for 5 h), DNA end repair and A-tailing as described by Boyle and colleagues 6 with subsequent heat inactivation as described by Smallwood and Kelsey 7 and AMPure XP beads purification (Beckmann Coulter) according to the manufacturer’s instructions.. Adapter ligation was performed at 16°C overnight 6 and subsequent heat inactivation at 65°C for 20 min 7 by using T4 DNA ligase (Thermo Fisher) and Illumina 5mC sequencing adapters for single-sample sequencing and Illumina TruSeq adapters for the multiplexed sequencing approach, respectively. After AMPure XP beads purification (Beckmann Coulter) samples were bisulfite converted using one-step modification of the Imprint DNA Modification Kit (Sigma). Final amplification of converted samples was performed by using PfuTurbo Cx Hotstart DNA Polymerase (Agilent) and Illumina primers PE1.0 and PE2.0 for single-samples sequencing and Illumina TruSeq primers for multiplexed sequencing 6 with PCR conditions as described in Gu and colleagues 8. PCR products were purified using AMPure XP beads (Beckmann Coulter) and indexed samples were pooled equimolar into pools of six. Libraries were analyzed with Agilent 2100 Bioanalyzer using the Agilent High Sensitivity DNA Kit and concentration was measured using Quant-iT DNA BR Assay Kit (Life Technologies). All sperm and epithelial samples were sequenced using an Illumina HiSeq 2500 platform (Illumina, San Diego, CA) at an average of 127 million singleend 50 bp reads (Supplementary Data 14).

!

RRBS data pre-processing and analysis. Illumina CASAVA filter version 0.1 and FastQC v0.10.1 were used for removing bad quality reads and for quality control, respectively. Adapter or primer contamination was removed using cutadapt. Alignment to bisulfite converted mm10 reference genome from UCSC and methylation calling was performed using Bismark v0.7.12 9,10. The R-package RnBeads v0.99.16 11 was employed for additional filtering steps, and differential methylation analysis. Low quality sites present in less than 50% of the samples and sites with extremely large total coverage (total coverage > 99% quantile of all sites) were removed and only autosomal sites were retained in the study. Two samples were removed from the analysis due to gender mix-up or a too low number of covered CpG sites. Site-based differential methylation analysis was performed applying the limma method 12 to mouse litters, library preparation batches, concentration, sequencing batches, gender as well as covariates wherever applicable. For our analysis, a site was called differentially methylated (DM) if the raw p-value was smaller than 0.05 and the absolute mean methylation difference was at least 0.20. DM sites were categorized as hyper- or hypo-methylated if they showed higher or lower methylation ratios in DSS-treated or offspring mice compared to control mice, respectively. A gene was annotated or called if at least one CpG site within the gene sequence or promoter was differentially methylated. A site can be located in gene body, promoter, intron or exon, therefore all sites irrespective of their location, whether downstream or upstream of the associated region were retained for further downstream analyses. Additionally, if more than 1 site was associated with a gene, the site with the largest methylation difference was selected for a particular gene. Direction of the biological effect had to be the same to classify it as an ‘overlap’. For differential expression, transcripts had to be either up- or down-regulated in both the F0

!

generation and their offspring. For differential methylation, CpG sites had to be either hypoor hyper-methylated in both F0 generation and their offspring in every analyzed tissue. To check if the observed number of overlapping genes is different from the expected number under independence of findings in the F0 and F1 generation, we performed the following permutation test. Observed p-values were randomly shuffled across all filtered CpG sites in F0 and F1 generation separately and the number of differentially methylated genes detected in both generations was calculated using the same definition described above. This process was repeated 10,000 times to estimate the mean number of overlapping genes and the p-value as the number of permutations with an overlap larger or equal than the observed number of overlaps.

Genomic annotation of differentially methylated sites. Differentially methylated CpG sites were annotated with respect to known CpG islands (CGIs), genes and regulatory regions. CGI definitions were downloaded from UCSC and CGI shores were defined as regions outside CGIs but within 2 kbp of any CGI and CGI shelves as regions within 2 kbp from a CGI shore. Information about genes including transcript, introns, exons, 3’ UTR and 5’ UTR was extracted from Bioconductor packages Mus.musculus and TxDb.Mmusculus.UCSC.mm10.knownGene version 3.1.2. Promoters were defined as regions 1 kbp upstream of a transcription start site (TSS). Regulatory information including enhancers and transcription factor binding sites was downloaded from EnsEMBL build GRCm38.p4 (Genome Reference Consortium Mouse Reference 38). Functional annotation of differentially methylated regions (DMRs) was performed using the gene enrichment tool GREAT 13. To reduce the number of false positives, basal plus extension rule was applied including default parameters. Target genes can be regulated by regulatory elements or domains that are located over one million bases away from them 14 as defined by the ‘basal plus extension rule’ of GREAT to even detect long-range interactions.

!

Correlation of methylation data with gene expression. For each gene that is either differentially methylated or has a differentially methylated site in its promoter region, the CpG site with the strongest absolute methylation difference was selected and correlation with the corresponding normalized gene expression values was estimated using Pearson's correlation coefficient.

Network analysis. Functional network analysis and gene enrichment was performed using the FNMT web tool (functional networks of tissues in mouse) 15 using ‘Colon’ as the reference tissue and our candidate genes as input (Supplementary Data 10). Minimum relationship confidence of 0.75 was used to observe major interacting and co-expressing genes.

!

Supplementary Figure Legends

Supplementary Figure 1. Experimental model to study epigenetic alterations induced by chronic inflammation and their potential of intergenerational inheritance. (a) C57Bl/6 littermate males (6 pups in one litter) and females (6 pups in one litter) were purchased from Charles River. At 5 weeks of age litters with male pups were split into two groups: F0Ctrl (kept on water) and F0DSS (undergoing DSS treatment to develop chronic colitis). Litters consisting of female pups were all kept on normal drinking water. (b) Representative weight change of 5 week-old male littermate mice in the course of chronic colitis using DSS in the drinking water (F0DSS) compared to littermate mice that were meanwhile kept on normal drinking water (F0Ctrl). F0Ctrl, n=3; F0DSS, n=3; Mean ± SEM. (c) Representative H&E stainings of the distal colon of F0DSS mice after healing colitis for 18 days after the last DSS cycle in the model of chronic DSS-induced colitis (scale bars, 200µm). (d) DSS subscores (left) and the resulting cumulative score (right) of the distal colon of F0DSS after recovering from colitis for 18 days after the last DSS cycle. F0DSS, n=5-6 from two representative litters; data are presented as Median. (e) Colon length of F0DSS mice after healing chronic DSS-induced colitis for 18 days compared to littermate mice kept on normal drinking water (F0Ctrl). F0Ctrl, n=21; F0DSS, n=15; Mean ± SEM; Unpaired, two-tailed Student’s t-test. (f) F0Ctrl and F0DSS male littermates were crossed with female littermates in order to generate F1 offspring.

Supplementary Figure 2. Increased susceptibility to DSS-induced colitis in offspring of mice with chronic experimental colitis. (a,b) DSS subscores (left) and the resulting cumulative score (right) of the proximal colon of F1Ctrl and F1DSS females (a) and males (b) after induction of an acute colitis by adding 3.5% DSS in the drinking water of mice of both groups (F1 DSS susceptibility test). F1Ctrl females, n=9; F1DSS females, n=9; F1Ctrl males, n=6; F1DSS males, n=3; Mean ± SEM; Mann–Whitney U-test. (c-d) Blood hematocrit values of !

F1Ctrl and F1DSS mice after 3.5% DSS treatment shown in Fig. 1e separated according to gender: females (c) and males (d). F1Ctrl females, n=11; F1DSS females, n=8; F1Ctrl males, n=6; F1DSS males, n=3; Mean ± SEM; Unpaired, 1-tailed Student’s t-test. (e-f) DSS subscores (left) and the resulting cumulative score (right) of the distal colon of F1Ctrl and F1DSS females (e) and males (f) after induction of an acute colitis by adding 3.5% DSS in the drinking water of mice of both groups. F1Ctrl females, n=10; F1DSS females, n=9; F1Ctrl males, n=6; F1DSS males, n=3; Mean ± SEM; Mann–Whitney U-test. (g) Body weight reduction (in %) of 7 week-old males and females that are offspring of either F0Ctrl males (F1Ctrl) or F0DSS males (F1DSS) in the course of an acute colitis induced by 3.5% DSS in the drinking water of both groups for 6 days. F1Ctrl females, n=11; F1DSS females, n=9; F1Ctrl males, n=6; F1DSS males, n=3; Mean ± SEM. (h) Colon length of F1Ctrl and F1DSS mice (female and males) that underwent a 3.5% DSS colitis model for 6 days. F1Ctrl females, n=9; F1DSS females, n=9; Mean ± SEM; Unpaired, two-tailed Student’s t-test.

Supplementary Figure 3. Quality and purity checks of samples analyzed in this study. (a) FACS gating strategy used for sorting EpCAM+ CD45– colonic intestinal epithelial cells (IECs). (b) Quality control of normalized counts for RNA-Seq samples. The y-axis shows the log transformed normalized counts for all IEC samples (F0Ctrl and F1Ctrl samples shown in green and F0DSS and F1DSS samples shown in orange) shown along the x-axis (c-e) Normalized counts for genes specifically expressed in epithelia (Cdh1, Actb, Mhy11), but not in isolated sperm cells, showing purity of isolated samples and no contaminations with surrounding tissues (i.e. epididymis). (e-g) Normalized counts for genes specifically expressed in sperm cells (Odf1, Smpc1), but not in epithelia (i.e. epididymis), showing purity of isolated samples. (h) Expression of the epididymis-specific gene Myh11 as well as of the sperm-specific genes Odf1 and Smcp in representative sperm samples used for RRBS as measured by qRT-PCR.

!

Supplementary Figure 4. Quality control of samples and genomic coverage annotation of methylated sites in sperm samples. (a) MDS analysis of all the filtered and quality controlled CpG sites identified in sperm and epithelium samples (F0 and F1) shows a clear separation of both cell types in two clusters. (b,c) Genomic coverage annotation of the quality controlled CpG sites in sperm samples of F0Ctrl and F0DSS mice (b) as well as of F1Ctrl and F1DSS mice (c) to genic regions, CGIs and regulatory regions. .

Supplementary Figure 5. Differentially methylated genes are overlapping between sperm samples of the F0 and F1 generation. (a) Permutation analysis was performed to determine whether the observed differential methylation seen in sperm (F0 and F1) is larger than expected by random chance. The p-value was estimated as the number of permutations with an overlap larger or equal than the observed number of overlaps (66) represented by the red line. This number is significantly larger than expected by chance in all 10,000 permutations represented by the histogram and the blue line (mean number after 10,000 permutations = 15.518 [3, 30]; Permutation based p-value = 0). (b,c) Heatmap of 66 differentially methylated overlapping genes comparing sperm samples of mice of the F0 generation with mice of the F1 generation, shows hierarchical clustering between F0Ctrl and F0DSS sperms (b) as well as between F1Ctrl and F1DSS sperms (c).

Supplementary Figure 6. Genomic coverage annotation of methylated sites in IECs. (a,b) Genomic coverage annotation of quality controlled CpG sites in IECs of F0Ctrl and F0DSS mice (a) as well as F1Ctrl and F1DSS mice (b).

Supplementary Figure 7. Patterns of imprinting in epithelial and sperm samples of mice of the F0 and F1 generation. (a-h) A few paternally imprinted genes were selected from the GeneImprint database and the housekeeping gene Actb was used as a negative control. Y-axis

!

shows the median methylation values for all the sites covered in the selected genes across xaxis. For IEC samples (a-d), the methylation value fluctuates between 0%, 50% or 100% for all genes as expected for imprinted genes. However, for sperm samples (e-h), the imprinting patterns of methylation for the selected genes were slightly perturbed.

Supplementary Figure 8. Differential methylation and differential expression in F1 IECs. (a,b) Heatmaps of F1Ctrl and F1DSS IECs showing hierarchical clustering of 13 differentially expressed (a) and methylated genes (b), respectively.

Supplementary Figure 9 | Patterns of epigenetic inheritance – suggested candidate genes. (a) Permutation analysis (see Supplementary Material and Methods) was performed to determine whether the observed differential methylation seen in sperm of both F0 and F1 generation and in F1 IECs is larger than expected by random chance. The p-value was estimated as the number of permutations with an overlap larger or equal than the observed number of overlaps (3) represented by the red line. This number is significantly larger than expected by chance in 9,700 out of 10,000 permutations represented by the histogram and the blue line (mean number after 10,000 permutations = 0.677 [0, 6]; Permutation based p-value = 0.03). (b,c) Regional browser-view plots for Hdac5 (b) and Mta1 (c) genes that are differentially methylated (DM) in F0 and F1 sperm cells and differentially expressed in F1 IECs, respectively. The plots show the EnsEMBL gene annotations, coverage (log10) distribution of quality-controlled sites, and their median methylation values (hypo-methylated or hyper-methylated). The red arrows indicate sites of Hdac5:102,204,587 (b) and Mta1:113,132,074 (c) that are significantly differentially methylated between F1Ctrl and F1DSS sperm samples.

!

Supplementary Data legends

Supplementary Data 1. Differentially methylated sites (RRBS) in sperm samples of F0DSS and F0Ctrl mice annotated either to transcript or promoter regions (methylation difference cut off 0.05 and p-value < 0.05). The methylation (AQ-BP) and coverage values (BQ-CP) for each sample, mean methylation (D-E) and mean coverage (Q-R) for each group and the annotations for each differentially methylated CpG site (Y-AP) are listed.

Supplementary Data 2. Differentially methylated sites (RRBS) in sperm samples of F1DSS and F1Ctrl mice annotated either to transcript or promoter regions (methylation difference cut off 0.05 and p-value < 0.05). The methylation (AQ-BE) and coverage values (BF-BT) for each sample, mean methylation (D-E) and mean coverage (Q-R) for each group and the annotations for each differentially methylated CpG site (Y-AP) are listed.

Supplementary Data 3. Overlap of differentially methylated genes (RRBS) in F0 sperm samples with differentially methylated genes (RRBS) in F1 sperm samples.

Supplementary Data 4. Differentially expressed genes (RNA-Seq) in IEC samples of F0DSS and F0Ctrl mice (adjusted p-value < 0.05). The normalized gene expression counts for each sample (G-O) are indicated.

Supplementary Data 5. Differentially expressed genes (RNA-Seq) in IEC samples of F1DSS and F1Ctrl mice (adjusted p-value < 0.05). The normalized gene expression counts for each sample (G-AC) are indicated.

!

Supplementary Data 6. Differentially methylated sites (RRBS) in IEC samples of F0DSS and F0Ctrl mice annotated either to transcript or promoter regions (methylation difference cut off 0.05 and p-value < 0.05). The methylation (AQ-BQ) and coverage values (BR-CR) for each sample, mean methylation (D-E) and mean coverage (Q-R) for each group and the annotations for each differentially methylated CpG site (Y-AP) are listed.

Supplementary Data 7. Differentially methylated sites (RRBS) in IEC samples of F1DSS and F1Ctrl mice annotated either to transcript or promoter regions (methylation difference cut off 0.05 and p-value < 0.05). The methylation (AQ-BL) and coverage values (BM-CH) for each sample, mean methylation (D-E) and mean coverage (Q-R) for each group and the annotations for each differentially methylated CpG site (Y-AP) are listed.

Supplementary Data 8. Overlap of differentially methylated genes (RRBS) in F0 IECs with differentially methylated genes (RRBS) in F1 IECs.

Supplementary Data 9. Overlap of differential methylated genes (RRBS) in F1 IECs with differential expressed genes (RNA-Seq) found in F1 IECs along with their correlations.

Supplementary Data 10. Potential candidates for trans-generational inheritance: Overlap of differentially methylated genes in F0 and F1 sperm samples with differentially expressed genes in F1 IECs.

Supplementary Data 11. Overlap between the herein identified differentially methylated regions in F1 sperm samples (RRBS) with differentially methylated regions identified in the study from Radford and colleagues 16.

!

Supplementary Data 12. Functional network analysis for the connected genes showing enriched Gene ontology (GO) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways.

Supplementary Data 13. RNA-Seq statistics of all analyzed F0 and F1 IEC samples.

Supplementary Data 14. RRBS statistics of all analyzed F0 and F1 samples from sperm cells and IECs.

!

Supplementary References

1

Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105-1111, doi:10.1093/bioinformatics/btp120 (2009).

2

Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357-359, doi:10.1038/nmeth.1923 (2012).

3

Anders, S., Pyl, P. T. & Huber, W. HTSeq--a Python framework to work with highthroughput sequencing data. Bioinformatics 31, 166-169, doi:10.1093/bioinformatics/btu638 (2015).

4

Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140, doi:10.1093/bioinformatics/btp616 (2010).

5

Huang, L. et al. GeneAnswers: Integrated Interpretation of Genes. R package version 2.10.0. (2014).

6

Boyle, P. et al. Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling. Genome Biol 13, R92, doi:10.1186/gb-201213-10-r92 (2012).

7

Smallwood, S. A. & Kelsey, G. Genome-wide analysis of DNA methylation in low cell numbers by reduced representation bisulfite sequencing. Methods Mol Biol 925, 187-197, doi:10.1007/978-1-62703-011-3_12 (2012).

8

Gu, H. et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat Protoc 6, 468-481, doi:10.1038/nprot.2010.190 (2011).

!

9

Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571-1572, doi:10.1093/bioinformatics/btr167 (2011).

10

Krueger, F., Kreck, B., Franke, A. & Andrews, S. R. DNA methylome analysis using short bisulfite sequencing data. Nat Methods 9, 145-151, doi:10.1038/nmeth.1828 (2012).

11

Assenov, Y. et al. Comprehensive analysis of DNA methylation data with RnBeads. Nat Methods 11, 1138-1140, doi:10.1038/nmeth.3115 (2014).

12

Ritchie, M. E. et al. limma powers differential expression analyses for RNAsequencing and microarray studies. Nucleic Acids Res 43, e47, doi:10.1093/nar/gkv007 (2015).

13

McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28, 495-501, doi:10.1038/nbt.1630 (2010).

14

Lettice, L. A. et al. A long-range Shh enhancer regulates expression in the developing limb and fin and is associated with preaxial polydactyly. Hum Mol Genet 12, 17251735 (2003).

15

Goya, J. et al. FNTM: a server for predicting functional networks of tissues in mouse. Nucleic Acids Res, doi:10.1093/nar/gkv443 (2015).

16

Radford, E. J. et al. In utero effects. In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science 345, 1255903, doi:10.1126/science.1255903 (2014).

!

!

Supplementary Figure 1 a

C57BL/6(N) mother with 6 female pups

c

F0Ctrl

F0DSS

H&E

C57BL/6(N) mother with 6 male pups

d

Male littermates were divided in 2 groups at 5 weeks of age

F0DSS

cumulative DSS score

5

DSS score

4 F0

3 2 1

n=3 kept on normal n=3 treated with 3 cycles Female littermates kept on drinking water (F0Ctrl ) of DSS in their normal drinking water drinking water (F0DSS)

0

tap water

2.5% DSS

2.5% DSS

tap water

tap water O/N mating dissection

weight change (g)

35 30

F0Ctrl F0DSS

15

da y da 0 y da 2 y4 da y da 6 da y 8 y1 da 0 y da 12 y da 14 y da 16 y da 18 y da 20 y da 22 y da 24 y da 26 y2 da 8 y da 30 y da 32 y da 34 y da 36 y da 38 y da 40 y da 42 y4 da 4 y da 46 y da 48 y da 50 y da 52 y da 54 y da 56 y da 58 y6 0

10

F0Ctrl

F0DSS X

X

F1Ctrl kept on normal drinking water

F1DSS kept on normal drinking water

F1

Comparative analysis RNA-seq RRBS EpCAM+ CD45– IECs EpCAM+ CD45– IECs & & sperm cells sperm cells

tra s tio n

n

8 6

F0DSS

fil in

cr

yp

tl

os

io

a

os

er

e

m

cl

de

oe

lli

ea 10

F0Ctrl

20

F0

P < 0.0001

4

25

f

Colon length (cm)

2.5% F0DSS: DSS

12

fo

ar

e

tap water

10

5

0

b F0Ctrl:

15

F0Ctrl

F0DSS

Supplementary Figure 2

0.5

0

n tio

tra

in

cr

fil

yp

tl

io

os

s

n

a

6 y

5

da

4

y da

3 y

y da

2

da

y

0 y da

1

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS

-10

DSS score

n tio

in

fil

tl

tra

os

s

n io cr 7 6 5 4 3

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS

P = 0.1603 females

8 Colon length (cm)

-5

yp

os

ea ar

fo

8

da

weight change (%)

6 y

5 y

da

da

4

da

y

3

2

y da

y

1

da

da

y

0

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS

0

-15

-15

y

0

fil

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS

da

0

in

-5

10 5

h

males

15

1

males

5

0

-10

2

n tio

tra

tl yp

cr

er

os

io

os

n

a m de

cl

oe

fo

lli

ar

females

5

P = 1.0

P = 0.357

er

0

females

3

a

0

s

5

es

1

P = 0.679

m

10

4

de

15

20

P = 0.375

P = 1.0

Colon length (cm)

2

5

oe

20

es

P = 0.561

P = 0.741

+ 3.5% DSS

P = 0.4798

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS P = 0.6786

distal Colon

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS

DSS score

P = 0.785

DSS

cl

F1

P = 0.122

4

f

F1Ctrl + 3.5% DSS

distal Colon

P = 0.211

3

m

males

lli

females

ea

DSS score weight change (%)

ea

20

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS

g

fo

40

y

20

60

da

40

5

ar

tio tra

fil in

Hematocrit (%)

Hematocrit (%)

P = 0.386

80

60

e

0

F1DSS + 3.5% DSS

P = 0.020

0

5

F1Ctrl + 3.5% DSS

F1DSS + 3.5% DSS 80

males

10

0

n

s os

n

tl yp

cr

os

io

m

a

es cl

de

er

d

F1Ctrl + 3.5% DSS

DSS score

c

oe

fo

lli

ar

ea

0

os

0.0

P = 1.0

P = 1.0

1

DSS score

5

P = 0.083

2

er

P = 0.131

1.0

10

3

es

1.5

15

P = 0.059

P = 0.667

de

P = 0.450 P = 1.0

4

females

oe

2.0

DSS score

DSS score

P = 0.063

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS P = 0.3333

proximal Colon

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS

P = 0.0253

15

P = 0.029

2.5

b

cl

+ 3.5% DSS + 3.5% DSS

DSS score

F F

F1Ctrl + 3.5% DSS F1DSS + 3.5% DSS

proximal Colon

Ctrl 1 DSS 1

lli

a

7 6 5 4 3

P = 0.2780 males

Supplementary Figure 3 a 10

10 104

DAPI

FSC-A

104 103

88.3% 103

100K

102 0

94.4%

102 43.2% 0

b Log transformed normalized cpm

1.64%

200K

0

100K 200K FSC-A

0 0

100K 200K FSC-H

0

102 103 104 10 : EpCAM

10

0

c

d

e

3,000 2,000

200 Myh11 (cpm)

4,000

Cdh1 (cpm)

Actb (cpm)

2,000

1,000

100

1,000 0

0 EpCAM+ sperm IECs cells

f

EpCAM+ sperm IECs cells

g

EpCAM+ sperm IECs cells

h

sperm 1 sperm 2 sperm 3 sperm 4 sperm

12,000

10,000

8,000 6,000

target/Hprt

Smcp (cpm)

Odf1 (cpm)

10,000

4,000 2,000

0

0

0 EpCAM IECs

+

sperm cells

0 400 300 200 100

EpCAM IECs

+

sperm cells

0

Myh11

Odf1

Smcp

4 0.20

b

c ̂

̂ ̂

̂

F Ctrl

F0DSS F

Sperm IEC

3e+05

e+05

e+05

0e+00

5e+05

e+05

3e+05

2e+05

e+05

0e+00 ̂ ̂

̂ ̂̂

̂

̂

DSS

Genes

5e+05 4e+05

4e+05

3e+05

0e+00

Genes 4e+05

3e+05

2e+05

O

̂

bi nd pe E ing n nh si c a te Pr om hro nce ot Pro ma r TF er m tin bi flanote nd k r in ing g s In ite te r

F

TC

C

0.00 ̂

e+05 e+05

0e+00 5.0e+04 0e+00

TC F b O ind pe E in n nh g s ch a it Pr om P ro nce e o r m r TF ter om atin bi fla ote nd nk r in in g g s In ite te r

Coordinate 2 0.05 ̂ ̂

C

F0Ctrl

C pG C Is pG la C S nd pG ho r In Sh e te el rC f G I

In t Tr erg an en sc ic r C ipt D E S In xon 5' tron U T 3 Pr 'U R om TR ot er 2 0.05

C pG C Is pG la C S nd pG ho r In Sh e te el rC f G I

In t Tr erg an en sc ic r C ipt D Ex S In on 5' tron U Pr 3'UTR om T ot R er

Supplementary Figure 4

a ̂̂ ̂ ̂ ̂ ̂ ̂̂ ̂

̂

̂̂ ̂̂ ̂

̂ ̂ ̂

̂ ̂ ̂ ̂

̂

̂ ̂

0.00 ̂

F0 sperm

CpG islands Regulatory

6e+05 4e+05

3e+05

2e+05 2e+05

e+05 e+05

0e+00

F sperm

CpG islands Regulatory

3.5e+05 3.0e+05 2.5e+05 2.0e+05

e+05

Supplementary Figure 5 a

Empirical distribution (10,000 permutations) 2,000

P=0

Frequency

1,500 1,000 500 0 0 10 20 30 40 50 60 70 significant overlap between F0 and F1 sperm

b

c 200 100 0

0

0.4 0.8 Methylation

Color Key and Histogram

F0Ctrl sperm F0DSS sperm

chr5:66026844_Rbm47 chr19:7422264_2700081O15Rik chr8:4778099_Shcbp1 chr15:37666208_Ncald chr13:15793586_4933412O06Rik chr18:37295846_Gm37013,Pcdhb2 chr11:113400078_Slc39a11 chr18:7000826_Mkx chr7:45794354_Lmtk3 chr17:91088197_Nrxn1 chr11:36944147_Tenm2 chr4:130006972_Bai2 chr17:12125073_Agpat4 chr8:48228654_Tenm3 chr10:70967267_Bicc1 chr16:92498352_Clic6 chr14:118703402_Abcc4 chr8:83902976_Lphn1 chr2:148396416_Sstr4 chr2:165632100_Eya2 chr11:68871833_Ndel1 chr4:127326514_Gjb3 chr7:45353568_Ppfia3 chr17:24436264_Eci1 chr7:25001175_Atp1a3 chr10:70548667_Fam13c chr1:175418576_Rgs7 chr9:62721604_Itga11 chr18:80699687_Nfatc1 chr15:35133827_Stk3 chr13:56536908_Fbxl21 chr1:187811066_Esrrg chr9:121419731_Trak1 chr12:99366126_Foxn3 chr17:87352900_Ttc7 chr1:39698759_Rfx8 chr2:35906401_Ttll11 chr11:118560564_Rbfox3 chr13:97955892_Arhgef28 chr5:65484143_Smim14 chr9:103474456_Bfsp2 chr16:4655600_Coro7 chr18:9778316_Colec12 chr10:116441631_Kcnmb4 chr18:58053558_Fbn2 chr10:68751122_Tmem26 chr14:70627919_Dmtn chr11:98721533_Med24 chr16:90407646_Hunk chr18:39284333_Arhgap26

Count

Count

Color Key and Histogram 150 100 50 0

0

0.4 0.8 Methylation

F1Ctrl sperm F1DSS sperm

chr8:123824633_Rab4a chr4:154390582_Prdm16 chr4:135565121_Grhl3 chr17:35243374_Ddx39b chr10:81291272_Tjp3 chr7:44866373_Ptov1 chr8:125871111_Pcnxl2 chr7:110802414_Ampd3 chr4:118243192_Ptprf chr19:3659722_Lrp5 chr11:118624919_Rbfox3 chr1:37986196_Txndc9 chr11:116145471_Fbf1 chr2:142655848_Kif16b chr1:133413288_Sox13 chr11:106606255_Tex2 chr1:62728437_Nrp2 chr2:32166804_Prrc2b chr7:110574563_Sbf2 chr2:165192241_Cdh22 chr19:42026211_Ubtd1 chr17:29859996_Mdga1 chr13:97249261_Enc1 chr16:35631726_Sema5b chr5:34920793_Msantd1 chr17:71105516_Myom1 chr4:128748077_Phc2 chr2:163870388_Rims4 chr4:107623185_Glis1 chr17:47468551_AI661453 chr7:128010261_Trim72 chr9:111467744_Dclk3 chr2:152933019_Foxs1 chr15:100720311_Galnt6 chr13:95604500_F2r chr3:89760715_Chrnb2 chr9:108106757_Bsn chr8:121541710_1700018B08Rik chr6:48438885_Zfp467 chr7:81150795_Slc28a1 chr1:3531676_Xkr4 chr2:84659012_Btbd18 chr10:68097204_Arid5b chr5:134217249_Gtf2ird2 chr10:127540028_Lrp1 chr4:117926732_Artn chr11:94409830_Cacna1g chr15:82223157_Tnfrsf13c chr7:45787732_Lmtk3 chr7:4993979_Zfp579

7e+05 6e+05 5e+05 4e+05 3e+05 2e+05 1e+05 0e+00 21%

22% 17% 33%

17%

1e+05

0e+00 3

14% 2%

1e+05

66%

31% 39% 32%

13% 2%

0e+00

Genes 5e+05

4 e+05

1e+05

0e+00

bi n pe E din n nh g s ch an ite Pr om P rom ce o r a r TF ter om tin bi fla ote nd nk r in in g g s In ite te r

F

38%

O

32% e+05

2e+05 10%

2%

47% 41%

10% 3%

n pe E din n nh g s ch an ite Pr om P rom ce o r a r TF ter om tin bi fla ote nd nk r in in g g si In te te r

3e+05 39%

O

TC

C

4e+05

bi

b 4e+05

F

2e+05 66%

TC

Genes

C

6e+05 5e+05

C pG C Is pG la C Sh nd pG o r In Sh e te e r C lf G I

In te Tr rge an n sc ic r C ipt D Ex S In on t 5' ron U T Pr 3'U R o m TR ot er

a

C pG C Is pG la C S nd pG ho r In Sh e te el rC f G I

In t Tr erg an en sc ic r C ipt EDx S In on 5' tron U Pr 3'UTR om TR ot er

Supplementary Figure 6 F0 IECs CpG islands Regulatory

49%

4e+05

3 e+05

2e+05

5e+05

3e+05 3e+05

2e+05 2e+05

0 e+00

43% 41%

20%

1e+05

0e+00 1% 2%

8%

CpG islands

4e+05

1e+05

1% 2%

1%

F1 IECs

Regulatory

44% 40%

19% 8% 1%

tb

Ac

i2

M ag

l1

Pl ag

Ig f2 D io 3

tb

Ac

ag i2

M

1

ag l

Pl

io 3

D

f2

Ig

t

M es

g i2 tb

Ac

ag

M

l1

3

io ag

Pl

D

f2

Ig

t

10

es

M

Pe g

1

pr

G

0.0

Median methylation ratio 0.2 0.4 0.6 0.8 1.0

f

es t

h Pe g1 0

1

Median methylation ratio 0.2 0.4 0.6 0.8 1.0

es t

ag i2

l1

es t

l1 ag

i2 Ac tb

M

ag

Pl

Ig f2 D io 3

M

10

pr 1 Pe g

G

Ac tb

M

ag

Pl

Ig f2 D io 3

M

10

pr 1

Pe g

G

Median methylation ratio 0.2 0.4 0.6 0.8 1.0

Median methylation ratio 0.2 0.4 0.6 0.8 1.0

0.0

0.0

e

M

F1DSS IECs - DNA methylation

G pr

0.0

F1Ctrl IECs - DNA methylation

Pe g1 0

Median methylation ratio 0.2 0.4 0.6 0.8 1.0

tb

Ac

i2

ag

M

3

l1

ag

Pl

io

D

f2

Ig

t

es

M

Median methylation ratio 0.2 0.4 0.6 0.8 1.0

F0DSS IECs - DNA methylation

pr 1

0.0

tb

Ac

i2

M ag

l1

Pl ag

Ig f2 D io 3

t

10

Pe g

1

pr

G

0.0

F0Ctrl IECs - DNA methylation

G

tb

Ac

2

ag i

M

1

ag l

Pl

io 3

D

f2

Ig

d es

Median methylation ratio 0.2 0.4 0.6 0.8 1.0

c

M

0

Pe g1

pr 1

G

0.0

b

es t

Median methylation ratio 0.2 0.4 0.6 0.8 1.0

a

M

Pe g1 0

1

G pr

0.0

Supplementary Figure 7 F0Ctrl sperm - DNA methylation

F0DSS sperm - DNA methylation

F1Ctrl sperm - DNA methylation

F1DSS sperm - DNA methylation

Supplementary Figure 8 b Count 0 2 4 6

Color Key and Histogram

F1Ctrl IECs F1

DSS

IECs

-2 0 2 Row Z-Score

Color Key and Histogram Count 0 20 40 60

a

F1DSS IECs 0

Anks4b Rnf31 Jmjd1c Plekha7 Ick Nrip1 Adcy6 Tmc8 Cmtm8 Abce1 Fam171a2 Kif26a Arhgef10

F1Ctrl IECs

0.4 0.8 Methylation

Anks4b Fam171a2 Abce1 Jmjd1c Kif26a Nrip1 Arhgef10 Rnf31 Adcy6 Cmtm8 Plekha7 Tmc8 Ick

Supplementary Figure 9 a Empirical distribution (10,000 permutations) 6,000 P = 0.03

Frequency

5,000 4,000 3,000 2,000 1,000 0

0 5 10 15 20 25 30 Overlap between F0 & F1 sperm methylation and F1 IECs methylation

b

c Chr 12

Chr 11 102.2 mb

G6pc3

Hdac5

G6pc3

Hdac5 Hdac5

G6pc3

102.21 mb

102.22 mb 102.23 mb 3' 5'

5' 113.10 mb 3'

Hdac5 Hdac5

ENSEMBL genes

Hdac5 G6pc3

Hdac5

Hdac5

G6pc3 G6pc3

Hdac5

Hdac5 Hdac5 Hdac5

Mta1 Mta1 Mta1 Crip2

Mta1 Mta1 Mta1

Hdac5

CGI

Hdac5

̂

̂ ̂ ̂ ̂

2 1.5 1

̂

̂

̂ ̂ ̂ ̂

̂

0.8 0.4 0

̂ ̂ ̂ ̂ ̂ ̂

̂

̂ ̂ ̂ ̂

̂ ̂ ̂ ̂ ̂̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂

̂

̂ ̂

̂ ̂

̂̂

̂

̂̂ ̂ ̂ ̂ ̂ ̂̂ ̂̂ ̂ ̂ ̂̂ ̂̂ ̂ ̂̂ ̂ ̂ ̂

̂ ̂ ̂

̂

̂ ̂

̂

̂

̂

̂

F1Ctrl sperm

̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂

̂ ̂̂ ̂

̂

̂

̂

̂ ̂

̂

̂ ̂

̂ ̂ ̂

̂

̂ ̂

̂ ̂ ̂ ̂ ̂

̂

1

̂ ̂ ̂ ̂ ̂̂ ̂ ̂̂ ̂ ̂

̂ ̂

̂ ̂ ̂

̂̂ ̂̂ ̂

̂ ̂ ̂

̂

̂

̂ ̂

̂ ̂

̂

̂̂ ̂ ̂

̂

̂ ̂̂

̂

̂

̂ ̂ ̂ ̂ ̂

̂ ̂ ̂

̂

̂̂

̂

̂ ̂ ̂ ̂ ̂ ̂

̂ ̂ ̂ ̂

̂

̂̂ ̂ ̂̂

̂ ̂ ̂

̂ ̂

̂ ̂

̂ ̂ ̂

̂

1.5

F1DSS sperm

significant DM site

Methylation

CGI

̂

̂ ̂

(log10)

Hdac5

Coverage

2

Hdac5

(log10)

113.13 mb 113.14 mb 3' 5'

Hdac5

Hdac5

Coverage

113.12 mb

Mta1

Hdac5

Methylation

113.11 mb

Mta1

mmu-mir-8101

ENSEMBL genes

5' 3'

̂̂ ̂ ̂ ̂

̂ ̂ ̂

̂ ̂ ̂ ̂ ̂

̂

̂ ̂ ̂

̂ ̂ ̂ ̂ ̂ ̂

̂ ̂ ̂ ̂

̂ ̂

0.8

̂

0.4

̂ ̂ ̂

̂ ̂

̂

̂ ̂ ̂ ̂ ̂̂ ̂ ̂ ̂ ̂ ̂̂ ̂̂ ̂ ̂ ̂ ̂̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂

0

̂

F1Ctrl sperm

̂ ̂

̂

F1DSS sperm

̂

̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂ ̂̂ ̂̂ ̂ ̂

significant DM site