www.sciencemag.org/cgi/content/full/345/6204/1251086/DC1

Supplementary Materials for

Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity

Sadia Saeed, Jessica Quintin, Hindrik H. D. Kerstens, Nagesha A. Rao, Ali Aghajanirefah, Filomena Matarese, Shih-Chin Cheng,

Jacqueline Ratter, Kim Berentsen, Martijn A. van der Ent, Nilofar Sharifi, Eva M. Janssen-Megens, Menno Ter Huurne, Amit Mandoli, Tom van Schaik,

Aylwin Ng, Frances Burden, Kate Downes, Mattia Frontini, Vinod Kumar, Evangelos J. Giamarellos-Bourboulis, Willem H. Ouwehand, Jos W. M. van der Meer,

Leo A. B. Joosten, Cisca Wijmenga, Joost H. A. Martens, Ramnik J. Xavier, Colin Logie,* Mihai G. Netea,* Hendrik G. Stunnenberg*

*Corresponding author. E-mail: h.stunnenberg@ncmls.ru.nl (H.G.S.); mihai.netea@radboudumc.nl (M.G.N.); c.logie@ncmls.ru.nl (C.L.)

Published 26 September 2014, Science 345, 1251086 (2014) DOI: 10.1126/science.1251086

This PDF file includes:

Materials and Methods Figs. S1 to S9 References

Other Supporting Online Material for this manuscript includes the following: (available at www.sciencemag.org/content/345/6204/1251086/suppl/DC1)

Tables S1 to S6 as Excel files

2

Materials and Methods

Healthy volunteers

Healthy volunteers gave written informed consent with regard to scientific use

(Sanquin Blood bank, Nijmegen, the Netherlands)

Reagents

Ficoll-Paque (GE Healthcare) was used to isolate PBMCs by differential

centrifugation. The RPMI 1640 medium (Sigma-Aldrich) was supplemented with 10

µg/mL gentamycin, 10 mM L-glutamine and 10 mM pyruvate (Life Technologies).

β1,3(D)glucan (β-glucan) was kindly provided by Prof. David Williams (University

of Tennessee). Pam3Cys were purchased from EMC microcollections and LPS (E.

coli serotype 055:B5) were purchased from Sigma-Aldrich, with an additional

purification step (64). For “inhibition” experiments H89 (Sigma-Aldrich, B1427),

propranolol (Sigma-Aldrich, P0884), 2’,5’-dideoxyadenosine (Sigma-Aldrich,

D7408), human recombinant IL10 (Merck-Millipore) were used.

Isolation of primary monocytes for genome-wide sequencing analysis

Monocytes were obtained by depletion of CD3, CD19 and CD56 positive cells from

PBMCs obtained upon Ficoll isolation of a buffycoat. CD3 MicroBeads (130-050-

101), CD19 MicroBeads (130-050-301) and CD56 were purchased at Miltenyi Biotec

and used according to the manufacturer protocol. Efficacy of depletion was controlled

by flow cytometry (Fig. S1A). Samples were measured on a FACS FC500, and data

were analyzed using the CXP software (Beckman Coulter). Before and after the

depletion, cells were incubated in PBS supplemented with 1% PBA and the following

antibodies: anti-CD45-PeCy7 (Beckman Coulter), anti-CD14-FITC (Beckman

Coulter), anti-CD3-PC5 (Biolegend (ITK)), anti-CD19-ECD (Beckman Coulter) and

anti-56-PE (BD Bioscience). For the additional transcriptomic analysis of the pop-1

and pop-2 samples (Fig. S1A), additional CD14 positive cells selection was

performed on the CD3-, CD19- and CD56- population using CD14 MicroBeads (130-

050-201) from Miltenyi Biotec. By this selection monocytes population (Mo) was

further defined into monocytes highly expressing CD14 (pop-1, ~88% of the Mo) and

the rest of the Mo (pop-2, ~12% of the Mo) (Fig. S1 A, B).

3

For ChIP analysis and DNase I treatment, 10x106 of CD3-,CD19- and CD56-

depleted monocytes were plated on 100 mm dishes. Monocytes were pre-incubated

either with cell culture medium (RPMI), β-glucan (5 µg/mL) or with LPS (100

ng/mL), for 24 hours in a total volume of 10 mL. After wash-out, cells were cultured

in RPMI supplemented with 10% human pooled serum containing homeostatic

levels of M-CSF that induces monocyte differentiation. Cell samples were

collected at 0 hours and on 6th day, and counted before further treatment for

chromatin immunoprecipitation or DNase I treatment.

For RNASeq 5x106 of CD3-CD19-CD56- depleted monocytes were seeded on 60 mm

dishes. Monocytes were pre-incubated either with cell culture medium (RPMI), β-

glucan (5 µg/mL) or with LPS (100 ng/mL) for 24 hours. After wash-out, cells were

cultured in RPMI supplemented with 10% human pool serum and further RNA

collection and library preparation (0 hours and 5 days after treatment) was done

according to standard BLUEPRINT protocols (www.blueprint-epigenome.eu) and

Illumina library preparation protocol.

For cytokines production, 1x104 of CD3-CD19-CD56- depleted monocytes were

plated out in a 96 well flat bottom plate. Monocytes were pre-incubated as above for

24 hours in a total volume of 200 µL. After a wash-out, cells were cultured in RPMI

supplemented with 10% human pooled serum. Cells were then subjected after 5 days

to a second stimulation for 24 hours with (LPS 10 ng/mL) or P3C (10 µg/mL) for

cytokine measurements with ELISA (Fig. S4E). Each volunteer used in this assay

presented an efficient training or tolerant phenotype (data not shown).

Inhibition experiments

PBMCs were diluted to a concentration of 5x106 cells per mL and 100 µL of this

suspension was added to the well of a 96-well plate. PBMCs were incubated for 1

hour at 37°C in 5% CO2 and adherent monocytes were selected by washing out non-

adherent cells with pre-warmed PBS. For training, monocytes were pre-incubated

with β-glucan (5 µg/mL) for 24 hours. For tolerance, cells were pre-incubated with

LPS (100 ng/mL) for 24 hours. Cells were then washed and maintained in RPMI

supplemented with 10% human pool serum. For cytokine measurements cells were

subjected to a second stimulation of cytokine production after a period of 6 days with

various stimuli in a volume of 200 µl of medium (LPS 10 ng/mL, Pam3Cys 10

4

µg/mL). After 24 hours, supernatants were collected and stored at -20°C until

assayed.

In the “inhibition of training” experiments, before the priming with β-glucan, adherent

monocytes were pre-incubated for 1 hour with H89 (30 µM), 2’,5’-dideoxyadenosine

(1 mM) or propranolol (200 µM).

Cytokine assays

TNFα, IL-6, Il-1β and IL10 were measured using ELISA according to the

manufacturer protocol (IL6, IL10: Sanquin; and TNFα, IL1β: R&D). For cytokines

production assays and qPCR analysis, the differences between groups were analyzed

using the Wilcoxon signed-rank test (unless otherwise stated). The level of

significance was defined as a p-value < 0.05.

Western-blot

Immunoblotting experiments were performed as previously described (17). The

quantification of protein expression was performed by densitometry (GS-670, Bio-

Rad, Hercules, CA) and signal analysis using Molecular Analyst software (Bio-Rad).

The ratio between the intensity of the protein of interest and β-actin was calculated.

The activation of caspase-1 was assessed by calculating the ratio between the p10 and

p45 fragments.

RNA-seq alignment and expression analysis

RNA-seq reads were aligned using GSNAP (65) using non-default parameters -m 1 -

N 1 -n 1 -Q -s Ensembl_splice_68. RNA-seq library data were initially subjected to a

quality control step, where, based on read distribution over the annotated genome,

libraries that are outliers were identified and discarded from further analysis. For

expression analyses reads were aligned to the Ensembl v68 human transcriptome

using Bowtie. Quantification of gene expression was performed using MMSEQ (31).

Differential expression

Differential expression was determined using MMDIFF (32). A two model

comparison was used to identify differentially expressed genes that confer cellular

identity Mo/Mf. Under the null-model the mean expression level is the same in both

cell types and under the alternative model the mean expression level is allowed to

5

differ between the two cell types. Genes with a larger posterior probability for the

second model, an RPKM value greater than 2 in any of Mo or Mf and minimally a

two-fold expression change were considered as differentially expressed.

Expression changes related to LPS and β-glucan were studied using a 15-model

comparison, a.k.a. polytomous comparison. Under the null-model, it is assumed that

the mean expression level is the same across differentiation and micro-organismal

ligand response. In the most complex model the mean expression level is assumed to

be different for all conditions (Mo, Mf, LPS-Mf, BG-Mf). Additionally, we defined 4

models that assume a mean expression in one condition that differs from the equal

expression in the three others, 3 models that assume two pairs of conditions with

equal mean expression within a pair but different between the two pairs and, 6 models

that assume the remaining possibility that two conditions have equal mean expression

while the two others have different mean expression levels that also differ from each

other. We calculated the Bayes factor for each model by comparing the differential

expression models to the (reference) null-model and applied Bayes’ theorem to

compute the posterior probability of each model per gene. The expression change

directionality of a gene was determined based on the model with the highest posterior

probability having to be at least 0.35 for the gene to be considered for downstream

analyses.

Condensing genes into six expression modules

Genes that were successfully assigned to expression models that assume differential

expression in monocyte-derived macrophages were grouped based on their expression

in LPS-Mf and BG-Mf relative to Mf. We applied a three group classification (up,

down and equal) to record the direction of expression change in LPS-Mf and BG-Mf

relative to Mf, resulting in eight possible expression scenarios. Scenarios “LPS-Mf is

down, BG-Mf is up” and “LPS-Mf is up, BG-Mf is down” were dropped because of

low (3 and 5 respectively) gene numbers. The six remaining scenarios were relabeled

as expression modules: M1=”LPS-Mf is down, BG-Mf is down”, M2=LPS-Mf is

down, BG-Mf is equal”, M3=”LPS-Mf is equal, BG-Mf is down”, M4=” LPS-Mf is

equal, BG-Mf is up”, M5=”LPS-Mf is up, BG-Mf is equal”, M6=”LPS-Mf is up, BG-

Mf is up.

ChIP-seq

6

H3K4me1, H3K4me3 and H3K27ac antibodies were extensively characterized (see

www.blueprint-epigenome.eu) and used for ChIP according to standard BLUEPRINT

protocols (www.blueprint-epigenome.eu).

Peak Calling and identification of differential regulatory regions

For peak calling the BAM files were first filtered to remove the reads with mapping

quality less than 15, followed by fragment size modeling

(http://code.google.com/p/phantompeakqual-tools/). The peak calling algorithm

MACS2 (http://github.com/taoliu/MACS/) was used to detect the binding sites for the

three studied histone marks at p-value of 10-10. H3K4me1 peaks were called using

the broad setting of MACS2 while H3K27ac and H3K4me3 were called using the

default (narrow) setting.

In order to identify differential H3K27ac-bound regulatory elements, initially,

H3K27ac peaks from different treatments were merged and sequenced reads were

counted from normalized data files (down scaling). H3K27ac peaks that do not

overlap with H3K4me3 marked promoters (transcription start site±2.5kb) were

regarded as distal regulatory elements (ACe’s) whereas those overlapping with

H3K4me3 marked promoters were labeled as active promoters (ACp’s, Table S3).

H3K27ac regions that show a change of mean ± 2x median absolute deviation (p-

vlaue < 0.05) acetylation signal in each data set were regarded as dynamic acetylated

regions. K-means clustering (Pearson correlation) was performed to identify different

clusters dynamic H3K27ac regions (ACe1-5 and ACp1-3). The same approach was

used to identify dynamic clusters of H3K4me1 (MM) or H3K4me3 (TM) marked

regions that lack H3K27ac (Fig. S2).

Principal Component Analysis

We performed principal component analysis (PCA) using prcomp function in R to

visualize differences between the cell types and examine reproducibility between

samples of the same cell type. Treating each histone modification separately, we

computed combinations of enrichment levels that capture a large proportion of the

variation between cell types (PC1 and PC2). This revealed that at the single histone

modification level monocytes and macrophages are distinguishable and cluster by

pattern similarity of a given cell type, suggesting a high degree of reproducibility

between the samples.

7

DNase I-seq

DNase I libraries were prepared for monocytes (Mo) and all three types of

differentiated macrophages (Mf, LPS-Mf and BG-Mf) as described (66). In brief

nuclei were isolated using Buffer A (15 mM NaCl, 60 mM KCl, 1 mM EDTA, pH

8.0, 0.5 mM EGTA, pH 8.0, 15 mM Tris-HCl, pH 8.0, 0.5 mM Spermidine)

supplemented with 0.015 % IGEPAL CA-630 detergent. DNase I treatment was done

for 3 minutes and the reaction was stopped with stop buffer (50 mM Tris-HCl, pH 8,

100 mM NaCl, 0.10 % SDS, 100 mM EDTA, pH 8.0, 1 mM Spermidine, 0.3 mM

Spermine). The sample was further fractionated on 9% Sucrose gradient for 24 hours

at 25000 rpm at 16 ºC. Fractions containing fragments smaller than 1kb were purified

and processed according to the Illumina library preparation protocol.

Hotspots analysis

DNase I hotspots (DHS) were scanned for the presence of TF-motifs, where a TF-

motif weight matrix was available. DHS of this study Mo, Mf, LPS-Mf and BG-Mf

(four donors merged) were collapsed and merged into a single set of DHS regions,

which were subsequently scanned for TF-motif occurrence. A non-redundant

collection of TF-motifs was generated from TF-motif repositories JASPAR,

UniPROBE, Jolma/Taipale and ht-SELEX

(http://dx.doi.org/10.6084/m9.figshare.819997). Out of a collection of 1406 motifs

(48), 544 motifs for which the assigned TF is expressed (>2 RPKM) in monocytes or

monocyte-derived macrophages, were obtained. The merged set of DHS regions were

scanned for TF-motif presence using Gimme motifs (48) with dynamic TF-motif

scoring cut-offs targeting a false discovery rate (FDR) of 0.01. To look at the motif

enrichments in each epigenetic cluster DHS were assigned to epigenomic clusters

ACe1-5 by intersection. Motif occurrences were acquired by intersection of the

assigned DHSs with the motif scanning results on the merged set of DHS. Enrichment

of motifs in each of the ACe’s was defined by applying a hypergeometric test using

the motif frequency in all distal (non-dynamic) H3K27ac regions as the background.

This results in TFs that putatively regulate the activity of the distal regulatory regions.

Subsequently, hierarchical clustering was performed (Euclidian distance) using the

motif occurrence frequencies in the ACe cluster. TF motifs that satisfy an arbitrary

8

cutoff of > 5% motif presence in at least one ACe cluster were used to generate the

heat map (Fig. 4D).

Animals

C57BL/6J female mice between 8 and 12 weeks of age were used (Jackson

Laboratories). Experiments were approved by the Ethics Committee on Animal

Experiments of the University of Athens. Mice were injected with a sublethal dose of

C. albicans yeast (2×104 CFU/mouse) in a 100 µL volume of sterile pyrogen-free

phosphate-buffered saline (PBS) or with saline alone. Seven days later, mice were

infected intravenously with a lethal dose of C. albicans yeast (2×106 CFU/mouse).

Animals received a daily intraperitoneal injections of either saline or propranolol (1

mg/kg) one day prior the sub-lethal dose C. albicans yeast up to 3 days after the lethal

Candida injection. Survival upon the lethal infection was monitored daily. Survival

curves were created using the product limit method of Kaplan and Meier. Comparison

of two survival curves was performed using the Logrank test. The level of

significance between groups was set at p-value < 0.05.

DNA replication analysis

EdU-incorporation was performed using the Click-iT EdU Imaging kit (Invitrogen)

according to the supplied protocol. Isolated monocytes were seeded on cover slips

placed in 6 well cell culture plates at density of 105 cells per each well. A day before

the EdU assay, HeLa cells were seeded at 25% confluence into control wells

containing cover slips. Both monocytes and HeLa cells were then exposed to EdU for

7 hours, followed by cell fixation, permeabilization and imaging according to the

manufacturer’s protocol.

9

Supplementary figure legends: Fig. S1. Representative flow cytometric analysis of CD3, CD56, CD19, and CD14

positive cells. (A) Cells were gated on CD45+ populations before and after

Microbead lymphocyte depletion (CD3, CD56, CD19) of PBMCs obtained from the

buffy coats. This Monocytes population (Mo) was further fractionated based on CD14

positive Mo (pop-1, ~88% of the Mo) and the rest of the Mo (pop-2, ~12% of the

Mo). (B) Transcriptome analysis of Mo, Mf, LPS-Mf and BG-Mf cells from different

donors. The most differential cell surface markers are plotted for cell populations pop-

1 and pop-2 along with the complete set of cell states obtained from the four donors

included for the transcriptome and polytomous analyses.

Fig. S2. Dynamic H3K4me DNA regions stratified by k-means clustering. (A)

H3K4me1-marked distal elements. (B) H3K4me3-marked elements. (C) Provenance

of the epigenetic clusters as assigned by Blueprint ChromHMM data for primary

human monocytes

(http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/current_release/homo_sapiens/hu

b/hub.txt;. For comparison, the whole-genome distribution of epigenetic states is

rendered.

Fig. S3. Comparison of three epigenetic marks in monocyte and macrophage

populations. PCA plots comparing positively and negatively selected monocytes and

macrophages as a function of the histone marks H3K4me3, H3K4me1 and H3K27ac.

Note that the plots discriminate between monocyte and macrophage as well as

macrophages with different differentiation stimuli. (A) Tags were counted on a

merged peak set (minimal two occurrences) and normalized for effective library size

and peak length (B) The top 5000 most variable peaks were selected from the merged

peak set. The PCA was performed with the prcomp function in R.

Fig. S4. Attenuation of innate immune defense pathways in resident

macrophages compared to circulating monocytes. Human primary monocytes were

obtained from PBMCs of healthy volunteers (Mo). Primary monocytes were

maintained in cell culture medium supplemented with 10% of human serum for a

week to obtain macrophages (Mf). (A) Secreted TNF-α, IL-6, IL-10, and IL-1β in

monocytes or macrophages upon 24 hours of LPS stimulation as determined by

ELISA. (B) Monocytes or macrophages were either stimulated with LPS alone (∅) or

co-stimulated with LPS and different doses of human IL10 recombinant protein

10

(rIL10). IL-6 production in supernatants was determined after 24 hours by ELISA.

Note the differential response at 1 ng/ml LPS. (C) Genome browser screen shot of the

IL-1β. Note the decreased H3K27ac signal over the promoter region of IL-1β in day 6

macrophages (Mf) as compared to naive monocytes (Mo) as well as an accompanying

decrease in the gene expression. (D) Caspase-1 activation in supernatants of

unstimulated monocytes and macrophages of three independent healthy volunteers

was analyzed by western blot using an antibody against the Caspase-1 p10 cleavage

product (upper panel). The caspase-1 activation ratio (p45/p10) is represented in the

lower panel. (E) Diagram showing the time line of the in vitro training and tolerance

experiment. Pure human primary monocytes are pre-exposed either to culture

medium, β-glucan (training) or to Lipopolysaccharide (LPS) for 24 hours (1st

stimulation). After the 1st stimuli is washed out the cells are further incubated for 5

days in culture medium supplemented with 10% human pooled serum. Cells are then

collected for genome-wide analysis. In parallel, a second in vitro stimulation (2nd

stimulation) of cytokine production with different pattern recognition receptor (PRR)

ligands is performed for an additional 24 hours.

Fig. S5. Absence of DNA replication in BG trained cells. The fluorescent

nucleotide analog EdU was used to label chromosomal DNA undergoing replication.

While HeLa cells efficiently incorporated EdU, in vitro monocyte-derived

macrophages (Mf) did not and nor did the β-glucan trained (BG-Mf) cells.

Fig. S6. Potentially drugable targets that are differentially regulated in

differentiating monocytes. (A-C) Kinases (A), G-coupled receptors (B) and histone

modifying enzymes (C) that display at least a 4-fold difference (RPKM) between Mo,

Mf, LPS-Mf and/or BG-Mf are plotted.

Fig. S7. Circadian cycle factors. The indicated GO terms were queried and

associated factors were stratified in four categories; (A) factors whose transcript level

is higher in monocytes than the derived macrophages, (B) those whose expression is

higher in macrophages, (C) those whose expression is relatively constant and (D)

those with low transcript levels.

Fig. S8. cAMP pathway factors. The average expression level (RPKM) of a selected

group of cAMP signaling pathway factors is shown. Error bars represent the standard

deviation across the four donors.

Fig. S9. The cAMP dependent protein kinase (PKA) inhibitor H89 had no effect

on the LPS-triggered immune response. The PKA inhibitor H89 did not modulate

11

the induction of IL-6 and TNFα, assayed as the response to LPS direct stimulation of

monocytes; p>0.05 (Wilcoxon signed rank test). Data show the cytokine production

(ELISA) upon LPS stimulation in the presence (H89) or absence (∅) of the PKA

inhibitor as mean ± SEM, n = 4 in 2 separate experiments. p>0.05.

Supplementary Tables

TableS1: List of genes that are differentially expressed during monocyte (Mo) to

macrophages (Mf) differentiation (Fig. 1J).

TableS2: Extended list of gene ontology terms associated with differentially

expressed genes during monocyte (Mo) to macrophage (Mf) differentiation (Fig 1J)

and genes assigned to the expression modules M1-6 (Fig. 3B).

TableS3: List of genomic loci associated with each epigenomic clusters (Fig. 2A-B,

Fig S2A-B).

TableS4: List of genes that fit into differential expression models and that are

assigned to expression modules (Fig. 3A).

TableS5: List of the 1581 human DNA sequence-specific transcription factors used in

this study (Fig. 4A).

TableS6: List of transcription factor motifs that are enriched in dynamic distal

regulatory elements (ACe, Fig. 4D).

pop−2

pop−2

pop−1

pop−1

Mo

Mo

Mo

Mo

Mf

Mf

Mf

Mf

LPS

-Mf

LPS

-Mf

LPS

-Mf

LPS

-Mf

BG

-Mf

BG

-Mf

BG

-Mf

BG

-Mf

MS4A6ACD36CD14CD163CXCR3KITCD200R1LCD27TNFSF4IL5RAKELIL18R1GP1BACD200R1CD177IL2RACLEC4CENPP3LILRA4SLC4A1CD3EIL18RAPGYPAITGA2BITGB3CD96GYPBICOSLGCD300LBTNFSF8CD1DCD93THBDCEACAM1IL3RACEACAM8MS4A3CEACAM6CCR3

−5

0

5

pop-1

pop-2

Total PBMCs Depleted (Mo) Total PBMCs Depleted (Mo)

A

B

Figure S1

A B

MM1

MM2

MM3

MM4

MM5

MM6

TM1TM2

TM4TM5

TM3

Transcribed region Inactive enhancer Active enhancer

Active promoter Repressed promoter

C

0

10

20

30

40

50

60

70

80

90

100

Whole genome

MM1 MM2 MM3 MM4 MM5 MM6 TM1 TM2 TM3 TM4 TM5

Heterochromatin C

Figure S2

H3K27ac H3K4me1 H3K4me3 DNaseIH3K27acDNaseI

Mo

Mf

LPS

-Mf

BG

-Mf

Mo

Mf

LPS

-Mf

BG

-Mf

Mo

Mf

LPS

-Mf

BG

-Mf

Mo

Mf

LPS

-Mf

BG

-Mf

Mo

Mf

LPS

-Mf

BG

-Mf

Mo

Mf

LPS

-Mf

BG

-Mf

PCA H3K4me1 − top 5000 peaks

PCA H3K27ac − top 5000 peaks

PCA H3K4me3 − top 5000 peaks

PCA All H3K27ac peaks

A B

PCA All H3K4me3 peaks

Cell typeMo: CD3- CD56- CD19-Mo: CD14+ CD16-MfLPS-Mf

BG-MfMf−M0Mf−M1Mf−M2

Figure S3

PC

2 (e

xpla

ins

21.8

%)

PC1 (explains 47.7%)PC1 (explains 27.3%)

PC

2 (e

xpla

ins

17.8

%)

PC1 (explains 38.0%)

PC

2 (e

xpla

ins

20.4

%)

PCA All H3K4me1 peaks

PC1 (explains 29.5%)

PC

2 (e

xpla

ins

16%

)

PC1 (explains 62.8%)

PC

2 (e

xpla

ins

15.1

%)

PC1 (explains 57.4%)

PC

2 (e

xpla

ins

14.2

%)

Cell typeMo: CD3- CD56- CD19-Mo: CD14+ CD16-MfLPS-Mf

BG-MfMf−M0Mf−M1Mf−M2

A B

2000

1500

1000

500

0TNFα IL6 IL1β IL10

LPS

pg /

ml

monocytes (Mo)macrophages (Mf)

monocytes (Mo)macrophages (Mf)

LPS

rIL10 (ng/ml): ∅ 0.1 1 10 1000

50

100

150

% o

f IL6

pro

duct

ion

C D

p45

p10

#1 #2 #3 #1 #2 #3 monocytes macrophages

casp

ase-

1

0.0

0.1

0.2

0.3

0.4

#1 #2 #3 #1 #2 #3

monocytes (Mo)macrophages (Mf)

Cas

pase

-1 a

ctiv

atio

n ra

tio

E

cytokines

training / tolerance scheme

Monocytes(Mo) +

1st stimuli 2nd stimuli 37°C

Wash-out & Resting time

days 0 1 7 2 3 4 5 6

sequencing

chr2: 10 kb hg19113,590,000 113,600,000

IL1B

250 _

1 _250 _

1 _200 _

1 _200 _

1 _80 _

1 _80 _

1 _60 _

-2500 _60 _

-2500 _

Mo

Mf

Mo

Mf

Mo

Mf

Mo

Mf

Figure S4

H3K

27ac

H3K

4me1

H3K

4me3

RN

Ase

q

EdU - DIC

EdU - DAPI

Hela Control (Mf) Trained ( BG-Mf)

A

Figure S5

Figure S6

RPS6KA1LIMK1DAPK1NEK6MATKCSF1RCLK1PRKACBCDK4DYRK4IKBKEMAPKAPK2DYRK1ARPS6KA2PINK1MAP3K8CSNK1DSTK38LPLK3MERTKPRKCHADCK2DYRK2FYNOXSR1TRIB1STK17BFLT1EIF2AK3SIK3RIPK2MAP2K3TNIKRNASELERN1SNRKEEF2KMAP3K6PSKH1LIMK2EPHB2LRRK2PDIK1LPIM2PRKCEMYLKLATS2SCYL3MAPK7PLK2CDK18CDK1IRAK2BMPR1ANEK11NUAK2NEK1PHKG1WEE1METULK1JAK3MAP2K6LCKPRKD2GRK5SIK1AXLYES1ERN2MAP4K1CAMK2AFGFR2MAKEPHA4ALPK2NTRK1

Mo

Mf

LPS

-Mf

BG

-Mf

KinasesA B C

FPR3C3AR1GPR183CXCR4FPR1GPR34CMKLR1GPR162P2RY11PTGER4SUCNR1EMR2ADORA3GPR84GPR35GPR132GPR68P2RY6FFAR2P2RY13GPR173TBXA2RHTR7S1PR1HRH2GPR18CYSLTR2GPBAR1RXFP4S1PR3HRH4ADORA2AOR2B11HCAR3S1PR5P2RY12

Mo

Mf

LPS

-Mf

BG

-Mf

GPCR’s Chromatin modifiers

Mo

Mf

LPS

-Mf

BG

-Mf

ELP3EP300CREBBPHDAC8HDAC9MLL5SMYD5PRDM1SETD1BPRDM8JMJD1CJARID2IL4I1JMJD6JHDM1DMINAJMJD4PAOXKDM8KDM6BPRMT2PRMT6

-3 2 8

Log RPKM2

-3 2 8

Log RPKM2

-3 2 8

Log RPKM2

HAT

HDAC

HMT

KDM

PRMT

Circadian cycle genes with higher expression in Monocytes

Figure S7

Mo

Mf

LPS-Mf

BG-Mf

Mo

Mf

LPS-Mf

BG-Mf

Mo

Mf

LPS-Mf

BG-Mf

Query: GO:0007623 GO:0010841 GO:0022410 GO:0032922 GO:0042320 GO:0042321 GO:0042322 GO:0042323 GO:0042745 GO:0042747 GO:0042748 GO:0045187 GO:0045188 GO:0045475 GO:0045938 GO:0046005 GO:0046010 GO:0048512 GO:0060086 GO:0097167

0 200 400 600 800

1000 1200

CST3

NCOR1

PTEN

DHX9

RBM4

ARNTL JU

N

SETX

GSK3B

METTL3

CRY1 ADA

HEBP1

GHRL

RBM4B

HOMER1

RPK

M

Circardian cycle genes that are relatively constant

0 50

100 150 200 250 300 350 400 450 500

DDX5

JUND

DYRK1A

CSNK1D

PTGDS

BHLHE40

NFIL3

PER1

ADORA2A

CRY2

EGR1

EGR3

BTBD9

PER2

PROK2

TYMS IL6

RPK

M

1500

Mo

Mf

LPS-Mf

BG-Mf

0

20

40

60

80

100

120

140

HDAC3

CDK4 ADK

BHLHE41

NAGLU

KCNMA1

CLOCK

HS3ST2

HTR7

ARNTL2

PER3

TIMELE

SS

RPK

M

Circadian cycle genes with higher expression in Macrophages

0 1 2 3 4 5 6 7 8 9

10

TNFRSF11A

TPH1

ADIPOQ

TPH2

DRD4 CRX

RORA

NR1D1

RPK

M

Circardian cycle genes with a relatively low expression

0

100

200

300

400

500

600

700

GNB4

GNB5

GNA13

GNA15

GNAS

GNAI2

GNAI3

GNG2

GNG5

GNG10

GNGT2

GNG11

ADCY7

ADCY3

RP

KM

Mo

Mf

LPS-Mf

BG-Mf

0

50

100

150

200

250

300

350

PDE4B

PDE7A

PDE4D

PDE3B

PDE1B

PDE2A

PDE6B

AKAP8

AKAP8L

AKAP11

PRKAR1A

PRKAR2A

PRKAR2B

PRKAA1

PRKACB

PRKX

PRKACA

RP

KM

Mo

Mf

LPS-Mf

BG-Mf

0

100

200

300

400

500

600

700

ATF4

CREM

ATF3

CREB1

ATF6B

ATF6

ATF1

ATF2

CREB3

ATF5

CREB3L2

ATF7

CREB5

CREB3L4

RP

KM

Mo

Mf

LPS-Mf

BG-Mf

Figure S8

Intracellular cAMP signal transduction molecules

Figure S9

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