ResourceAn IL-27-Driven Transcriptional Network Identifies
Regulators of IL-10 Expression across T Helper Cell
SubsetsGraphical AbstractTemporal RNA profiling TFs associated with 
Il10 expression 
Naive T cell Tr1
+ IL-27 IL-10+ IL-10-
IL-10 Th1 Th1
0h 72h Th2 Th2
17 time points
Th17 Th17
Prdm1 Treg Treg
Maf ... In vitro & in vivoTranscriptional network Meta analysis of in-house 
driven by IL-27 and public RNA-seq
 Colon T cells scRNA-seq
TF
TF Normal colon
CCR2
TF WT Treg
TF Il10 IL-10
TF TF CCR7 CD103
TF Prdm1/Maf XCL1
DKO Treg
RNA-seq of 16 TF KOs CXCL-10 ColitisLT-α
IFN-γHighlightsd IL-27-driven transcriptional network in CD4 T cells unravels
key Il10 regulators
d Systematic characterization of the function of 16 Il10
regulators by RNA-seq
d Identification of transcription factors associated with Il10 in
multiple T cell subsets
d Prdm1 and Maf are critical for Il10 production and intestinal
immune homeostasisZhang et al., 2020, Cell Reports 33, 108433
November 24, 2020 ª 2020 The Authors.
https://doi.org/10.1016/j.celrep.2020.108433Authors
Huiyuan Zhang, Asaf Madi, Nir Yosef, ...,
Ana C. Anderson, Aviv Regev,
Vijay K. Kuchroo
Correspondence
asafmadi@tauex.tau.ac.il (A.M.),
aregev@broadinstitute.org (A.R.),
vkuchroo@evergrande.hms.harvard.edu
(V.K.K.)
In Brief
Zhang et al. construct a transcriptional
network for IL-27-mediated Il10
production in CD4 T cells, characterize
the function of 16 Il10 regulators, and
uncover the role of two transcription
factors, Prdm1 and Maf, in driving Il10
production in all T helper cells and in
maintaining immune homeostasis in the
colon.ll
OPEN ACCESS
llResource
An IL-27-Driven Transcriptional Network
Identifies Regulators of IL-10 Expression
across T Helper Cell Subsets
Huiyuan Zhang,1,14 Asaf Madi,1,2,14,* Nir Yosef,1,3 Norio Chihara,1,4 Amit Awasthi,1,5 Caroline Pot,1,6 Conner Lambden,1,7
Amitabh Srivastava,8 Patrick R. Burkett,1,9 Jackson Nyman,1,7 Elena Christian,1,7 Yasaman Etminan,1 Annika Lee,1
Helene Stroh,1 Junrong Xia,1 Katarzyna Karwacz,1,10 Pratiksha I. Thakore,7 Nandini Acharya,1 Alexandra Schnell,1
ChaoWang,1 Lionel Apetoh,1,11 Orit Rozenblatt-Rosen,7 AnaC. Anderson,1 Aviv Regev,7,12,13,* and Vijay K. Kuchroo1,7,15,*
1Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and
Women’s Hospital, Boston, MA, USA
2Department of Pathology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
3Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, CA,
USA
4Division of Neurology, Kobe University Graduate School of Medicine, Kobe, Japan
5Center for Human Microbial Ecology, Translational Health Science and Technology Institute(an autonomous institute of the Department of
Biotechnology, Government of India), NCR Biotech Science Cluster, Faridabad, India
6Laboratories of Neuroimmunology, Division of Neurology and Neuroscience Research Center, Department of Clinical Neurosciences,
Lausanne University Hospital, Lausanne, Switzerland
7Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
8Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
9Biogen, 300 Binney St., Cambridge, MA, USA
10Regeneron Pharmaceuticals, 777 Old Saw Mill River Road, Tarrytown, NY, USA
11INSERM, U1231, Dijon, France
12Howard Hughes Medical Institute, Department of Biology, Koch Institute and Ludwig Center, Massachusetts Institute of Technology,
Cambridge, MA, USA
13Genentech, 1 DNA Way, South San Francisco, CA, USA
14These authors contributed equally
15Lead Contact
*Correspondence: asafmadi@tauex.tau.ac.il (A.M.), aregev@broadinstitute.org (A.R.), vkuchroo@evergrande.hms.harvard.edu (V.K.K.)
https://doi.org/10.1016/j.celrep.2020.108433SUMMARYInterleukin-27 (IL-27) is an immunoregulatory cytokine that suppresses inflammation through multiple mech-
anisms, including induction of IL-10, but the transcriptional network mediating its diverse functions remains
unclear. Combining temporal RNA profiling with computational algorithms, we predict 79 transcription fac-
tors induced by IL-27 in T cells. We validate 11 known and discover 5 positive (Cebpb, Fosl2, Tbx21, Hlx,
andAtf3) and 2 negative (Irf9 and Irf8) Il10 regulators, generating an experimentally refined regulatory network
for Il10. We report two central regulators,Prdm1 andMaf, that cooperatively drive the expression of signature
genes induced by IL-27 in type 1 regulatory T cells, mediate IL-10 expression in all T helper cells, and deter-
mine the regulatory phenotype of colonic Foxp3+ regulatory T cells. Prdm1/Maf double-knockout mice
develop spontaneous colitis, phenocopying ll10-deficient mice. Our work provides insights into IL-27-driven
transcriptional networks and identifies two shared Il10 regulators that orchestrate immunoregulatory pro-
grams across T helper cell subsets.INTRODUCTION
Interleukin-27 (IL-27) is an immunoregulatory cytokine that regu-
lates immune responses bymultiple mechanisms, including inhi-
bition of differentiation of effector T cell subsets (Artis et al., 2004;
Stumhofer et al., 2006; Villarino et al., 2006; Yoshida and Hunter,
2015), induction of a ‘‘co-inhibitory’’ gene module to promote
T cell exhaustion (Chihara et al., 2018; DeLong et al., 2019),
and polarization of Foxp3+ regulatory T (Treg) cells to a T-bet+C
This is an open access article under the CC BY-Nsubset that specializes in controlling Th1 immunity (Hall et al.,
2012). In addition, we and others have described that IL-27
can differentiate naive T cells into type 1 regulatory T (Tr1) cells
(Awasthi et al., 2007; Fitzgerald et al., 2007; Stumhofer et al.,
2007; Wang et al., 2011), a Foxp3
 IL-10-producing regulatory
cell population identified in mouse and human, that suppresses
tissue inflammation, autoimmune reactions, and graft versus
host disease (GVHD) largely via IL-10 (Roncarolo et al., 1988).
IL-27 has the unique capability to induce IL-10 production fromell Reports 33, 108433, November 24, 2020 ª 2020 The Authors. 1
C-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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OPEN ACCESS Resourcea wide range of cell types, including Th1, Th2, Th17, and Treg
cells (Awasthi et al., 2007; Fitzgerald et al., 2007; Hall et al.,
2012; Stumhofer et al., 2006, 2007). Consistent with these obser-
vations, Il27ra
/
 T cells have defects in producing IL-10 in vitro
and in vivo (Batten et al., 2008). Il27ra
/
 mice suffer from lethal
immunopathology in parasitic diseases, which is reminiscent of
Il10
/
mice (Villarino et al., 2003), and they aremore susceptible
to experimental autoimmune encephalomyelitis (Batten et al.,
2006; Fitzgerald et al., 2007). The molecular mechanisms by
which IL-27 induces these diverse regulatory functions in
T cells are not fully understood.
The anti-inflammatory cytokine IL-10 has an indispensable
role in maintaining immune tolerance and limiting immunopa-
thology during homeostasis, inflammation, infection, and auto-
immune diseases (Iyer and Cheng, 2012; Ouyang et al., 2011).
Mutations in IL-10 or IL-10R lead to early-onset inflammatory
bowel disease (IBD) in humans (Shim, 2019), and mice deficient
in IL-10 or IL-10R develop spontaneous colitis (Ku€hn et al., 1993;
Spencer et al., 1998). Importantly, all T helper cell subsets,
including Th1, Th2, and Th17 cells, can produce IL-10 tomitigate
hyperactive immune responses (Gabrysová et al., 2014). Several
transcription factors (TFs) have been shown to regulate IL-10
(Gabrysová et al., 2014; Zhang and Kuchroo, 2019). However,
a comprehensive model that systemically examines the dynamic
transcriptional network that regulates Il10 in a temporal context
of induction and maintenance is lacking.
Here we combined computational algorithms with high-reso-
lution temporal transcriptional profiling to predict the TF network
driven by IL-27 during Tr1 differentiation. Network analysis sys-
tematically identified regulators for Il10 and highlighted Prdm1
and Maf as two central nodes of the Il10 regulatory circuits that
cooperatively promoted IL-10 production not only in Tr1 cells
but also in Th1, Th2, Th17, and Treg cells. Genetic deletion of
Prdm1 andMaf in T cells (Prdm1/Maf DKO), but not either alone,
led to spontaneous colitis in mice that exhibits features of human
IBD, underscoring the importance of Prdm1 andMaf crosstalk in
regulating immune homeostasis in vivo. Single-cell RNA
sequencing (scRNA-seq) of colonic CD4+ T cells in DKO mice
identified a unique cluster of Treg cells that lost Il10 expression
and acquired proinflammatory signatures.
RESULTS
Building a Predictive Model for the IL-27-Driven
Transcriptional Program in CD4 T Cells by High-
Resolution Temporal Transcriptional Profiling
To understand the gene expression program induced by IL-27 in
CD4 T cells, we activated naive CD4 T cells in vitro in the pres-
ence (Tr1) or absence (Th0) of IL-27 for 72 h and performed
whole-genome microarrays at 17 time points. 790 genes were
differentially expressed in Tr1 cells compared with Th0 cells,
which partitioned into 24 co-expression clusters with distinct
temporal profiles (Figure 1A). After activation with IL-27, the cells
underwent several transcriptional waves before acquiring a sta-
ble phenotype (Figure 1B): an early phase from 0–4 h, when the
global transcriptional profile changes dynamically; a stable early
phase from 8–20 h; an intermediate phase from 25–42 h; and a
late phase from 48–72 h. The dynamic transcriptional changes2 Cell Reports 33, 108433, November 24, 2020during the first 4 h are a distinct feature of Tr1 cell differentiation
compared with Th17 cells, whichmanifest a relatively stable pro-
file during the early phase from 0–2 h (Yosef et al., 2013).
To identify TFs that drive the distinct transcriptional waves, we
hypothesized that genes co-expressed in a cluster (Figure 1A) are
likely to share regulators that are active at the relevant time point.
We predicted regulator-target associations in the IL-27-driven
transcriptional programs based on significant overlap between
genes in a specific cluster and a regulator’s putative targets in a
regulator-target association database (Yosef et al., 2013). This
generated a predictive network containing 79 TFs that were puta-
tive regulators of the gene clusters induced by IL-27 (Figure 1C).
The 79 TFs fall into six major expression patterns, each containing
both knownand previously uncharacterized regulators, that are (1)
highly expressed during the dynamic early phase (0–4 h), including
Irf1 and Batf, which are the pioneer TFs of Tr1 cell differentiation
(Karwacz et al., 2017), as well as Eomes (Zhang et al., 2017); (2)
increased during the stable early phase (8–20 h), including Stat1
and Stat3, which mediate signaling downstream of the IL-27 re-
ceptor (Stumhofer et al., 2007) and IL-21 receptor (Leonard and
Wan, 2016; Pot et al., 2009); (3) increased during the intermediate
phase (25–42 h), including Ahr (Apetoh et al., 2010); (4) increased
during the late phase (48–72 h), such as Prdm1 (Montes de Oca
et al., 2016); (5) increased gradually over time, such as Maf (Pot
et al., 2009) andHif1a (Mascanfroni et al., 2015); and (6) decreased
specifically during the stable early phase and may act as gate-
keepers for the IL-27-induced gene program, which, interestingly,
include a potent IL-10 inhibitor, Bhlhe40 (Huynh et al., 2018; Lin
et al., 2014; Yu et al., 2018). Our computational analysis identified
almost all TFs known to be required for Tr1 cells, indicating a good
predictive power, and predicted 69 TFs thatwere not implicated in
Tr1 differentiation.
Experimental Validation of the IL-27 Predicative
Network Identifies TFs that Regulate IL-10 In Vitro
Because IL-10 production is themost representative feature of IL-
27-induced Tr1 cells, we used IL-10 expression as a readout to
validate the predicative IL-27 network. We gathered 24 available
knockout mice, differentiated their naive CD4 T cells into Tr1 cells
with IL-27, and compared their Il10mRNA levels with the respec-
tive controls (Figure 2A). We validated 11 known Il10 regulators in
T cells: 8 positive regulators (Prdm1, Stat3, Ahr, Maf, Irf1, Batf,
Hif1a, and Nfil3) and 3 negative regulators (Irf4, Bhlhe40, and
Ets1) (Huynh et al., 2018; Karwacz et al., 2017; Lee et al., 2012;
Lin et al., 2014; Yu et al., 2018). We found that Cebpb, a TF that
induces ll10 in M2 macrophages (Liu et al., 2003), was also
required for Il10 production in Tr1 cells. Importantly, among the
12 tested factors that were not known to regulate Il10, we discov-
ered 4 positive (Atf3, Fosl2, Tbx21, and Hlx) and 2 negative (Irf9
and Irf8) Il10 regulators. These TFs also regulated IL-10 at the pro-
tein level (Figure S1A). Of the 24 genetically perturbed TFs, we
successfully validated 18 (75%) of our computational predictions.
We examined binding of the aforementioned Il10 regulators to
the Il10 locus in public chromatin immunoprecipitation
sequencing (ChIP-seq) data. ATF-3 (Garber et al., 2012), T-bet
(Nakayamada et al., 2011), Fosl2 (Ciofani et al., 2012), and IRF8
(Xu et al., 2015) have significant binding in the Il10 locus, some
of which lies in chromatin-accessible regions in Tr1 cells
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Resource OPEN ACCESS
A B C
72 Id3
60 6 Pou2f2
1 Rorc54 Bhlhe40
Nfyb
48 Zkscan6
42 JunbAtf6
36 5 Fli1
Klf10
30
2 Hif1a
25 MafSp4
20 Cebpb
Jarid2
3 18 Myst4
16 Tulp4Tle6
14 Klf7
4 8 Chd74 Hlx
5 4 Sertad1Fosl2
2 Sap30
6
1 Id27 Ets1
0.5 Prdm1
8 Fosb
9 0.5 1 2 4 8 14 16 18 20 25 30 36 42 48 54 60 72 Sox4
Time (h) Rai1Arid5a
10 3 Bcl3
0 0.4 0.8 Nfe2L2
11 Pearson Batf3Ahr
correlation Satb1
12 Nfil3
Atf3
Litaf
Klf6
13 Crem
Gfi1
14 Gtf3c5
15 2 Etv6
16 Mllt6
17
18 JunNotch1
19 Tbx21
Stat3
20 Stat1Pml
Myb
Nrip1
21 Daxx
Irf4
Hivep1
Nfkbia
Ets2
Eomes
22 Notch2
Plagl1
Irf1
Irf8
Irf9
1 Nfkb2
Mbnl3
Zbtb12
Stat2
Irf7
23 Trim25
Zfp281
Ifi35
Batf
Talsss2
Tle1
Gata3
RxrA
24 Utf1
Bc006779
0.5 1 2 4 8 14 16 18 20 25 30 36 42 48 54 60 72 0.5 1 2 4 8 14 16 18 20 25 30 36 42 48 54 60 72
Time (h) Time (h)
−3 0 3 −3 0 3
Row z-score Row z-score
Figure 1. Building a Predicative Model of the IL-27-Driven Transcriptional Program in CD4 T Cells by High-Resolution Temporal Tran-
scriptional Profiling
Gene expression profiles during IL-27-driven in-vitro Tr1 differentiation were measured by microarray at 17 time points with the Th0 condition as a control.
(A) Relative expression (log2(Tr1/Th0)) of 790 differentially expressed genes (rows).
(B) Pearson correlation matrix of the transcriptome at every pair of time points.
(C) Relative expression (log2(Tr1/Th0)) of 79 TFs predicted to regulate gene clusters. Underlined are TFs known to regulate Tr1 differentiation.
Cluster
Time (h)(Figure 2B), indicating that they may directly regulate Il10 tran-
scription in Tr1 cells. To investigate whether these TFs can trans-
activate or inhibit Il10, we performed luciferase reporter assays in
293T cells using reporters for the proximal Il10 promoter and the
CNS-9, HSS+2.98, and HSS+6.45 enhancers (Hedrich and
Bream, 2010; Figure 2C). Atf3, Fosl2, and Hlx transactivated
the Il10 promoter and the three enhancers. Transactivation by
Cebpb was more restricted to the promoter and CNS-9 region,
and T-bet only transactivated the CNS-9 region. Irf8 inhibited
the baseline activity of the Il10 promoter, HSS+2.98, HSS+6.45,
and, to a lesser extent, CNS-9. We found that transactivation of
Il10 by the pioneer factor Irf1 (Karwacz et al., 2017) was
completely blocked by Irf8 co-expression at the three enhancers
but not the proximal promoter (Figure S1B). In contrast, the puta-
tive negative regulator Irf9 transactivated Il10 at all four cis-regu-
latory sites, indicating that inhibition of Il10 by Irf9 in Tr1 cells may
bemediated by indirectmechanisms (Figure 2C). In summary,wevalidated 11 known and discovered 7 direct and indirect regula-
tors of Il10 during IL-27-driven Tr1 differentiation.
Hlx Regulates Il10 Expression and Tr1 Function In Vivo
It has been shown previously that Hlx cooperates with T-bet to
promote interferon (IFN)-gexpression in Th1 cells in vitro (Mullen
et al., 2002); however, whether it regulates T cell function in vivo
and whether it has an immunoregulatory role has not been inves-
tigated. To address these questions, we first tested the role ofHlx
in amodel of self-limiting inflammation induced by intraperitoneal
injection of an anti-CD3 antibody, which spontaneously resolves
in a IL-10-dependent manner (Huber et al., 2011; Kamanaka
et al., 2006). Because Hlx deficiency is embryonically lethal
(Hentsch et al., 1996), we compared Il10 expression in CD4
T cells from Hlx+/
 and WT mice following anti-CD3 injection
and observed less Il10 inHlx+/
 T cells (Figure 2D).We next inves-
tigated how Hlx regulates the immunoregulatory function of Tr1Cell Reports 33, 108433, November 24, 2020 3
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OPEN ACCESS Resource
A B 5 kb
7 Il10
6 Positive Negative
5 regulators regulators
HSS HSS
CNS-9 Promoter 2.98 6.45
4
3
2 ATAC-seq (Tr1)
1
0 ATF-3 -ChIP
–1
Fosl2-ChIP
–2
–3 T-bet - ChIP
–4
–5 IRF8 -ChIP
t3 l2 hr 1 tf a fs 1 1 b
x 3 3 4 3 2 7 2 1 9 8 1 0 4
ta o A Ir
f
Ba if1
a l l f t f l f i f f f
S H M
m x2 bp H i td b N
f A ta at 2 Ir Id F
l
fe I
r Ir ts 4 Ir
F Pr T Ce S B
E e
N lh
Bh
C Control Atf3 Control Cebpb Control Fosl2 Control Hlx
4 3 2.0 1.5
**** **** *** ****
3 **** 1.5 **** ****
**** 2 NS **** 1.0NS
2 ***
****
1.0 **** ****
1 0.5
1 0.5
****
0 0 0.0 0.0
te
r 9 8 5
o NS
- .9 .4
Control Irf8 Control Irf9 Control Tbx21 m 2 6o C S S 0.6 6 2.0 Pr HS HS
* ***
1.5
0.4 4 NS
** NS NS
*** *** **** 1.0
**
0.2 **** 2
** 0.5
0.0 0 0.0
ert -9 98 45
r
te -9 98 45 te
r -9 98 45o NS 2
. . . . . .
m C S S 
6 om CN
S 2 6 o
S S N
S  2  6
ro S S ro S S ro
m C S S
P H H P H H P HS HS
D WT Hlx+/– E Rag–/– WT Tr1 Hlx+/– Tr1
1500 120
*
110
1000
100 p = 0.0001
500
90
0 80
ro
l
D3
1 2 3 4 5 6 7 8
t
on i-C WeeksC an
t
Figure 2. Experimental Validation of IL-27 Predicative Network Identifies TFs that Regulate Il10 In Vitro and In Vivo
(A) Log2 fold change of Il10 mRNA levels in WT versus KO Tr1 cells differentiated in vitro with IL-27 for 72 h, quantified by qPCR. Blue, positive regulator; red,
negative regulator; gray, not statistically significant. Data are displayed as mean of 2–3 replicates.
(B) Statistically significant ChIP-seq binding sites of ATF-3 ATF-3, Fosl2, T-bet, and IRF8 in the Il10 locus.
(C) Luciferase activity in 293T cells transfected with luciferase reporters for the indicated cis-regulatory elements of Il10 and plasmids encoding the depicted TFs.
Firefly luciferase activity is normalized to constitutive Renilla luciferase activity.
(D) WT andHlx+/
mice were injected intraperitoneally (i.p.) with anti-CD3. Il10mRNA in CD4+ T cells MACS purified frommesenteric lymph nodes wasmeasured
by qPCR.
(E) 53 105 in vitro differentiated WT (diamonds) and Hlx+/
(squares) Tr1 cells were transferred i.p. into Rag1
/
 recipients. Rag1
/
 (circles) did not receive any
cells. Changes in body weight were monitored weekly. n = 5.
Il10 mRNA RLU RLU Log2 (fold change) of Il10 mRNA
(relative expression)
% body weightcells in a T cell transfer colitis model. Rag
/
 mice receiving wild-
type (WT) Tr1 cells were able to maintain their body weight
because Tr1 cells normally do not induce colitis. However, the re-
cipients of Hlx+/
 Tr1 cells lost weight over time, indicating that
Tr1 cells haplodeficient for Hlx might lose the regulatory pheno-
type and become proinflammatory (Figure 2E).4 Cell Reports 33, 108433, November 24, 2020A Comprehensive Transcriptional Network Focused on
Regulation of IL-10 by IL-27
We identified causal genetic targets of the Il10-regulating TFs in
the IL-27 network by performing RNA-seq on Tr1 cells geneti-
cally deficient in each of them, generating a comprehensive
network showing the effect of the TFs on Il10 as well as on the
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Resource OPEN ACCESS
A B
Positive regulation Betweenness centrality 0.15 Positive regulators
Negative regulation 0.00 0.14 Negative regulators
Cebpb Stat1 Nfil3 0.10
Fosl2 DowN
DowN
UP
Hlx Maff
UP
0.05
Atf3 UP Rorc
UP
DowN
UP
DowN
UP Jun
UP
Irf1 UPUP UP 0.00
UP
DowN
UP 1 af frf4 at 1a t3 ts1 21 h
r
UP dm M IDowN r B Hi
f ta
UP Stat2 P S
E bx AT
UP
Ahr UP
UP
UP
UP
UP
DowN
UP
UP Pou2f2
UP C Gene expression over time Il10
Tbx2UP 1 UPUP UP
UP
UP Il1 6DowNUP UPUP 0
DowN
UP Control
Myb
UP
UP
UP Latency Induction Tr1
UP
UP
UP
UP
DowN
Stat3 UP
DowN
UP
DowN
DowN
DowN
DowN 5
DowN
DowN
DowN
UP
DowN
UP Irf8 Maintenance
UP
DowN
Hif1a UP
DowN
DowN
UP
UP
UP
UP
DowN
UP
UP
UP
DowN
Bhlhe40
UP
UP 4
Batf DowN
UP
DowN
UP
Irf9
UP
UP
DowN
UP
UP
UP
UP
DowN
Maf DowN Ets1
UP
UP
DowN
Prdm1 DowN Irf4
UP 0.5 1 2 4 8 14 16 18 20 25 30 36 42 48 54 60 72
up
Time (h)
D Latency Induction Maintenance
Bhlhe40 Irf1
Maf Bhlhe40
Fosl2
Maf Fosl2 Maf
Prdm1 Prdm1 Prdm1
Irf8
Batf Hif1a Hif1a
IL-10 Ets1 IILl-1-100 Ets1 IL-10
Tbx21 Tbx21
Irf9
Nfil3 Nfil3
Stat1
Stat3 Hlx Ahr Hlx Ahr
Irf4 Cebpb Cebpb
Cebpb
Atf3
Figure 3. A Comprehensive Transcriptional Network Focused on Regulation of IL-10 by IL-27
(A) General network of Il10 regulation by TFs in Tr1 cells, visualized using Cytoscape. Edges indicate causal regulatory targets identified using genetic pertur-
bation by RNA-seq or qPCR. Blue and red edges indicate positive and negative regulations, respectively. Nodes are colored by betweenness centrality score.
(B) Betweenness centrality scores of the regulators in (A). Blue, positive regulator; red, negative regulator.
(C) Temporal expression of Il10 in Tr1 versus Th0 cells measured by microarray.
(D) Temporal regulation of Il10 in Tr1 cells, divided into 3 main phases: latency (0–20 h), induction (25–48 h), and maintenance (54–72 h). Purple nodes, increased
by IL-27; gray nodes, decreased by IL-27.
Log2 expression Betweenness centralityexpression of each other (Figure 3A). The network showed dense
inter-connectedness betweenmultiple negative and positive Il10
regulators, with substantial cross-regulation between them, ex-
plaining how indirect regulators affect Il10 expression through
direct regulation of other regulators (Figure 3A). Besides direct
inhibition of Il10 by Irf8, the negative regulators, including Irf8,
Irf4, Ets1, and Irf9, may regulate Il10 by inhibiting the expression
of positive regulators such as Prdm1,Maf, Ahr, and Batf. Except
for inhibition of Irf4 by Stat1 and Bhlhe40 by Maf, very few pos-
itive regulators inhibited expression of the negative regulators;
rather, they reinforced the expression of each other. Quantifica-
tion of TF connectivity within the network by betweenness cen-
trality score revealed Prdm1 andMaf as themost central positiveregulators and Irf4 as the most central negative regulator, iden-
tifying these TFs as central hubs for regulation of Il10 in Tr1 cells
(Figure 3B).
We observed three distinct phases of Il10 expression during
Tr1 differentiation from the temporal microarray data (Figure 3C):
a latency phase (0–20 h) with no detectable Il10, an induction
phase (20–48 h), and a maintenance phase (48–72 h). To further
understand the temporal dynamics of Il10 regulation, we divided
the global network into three phase-specific networks based on
the regulator’s temporal expression pattern (Figure 3D). The IRFs
and Atf3 were mainly increased during the latency phase; Hlx
during the induction phase; Tbx21, which has been shown to
cooperate with Hlx (Mullen et al., 2002), at the latency andCell Reports 33, 108433, November 24, 2020 5
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OPEN ACCESS Resourceinduction phase; and Fosl2, during the late induction phase and
the maintenance phase. Notably, Prdm1, Maf, and Cebpb were
increased at all three phases. Multiple regulators that suppress
Il10 were expressed at the latency phase and decreased at the
induction and maintenance phases. This may be one of the rea-
sons why Il10 induction is relatively late during Tr1 differentiation.
Prdm1 andMaf Have Complementary but Indispensable
Roles in Regulating Tr1 Identity at the Transcriptional
and Chromatin Level
Despite being the most central nodes in the Il10 regulatory
network, Il10 expression in Prdm1 or Maf single-knockout
(cKO) Tr1 cells is only partially reduced, suggesting a comple-
mentary relationship between the two TFs. We therefore gener-
ated mice that lack both Prdm1 and Maf (DKO) in T cells, using
conditional deletion driven by Cd4-Cre. Prdm1/Maf double
deficiency led to almost complete loss of Il10 in Tr1 cells
(Figure 4A).
To investigate how loss of Prdm1 and Maf influences the Il10
regulatory network (Figure 3A), we performed RNA-seq on sin-
gle- and double-KO Tr1 cells at 72 h (Figure 4B). Deficiency in
Prdm1 and Maf led to a collapse in expression of several TFs
important for Il10 expression, including Fosl2, Hif1a, Hlx, and
Notch1 (Rutz et al., 2008), which was not observed in Prdm1
or Maf single KO. In addition, a number of other transcriptional
regulators that are induced by IL-27 were also specifically
decreased in DKO cells, such as Sp1, Ets2, Mbnl3, Klf10,
Nfyb, Satb1, Crem, Nfkbia, Chd7, Trim25, Klf6, and Nfkb2,
although their role in regulating Il10 remains to be investigated.
Furthermore, Prdm1 and Maf transcriptionally regulated each
other’s expression (Figure 4B). Bhlhe40, a potent Il10 inhibitor,
was increased dramatically in DKO mice, indicating that
Prdm1 and Maf are critical not only for driving Il10 expression
but also for antagonizing the expression of TFs that repress
Il10. Although some positive regulators, such as Irf1 and Atf3,
were increased in DKO cells, these early-stage Il10 inducers
could not rescue the loss of Il10 in the absence of Prdm1 and
Maf, perhaps because of their inability to overcome inhibition
(Figure S1B).
To assess whether Prdm1 or Maf regulated the chromatin
landscape of Tr1 cells, we profiled chromatin accessibility in sin-
gle- and double-KO Tr1 cells using Assay for Transposase-
Accessible Chromatin with high-throughput sequencing
(ATAC-seq). Although the chromatin landscape in the Il10 locus
remains largely unchanged in Prdm1 or Maf single-KO cells, we
detected a reduction in accessibility at specific enhancer regions
in the Il10 locus in DKO cells (Figure 4C). In addition, we found
that Fosl2 and Hlx, two other positive regulators of Il10, also
became less accessible in DKO but not either single-KO cells
(Figure 4C), which is consistent with their gene expression pro-
file. Moreover, DKO Tr1 cells showed a unique reduction in chro-
matin accessibility in co-inhibitory receptor gene loci such as
Ctla4, Pdcd1 (PD-1), Tigit, Havcr2 (Tim-3) (Figure S2), another
hallmark of Tr1 cells that is transcriptionally regulated by
Prdm1 and Maf (Chihara et al., 2018). In summary, these data
suggest that Prdm1 and Maf have complementary but indis-
pensable roles in regulating the hallmark genes for Tr1 identity
at the transcriptional and chromatin levels.6 Cell Reports 33, 108433, November 24, 2020IL-10 Regulators Are Induced in Diverse IL-10-
Producing T Helper Cells
All T helper cells can produce IL-10, but the regulation of IL-10
expression in these contexts is unclear. We therefore examined
whether the regulators we identified in the IL-27 network were
also utilized for IL-10 regulation in other T helper cells. We
differentiated naive CD4 T cells from IL-10Thy1.1 reporter mice
(10BiT) (Maynard et al., 2007) into Th1, Th2, non-pathogenic
Th17 (Th17), pathogenic Th17 (pTh17), and Tr1 cells; sorted
out the IL-10+ and IL-10- compartments; and performed
RNA-seq. We also analyzed the RNA profiles of IL-10+ versus
IL10
 T cells purified from several other in vivo and in vitro con-
ditions (Boks et al., 2016; Burton et al., 2014; Gagliani et al.,
2015; Langenhorst et al., 2012; Neumann et al., 2014; Trandem
et al., 2011; Table S1). We identified TFs whose expression was
associated with IL-10 in each T cell subset or condition (Figures
5A and 5C) and ranked them based on the number of condi-
tions where their expression is enriched in the IL-10-producing
compartment (Figures 5B and 5D). Many of the regulators iden-
tified in our IL-27 network were also identified by this analysis,
including Prdm1, Maf, Hlx, Tbx21, Batf, Nfil3, Ahr, Bhlhe40, and
Irf8, indicating that they might also regulate IL-10 in other con-
texts (Figures 5A and 5C). Prdm1 and Maf, the two positive reg-
ulators with highest centrality in the IL-27 network (Figure 3B),
were enriched in the IL-10-producing compartment of all T
helper cell subsets (Figure 5B) as well as under many other
in vivo and in vitro conditions (Figure 5D), whereas the other
TFs were more restricted to certain conditions. A combined
ranking scheme of IL-10 regulators evaluating the centrality
score in the IL-27 network and generalizability in other IL-10-
producing T cell subsets derived from in vitro and in vitro con-
texts revealed Prdm1 and Maf as the two top TFs that regulate
IL-10 production across T helper cells (Figure 5E).
Role of Prdm1 andMaf in Regulating IL-10 in Different T
Helper Cells
We validated the association of Prdm1 and Maf expression with
Il10 in various contexts by qPCR. The IL-10+ compartment of
Th1, Th2, Th17, Tr1, and Treg cells expressed higher levels of
Prdm1 andMaf than their IL-10
 counterparts (Figure 6A). More-
over, analysis of public ChIP-seq datasets confirmed binding of
Prdm1 andMaf at accessible chromatin regions in the Il10 locus
(Figure 6B). These findings further support the hypothesis that
Prdm1 and Maf may be critical regulators of IL-10 in multiple
settings.
We tested the interaction between Prdm1 orMaf in regulating
Il10 using luciferase assays. Although Prdm1 alone had very
limited capability to transactivate Il10, it significantly enhanced
transactivation of Il10 by Maf (Figure 6C), suggesting that co-
operation between Prdm1 and Maf is required for optimal IL-
10 production. Of note, the synergistic effect between Prdm1
and Maf is specific to enhancer regions (tested by the interac-
tion term in the linear regression model; CNS-9, p = 0.00255;
HSS+6.45, p = 0.000154) but not the promoter. We further
studied the synergy between Prdm1 and Maf in regulating
Il10 in primary CD4 T cells using a gain-of-function approach
by transducing Th1, Th2, Th17, Tr1, and Treg cells with
retroviruses encoding Prdm1 (MSCV-IRES-Thy1.1) and Maf
ll
Resource OPEN ACCESS
A B
Il10
15 WT 3
WT 2
WT 1
10 + + + + + + + + + + + + + + + + + + + + Prdm1 KO 3
+ + + + + + + + + + + + + + + + + + + + Prdm1 KO 2
+ + + + + + + + + + + + + + + + + + + + Prdm1 KO 1
5
+ + + + + + + + + + + + + + + + + Maf KO 3
+ + + + + + + + + + + + + + + + +
z-score Maf KO 2
0 2 + + + + + + + + + + + + + + + + + Maf KO 1
ol O O O 1r + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + DKO 3
on
t
 cK  cK1 f D
K 0
−1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + DKO 2C a
dm M −2 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +r DKO 1P
C
5 kb
[0–18]
Control
[0–18]
Prdm1 cKO
[0–18]
Maf cKO
[0–18]
DKO
Refseq genes Il10
5 kb
[0–18]
Control
[0–18]
Prdm1 cKO
[0–18]
Maf cKO
[0–18]
DKO
Refseq genes Fosl2
5 kb
[0–18]
Control
[0–18]
Prdm1 cKO
[0–18]
Maf cKO
[0–18]
DKO
Refseq genes Hlx
Figure 4. Prdm1 andMafHave Complementary but Indispensable Roles in Regulating Tr1 Identity at the Transcriptional andChromatin Level
(A) Naive CD4 T cells from the indicated mice were differentiated in vitro into Tr1 cells with IL-27. Il10 expression was measured by qPCR on day 3.
(B and C) Control, Prdm1 cKO, Maf cKO, and Prdm1/Maf DKO Tr1 cells generated as described in (A) were analyzed by RNA-seq (B) and ATAC-seq (C).
(B) Heatmap showing expression of 79 predicted regulators in the Tr1 network. ‘‘+’’ indicates statistically significant differential expression.
(C) Chromatin accessibility in the Il10, Fosl2, andHlx loci in Tr1 cells of the indicated genotype. Red bars represent regions with differential chromatin accessibility
in DKO cells.
Relative expression
Zbtb12
Tle1
Utf1
Stat1
Junb
Rai1
Pml
Batf
Nrip1
Jun
Zfp281
Tbx21
Tulp4
Sp4
Irf4
Hif1a
Ets2
Irf9
Fosl2
Mbnl3
Bcl3
Klf10
Nfyb
Satb1
Crem
Nfkbia
Fli1
Chd7
Il10
Id2
Prdm1
Hlx
Notch1
Atf6
Trim25
Irf7
Rorc
Maf
Ets1
Tal2
Cebpb
Klf7
Zkscan6
Eomes
Hivep1
Gfi1
Gtf3c5
Sox4
Ifi35
Daxx
Mllt6
Myb
Tle6
Atf3
Bhlhe40
Klf6
Irf1
Sertad1
Stat3
Nfkb2
Id3
Stat2
Nfe2l2
Ahr
Litaf
Notch2
Fosb
Irf8
Sap30
Batf3
Gata3
Jarid2
Arid5a
Pou2f2
Rxra
Plagl1
Etv6(MSCV-IRES-GFP). IL-10 production was enhanced dramati-
cally when Prdm1 and Maf were co-expressed (Figure 6D).
Importantly, although Prdm1 and Maf cooperatively promoted
IL-10 production across all T helper cells, they did not inhibit
production of signature cytokines of the T helper cell subsets
(Figure S3). Thus, Prdm1 and Maf enabled expression of a
gene module that induced IL-10 in all T helper cell subsets
without disrupting their cell differentiation program.Genetic Deficiency of Prdm1 and Maf, but Not Either
Alone, in T Cells Leads to Human IBD-like Spontaneous
Colitis Driven by a Unique Cluster of Treg Cells
IL-10 has a critical role in maintaining intestinal homeostasis.
Mutations in IL-10 or IL-10R are associated with human ulcera-
tive colitis presenting in early childhood (Zhu et al., 2017). We
therefore monitored Prdm1/Maf DKO mice for spontaneous
development of colitis. Strikingly, loss of Prdm1 and Maf inCell Reports 33, 108433, November 24, 2020 7
ll
OPEN ACCESS Resource
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Mxd1
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Bhlhe40 conditions
Atf4 Id2
Batf Jun
Csda Nfil34 enriched Bcl3
Etv5 conditions Cux1
Foxm1 Ebf1
Hmgb1 Elf4
Hmgb2 Hopx
Hmgb3 Ikzf3 3 enriched
Litaf Klf7 conditions
Nfil3 Med13l
Nfat5
Carhsp1 Plekhf1
Crem Pou6f1
E2f4 Rorc
E2f7 Runx3
E2f8 Smad3
Hlx Tulp4
Hmga1-rs1
h17 h17 reg D3 D3  Ag and D4 sId2 g
s. T xT I 
T C
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Irf5 3 enriched r1 v . a a
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d
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Mxd3 S xTh
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ntra  to
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+ tDC SA
-
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Nfatc2 TTr1
E I D4C um
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Srebf1 SI H
Srebf2
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Whsc1 Not enriched
Xbp1
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Th1 Th2 Th1
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E Combined score
Score 0.5 2.5
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Network rank
0.0 1.0
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(legend on next page)
8 Cell Reports 33, 108433, November 24, 2020
Prdm1
Maf
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Nfatc2
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f1
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Elk3 Phf1Nupr1
Tcf4 Nr1h3N
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Egr1 treated Treg
Gzf1
Ikzf2
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ll
Resource OPEN ACCESST cells led to spontaneous weight loss over time (Figure 7A),
similar to that observed in IL-10-deficient mice (Ku€hn et al.,
1993). The presence of one copy of the Maf allele protected
the mice from weight loss until 16 weeks of age, and one copy
of Prdm1 protected the mice until at least 24 weeks of age (Fig-
ure S4A). DKOmice, but not single-KO mice, had shorter colons
(Figure 7B), and histological analysis of the entire intestine
confirmed the presence of active colitis in DKO mice (Figure 7C;
Figure S4B) with features reminiscent of human ulcerative colitis.
Severe chronic active colitis with cryptitis, crypt abscess, and
crypt loss with mucosal ulcers reminiscent of ulcerative colitis
was themost prevalent pathology in DKOmice. DKOmice occa-
sionally showed flask-shaped aphthous erosions that are com-
mon in human Crohn’s disease, but other defining features of
Crohn’s disease, such as transmural lymphoid aggregates,
were absent in all mice (Figure 7C; Figure S4C). Although Cd4-
Cre; Prdm1fl/fl mice have been reported to develop spontaneous
colitis (Martins et al., 2006), we observed spontaneous intestinal
disease very rarely only inmale but not in any femalemice. More-
over, the pathology in thesemice wasmainly located in the prox-
imal end of the small intestine rather than the colon (Figures S4D
and S4E).
To characterize the transcriptional changes in T cells that lead
to spontaneous colitis in DKO mice, we performed scRNA-seq
on CD4 T cells from the colonic lamina propria at 3 weeks of
age before disease onset. We included two biological replicates
for each genotype (Figure 7D) and collected a total of 13,535
high-quality single-cell profiles that were partitioned into eight
distinct clusters (Figure 7E). Cluster identity was designated
based on bulk RNA-seq-derived gene signatures (Immgen) and
confirmed by expression of key marker genes (Figures S5A
and S5B). Cells of the four genotypes distributed evenly within
the naive T cell clusters (clusters 1–3), indicating negligible batch
effects between samples and similar phenotypes of naive T cells
in the absence of Prdm1 or Maf. However, Maf cKO and DKO
cells formed distinct sub-clusters within the effector T cell cluster
(cluster 4), and each of the Treg cell clusters (clusters 5–8) was
dominated by a different genotype (Figures 7D–7F; Figure S5C).
The proportion of effector T cells was increased in both Maf
cKO and DKO (Figure 7F), but these cells were qualitatively
different (Figures 7G and 7H) in that DKO effector cells had
dramatically increased expression of the Th1 gene signature,
which is a major pathogenic cell population implicated in IBD
(Ito and Fathman, 1997; Neurath et al., 2002). The Th17 gene
signature was also increased significantly in DKO but to a lesser
extent (Figure 7G). A subset of effector cells in DKO mice ac-
quired a signature that resembles CD4 T cells from inflamed in-
testinal lesions of humans with ulcerative colitis (Smillie et al.,
2019). In addition, the differentially expressed genes in DKOFigure 5. TFs Associated with IL-10 Production in Different T Helper C
(A and C) TFs enriched in the IL-10+ compartments compared with their IL-10
 co
conditions where a direct comparison between the transcriptome of IL-10+ and I
least 3 conditions were magnified.
(B and D) A different display of same data in (A) and (C), respectively, showing TF
expression is enriched in the IL-10+ compartment. SI, small intestine.
(E) A ranking scheme (STAR Methods) for all potential regulators of IL-10, taking
(Figure 5A), and enrichment in public datasets (Figure 5C).effector T cells (compared with control cells) showed unique
enrichment for IBD-associated genome-wide association study
(GWAS) genes that are involved in adaptive immunity (Graham
and Xavier, 2020; Figure 7H). These data indicate that loss of
Prdm1 and Maf leads to spontaneous colitis that resembles hu-
man IBD in terms of not only pathological features but also mo-
lecular signatures.
Consistent with previous reports (Maynard et al., 2007), Treg
cells (clusters 7 and 8) were a major source of IL-10 in the colon
(Figure 7I). We observed that the average expression level of Il10
was reduced in colonic Treg cells in the absence of Maf; the
average expression level and percentage of Il10-positive cells
were reduced in the absence of Prdm1, and Il10 expression
was barely detectable in the absence of both (Figure 7J). There-
fore, Prdm1 and Maf were also required for Il10 expression in
Treg cells in vivo.
Besides downregulation of Il10, DKO Treg cells, compared
with single-KO or control Treg cells, exhibited a unique gene
expression profile (cluster 5; Figures 7D–7F). DKO Treg cells
lost immunoregulatory phenotypes, including expression of
TFs critical for Treg cell stability and function (e.g., Ikzf2 and
Gata3), co-stimulatory receptors (e.g., Cd28, Tnfrsf18, and
Tnfrsf4), and soluble immunosuppressive molecules (e.g., Areg
and Apoe). On the other hand, DKO Treg cells acquired Th1-
associated (e.g., Nkg7, Xcl1, Lta, and Cxcl10) and Cytotoxic T
Lymphocytes (CTL)-associated (e.g., Cd160 and Gpr18) genes
that are known to actively promote inflammation. Moreover,
DKO Treg cells showed profound changes in their chemotaxis
profile, turning off Ccr2 and Cxcr6 while dramatically upregulat-
ing Ccr7 (Figure 7K). These data indicated that Prdm1 and Maf
cooperatively regulate the identity and function of Treg cells
and are indispensable for immune tolerance in vivo.
DISCUSSION
Computational inference of gene regulation from temporal
profiling of gene expression has shown great potential to delin-
eate the dynamic transcriptional circuits that regulate T cell dif-
ferentiation. We and others have successfully built models of
regulatory networks for Th17 cells (Wu et al., 2013; Yosef
et al., 2013; Ciofani et al., 2012) and Th2 cells (Henriksson
et al., 2019), discovering key regulators and revealing general
principles governing T cell differentiation. Here we computed a
transcriptional network induced by IL-27 in CD4 T cells that
showed strong predicative power for identification of Il10 regula-
tors: 18 (75%) of the 24 predicted TFs we validated experimen-
tally were confirmed to be regulating Il10 expression. In addition
to IL-10, IL-27-induced Tr1 cells feature expression of IFN-g
(Awasthi et al., 2007; Pot et al., 2009) and co-inhibitory receptorsells
mpartments in (A) in-vitro-generated T helper cell subsets, (C) 10 in vivo/ex vivo
L-10
 cells was made in public data (Table S1). TFs that are enriched under at
s that are enriched under at least 3 conditions and the conditions where their
into account network centrality (Figure 3B), enrichment in in vitro conditions
Cell Reports 33, 108433, November 24, 2020 9
ll
OPEN ACCESS Resource
A Prdm1 Maf
3 4
3
2
2
1
1
0 0
Th1 Th2 Th17 Tr1 Treg Th1 Th2 Th17 Tr1 Treg
B CNS-9 Promoter HSS 2.98 HSS 6.45
[0 – 2399] 5 kb
Maf ChIP
[0 – 14]
Prdm1 ChIP
[0 – 33]
ATAC-seq Tr1
Il10
C Promoter CNS-9 HSS 2.98 HSS 6.45 D Th1 Th2
6 15 *** 40 *
5 NS NS
4 ** 3010 *
3 Prdm1 20
2 5
1 10
0 0 0
6 Th17 Tr1
5 20 * 15 ****
4
15 NS **3 Maf NS 10 NS
2 10
1 5
5
0
0 25 50 100 0 25 50 100 0 25 50 100 0 25 50 100
(ng) (ng) (ng) (ng) 0 0 l f f
Treg tro
1
m aM aM
5 NS 6 *** 8 *** n d8 **** r  8
*** ** C
o P +1 
4 m
4 ****
6 **** 6 **** 6 dPr
3
* ** 4 NS 4 **** Combination 4 *
2
2 **
1 2 2 2
0 0 0 0 0
ol 1 af af ol 1 af af lr r ro 1
f f l 1 f f l 1 f f
nt dm M  M nt dm M  M nt dm
a a
M M tr
o
 n dm
a a tro m a a
o Pr + o r
M M
+ o  n d
M  M
C 1 C P 1 C P
r  + o r1 C P 1 
+ rCo P 1 
+
dm dm dm dm m
Pr Pr Pr Pr Pr
d
Figure 6. Prdm1 and Maf Synergistically Regulate IL-10 in All T Helper Cells
(A) Enrichment of Prdm1 and Maf mRNA in in-vitro-generated T helper cells validated by qPCR.
(B) ChIP-seq of Maf in Th17 cells and Prdm1 in tissue-resident memory T cells aligned with ATAC-seq data of Tr1 cells differentiated in vitro at 72 h.
(C) Luciferase activity in 293T cells transfected with Il10 luciferase reporters along with constructs encoding Prdm1, Maf, or both. n = 3.
(D) T helper cells differentiated in vitro were transduced with two retroviruses expressing Prdm1 andMaf, respectively. IL-10 expression in control cells, Prdm1-
overexpressing cells, Maf-overexpressing cells, and cells overexpressing Prdm1 and Maf was measured by flow cytometry 48 h after transduction.
RLU RLU RLU
Log2 (IL-10+ / IL-10–)
Log2 (IL-10+ / IL-10–)
IL-10+ /CD4+ (%) IL-10+ /CD4+ (%) IL-10+ /CD4+ (%)(Chihara et al., 2018; DeLong et al., 2019). Therefore, the pre-
dicted TFs in the IL-27 network presented here could also be uti-
lized for defining the transcriptional regulation of these mole-
cules in a manner similar to what is presented here for Il10.
By genetically perturbing the IL-27 network, we identified crit-
ical regulators of Il10, which may shed light on previously unap-
preciated roles of IL-10 in physiological processes and deepen
our understanding of IL-10-related immune disorders such as
IBD (Zhu et al., 2017). For example, Atf3 is a TF that is induced10 Cell Reports 33, 108433, November 24, 2020by endoplasmic reticulum stress (Schmitz et al., 2018) with
anti-inflammatory properties (De Nardo et al., 2014; Gilchrist
et al., 2006); induction of IL-10 by Atf3 may provide a negative
feedback loop to dampen endoplasmic reticulum (ER) stress
(Hasnain et al., 2013; Shkoda et al., 2007) and suppress inflam-
mation. Further, Fosl2 is a member of the AP1 family, which con-
tains several members that were implicated in Il10 regulation (Hu
et al., 2006; Kremer et al., 2007). Fosl2 has been shown previ-
ously to regulate the pathogenicity of Th17 cells (Ciofani et al.,
ll
Resource OPEN ACCESS
A B
Control (Cre–) Maf cKO Control Maf Prdm1 Colon(Cre–) cKO cKO DKO 10
Prdm1 cKO DKO NS
25 9 NS
20 8
p < 0.0001 ****
7
15
6
10
5
5 – )e KO O O
Cr( f c  c
K DK
ol a
1
0 tr M m
on
d
Pr
C
30
C Control (Cre–) Prdm1 cKO Maf cKO DKO25
20 p < 0.0001
15 5x
10
5
0
0 4 8 12 20x
Age (wk)
D Control 1 Maf cKO 1 E 1: Naïve CD4 5: Treg 1: Naïve CD4 4: Teff 7: TregControl 2 Maf cKO 2 F2: Naïve CD4 6: Treg 2: Naïve CD4 5: Treg 8: Proliferative Treg
Prdm1 cKO 1 DKO 1 3: Naïve CD4 7: Treg 3: Naïve CD4 6: Treg
Prdm1 cKO 2 DKO 2 4: Teff 8: Proliferative Treg 100
3 3
6
3 80
7
2 60
0 0 1 5
40
–3 –3 4
8 20
–6 –6
0
–10 –5 0 5 10 –10 –5 0 5 10 1 2 1 2 1 2 1 2
UMAP 1 UMAP 1 Control Prdm1 Maf DKO
cKO cKO
G Th1 signature Th17 signature H UC CD4 T cell signature IBD GWAS genes I Il10
–16 (Smillie et al. 2020) (Graham et al. 2020)p < 2.2 × 10 p < 2.2 × 10–16
p < 2.2 × 10–16
p < 2.2 × 10–16 p = 0.0004 p = 4.2 × 10–12
NS 0.2 2.0 4
NS NS
0.1 *
0.5
1.5 3
0.1
0.0 1.0 2
0.0 0.0
0.5 1
–0.1
–0.1
0
0.0
l l l 1 2 3 4 5 6 7 8
tro KO KO KO tro KOn  c  c D n  c  cK
O KOD nt
ro
 cK
O KO O O O O
o f o f o f c D
K  cK  cK DK Cluster
C rd
m a
M C d
m a
r M C d
m a m af
P P Pr M
d
Pr M
J Il10 K
DKO DKO
4
Maf cKO Percent 3 Maf cKO Percent
expressed expressed
10 25
20 50
2
Prdm1 cKO 30 Prdm1 cKO 75
Average Average
expression 1 expression
1.0 1.0
Control 0.5 Control 0.5
0.0 0.0
–0.5 0
–0.5
–1.0
Il10 ltro KO KO KO g
7 cl1 tak X L cl1
0 60 18 7 e 7 2 31 r cr ga d zf ta d2
8 8 4 g e 0 6
f1 rs
f re po Il1 cr
2
cr
on  c f c D N d
p C It C Ik a
Cx C G G C f
rs nf A A C Cx
C dm ar M T
n T
P
(legend on next page)
Cell Reports 33, 108433, November 24, 2020 11
Score UMAP 2 Body weight (g) Body weight (g)
Expression level
UMAP 2
Score
–log10(p-value)
Length (cm)
Proportion (%)
Expression level
ll
OPEN ACCESS Resource2012), which play an important role in the pathogenesis of IBD. A
single-nucleotide polymorphism (SNP) in Fosl2 (rs925255) has
been linked genetically to IBD in a GWAS (Jostins et al., 2012).
Our study raises the possibility that the SNP in Fosl2may further
influence susceptibility to IBD by regulating IL-10 expression.
Last, we identified Hlx as a regulator of IL-10 and showed that
its haplodeficiency is sufficient to convert Tr1 cells to proinflam-
matory cells that exacerbate T cell transfer colitis. Interestingly,
theHlx locus has been shown to be hypermethylated in epithelial
cells in humans with IBD, and these data suggest that Hlx might
have a role in regulating immune responses beyond T cells.
The lineage-defining TFs for Th2 (GATA-3) and Th17 (ROR-gt)
have been shown to contribute to IL-10 expression (Shoemaker
et al., 2006; Wang et al., 2015). However, the role of T-bet, the
master TF for Th1 cells, in Il10 regulation has been controversial.
One study has reported that IL-10 production is increased in CD4
T cells in the absence of T-bet (Shin et al., 2014), which could be
due to the indirect effect of a decrease in IFN-g (Hu et al., 2006).
Other studies have reported that T-bet can induce IL-10 produc-
tion but under conditions where T-bet or other TFs are overex-
pressed (Rutz et al., 2008; Zhu et al., 2015). Here we show that
genetic deficiency in T-bet leads to impaired IL-10 production
in Tr1 cells. Additionally, we show, by ChIP-seq and luciferase
assays, that T-bet can directly bind and transactivate Il10.
Thus, the master TFs for all T helper cell subsets can induce
immunoregulatory genes to mitigate overexuberant responses.
Eomes, another T-box TF that is highly homologous to T-bet in
its DNA binding domain (Pearce et al., 2003), has been reported
to regulate IL-10 in a GVHDmodel (Zhang et al., 2017). Our study
suggests that the relative contribution of these two TFs to IL-10
regulation may be highly context dependent (Zhang et al., 2017).
Master TFs have been identified for other T cell subsets but not
for Tr1 cells. Our network analysis in Tr1 cells highlighted Prdm1
and Maf as central hubs in regulating Il10. Not only are they
heavily regulated, but, more importantly, they orchestrate a reg-
ulatory circuit composed of multiple other transcriptional modu-
lators. Prdm1/Maf DKO Tr1 cells, but not either single KO Tr1
cells, exhibited complete loss of Il10 and collapse of the Il10 reg-
ulatory circuit, both accompanied by reduced chromatin acces-
sibility, and a notable upregulation of the Il10 repressor Bhlhe40.
Expression of co-inhibitory receptors, another hallmark of Tr1Figure 7. Genetic Deficiency of Prdm1 and Maf, but Not Either Alone,
Unique Cluster of Treg Cells
(A) Weekly monitored body weights. Top: female mice. Bottom: male mice. n R
(B) Colon length of the indicated mice, presented as seen by gross anatomy and
(C) Hematoxylin and eosin staining of colon Swiss rolls. Pictures are representative
250 mm.
(D–K) CD4 T cells from colonic lamina propria were profiled by scRNA-seq.
(D and E) Uniform manifold approximation and projection (UMAP) plots show 13
(F) Distribution of cells with different genotypes in clusters.
(G and H) Left: distribution of gene signature scores of Tconv cells (clusters 1–4
GWAS genes that are involved in adaptive immunity in differentially expressed gen
the control. Significance of enrichment was tested by hypergeometric test.
(I-K) Gene expression level represented as log(TP10K+1).
(I) Il10 expression by control (WT) cells across clusters.
(J) Il10 expression by Treg cells (clusters 5–8) across genotypes.
(K) Representative differentially expressed genes of DKO Treg cells compared wit
express the gene; color indicates mean expression in expressing cells relative to
12 Cell Reports 33, 108433, November 24, 2020cells (Brockmann et al., 2018; Chihara et al., 2018; DeLong
et al., 2019), are induced by IL-27 and controlled by Prdm1
and Maf transcriptionally and at the chromatin level. These
data suggest that the key signature of Tr1 cells might be estab-
lished through collaboration between two TFs with complemen-
tary roles.
We and others have shown that c-Maf is a universal regulator of
IL-10 in Th1, Th2, Th17, Tr1, aswell as Treg cells (Gabrysová et al.,
2018). Further, we discovered that Maf needs to cooperate with
other TFs, such as Ahr, to achieve robust Il10 transcription (Ape-
toh et al., 2010). In this study, we identified Prdm1 as a critical
partner ofMaf and that together they synergistically transactivate
Il10 in all CD4 T cell subsets, including IL-10-producing Tr1 cells.
Commitment of T helper cells to specific subsets requires in-
duction of master TFs that not only induce specific transcrip-
tional programs that push T cell subsets in one direction but
also initiate repressive programs that antagonize other fates
(Sungnak et al., 2019). Interestingly, we observed that, although
Prdm1 and Maf synergistically promote IL-10 production, they
do not inhibit production of signature cytokines of the different
T helper cell subsets, enabling them to co-produce IL-10 while
maintaining their original transcriptional program.
We found that Prdm1/Maf DKO mice, but not single KO mice,
phenocopy Il10-deficient mice and develop spontaneous colitis
that presents pathological andmolecular features of human IBD.
Prdm1 is a well-recognized GWAS gene associated with IBD (El-
linghaus et al., 2013). It would therefore be interesting to investi-
gate whether SNPs related to Maf can further enhance IBD sus-
ceptibility. With scRNA-seq analysis, we discovered a unique
cluster of colonic Treg cells in Prdm1/Maf DKO mice that was
not observed when Prdm1 (Cretney et al., 2011; Garg et al.,
2019; Ogawa et al., 2018) or Maf (Neumann et al., 2019; Xu
et al., 2018) was perturbed individually. These DKO Treg cells
lose immunoregulatory phenotypes, including production of IL-
10, and acquire strong Th1- and CTL-associated gene signa-
tures, indicating potential to actively exacerbate inflammation.
In addition, this DKO Treg cell cluster shows a profound shift in
the use of chemokine receptors from those that drive T cells to
tissue with active inflammation (e.g., Ccr2 and Cxc6) (Hamano
et al., 2014; Loyher et al., 2016; Mondini et al., 2019; Zhang
et al., 2009) to Ccr7, which, together with two other markersin T Cells Leads to Human IBD-like Spontaneous Colitis Driven by a
8. Data are represented as mean ± SD.
measurement.
of 10 control, 4 Prdm1 cKO, 6Maf cKO, and 7DKOmice. Scale bars represent
,535 cells (dots) colored by genotype (D) or cluster (E).
) by genotype. ‘‘+’’ indicates median. (H) right: enrichment of IBD-associated
es of Prdm1 cKO,Maf cKO, and DKO Tconv cells, respectively, compared with
h all other genotypes. Dot size represents the fraction of cells in the cluster that
other genotypes.
ll
Resource OPEN ACCESShighly expressed by DKO Treg cells, Lta (Upadhyay and Fu,
2013) and Itgae (Leithäuser et al., 2006), are associated with
development of lymphoid-like structures. Therefore, it would
be interesting to further study how Prdm1 and Maf regulate the
migration and location of Treg cells.
We have shown that Prdm1 andMaf co-operatively induce the
co-inhibitory receptor gene module on exhausted CD8 T cells
(Chihara et al., 2018), which not only have a dysfunctional
effector program but also co-produce IL-10 to actively suppress
the immune responses in chronic viral infections and cancer.
Further, Maf was also implicated in IL-10 expression in B cells
(Liu et al., 2018) and macrophages (Cao et al., 2005), and
Prdm1 has a regulatory role in dendritic cells (Kim et al., 2011;
Watchmaker et al., 2014). These data, together with our current
study, emphasize the importance of cooperativity between
Prdm1 and Maf in regulating immunoregulatory gene programs
across multiple immune cell types.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
d KEY RESOURCES TABLE
d RESOURCE AVAILABILITYB Lead Contact
B Materials Availability
B Data and Code Availability
d EXPERIMENTAL MODEL AND SUBJECT DETAILS
B Mice and Ethics Statement
d METHOD DETAILS
B Experimental methods
B Computational Methods
d QUANTIFICATION AND THE STATISTICAL ANALYSIS
SUPPLEMENTAL INFORMATION
Supplemental Information can be found online at https://doi.org/10.1016/j.
celrep.2020.108433.
ACKNOWLEDGMENTS
The authors thank Christophe Benoist, Arlene Sharpe, Mikael Pittet, Mary
Collins, andKaren Dixon for constructive criticism anddiscussions andDeneen
Kozoriz, Rajesh K. Krishnan, Leslie Gaffney, Sarah Zaghouani, Haoxin Li, Rui-
han Tang, Qianxia Zhang, Yu Hou, Jingwen Shi, Danyang He, Sheng Xiao, and
Ido Amit for technical assistance. This work was supported by NIH grants
R01NS30843, R01AI144166, P01AI073748, P01AI039671, P01AI056299, and
P01AI129880 (to V.K.K.) and the Klarman Cell Observatory (to A.R.). A.R. is
an Investigator of the Howard Hughes Medical Institute. A.C.A. is a recipient
of the Brigham and Women’s President’s Scholar Award and is supported by
NIH grant CA229400. A.M. was supported by the Alon Fellowship for
Outstanding Young Scientists, Israel Council for Higher Education. L.A. was
supported by the European Research Council (grant agreement 677251).
A.A. was supported by DST-SERB grant CRG/2018/002653 from the Depart-
ment of Science and Technology, Government of India.
AUTHOR CONTRIBUTIONS
V.K.K. and A.R. conceived the study. H.Z., A.M., N.Y., N.C., C.W., L.A., A.R.,
and V.K.K. designed experiments and interpreted results. H.Z., N.C., A.A.,C.P., and L.A. performed and analyzed experiments with assistance from
Y.E., A.L., H.S., J.X., K.K., and N.A. A.M., N.Y., and C.L. analyzed bulk RNA-
seq and performed network analyses. H.Z. and C.L. analyzed scRNA-seq
and ATAC-seq. J.N., E.C., P.I.T., A. Schnell, and H.Z. performed sequencing
under the supervision of O.R.-R. A. Srivastava analyzed pathology. A.C.A.,
A.R., and V.K.K. supervised the project. H.Z. and A.M. drafted the manuscript.
L.A., A.C.A., A.R., and V.K.K. edited the manuscript.
DECLARATION OF INTERESTS
A.R. is a founder and equity holder of Celsius Therapeutics, an equity holder in
Immunitas Therapeutics, and, until August 31, 2020, an SAB member of Syros
Pharmaceuticals, Neogene Therapeutics, Asimov, and Thermo Fisher Scienti-
fic. From August 1, 2020, A.R. is an employee of Genentech, a member of the
Roche Group. V.K.K. has an ownership interest in Tizona Therapeutics,
Celsius Therapeutics, and Bicara Therapeutics. V.K.K. has financial interests
in Biocon Biologic, BioLegend, Elpiscience Biopharmaceutical Ltd., Equilium
Inc., and Syngene Intl. V.K.K. is a member of SABs for Elpiscience Biopharma-
ceutical Ltd., GSK, Kintai Therapeutics, Repertoir Immune Medicines, Rubius
Therapeutics, and Tizona Therapeutics. A.C.A. is a member of SAB for Tizona
Therapeutics, Compass Therapeutics, Zumutor Biologics, ImmuneOncia, and
Astellas Global Pharma Development Inc. A.C.A.’s and V.K.K.’s interests were
reviewed and managed by the Brigham and Women’s Hospital and Partners
Healthcare and A.R.’s interests by the Broad Institute and HHMI in accordance
with their conflict of interest policies.
Received: March 4, 2020
Revised: July 14, 2020
Accepted: November 4, 2020
Published: November 24, 2020
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Gastroenterol. Res. 10, 65–69.Cell Reports 33, 108433, November 24, 2020 17
ll
OPEN ACCESS ResourceSTAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
InVivoMab anti-mouse CD3ε Bio X Cell Cat# BE0001-1
InVivomAb anti-mouse CD28 Bio X Cell Cat# BE0015-5
InVivomAb anti-mouse TGF-b Bio X Cell Cat# BE0057
InVivomAb polyclonal Armenian hamster Bio X Cell Cat# BE0091
IgG
Chemicals, Peptides, and Recombinant Proteins
Recombinant Mouse IL-27 (NS0- R&D SYSTEMS Cat# 2799-ML-010
expressed) Protein
Recombinant Mouse IL-12 Protein R&D SYSTEMS Cat# 419-ML-050
Mouse IL-4, research grade Miltenyi Biotec Cat# 130-097-757
Recombinant Mouse IL-1 beta/IL-1F2 R&D SYSTEMS Cat# 419-ML-010
Protein
Recombinant Mouse IL-6 Protein R&D SYSTEMS Cat# 406-ML-025
Recombinant Mouse IL-23 Protein R&D SYSTEMS Cat# 1887-ML-010
Human TGF-b1, premium grade Miltenyi Biotec Cat# 130-095-067
Liberase TL Research Grade Sigma Cat# 5401020001
DNase I Sigma Cat# 10104159001
Fixable viability dye eFluor506 eBioscience Cat# 65-0866-14
7AAD BD Biosciences Cat# 559925
Digitonin Promega Cat# G9441
Critical Commercial Assays
RNeasy Plus Mini Kit QIAGEN Cat# 74134
iScript Reverse Transcription Supermix Bio-Rad Cat# 1708841
TaqMan Fast Advanced Master Mix Thermo Fisher Scientific Cat# 4444557
PolyJet In Vitro DNA Transfection Reagent SignaGen Laboratories Cat# SL100688
Dual-Luciferase Reporter Assay System Promega Cat# E1960
GeneChip Mouse Genome 430 2.0 Array Affymetrix Cat# 900497
Nextera DNA Sample Preparation Kit Illumina Cat# FC-121-1030
MinElute Reaction Cleanup kit QIAgen Cat# 28204
Chromium Single Cell 30 Library & Gel Bead 10x Genomics Cat# PN-120237
Kit v2
Chromium Single Cell A Chip Kit 10x Genomics Cat# PN-1000009
Deposited Data
Raw and analyzed data This paper GEO: GSE159208
Tr1 ATAC-seq (related to Figures 2B and Karwacz et al., 2017 GEO: GSE92993
6B)
Atf3 ChIP-seq (related to Figure 2B) Garber et al., 2012 GEO: GSE36104
Fosl2 ChIP-seq (related to Figure 2B) Ciofani et al., 2012 GEO: GSE40918
Tbx21 ChIP-seq (related to Figure 2B) Nakayamada et al., 2011 GEO: GSE33802
Irf8 ChIP-seq (related to Figure 2B) Xu et al., 2015 GEO: GSE70712
Experimental Models: Cell Lines
293T cells GenHunter Cat# Q401
Platinum-E (Plat-E) Retroviral Packaging Cell Biolabs Cat# RV-101
Cell Line
(Continued on next page)
e1 Cell Reports 33, 108433, November 24, 2020
ll
Resource OPEN ACCESS
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
Experimental Models: Organisms/Strains
Mouse: C57BL/6J The Jackson Laboratory JAX: 000664
BALB/cJ The Jackson Laboratory JAX: 000651
Mouse: Foxp3-GFP Bettelli et al., 2006 N/A
Mouse: 10BiT Maynard et al., 2007 N/A
Mouse: Prdm1f/f Shapiro-Shelef et al., 2003 JAX: 008100
Mouse: Maff/f Wende et al., 2012 N/A
Mouse: Ahr KO Schmidt et al., 1996 JAX: 002831
Mouse: Atf3f/f Taketani et al., 2012 N/A
Mouse: Batf KO Schraml et al., 2009 JAX: 013758
Mouse: Cebpbf/f Sterneck et al., 2006 N/A
Mouse: Ets1 KO Muthusamy et al., 1995 N/A
Mouse: Fosl2f/f Karreth et al., 2004 N/A
Mouse: Hif1af/f Ryan et al., 2000 JAX: 007561
Mouse: Irf1 KO Matsuyama et al., 1993 JAX: 002762
Mouse: Irf4 KO Klein et al., 2006 JAX: 009380
Mouse: Irf8f/f Feng et al., 2011 JAX:014175
Mouse: Irf9 KO Gift from Paul J. Utz RIKEN: RBRC00915
Mouse: Nfil3f/f Gascoyne et al., 2009 N/A
Mouse: Stat3f/f Moh et al., 2007 JAX: 016923
Mouse: Tbx21 KO Finotto et al., 2002 JAX: 004648
Mouse: Bhlhe40 KO Jiang et al., 2008 JAX: 029732
Mouse: Hlx+/
 Hentsch et al., 1996 JAX: 008313
Mouse: Stat4 Kaplan et al., 1996 JAX: 002826
Mouse: Batf3 KO Hildner et al., 2008 JAX: 013755
Mouse: Nfe2l2 KO Chan et al., 1996 JAX: 017009
Mouse: Irf7 KO Gift from Ian Rifkin N/A
Mouse: Id2f/f Seillet et al., 2013 N/A
Mouse: Fli1+/
 Gift from Maria Trojanowska N/A
Mouse: Cd4-Cre Lee et al., 2001 Taconic: 4196
Mouse: Actin-Cre Lewandoski et al., 1997 JAX: 033984
Mouse: Lck-Cre Hennet et al., 1995 JAX: 003802
Mouse: dLck-Cre Wang et al., 2001 JAX: 012837
Recombinant DNA
pGL4.10[luc2] Vector Promega Cat# E665A
pGL4.10-Il10 proximal promoter-luc2 This paper N/A
pGL4.10-Il10 CNS-9-luc2 This paper N/A
pGL4.10-Il10 HSS+2.98-luc2 This paper N/A
pGL4.10-Il10 HSS+6.45-luc2 This paper N/A
MSCV-IRES-GFP Gift from Tannishtha Reya Addgene #20672
MSCV-Maf-IRES-GFP This paper N/A
MSCV-IRES-Thy1.1 Gift from Philippa Marrack N/A
MSCV-Prdm1-IRES-Thy1.1 This paper N/A
Software and Algorithms
GenePattern Reich et al., 2006 https://www.genepattern.org/
EDGE Leek et al., 2006 https://www.bioconductor.org/
Tophat Trapnell et al., 2009 https://github.com/infphilo/tophat
RSEM Li and Dewey, 2011 http://deweylab.github.io/RSEM/
(Continued on next page)
Cell Reports 33, 108433, November 24, 2020 e2
ll
OPEN ACCESS Resource
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
DESeq2 Love et al., 2014 https://bioconductor.org/packages/
release/bioc/html/DESeq2.html
RStudio RStudio https://www.rstudio.com/
ATAC-seq pipeline Lee et al., 2016 https://zenodo.org/record/211733
Integrative Genomics Viewer Robinson et al., 2017 http://software.broadinstitute.org/
software/igv/
R package Seurat v3 Butler et al., 2018 https://satijalab.org/seurat/
FlowJo FlowJo https://www.flowjo.com
Prism GraphPad https://www.graphpad.comRESOURCE AVAILABILITY
Lead Contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Vijay
Kuchroo (vkuchroo@evergrande.hms.harvard.edu).
Materials Availability
This study did not generate new unique reagents.
Data and Code Availability
Data generated in this paper has been deposited in the Gene Expression Omnibus (GEO) under accession number GEO:
GSE159208.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Mice and Ethics Statement
C57BL/6, BALB/cJ, dLckCre,Hlx+/
 (Hentsch et al., 1996), Tbx21
/
 (Finotto et al., 2002), Irf8fl/fl (Feng et al., 2011), Prdm1fl/fl (Shapiro-
Shelef et al., 2003), Nfe2l2
/
 (Chan et al., 1996), Hif1afl/fl (Ryan et al., 2000), Ahr
/
 (Schmidt et al., 1996), Batf3
/
 (Hildner et al.,
2008), Stat3fl/fl (Moh et al., 2007), Stat4
/
 (Kaplan et al., 1996), Irf1
/
 (Matsuyama et al., 1993), Batf
/
 (Schraml et al., 2009),
Irf4fl/fl (Klein et al., 2006), Bhlhe40
/
 (Jiang et al., 2008) and 10BiT (Maynard et al., 2007) mice were purchased from Jackson Lab-
oratory. Cd4Cre (Lee et al., 2001) mouse was purchased from Taconic. Maffl/fl (Wende et al., 2012), Nfil3fl/fl (Gascoyne et al., 2009),
Id2fl/fl (Seillet et al., 2013), Ets1
/
 (Muthusamy et al., 1995) and Foxp3-GFP mouse (Bettelli et al., 2006) has been previously
described. Other previously described mutant strains were kindly provided by the following researchers: Fosl2fl/fl (Karreth et al.,
2004), D. Littman;Atf3fl/flActb-Cre (Taketani et al., 2012), H.Weiner;Cebpbfl/fl Lck-Cre+ (Sterneck et al., 2006),M. Rincon. In addition,
spleens from Irf7
/
, Fli1+/
, Irf9
/
mice were obtained from Ian R. Rifkin, Maria Trojanowska, and Paul J. Utz, respectively. In vitro
experiments were performed using 6-10 weeks old female and male mice.
All animals were housed and maintained in conventional pathogen-free facilities at the Harvard Institute of Medicine and Hale
Building for Transformative Medicine in Boston (IUCAC protocols: 2016N000444 (V.K.K.)). All experiments were performed in accor-
dance to guidelines outlined by Harvard Medical Area Standing Committee on Animals and the Brigham andWomen’s Hospital Insti-
tutional Animal Care and Use Committee.
METHOD DETAILS
Experimental methods
T cell sorting and in-vitro T helper cell differentiation
For the generation of time-course microarray data of Tr1 cells and validation of Il10 regulators in Tr1 cells in vitro,
CD4+CD44-CD62L+CD25- naive cells were sorted from WT B6 or indicated KO and their corresponding control mice with BD
FACSAria sorter, and then activated with plate-bound anti-CD3 and anti-CD28 (both at 1ug/ml) in the presence of 25ng/ml rmIL-
27 (R & D systems). 10ug/ml anti-TGFb (Bioxcell, Clone# 1D11.16.8) was also added for the microarray experiment.
For RNA-seq and qPCR analysis of IL-10 producing and non-producing T helper cells, naive CD4+CD44-CD62L+GFP- cells were
sorted from Foxp3-GFP; Il10-Thy1.1 double reporter mice using BD FACSAria sorter and were activated with irradiated splenocytes
depleted of CD4 T cells (at the T: APC ratio of 1:6) and 2.5ug/ml soluble anti-CD3 in the presence of polarizing cytokines. Concen-
tration of cytokines are as follows: 20ng/ml rmIL-12 (R & D systems) for Th1; 20ng/ml rmIL-4 (Miltenyi Biotec) for Th2; 2ng/ml ofe3 Cell Reports 33, 108433, November 24, 2020
ll
Resource OPEN ACCESSrhTGFb1 and 25ng/ml rmIL-6(both fromMiltenyi Biotec) for non-pathogenic Th17; 20ng/ml rmIL-1b(Miltenyi Biotec), 25ng/ml rmIL-6
(Miltenyi Biotec), and 20ng/ml rmIL-23 (R &D systems) for pathogenic Th17; 25ng/ml of rmIL-27 (R &D systems) for Tr1. IL-10 positive
(Thy1.1+) and negative (Thy1.1+) 7-AAD-TCRb+CD4+GFP- cells were re-sorted at 72 hours.
Isolation of lymphocytes from colonic lamina propria
To remove epithelial cells, colons were first washed for 20min in RPMI medium (GIBCO) with 3% FBS (Sigma-Aldrich), 5mM EDTA
(Invitrogen) and 1mMDTT (Sigma) in a shaking incubator at 400rpm at 37C, followed by three other washes each for 30 s by vibrant
vortexing in RPMI with 2mM EDTA. The tissue was then cut into little pieces and digested for 30min in RPMI with 100ug/mL Liberase
TL (Sigma) and 500ug/mL DNase I (Sigma) Digestion in a shaking incubator at 400rpm at 37C. Digestion was terminated by addition
of ice-cold RPMIwith 3%FCS. Cells were washed twice in RPMIwith 3%FCS, passed through a 40mmcell strainer and resuspended
in ice-cold RPMI with 3% FCS and 1mM EDTA for sorting.
RNA profiling by microarrays, population RNA-seq and single cell RNA-seq
The temporal gene expression profiling of Tr1 cells at 17 time points during in vitro differentiation by IL-27 were measured by Affy-
metrix GeneChipMouse Genome 430 2.0 Arrays. For genetic validation of the 24 TFs, naive CD4+ T cells were isolated from spleen of
knockout mice and matched controls and differentiated in vitro in Tr1 polarizing conditions for 72 hours. Cells were collected and
processed using an adaptation of the SMART-Seq 2 protocol (Tirosh et al., 2016), using 5uL of lysate from bulk CD4+ T cells as
the input for each sample during RNA cleanup via SPRI beads (2,000 cells lysed on average in RLT). Libraries were prepared using
the Nextera XT DNA Sample Prep Kit (Illumina), quantified, pooled, and then sequenced on the HiSeq 2500 (Illumnia) to an average
depth of 20M reads.
For scRNA-seq profiling of colonic CD4 T cells in control, Prdm1 cKO, Maf cKO, and DKO mice, live (7-AAD-) TCRb+CD4+ cells
were sorted from colonic lamina propria of eachmouse with two biological replicates for each genotype. Cells were processed using
Chromium Single Cell 30 Reagent Kits v2 according to manufacturer’s protocol (10X Genomics). For each biological replicate, an
input of 7,000 single cells was added to an individual channel with a recovery rate of approximately 1,3002100 cells. The generated
scRNA-seq libraries were sequenced on HiSeq X Ten.
ATAC-seq
Control, Prdm1 cKO, Maf cKO, and DKO Tr1 cells were cultured as described above for 72h with IL-27. Three to five replicates
were included for each genotype. Subsequently, 6,000 viable Tr1 cells were sorted and frozen in BambankerTM cell freezing
media (LYMPHOTEC Inc.) at 80C. For ATAC-seq library preparation, cells were thawed at 37C, washed once with PBS,
and lysed and tagmented in 1X TD Buffer, 0.2ml TDE1 (provided in Nextera DNA Sample Preparation Kit from Illumina),
0.01% digitonin, and 0.3X PBS in 40ml reaction volume following the protocol described by Corces et al. (2016). The DNA
was purified immediately with the MinElute PCR purification kit (QIAGEN), and then PCR amplified and quantified as we previ-
ously described (Wallrapp et al., 2019). The library was sequenced on an Illumina NextSeq 550 system with paired-end reads of
37 base pairs in length.
Quantitative RT-PCR
RNA was extracted using RNeasy Plus Mini Kit (QIAGEN), cDNA was prepared using iScript Reverse Transcription Supermix (Bio-
rad) and used as template for real-time qPCR run with TaqMan Fast Advanced Master Mix (Thermo Fisher Scientific) on the ViiA 7
Real-Time PCR System (Applied Biosystems). Expression was normalized to Actb. The following probes used for qPCR were pur-
chased from Applied Biosystems: Il10 (Mm01288386_m1), Maf (Mm02581355_s1), Prdm1 (Mm00476128_m1), Actb
(Mm00607939_s1).
Flow Cytometry
Single cell suspensions were stained with antibodies against surface molecules. Fixable viability dye eF506 or 7-AAD was used to
exclude dead cells. For intra-cytoplasmic cytokine staining, cells were stimulated with 12-myristate 13-acetate (PMA) (50ng/ml,
Sigma), ionomycin (1 mg/ml, Sigma) in the presence of Brefeldin A (Golgiplug, BD Biosciences) and Monensin (Golgistop, BD Biosci-
ences) for 4-5 hours prior to staining with antibodies against surface proteins followed by fixation, permeabilization with Fixation/Per-
meabilization Solution Kit (BD Biosciences) and staining with antibodies against intracellular cytokines. Data was analyzed with
Flowjo.
Luciferase assays
5x104 293T cells were seeded in 96 well plate one day before transfection and then transfected with Firefly luciferase reporter con-
structs for Il10, Renilla luciferase reporter (internal control) and plasmids expressing specific transcription factors using PolyJet
In Vitro DNA Transfection Reagent (SignaGen Laboratories). Cells were analyzed 48h later with Dual-Luciferase Reporter Assay Sys-
tem (Promega). To construct reporters for Il10 enhancers, previously described enhancer regions, including the CNS-9, HSS+2.98,
and HSS+6.45 (Lee et al., 2009), were cloned upstream of the Il10 minimal promoter. Fragments containing the proximal Il10 pro-
moter (
1.5 kb including the HSS-0.12 site) or the aforementioned enhancers were cloned into pGL4.10 Luciferase reporter plasmid
(Promega).
In-vivo treatment of anti-CD3
Mice were treated with 20 mg anti-CD3 monoclonal antibody (clone 145-2C11, Bio X Cell) or an isotype control (Bio X Cell) intraper-
itoneally every 3 days for a total of three times. Mice were sacrificed 4h after the last treatment. CD4 T cells were purified frommesen-
teric lymph node by MACS cell separation and Il10 expression was measured by qPCR.Cell Reports 33, 108433, November 24, 2020 e4
ll
OPEN ACCESS ResourceT cell transfer colitis
CD4+CD62L+ T cells were sorted from WT and Hlx+/
 mice and cultured with plate-bound anti-CD3 and anti-CD28 antibody in the
presence of 25ng IL-27. 72 h later cells were detached from the plate and rested for 48h before transferred into Rag1
/
 KO recip-
ients. 53 105 WT or Hlx heterozygous Tr1 cells were transferred intraperitoneally into Rag1
/
 animals and changes in body weight
were monitored weekly.
Retroviral infection
T cells activated with plate-bound anti-CD3 and anti-CD28 antibody in the presence of polarizing cytokines were transduced with
MSCV expressing Prdm1 (marked by Thy1.1) and Maf (marked by GFP) at 24h after activation. IL-10 expression in control
(Thy1.1-GFP-), Prdm1-overexpressing (Thy1.1+GFP-), Maf-overexpressing (Thy1.1-GFP+) and cells overexpressing both
(Thy1.1+GFP+) was analyzed by flow cytometry. For preparation of retroviruses, Plat-E cells were transfected with MSCV vectors
with PolyJet. Supernatant containing virus was harvested 48hr after transfection of Plat-E cells and then used for spin transduction
of T cells with polybrene (8 mg/ml) at 2000rpm, 32C for 1hr.
Computational Methods
Microarray data pre-processing and analysis
Individual .CEL files were RMA normalized and merged to an expression matrix using the ExpressionFileCreator of GenePattern with
default parameters (Reich et al., 2006). Gene-specific intensities were then computed by taking for each gene j and sample i the
maximal probe value observed for that gene. Samples were then transferred to log-space by taking log2(intensity).
Differentially expressed genes (comparing to the Th0 control) were found using a method we previously described (Yosef et al.,
2013). Briefly, genes that were detected in two of the four methods used were defined as differentially expressed: (1) Fold change.
Requiring a 2-fold change (up or down) during at least two time points. (2) Polynomial fit. We used the EDGE software (Leek et al.,
2006; Storey et al., 2005), designed to identify differential expression in time course data, with a threshold of q-value % 0.01. (3)
Sigmoidal fit. We used an algorithm similar to EDGE while replacing the polynomials with a sigmoid function, which is often more
adequate for modeling time course gene expression data (Chechik and Koller, 2009). We used a threshold of q-value % 0.01. (4)
ANOVA. Gene expression was modeled by time (using only time points for which we have more than one replicate) and treatment.
Themodel takes into account each variable independently, as well as their interaction. We report cases in which the P value assigned
with the treatment parameter or the interaction parameter passed an FDR threshold of 0.01.
To associate the regulation activity of a differentially expressed transcription factor with the three phases of IL-10 expression (la-
tency, induction and maintenance) we segmented our time course dataset into three corresponding time windows: 0-20h, 25-48h
and 54-72h. TFs were assigned to specific phases if they were differential expressed (> 1.8 Fold change) anytime during this time
window.
Prediction of TFs regulating the IL-27 network
Using approaches as we previously described (Yosef et al., 2013), we identified potential regulators of Tr1 differentiation by
computing overlaps between their putative targets and sets of differentially expressed genes grouped by k-means clustering. For
every TF in our database, we computed the statistical significance of the overlap between its putative targets and each of the groups
defined above using Fisher’s exact test. We included cases where p < 5 3 10
5 and the fold enrichment > 1.5.
Population RNA-seq data pre-processing and analysis
RNA-seq reads were aligned using Tophat (Trapnell et al., 2009) and RSEM-based quantification (Li and Dewey, 2011) using known
transcripts (mm9), followed by further processing using the Bioconductor package DESeq2 in R (Anders and Huber, 2010). The data
was normalized using TMM normalization. The TMMmethod estimates scale factors between samples that can be incorporated into
currently used statistical methods for DE analysis. Post-processing and statistical analysis was carried out in R (Li and Dewey, 2011).
For the analysis of the effect of different regulator KOs, differentially expressed genes were defined as genes with abs (logFC be-
tween control and KO) > 1.
For comparison between IL-10+ and IL-10- cells, differentially expressed genes were defined based on the raw counts with a single
call to the function DESeq2 (Love et al., 2014) (FDR-adjusted P value < 0.05). Heatmap figures were generated using pheatmap pack-
age (https://cran.r-project.org/web/packages/pheatmap/index.html).
ATAC-seq analysis
Generation and analysis of ATAC-seq data for in-vitro differentiated Tr1 cells at 24 h and 72 h were performed in our previously pub-
lished study (Karwacz et al., 2017). A publicly available ATAC-seq pipeline (Lee et al., 2016) was used for the processing of ATAC-seq
on Prdm1 cKO, Maf cKO and DKO Tr1 cells. Briefly, reads were aligned to the mm10 genome using Bowtie2 and filtered to remove
duplicates and mitochondrial reads. Biological replicate for each group were merged peaking-calling using MACS2 (Zhang et al.,
2008). Integrative Genomics Viewer (IGV) was used for visualization of ATAC-seq peaks.
Single cell RNA-seq analysis
Data preprocessing. De-multiplexing, alignment to the mm10 mouse transcriptome and UMI-collapsing were performed using the
Cellranger toolkit (version 2.1.0, 10X Genomics). Subsequent analysis was performed with R package Seurat v3 (Butler et al.,
2018). For downstream processing we filtered out low quality cells that had (1) a low number (< 500) of unique detected genes,
and (2) a high mitochondrial content (15%) determined by the ratio of reads mapping to the mitochondria. A small proportion of cells
were identified as contamination by macrophages, innate lymphoid cells, intraepithelial lymphocytes and fibroblasts, and weree5 Cell Reports 33, 108433, November 24, 2020
ll
Resource OPEN ACCESSexcluded from downstream analysis. To account for differences in sequencing depth across cells, UMI counts were normalized by
the total number of UMIs per cell and converted to transcripts-per-10,000 before being log transformed (henceforth ‘‘log(TP10K+1)’’).
PCA and clustering
Highly variable geneswere selected using the ‘mean.var.plot’ method in FindVariableFeatures functionwith default settings, resulting
in 341 genes which are then used for PCA analysis by RunPCA function. We used the first 40 PCs for subsequent analyses as they
capture the majority of signal in an elbow plot, but we also confirmed that the resulting analyses were not particularly sensitive to the
above-mentioned choice of parameters. The cells were clustered via Seurat’s FindClusters function, which optimizesmodularity on a
K-nearest-neighbor (KNN) graph computed from the top eigenvectors using Louvain algorithm, with nn.eps at 0.5, resolution at 0.4,
and n.start at 10. These parameters resulted in clusters that captured major genotype- related separations, known T cell subgroups,
and statistically validated transcriptional distinct sections of interest while avoiding subdivisions of relatively uniform parts of the data.
To visualize the data, UMAP plots were generated using Seurat’s RunUMAP function with min.dist at 0.75.
Cell type assignment
To identify which T cell subtype each cluster represents, we identifiedmarkers of each cluster using Seurat’s FindAllMarkers function
with min.pct at 0.25. The top 200 genes of each cluster were then used as input for My Geneset module of Immgen (immgen.org). We
assigned identity to each cluster based on the cell population in the Immgen database that display highest expression of its marker
genes and confirmed the designation by expression of known marker genes (Figure S5A).
Gene signatures
Scoring gene signature was performed using AddModuleScore function of Seurat based on strategies described by Tirosh et al.
(2016). Markers of cell cycles including G2/M phase and S phase were provided by Tirosh et al. (2016). Th1 signature was manually
curated based on literature. Th17 signature was generated by comparing microarrays of in vitro cultured Th17 cells to other T helper
cells, including naive, Th1, Th2, iTreg and nTreg cells (Wei et al., 2009; Xiao et al., 2014). CD4 T cells signature from ulcerative colitis
patients contains genes upregulated in CD4 T cells from biopsies of inflamed intestinal tissue in patients compared to those from
healthy tissue in healthy controls profiled by scRNA-seq (Smillie et al., 2019). IBD associated GWAS genes were compiled from liter-
ature (Graham and Xavier, 2020).
Differential expression analysis
Differentially expressed genes were tested using MAST (Finak et al., 2015) by calling FindMarkers function in Seurat. To find unique
markers for Prdm1/Maf DKO Tregs, DKO Tregs were compared against Treg cells in both single KO and control groups.
QUANTIFICATION AND THE STATISTICAL ANALYSIS
Unless otherwise specified, all statistical analyses were performed using the two-tail Student’s t test using GraphPad Prism software.
P value less than 0.05 is considered significant (p < 0.05 = *; p < 0.01 = **; p < 0.001 = ***, p < 0.0001 = ****. Data were represented as
mean ± s.e.m. unless otherwise specified. For certain types of numeric computations for transcriptomic data, the smallest P value
that R can report is < 2.2 3 10
16.Cell Reports 33, 108433, November 24, 2020 e6