Transcriptional Regulatory Networks That Promote and Restrict Identities and Functions of Intestinal Innate Lymphoid Cells

Home , ARNTL, ELF1, ELK3, EPAS1, FOXF1, FOXS1

bioRxiv preprint doi: https://doi.org/10.1101/465435; this version posted November 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Transcriptional regulatory networks that promote and restrict identities and functions of intestinal innate lymphoid cells

Maria Pokrovskii1, Jason A. Hall1, David E. Ochayon2,3, Ren Yi4, Natalia S. Chaimowitz2,3, Harsha Seelamneni2,3, Nicholas Carriero5, Aaron Watters5, Stephen N. Waggoner2,3, Dan R. Littman1,6*, Richard Bonneau4,5*, Emily R. Miraldi3,7

1The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, NY 10016, USA 2Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA 3Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA 4Departments of Biology and Computer Science, New York University, NY 10003, USA 5Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA 6Howard Hughes Medical Institute 7Divisions of Immunobiology and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA *Correspondence to [email protected] and [email protected]

Key Words: Gene regulation; ATAC-seq; lineage commitment; inferelator; lymphocyte development

1 bioRxiv preprint doi: https://doi.org/10.1101/465435; this version posted November 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Summary:

Innate lymphoid cells (ILCs) can be subdivided into several distinct cytokine-secreting lineages that promote tissue homeostasis and immune defense but also contribute to inflammatory diseases. Accumulating evidence suggests that ILCs, similarly to other immune populations, are capable of phenotypic and functional plasticity in response to infectious or environmental stimuli. Yet the transcriptional circuits that control ILC identity and function are largely unknown. Here we integrate gene expression and chromatin accessibility data to infer transcriptional regulatory networks within intestinal type 1, 2, and 3 ILCs. We predict the “core” sets of transcription-factor (TF) regulators driving each ILC subset identity, among which only a few TFs were previously known. To assist in the interpretation of these networks, TFs were organized into cooperative clusters, or modules that control gene programs with distinct functions. The ILC network reveals extensive alternative-lineage-gene repression, whose regulation may explain reported plasticity between ILC subsets. We validate new roles for c-MAF and BCL6 as regulators affecting the type 1 and type 3 ILC lineages. Manipulation of TF pathways identified here might provide a novel means to selectively regulate ILC effector functions to alleviate inflammatory disease or enhance host tolerance to pathogenic microbes or noxious stimuli. Our results will enable further exploration of ILC biology, while our network approach will be broadly applicable to identifying key cell state regulators in other in vivo cell populations.

2 bioRxiv preprint doi: https://doi.org/10.1101/465435; this version posted November 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Introduction:

Innate lymphoid cells (ILCs) are recently characterized cells that regulate critical aspects of tissue homeostasis, inflammation, and repair (Cherrier et al., 2012; Eberl et al., 2015; van de Pavert et Vivier, 2015; Sonnenberg et Artis, 2015; Spits et Cupedo, 2012; Walker et al., 2013). As the innate counterparts of adaptive T lymphocytes, ILCs lack somatically rearranged antigen receptors and respond rapidly upon encountering specific metabolites or cytokines (Tait Wojno et Artis, 2016; Vivier et al., 2018). In addition to this hallmark feature, ILCs are enriched at mucosal and barrier surfaces where they are poised to coordinate tissue immunity and homeostasis (Tait Wojno et Artis, 2012).

Although recent studies highlight the heterogeneity of intestinal ILCs (Björklund et al., 2016; Gury-BenAri et al., 2016), the cells can nevertheless be broadly classified into three principal groups based on their signature effector cytokines, functional characteristics, and dependence on lineage-determining transcription factor expression (Eberl et al., 2015; Spits et al., 2013; Walker et al., 2013). Type 1 ILCs include ILC1 and natural killer (NK) cells that produce interferon gamma (IFNg) and require the transcription factor T-bet (Tbx21). NK cells also produce IFNg, but they are unique in their potent cytolytic function and additional dependence on eomesodermin (Eomes) (Pikovskaya et al., 2016). Type 2 ILCs (ILC2s) require GATA3 and secrete the cytokines IL-4, IL-5, and IL-13. ILC3s comprise a heterogeneous group of related cells defined by their dependence on RORgt and production of IL-22 and IL-17 cytokines. During development, the T-helper-like ILCs (ILC1, ILC2, and a subset of ILC3) arise from a common progenitor that diverges from NK cell and lymphoid tissue inducer (LTi) cell progenitors (Constantinides et al., 2014; Klose et al., 2014). Unlike NK cells, these ILCs are dependent on IL7Ra. While some of the TFs driving early lineage commitment of ILCs have been discovered (Constantinides et al., 2014, 2015; Fang et Zhu, 2017; Lim et al., 2017; Serafini et al., 2015; Yu et al., 2013), the transcriptional mechanisms that maintain ILC identity and function are less well studied.

An emerging property of ILCs is their apparent functional and phenotypic flexibility in response to changing tissue environments (Lim et al., 2016, 2017; Melo-Gonzalez et Hepworth, 2017; Ohne et al., 2016). For example, ILC1 and ILC3 exist on a continuum marked at its extremes by the exclusive expression of lineage-specifying factors T-bet or RORgt, while intermediate populations express varying levels of both TFs (Bernink et al., 2015; Klose et al., 2013; Verrier et al., 2016; Vonarbourg et al., 2010). Their interconversion can result in the accumulation of a pro-inflammatory IFNg-producing “ex- ILC3” that drive intestinal inflammation (Bernink et al., 2013; Vonarbourg et al., 2010). This property mirrors the relationship between Th1 and Th17 cells, with conversion of RORgt+ IL-17A-expressing cells into T-bet+ IFNg-producing Th1-like “pathogenic Th17 cells” in the presence of IL-23 (Hirota et al., 2011). Plasticity between ILC1 and ILC2 as well as ILC1 and NK cells has also been reported (Lim et al., 2017). These findings highlight the need for a better understanding of the molecular mechanisms that govern ILC functional plasticity.

Recent studies have described epigenetic and transcriptional signatures for ILC subsets and suggested subset-specific TF regulators based on TF-motif analysis of differentially accessible chromatin regions (Gury-BenAri et al., 2016; Koues et al., 2016; Robinette et al., 2015; Shih et al., 2016). However, the TF regulators of individual genes in each ILC subset remain largely unknown. These regulatory relationships are the building blocks of

3 bioRxiv preprint doi: https://doi.org/10.1101/465435; this version posted November 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

larger gene programs driving ILC function and require broad, unbiased and systematic investigation.

Transcriptional regulatory networks (TRNs) describe the regulatory interactions between TFs and their target genes. TRN construction is especially challenging in relatively rare ex vivo cell types like ILCs, because many transcriptional regulators are unknown, and it is technically difficult to obtain direct TF occupancy data for those that are. In this context, integration of chromatin accessibility with TF motif databases provides a powerful means to identify putative regulatory regions and the candidate TFs binding to those regions (Pique-Regi et al., 2011). We recently developed a version of Inferelator algorithm that integrates TF motif analysis of chromatin accessibility and gene expression data to infer TRNs (Miraldi et al., 2018). Importantly, we showed that this method, applied to accessibility and gene expression data alone, can recapitulate a “gold standard” network (Ciofani et al., 2012; Yosef et al., 2013) that was built through the more laborious approach of TF chromatin immunoprecipitation and TF KO RNA-seq, in related Th17 cells (Miraldi et al., 2018).

In this study, we applied our newest Inferelator method to construct TRNs for five intestinal ILC subsets (CCR6+ ILC3, CCR6- ILC3, ILC1, NK cells, and ILC2) by leveraging recently published ILC genomics studies and our own intestinal ILC gene expression and chromatin accessibility datasets. This unbiased approach recovers the few known TF regulators of subset-specific genes and predicts new roles for nearly a hundred additional TFs in ILC regulation. Clustering of TFs, based on shared regulatory interactions, reveals core modules: groups of TFs that cooperatively regulate distinct functional pathways. Strikingly, the combined intestinal ILC network not only identifies activators of genes within each subset but also repressors of genes corresponding to alternative ILC lineages. We experimentally validate TFs predicted to repress alternative-lineage genes. These proof- of-concept validation experiments highlight how TRNs can be used to elucidate control points of cell identity and plasticity in mature intestinal ILCs.

Results

Innate lymphoid cell chromatin accessibility landscapes suggest activating and repressive regulatory elements

For construction of the transcriptional regulatory networks (TRNs) that underlie mature ILC maintenance and function, we globally identified putative regulatory elements in small (SI) and large intestine (LI) ILCs by the genome-wide assay for transposase-accessible chromatin using sequencing (ATAC-seq) (Buenrostro et al., 2013). In parallel, we also assessed gene expression by RNA-seq (Figure 1). Five ILC subsets were sort-purified from C57BL/6 mice using surface markers that discriminated lineage-restricted TF expression (Figure S1). Canonical NK cells were negative for IL7ra and other lineage markers but expressed NK1.1 while T helper-like ILCs were positive for IL7ra and expressed either Klrg1 and Gata3 (ILC2) or Klrb1b (ILC1/ILC3 continuum). Expression of NK1.1 and CCR6 among Klrb1b+ cells further discriminated T-bet positive ILC1 from two ILC3 sub populations that either expressed RORgt alone (CCR6+, which includes LTi cells) or co-expressed RORgt and T-bet (CCR6-).

4 bioRxiv preprint doi: https://doi.org/10.1101/465435; this version posted November 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 1

Figure 1. Study design for generation and biological validation of ILC TRN. ILCs were isolated from the small and large intestine of mice for measurement of gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) to build a transcriptional regulatory network (TRN) explaining subset-specific gene expression of intestinal ILCs. bioRxiv preprint doi: https://doi.org/10.1101/465435; this version posted November 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Quantitative analyses of the RNA-seq and ATAC-seq data led to the identification of differentially expressed genes and regulatory elements for each ILC subset. Correlation- based clustering of samples yielded expected groupings of ILCs based on the three broad ‘types’ with smaller differences between subsets, i.e., ILC1 and NK cells differed more from ILC2 and ILC3 than from each other (Figure S2). Examination of differentially expressed genes revealed the expected lineage-specific TF and cytokine/receptor genes (Figure S3). These include Rorc, Il23r, Il22, Il1r1, Il1r2 in ILC3; Gata3, Hes1, Pparg, Areg, Il1rl1 (ST2), Bmp2, Bmp7 in ILC2; Tbx21 in ILC1; and Eomes and Gzma in NK. In addition to these canonical ILC-subset markers, we identified hundreds of TFs and immunological signatures that also distinguished the subsets (Figure S3).

To explore major trends in the regulatory elements identified by ATAC-seq, we performed principal component analysis (PCA) on all (~127K) accessible peaks in the 5 ILC subsets (Figure 2A, left panel). The largest accessibility pattern distinguished ILC1/NKs from ILC3s (39% of the variance, PC 1), while the second largest separated ILC2s from ILC1s and ILC3s (27% of the variance, PC 2). Principal components 3 and 4 (not shown) captured smaller trends distinguishing ILC1 from NK and CCR6+ ILC3 from CCR6- ILC3. Approximately 104 peaks were unique to each broad class of ILC (ILC1/NK, ILC2, ILC3) (light colored circles, Loadings Plot, Figure 2A; light colored bars Figure 2B).

We next integrated differential gene expression into our analysis to determine if the putative regulatory elements correlated with expression of nearby genes. Peaks were assigned to genes based on their proximity (+/- 10kb) to genes uniquely expressed in one of the major ILC subsets (dark green, blue and red dots on the loadings plot Figure 2A, and dark colored bars Figure 2B). Indeed, the majority of proximal subset-specific peaks were adjacent to genes that were uniquely expressed in the same subset, consistent with putative promoter or enhancer activities (Figure 2B). One example is an ILC3-specific peak lying upstream of the Il17re locus, a gene that was uniquely expressed in ILC3s (Figure 2C, upper panel). Given the correlation between the gene expression and accessibility pattern, we posited that the accessible region might promote Il17re expression. A motif search in the ILC3-associated peak yielded a Retinoic Acid-Related Orphan Receptor Response Element (RORE), to which the ILC3-expressed TFs RORgt or RORa might bind and potentially promote Il17re expression.

Strikingly, a smaller number of ATAC-seq peaks proximal to genes uniquely expressed in ILC3 (dark blue dots and bars) were instead uniquely accessible in other subsets (ILC2s and ILC1/NKs, light green and pink) (Figure 2A,B). For example, two peaks, which were uniquely accessible in ILC1/NKs, are near a gene (Il23r) that is uniquely expressed in ILC3s (Figure 2C, lower panel). This pattern might arise from mechanisms of active repression. Specifically, NK/ILC1 lineage TFs might bind at distinct sites to suppress expression of alternative-lineage genes, such as Il23r. Based on TF gene expression patterns (Figure S3) and motif analysis, three potential ILC1/NK TFs (IRF8, TBX21 and EOMES) are candidate negative regulators of Il23r in type 1 ILCs. Similar to ILC3s, a smaller number of subset-specific peaks negatively correlate with the expression of proximal alternative-lineage genes in type 1 and 2 ILCs (Figure 2B).

To determine which TFs might bind to predicted activating or repressive regulatory elements in each ILC subset, we performed TF motif analysis (Methods). Specifically, we tested whether motifs of TFs uniquely expressed in a particular lineage were enriched in accessible regions proximal to genes expressed in that lineage (putative activation) or other lineages (putative repression). This analysis recovered motifs of the key known

5 CCR6+ CCR6+ ILC3 ILC3 CCR6+ CCR6+ ILC3 CCR6ILC3- CCR6- ILC3 RNA-seq ILC3 CCR6- CCR6- ILC3 RNA-seq Inference of ILC3 ILC1 ILC1 Small ILC TRNs Inference of Intestine ILC1 ATAC-seq ILC1 Small NK ILC TRNs NK ILargentestine ATAC-seq NK NK Intestine ILC2 ILC2 Large Intestine ILC2 ILC2 Figure 1 Figure 1 A CCR6+ CCR6+ ILC3 ILC3 A CCR6+- CCR6+CCR6- ILC3 RNA-seq ILC3ILC3 CCR6- CCR6- ILC1ILC3 ILC1 Small RNA-seq ILC3 Intestine ATAC-seq bioRxiv preprint doi: https://doi.org/10.1101/465435ILC1NK; this version posted NovemberInference 8, 2018. of The copyright holderILC1NK for this preprint (which was not certified by peerSmall review) is the author/funder. All rights reserved.ILC TRN No reuse allowed without permission. ILargentestine Intestine ATAC-seq ILC2NK Inference of ILC2NK ILC TRN Figure 2 Large Intestine ILC2 A B ATAC Scores Plot ILC2 CB fineILCATACfineILC 1607 1607sqrtZ Scores sqrtZ Scores Scores Plot (, 172111) (, 172111) ATACATAC Loadings Loadings Plot Plot ATAC peaks unique to: ILC2 ATAC peaks unique to: Subset Genes ILC2 ILC1 Distal to ILC BfineILCATAC 1607 sqrtZ Scores Scores Plot (, 172111) C Not

ATAC Loadings Plot ILC1+NK to ILC2 ΔGene ATACILC2 peaks unique to: proximal ILC2 ILC3 CCR6nILC3 SI ILC3s CCR6pILC3 SI ILC1 CCR6nILC3 SI And near genes Not

CCR6pILC3ILC1 SI SI to ILC2 SI Anduniquely ILC2near expressed genes in: ΔGene ILC1 SI proximal PC 2 (27%) 2 PC PC 2 (27%) 2 PC NK SI Subset Genes PC 2 (27.46%) ILC2 SI uniquelyILC3 expressed in: Proximal to PC 2 (27%) 2 PC CCR6nILC3 SI • ILC3 proximal PC 2 (27%) 2 PC ILC3

NK SI ΔGene PC 2 (27.46%) ILC1 CCR6pILC3 SI And• ILC1nearILC1+NKILC1ILC3 genes CCR6 ILC1 SI uniquely expressed in: ILC1 CCR6-ILC3 ILC2 SI • ILC2ILC2ILC1 PC 2 (27%) 2 PC +ILC3 (27%) 2 PC NK SI PC 2 (27.46%) NKCCR6-- CCR6+

PC 1 (38.96%) • ILC3ILC3sILC3ILC2 proximal NK ILC3 ΔGene ILC1 PCILC3 1 (39%) PC 1 (39%) PC 1 (38.96%) • ILC1ILC1 PC 1 (39%) CCR6 PC 1 (39%) ILC2 D NK CCR6-ILC3 +ILC3 • ILC2 GenesPC 1 (38.96%) ILC1 Peaks ILC2 Peaks ILC3 Peaks C PC 1Il17re (39%)(ILC3 gene) locus (ATACPC 1 (39%)-seq, putative activation) ILC1 Tbx21, Eomes, Tbx6, Irf8, Zbtb49, (none recovered) Rorc, Nrd1d D Zfp287, Zfp646, Zfp341 Genes ILC1 Peaks ILC2 Peaks ILC3+ Peaks Tbx21, Eomes, Nr4a2, Zbtb49, Zfp287, Mecom, Gata3, Rorc, Nrd1d1, Nkx6CCR6-2, Hoxa7,ILC3 Tcf7 ILC2 - ILC1 Tbx21,Zfp646, Eomes Zfp341, Tbx6, Irf8, Zbtb49, Pou2f2(none recovered) Rorc, Nrd1d CCR6 ILC3 Zfp287, Zfp646, Zfp341 ILC1 ILC3 Tbx21, Eomes, Irf8 (none recovered) Rorc, Nrd1d, Rela, Atf3, Batf3, Irf5, Mef2c, Nkx6-2, Pou6f2, Rax, Stat6,NK Tcf7, Zfp462, Zfp57 Tbx21, Eomes, Nr4a2, Zbtb49, Zfp287, Mecom, Gata3, Rorc, Nrd1d1, Nkx6-2, Hoxa7, Tcf7 ILC2 ILC2 Zfp646, Zfp341 PotentialPou2f2 transcriptional activation Potential repression Il23r (ILC3 gene) locus (ATAC-seq, putative repression) ILC3 Tbx21, Eomes, Irf8 (none recovered) Rorc, Nrd1d, Rela, Atf3, Batf3, Irf5, Mef2c, Nkx6-2, E Il17re (ILC3 gene) locus (ATAC-seq, putative activation)Pou6f2, Rax, Stat6, Tcf7, Zfp462, Zfp57 Potential transcriptional activation Potential repressionCCR6+ ILC3 - CCR6+ ILC3 CCR6 ILC3 ILC1 E Il17re (ILC3 gene) locus (ATAC-seq, putative activation) CCR6- ILC3 RORE motif ILC1 NK NK ILC2 ILC2CCR6+ ILC3 CCR6- ILC3 Il23r (ILC3 gene) locus (ATAC-seq, putative repression) ILC1 NK ILC2 CCR6+ ILC3 CCR6- ILC3 D IRFIl23r motif (ILC3 gene) Tbx21locus (ATAC motif-seq, putative repression) ILC1 RORE motif NK ILC2CCR6+ ILC3 Genes ILC1 Peaks ILC2 Peaks ILC3 Peaks CCR6- ILC3 ILC1 Tbx21, Eomes, Tbx6, Irf8, Zbtb49, (none recovered) Rorc, Nrd1d ILC1 ROREZfp287, motif Zfp646, Zfp341 NK ILC2 ILC2 Tbx21, Eomes, Nr4a2, Zbtb49, Zfp287, Mecom, Gata3, Rorc, Nrd1d1, Nkx6-2, Hoxa7, Tcf7 Zfp646, Zfp341 IRF motif Pou2f2Tbx21 motif ILC3 Tbx21, Eomes, Irf8 (none recovered) Rorc, Nrd1d, Rela, Atf3, Batf3, Irf5, Mef2c, Nkx6-2, Pou6f2, Rax, Stat6, Tcf7, Zfp462, Zfp57

IRF motifPotentialTbx21 transcriptional motif activation Potential repression

Figure 2. Characterization of genomic regulatory elements in ILCs. (A) 127k accessible regions (peaks) were identified in ILCs and accessibility levels quantified across samples. In the PCA scores plot, SI ILC samples are plotted according to accessibility patterns, while the PCA loadings plot highlights the peaks that contributed most to those patterns. In the loadings plot, peaks are annotated according to lineage-specific accessibility (pastel circles) and proximity to lineage-specific ILC genes (dark colored dots). (Lineage-specific peaks and genes are defined to have increased accessibility or expression in ILC1+NK, ILC2, or ILC3s at FDR=10%, log2(FC)>1 for each pairwise comparison.) (B) Subset-specific ATAC-seq peaks were categorized as proximal (+/-10kb gene body) or distal to subset-specific genes; color scheme as in (A). (C) Representative ATAC-seq data tracks are displayed for each ILC subset and TF motif occurrences in putative activating and repressive regions are highlighted in upper and lower panels, respectively. (D) The table displays subset-specific TFs whose motifs were enriched in subset-specific peaks proximal to subset-specific genes (FDR=10%, log2(FC)>1). bioRxiv preprint doi: https://doi.org/10.1101/465435; this version posted November 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

lineage-specifying TFs RORgt, T-bet and GATA3, as well as new candidate regulators (Figure 2D). For example, in unique ILC1/NK accessible regions, motifs for T-bet, Eomes, and other ILC1-expressed TF motifs were enriched proximal not only to ILC1 genes (suggesting activation) but also to ILC2 and ILC3 genes (suggesting that these TFs may have repressive functions across multiple gene loci).

Thus, simple motif-enrichment analysis, integrating differential accessibility with gene expression patterns of peak-proximal genes, can be used to derive a noisy initial set of TF-gene regulatory interactions (Blatti et al., 2015) and can predict whether these interactions may be activating or repressive (Karwacz et al., 2017). However, such a regulatory network would be expected to contain many false positives and negatives. For example, sources of error would include: 1) A TF motif occurrence within a peak indicates the potential for, rather than evidence of, TF binding; 2) TF motifs are often shared by members of the same TF subfamily, leading to ambiguity; 3) long-range regulatory interactions are difficult to assign to genes in the absence of 3D-chromatin conformation data; and 4) not all TF motifs have been determined (leading to the exclusion of many potential regulators). Thus, we refer to this initial, noisy set of TF-gene interactions as a “prior” network (∆ATAC), serving as input to a more sophisticated method for TRN inference (Miraldi et al., 2018).

ILC subsets have shared and unique core regulatory networks

We constructed an ILC TRN using our most recent Inferelator method (Miraldi et al., 2018) (Figure 1). Like the original (Bonneau et al., 2006), this method models gene expression as a function of TF activities, leveraging (partial) correlations between gene expression and TF activity profiles across many sample conditions to learn TF-target gene interactions (Methods). As increasing the number of gene expression samples boosts network inference, we incorporated publicly available ILC genomics datasets (Gury- BenAri et al., 2016; Shih et al., 2016) into our analysis (Figure S4A). As a result, our gene expression matrix tripled in size (to 62 ILC samples total: 40 from SI, 8 from LI, and the remainder from bone marrow, lung, liver and spleen). Importantly, recent versions of the Inferelator (Arrieta-Ortiz et al., 2015; Greenfield et al., 2013; Miraldi et al., 2018) enable incorporation of prior information (e.g., DATAC prior) and have markedly improved inference in another mammalian context, Th17 cell differentiation (Miraldi et al., 2018).

The ILC TRN contains models for 6,418 genes, resulting in 63,832 TF-gene interactions mediated by 445 TFs across all subsets (detailed in Methods, Figure S4). TFs were ranked according to number of target genes in the network; fourteen of the top 100 TFs (e.g., ARNT2, IKZF2, FOXJ1) were not in the DATAC prior but had strong support from gene expression modeling alone, confirming that our ILC TRN was not limited to TFs with known motifs (Figure S5). Positive regulatory interactions outnumbered repressive interactions by 1.6:1 in the ILC TRN. This positive-edge bias was consistent with the distribution of lineage-specific ATAC-seq peaks, in which putative promoter elements were proximal to genes specific to that lineage more frequently than genes unique to another lineage (Figure 2B); this trend was mirrored in subset-specific TF-gene interactions (Figure S4B,C). Thus, the ILC TRN predicted both activating and repressive mechanisms driving subset-specific gene expression patterns.

To identify the key transcriptional regulators associated with each cell type, we derived “core” TRNs for each of the five ILC subsets. We identified TFs that strongly promoted the gene expression patterns of individual ILC subsets based on enrichment of (1) positive

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Figure 3. Core TRNs for the ILC subsets. (A) Core TFs are clustered according to enriched positive regulation of up-regulated genes (red) or negative regulation of down-regulated genes (blue) in a given ILC subset, as both types of regulation would contribute to subset-specific gene expression. The significance of enrichment is displayed in the left panel. The right panel displays the number of positive (red) and negative (blue) regulatory interactions per TF; interactions supported by the DATAC prior are shaded pink (+) and blue (-). TFs previously associated with a given ILC subset are bolded. Core TRNs are displayed for (B) ILC3, (C) ILC1, and (D) ILC2 subsets, representing subset-specific as well as shared regulatory modalities, for select TFs in (A). Target genes are limited to differentially expressed cytokines, chemokines, and receptors. Node color represents z-scored gene expression in the given cell type (red for up-regulation and blue for down-regulation); TF node size is determined by its degree (number of target genes). Edge color denotes positive (red) and negative (blue) regulation. Solid edges are supported by the DATAC prior and gene expression modeling, while dotted edges are supported by gene expression modeling alone. bioRxiv preprint doi: https://doi.org/10.1101/465435; this version posted November 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

targets in the genes up-regulated in that subset or (2) repressed targets among down- regulated genes (Miraldi et al., 2018). This enabled recovery of both activating and repressive modalities for promotion of subset-specific gene expression patterns. TFs regulating subset gene signatures were clustered to identify classes of TFs unique to or shared between ILCs (Figure 3A). This analysis correctly recovered well-known subset- specific regulators. For example, RORgt is recovered as a shared regulator of both ILC3 subsets (Sawa et al., 2010). GATA3 is recovered in the ILC2 network (Hoyler et al., 2012). T-bet is a shared regulator in the NK and ILC1 networks (Powell et al., 2012; Sciumé et al., 2012; Townsend et al., 2004), while Eomes is unique to NK (Pikovskaya et al., 2016). Beyond these lineage-defining TFs, the ILC networks accurately predicted several other TFs with known functions in various ILC lineages. These include TCF-1 (Tcf7) in CCR6+ ILC3 (Mielke et al., 2013); LEF1 (Held et al., 2003) and BLIMP-1 (Prdm1) (Kallies et al., 2011; Smith et al., 2010) in NK; KLF2 in NK and ILC1 (Rabacal et al., 2016); and PPARg in ILC2 (highlighted in bold, Figure 3A). Importantly, the majority of the 125 TFs are novel predictions, highlighting the utility of our network approach to markedly expand TF predictions beyond what is possible by standard motif enrichment analysis.

To examine what genes were regulated by core TFs, we visualized the subset-specific TRNs, limiting target genes to cytokines and receptors (Figure 3B). Several of the cytokine interactions have been previously verified. For example, RORgt in ILC3 was predicted to regulate its canonical target genes, including Il23r, Il17a, and Il1r1. Thus, the recovery of known TFs and their regulatory interactions provided confidence in the overall quality of the networks and their value for pursuing novel interactions. These searchable core TRNs are available to explore via an interactive web-based interface: https://github.com/flatironinstitute/ILCnetworks (see Supplemental Information for instructions).

Transcription factor co-regulation of distinct functional pathways

The analyses described above implicated individual TFs in transcriptional control of genes specific to each ILC subset. However, many TFs function in a combinatorial manner to co- regulate distinct gene programs that enable specific cellular functions (Pope et Medzhitov, 2018). To determine how the TFs in the ILC TRN connect to ILC functions we clustered TFs into “co-regulating modules” based on overlap in their target genes (Miraldi et al., 2018). This analysis identified several groups of TFs with significant overlap in their positive target genes and helped organize the network into easier-to-interpret modules (Figure 4A, Methods). To help identify functional roles for each module, we performed gene-set enrichment analysis on the module’s co-regulated target genes (Figure S6, representative gene ontology and cellular pathway enrichments are summarized in Figure 4A). A cluster composed of TGIF, NR2F6, IRF7, VDR, MAF, and BCL6 is predicted to positively regulate chemotaxis and cytokine signaling, suggesting that it may be relevant to ILC identity and trafficking. Each of these factors, with the exception of TGIF1, were predicted to be both activators and repressors of ILC3 genes. These TFs are most highly expressed in SI CCR6- ILC3, and are hence most likely to coordinate cytokine and chemokine programs in these cells (Figure 4B). The targets of known circadian regulators NR1D1, RORA, and NR1D2 (Sato et al., 2004; Zhang et al., 2014) are most highly expressed in ILC3, and form another distinct cluster (Figure 4A). A cluster of genes predicted to regulate adipogenesis is most highly expressed in ILC2 (in both SI and LI), and two of the TFs (GATA3 and STAT1) are themselves in the adipogenesis gene set (Figure 4A,C). ILC3s have the ability to process and present antigen to regulate T cells in response to microbiota (Hepworth et al., 2013, 2015), yet the transcriptional regulation of

7 TF-TF module analysis – positive edges, 15 top clusters (out of 57) 10 TF / gene model CCR6+ILC3, Citro LI CCR6-ILC3, Citro LI CCR6+ILC3, SI CCR6+ILC3, LI CCR6-ILC3, SI CCR6-ILC3, LI ILC2, Citro LI Figure 4 ILC2, SI ILC1, SI ILC2, LI NK, SI ILC3 ILC3 - A - CCR6+ILC3 CCR6 ILC1 NK ILC2 posEdge posEdgeCCR6+ILC3 CCR6 ILC1 NK ILC2 negEdge negEdge P <= 0.01 P <= 0.01 Gene Expression adj adj TGIF1 TGIF1TGIF1 TGIF1 Tgif1 TGIF1 media(1h) >2 2 % Overlap between networks, TF/gene = 15 ≥ 1 NR2F6 NR2F6 NR2F6 Nr2f6 sATAC (Th17NR2F6 48h), b=50, noTFA 13 Th0(1h) NR2F6 ChIP/sA(Th17), b=50, noTFA Th0(3h) ChIP/sA(Th17)+KO+sA(Th), b=50, noTFA IRF7 IRF7 IRF7 Irf7 IRF7 Th0(6h) IRF7 No Prior 0.9 ChIP, b=50, noTFA Th0(16h) ) VDR VDR VDR VDR KO, b=50, noTFA Th0(48h) VDR Vdr qATAC (All Th), b=50, noTFA KO, b=10, noTFA Th17(1h) adj 0.8 MAF MAF MAF MAF ChIP, b=100 Th17(3h) MAF Maf ChIP, b=50 P qATAC (All Th), b=10, noTFA Th17(6h) ( 0 Th17(9h) BCL6 BCL6 BCL6 Bcl6 BCL6 ChIP, b=10 score 0.7 12 BCL6 ChIP, b=10, noTFA - Th17(12h) 10 KO, b=100 Th17(16h) NR1D2 NR1D2 NR1D2 Nr1d2 NR1D2 KO, b=50 NR1D2 KO, b=10 0.6 Th17(24h) qATAC (All Th), b=100 Th17(48h) RORA log RORARORA RORA Rora RORAqATAC (All Th), b=50 Th1(48h) - qATAC (All Th), b=10 NR1D1 NR1D1 sATAC (Th17 48h), b=100 0.5 Th2(48h) NR1D1NR1D1 Nr1d1 sATACNR1D1 (Th17 48h), b=50 Treg(48h) ≤-2 ChIP/sA(Th17), b=100 PBX2 * edges) of (Sign <-2 PBX2 PBX2 PBX2 ChIP/sA(Th17), b=50 PBX2 Pbx2 ChIP/sA(Th17)+KO+sA(Th), b=100 0.4 ChIP/sA(Th17)+KO+sA(Th), b=50 Normalized Rlf

Irf9 Irf2 Irf1 Irf7 Vdr Lbr Cic IKZF1Id1 Id2 Erg IKZF1 sATAC (Th17 48h), b=10 Sp9 Atf5 Atf3 Txk Pcx Pml Maf Tal2 Tox Utf1 Klf3 Batf Aff3 IKZF1 Ikzf1 IKZF1

Dpf1 Ing2 Mxi1 Rest Sirt6 Etv4 Ikzf3 Rorc Rora Vax2 Edf1 Ift57 Bcl3 Mlf1 Ikzf2 Spib Rbpj Dffb IKZF1 ChIP/sA(Th17), b=10 Scrt1 Tcf25 Hes6 Hif1a Arrb1 Glis3 Hif3a Foxj1 Sos2 Jdp2 Msh5 Mafg Ddit3 Stat2 Stat1 Zbp1 Eno1 Stat3 Mxd1 Ssh2 Chd7 Phtf2 Nrip1 Akna Tnp2 Hhex 0.3 Sap30 Rcor2 Plscr1 Nupr1 Tbx21 Oasl1 Dhx58 Nr1d2 Nr1d1 Foxs1 Thap3 Tead1 Ssbp4 Prkdc Mbd5 Foxp2 Bcl6b Bmyc Plagl2 Nfkb2 Nfkb1 Plagl1 Gata3 Gata1 Pparg Epas1 Zbtb5 Foxo3 Trp53 Trp73 ChIP/sA(Th17)+KO+sA(Th), b=10 Zfp654 Zfp263 Zfp395 Ankzf1 Rbbp8 Cebpg Zfp790 Cebpb Trim21 Nfkbil1 Srebf1 Zfp608 Zfp827 Nfkbib Nfkbie Ncoa3 Zfp266 Trim24 Aebp2 Zfp750 Zfp365 Overlap Z Overlap Snapc1 Fbxo21 Kdm5b Setdb2 Ppfibp2 STAT1 STAT1 sATAC (Th17 48h), b=10, noTFA Dnajc21 Trim30a Zfp280b Ppp1r10 Zfp385a STAT1 Stat1 STAT1 Ppp1r13l Tsc22d3 STAT1 ChIP/sA(Th17), b=10, noTFA ChIP/sA(Th17)+KO+sA(Th), b=10, noTFA GATA3 GATA3GATA3 GATA3 Gata3 GATA3 00.2 C030039L03Rik

POU2F2 POU2F2 POU2F2 Pou2f2 POU2F2 No Prior

POU2F2 KO, b=50 KO, b=10

P = .1 KO, b=100 10 adj Padj = .1 ChIP, b=50 ChIP, b=10 ELF1 ELF1ELF1 ELF1 Elf1 ELF1 ChIP, b=100 KO, b=50, noTFA KO, b=10, noTFA ChIP, b=50, noTFA ChIP, b=10, noTFA ChIP/sA(Th17), b=50 ChIP/sA(Th17), b=10 qATAC (All Th), b=50 qATAC (All Th), b=10 E2F6 E2F6E2F6 E2F6 E2f6 E2F6 qATAC (All Th), b=100 ChIP/sA(Th17), b=100 sATAC (Th17 48h), b=50 sATAC (Th17 48h), b=10 sATAC (Th17 48h), b=100 ChIP/sA(Th17), b=50, noTFA ChIP/sA(Th17), b=10, noTFA KLF5 KLF5KLF5 KLF5 Klf5 KLF5 qATAC (All Th), b=50, noTFA qATAC (All Th), b=10, noTFA sATAC (Th17 48h), b=50, noTFA sATAC (Th17 48h), b=10, noTFA ChIP/sA(Th17)+KO+sA(Th), b=50 ChIP/sA(Th17)+KO+sA(Th), b=10 E2F1 E2F1E2F1 E2F1 E2f1 E2F1 ChIP/sA(Th17)+KO+sA(Th), b=100 ZFP654 ZFP654ZFP654 ZFP654 Zfp654 ZFP654 ChIP/sA(Th17)+KO+sA(Th), b=50, noTFA ChIP/sA(Th17)+KO+sA(Th), b=10, noTFA

ZFP51 ZFP51ZFP51 ZFP51 Zfp51 Gene Expression ZFP51 ZFP120 ZFP120ZFP120 ZFP120 Zfp120 ZFP120 PURB PURBPURB PURB Purb PURB ZFP260 ZFP260ZFP260 ZFP260 Zfp260 ZFP260 8 NR2C2 NR2C2NR2C2 NR2C2 Nr2c2 NR2C2 ZFP397 ZFP397ZFP397 ZFP397 Zfp397 ZFP397 ZFP800 ZFP800ZFP800 ZFP800 Zfp800 ZFP800 EEA1 EEA1EEA1 EEA1 Eea1 EEA1 TSC22D1 TSC22D1 TSC22D1 Tsc22d1 TSC22D1 P TSC22D1= 1 P = 1 adj Padj = 1 adj Padj = 1 SOX7 SOX7SOX7 SOX7 Sox7 SOX7 NFIB NFIBNFIB NFIB Nfib NFIB

FOXF1 FOXF1FOXF1 FOXF1 Foxf1 FOXF1 6 ELF3 ELF3ELF3 ELF3 Elf3 ELF3 ONECUT2 ONECUT2ONECUT2 ONECUT2 Onecut2 ONECUT2 EHF EHFEHF EHF Ehf EHF ZFP385A ZFP385AZFP385A ZFP385A Zfp385a ZFP385A SPI1 SPI1SPI1 SPI1 Spi1 SPI1 POU2AF1 POU2AF1POU2AF1 POU2AF1 Pou2af1 POU2AF1 GATA2 GATA2GATA2 GATA2 Gata2 GATA2 ZFP367 ZFP367ZFP367 ZFP367 Zfp367 ZFP367 4 FOXM1 FOXM1FOXM1 FOXM1 Foxm1 FOXM1 KLF8 KLF8KLF8 KLF8 Klf8 KLF8 E2F2 E2F2E2F2 E2F2 E2f2 E2F2 P = .1 adj Padj = .1 TRERF1 TRERF1TRERF1 TRERF1 Trerf1 TRERF1 SCMH1 SCMH1SCMH1 SCMH1 Scmh1 SCMH1 ZFP574 ZFP574ZFP574 ZFP574 Zfp574 ZFP574 PLAGL2 PLAGL2PLAGL2 PLAGL2 Plagl2 PLAGL2 ZFP668 ZFP668ZFP668 ZFP668 Zfp668 ZFP668 ZFP605 ZFP605ZFP605 ZFP605 Zfp605 ZFP605 2 TFAP4 TFAP4TFAP4 TFAP4 Tfap4 TFAP4 PRMT3 PRMT3PRMT3 PRMT3 Prmt3 PRMT3 PA2G4 PA2G4PA2G4 PA2G4 Pa2g4 PA2G4 CHAMP1 CHAMP1CHAMP1 CHAMP1 Champ1 CHAMP1 ZFP1 ZFP1ZFP1 ZFP1 Zfp1 ZFP1 DACH2 DACH2DACH2 DACH2 Dach2 DACH2 NME2 NME2NME2 NME2 Nme2 NME2 MXI1 MXI1MXI1 MXI1 Mxi1 MXI1 Gene Expression P <= 0.01 P <= 0.01 0 adj adj >2 CCR6+ILC3, SI Gene Expression Irf7 Vdr Ikzf1 Elf1 Klf5 Elf3 Ehf Klf8 Tgif1 Maf Nfib Bcl6 Rora Stat1 E2f6 E2f1 Purb Spi1 E2f2 Trerf1 Zfp1 Nr2f6 Pbx2 Eea1 Sox7 Foxf1 Plagl2 Tfap4 Mxi1 Nr1d2 Nr1d1 Gata3 Zfp51 Nr2c2 Gata2 Prmt3 Pou2f2 Zfp654 Zfp120 Zfp260 Zfp397 Zfp800 Zfp367 Zfp574 Zfp668 Zfp605 Pa2g4 Dach2 Foxm1 Nme2 NK SI up NK SI up Onecut2 Zfp385a Pou2af1 Scmh1

>2 NK SI up NK SI up CCR6-ILC3, SI Tsc22d1 Champ1 media(1h) ILC1 SI up ILC2 SI up ≥2 ILC1 SI up ILC2 SI up Th0(1h) ILC1 SI up ILC2 SI up ILC1 SI up ILC2 SI up Th0(3h) ILC1, SI Th0(6h) CCR6-ILC3 SI up CCR6-ILC3 SI up CCR6-ILC3 SI up CCR6-ILC3 SI up CCR6+ILC3 SI up CCR6+ILC3 SI up Th0(16h) CCR6+ILC3 SI up NK, SI CCR6+ILC3 SI up

Th0(48h) <-2 0 >2 Th17(1h) ILC2, SI Th17(3h) Th17(6h) 0 CCR6+ILC3, LI 0 Th17(9h) Th17(12h) CCR6-ILC3, LI Th17(16h) Th17(24h) ILC2, LI

Th17(48h) Log2(FC) Th1(48h) CCR6+ILC3, Citro LI Th2(48h) Treg(48h) <-2 ≤-2 CCR6-ILC3, Citro LI Gene Expression Expression Gene ILC2, Citro LI Rlf Irf9 Irf2 Irf1 Irf7 Vdr Lbr Cic Id1 Id2 Erg

Sp9 Atf5 Atf3 Txk Pcx Pml Maf Tal2 Tox Utf1 Klf3 Batf Aff3 <-2 Dpf1 Ing2 Mxi1 Rest Sirt6 Etv4 Ikzf3 Rorc Rora Vax2 Edf1 Ift57 Bcl3 Mlf1 Ikzf2 Spib Rbpj Dffb Scrt1 Tcf25 Hes6 Hif1a Arrb1 Glis3 Hif3a Foxj1 Sos2 Jdp2 Msh5 Mafg Ddit3 Stat2 Stat1 Zbp1 Eno1 Stat3 Mxd1 Ssh2 Chd7 Phtf2 Nrip1 Akna Tnp2 Hhex Sap30 Rcor2 Plscr1 Nupr1 Tbx21 Oasl1 Dhx58 Nr1d2 Nr1d1 Foxs1 Thap3 Tead1 Ssbp4 Prkdc Mbd5 Foxp2 Bcl6b Bmyc Plagl2 Nfkb2 Nfkb1 Plagl1 Gata3 Gata1 Pparg Epas1 Zbtb5 Foxo3 Trp53 Trp73 Zfp654 Zfp263 Zfp395 Ankzf1 Rbbp8 Cebpg Zfp790 Cebpb Trim21 Nfkbil1 Srebf1 Zfp608 Zfp827 Nfkbib Nfkbie Ncoa3 Zfp266 Trim24 Aebp2 Zfp750 Zfp365 Snapc1 Fbxo21 Kdm5b Setdb2 Ppfibp2 Dnajc21 Trim30a Zfp280b Ppp1r10 Zfp385a Ppp1r13l Tsc22d3 EHF IRF7 VDR MAF NFIB SPI1 ELF1 E2F6 KLF5 E2F1 ELF3 KLF8 E2F2 ZFP1 MXI1 BCL6 PBX2 EEA1 SOX7 TGIF1 IKZF1 RORA PURB NME2 STAT1 ZFP51 TFAP4 C030039L03Rik NR2F6 NR1D2 NR1D1 GATA3 NR2C2 FOXF1 GATA2 PA2G4 PRMT3 DACH2 ZFP654 ZFP120 ZFP260 ZFP397 ZFP800 ZFP367 FOXM1 SCMH1 ZFP574 ZFP668 ZFP605 POU2F2 TRERF1 PLAGL2 ZFP385A CHAMP1 TSC22D1 POU2AF1 ONECUT2 B C D

Figure 4. TF-TF modules co-regulate expression of gene pathways. (A) The normalized target gene overlap between the top 15 “TF-TF modules” (sets of TFs with significant overlap in positively regulated target genes) are displayed and annotated with enriched gene pathways in the main panel. TF-TF module positive and negative target gene enrichments in ILC subset gene sets are displayed in the left panels, while gene expression of TF constituents is displayed in the bottom panel. Positive TF- TF modules are displayed for the (B) “chemotaxis, cytokine signaling”, (C) “adipogenesis”, and (D) “antigen presentation” modules, and nodes represent z-scored relative gene expression in SI CCR6- ILC3, SI ILC2, and LI CCR6- ILC3, respectively. Target genes were included in the TRN visualization if positively regulated by at least two TFs in the module. this process is not fully understood. This analysis predicted that a set of TFs (ZFP385A, SPI1, POU2AF1, and GATA2), most highly expressed in CCR6- ILC3 of the LI, regulate genes involved in “Antigen presentation”, “Asthma”, “Rheumatoid Arthritis”, and “Intestinal IgA production network”. Indeed, several HLA genes are regulated by TFs in this module, including H2-DMa, H2-DMb2, H2-Ab1, H2-DMb1, and H2-Ob (Figure 4D). Thus, our co- regulator module analysis highlights sets of TFs with coordinated regulation of key pathways in ILCs, recovering some known TF-pathway associations and many more novel predictions for TF regulation of ILC function.

The ILC TRN reveals TF repressors of alternate lineage genes

The phenomenon of ILC functional and phenotypic plasticity is poorly understood. We hypothesized that regulatory circuits directing the conversion of one ILC subset into another might function via de-repression of alternative ILC subset genes. We thus focused on TFs in the network that were predicted to act as suppressors of such genes. We identified TFs in the TRN whose negative target genes were enriched in signature genes of another ILC subset (Figure 5A). Several of the TFs (TBX21, EOMES, IRF8) identified by DATAC motif enrichment analysis (Figure 2D) were again recovered by this TRN analysis. Importantly, the TRN also uncovered numerous additional factors, several of which did not have recognizable TF motifs and were thus unrecoverable from ATAC-seq analysis alone. We further examined how these negative regulators were predicted to regulate one another. We thus generated a TF-TF lineage-regulating network for some of the most significant alternative-lineage gene repressors identified (Figure 5B). In the repressive TRN, ILC3-specific Rorc antagonizes ILC1/NK regulators Tbx21 and Bach2, while NK/ILC1 regulators, including Tbx21 and Bcl6, repress Rorc. Tbx21 additionally antagonizes the hallmark ILC2 TF Gata3. Reciprocally, ILC2 TFs Klf5, Pou2f2 and Irf4 repress Tbx21 and Rorc. These analyses implicate a number of ILC regulators in the control of ILC plasticity. We chose BCL6 and c-MAF as candidates for further validation due to their predicted reciprocal regulation of genes in the ILC1/ILC3 continuum as well as for their predicted roles in controlling a module of cytokine and cytokine receptor genes (Figure 4A,B). c-MAF regulates the balance between intestinal ILC1 and ILC3

The transcription factor c-MAF has critical functions in effector T cells, contributing to the expression of IL-10 in multiple T cell subsets, the differentiation of induced Treg cells (Xu et al., 2018), and the repression of IL-22 transcription (Rutz et al., 2011). Its function in ILCs has not been explored. Our ILC TRN predicted that c-MAF could repress genes unique to the CCR6+ ILC3 lineage and promote expression of genes in the CCR6- ILC3 and ILC1 lineages, where c-MAF is most highly expressed (Figures 3A, 4A, 5A). To address whether c-MAF regulates these cell types, we examined ILCs in the SI lamina propria of mice lacking c-MAF in all lymphocyte populations (Il7racre; Maffl/fl) (Wende et al., 2012) (Figure 6A). Indeed, there was a reduction in the number of RORgt+ T-bet- ILC3 accompanied by an increase in the frequencies and total numbers of the other ILC subsets, particularly ILC1, in Maffl/fl;Il7racre animals compared to Maffl/fl (or Il7racre) controls (Figure 6A,C). Notably, Maffl/fl;Il7racre ILCs exhibited increased expression of T-bet, suggesting that c-MAF may regulate the balance of ILC1 and CCR6+ ILC3 via direct or indirect suppression of Tbx21 (Figure 6B). Thus, as predicted by the ILC TRN, c-MAF regulates the balance between ILC1 and ILC3.

BCL6 promotes abundance of intestinal type 1 ILCs

8 Zfp263 Bcl6 Irf4 Irf7 Maf Egr2 Irf1 Spi1 Hic1 Gata2 Foxj1 Prdm1 Zfp105 Nr3c1 Egr1 Stat1 Pou2f2 Ikzf2 Trp53 Srf Elf1 Sp3 Vdr Mecom Rest Tfcp2 Rfx2 Pparg Max Tbx21 Irf5 Klf4 Zfp219 Klf5 Tcf7l1 Myc Mxi1 Plag1 Foxa1 Bcl11a Elf3 Egr3 Pou2af1 Figure 5 Klf2 Pura Nfkb1 Bach2Subset Genes re+ress1E-5, geneEx+ TF RNAseqTcf7l2Repressed # of edges A Thra ZEB1ZEB1 ZEB1Jund ZEB1 ELK1ELK1 Stat3ELK1 Irf8 ELK1 PHF21APHF21A PHF21A Zfhx3 PHF21A ZBTB24ZBTB24 ZBTB24Lef1 ZBTB24 ILC2/3--|NK ZBTB4ZBTB4 ZBTB4Sox7 ZBTB4 ZSCAN26ZSCAN26 ZSCAN26Elk3 Nfatc1 ZSCAN26 ZFP131ZFP131 ZFP131 Zfp110 ZFP131 ZFP516ZFP516 ZFP516Sox8 ZFP516 RORARORA Nfe2l2RORA RORA D3ERTD254ED3ERTD254E D3ERTD254EFoxo1 D3ERTD254E ZFP608ZFP608 ZFP608Zfp1 Rora ZFP608 ZSCAN12ZSCAN12 ZSCAN12 ZSCAN12 + Foxm1 CCR6 ILC3 ETV6ETV6 ETV6Tef ETV6 --|ILC1/NK FOXA1FOXA1 Foxf1FOXA1 FOXA1 MEIS1MEIS1 Gata3MEIS1 Smad1 MEIS1 ZKSCAN3ZKSCAN3 ZKSCAN3 Foxc1 ZKSCAN3 RFX5RFX5 Zbtb16RFX5 RFX5 ZEB2ZEB2 Stat6ZEB2 ZEB2 IRF4IRF4 Sox4IRF4 Nfix IRF4 EPAS1EPAS1 EPAS1 ILC2--|ILC1… Myb EPAS1 KLF5KLF5 KLF5E2f3 KLF5 POU2F2POU2F2 POU2F2Pbx1 POU2F2 --|ILC1/NK EGR1EGR1 EomesEGR1 EGR1 ATF6ATF6 Setdb1ATF6 Nr4a2 ATF6 NFATC1 NFATC1 media(1h) Th17(12h) Th17(16h) Th17(24h) Th17(48h) Treg(48h) NFATC1 Th0(16h) Th0(48h) Th17(1h) Th17(3h) Th17(6h) Th17(9h) Th1(48h) Th2(48h) Th0(1h) Th0(3h) Th0(6h) Zeb1 NFATC1 --|ILC2 ZFP606 ZFP606 ZFP606 Scrt1 Klf7 ZFP606 Tcf25 Dpf1 Sap30 PRDM1 Ing2 PRDM1 Zfp654 PRDM1 Tcf7 Sp9 PRDM1 Zfp263 Mxi1 Hes6 TRP53 Zfp395 TRP53 Hey1 Rlf TRP53 Ppp1r13l TRP53 Snapc1 C030039L03Rik Hif1a Elk1 Arrb1 ESR1 ESR1 Glis3 Rcor2 Treg(48h) Th17(48h) Th17(24h) Th17(16h) Th17(12h) media(1h)

ESR1Th2(48h) Th1(48h) Th17(9h) Th17(6h) Th17(3h) Th17(1h) Th0(48h) Th0(16h) Hif3a Th0(6h) Th0(3h) Th0(1h) ESR1 Fbxo21 Atf3 Dnajc21 Rest MAF MAF Sirt6 Scrt1 Foxj1 Tcf25MAF + Kdm5b Dpf1 MAF Sox5 Ankzf1 Sap30 --|CCR6 ILC3 Ing2 Sos2 Zfp654 Plscr1 Sp9JUN JUN Jdp2 Zfp263 Rbbp8 Mxi1JUN Etv5 Etv4 Hes6 JUN Msh5 Zfp395 Rlf Mafg Ppp1r13l Tsc22d3 Snapc1JUNB JUNB Nupr1 C030039L03Rik Arnt2 Ddit3 Hif1a JUNBArrb1 Cebpg Glis3 JUNB Zfp790 Rcor2 Atf5 Hif3a Cebpb Fbxo21 Ar Atf3 Dnajc21ATF3 ATF3 Rest Trim21 ATF3Sirt6 Irf9 Foxj1 ATF3 Irf2 Kdm5b

Gene Expression Tbx21 Ankzf1 Bhlhe40 Stat2 Sos2 Plscr1BACH2 BACH2 Stat1 Jdp2 Zbp1 BACH2Rbbp8 Irf1 Etv4 BACH2 Ikzf3 Msh5 Ikzf3 Mafg Oasl1 Tsc22d3 Irf7 Nupr1 FOXC2 Trim30a Ddit3FOXC2 Dhx58 Cebpg Txk FOXC2Zfp790 Nfic Atf5 FOXC2 Rorc Cebpb Rora Atf3 Nr1d2 - Trim21 NR2F6 Setdb2 Irf9NR2F6 Vdr Irf2 Mef2d Vax2 --|CCR6 ILC3 NR2F6Tbx21 Gene Expression Stat2 NR2F6 Nr1d1 Stat1 Foxs1 Zbp1 Eno1 Irf1 Thap3 Ikzf3BCL6 Meis3BCL6 Oasl1 Nfkbil1 Irf7 Edf1 Trim30aBCL6 Srebf1 Dhx58 BCL6 Lbr Txk Tead1 Rorc Klf8 Rora Zfp608 Nr1d2MBD1 MBD1 Pcx Setdb2 Ift57 MBD1Vdr Ssbp4 Vax2 MBD1 Nr1d1 Zbtb4 Stat3 Foxs1 Mxd1 Eno1 Pml Thap3ZFHX3 ZFHX3 Ssh2 Nfkbil1 Chd7 Edf1 ZFHX3Srebf1 Ybx1 ZFHX3 Maf Lbr Cic Tead1 Bcl3 Zfp608 Prkdc Pcx IRF8 Mbd5 Ift57 IRF8 Ssbp4 Sp6 Ppfibp2 Stat3IRF8 Foxp2 NK/ILC1--|ILC2 Mxd1 IRF8 Tal2 Pml Mlf1 Ssh2 Bcl6b Chd7 TBX21 MafTBX21 Bach1 Zfp827 Cic Ikzf2 Bcl3 Phtf2 TBX21Prkdc TBX21 Bmyc Mbd5 Ppfibp2 Plagl2 Foxp2 Nr1d1 Nfkb2 Tal2 Nfkb1 Mlf1 Tox Bcl6b Nrip1 0 Zfp827 Ikzf2 Utf1 NK, SI Phtf2 NK, SI Klf3 Bmyc Plagl1 ILC1, SI ILC2, SI NK ILC1, SI ILC2, SI Plagl2 NK -200 Nfkbib 0 200 400 Nfkb2 -200 -100 0 100 200 Spib Nfkb1 Tox 200 Nfkbie 200 400 Akna

Nrip1 ILC3 ILC3 ILC1 ILC2 Utf1 ILC3 ILC3 ILC1 ILC2 Rbpj - Klf3 Ncoa3

Plagl1 - -

CCR6-ILC3, SI Id1 CCR6+ILC3, SI CCR6-ILC3, SI Nfkbib + + Spib CCR6+ILC3, SI Tnp2 Nfkbie # of NegativeGata3 and Positive Targets per TF Akna # of Negative andGata1 Positive Targets per TF Rbpj Pparg Ncoa3 Id1 Id2 Tnp2 Epas1 Gata3 Zfp280b Gata1 Erg Pparg Hhex Id2 Epas1 Batf Zfp280b Aff3 Erg Zfp266 Hhex Trim24 + Batf CCR6 CCR6 Zbtb5 +, not in Prior Aff3 CCR6 Zfp266 CCR6 Foxo3 Trim24 Aebp2 Zbtb5 Ppp1r10 Foxo3 Dffb Aebp2 Trp53 Ppp1r10 Dffb Trp73 +, ΔATAC Trp53 Zfp750 +, w/Prior Support Trp73 Zfp385a Zfp750 Zfp365 Zfp385a -log (P ) Zfp365 log2(Δgene) 10 adj -,- not in Prior <-2 0 >2 >2 0 <-2 -,- ,w/Prior ΔATAC Support -2 2 0 10

B ILC3 ILC2 ILC1/NK

Figure 5. Predicted TF repressors of alternative ILC subset genes. (A) TFs predicted to be repressors of subset-specific gene expression patterns are clustered according to the significance of negative edge enrichment in genes upregulated per subset (FDR=1E-5; blue heatmap, middle panel). The left panel displays relative gene expression for the ILC subsets in the SI, while the right panel indicates number of target genes per TF, as in Figure 2A. (B) A TRN containing only TFs was constructed from a selection of lineage-repressors in addition to the “master regulators” RORC and GATA3. Nodes are colored according to ILC subset. Figure 6

A (Lin- Gata3-) B Maffl/fl Maffl/fl;Il7rCre (Lin- RORgt+ T-bet+)

5 100 10 6.43 43.2 11.3 63.4 MFI:

4 80 Ctrl: 10,609+/-482 PE 10 - 60 Maf KO: 15,940+/-1,475 3

bet 10

- 40 T

0 % of Max 20 17.5 32.9 11.5 13.7 0 3 4 5 0 103 104 105 0 10 10 10 RORgt-APC T-bet-PE

C ILC2 ILC3 ILC3 ILC1 (GATA3+) (RORγt+ T-bet-) (RORγt+ T-bet+) (RORγt- T-bet+) P = 0.06 P = 0.005 Maf fl/fl 40 P = 0.008 150 P = 0.17 25 30 ) Cre 4 Maf fl/fl; Il7rα 20 30 100 20 15 20 10 50 10 10 5 Cells in SILP (x10 0 0 0 0

D (Lin-) F (Lin- Il7ra+ Klrg1- Klrb1b+) Bcl6fl/fl Bcl6fl/fl; Ncr1Cre Bcl6fl/fl Bcl6fl/fl; Ncr1Cre BV711 - BV711 - NK1.1 NK1.1

IL7ra-PE-Cy7 CCR6-BV421 E 2.0 2.5 6 20 ) ) ) ) P = 0.030 P = 0.007 P = 0.832 5 4 4 4 P = 0.536 Bcl6fl/fl 2.0 1.5 5 15 Bcl6fl/fl; Ncr1Cre 1.5 P = 0.936 10 1.0 2 1.0

0.5 0.5 1 5 Cells in SILP (x10 Cells in SILP (x10 Cells in SILP (x10 Cells in SILP (x10 0 0 0 0 NK ILC1 ILC3 ILC3 ILC2 CCR6- CCR6+

Figure 6. Functional roles for c-MAF and BCL6 in intestinal ILCs. (A) Indicated ILC populations from the SILP of Maffl/fl;Il7rCre or control Maffl/fl mice. Lineage negative is (CD3-TCRb-TCRgd-CD11b- CD11c-CD14-CD19-). (B) Histogram depicting T-bet expression in RORgt+T-bet+ ILC3 gate. MFI is mean fluorescence intensity +/- standard deviation. (C) Quantification of absolute numbers of indicated ILC populations from one of three representative experiments. Statistics calculated by Welch’s t test. (D, E) Indicated ILC populations from the SILP of Bcl6fl/fl; Ncr1Cre or control Bcl6fl/fl mice. Lineage negative is as in (A). (F) Quantification of absolute numbers of indicated SILP ILC populations. Each line corresponds to an independent experiment (n=6) containing between 2-4 mice of each genotype. Each dot represents mean value for each experiment. Statistics calculated by ratio paired t test.

The ILC TRN predicted that BCL6 was a core regulator of NK and CCR6- ILC3 lineages (Figure 3A). BCL6 is a transcription factor critical for controlling germinal center B cell and follicular helper T (Tfh) cell differentiation (Hatzi et al., 2013, 2015), with a known role for alterative-lineage repression of Th17, Th2 and Th1 programs in Tfh (Hatzi et al., 2015). However, a role for BCL6 in ILC function has not been previously characterized. Conditional deletion of BCL6 in NK, ILC1 and NKp46-expressing ILC3 was achieved by crossing Bcl6fl/fl (Hollister et al., 2013) to Ncr1Cre (Narni-Mancinelli et al., 2011) mice. Comparison of SI lamina propria ILC populations between Bcl6fl/fl; Ncr1Cre (Bcl6 KO) and control Bcl6fl/fl mice (or Ncr1Cre mice) revealed a decrease in NK and ILC1 cell frequencies and numbers (Figure 6D, E, F). There was no change in the frequency or numbers of ILC3 and ILC2. Bcl6 expression was highest in NK and then decreased progressively from ILC1 to CCR6- ILC3 to CCR6+ ILC3 (Figure S3A, Table S3). Thus, we observed a decrease in the two ILC populations with highest Bcl6 expression. Intriguingly, the ILC TRN additionally predicted that BCL6 both positively and negatively regulated ILC3 signature genes (Figures 3A, 5A). Thus we sought to investigate BCL6-dependent gene changes to test the accuracy of target gene prediction by the Inferelator and to shed light on how BCL6 regulates ILC homeostasis and function.

Validation of BCL6 as a modulator of NK cell and ILC3 gene programs

To gain further insight into the regulatory footprint of BCL6 in the ILC lineages, we compared transcriptomes of NK, ILC1, and CCR6- ILC3 from Bcl6 KO and control mice. Bcl6 deletion led to gene expression changes in each subset with greatest effects in NK cells, resulting in nearly 1000 differentially expressed genes (FDR = 25%), composed of a near-equal number of up-regulated and down-regulated genes (Figure 7A). Although less highly expressed in these cells, deletion of Bcl6 also significantly perturbed expression patterns in CCR6- ILC3, leading to 800 differentially expressed genes. The majority of the Bcl6-dependent genes in CCR6- ILC3 were up-regulated, suggesting a largely repressive role for BCL6 in ILC3 (Figure 7A). Although SI ILC1 have the second- highest level of Bcl6 expression (Figure S3A), perturbation of Bcl6 in ILC1 led to the smallest number of gene expression changes (fewer than 300).

We clustered the Bcl6 KO data to visualize overlap in differentially expressed genes across the subsets (Figure 7B) and observed several key trends. Across the four subsets, BCL6 has a greater number of putative repressed targets than positive targets consistent with its known roles as a transcriptional repressor (Chang et al., 1996; Crotty et al., 2010; Hatzi et al., 2015) (Figure 7B). Among both repressed and positive targets, genes cluster into those that are conserved across NK, ILC1, and ILC3 and those that are unique to NK cells. Most of the putative repressed targets (70%) are conserved among ILCs and notably include canonical ILC3 genes such as Rora, Stat3, Il17a, Il17f, while 67% of positive targets are NK-cell-specific (Figure 7B). It should be noted that, although fewer genes were significantly differentially expressed in ILC1 (Figure 7A), Bcl6-dependent gene patterns in ILC1 were qualitatively similar to CCR6- ILC3, suggesting conservation of regulatory mechanisms (Figure 7B). Genes that appear to be positively regulated by BCL6 include several with known roles in NK cell differentiation and functions (Klf2, Fasl, Clec2d, CD48, CD160, Ptpn6, Klrk1, Klrb1b, Klrb1c, Klrb1c, Klrb1f). These results suggest that in ILC1 and ILC3, BCL6 is mainly a repressor, while, in NK cells, BCL6 is both a repressor and an activator (Figure 7B, S7C,D).

9 10tfsPerGene qATAC 10 1 bias25 Activating, P <= 0 MAFG adj MITF ZUFSP ZFP131 ILC3 CCR6pos Bcl6 KO 3 ILC3 CCR6pos Bcl6 KO 2 ILC3 CCR6pos Bcl6 KO 1 ILC3 CCR6neg Bcl6 KO 3 ILC3 CCR6neg Bcl6 KO 2 ILC3 CCR6neg Bcl6 KO 1 ELK1 ILC3 CCR6pos WT 3 ILC3 CCR6pos WT 2 ILC3 CCR6pos WT 1 ILC3 CCR6neg WT 3 ILC3 CCR6neg WT 2 ILC3 CCR6neg WT 1 PHF21A 10tfsPerGene qATAC 10 1 bias25

ILC1 Bcl6 KO 3 ILC1 Bcl6 KO 2 ILC1 Bcl6 KO 1 Activating, Padj <= 0 NK Bcl6 KO 3 NK Bcl6 KO 2 NK Bcl6 KO 1

ILC3

ILC3 ZEB1 MAFG

ILC1 ILC1 WT 3 ILC1 WT 2 ILC1 WT 1 NK

NK WT 3 NK WT 2 NK WT 1 CCR6+ CCR6+ -

CCR6 ZSCAN26 MITF

- + - + - + - +

Bcl6 NPAS2 ZUFSP

<-1

Cxcr5 ZFP131

Il7r ZFP516

Klrb1c ELK1

Klrb1f ZBTB24

Klrb1b PHF21A

Il16 ARHGAP35

Klrk1 ZEB1

Cd83 MEIS1

Cxcl3 ZSCAN26

Ccr2

0 0 D3ERTD254E

Tnfsf11 NPAS2

Tnfrsf1a PLAGL1 Il2ra ZFP516

Il4ra ZKSCAN3 ZBTB24 Il2rb ≤ 2 - ≥ 2

Ifngr1 CLOCK ARHGAP35 Areg

>2 0 <-2

Cxcl10 MEIS1

Il17f EEA1

Il17a

>1 >1 D3ERTD254E

Zfp365 ILC3 CCR6pos Bcl6 KO 3 ILC3 CCR6pos Bcl6 KO 2 ILC3 CCR6pos Bcl6 KO 1 ILC3 CCR6neg Bcl6 KO 3 ILC3 CCR6neg Bcl6 KO 2 ILC3 CCR6neg Bcl6 KO 1

) Δgene log2(

2 Zfp385a ETV6

Zfp750

|FC|>0, 20 |FC|>0, log FDR=10%, ILC: Bcl6

Trp73

Trp53

Dffb

Ppp1r10 PLAGL1

Aebp2

ILC3 CCR6pos WT 3 ILC3 CCR6pos WT 2 ILC3 CCR6pos WT 1 ILC3 CCR6neg WT 3 ILC3 CCR6neg WT 2 ILC3 CCR6neg WT 1

Foxo3

Zbtb5 ZEB2

Trim24

Zfp266

Aff3

Batf ZKSCAN3

Hhex

Erg

ILC1 Bcl6 KO 3 ILC1 Bcl6 KO 2 ILC1 Bcl6 KO 1

Zfp280b NFE2L3 ILC3 Epas1 ILC3

Id2 NK Bcl6 KO 3 NK Bcl6 KO 2 NK Bcl6 KO 1

Pparg

Gata1 CLOCK ILC1

Gata3 NK

Tnp2

Id1 ILC1 WT 3 ILC1 WT 2 ILC1 WT 1 Ncoa3 CCR6+ CCR6+ -

CCR6 STAT6

Rbpj NK WT 3 NK WT 2 NK WT 1

Akna

Nfkbie EEA1

Spib

Nfkbib

Plagl1

Klf3 Utf1

- + - + - + - +

Bcl6 ZFP608

Nrip1

Tox ETV6 Nfkb1

Nfkb2

Plagl2

Bmyc <-1

Phtf2

Ikzf2 ZSCAN12 Zfp827

Bcl6b ZEB2

Mlf1

Tal2

Foxp2

Ppfibp2

Mbd5

Prkdc ATF6

Bcl3 NFE2L3

Cic

Maf

Chd7

Ssh2

Pml

Mxd1

Stat3 MECP2 STAT6 Ssbp4

Ift57

Pcx

Zfp608

Tead1

Lbr Srebf1

NK ZFP608

Edf1 IRF8 Nfkbil1

Thap3

Eno1

Foxs1

Nr1d1 Vax2

Targets ZSCAN12 Vdr

Setdb2 NFATC1 Nr1d2

Rora

Rorc

Txk Dhx58

Positive Bcl6 Bcl6 Positive Trim30a ATF6

Irf7

Oasl1 ZFP606 Ikzf3

Irf1

Zbp1

Stat1

Stat2

Putative Putative MECP2 Gene Expression Tbx21

dependent TFs to explain… to TFs dependent Irf2 Irf9

ILC PREB Trim21

Atf3

Cebpb

Atf5 IRF8

Zfp790

Cebpg

Ddit3

Nupr1 ZFP768

Tsc22d3

Mafg -

Msh5 NFATC1

Etv4 conserved Rbbp8

- try to use the network and Bcl6 and network the use to try Jdp2

Plscr1

Sos2 ZFP574

Ankzf1

Kdm5b ZFP606

Foxj1 Activating, P = .1

Sirt6

Rest adj

Dnajc21

Fbxo21

Hif3a MEF2D Rcor2

Glis3 PREB

Arrb1 Hif1a

targets, some indirect), for indirect indirect for indirect), some targets, C030039L03Rik

Snapc1

Ppp1r13l

Rlf ZXDB

Zfp395 ZFP768

Hes6

Mxi1

Zfp263

Sp9

Zfp654

Ing2

Sap30 TRP53 ZFP574

Dpf1

Tcf25 Activating, P = .1 Scrt1 expression (some will be direct Bcl6 Bcl6 direct be will (some expression adj

MEF2C MEF2D P = 1 Th0(1h) Th0(3h) Th0(6h)

Th0(16h) Th0(48h) Th17(1h) Th17(3h) Th17(6h) Th17(9h) Th1(48h) Th2(48h) adj media(1h) Th17(12h) Th17(16h) Th17(24h) Th17(48h) Treg(48h)

NK NFAT5 ZXDB Use TRN to explain changes in gene gene in changes explain to TRN Use • 0 RFX5 TRP53 MEF2C P = 1 ZFHX3 adj NFAT5 Repressive, P = .1

ELF1 adj

RFX5

Bcl6 doing in each cell type? type? cell each in doing Bcl6 NFKB1 ZFHX3 Targets Figure 7Repressive, P = .1

ZFP827 ELF1 adj Repressed Repressed

targets in each cell type. What is is What type. cell each in targets ILC PPARG NFKB1

Putative Bcl6 Bcl6 Putative KLF5 ZFP827

- TFs per Gene per TFs A C conserved

between TRN Bcl6 targets and and targets Bcl6 TRN between IKZF1 PPARG

20 15 10 5

0 ΔGenes upon Bcl6 KO Overlap between Bcl6 KO

Bcl6, FDR=25%, |log2(FC)|>0 KLF5

10 NFIX

-10 qATAC 10 1, bias = 0.25, maxComb, FDR = 25% qATAC 10 1, bias = 0.25, maxComb, FDR = 25% set enrichment enrichment set - Compare gene Compare -- ΔGenes and TRN BCL6 targets • IKZF1 CCR6 ILC33 POU2F2 CCR6Bcl6 KO-- CCR6negILC3MBD1 ILC3 Bcl6 KO CCR6neg ILC3 NFIX ILC12 POU2F2

Bcl6BACH2ILC1 KO ILC1 Bcl6 KO ILC1

10 MBD1 -8 NK1 FOXC2Bcl6NK KO NK Bcl6 KO NK

Bcl6 KO BACH2

UnionBcl6 KO union Bcl6 KO union

NK (267) NK 0 100 200 300 400 500 600 EPAS1 FOXC2

0 200 400 600 0 0.5 1 1.5 2 0 2 4 6 8 10 12

>1 >1 log (Odds Ratio) -log (P)

2 10

2 Number of Genes of Number

ILC1 (73) ILC1 0 1 2 0 4 8 12

|FC|>0, 992 |FC|>0, Bcl6 ILC: FDR=10%, log FDR=10%, ILC: Bcl6 EGR2 EPAS1

10 # of ΔGenes

-6

CCR6pos ILC3 (204) ILC3 CCR6pos log (Odds) -log10(P) Decreased 2 10 10 10

10 EOMES EGR2 ILC2 SI up SI ILC2

3 2 1 0

CCR6neg ILC3 (284) ILC3 CCR6neg

Genes (P Increased Up-regulated / “Repressed” MXD1 EOMES

NK SI up SI NK

union (123) union Down-regulated / “Positive” ELK3 MXD1

intersect (2) intersect ILC1 SI up SI ILC1 10

Number of Genes of Number ELK3

-4 TBX21 ILC ATAC: FDR=10%, log combinedP (197) combinedP ILC2 SI 2

10 10 10 10

CCR6-ILC3 SI up SI CCR6-ILC3

NK TBX21

ILC2 SIDecreased 1 3 2 1 0

P = .05 = P D EGR1

hyg ILC1 SI 1 Increased Bcl6EGR1 KO FDR = 10

CCR6+ILC3 SI up SI CCR6+ILC3 IRF4BCL6 regulation of Bcl6subset KO signature FDR = genes10

TFs per Gene per TFs ILC1 SI 4 Activating, P <=<= 00

) IRF4

TFs per Gene per TFs adj

10 Bcl6 ILC: FDR=10%, log |FC|>0, ILC1992 SI 3 BCL6 CCR6+ILC3

20 15 10 5 0

-2 2 ATF3 NK

20 15 10 5 0 - ATF3

ILC1 SI 2 >1 BCL6 CCR6-ILC3CCR6 ILC3 B BCL6- CCR6-ILC3 adj

10 ILC1 LI 4 ESR1 Activating, P == .1.1

) (edge sign) * -log10(P * sign) (edge ESR1 -10 ILC1 P == 11 adj

ILC1 LI 3 JUN BCL6 ILC1 Repressive,adj P == .1.1

10 JUN

Bcl6 adj 4 2 0 -2 -4 -6

-10 Ifngr1Ifngr1 CCR6-ILC3 LI 3 BCL6 NK NK

CCR6+ILC3 CCR6+ILC3 LI 3 JUNB ΔGenes JUNB

CCR6+ILC3 SI 3 BCL6 UNION

10 BCL6 BCL6 Repressive, P <=<= 00 BCL6 ILC1 ILC1 BCL6 ILC TRN -8

CCR6+ILC3 CCR6+ILC3 SI 2 adj Bcl6 KO 2

qATAC 10 1, bias = 0.25, maxComb, FDR = 10% = FDR maxComb, 0.25, = bias 1, 10 qATAC

10 |FC|>4

Bcl6 KO NR2F6

-8 CCR6+ILC3 SI 1 NR2F6

NK (525) NK NK NK NK (267) NK

BCL6 intersection BCL6 CCR6-ILC3 SI 3 MAF

ILC1 (143) ILC1 Il17a, Il17fIl17aIl17f MAFSign of edges * ILC3 ILC3 ILC1 ILC2 ILC3 ILC3 ILC1 ILC2

ILC1 (73) ILC1 CCR6-ILC3 SI 2 - -

Nfkb2Nfkb1Stat3 + +

10 PRDM1

CCR6pos ILC3 (349) ILC3 CCR6pos Nfkb1/2, Stat3 -6 PRDM1 - log (P )

BCL6 CCR6+ILC3 CCR6+ILC3 BCL6 CCR6-ILC3 SI 1 CCR6pos ILC3 (204) ILC3 CCR6pos 10 adj

CCR6neg ILC3 (477) ILC3 CCR6neg FOXA1

-6 Genes (P

CCR6-ILC3

CCR6neg ILC3 (284) ILC3 CCR6neg FOXA1

Genes (P CCR6-ILC3 union (145) union

comb <-2 0 >2

Rora NFATC2CCR6 CCR6 CCR6 CCR6 union (123) union Rora P BCL6 intersect (3) intersect NFATC2- 5 5

NK SI up NK SI up

NKX6-2 NK SI up NK SI up intersect (2) intersect ILC1 SI up ILC2 SI up ILC1 SI up ILC2 SI up ILC1 SI up ILC2 SI up ILC1 SI up ILC2 SI up

10 conserved 10 combinedP (232) combinedP

-4 Hif1aHif1a ILC Subset Gene Sets

BCL6 CCR6-ILC3 CCR6-ILC3 BCL6 NKX6-2 - -4 combinedP (197) combinedP RORA

P = .05 = P hyg

P = .05 = P RORA CCR6-ILC3 SI up CCR6-ILC3 SI up ZBTB4CCR6-ILC3 SI up CCR6-ILC3 SI up hyg CCR6+ILC3 SI up CCR6+ILC3 SI up

CCR6+ILC3 SI up CCR6+ILC3 SI up Repressive, P <= 0

BCL6 union union BCL6 ILC adj

) Areg ZBTB4

Areg, Tnfs11 ILC1 ) Tnfsf11

10 Repressive, P <= 0

10 adj

-2

ILC1

BCL6 NK NK BCL6 Foxa1Foxa1 E

Bcl6 KO FDR = 10 = FDR KO Bcl6 Bcl6, FDR=10%, |log2(FC)|>0 FDR=10%, Nrfa2Nr4a2Bcl6, NK SI up NK SI up

Lin54Lin54 ILC1 SI up ILC2 SI up ILC1 SI up ILC2 SI up

NK SI up NK SI up

Repressed by BCL6 Validation of Bcl6 Target Predictions Target Bcl6 of Validation

Bcl6, FDR=1%, |log2(FC)|>0 FDR=1%, Bcl6, Nr1d1

Nrd1d ILC1 SI up ILC2 SI up ILC1 SI up ILC2 SI up

qATAC 10 1, bias = 0.25, maxComb, FDR = 25% = FDR maxComb, 0.25, = bias 1, 10 qATAC -

qATAC 10 1, bias = 0.25, maxComb, FDR = 10% = FDR maxComb, 0.25, = bias 1, 10 qATAC NK CCR6-ILC3 SI up CCR6-ILC3 SI up CCR6 ILC3CCR6+ILC3 SI up CCR6+ILC3 SI up

0 Bcl6 validation Bcl6 – Figure 5 5 Figure Hey1Hey1 CCR6-ILC3 SI up CCR6-ILC3 SI up NK CCR6+ILC3 SI up CCR6+ILC3 SI up Pathways: 30 20 74 • IL12- • IL23- Klrb1f, Klrb1cKlrb1cKlrb1f • IL2- signaling • cytokine conserved ILC ATAC: FDR=10%, log Klrb1b, Batf3Klrb1b - ILC2 SI 2 Batf3Jund production ILC2 SI 1 Jund, Fos, JunJunFos BCL6 Klf2Klf2

ILC1 SI 1 ILC ILC1 SI 4 ILC1 SI 3 ILC1 SI 2 ILC1 LI 4 ILC3 genes ILC1 LI 3 CCR6-ILC3 LI 3 Klrk1Klrk1 NK 60 65 CCR6+ILC3 LI 3

CCR6+ILC3 SI 3 Activated by BCL6 NK genes CCR6+ILC3 SI 2 2

CCR6+ILC3 SI 1 |FC|>4 CCR6-ILC3 SI 3 CCR6-ILC3 SI 2 <-1 CCR6-ILC3 SI 1 log2(Δgenesubtype) - + - + - + Bcl6 <-1 >1 NK ILC1 CCR6 -- <-2 0 >2

NK WT 1 NK WT 2 NK WT 3 ILC3 ILC1 WT 1 ILC1 WT 2 ILC1 WT 3 NK Bcl6 KO 1 NK Bcl6 KO 2 NK Bcl6 KO 3 ILC1 Bcl6 KO 1 ILC1 Bcl6 KO 2 ILC1 Bcl6 KO 3 Figure 7. Validation of TRN predictions for BCL6. (A) The number of up-regulated and down- ILC3 CCR6neg WT 1 ILC3 CCR6neg WT 2 ILC3 CCR6neg WT 3 fl/fl Cre+/- fl/fl

regulated genes comparing BclILC3 CCR6neg Bcl6 KO 1 ILC3 CCR6neg Bcl6 KO 2 ILC3 CCR6neg Bcl6 KO 3 6 KO (Bcl6 -Ncr1 ) to control (Bcl6 ) per ILC subset in the SI (FDR=25%). (B) Hierarchical clustering of 902 differentially expressed genes (Bcl6 KO versus control (FDR=10%), in at least one of the three ILC subsets). Genes are normalized independently for each subset to highlight Bcl6-dependent trends. (C) Significance of overlap between TRN-predicted BCL6 targets and the Bcl6-KO-dependent genes (TRN BCL6 targets were >2-fold more likely to be differentially expressed; Fisher Exact Test P<1E-3, all subsets, and P<1E-10 for union of differential genes across subsets). (D) Positively and negatively-regulated BCL6 targets were (1) inferred from the Bcl6 KO differential expression analysis in each cell type as well as the union across cell types (FDR=10%) and (2) tested for enrichment in genes upregulated per subset; TRN predictions for BCL6 (from Figure 4A) are provided for reference. (E) BCL6 was a significant regulator of lineage genes in NK cells and CCR6- ILC3. GSEA of Bcl6-repressed targets in Bcl6 KO CCR6- ILC3 revealed enrichment of several gene pathways. To assess the accuracy of network predictions, we tested how well ILC TRN-predicted BCL6 targets matched Bcl6 KO gene expression data. Indeed, for each ILC subset, TRN- predicted targets were two-fold more likely to be differentially expressed in Bcl6-deleted cells (P<1E-4, Fisher Exact Test, Figure 7C), and the enrichment grew to four-fold upon combining differential genes across the ILC subsets (“Union”) (P<1E-10). These results were robust over a range of FDR cutoffs and TRN model sizes (Figure S7A). Importantly, TRNs built from gene expression data alone (without ATAC-seq data) were poor at predicting Bcl6-dependent genes (Figure S7B), highlighting the importance of integrating ATAC-seq data into TRN inference for prediction of BCL6 targets.

We next asked what genes BCL6 regulated and in which direction (Figure 7D). Strikingly, both up-regulated and down-regulated gene sets in Bcl6 KO NK cells were enriched for ILC3-signature genes, supporting the ILC TRN prediction that BCL6 is both a positive and negative regulator of ILC3 genes in NK cells (Figure 7D). In CCR6- ILC3, BCL6 also tuned down a subset of CCR6- ILC3 signature genes, but too a lesser degree than in NK, perhaps due to its lower level of expression there. The CCR6- ILC3 Bcl6 KO data additionally suggested that BCL6 is a positive regulator of ILC1/NK-signature genes; some promotion of ILC1/NK-signature genes was observed in NK cells and ILC1s as well (Figure S8A). This result was not predicted by analysis of the ILC TRN and highlights the value of ILC subset-specific TF perturbation data to tease out additional targets of BCL6. There was significant overlap between BCL6-repressed CCR6- ILC3 genes across NK cells and CCR6- ILC3, while the BCL6-activated CCR6-ILC3 genes in NK do not overlap with the BCL6-repressed genes in any of the subsets (Figure 7E). Among TFs suppressed by BCL6, RORA (highest expression in ILC2 and ILC3) was also predicted to repress NK/ILC1 signature genes (Figure 5), highlighting a potential hypothesis to explain increased ILC1/NK gene expression in Bcl6 KO ILCs.

BCL6 (and c-MAF) belong to a cluster of TFs predicted to co-regulate cytokine signaling (Figure 4B). Gene-set enrichment analyses on BCL6 targets from Bcl6 KO confirmed that, in ILC3, BCL6 negatively regulated IL12-, IL23- and IL-2-signaling pathways, cytokine production, and endogenous TLR signaling (FDR<1%) (Figure 7E, S8B. Taken together, these results highlight the utility of the Inferelator in predicting gene regulation by validating activated and repressed target genes of BCL6 in ILCs. These analyses confirm a role for BCL6 as a modulator of the ILC3 program and activator of NK genes, with distinct roles in ILC3 and NK cells. These data suggest potential mechanisms whereby BCL6 maintains SI NK/ILC1 cells through repression of alternative lineage (ILC3) genes and promotion of select genes required for maintenance of these lineages.

Discussion

Innate lymphoid cells are important mediators of mucosal immunity, especially in early immune responses. However, they also contribute to diverse pathologies, from autoimmune disease to metabolic syndrome. Previous ILC genomics studies identified genes, gene pathways and putative regulomes of the ILC lineages (Gury-BenAri et al., 2016; Koues et al., 2016; Robinette et al., 2015; Shih et al., 2016). In this study, we integrate our own substantial ILC genomics dataset with previous work (Gury-BenAri et al., 2016; Koues et al., 2016) to reconstruct the first genome-scale transcriptional regulatory network (TRN) for gut-resident ILCs. This effort also represents the first application of our TRN inference method (Miraldi et al., 2018) in vivo, providing an important proof of concept. The ILC TRN de novo predicted known critical regulators of

10 ILC lineage as well as many new candidate TFs, two of which, BCL6 and c-MAF, we have confirmed to be functionally relevant in the contexts predicted. Moreover, the networks identify specific gene targets of each TF, further facilitating biological hypothesis development and testing. This TRN and co-regulated gene modules are intended to serve as a resource for the ILC field. Networks and gene expression data are available via a user-friendly web interface that enables searchable interactive visualization of both networks and gene expression.

By integrating gut ILC subsets into a single network, we were able to explore regulatory relationships between as well as within ILC subsets. One of the striking findings from analysis of the combined ILC TRN was an extensive chromatin landscape of alternative lineage gene repression. Many of the repressive interactions were supported by ATAC- seq peaks with high accessibility and binding motifs for TFs expressed within a particular subset but adjacent to alternative lineage genes that were not expressed. One regulatory element in the Ifng gene with this pattern of accessibility in ILC2 was highlighted previously (Koues et al., 2016). Our work demonstrates that this regulatory pattern is broadly present within all ILC subsets and may represent a general mechanism underpinning ILC lineage regulation. Such elements may function as silencers and, together with poised and active enhancers, respond to environmental cues to modulate ILC identity.

For each of the five ILC subsets in our study, we defined “core” TFs and TRNs, recovering known TF lineage regulators and implicating nearly 100 additional TFs in the regulation of lineage-specific gene expression patterns. Additionally, we detected over 50 TFs whose repressed targets in the TRN were enriched in lineage-specific genes. These TFs represent candidate lineage control points. Not only could they be key to maintenance of subset-specific gene expression patterns, but their perturbation could potentially be used to alter ILC lineage identity and functionality. Several autoimmune and inflammatory diseases are associated with altered abundances and activity of ILC subsets (Eberl et al., 2015). For example, maternal antibiotic exposure is associated with a decrease in lung ILC3s and an increased susceptibility to infection (Gray et al., 2017). Thus, our ILC TRN could be used to design disease interventions that limit the function or abundances of specific ILC subsets through targeting of lineage-promoting or lineage-repressing TFs. Construction of ILC TRNs using human ILCs derived from disease contexts could provide additional clues as to what TFs are active in these states. We also emphasize that our approach is entirely general and readily applicable to other plastic cell populations in physiological settings, including T helper cells, macrophages, and others.

We functionally tested predictions from our “core” TRN and alternate-lineage repressor analyses. We predicted that MAF was a “core” positive regulator of lineage-specific gene programs in ILC1 and CCR6- ILC3. We also predicted that MAF suppressed the CCR6+ ILC3 program. Thus, we anticipated that deletion of MAF would shift the ILC3-ILC1 continuum. Indeed, these populations were altered in Maffl/fl Il7rCre KO mice relative to control. We also tested the predicted role of BCL6 as a “core” regulator of NK and CCR6- ILC3 lineages using conditional Bcl6 KO mice. Bcl6 deletion led to a decrease in SILP NK and ILC1, while there was no change in CCR6- ILC3 frequencies.

By profiling gene expression in the three Bcl6-expressing ILC populations of the SI, we confirmed that BCL6 was both a positive and negative regulator of ILC3 genes. The subset-specific Bcl6 KO data was critical to confirming TRN BCL6 target predictions and pinpointing the specific ILC subsets in which BCL6 positively and/or negatively regulated ILC3 and other target genes. BCL6 was found to significantly antagonize the ILC3 program

11 in NK and CCR6-ILC3, and less so in ILC1. The Bcl6 KO gene expression experiments and their enrichment analyses provide a potential explanation for the reduction of SI ILC1 in Bcl6 KO animals. BCL6 might contribute to ILC3-to-ILC1 plasticity through repression of ILC3-promoting signaling pathways (e.g., IL23 signaling). BCL6 also positively contributes to NK and ILC1 gene expression (e.g. Klf2), and this represents a second mechanism by which BCL6 might contribute to maintaining SI type 1 ILCs via promotion of genes required for survival or homing. We speculate that these two mechanisms may in fact be connected as BCL6 may repress ILC3 TFs (e.g. RORA) in type 1 ILCs that in turn repress type 1 signature genes affecting ILC1 and NK cell accumulation within the intestines. Further experimental evidence is required to explore which BCL6 target genes enumerated by the ILC TRN are critical for the observed phenotypes.

Notably, BCL6-repression of the ILC3 program mirrors BCL6 repression of the Th17 program in Tfh cells. For example, in both ILCs and Tfh, Rora, Stat3, Il17a, Il17f are BCL6 targets while Rorc is not (Hatzi et al., 2015). Thus, like other TFs (Walker et al., 2013), BCL6 shares some regulatory roles between ILC and T Helper subsets. Application of our methods to construct TRNs of intestinal T helper cell TRNs would provide an opportunity for a comparative analysis of shared and unique regulatory mechanisms in T helper cells and ILCs.

In NK cells, the purpose of BCL6 repression of ILC3 genes is less clear, as plasticity between NK and ILC3 (or NK and ILC1) has not been reported. We note that our study is the first to contribute both NK and ILC1 RNA-seq and ATAC-seq data from the intestine, and to show that these two subsets have stronger correlation in accessibility patterns than previous studies comparing intestinal helper ILC subsets to spleen and bone marrow NK cells (Koues et al., 2016). As another potential explanation, NK cells might need to express ILC3 signature genes under some contexts.

A number of ILC TRN predictions are consistent with current knowledge of ILC lineage regulation, and a limited number of novel predictions have been experimentally tested in this study. However, the vast majority of predictions remain to be tested. We anticipate that the ILC TRNs will be useful to unraveling the molecular mechanisms driving complex phenotypes (e.g., due to TF KO or other experimental perturbations) in ILCs. We hope that these maps of ILC lineage-specific transcriptional regulation increase the pace of discovery in ILC biology, eventually leading to the design of precise genetic and chemical perturbation of subset-specific ILC behaviors in the context of human health and disease.

Acknowledgements

We thank the Flatiron Institute Scientific Computing Core (I. Fisk) for enabling the computational aspects of this work and the New York University Langone Medical Center Genomics Core (A. Heguy and P. Zappile) for help with sequencing. This work was supported by the Cincinnati Children’s Research Foundation (Trustee Award to E.R.M., Research Innovation Award to S.N.W.; Research In Residency Award to N.S.C.; and Arnold P. Strauss Fellowship to D.O.), the Simons Foundation (E.R.M., A.W., N.D., N.C., R.B.), U.S. National Institute of Health (5T32AI100853 to M.P.; R01-DK103358-01 to R.B. and D.R.L.; DA038017 to S.N.W.; and R01-GM112192-01 to R.B., T32 CA009161 (Levy) to J.A.H.), Damon Runyon Cancer Research Foundation (Dale and Betty Frey Fellowship to J.A.H.), the Laura and Isaac Perlmutter Cancer Center (P30CA016087 to A.H.), and the Colton Center for Autoimmunity (D.R.L). Cell sorting and flow cytometric data acquired

12 at Cincinnati Children’s relied on equipment maintained by the Research Flow Cytometry Core, which is supported in part by NIH grants AR47363, DK78392 and DK90971.

Author Contributions

ERM, MP, JAH, DRL, and RB designed and contributed to analysis throughout the study. MP and JAH generated ILC genomics data. MP contributed c-MAF data. SNW conceived of BCL6 experiments and generated the mouse model. ERM, MP, JAH, DEO, SNW, DRL designed BCL6 experiments. DEO, NSC, HS performed BCL6 experiments. ERM built the ILC TRNs and computational analyses. RY compiled public ILC genomics data. NC mapped RNA-seq and ATAC-seq data. AW designed interactive network visualization software with input from ERM, MP and RB. ERM and MP wrote the manuscript with input from JAH, DEO, SNW, DRL, and RB.

Declarations

The authors declare no competing interests.

Methods

Mice Wild type and Maf KO experiments. Maffl/fl and Il7raCre mice were kindly provided by C. Birchmeier and H. R. Rodewald (Schlenner et al., 2010; Wende et al., 2012). Maf conditional KO mice were generated by crossing Maffl/fl to Il7raCre animals. Maf KO mice were bred and maintained in the animal facility of the Skirball Institute (New York University School of Medicine) in SPF conditions. All animal procedures were performed in accordance with protocols approved by the Institutional Animal Care and Usage Committee of New York University School of Medicine.

Bcl6 KO experiments. Mice were housed under pathogen-free conditions, and experiments were performed using ethical guidelines approved by the Institutional Animal Use and Care Committees of Cincinnati Children’s Hospital Medical Center. Ncr1iCre mice were previously described (Narni-Mancinelli et al., 2011) and were kindly provided by Eric Viviér (University of Marseille). Bcl6fl/fl mice were purchased from Jackson laboratories (Bar Harbor, ME). Both strains of mice were on C57BL/6 background. Ncr1iCre/iCre mice and Bcl6fl/fl mice were bred to obtain Bcl6fl/fl Ncr1wt/iCre (Bcl6 KO) or Bcl6fl/fl Ncr1wt/wt (WT). After breeding, all mice were kept with their littermates (males and females were separated). Bcl6 deletion was verified by examining NCR1 expression and by PCR. Male mice at 16–20 weeks of age were utilized in experiments. In each experiment, an equal number of mice was used.

Isolation of SILP and LILP lymphocytes and TF analysis. Intestinal tissues were sequentially treated with PBS containing 1 mM DTT at room temperature for 10 min, and 5 mM EDTA at 37°C for 20 min to remove epithelial cells, and then minced and dissociated in RPMI containing collagenase (1 mg/ml collagenase II; Roche), DNase I (100 µg/ml; Sigma), dispase (0.05 U/ml; Worthington) and 10% FBS with constant stirring at 37°C for 45 min (SI) or 60 min (LI). Leukocytes were collected at the interface of a 40%/80% Percoll gradient (GE Healthcare). For transcription factor analysis, lamina propria mononuclear

13 cells were first stained for surface markers before fixation and permeabilization, and then subjected to intracellular TF staining according to the manufacturer’s protocol (eBioscience Intracellular Fixation & Permeabilization buffer set from eBioscience).

Sorting of intestinal ILCs NK, ILC1, ILC2, and ILC3 (CCR6+ and CCR6-) were sorted from the small or large intestine lamina propria preparation using the surface marker panel described in Figure S1. A portion of sorted cells was stained intracellularly with ILC lineage transcription factors to confirm that the correct populations were isolated. Lin-Klrg1hi cells were uniformly Gata3hi and thus mature ILC2 (Hoyler et al., 2012). We have found Klrb1b to be a good marker of ILC1 and ILC3, which can then be further stratified based on expression of NK1.1hi (ILC1) and NK1.1lo-NK1.1neg (ILC3) (Figure S1). ILC1 are T-bethi while ILC3 are RORgt+ and can be either T-bet- (CCR6+) or T-bet+ (CCR6-) (Figure S1).

Cell staining for flow cytometry For analysis of ILCs, live LP cells were stained with anti-NK1.1, anti-IL7ra, anti-Klrg1, anti- Klrb1b (clone 2D12, subclone from 2D9) as previously described (Aust et al., 2009), and anti-CCR6. Anti-CD11b, anti-CD11c, anti-CD14, anti-CD19, anti-B220, anti-TCRβ, anti- TCRγδ, and anti-CD3 were used as dump gate. For transcription factor staining, cells were stained for surface markers, followed by fixation and permeabilization before nuclear factor staining according to the manufacturer’s protocol (FOXP3 staining buffer set from eBioscience) with individually or in combination anti-RORgt, anti-T-bet, and anti-Gata3. 4′,6-diamidino-2-phenylindole (DAPI) or Live/dead fixable blue (ThermoFisher) was used to exclude dead cells. Flow cytometric analysis was performed on an LSRII (BD Biosciences) or an Aria (BD Biosciences). All data were re-analyzed using FlowJo (Tree Star). Commercially available antibodies used in flow cytometry experiments above are listed in Table S1.

ATAC-seq Sorted ILC populations were prepared according to (Buenrostro et al., 2013). Paired-end 50bp sequences were generated from samples on an Illumina HiSeq2500. Sequences were mapped to the murine genome (mm10) with bowtie2 (2.2.3), filtered based on mapping score (MAPQ > 30, Samtools (0.1.19)), and duplicates removed (Picard). For each sample individually, we ran Peakdeck (parameters –bin 75, -STEP 25, -back 10000, -npBack100000) and filtered peaks with Praw<1E-4. To enable quantitative comparison of accessibility across samples, we generated a reference set of accessible regions, taking the union (Bedtools) of peaks detected in individual samples. The reference set of ATAC- seq peaks contained 65,281 potential regulatory loci, ranging from 100 base pairs (bp) to 3750 bp (median length 375 bp). Reads per reference peak were counted with HTSeq- count. This pipeline resulted in ~14 million uniquely mapped reads per sample, ~42% of which mapped to the identified peaks. We used DESeq2 to normalize and identify differentially accessible peaks across conditions. Downstream analysis and data visualization were performed in MATLAB R2014B. For PCA analysis (Figure 1), ATAC- seq data was treated as in (Karwacz et al., 2017) to yield ~127k accessible regions, and robustly normalized DESeq2 counts were mean-centered and variance-normalized by the square root of standard deviation. ATAC-seq data were deposited in the GEO database under accession GSE116093.

RNA-seq For the initial RNA-seq measurements in wildtype ILCs, cells were sorted (FACSAria II, BD) directly into Trizol and snap-frozen, thawed for chloroform extraction and purified

14 using the RNAeasy minElute kit (Qiagen). Samples were evaluated for RNA quality by sampling and analyzing one µl of library by Bioanalyzer (Agilent, Santa Clara, CA), using DNA high sensitivity chip. An accurate quantification of library concentration, was performed by NEBNext Library Quant Kit (New England BioLabs). Libraries were prepared using Nugen Ovation RNAseq System V2 and Nugen Ovation Ultralow Library System kits and sequenced on an Illumina HiSeq2500. Sequences were mapped to the murine genome (mm10) with STAR. HTSeq-count was used to count reads per gene. DESeq2 was used to normalize and identify differentially expressed transcripts among ILC lineages. Additional ILC RNA-seq data were downloaded from GEO: GSE85152, GSE77695, and processed similarly. We controlled for batch effects across the three datasets using COMBAT, yielding 62 samples in the final gene expression matrix. The combined samples clustered by ILC subsets (data not shown) and exhibited similar lineage-specific gene signatures for TFs, receptors and cytokines (Figures S5-S7). The full batch-corrected gene expression values for the combined studies is contained in Table S3.

For RNA-seq of Bcl6 KO and control animals, ILC populations were sorted as described above. Briefly, cells were sorted into 100% FBS, and immediately spun at 600 g for 6 min. After removal of supernatant, the cell pellet was lysed with Lysis/Binding Buffer from mirVana miRNA Isolation Kit (Thermo Fisher, Grand Island, NY). Samples were evaluated for RNA quality as described above. The library for RNA-seq was prepared by using NEBNext Ultra II Directional RNA Library Prep kit (New England BioLabs, Ipswich, MA). To study differential gene expression, individually indexed and compatible libraries were proportionally pooled (~25 million reads per sample in general) for clustering in cBot system (Illumina, San Diego, CA). Libraries at the final concentration of 15 pM were clustered onto a single read (SR) flow cell v3 using Illumina TruSeq SR Cluster kit v3, and sequenced to 51 bp using TruSeq SBS kit v3 on Illumina HiSeq system. Sequences were mapped and gene expression analysis performed as described above.

RNA-seq data were deposited in the GEO database under accession GSE116093.

Core ILC subset gene sets. ILC subset gene sets were used to develop core networks (Figure 3) and for detection of lineage regulators (Figure 5). They were constructed from our gene expression dataset (20 samples). For a given lineage, ILC subset genes were included in the set if they were more highly expressed (log2(FC)>1, FDR=10%) in that subset relative to at least one other subset and not decreased (log2(FC)<-1, FDR=10%) relative to any other subset in the SI. This analysis yielded overlapping subset-defining gene sets ranging from ~1500-~2500 genes per subset. We also define sets of subset- suppressed genes, which, for a given subset, had to be less expressed in that subset relative to at least one other subset and not increased relative to any other subset in the SI. These core ILC gene signatures of up- and down-regulated genes are contained in Table S3.

Transcriptional regulatory network inference. Motif Analysis and Generation of DATAC Prior Matrix. Peaks were associated with putative transcription-factor (TF) binding events and target genes to generate a “prior” network, " ∈ ℝ|&'(')|×|+,)|, of TF-gene interactions. We collected mouse motifs for 859 TFs from CisBP (Weirauch et al., 2014) and ENCODE (Kheradpour et Kellis, 2014), as described in (Miraldi et al., 2018). We scanned peaks for individual motif occurrences with FIMO (parameters --thresh .00001, --max-stored-scores 500000, and a first-order background model) and included motif occurrences with Praw<1E-4 in our analysis. To

15 construct the DATAC prior, core ILC subset ATAC-seq peak sets were defined based on differential accessibility, analogously, with the same parameters, to the core ILC subset gene sets above. For each subset and core TF, we began with all ATAC-seq peaks containing at least one motif occurrence (as the background set), we then tested whether core subset ATAC-seq peaks were enriched nearby core subset genes. Note that to perform the enrichment, genes were mapped to motif-containing peaks; gene sets were converted to “peak sets” and included the union of motif-containing peaks falling within +/- 10kb of a gene body in the gene set. We used the hypergeometric CDF to estimate enrichment and applied a nominal significance cutoff of Praw<1E-4. TFs significantly enriched in DATAC proximal to core gene sets were included as regulators of the genes in the set that contained a DATAC-proximal TF-motif-containing peak. To detect repressive interactions, we repeated the procedure above looking for enrichment of core ATAC-seq peaks nearby subset-suppressed genes. This procedure was also repeated for gene sets derived from CCR6- ILC3, CCR6+ ILC3 and ILC2 samples from the LI. Finally, we also derived LI- and SI-dependent TF-gene interactions, similarly testing for enrichment between DATAC and Dgene sets based on LI versus SI accessibility / expression in the three cell types above. The resulting “DATAC” prior matrix contained 42,850 TF-gene interactions for 4226 genes and 129 TFs (Table S4).

Inference framework. We built gene expression models according to (Miraldi et al., 2018), using a modified LASSO-StARS framework to solve for TF-gene interaction terms {bik}:

> 01 = 3456789 |; − 0=|> + |Λ ∘ 0|B , [Equation 1] where, ; ∈ ℝ|&'(')|×|)CDEF')| is the expression matrix for genes in the prior, = ∈ ℝ|+,)|×|)CDEF')| contains TF activities, B ∈ ℝ|&'(')|×|+,)| is the matrix of inferred TF-gene interaction coefficients, Λ ∈ ℝ|&'(')|×|+,)| is a matrix of nonnegative penalties, and ∘ represents a Hadamard (entry-wise matrix) product (Gustafsson et al., 2015; Studham et al., 2014). Representing the LASSO penalty as a Hadamard product involving a matrix of penalty terms, as opposed to a single penalty term, enabled us to incorporate prior information into the model-building procedure. Specifically, a smaller penalty ΛHI is used if there is evidence for the TF-gene interaction in the prior matrix. This procedure encourages selection of interactions supported by the prior (e.g., containing ATAC-seq evidence), if there is also support in the gene expression data.

For this study, our target gene expression matrix contained the 6418 “core” ILC genes (defined above) for the 62 samples. We considered 445 potential TF regulators (intersection of “core” ILC genes with our curated list of mouse TFs (Miraldi et al., 2018)). Entries of the Λ matrices were limited to two values: the nonnegative value J, for TF-gene interactions without evidence in the prior, and . 25 × J, for TF-gene interactions with support in the prior. Note that . 25 × J weights interactions in the prior twice as strongly as in our previous Th17 TRN (Miraldi et al., 2018), because, in this context, we have fewer gene expression samples (62 versus 254) and a presumed higher confidence prior (due to DATAC method above).

As described previously, we used the stability-selection method StARS (Liu et al., 2010) with 50 subsamples of size . 63 × |P36QRSP| and an instability cutoff = .05 to solve for the TF-gene interactions B. We then ranked TF-gene interactions based on stability as described in (Miraldi et al., 2018) and used out-of-sample prediction (described below) to determine how many interactions to include in the final TRN. To estimate the TF activity

16 matrix =, we used two methods: (1) TF gene expression and (2) prior knowledge of TF target gene expression (Arrieta-Ortiz et al., 2015). For (2), we used the following relationship to solve for TF activities: ; = "= [Equation 2], where " ∈ ℝ|&'(')|×|+,)| is the (DATAC) prior. Based on previous results (Miraldi et al., 2018), we generated separate TRNs using each of the TF activity methods and rank-combined TRNs using the maximum. Based on gene-expression prediction (described below, Figure S4D,E), our ILC TRN size was set to an average of 10 TFs per gene (total 63,832 TF-gene interactions). At this model size, 25,113 TRNs interactions (39%) had DATAC prior support, and the TRN contained 59% of the 42,850 prior interactions. Thus, many DATAC are filtered out (41% of the prior) because they are not supported by the gene expression data and new interactions (61% of the final TRN) are learned from gene expression patterns alone.

Gene Expression Prediction. TRN model quality was computationally assessed by out-of- sample gene expression prediction. To ensure robustness of results, four separate out-of- sample gene expression prediction tasks were designed, where, for each independently, ILC TRN models were built in the absence of (1) all ILC1 (5 samples) from (Gury-BenAri et al., 2016), (2) all ILC2 (5 samples) from (Gury-BenAri et al., 2016), (3) all ILC3 (6 samples) from (Gury-BenAri et al., 2016), and (4) all eight large intestine (LI) samples. For each out-of-sample prediction challenge, models were built using prior-based TFA (TFA=P+A) and TF mRNA (see “Inference Framework”). Because the LI gene expression data had been used to construct the DATAC prior, TF-gene interactions based on gene sets derived from the LI samples were omitted and the reduced prior was used to train the model.

Training TFA matrices were mean-centered and variance-normalized according to the |+,)| C |+,)| training-set means 3TUVCH( ∈ ℝ and standard deviations WTUVCH( ∈ ℝ . Target gene expression vectors were mean-centered according to the training-set mean X̅UVCH( ∈ |&'(')| > ℝ . Predictive performance was quantified with ZEV'[ as a function of mean model size (TFs / gene) (Figure S4D,E). In brief, for each model-size cutoff, we regressed the vector of normalized training gene expression data onto the reduced set of normalized training TFA estimates to arrive at a set of multivariate linear coefficients 0UVCH( ∈ |&'(')|×|+,)| > ℝ . ZEV'[ is defined as: > ]]^_`ab > ZEV'[ = 1 − , ZEV'[ ∈ (−∞, 1] , [Equation 3a] ]]^cdee where > CpqrCTp,s`tuc jjk = ∑Hx|&'(')| mX − ∑ o m w − X̅ w [Equation 3b] EV'[ Hn Ix|+,)| HI,UVCH( vt H,UVCH( nx{U')U} p,s`tuc and > jjk({FF = ∑Hx|&'(')||XHn − X̅H,UVCH(} . [Equation 3c] nx{U')U} > Any ZEV'[>0 indicates that the numerator model has predictive benefit over the null model (simply using mean gene expression from the observed training data). For all leave-out > gene expression prediction tasks, median ZEV'[ increased dramatically from model-size two to five TFs / gene and began to plateau at 10 TFs / gene, suggesting 10 TFs / gene as model-size cutoff (Figure S4D,E). We note that ILC TRN analyses (Figures 3-5, 7) were tested for robustness to variation in this cutoff (e.g., Figure S7A).

Network Visualization

17 Networks were visualized using a newly designed interactive interface, based on iPython and packages: igraph, numpy and scipy (https://github.com/simonsfoundation/jp_gene_viz). ILC TRNs are available from the following link: https://github.com/flatironinstitute/ILCnetworks (see Supplemental Text 1 for detailed instructions).

TF-TF Module Analysis TF-TF modules were constructed as described in (Miraldi et al., 2018). In brief, for positive and negative edges separately, we calculated a background-normalized overlap score ~Hn between TF i and TF j as:

> > Å~H|n + ~n|H , 7Ç ~H|n, ~n|H > 0 ~Hn = Ä , [Equation 4] 0, ÖÜℎS4à7PS where ~H|n is the z-score of the overlap between TF i and TF j, using the mean and standard deviation associated with the overlaps of TF j to calculate the z-score. We filtered the normalized overlap matrix so that it contained only TFs with at least one significant overlap (FDR = 10%). We then converted the similarity matrix of normalized overlaps to a distance matrix for hierarchical clustering using Ward distance. To arrive at a final number of clusters, we calculated the mean silhouette score for solutions over a range of total clusters and selected the solution that maximized silhouette score. For positive interactions, this analysis lead to 57 clusters, and 12 clusters for negative interactions. We estimated the significance of individual clusters based on cluster size and overlap between TFs in the cluster (Miraldi et al., 2018). We only report positive TF-TF clusters, because, as observed previously (Miraldi et al., 2018), positive TF-TF clusters were orders of magnitude more significant than TF-TF clusters based on repressive interactions.

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Supplementary Table Legends

Table S1. Commercially available antibodies used in flow cytometry experiments.

Table S2. Batch-corrected gene expression data combining samples from this study, Gury-BenAri et al., and Shih et al.

Table S3. Up-regulated and down-regulated ILC subset gene signatures (see Methods).

Table S4. DATAC prior matrix.

Table S5. The ILC TRN.