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ABSTRACT Genome-wide association studies have identified risk loci linked to inflammatory bowel disease (IBD)1—a complex chronic inflammatory disorder of the gastrointestinal tract. The

increasing prevalence of IBD in industrialized countries and the augmented disease risk observed in migrants who move into areas of higher disease prevalence suggest that environmental

factors are also important determinants of IBD susceptibility and severity2. However, the identification of environmental factors relevant to IBD and the mechanisms by which they influence

disease has been hampered by the lack of platforms for their systematic investigation. Here we describe an integrated systems approach, combining publicly available databases, zebrafish

chemical screens, machine learning and mouse preclinical models to identify environmental factors that control intestinal inflammation. This approach established that the herbicide

propyzamide increases inflammation in the small and large intestine. Moreover, we show that an AHR–NF-κB–C/EBPβ signalling axis operates in T cells and dendritic cells to promote intestinal

inflammation, and is targeted by propyzamide. In conclusion, we developed a pipeline for the identification of environmental factors and mechanisms of pathogenesis in IBD and, potentially,

other inflammatory diseases. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access through your

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SIMILAR CONTENT BEING VIEWED BY OTHERS INFLAMMATION STATUS MODULATES THE EFFECT OF HOST GENETIC VARIATION ON INTESTINAL GENE EXPRESSION IN INFLAMMATORY BOWEL DISEASE Article Open access 18

February 2021 MULTIOMICS TO ELUCIDATE INFLAMMATORY BOWEL DISEASE RISK FACTORS AND PATHWAYS Article 17 March 2022 IDENTIFYING HIGH-IMPACT VARIANTS AND GENES IN EXOMES OF ASHKENAZI JEWISH

INFLAMMATORY BOWEL DISEASE PATIENTS Article Open access 20 April 2023 DATA AVAILABILITY RNA-seq and scRNA-seq data have been deposited at the GEO database under the following accession

number GSE194412 and GSE175766. 16S rRNA-sequencing data have been submitted to the NCBI sequence-read archive under BioProject number PRJNA804134. The machine learning codes used for this

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Protoc._ 12, 828–863 (2017). CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank all of the members of the Quintana laboratory for advice and

discussions; R. Krishnan for technical assistance with flow cytometry studies; E. Buys and the BWH aquatics facility for assistance with breeding and maintaining the zebrafish; the staff at

the Tufts and Harvard histology core facilities for providing histopathology services; the staff at the NeuroTechnology Studio at Brigham and Women’s Hospital for providing instrument

access; L. Zon and G. Stirtz at Boston Children’s Hospital for providing zebrafish lines and advice; D. Rojas Marquez (@darwid_illustration) for help with the model figure. All of the other

illustrations were created using BioRender. This work was supported by grants NS087867, ES025530, ES032323, AI126880 and AI093903 from the National Institutes of Health. C.-C.C. received

support (104-2917-I-564 −024) from the Ministry of Science and Technology, Taiwan. Y.-C.W. received support by grants and 109-2221-E-010-013-MY3 and 107-2221-E-010-019-MY3 from the Ministry

of Science and Technology, Taiwan. C.M.P. was supported by a fellowship from the FAPESP BEPE (2019/13731-0). C.G.-V. was supported by an Alfonso Martín Escudero Foundation postdoctoral

fellowship and by a postdoctoral fellowship from the European Molecular Biology Organization (ALTF 610-2017). G.P. is a trainee in the Medical Scientist Training Program funded by NIH T32

GM007356. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical

Science or NIH. H.-G.L. was supported by a Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A3A14039088).

M.J. was supported by a post-doctoral fellowship from Sigrid Juselius, personal post-doctoral grants from Saastamoinen Foundation, Paulo Foundation, The Finnish MS-Foundation, Orion Farmos

Research Foundation and Maud Kuistila Memory Foundation. M.A.W. was supported by the NIH (1K99NS114111, F32NS101790), a training grant from the NIH and Dana-Farber Cancer Institute

(T32CA207201), a travelling neuroscience fellowship from the Program in Interdisciplinary Neuroscience at the Brigham and Women’s Hospital and the Women’s Brain Initiative at the Brigham and

Women’s Hospital. V.R. received support from an educational grant from Mallinkrodt Pharmaceuticals (A219074) and by a fellowship from the German Research Foundation (DFG RO4866 1/1). R.C.

received support by a postdoctoral fellowship from the Swedish Research Council. B.M.A. received support from K12CA090354 from the NIH. AUTHOR INFORMATION Author notes * These authors

contributed equally: Liliana M. Sanmarco, Chun-Cheih Chao, Yu-Chao Wang, Jessica E. Kenison AUTHORS AND AFFILIATIONS * Ann Romney Center for Neurologic Diseases, Brigham and Women’s

Hospital, Harvard Medical School, Boston, MA, USA Liliana M. Sanmarco, Chun-Cheih Chao, Jessica E. Kenison, Zhaorong Li, Joseph M. Rone, Claudia M. Rejano-Gordillo, Carolina M. Polonio, 

Cristina Gutierrez-Vazquez, Gavin Piester, Agustin Plasencia, Lucinda Li, Federico Giovannoni, Hong-Gyun Lee, Camilo Faust Akl, Michael A. Wheeler, Ivan Mascanfroni, Merja Jaronen, Moneera

Alsuwailm, Patrick Hewson, Ada Yeste, Brian M. Andersen, Millicent Ekwudo, Emily C. Tjon, Veit Rothhammer, Maisa Takenaka, Kalil Alves de Lima, Mathias Linnerbauer, Lydia Guo, Ruxandra

Covacu, Hugo Queva, Pedro Henrique Fonseca-Castro, Maha Al Bladi, Laura M. Cox, Kevin J. Hodgetts & Francisco J. Quintana * Institute of Biomedical Informatics, National Yang Ming Chiao

Tung University, Taipei, Taiwan Yu-Chao Wang & Chien-Jung Huang * Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, Rochester, NY, USA Gavin

Piester * The Broad Institute of Harvard and MIT, Cambridge, MA, USA Michael A. Wheeler & Francisco J. Quintana * Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical

School, Boston, MA, USA Brian M. Andersen * Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA Diana G. Franks & Mark E. Hahn * Max-Delbrück-Center for

Molecular Medicine (MDC), Berlin, Germany Alexander Mildner * Department of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Joshua Korzenik * Harvard T. H. Chan School of Public Health, Boston, MA, USA Russ Hauser * Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston

Children’s Hospital, Boston, MA, USA Scott B. Snapper * Department of Medicine, Harvard Medical School, Boston, MA, USA Scott B. Snapper Authors * Liliana M. Sanmarco View author

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Francisco J. Quintana View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS L.M.S., C.-C.C., J.E.K. and F.J.Q. designed research. L.M.S.,

C.-C.C., J.E.K., J.M.R., C.M.R.-G., C.M.P., C.G.-V., G.P., A.P., L.L., F.G., H.-G.L., C.F.A., M.A.W., I.M., M.J., M.A., P.H., A.Y., B.M.A., D.G.F., M.E., V.R., M.T., K.A.d.L., M.L., L.G.,

R.C., H.Q., P.H.F.-C. and M.A.B. performed experiments. L.M.S., C.-C.C., J.E.K., Z.L., E.C.T., V.R., L.M.C., K.J.H, M.E.H., J.K., R.H., S.B.S. and F.J.Q. analysed data. A.M. contributed mice

and reagents. Y.-C.W., E.C.T. and C.-J.H. performed machine learning. L.M.S., C.-C.C., J.E.K. and F.J.Q. wrote the paper with input from all of the authors. F.J.Q. directed and supervised

the study. CORRESPONDING AUTHOR Correspondence to Francisco J. Quintana. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW

INFORMATION _Nature_ thanks Judy Cho, Mark Sundrud and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ADDITIONAL INFORMATION PUBLISHER’S NOTE

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA FIGURES AND TABLES EXTENDED DATA FIG. 1 TNBS-INDUCED

INTESTINAL INFLAMMATION IN ZEBRAFISH. (A) Zebrafish survival when treated with 25, 50, or 75 μg ml−1 TNBS for 24, 48, 72, or 96 h. Data shown as mean percent survival ±SEM of 3 independent

experiments with n = 20 fish per group per experiment. (B) Intestinal scores of naive 10 d.p.f. zebrafish, or zebrafish exposed for 24 or 72 h starting at 7 d.p.f. to TNBS (25 μg ml−1). (n =

 24 per group). (C) Intestinal scores of zebrafish exposed for 72 h starting at 7 d.p.f. to TNBS plus vehicle, NOS inhibitor (NOSi, 10 μM), or FICZ (10 μM). (n = 24 per group). (D–F)

Intestinal scores (D) and _lck_ (E), _il17a/f_, _tnfa_, _ifng_, _il1b_ and _nos2a_ (F) expression in 10 d.p.f. naive, or TNBS-exposed (25 μg ml−1, 72 h) vehicle- or lenaldekar-treated (LDK,

5 μM) zebrafish (n = 24 per group for intestinal scores, n = 6 for _lck_ naive vehicle, n = 11 otherwise). (G) Intestinal scores in 10 d.p.f. naive vehicle or TNBS-exposed (25 μg ml−1, 72 h)

WT, _rag1-_ and _rag2_-deficient zebrafish. (n = 24 for WT naive and TNBS, n = 44 for _rag1_ and _rag2_ KO groups). (H–I) _lck_ (H), _il17a/f_, _tnfa_, _ifng_, _il1b_ and _nos2a_ (I)

expression in 10 d.p.f. TNBS-exposed (25 μg ml−1, 72 h) WT or _rag2_-deficient zebrafish. (n = 18 per group). (J) T cells expressing GFP under the control of the _lck_ promoter in the

intestine of naive 10 d.p.f. lck:gfp zebrafish, or lck:gfp zebrafish exposed for 72 h starting at 7 d.p.f. to TNBS (25 μg ml−1) alone or in combination with lenaldekar (LDK, 5 μM) or

propyzamide (20 μM). Top panels are brightfield, middle panels are _lck:gfp_ expression, bottom panels are a composite of both. (K) Quantification of intestinal GFP-positive T cells in

lck:gfp zebrafish shown in (J). (n = 12 per group). (L) Activity of candidate chemicals in 49 ToxCast bioassays targeting genes linked to IBD; shown as active (red), inactive (blue), or no

data (grey). (M) Principal component analysis based on ToxCast bioassays in chemicals found to ameliorate, promote, or have no effect on intestinal inflammation in the TNBS-induced zebrafish

model. (N) Intestinal scores in zebrafish exposed for 72 h to TNBS and chemicals randomly selected from the ToxCast database at 0.2, 1, 5, or 20 μM. Each panel represents the average of n =

 12 zebrafish per group. Grey panels indicate concentrations lethal to zebrafish larvae. (O,P) Intestinal scores (O) and _il17a/f_, _tnfa_, _ifng_, _il1b_ and _nos2a_ expression (P) in naive

zebrafish treated with vehicle or propyzamide (0.2, 1, 5, or 20 μM) (n = 24 per group for intestinal scores, n = 9 per group for gene expression). (Q) Intestinal scores of TNBS-exposed (25 

μg ml−1, 72 h) zebrafish treated with vehicle, NOS inhibitor (10 μM), FICZ (10 μM), or propyzamide (20 μM), or a combination as indicated. (n = 36 for TNBS+vehicle and TNBS+propyzamide

groups, n = 24 otherwise). Two-way ANOVA followed by Šídák’s multiple comparisons test for B. One-way ANOVA followed by Šídák’s or Dunnett’s multiple comparisons test for C–G,K,N,O,Q.

Unpaired student’s T test for H,I. Data shown as mean±SEM. Source Data EXTENDED DATA FIG. 2 PROPYZAMIDE BOOSTS TNBS-INDUCED COLITIS IN MICE. (A) Gating strategy used to analyse CD4+ T cells.

(B) CD3+ lymphocytes in colon normalized by tissue length from vehicle- or propyzamide-treated (100 mg kg−1) mice during TNBS-induced colitis (n = 5 mice per group). (C) Representative dot

plots of IFNγ and IL17 expression in CD4+ T cells. (D) Representative dot plots of IL-17 and RORγt expression in CD4 T cells and number of IL17+RORγt+ CD4 T cells in vehicle- or

propyzamide-treated TNBS mice (n = 4 for vehicle, n = 3 for propyzamide). Unpaired student’s T test. (E) _Ifng_ and _Il17_ expression determined by qPCR in lamina propria mononuclear cells

(LPMC) from naive mice (n = 5) and vehicle- or propyzamide-treated TNBS mice (n = 4 mice per group). (F) IL17+γδ+ or CD8+ T cells isolated from colons of vehicle- (n = 7) or

propyzamide-treated (n = 9) mice during TNBS-induced colitis. (G,H) Weight change (g) and colon length (h) of mice treated with vehicle or propyzamide (100 mg kg−1) for 10 days (n = 20 mice

per group). (I) Total CD4+ (n = 10 propyzamide, n = 9 vehicle), IFNγ+ CD4+ (n = 17 per group) and IL-17+ CD4+ (n = 9 per group) T cells in colons of vehicle- or propyzamide-treated mice. (J)

CD8+and IFNγ+ CD8+ T cells in colons of vehicle- (n = 9 for CD8+, n = 10 for IFNγ+ CD8+) or propyzamide-treated (n = 8 per group) mice. (K) γδ+T and IL-17+ γδ+T cells in colons of vehicle-

or propyzamide-treated mice (n = 10 per group). (L) CD127+ ILCs (n = 10 vehicle, n = 7 propyzamide) and IL-17+ILC3s (n = 6 per group) in colons of vehicle- or propyzamide-treated mice. (M)

_Rela_ and _Cebpb_ expression determined by qPCR from colonic CD45+ cells isolated from vehicle- or propyzamide-treated mice (n = 4 for propyzamide _Rela_, n = 5 otherwise). (N) Propyzamide

concentrations in plasma (n = 6 per timepoint), faeces (n = 1) and urine (n = 1) after propyzamide administration (100 mg kg−1). (O) Propyzamide levels in plasma, faeces and urine collected

from naive or TNBS-induced colitis mice (n = 3 per group). One-way ANOVA followed by Holm-Šídák’s or Tukey’s of multiple comparisons test for B and E. Data shown as mean±SEM. Source Data

EXTENDED DATA FIG. 3 EFFECTS OF PROPYZAMIDE ON THE GUT MICROBIOME. (A) α-diversity of the faecal microbiome (n = 6 for Jejunum vehicle, n = 7 for Ileum, Caecum and Colon vehicle, n = 8 for

Ileum Propyzamide, n = 9 for Jejunum, Caecum and Colon propyzamide). Kruskal–Wallis nonparametric ANOVA test. (B) β-diversity shown as Principal-coordinate analysis (PCoA) based on

unweighted UniFrac metrics. (C) Relative abundance of bacteria classified at a family-level taxonomy. (D) Relative abundance of the _Suterellaceae_ family (n = 7 for TNBS vehicle, n = 9 for

TNBS propyzamide, n = 10 for naive vehicle and propyzamide) Kruskal–Wallis nonparametric ANOVA test. (E) Schematic of faecal microbiota transplant (FMT) to germ free mice. This schematic was

created using BioRender. (F) 16S quantification by qPCR after FMT (n = 4 control, n = 20 before reconstitution, n = 8 for FMT from vehicle- and propyzamide-treated mice). (G,H,I) Weight

loss (g) (n = 10 vehicle, n = 6 propyzamide), colon length (h) (n = 10 vehicle, n = 7 propyzamide)and representative hematoxylin & eosin staining in colons (i) from germ free mice after

FMT from propyzamide- or vehicle-treated mice (n = 8 vehicle, n = 5 propyzamide for quantification). (J) CD4+, IFNγ+ CD4+ and IL-17+CD4+ T cells in colons of germ free mice after FMT from

propyzamide- or vehicle-treated mice. (n = 10 mice for vehicle CD4+ and IFNγ+ CD4+, n = 9 for vehicle IL-17+CD4+, n = 8 mice for all propyzamide groups). Data shown as mean±SEM. ***p < 

0.001, ** p < 0.01, *p < 0.05. Source Data EXTENDED DATA FIG. 4 TRANSCRIPTIONAL ANALYSIS OF COLONIC T CELLS AND DCS. (A) _Tnf, Il23, Il1b_ and _Il6_ expression determined by qPCR in

LPMC from naive mice (n = 7) and vehicle- or propyzamide-treated mice during TNBS-induced colitis (n = 6 mice per group). (B) _Tgfb and Il10_ expression determined by qPCR in LPMC from naive

mice (n = 7) and vehicle- or propyzamide-treated mice during TNBS-induced colitis (n = 6 mice per group). Data shown as mean±SEM. (C) Dot plot visualization of features that define cell

clusters in Fig. 2i. (D) UMAP plots of colonic cells from naive or TNBS-induced colitis mice treated with vehicle or propyzamide (100 mg kg−1). (E) Cluster distribution per replicates of

colonic cells from naive or TNBS-induced colitis mice treated with vehicle or propyzamide (n = 5 mice per group). (F) Heatmap of differentially expressed genes that cluster colonic DC

populations from scRNAseq analysis. (G) UMAP plots of DCs from colons from naive or TNBS-induced colitis mice treated with vehicle or propyzamide (100 mg kg−1). (H) GSEA analysis showing

pathways activated in DCs from propyzamide-treated mice during TNBS-colitis. (I) Percentage of each DC subpopulation from vehicle- or propyzamide-treated naive mice. (J) mRNA expression

determined by bulk RNA-seq in colon samples from vehicle- or propyzamide-treated mice 24 h after anti-CD3 administration (n = 4 mice per group). (K) _Cyp1a1_ and _Cyp1b1_ expression in

colonic CD45+ cells from vehicle- or propyzamide-treated mice 24 h after anti-CD3 injection (n = 6 mice per group). (L) IPA showing pathways significantly upregulated in propyzamide-treated

mice analysed by bulk-RNA-seq. (M,N) _Rela_ and _Cebpb_ (m) and _Ifng, Il17, Rorc_ and _Il12rb1_ (n) expression in colonic CD45+ cells from vehicle- and propyzamide-treated mice 24 h after

anti-CD3 administration (n = 10 mice for _Cebpb_ vehicle, n = 8 mice for _Cebpb_ propyzamide, n = 5 for _Ifng_ vehicle, n = 4 for _Ifng_ propyzamide, n = 5 for _Il17_ propyzamide, n = 5 for

_Rorc_ vehicle, n = 5 for _Il12rb1_ vehicle, n = 6 mice otherwise). (O) T cells, IFNγ+ and IL-17+ CD4 T cells and IFNγ+ CD8 T cells in colon from propyzamide- and vehicle-treated mice 24 h

after anti-CD3 injection. Data shown as mean±SEM. ***p < 0.001, ** p < 0.01, *p < 0.05. Source Data EXTENDED DATA FIG. 5 EFFECTS OF PROPYZAMIDE ON THE SMALL INTESTINE. (A) mRNA

expression determined by bulk RNA-seq in jejunal CD45+ cells from vehicle- and propyzamide-treated mice (n = 4 mice per group. (B,C,D,E) CD4, IFNγ+ CD4 and IL-17+ CD4 T cells (b), CD8 and

IFNγ+ CD8 T cells (c), γδ and IL-17+ γδ T cells (d), ILC and IL-17+ILC3 (e) in jejunum from vehicle- or propyzamide-treated mice. (n = 10 mice for IL-17+CD4 T cells vehicle, IFNγ+ CD8 T

cells vehicle, γδ and IL-17+ γδ T cells vehicle, ILC and IL-17+ILC3 vehicle, n = 7 mice for CD8 T cells propyzamide and γδ T cells propyzamide, n = 8 for ILC propyzamide, n = 9 mice

otherwise). (F) Transactivation of the RARα promoter in _RARa_-luciferase transfected HEK293T cells treated with retinoic acid or retinoic acid and propyzamide. (G) Transactivation of the

PPARα promoter in _PPARa_-luciferase transfected HEK293T cells treated with fenofibrate, or propyzamide and fenofibrate for 24 h. (H) _Rorc_ and _Il12rb1_ expression evaluated by qPCR in

colonic CD4 T cells sorted from vehicle- or propyzamide-treated WT or AHRd mice after TNBS-colitis. (I) _Il1b, Tnf_ and _Il23_ expression in sorted DCs. (J) _Rela_ expression in BMDCs (n = 5

per group). (K) C/EBPβ expression, determined by ELISA, following _Rela_ knockdown in BMDCs (n = 3 per group). (L) Relative expression of p65 subunit of NF-κB in primary murine DCs as a

result of the depicted chemical treatment previously identified to be linked to IBD in Fig. 1d. (M) Microtubule destabilization after paclitaxel and/or propyzamide incubation with

fluorescent tubulin. Data shown as mean±SEM. ****p < 0.0001, ***p < 0.001, ** p < 0.01, *p < 0.05. Source Data EXTENDED DATA FIG. 6 C/EBPΒ ACTIVATION IN DCS BOOSTS COLITOGENIC

T-CELL DIFFERENTIATION. (A) _Cebpb_ expression following propyzamide treatment in primary DCs from WT and _Cebpb__−/−_ mice (n = 14 vehicle-treated WT cells, n = 9 propyzamide-treated WT

cells, n = 4 vehicle-treated _Cebpb__−/−_ cells, n = 5 propyzamide-treated _Cebpb__−/−_ cells). One-way ANOVA test followed by Tukey’s post-hoc test. (B) _Il1b, Il23_ and _Tnf_, expression

following _Cebpb_ knockdown in BMDCs. (For _Il1b_ n = 6 siNT vehicle, n = 4 siNT propyzamide, n = 5 siCebpb vehicle and n = 6 siCebpb propyzamide. For _Tnf_, n = 6 siNT vehicle, n = 3 siNT

propyzamide, n = 6 siCebpb vehicle and n = 6 siCebpb propyzamide. For _Il23_, n = 5 siNT vehicle, n = 3 siNT propyzamide, n = 7 siCebpb vehicle and n = 9 siCebpb propyzamide). One-way ANOVA

test followed by Holm-Šidák’s post-hoc test. (C) _Cebpb_ expression following _Cebpb_ silencing in BMDCs (n = 8 siNT vehicle, n = 5 siNT propyzamide, n = 9 siCebpb vehicle and n = 5 siCebpb

propyzamide). (D) Gate strategy used to sort human DCs from PBMCs. (E) _CEBPB_ expression in human DCs treated with propyzamide after knockdown with _CEBPB-_targeting (siCEBPB) or

non-targeting (siNT) siRNAs (n = 6 per group). Two-way ANOVA followed by Tukey’s multiple comparisons post-hoc test. (F) ChIP-seq and ATAC-seq re-analyses from23 of Cebpb binding to _Il1b_,

_Tnf_ and _Il23_ promoter in GM-CSF or Flt3 differentiated DCs. (G) Analysis of splenic DCs from TNBS-induced colitis mice. This schematic was created using BioRender. (H) IFNγ, IL-17 and

TNF representative histograms in T cells co-cultured with splenic DCs from vehicle- or propyzamide-treated TNBS-induced colitis mice. MFI of IL-17, IFNγ and TNF OT-II CD4+ T cells following

co-culture with splenic DCs from propyzamide- or vehicle-treated mice (n = 6 vehicle-treated mice, n = 4 propyzamide-treated mice). Unpaired t-test. (I) _Ifng_ and _Il17_ expression

determined by qPCR in co-cultures from WT and _Cebpb__−/−_ DCs pre-treated with vehicle or propyzamide in presence of OVA peptide and co-culture with OT-II naive CD4 T cells for 48 h (For

_Ifng_, n = 4 WT vehicle, n = 4 WT propyzamide, n = 7 _Cebpb__−/−_ vehicle and n = 6 _Cebpb__−/−_ propyzamide. For _Il17_, n = 7 WT vehicle, n = 4 WT propyzamide, n = 4 _Cebpb__−/−_ vehicle

and n = 6 _Cebpb__−/−_ propyzamide). One-way ANOVA test followed by Holm-Šidák’s multiple comparisons post-hoc test. (J) TNF, IL-17 and IL-6 relative levels determined in co-culture

supernatants of human DCs pre-treated as specified in the figure, and then incubated with allogenic T cells for 48 h. (For _Tnf_, n = 3 per group. For _Il17_, n = 4 per group). One-way ANOVA

test followed by Holm-Šidák’s multiple comparisons post-hoc test. (K–L) _Cebpb_ expression (k) and _Il1b, Il23_ and _Tnf_ expression (l) determined by qPCR in primary splenic DCs

transfected with cumate-inducible _Cebpb_-expressing plasmid after cumate treatment as depicted for 96 h. (For _Cebpb_ (k), n = 5 vehicle-treated and n = 3 for each cumate-treated group. For

_Tnf_ and _Il23_ (l) n = 4 vehicle-treated and n = 3 for each cumate-treated group). One-way ANOVA test followed by Holm-Šidák’s post-hoc test. Data shown as mean±SEM. Source Data EXTENDED

DATA FIG. 7 SCRNASEQ ANALYSIS OF INTESTINAL DCS FROM IBD PATIENTS. (A) UMAP plot and cluster distribution per replicates of colonic leukocytes isolated from _Cebpb__−/−_ (n = 2 mice) and WT

(n = 5 mice) DC chimeras after TNBS-colitis. (B) UMAP plot showing DC clusters analysed in colon from WT and _Cebpb__−/−_ DC chimeras analysed by scRNAseq. (C) Number of cells per DC cluster

and cluster distribution per replicates. (D) _Cebpb_ expression in DCs recovered from WT or _Cebpb__−/−_ DC chimera. (E) UMAP plot of 58 samples from 44 IBD patients and healthy controls.

(F) Dot Plot visualization of features that define DCs and T cells. (G) Differentially regulated pathways in DCs from IBD patients. (H) Violin plot depicting _CEBPB_ expression in DCs from

IBD and healthy controls. (I) UMAP depicting intestinal DCs from IBD and healthy controls analysed by scRNA-seq. (J) _CEBPB_ expression in DC1 and DC2 subsets. (K) Differentially regulated

pathways in _CEBPB_ expressing DCs. EXTENDED DATA FIG. 8 VCAM-1 BLOCKADE AMELIORATES INTESTINAL INFLAMMATION. (A) Network analysis of molecules reported in ToxCast database to be induced by

propyzamide. (B) Effect of propyzamide on _Vcam1_ promoter activity (n = 10 for 0.2 μM propyzamide, n = 7 otherwise). (C) _Vcam1_ expression in colons from naive and TNBS-induced colitis

mice treated with vehicle or propyzamide (100 mg kg−1) (n = 5 mice per group). Unpaired t-test. (D) Effect of propyzamide on _Itga4_ and _Itga1_ expression in T cells (n = 3 for Itga4

propyzamide, n = 4 otherwise). Unpaired t test. (E) Evaluation of VCAM-1 blocking antibodies in TNBS-induced colitis. This schematic was created using BioRender. (F) Effect of VCAM-1

blocking antibodies (100 μg per mouse) or isotype controls on TNBS-induced colitis (Weight: n = 12 for vehicle isotype control, n = 9 for vehicle anti-VCAM1, n = 7 for propyzamide

anti-VCAM1, n = 13 otherwise, Colon length: n = 15 for naive, n = 5 for vehicle isotype control, n = 13 for propyzamide isotype control, n = 7 otherwise). (G,H) Representative hematoxylin

& eosin staining (g) and clinical histomorphology scores (h) (n = 4 mice for antiVCAM1-treated mice, n = 5 mice otherwise). Arrows show leukocyte infiltrates. (I) Effect of VCAM-1

blocking antibodies (100 μg per mouse) or isotype controls on colonic IFNγ+ and IL17+ CD4+ T cells during TNBS-induced colitis determined by flow cytometry (IFNγ: n = 6 mice for vehicle

isotype control, n = 5 mice for propyzamide isotype control, n = 4 otherwise. IL17: n = 4 mice for vehicle and propyzamide anti-VCAM1, n = 5 otherwise). One-way ANOVA followed by post-hoc

tests Tukey’s or Holm-Sidak’s test for selected multiple comparisons for B, F, H and I. Data shown as mean±SEM. Source Data EXTENDED DATA FIG. 9 PROPYZAMIDE BOOSTS COLITOGENIC T-CELL

DIFFERENTIATION. (A) Gate strategy used to study CD4 T cells. (B) Percentage of IFNγ+ CD4+ T cells and _Ifng_ expression of CD4+ T cells activated under Th1-polarizing conditions in the

presence of vehicle or propyzamide (n = 3 per group). Unpaired t-test. (C) Percentage of IL-17+ CD4+ T cells and _Il17_ expression of CD4+ T cells activated under Th17-polarizing conditions

in the presence of vehicle or propyzamide (n = 3 per group). Unpaired t-test. (D) _Tbx21_, _Csf2_ and _Il12rb1_ expression in CD4+ T cells activated under Th1 polarizing conditions in the

presence of vehicle or propyzamide (n = 3 per group, except propyzamide-treated cells expressing _Il12rb1_, n = 4). Unpaired t-test (E) _Csf2, Tnf_ and _Rorc_ expression in CD4+ T cells

activated under Th17 polarizing conditions in the presence of vehicle or propyzamide (For _Csf2_ n = 3per group, for _Rorc_ n = 3 vehicle-treated and n = 4 propyzamide-treated, for _Tnf_ n =

 5 per group) Unpaired t-test. (F) Nuclear p65 translocation in splenic CD4+ T cells activated with anti-CD3 and anti-CD28 in the presence of vehicle or propyzamide (n = 3 per group).

Unpaired t-test. (G,H) _Rela_ (g) and _Cebpb_ (h) expression following _Rela_ knockdown in T cells and treated with vehicle or propyzamide (For (g) n = 3 per siNT group and n = 4 per siRela

group, for (h) n = 5 per siNT group and n = 4 per siRela group). One-way ANOVA test followed by Holm-Šidák’s multiple comparisons post-hoc test. (I) _Cebpb_ expression in murine splenic WT

and _Cebpb__−/−_ T cells (n = 9 vehicle-treated WT cells, n = 7 propyzamide-treated WT cells, n = 3 vehicle-treated _Cebpb__−/−_ cells, n = 3 propyzamide-treated _Cebpb__−/−_ cells). One-way

ANOVA test followed by Holm-Šidák’s multiple comparisons post-hoc test. (J) _CEBPB_ expression following CEBPB knockdown in human T cells.(n = 4 per group except n = 3 siCEBPB

propyzamide-treated group). (K) ILCs and CD8 T cells in colons from _Rag1__−/−_ mice reconstituted with WT or _Cebpb__−/−_ T cells. (n = 4 per group except _Cebpb__−/−_ CD8 T cells n = 5).

Data shown as mean±SEM. ***p < 0.001, ** p < 0.01, *p < 0.05. Source Data EXTENDED DATA FIG. 10 SCRNASEQ ANALYSIS OF INTESTINAL T CELLS FROM IBD PATIENTS. (A) UMAP depicting total

intestinal T cells analysed by scRNA-seq. (B) Dot plot visualization of features to identify T-cell subsets. (C) Heat map of differentially expressed genes in T cells from IBD patients and

healthy controls. (D) Upstream analysis of NF-κB-driven _CEBPB_ expression in T cells. (E) _CEBPB_ expressing T cells from IBD and HC samples. (F,G,H) Pathway analysis of differentially

expressed genes in Resident Memory T cells (f), CD8 T cells (g) and ILCs (h) from IBD and healthy control samples. (I) Graphical model of modulation of colitogenic T cell responses by

NF-κB-driven C/EBPβ signalling and propyzamide. SUPPLEMENTARY INFORMATION REPORTING SUMMARY SUPPLEMENTARY TABLE 1 Effect of 936 candidate chemicals in ToxCast assays related to genes

relevant to IBD pathogenesis. Red highlights indicate that the tested chemical is active in the assay, blue highlights indicate that the chemical is inactive and ND indicates that no data

are available in the assay. SUPPLEMENTARY TABLE 2 Descriptions and details of the assays used in Supplementary Table 1, including citations for the assays themselves, and references for the

assay targets’ relevance to IBD. SUPPLEMENTARY TABLE 3 Results of the in vivo zebrafish screen of 111 candidate chemicals on TNBS-induced intestinal inflammation, and predicted exposure

levels for each chemical in zebrafish and humans (based on data from the EPA ToxCast website). SUPPLEMENTARY TABLE 4 Effect of 327 chemicals (including the 30 used as the training set, plus

an additional 297 identified from the ToxCast database) in the 16 ToxCast bioassays defined as an IBD bioactivity feature. Results shown are the AC50 values determined in each bioassay and

publicly available from the ToxCast website. SUPPLEMENTARY TABLE 5 Description of the assays used in Supplementary Table 1, including citations for the assays themselves, SUPPLEMENTARY TABLE

6 Candidate chemicals identified from the ToxCast database predicted to worsen intestinal inflammation by a RF model and ranked using a RWR algorithm. Additional details about the

classification and uses of the top 20 predicted chemicals are provided, along with relevant sources. SUPPLEMENTARY TABLE 7 Statistical analyses of _α_-diversity of the microbiome as a result

of propyzamide treatment in naive and TNBS-colitis mice. SUPPLEMENTARY TABLE 8 Statistical analysis of the _β_-diversity in the gut microbiome after propyzamide administration in naive and

TNBS-colitis mice. SUPPLEMENTARY TABLE 9 Demographic information of the samples analysed by scRNA-seq in Extended Data Figs. 7 and 10. SOURCE DATA SOURCE DATA FIG. 1 SOURCE DATA FIG. 2

SOURCE DATA FIG. 3 SOURCE DATA FIG. 4 SOURCE DATA EXTENDED DATA FIG. 1 SOURCE DATA EXTENDED DATA FIG. 2 SOURCE DATA EXTENDED DATA FIG. 3 SOURCE DATA EXTENDED DATA FIG. 4 SOURCE DATA EXTENDED

DATA FIG. 5 SOURCE DATA EXTENDED DATA FIG. 6 SOURCE DATA EXTENDED DATA FIG. 8 SOURCE DATA EXTENDED DATA FIG. 9 RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a society or

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this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Sanmarco, L.M., Chao, CC., Wang,

YC. _et al._ Identification of environmental factors that promote intestinal inflammation. _Nature_ 611, 801–809 (2022). https://doi.org/10.1038/s41586-022-05308-6 Download citation *

Received: 31 May 2021 * Accepted: 01 September 2022 * Published: 20 October 2022 * Issue Date: 24 November 2022 * DOI: https://doi.org/10.1038/s41586-022-05308-6 SHARE THIS ARTICLE Anyone

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