A functional genomics predictive network model identifies regulators of inflammatory bowel disease

Abstract

A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.

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Figure 1: An integrative approach for constructing a predictive network model of IBD, and identifying and validating master regulators of these networks.
Figure 2: Ranking KDGs of the CIC IBD networks.
Figure 3: Transcriptional responses in stimulated macrophages to perturbations in macrophage KDGs are predicted by the IBD networks.
Figure 4: Enrichment analysis of KDG subnetworks.
Figure 5: FACS analysis of immune cells in the colonic lamina propria of KDG-knockout mice as compared to wild-type littermate controls.
Figure 6: Differential weight loss and intestinal inflammation of KDG-knockout models as compared to sex-matched wild-type littermate controls.
Figure 7: Schematic of crosstalk of KDG molecular and network pathways.

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Acknowledgements

We acknowledge P. Chinnasamy for backcrossing of Aif1−/− mice; E. Esplugues; K. Saulnier from Charles River Laboratories; S. Graham, J. Mena, and G. Lyng from Biomodels; K. Amin from Qiagen; E. Venturini and the New York Genome Center; M. Mahajan, Y. Kasai, and the Genome Core at Mount Sinai; H. Thomas; R. Ng; the Pathology Department and the Histology core at MSH; and Sinai Innovations. This work was supported in part through the computational resources and staff expertise provided by the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai. This work was funded by the Schadt laboratory at the Icahn Institute for Genomics and Multi-scale Biology, Icahn School of Medicine at Mount Sinai (NewYork). This work was partially funded by NIH/NIA grant R01AG046170 (to E.E.S. and B.Z.), a component of the AMP-AD Target Discovery and Preclinical Validation Project, the Rheumatology Research Foundation (to T.K.T.), the Leading Advanced Projects for Medical Innovation (LEAP; to Y.F.) from the Japan Agency for Medical Research and Development (AMED), U01HG008451 (to J.Z.), NIH R01HL128066 (to N.S.), and RO1 AI092093 and R21 AI109020 (to B.M.I.).

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Contributions

L.A.P. and E.E.S. conceived of, designed, managed, and performed all analysis in the study. J.P. and E.M.N. performed macrophage knockdown experiments. W.-m.S. performed network enrichments. A.M. performed flow cytometry. A.I. performed histological scoring of mouse tissue, and S.R.L. and L.A.P. performed adoptive T cell transfer colitis experiments. L.A.P. designed and managed DSS experiments, A.D.N. and K.H. performed polygenic risk score and variant calling, A.D.N., K.H., and P.R. performed CRE SNP enrichments, B.A.K. generated differential expression signatures, Y.Z. generated clinical correlations, A.S. performed cell enrichments, J.S., S.E.T., W.-m.S., and Y.Z. performed statistical analysis on macrophage experiments, K.S., R.M., P.R., and L.A.P. constructed the eQTL database, B.L. and H.S. performed RNA-seq analysis, E.L. performed transcription factor analysis, M.W. and C.A. provided visualization tools, C.B., M.C., A.D., J.R.F., J.P., and L.F.M. provided patient population guidance, Y.F., M.B.H., B.M.I., N.S., and T.K.T. provided reagents and guidance, A.K., C.A., and J.J.F. provided project support, J.Z. constructed Bayesian networks, B.Z. constructed coexpression networks, and L.A.P. and E.E.S. wrote the manuscript. C.A., B.Z., J.P., J.R.F., and R.D. provided critical review of the manuscript.

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Correspondence to Eric E Schadt.

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Competing interests

J.P., E.M.N., A.S., J.S., S.E.T., C.B., M.C., A.D., J.R.F., and R.D. were employees of Janssen during the time this work was completed.

Integrated supplementary information

Supplementary Figure 1 Distribution of the IBD polygenic score based on 86 SNPs represented in the three IBD populations (from top to bottom): RISK, CERTIFI, and MSH.

To assess whether the mean polygenic scores for each pair of populations were significantly different, we employed a pairwise t test. None of the tests were significant at a nominal 0.05 P-value threshold: the P value for CERTIFI versus MSH was 0.71; for RISK versus MSH the P value was 0.29; and for RISK versus CERTIFI the P value was 0.40.

Supplementary Figure 2 Heat map of the odds ratios of overlaps between gene lists by cell type (rows) and inflamed anatomical region (columns) in CD (top) and UC (bottom).

The full list of genes reported in any cell type and/or region was used as the background. Overlaps with Fisher’s test P < 0.001 are marked with a black circle. Log-odds ratios quantify the overlap between different anatomical regions (in columns) and cell types (in rows) within disease conditions (horizontal panels). We observed, for example, a significant overlap between the myeloid and rectum gene lists within patients with CD but not UC. Included in the heat map is ileum, plus any cell type and anatomical region with at least one overlap with significance, P < 0.001.

Supplementary Figure 3 Extent of KDG mRNA knockdown.

(ak) Three siRNA, mock, and non-target control experiments were performed per KDG target, including DOK3 (a), FPR1 (b), TNFAIP3 (c), LAPTM5 (d), SLAMF1 (e), AIF1 (f), NCKAP1L (g), GPR65 (h), GBP5 (i), MAFB (j), and GPSM3 (k), in human primary monocyte-derived macrophages with and without LPS stimulation. Data are representative of duplicate or triplicate samples per experiment.

Supplementary Figure 4 Effect of KDG knockdown in macrophages on cytokine expression.

(ah) Differential production of cytokines following siRNA-mediated knockdown of KDGs: IL-6 (a), IL-10 (b), TNF-α (c), IL-12p40 (d), IL-1RA (ILRN) (e), MCP-3 (CCL7) (f), MIP-1β (CCL4) (g), and IP-10 (CXCL10) (h). Comparisons were between LPS-treated siRNA versus LPS-treated non-targeting control siRNA cells. Three donors were tested for each siRNA in three separate experiments with two or three replicates per experiment.

Supplementary Figure 5 The KDG module and network structure are conserved across species.

(ac) Overlap of the mouse brown coexpression module with the CIC subnetwork of the MSH IBD network (184 genes) (a), the CERTIFI IBD network (191 genes) (b), and the RISK IBD network (308 genes) (c). Intestine KDGs are shown as red diamonds. Macrophage KDGs are shown as green triangles.

Supplementary Figure 6 Network validation: significant enrichment of KDG perturbation signatures in various IBD Bayesian networks.

Mouse intestine KDG knockout versus wild-type control interaction with DSS differential expression signature –log10 Fisher’s exact test P-value enrichment within a two-path-length neighborhood of each mouse experimental KDG in the CERTIFI, MSH, and RISK IBD networks.

Supplementary Figure 7 Enrichment of mouse and human transcription factors in KDG subnetworks on each IBD network.

These are transcription factors for regulation of genes in each subnetwork. (a) In monocyte results based on DNase I hypersenstivity regions at a significance threshold of P = 10−4. (b) In T cell ENCODE data at a significance threshold of P = 10−5.

Supplementary Figure 8 Colon weight/length ratio of KDG-knockout mice following treatment.

(a) Gpsm3 DSS: 9 Gpsm3−/− and 8 WT male mice. Pooled data representative of two independent experiments are shown. (b) Dock2 TNBS: 3 Dock2−/− and 6 WT and female mice. Data are representative of one of two independent experiments. An unpaired t test was performed. Data are expressed as ±s.e.m. Statistical significance is indicated: *P < 0.05, **P < 0.01, ***P < 0.001.

Supplementary Figure 9 Stool scores from KDG-knockout mice from DSS (day 7) or TNBS.

(a) Dock2 DSS: 6 Dock2−/− and 5 WT mice. (b) Dock2 TNBS: 2 Dock2−/− and 6 WT mice. The data in a and b are representative of a single experiment. (c) Dok3 DSS: 20 Dok3−/− and 20 WT mice per group. Pooled data representative of two independent experiments are shown. (d) Gpsm3 DSS: 3 Gpsm3−/− and 3 WT mice per group. Data are representative of one of two independent experiments. An unpaired two-tailed Student's t test was performed. Data are expressed as ±s.e.m. Statistical significance is indicated: *P < 0.05, **P < 0.01, ***P < 0.001.

Supplementary Figure 10 Differential weight loss from the T cell adoptive transfer colitis model.

(ad) C57BL/6 Rag−/− mice were transferred with cells from Aif1−/− (a), Gpsm3−/− (b), Dock2−/− (c), Nckap1l−/− (d), or respective wild-type littermate control mice. Data are expressed as ±s.e.m. Comparisons were performed using an autoregressive model to maximize use of the time series data. Data are expressed as mean ± s.e.m. *P < 0.05; **P < 0.01; ***P < 0.001.

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Peters, L., Perrigoue, J., Mortha, A. et al. A functional genomics predictive network model identifies regulators of inflammatory bowel disease. Nat Genet 49, 1437–1449 (2017). https://doi.org/10.1038/ng.3947

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