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Mapping the effects of drugs on the immune system

Nature Biotechnology volume 34, pages 47–54 (2016)Cite this article

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Abstract

Understanding how drugs affect the immune system has consequences for treating disease and minimizing unwanted side effects. Here we present an integrative computational approach for predicting interactions between drugs and immune cells in a system-wide manner. The approach matches gene sets between transcriptional signatures to determine their similarity. We apply the method to model the interactions between 1,309 drugs and 221 immune cell types and predict 69,995 interactions. The resulting immune-cell pharmacology map is used to predict how five drugs influence four immune cell types in humans and mice. To validate the predictions, we analyzed patient records and examined cell population changes from in vivo experiments. Our method offers a tool for screening thousands of interactions to identify relationships between drugs and the immune system.

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Figure 1: Constructing the drug-cell interactome.
Figure 2: Overview of the IP map.
Figure 3: Predicting immune cell influence from the IP map.
Figure 4: Influencing immune cell migration using the IP map.

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Acknowledgements

We thank G. Hoffman and B. Readhead for useful conversations about the computational methods and suggestions on the manuscript, L. Li for assistance with the electronic medical records from Mount Sinai, K. Oguntuyo for assistance with the qPCR reactions, and A. Rahman for sample preparation and acquisition on the CyTOF. This work was supported by the US National Institutes of Health grants R01AI104848, R33CA182377 and R01AI113221 (to B.D.B.), U54CA189201 and R01DK098242, and Jonathan E. Gray TCI Young Scientist Cancer Research Award (to J.T.D.).

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  1. Brian A Kidd and Aleksandra Wroblewska: These authors contributed equally to the work.

Authors and Affiliations

  1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Brian A Kidd, Aleksandra Wroblewska, Judith Agudo, Brian D Brown & Joel T Dudley

  2. Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Brian A Kidd & Joel T Dudley

  3. Department of Biomedical Informatics, Systems Biology and Medicine, Columbia University Medical Center, New York, New York, USA

    Mary R Boland & Nicholas P Tatonetti

  4. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Miriam Merad

  5. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Miriam Merad & Brian D Brown

  6. Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Miriam Merad & Brian D Brown

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Contributions

B.A.K. and J.T.D. conceived of and designed the study. B.A.K. implemented the computational methods and performed the data analysis. A.W., J.A., M.M. and B.D.B. designed the experiments. A.W. conducted all in vivo assays. M.R.B. analyzed electronic medical records from Columbia University Medical Center. B.A.K. and J.T.D. wrote the manuscript with review and feedback from A.W., B.D.B. and N.P.T.

Corresponding authors

Correspondence to Brian D Brown or Joel T Dudley.

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Competing interests

B.A.K. has received consulting fees from Merck. J.T.D. owns equity in NuMedii Inc. and has received consulting fees or honoraria from Janssen Pharmaceuticals, GlaxoSmithKline, AstraZeneca and LAM Therapeutics.

Integrated supplementary information

Supplementary Figure 1 Cell type and tissue location counts in the Immunological Genome data set.

Barplots indicate the sums of the table rows and columns. Abbreviated cell type labels represent: Neut = neutrophil, Mono = monocyte, NKT = natural killer T cell, NK = natural killer cell, Mac = macrophage, T4 = CD4+ T cell, T8 = CD8+ T cell, Tgd = gamma-delta T cell, DC = dendritic cell. Abbreviated tissue labels represent: BM = Bone marrow, scLN = Subcutaneous lymph nodes, msLN = mesenteric lymph nodes, sdLN = skin-draining lymph nodes, SI = Small intestine, FLN = Follicular lymph nodes, IE = Intraepithelial, mdLN = mediastinal lymph nodes, PP = Peyer’s patch, AA = abdominal adipose, LN = Lymph nodes, MsLN = Medullary sinus lymph nodes, PLN = Pancreatic lymph nodes, SF = synovial fluid, ssLN = Subcapsular lymph nodes.

Supplementary Figure 2 Clustered heatmap of cell state change similarity.

Hierarchical clustering applied to the matrix of Jaccard distances calculated between gene sets from the extremes—top/bottom 5% fold-change—of each cell state change expression profile. The full heatmap shows complete linkage clustering for all pairwise combinations of the 304 cell state changes. Colored bars on the rows and columns represent the categories of state changes and the cell types respectively.

Supplementary Figure 3 Jaccard distance between immune cell state change gene sets.

Sets based on the extremes—top/bottom 5% fold-change—of each cell state change expression profile. Each bar height reflects the average distance within a cell type and the error bars are the s.e.m. Gray horizontal line shows the average distance between all gene sets. Group differences assessed by ANOVA.

Supplementary Figure 4 Schematic depiction of our method to test for significant interactions and identify robust connections.

(a) Diagram of ranked list comparison to calculate the immunemod score for each pair of drug (j) and cell state change (k) at a particular gene set size (l). (b) Gene set size variation procedure to evaluate the robustness of each predicted interaction. (c) Schematic of the permutation testing to assess the significance of an immunemod score for a given drug (j), cell state change (k), and gene set size (l).

Supplementary Figure 5 Quantile-Quantile plot for gene expression models.

P-value distribution of independent (naïve) and correlated permutation models for the IP map.

Supplementary Figure 6 Number of predicted interactions between drugs and immune cells across all categories of immunological cell state changes.

(a) Overall, (b) Subset, (c) Tissue, (d) Genetic, (e) Time, and (f) Perturbation.

Supplementary Figure 7 Drug class enrichments among the stable drug clusters.

Enrichment value for each of the 13 level 1 ATC categories: H = Systemic hormonal preparations, excl. sex hormones and insulins, V = Various, B = Blood and blood forming organs, P = Anti-parasitic products, M = Musculo-skeletal system, L = Anti-neoplastic and immunomodulating agents, G = Genito urinary system and sex hormones, R = Respiratory system, A = Alimentary tract and metabolism, D = Dermatologicals, J = Anti-infectives for systemic use, S = Sensory organs, N = Nervous system, and C = Cardiovascular system. Asterisks indicate significant over- or under-enrichment (P < 0.01 and E > 1 or E < 1 respectively). Significance assessed by hypergeometric distribution.

Supplementary Figure 8 Circular layout of the predicted connections between drugs and cell subsets found in different tissues.

All connections statistically significant P < 0.05 with FDR < 5%, see methods. Line widths correspond to the number of interactions. A drug-tissue connection represents a drug that is predicted to influence a particular immune cell in the specific tissue as compared to the same cell in a contrasting tissue. The diagram has been organized by sorting the tissues counter-clockwise (drugs clockwise) in order of decreasing number of connections. Each single letter for of the drug half corresponds to a level 1 label of the ATC system. H = Systemic hormonal preparations, excl. sex hormones and insulins, V = Various, B = Blood and blood forming organs, P = Anti-parasitic products, M = Musculo-skeletal system, L = Anti-neoplastic and immunomodulating agents, G = Genito urinary system and sex hormones, R = Respiratory system, A = Alimentary tract and metabolism, D = Dermatologicals, J = Anti-infectives for systemic use, S = Sensory organs, N = Nervous system, and C = Cardiovascular system. Codes for each tissue label are as follows: PC = peritoneal cavity, br = brain, AA = abdominal adipose, pan = pancreas, SI = small intestine, SF = synovial fluid, LN = lymph nodes, PLN = pancreatic lymph nodes, mdLN = mediastinal lymph nodes, FLN = follicular lymph nodes, thy = thymus, sdLN = skin-draining lymph nodes, PP = Peyer’s patch, scLN = subcutaneous lymph nodes, msLN = mesenteric lymph nodes, and BM = bone marrow.

Supplementary Figure 9 Distribution and enrichment of drug hubs and islands.

(a) Histogram of drugs binned according to the number of unique cell types they are predicted to interact with (top right shows drug hubs with connections to 11 or more cell types). ATC level 1 categories enriched in drug (b) islands and (c) hubs. Significance assessed by fold-enrichment analysis and hypergeometric distribution.

Supplementary Figure 10 Drug hubs and islands.

Examples of drug (a) hubs or (b) islands associated with immune cell types.

Supplementary Figure 11 Two-dimensional colored heatmap of the T cell subnetwork pulled from the IP map.

Data organized by drug (rows) and T cell categories (columns). Predicted interactions between drug categories (ATC level 1 categories: H = Systemic hormonal preparations, excl. sex hormones and insulins, B = Blood and blood forming organs, V = Various, L = Anti-neoplastic and immunomodulating agents, P = Anti-parasitic products, M = Musculo-skeletal system, G = Genito urinary system and sex hormones, R = Respiratory system, S = Sensory organs, D = Dermatologicals, A = Alimentary tract and metabolism, N = Nervous system, J = Anti-infectives for systemic use, and C = Cardiovascular system) and (left) T cell subsets “Cell Types” or (right) identical T cell subset found in different tissues. All connections are statistically significant (P < 0.05 and FDR < 5%, see methods). Each colored cell in the heatmap represents the fraction of drugs from a particular ATC level 1 category (0–0.7, max = 1.0) predicted to influence the specific Cell Type or Tissue location. Colored circles represent the number of drugs in each category where the size is proportional to the number of unique drugs (16–144).

Supplementary Figure 12 Drugs predicted to influence T cell subset changes.

T4 = CD4+ cells, T8 = CD8+ cells, FP3 = FoxP3, mem = memory subset. Drugs ranked in descending order by immunemod score.

Supplementary Figure 13 Influence of guanfacine on regulatory T cells.

(a) Graph showing connections for a single drug, Guanfacine, predicted to influence regulatory T cells based on the immunemod score (shown as numbers next to each node). (b) Percentage of regulatory T cells (CD4+FoxP3+CD62L+) isolated from mouse spleens following treatment with the anti-hypertensive drug guanfacine. Each circle is a separate mouse from five separate experiments—grouped by color—for a total of 14 or 15 mice in the Ctrl and Guanfacine groups respectively. Dashed lines connect the mean frequency values between groups in each experiment. Significance assessed by meta analysis (see below).

Morgan, A.A., Khatri, P., Jones, R.H., Sarwal, M.M. & Butte, A.J. Comparison of multiplex meta analysis techniques for understanding the acute rejection of solid organ transplants. BMC Bioinformatics 11 Suppl 9, S6 (2010).

Supplementary Figure 14 Flow cytometry plots.

Representative biaxial flow cytometry plots showing the gating strategy for identifying Ly6G+CD11b+ cells from blood, bone marrow and peritoneal cavity of treated and untreated mice. Data are representative of three to four experiments with 8 mice in each condition.

Supplementary Figure 15 SPADE trees of CyTOF data collected during a single experiment.

Cells isolated from spleens of mice treated with drug (clioquinol) and vehicle control (PEG400). Trees show the lymphocyte lineage of CD45+ cells. Key for trees shown to orient toward CD4+ and CD8+ cells. Arrows highlight CD45+CD3+CD4–CD8+Thy1.2+TCRb+CD62LhiCD44lo population increase after clioquinol treatment. Horizontal color scales represent median intensity (blue = low, red = high) for marker indicated.

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Kidd, B., Wroblewska, A., Boland, M. et al. Mapping the effects of drugs on the immune system. Nat Biotechnol 34, 47–54 (2016). https://doi.org/10.1038/nbt.3367

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