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Understanding multicellular function and disease with human tissue-specific networks

Nature Genetics volume 47, pages 569–576 (2015)Cite this article

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Abstract

Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.

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Figure 1: Regularized Bayesian integration based on tissue ontology.
Figure 2: Predicted IL1B functional interaction partners from the blood vessel network are significantly upregulated after stimulation of blood vessel cells with IL-1β.
Figure 3: Tissue networks capture tissue-specific functional rewiring.
Figure 4: A disease map centered on Parkinson's disease summarizing its molecular associations with other diseases in substantia nigra.
Figure 5: Network reprioritization of hypertension GWAS identifies hypertension-associated genes.

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Acknowledgements

The first three authors are co-first authors and are listed alphabetically.

We sincerely thank Y. Lee and D. Gorenshteyn for help in curating disease associations and L. Bongo and M. Homilius for help in processing expression data. We are grateful to all members of the Troyanskaya laboratory for help in curating specific GO biological processes and for valuable discussions.

This work was primarily supported by US National Institutes of Health (NIH) grants R01 GM071966 and R01 HG005998 to O.G.T. and U54 HL117798 to G.A.F. C.S.G. was supported in part by US NIH grants T32 CA009528 and P20 GM103534. A.K.W. was supported in part by US NIH grant T32 HG003284. This work was supported in part by US NIH grant P50 GM071508 and by US NIH contract HHSN272201000054C. O.G.T. is a senior fellow of the Genetic Networks program of the Canadian Institute for Advanced Research (CIFAR).

Author information

Author notes

  1. Casey S Greene, Arjun Krishnan and Aaron K Wong: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA

    Casey S Greene & Rene A Zelaya

  2. Dartmouth-Hitchcock Norris Cotton Cancer Center, Lebanon, New Hampshire, USA

    Casey S Greene

  3. Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire, USA

    Casey S Greene

  4. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA

    Arjun Krishnan, Kara Dolinski & Olga G Troyanskaya

  5. Department of Computer Science, Princeton University, Princeton, New Jersey, USA

    Aaron K Wong & Olga G Troyanskaya

  6. Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Emanuela Ricciotti, Garret A FitzGerald & Tilo Grosser

  7. Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Emanuela Ricciotti, Garret A FitzGerald & Tilo Grosser

  8. Biology and Medical Informatics, University of California, San Francisco, San Francisco, California, USA.,

    Daniel S Himmelstein

  9. Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA

    Ran Zhang

  10. Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Boris M Hartmann, Elena Zaslavsky & Stuart C Sealfon

  11. Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Daniel I Chasman

  12. Simons Center for Data Analysis, Simons Foundation, New York, New York, USA

    Olga G Troyanskaya

Authors
  1. Casey S Greene
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  2. Arjun Krishnan
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  15. Olga G Troyanskaya
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Contributions

C.S.G., A.K., A.K.W. and O.G.T. conceived and designed the research. C.S.G., A.K. and A.K.W. performed computational analyses with contributions from D.S.H. and R.Z., and E.R. performed the molecular experiments. A.K.W., R.A.Z. and C.S.G. developed the web interface. D.I.C., B.M.H., E.Z., S.C.S. and K.D. provided data. C.S.G., A.K., A.K.W. and O.G.T. wrote the manuscript with input from E.R., T.G., G.A.F. and K.D. and revisions from all co-authors.

Corresponding author

Correspondence to Olga G Troyanskaya.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Integrating the entire data compendium with hierarchy-aware tissue-specific knowledge generates networks that better capture tissue-specific interactions than limiting the integration to tissue-specific data (P = 1.3 × 10−9).

For each tissue, two networks—one integrating the entire data compendium and the other integrating only tissue-labeled data—were generated, and their performance was measured using area under the receiver operator curve (AUC) on the basis of cross-validation. The scatterplot shows that the performance for 64 tissues (points) with tissue-labeled data (x axis) and all data (y axis), with 62 of 64 performing better with all data (above the diagonal line; P = 3.2 × 10−12). The remaining 80 tissues did not have sufficient tissue-specific data (fewer than 5 data sets) available to perform a tissue-restricted integration. The performance of our Bayesian integration for these tissues is shown on the disconnected axis.

Supplementary Figure 2 The blood vessel and cardiovascular system networks show the best correspondence with the experiment, over and above the tissue-naive network and the bulk of other unrelated tissue networks.

For each network, genes were ranked on the basis of their connectivity to IL-1β in that network. Then, at each rank, the precision of the predictions up to that rank was calculated as the fraction of genes that are differentially expressed in the experiment. Plotted in each of the three graphs is the precision (y axis) at incremental sets of top-ranked genes (1–100; x axis). The precision for the blood vessel and tissue-naive networks is plotted in solid blue and dashed dark gray, respectively. The median precision at each rank for the cardiovascular system of tissues and all tissues are plotted in dotted blue and gray, respectively. Further, the gray band around the all_tissues median represents the interquartile range of precision values at each rank calculated across all tissues. The three plots correspond to different choices of differentially expressed genes (DEGs) from the microarray, with (a) 500 genes, (b) 250 genes and (c) 1,000 genes. The results in the main text are based on choosing genes from (a) at rank 20.

Supplementary Figure 3 We analyzed publicly available gene expression data sets that included IL-1β treatment and found that genes connected to IL-1β in tissue-specific networks for the corresponding tissue responded significantly to treatment.

Each plot shows the mean log2 fold change after IL-1β treatment of the 20 genes most tightly connected to IL-1β in the network listed on the x axis, and error bars represent the standard error (s.e.). Also plotted alongside as controls are the mean and s.e. of 20 random genes from the data set. The first plot (GSE59671) corresponds to the blood vessel experiment elaborated in the main text (Fig. 2). The cell type and GEO identifier of the other data sets from which gene expression data were extracted is listed above the plot. Of these data sets, only GSE7216 (epidermal keratinocytes) is included in the data compendium used for integration. The rest are independent of the integration.

Supplementary Figure 4 Single-tissue query of LEF-1 in the GIANT interface.

(a) LEF-1’s functional network neighborhood in B lymphocytes. (b) The functional enrichment of LEF-1’s neighborhood. (c) A table of the most connected genes to LEF-1 in the network.

Supplementary Figure 5 This stacked bar plot shows the results of our blinded literature evaluation.

Only 10% of randomly selected diseases were associated strongly to Parkinson’s disease in the literature, while more than 75% of disease map–selected diseases were associated.

Supplementary Figure 6 Tissue-specific networks can capture additional disease associations.

(a) Alzheimer’s disease in the temporal lobe network (z score ≥ 2.25), (b) glycogen metabolism disorder in liver (z score ≥ 1.75) and (c) glomerulonephritis in renal glomerulus (z score ≥ 1.5). The appropriate tissue network was chosen on the basis of connectivity of diseases in their relevant tissues (see “Network connectivity in tissue-specific processes” in the Online Methods).

Supplementary Figure 7 Relevant tissue networks show the best performance in reprioritizing hypertension GWAS and are enriched with targets of antihypertensive drugs.

To evaluate the choice of tissues for reprioritization, we evaluated all tissue networks (along with the tissue-naive network) in the same setting we used for the kidney network. (a) The distribution of performance (measured using AUC) shows that the right tissue network, kidney, and other relevant tissues, heart and liver, are among the best, while the tissue-naive network sits amidst tissue networks that provide an average performance. (b) Top-ranked genes by NetWAS are significantly enriched with targets of antihypertensive drugs. Drug targets were obtained from four databases—DrugBank, TTD, PharmGKB and CTD—which curate this information using different criteria. We evaluated both the original GWAS (gray) and NetWAS using the kidney network (dark red) for enrichment of drug targets from each of these sources among the top-ranked genes. Enrichment was measures using z scores (Online Methods), with higher scores indicating greater enrichment near the top of the list. In nearly all cases—target data sources and phenotypic end points—NetWAS reprioritization resulted in significant top ranking of therapeutic targets, over the original GWAS.

Supplementary Figure 8 NetWAS reprioritization is effective across studies, phenotypes and relevant networks.

Each bar shows the performance of NetWAS reprioritization as measured by the area under the curve (AUC) of documented disease associations with the disease specified in the label above the plot. The horizontal axis shows relevant networks (colored bars) and GWAS alone (gray bars), and the horizontal axis label describes the GWAS phenotype from which associations were obtained.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Note. (PDF 6544 kb)

Supplementary Table 1

Tissue model weights of expression data sets. (XLSX 2437 kb)

Supplementary Table 2

Pathways known to be specifically active in a tissue are tightly connected in the corresponding tissue network. This table provides the list of tissues, their organ system categories (tissue-slim) and attributes of tissue-specific pathways mapped to those tissues. (XLSX 45 kb)

Supplementary Table 3

Top 20 genes tightly connected to IL1B in the blood vessel network. (XLSX 29 kb)

Supplementary Table 4

NetWAS results for combined hypertension phenotypes. (XLSX 1856 kb)

Supplementary Table 5

Many lines of evidence in the literature link the top predicted genes to hypertension via mechanistic relationship to known disease genes and pathways or association with hypertension risk factors. (XLSX 41 kb)

Supplementary Table 6

Expert-curated GO terms used to generate a global functional interaction standard. (XLSX 37 kb)

Supplementary Table 7

HPRD tissues were linked by direct text matching to terms in the BTO. (XLSX 41 kb)

Supplementary Table 8

This table contains the pruned BTO terms. (XLSX 55 kb)

Supplementary Table 9

We used simple text mining followed by manual curation to map biological process (BP) terms in GO to tissue terms in the BTO. (XLSX 136 kb)

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Greene, C., Krishnan, A., Wong, A. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet 47, 569–576 (2015). https://doi.org/10.1038/ng.3259

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