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Abstract

Cytokines are signaling molecules secreted and sensed by immune and other cell types, enabling dynamic intercellular communication. Although a vast amount of data on these interactions exists, this information is not compiled, integrated or easily searchable. Here we report immuneXpresso, a text-mining engine that structures and standardizes knowledge of immune intercellular communication. We applied immuneXpresso to PubMed to identify relationships between 340 cell types and 140 cytokines across thousands of diseases. The method is able to distinguish between incoming and outgoing interactions, and it includes the effect of the interaction and the cellular function involved. These factors are assigned a confidence score and linked to the disease. By leveraging the breadth of this network, we predicted and experimentally verified previously unappreciated cell–cytokine interactions. We also built a global immune-centric view of diseases and used it to predict cytokine–disease associations. This standardized knowledgebase (http://www.immunexpresso.org) opens up new directions for interpretation of immune data and model-driven systems immunology.

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  • 16 August 2018

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Acknowledgements

We thank A. Butte and M. Davis for fruitful discussions and advice, N. Geifman for assistance with cytokine ontology development, D. Dougall for contribution to the cell lexicon, members of the Shen-Orr lab for reference book curation, D. Cohen for the high-performance computing cluster support, R. Reichart for Text Mining insights, and P. Dunn and S. Bhattacharya for the user interface development support. This work was supported by the US National Institutes of Health (NIH)-National Institute of Allergy and Infectious Diseases (U19 AI057229, and BISC contract HHSN272201200028C) and an award from the Rappaport Family Institute for Research in the Medical Sciences (S.S.S.-O.).

Author information

Author notes

    • Amit Ziv-Kenet
    • , Jan C Rieckmann
    •  & Jeff Wiser

    Present addresses: Augury Systems Ltd., Haifa, Israel (A.Z.-K.); Roche Diagnostics GmbH, Penzberg, Germany (J.C.R.); Medidata Solutions, New York, New York, USA (J.W.).

Affiliations

  1. Faculty of Medicine, Technion–Israel Institute of Technology, Haifa, Israel.

    • Ksenya Kveler
    • , Elina Starosvetsky
    • , Amit Ziv-Kenet
    • , Yuval Kalugny
    • , Yuri Gorelik
    • , Gali Shalev-Malul
    • , Netta Aizenbud-Reshef
    • , Tania Dubovik
    • , Mayan Briller
    • , Nuaman Asbeh
    • , Doron Rimar
    •  & Shai S Shen-Orr
  2. CytoReason, Tel-Aviv, Israel.

    • Yuval Kalugny
  3. Northrop Grumman IT Health Solutions, Rockville, Maryland, USA.

    • John Campbell
    •  & Jeff Wiser
  4. Experimental Systems Immunology, Max Planck Institute of Biochemistry, Bayern, Germany.

    • Jan C Rieckmann
    •  & Felix Meissner
  5. Rheumatology Unit, Bnai Zion Medical Center, Haifa, Israel.

    • Doron Rimar
  6. Faculty of Biology, Technion–Israel Institute of Technology, Haifa, Israel.

    • Shai S Shen-Orr

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Contributions

K.K. designed, performed and interpreted the analyses, led the design and development of the software, implemented relation extraction, filtering and network assembly, and wrote the manuscript; E.S. interpreted the analyses, performed quality control, designed and performed the experimental validations, and wrote the manuscript; A.Z.-K. implemented indexing, ontology support and reference book annotation software, and broadly contributed to the entire computational pipeline development; Y.K. conceived the pipeline architecture and broadly contributed to the software formation; Y.G. assisted with cell entity recognition; G.S.-M. performed quality control on Text Mining output and assisted with cytokine ontology development; T.D. assisted with quality control on Text Mining output; M.B. contributed to quality control and prediction evaluation; N.A.-R. wrote the software and implemented the website back end; J.C. and J.W. designed and developed the user interface; J.C.R. and F.M. provided and interpreted the proteomic data; N.A. assisted with machine-learning for quality control; D.R. contributed to quality control and interpretation of disease profiles; and S.S.S.-O. conceived the idea, oversaw, designed and interpreted the analyses, and wrote the manuscript.

Competing interests

K.K. and Y.K. are employees and co-founders of CytoReason. S.S.S.-O. and E.S. are co-founders of, and serve as scientific advisors and/or consultants to, CytoReason.

Corresponding author

Correspondence to Shai S Shen-Orr.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8

  2. 2.

    Life Sciences Reporting Summary

  3. 3.

    Supplementary Notes

    Supplementary Notes 1–9

Excel files

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    Supplementary Table 1

    Cell seed recognition statistics.

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    Supplementary Table 2

    Blacklist of Cell Ontology nodes.

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    Supplementary Table 3

    Cytokine recognition statistics.

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    Supplementary Table 4

    Cytokine lexicon fragment for CXC chemokine family.

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    Supplementary Table 5

    Verb classification lexicon

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    Supplementary Table 6

    Cell entity fields and manual precision evaluation results.

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    Supplementary Table 7

    Cytokine entity fields and manual precision evaluation results. 2

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    Supplementary Table 8

    Disease entity fields and manual precision evaluation results.

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    Supplementary Table 9

    Manual precision evaluation for noun phrase-internal relation 43 evidence records.

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    Supplementary Table 10

    Manual precision evaluation for non-noun phrase-internal 52 relation evidence records.

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    Supplementary Table 11

    ImmuneXpresso counts

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    Supplementary Table 12

    PubMed ids and statistics for relation evidence records

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    Supplementary Table 13

    Disease term recognition statistics.

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    Supplementary Table 14

    Novel incoming cell-cytokine interaction candidates.

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    Supplementary Table 15

    Novel outgoing cell-cytokine interaction candidates.

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    Supplementary Table 16

    Detailed profiles of 188 top-cited diseases.

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    Supplementary Table 17

    Cytokine sampling for 188 top-cited diseases

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    Supplementary Table 18

    Novel cytokine-disease association candidates

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DOI

https://doi.org/10.1038/nbt.4152