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Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host–microbiota and other multi-omic interactions

Nature Protocols (2024)Cite this article

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Abstract

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host–microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network’s topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/.

Key points

  • Transkingdom Network Analysis (TkNA) is a unique analytical framework for inferring causal factors underlying host–microbiota and other multi-omic interactions, by integrating data from multiple cohorts and diverse omics types.

  • Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data, which it achieves without requiring huge sample sizes.

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Fig. 1: Flowchart of the TkNA pipeline.
Fig. 2: The TkNA pipeline is used in a cyclical manner.
Fig. 3: Example output plots produced by TkNA.
Fig. 4: Example configurations of experimental classes for meta-analysis of edges.
Fig. 5: The structure of the TkNA repository from GitHub.
Fig. 6: Example metadata and configuration (config) .json files.
Fig. 7: Schematic of how a basic network is reconstructed from two independent datasets.
Fig. 8: A comparison of network metrics across previously published networks.
Fig. 9: An example of clusters and regulatory nodes that may be identified in a network.
Fig. 10: Examples of good and bad networks.

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Data availability

Raw data used for the analysis that generated Fig. 3 and Supplementary Fig. 2 are available in the supporting primary research article, ref. 3. A graphical user interface version of TkNA is also available at https://bioinfo-abcc.ncifcrf.gov/TkNA.

Code availability

The TkNA pipeline is publicly available at https://github.com/CAnBioNet/TkNA. The code in this protocol has been peer reviewed.

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Acknowledgements

The funding that supports this work is AI157369 (to A.M.), DK103761 (to N.S.), DK107603 (to A.M.) and BC011153 (to G.T.), NCI/NIH (Contract No. 75N91019D00024 (to J.S. and J.W.)). S.S.P. and M.S.M. were supported by summer fellowships from the College of Pharmacy at Oregon State University. We thank Isaiah Shriver for aiding in testing the code.

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Author notes

  1. These authors contributed equally: Nolan K. Newman, Matthew S. Macovsky, Richard R. Rodrigues.

  2. These authors jointly supervised this work: Giorgio Trinchieri, Kevin Brown, Andrey Morgun.

Authors and Affiliations

  1. College of Pharmacy, Oregon State University, Corvallis, OR, USA

    Nolan K. Newman, Matthew S. Macovsky, Amanda M. Bruce, Jyothi Padiadpu, Sankalp S. Patil, Kevin Brown & Andrey Morgun

  2. Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Richard R. Rodrigues

  3. Microbiome and Genetics Core, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Richard R. Rodrigues

  4. Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA

    Jacob W. Pederson & Natalia Shulzhenko

  5. Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Jigui Shan & Joshua Williams

  6. Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Amiran K. Dzutsev & Giorgio Trinchieri

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Contributions

N.S. and A.M. conceived the original version of TkNA. N.K.N., M.S.M., R.R.R., A.K.D., N.S., G.T., K.B. and A.M. designed the current TkNA framework. N.K.N. and M.S.M. implemented the coding part of the TkNA workflow. R.R.R. and J.P. prepared parts of the TkNA workflow that require additional software. A.M.B., J.W.P. and S.S.P. performed the validation. N.K.N., R.R.R. and A.M.B. prepared the simulated data. R.R.R. prepared the experimental data. N.K.N. and A.M.B. prepared the figures. J.S. and J.W. built the web tool version of TkNA. N.K.N., M.S.M., R.R.R., J.P. and K.B. wrote the paper. A.M.B., J.W.P., S.S.P., J.P., A.K.D., N.S., G.T., K.B. and A.M. edited the paper. N.S., G.T., K.B. and A.M. supervised various aspects of this study.

Corresponding authors

Correspondence to Giorgio Trinchieri, Kevin Brown or Andrey Morgun.

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Nature Protocols thanks Nikita Agarwal, Torgeir Hvidsten and Jayadev Joshi for their contribution to the peer review process.

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Key references using this protocol

Morgun, A. et al. Gut 64, 1732–1743 (2015): https://doi.org/10.1136/gutjnl-2014-308820

Rodrigues, R. R. et al. Nat. Commun. 12, 101 (2021): https://doi.org/10.1038/s41467-020-20313-x

Li, Z. et al. J. Exp. Med. 219, e20220017 (2022): https://doi.org/10.1084/jem.20220017

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Newman, N.K., Macovsky, M.S., Rodrigues, R.R. et al. Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host–microbiota and other multi-omic interactions. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00960-w

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