Transkingdom network analysis (tkna): a systems framework for inferring causal factors underlying host–microbiota and other multi-omic interactions

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

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SIMILAR CONTENT BEING VIEWED BY OTHERS META-ANALYSIS OF MICROBIOME ASSOCIATION NETWORKS REVEAL PATTERNS OF DYSBIOSIS IN DISEASED MICROBIOMES Article Open access 19 October 2022 CAUSAL

EFFECTS IN MICROBIOMES USING INTERVENTIONAL CALCULUS Article Open access 11 March 2021 OPEN CHALLENGES FOR MICROBIAL NETWORK CONSTRUCTION AND ANALYSIS Article Open access 09 June 2021 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

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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. AUTHOR INFORMATION Author notes * These authors contributed equally: Nolan K.

Newman, Matthew S. Macovsky, Richard R. Rodrigues. * These authors jointly supervised this work: Giorgio Trinchieri, Kevin Brown, Andrey Morgun. AUTHORS AND AFFILIATIONS * 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 * Basic

Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA Richard R. Rodrigues * Microbiome and Genetics Core, Laboratory of Integrative Cancer Immunology,

Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA Richard R. Rodrigues * Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA Jacob W.

Pederson & Natalia Shulzhenko * Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA Jigui Shan & Joshua Williams * Cancer

Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA Amiran K. Dzutsev & Giorgio Trinchieri

Authors * Nolan K. Newman View author publications You can also search for this author inPubMed Google Scholar * Matthew S. Macovsky View author publications You can also search for this

author inPubMed Google Scholar * Richard R. Rodrigues View author publications You can also search for this author inPubMed Google Scholar * Amanda M. Bruce View author publications You can

also search for this author inPubMed Google Scholar * Jacob W. Pederson View author publications You can also search for this author inPubMed Google Scholar * Jyothi Padiadpu View author

publications You can also search for this author inPubMed Google Scholar * Jigui Shan View author publications You can also search for this author inPubMed Google Scholar * Joshua Williams

View author publications You can also search for this author inPubMed Google Scholar * Sankalp S. Patil View author publications You can also search for this author inPubMed Google Scholar *

Amiran K. Dzutsev View author publications You can also search for this author inPubMed Google Scholar * Natalia Shulzhenko View author publications You can also search for this author

inPubMed Google Scholar * Giorgio Trinchieri View author publications You can also search for this author inPubMed Google Scholar * Kevin Brown View author publications You can also search

for this author inPubMed Google Scholar * Andrey Morgun View author publications You can also search for this author inPubMed Google Scholar 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. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Protocols_

thanks Nikita Agarwal, Torgeir Hvidsten and Jayadev Joshi for their contribution to the peer review process. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with

regard to jurisdictional claims in published maps and institutional affiliations. RELATED LINKS 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 SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Figs. 1 and 2 RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a

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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_ 19, 1750–1778 (2024). https://doi.org/10.1038/s41596-024-00960-w Download citation * Received: 13 March 2023 * Accepted: 29 November 2023 * Published: 12 March 2024 * Issue Date:

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