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Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease.TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the network’s local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks.The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.

Clinical and preclinical studies have established that supplementing diets with ω3 polyunsaturated fatty acids (PUFA) can reduce hepatic dysfunction in nonalcoholic steatohepatitis (NASH). Herein, we used multi-omic network analysis to unveil novel mechanistic targets of ω3 PUFA effects in a preclinical mouse model of western diet induced NASH. After identifying critical molecular processes responsible for the effects of ω3 PUFA, we next performed meta-analysis of human liver cancer transcriptomes and uncovered betacellulin as a key EGFR-binding protein that was induced in liver cancer and downregulated by ω3 PUFAs in animals with NASH. We then confirmed that betacellulin acts by promoting proliferation of quiescent hepatic stellate cells, stimulating transforming growth factor–β2 and increasing collagen production. When used in combination with TLR2/4 agonists, betacellulin upregulated integrins in macrophages thereby potentiating inflammation and fibrosis. Taken together, our results suggest that suppression of betacellulin is one of the key mechanisms associated with anti-inflammatory and antifibrotic effects of ω3 PUFA during NASH.SynopsisMulti-omic network analysis points to mitochondrial cardiolipin precursors as candidate key lipids whereby ω3 fatty acids restore mitochondrial functioning.Multi-omic network analysis suggests betacellulin (BTC) as one of the key mediators of NASH suppressed by ω3 polyunsaturated fatty acids.Reduction of liver fibrosis by omega-3 fatty acids (especially by docosahexaenoic acid, DHA) is accomplished by simultaneous inhibition of betacellulin and TLR agonists.BTC promotes collagen production and induces TGFB2 in hepatic stellate cells.BTC together with TLR2/4 agonists stimulate expression of integrins in macrophages.DHA suppresses BTC-EGFR pathway in NASH animal model potentially preventing progression to hepatocellular carcinoma.