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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.

Microbiota contribute to the induction of type 2 diabetes by high-fat/high-sugar (HFHS) diet, but which organs/pathways are impacted by microbiota remain unknown. Using multiorgan network and transkingdom analyses, we found that microbiota-dependent impairment of OXPHOS/mitochondria in white adipose tissue (WAT) plays a primary role in regulating systemic glucose metabolism. The follow-up analysis established that Mmp12+ macrophages link microbiota-dependent inflammation and OXPHOS damage in WAT. Moreover, the molecular signature of Mmp12+ macrophages in WAT was associated with insulin resistance in obese patients. Next, we tested the functional effects of MMP12 and found that Mmp12 genetic deficiency or MMP12 inhibition improved glucose metabolism in conventional, but not in germ-free mice. MMP12 treatment induced insulin resistance in adipocytes. TLR2-ligands present in Oscillibacter valericigenes bacteria, which are expanded by HFHS, induce Mmp12 in WAT macrophages in a MYD88-ATF3–dependent manner. Thus, HFHS induces Mmp12+ macrophages and MMP12, representing a microbiota-dependent bridge between inflammation and mitochondrial damage in WAT and causing insulin resistance.

The global mechanisms and associated molecular alterations that occur in drug-resistant mycobacteria are poorly understood. To address this, we obtain genomics data and then construct a genome-scale response network in isoniazid-resistant Mycobacterium smegmatis and apply a network-mining algorithm. Through this, we decipher global alterations in an unbiased manner and identify emergent vulnerabilities in resistant bacilli, of which redox response was prominent. Using phenotypic profiling, we find that resistant bacilli exhibit collateral sensitivity to several compounds that block antioxidant responses. We find that nanogram/milliliter concentrations of ebselen, vancomycin, and phenylarsine oxide, in combination with isoniazid, are highly effective against Mycobacterium tuberculosis H37Rv and three clinical drug-resistant strains. Dynamic measurements of cytoplasmic redox potential revealed a surprisingly diminished capacity of clinical drug-resistant strains to counteract oxidative stress, providing a mechanistic basis for efficient and synergistic mycobactericidal activity of the drug combinations. Ebselen and vancomycin appear to be promising repurposable drugs.

The biosynthesis of NAD constitutes an important metabolic module in the cell, since NAD is an essential cofactor involved in several metabolic reactions. NAD concentrations are known to be significantly increased in several cancers, particularly in glioma, consistent with the observation of up-regulation of several enzymes of the network. Modulating NAD biosynthesis in glioma is therefore an attractive therapeutic strategy. Here we report reconstruction of a biochemical network of NAD biosynthesis consisting of 22 proteins, 36 metabolites, and 86 parameters, tuned to mimic the conditions in glioma. Kinetic simulations of the network provide comprehensive insights about the role of individual enzymes. Further, quantitative changes in the same network between different states of health and disease enable identification of drug targets, based on specific alterations in the given disease. Through simulations of enzyme inhibition titrations, we identify NMPRTase as a potential drug target, while eliminating other possible candidates NMNAT, NAPRTase, and NRK. We have also simulated titrations of both binding affinities as well as inhibitor concentrations, which provide insights into the druggability limits of the target, a novel aspect that can provide useful guidelines for designing inhibitors with optimal affinities. Our simulations suggest that an inhibitor affinity of 10 nM used in a concentration range of 0.1 to 10 μM achieves a near maximal inhibition response for NMPRTase and that increasing the affinity any further is not likely to have a significant advantage. Thus, the quantitative appreciation defines a maximal extent of inhibition possible for a chosen enzyme in the context of its network. Knowledge of this type enables an upper affinity threshold to be defined as a goal in lead screening and refinement stages in drug discovery.

The global variations in the gene expression pattern of drug treated (½X) and laboratory evolved drug-resistant strains (2XR and 4XR) of Mycobacterium smegmatis were obtained and compared with the M. smegmatis mc2 155 (WT) strain. The genes exhibiting two-fold change and p-value

We report the whole genome sequences of a Mycobacterium smegmatis laboratory wild-type strain (MC 2 155) and mutants (4XR1, 4XR2) resistant to isoniazid. Compared to Mycobacterium smegmatis MC 2 155 (NC_008596), a widely used strain in laboratory experiments, the MC 2 155, 4XR1, and 4XR2 strains are 60, 128 and 93 bp longer, respectively.