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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

Introduction: Advances in genomics technologies are providing a very large amount of data on genome-wide gene expression profiles, protein molecules and their interactions with other macromolecules and metabolites. Molecular interaction networks provide a useful way to capture this complex data and comprehend it. Networks are beginning to be used in drug discovery, in many steps of the modern discovery pipeline, with large-scale molecular networks being particularly useful for the understanding of the molecular basis of the disease. Areas covered: The authors discuss network approaches used for drug target discovery and lead identification in the drug discovery pipeline. By reconstructing networks of targets, drugs and drug candidates as well as gene expression profiles under normal and disease conditions, the paper illustrates how it is possible to find relationships between different diseases, find biomarkers, explore drug repurposing and study emergence of drug resistance. Furthermore, the authors also look at networks which address particular important aspects such as off-target effects, combination-targets, mechanism of drug action and drug safety. Expert opinion: The network approach represents another paradigm shift in drug discovery science. A network approach provides a fresh perspective of understanding important proteins in the context of their cellular environments, providing a rational basis for deriving useful strategies in drug design. Besides drug target identification and inferring mechanism of action, networks enable us to address new ideas that could prove to be extremely useful for new drug discovery, such as drug repositioning, drug synergy, polypharmacology and personalized medicine.

Oxidative/nitrosative stress and mitochondrial dysfunction have been implicated in the degeneration of dopaminergic neurons in the substantia nigra during Parkinson’s disease (PD). During early stages of PD, there is a significant depletion of the thiol antioxidant glutathione (GSH), which may lead to oxidative stress, mitochondrial dysfunction, and ultimately neuronal cell death. Mitochondrial complex I (CI) is believed to be the central player to the mitochondrial dysfunction occurring in PD. We have generated a dynamic, mechanistic model for mitochondrial dysfunction associated with PD progression that is activated by rotenone, GSH depletion, increased nitric oxide and peroxynitrite. The potential insults independently inhibit CI and other complexes of the electron transport chain, drop the proton motive force, and reduce ATP production, ultimately affecting the overall mitochondrial performance. We show that mitochondrial dysfunction significantly affects glutathione synthesis thereby increasing the oxidative damage and further exacerbating the toxicities of these mitochondrial agents resulting in neurodegeneration. Rat dopaminergic neuronal cell culture and in vitro experiments using mouse brain mitochondria were employed to validate important features of the model. Major conclusions Using a combination of experimental and in silico modeling approaches, we have demonstrated the interdependence of mitochondrial function with GSH metabolism in relation to neurodegeneration in PD.

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.

Resistance to therapy limits the effectiveness of drug treatment in many diseases. Drug resistance can be considered as a successful outcome of the bacterial struggle to survive in the hostile environment of a drug-exposed cell. An important mechanism by which bacteria acquire drug resistance is through mutations in the drug target. Drug resistant strains (multi-drug resistant and extensively drug resistant) of Mycobacterium tuberculosis are being identified at alarming rates, increasing the global burden of tuberculosis. An understanding of the nature of mutations in different drug targets and how they achieve resistance is therefore important. An objective of this study is to first decipher sequence as well as structural bases for the observed resistance in known drug resistant mutants and then to predict positions in each target that are more prone to acquiring drug resistant mutations. A curated database containing hundreds of mutations in the 38 drug targets of nine major clinical drugs, associated with resistance is studied here. Mutations have been classified into those that occur in the binding site itself, those that occur in residues interacting with the binding site and those that occur in outer zones. Structural models of the wild type and mutant forms of the target proteins have been analysed to seek explanations for reduction in drug binding. Stability analysis of an entire array of 19 mutations at each of the residues for each target has been computed using structural models. Conservation indices of individual residues, binding sites and whole proteins are computed based on sequence conservation analysis of the target proteins. The analyses lead to insights about which positions in the polypeptide chain have a higher propensity to acquire drug resistant mutations. Thus critical insights can be obtained about the effect of mutations on drug binding, in terms of which amino acid positions and therefore which interactions should not be heavily relied upon, which in turn can be translated into guidelines for modifying the existing drugs as well as for designing new drugs. The methodology can serve as a general framework to study drug resistant mutants in other micro-organisms as well.

Emergence of drug resistance is a major problem in the treatment of many diseases including tuberculosis. To tackle the problem from a wholistic perspective, it is essential to understand the molecular mechanisms by which bacteria acquire drug resistance using a systems approach. Availability of genome-scale data of expression profiles under different drug exposed conditions and protein–protein interactions, makes it feasible to reconstruct and analyze systems-level models. A number of proteins involved in different resistance mechanisms, referred to as the resistome are identified from literature. The interaction of the drug directly with the resistome is unable to explain most resistance processes adequately, including that of increased mutations in the target’s binding site. We recently hypothesized that some communication might exist from the drug environment to the resistome to trigger emergence of drug resistance. We report here a network based approach to identify most plausible paths of such communication in Mycobacterium tuberculosis. Networks capturing both structural and functional linkages among various proteins were weighted based on gene expression profiles upon exposure to specific drugs and betweenness centrality of the interactions. Our analysis suggests that different drug targets and hence different drugs could trigger the resistome to different extents and through different routes. The identified paths correlate well with the mechanisms known through experiment. Some examples of the top ranked hubs in multiple drug specific networks are PolA, FadD1, CydA, a monoxygenase and GltS, which could serve as co-targets, that could be inhibited in order to retard resistance related communication in the cell.

Pathway Alignment Tool (PAT) executables are availableupon request; Pathway Hunter Tool (PHT) is available onhttp://www.pht.uni-koeln.deLocal metabolic demand and supply regulate networkcomponents and control their activity globally. The use ofmetabolite structural information to calculate the shortestpath generates valid biochemical connectivity [1]. Weintroduce a new concept of "load points" for identifyingand empirically ranking the important points (metabo-lites/enzymes) in the network. The load point analysisprovides a global insight into the metabolic network,which cannot be obtained from connectivity informationor metabolic concentration data. We propose a new com-putational model based on extended graph theory to find"Choke point (CP)" and "Load point" enzymes (enzymesthat uniquely consume or produce a certain metabolite inthe network) [2]. Identifying such enzymes is the key tonetwork-based potential drug targeting [3]. We obtainedfew potential drug targets based on the "choke point"analysis of the network in the malarial parasite