Mycobacterium tuberculosis owes its high pathogenic potential to its ability to evade host immune responses and thrive inside the macrophage. The outcome of infection is largely determined by the cellular response comprising a multitude of molecular events. The complexity and inter-relatedness in the processes makes it essential to adopt systems approaches to study them. In this work, we construct a comprehensive network of infection-related processes in a human macrophage comprising 1888 proteins and 14,016 interactions. We then compute response networks based on available gene expression profiles corresponding to states of health, disease and drug treatment. We use a novel formulation for mining response networks that has led to identifying highest activities in the cell. Highest activity paths provide mechanistic insights into pathogenesis and response to treatment. The approach used here serves as a generic framework for mining dynamic changes in genome-scale protein interaction networks.

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Microrchidia family CW‐type zinc finger 2 (MORC2) is a recently identified chromatin modifier with an emerging role in cancer metastasis. However, its role in glucose metabolism, a hallmark of malignancy, remains to be explored. We found that MORC2 is a glucose‐inducible gene and a target of c‐Myc. Our meta‐analysis revealed that MORC2 expression is positively correlated with the expression of enzymes involved in glucose metabolism in breast cancer patients. Furthermore, overexpression of MORC2 in MCF‐7 and BT‐549 cells augmented the expression and activity of a key glucose metabolism enzyme, lactate dehydrogenase A (LDHA). Conversely, selective knockdown of MORC2 by siRNA markedly decreased LDHA expression and activity and in turn reduced cancer cell migration. Collectively, these findings provide evidence that MORC2, a glucose‐inducible gene, modulates the migration of breast cancer cells through the MORC2–c‐Myc–LDHA axis.

Dengue virus (DENV) is a human pathogen and its etiology has been widely established. There are many interactions between DENV and human proteins that have been reported in literature. However, no publicly accessible resource for efficiently retrieving the information is yet available. In this study, we mined all publicly available dengue–human interactions that have been reported in the literature into a database called DenHunt. We retrieved 682 direct interactions of human proteins with dengue viral components, 382 indirect interactions and 4120 differentially expressed human genes in dengue infected cell lines and patients. We have illustrated the importance of DenHunt by mapping the dengue–human interactions on to the host interactome and observed that the virus targets multiple host functional complexes of important cellular processes such as metabolism, immune system and signaling pathways suggesting a potential role of these interactions in viral pathogenesis. We also observed that 7 percent of the dengue virus interacting human proteins are also associated with other infectious and non-infectious diseases. Finally, the understanding that comes from such analyses could be used to design better strategies to counteract the diseases caused by dengue virus. The whole dataset has been catalogued in a searchable database, called DenHunt (http://proline.biochem.iisc.ernet.in/DenHunt/).