The success of Mycobacterium tuberculosis as a pathogen derives from its facile adaptation to the intracellular milieu of human macrophages. To explore this process, we asked whether adaptation also required interference with the metabolic machinery of the host cell. Temporal profiling of the metabolic flux, in cells infected with differently virulent mycobacterial strains, confirmed that this was indeed the case. Subsequent analysis identified the core subset of host reactions that were targeted. It also elucidated that the goal of regulation was to integrate pathways facilitating macrophage survival, with those promoting mycobacterial sustenance. Intriguingly, this synthesis then provided an axis where both host- and pathogen-derived factors converged to define determinants of pathogenicity. Consequently, whereas the requirement for macrophage survival sensitized TB susceptibility to the glycemic status of the individual, mediation by pathogen ensured that the virulence properties of the infecting strain also contributed towards the resulting pathology.

We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across diverse age-groups and ethnicities. A two-step approach was employed wherein cancer-positive samples were first identified as a group. A second multi-class algorithm then helped to distinguish between the individual cancers of the group. The approach yielded high detection sensitivity and specificity, highlighting its utility for the development of multi-cancer detection tests especially for early-stage cancers.

Even though endoplasmic reticulum (ER) stress associated with mycobacterial infection has been well studied, the molecular basis of ER as a crucial organelle to determine the fate of Mtb is yet to be established. Here, we have studied the ability of Mtb to manipulate the ultrastructural architecture of macrophage ER and found that the ER-phenotypes associated with virulent (H37Rv) and avirulent (H37Ra) strains were different: a rough ER (RER) with the former against a smooth ER (SER) with the later. Further, the functional attributes of these changes were probed by MS-based quantitative proteomics (133 ER proteins) and lipidomics (8 phospholipids). Our omics approaches not only revealed the host pathogen cross-talk but also emphasized how precisely Mtb uses proteins and lipids in combination to give rise to characteristic ER-phenotypes. H37Ra-infected macrophages increased the cytosolic Ca2+ levels by attenuating the ATP2A2 protein and simultaneous induction of PC/PE expression to facilitate apoptosis. However, H37Rv inhibited apoptosis and further controlled the expression of EST-1 and AMRP proteins to disturb cholesterol homeostasis resulting in sustained infection. This approach offers the potential to decipher the specific roles of ER in understanding the cell biology of mycobacterial infection with special reference to the impact of host response.

ObjectiveSignal transduction not only initiates entry into the cell cycle, but also reprograms the cell’s metabolism. To control abnormalities in cell proliferation, both the aspects should be taken care of, thus pleiotropic signaling molecules are considered as crucial modulators. Considering this, we investigated the role of AKT1 in central carbon metabolism. The role of AKT1 has already been established in the process of cell cycle, but its contribution to the central carbon metabolism is sparsely studied.ResultsTo address this, we combined the metabolomics and proteomics approaches. In accordance to our hypothesis, we found that the AKT1 kinase activity is regulating the levels of acetyl CoA through pyruvate dehydrogenase complex. Further, the decreased levels of acetyl CoA and dependency of acetyl CoA acetyl transferase protein on AKT1 kinase activity was also found to perturb the synthesis rate of palmitic acid which is a representative of fatty acid. This was analyzed in the present study using lipid labeling method through mass spectrometry.Electronic supplementary materialThe online version of this article (10.1186/s13104-018-3364-z) contains supplementary material, which is available to authorized users.