Pathogenicity of Mycobacterium tuberculosis is expressed by regulating metabolic thresholds of the host macrophage.

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.

The High Sensitivity of the Multi-Cancer Detection Test ONCOVERYX-F Offers a Promising Platform for Ovarian Cancer Screening.

We evaluated the potential relevance of our multi-cancer detection test, OncoVeryx-F, for ovarian cancer screening. For this, we compared its accuracy with that of CA125-based screening. We demonstrate here that, in contrast to CA125-based detection, OncoVeryx-F detected ovarian cancer with very high sensitivity and specificity. Importantly here, Stage I cancers too could be detected with an accuracy of >98%. Furthermore, again unlike CA 125, the detection accuracy of OncoVeryx-F remained comparable in both Caucasian and South Asian/Indian women. Thus, the robustness and accuracy of OncoVeryx-F, particularly for early-stage detection, underscores its potential utility for ovarian cancer screening.

A non-invasive method for concurrent detection of multiple early-stage cancers in women.

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.

Untargeted metabolomics reveals altered branch chain amino acids, glucose and fat metabolism contributing to coronary artery disease among Indian diabetic patients.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterised by increased blood glucose levels. Patients with T2DM have a high risk of developing atherosclerotic coronary artery disease (CAD). CAD with T2DM has a complex etiology and the understanding of the pathophysiology of coronary artery disease (CAD) in the presence of diabetes is poor. Here, we have used LC-MS/MS-based untargeted metabolomics to unveil the alterations of metabolites in the serum of South-Indian patients diagnosed with T2DM, CAD and T2DM along with CAD (T2DM-CAD) compared with the healthy subjects (CT). Using untargeted metabolomics and network-based approaches, a set of metabolites highly co-expressed with T2DM-CAD pathogenesis were identified. Our results revealed that these metabolites belong to essential pathways such as amino acid metabolism, fatty acid metabolism and carbohydrate metabolism. The candidate metabolites identified by metabolomics study are branch chain amino acids, L-arginine, linoleic acid, L-serine, L-cysteine, fructose-6-phosphate, glycerol, creatine and 3-phosphoglyceric acid, and explain the pathogenesis of T2DM-assisted CAD. The identified metabolites could be used as potential prognostic markers to predict CAD in patients diagnosed with T2DM.

A non-invasive method for concurrent detection of early-stage women-specific cancers.

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.

Concurrent interactome and metabolome analysis reveals role of AKT1 in central carbon metabolism.

OBJECTIVE: Signal 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. RESULTS: To 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.

Quantitative Proteomics and Lipidomics Analysis of Endoplasmic Reticulum of Macrophage Infected with Mycobacterium tuberculosis.

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 Ca(2+) 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.