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

Abstract 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

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

Abstract 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 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

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Additional file 3: Table S1. Gradient profile for LC–MSMS method on Agilent Polaris 5NH2 2 * 150 mm

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

AbstractWe 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.</jats:p

MOESM5 of Concurrent interactome and metabolome analysis reveals role of AKT1 in central carbon metabolism

Additional file 5: Table S3. Sheet 1: Consolidated list of proteins identified as AKT1 interactors from AKT1 pull down samples after targeting HA and Strep tags. Interacting partners of AKT1 were analyzed by LC-MS/MS in duplicate and proteins identified in both replicate sets were only included to generate a union file at 1% global FDR (from fit); 95% confidence. Sheet 2: Represents the list of interacting partners showing perturbed association with AKT1 after inhibiting its kinase activity. Any protein with a heavy: light ratio of SILAC labels greater than 2and less than 0.5 was considered perturbed

REPAiR: REsource Management in Peri-urban AReas: Going Beyond Urban Metabolism: D8.1 Corporate Identity Guidelines

Version Final This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 688920.Environmental Technology and Desig

MOESM4 of Concurrent interactome and metabolome analysis reveals role of AKT1 in central carbon metabolism

Additional file 4: Table S2. Sheet1: Consolidated union file generated by integrating replicate data sets from the eGFPpull-down samples after LC–MS/MS analysis. Sheet2: Represent the common interacting partners between AKT1 and eGFP

MOESM2 of Concurrent interactome and metabolome analysis reveals role of AKT1 in central carbon metabolism

Additional file 2: Figure S1. Detailed workflow to identify interacting partners of AKT1 followed by their subsequent analysis. Figure S2. Filters employed to generate the final list of AKT1 Interactors. Figure S3. KEGG pathway enrichment for AKT1 binding partners using WebGestalt. Figure S4. Validating the effect of AKT1 inhibitor (MK-2206) on AKT1 activity by western blotting. Figure S5. Specimen depiction of relative incorporation of labeled carbon in G6P and FBP metabolites in MK-2206 treated and untreated samples. Figure S6. Possible labeling pattern for key metabolite upon feeding cells with 13C6 Glucose. Figure S7. Specimen chromatograms depicting estimated false discovery rates in Lys C and Trypsin digested MK-2206 treated (Lys8Arg10) and untreated (Lys0Arg0) samples. Figure S8. Specimen chromatograms showing metabolite peaks in the untreated and MK-2206 treated samples for five different metabolites at one of the targeted time points. Figure S9. Chromatograms are corresponding to ion spectra of palmitic acid (as standard) and free fatty acids. Figure S10. Schematic diagram of the CCM pathways depicting uptake of glucose by the cells and its subsequent utilization across different metabolites. Proteins showing perturbed association with AKT1 inhibition and their corresponding reaction steps are highlighted in red colour in figure