M. thermoacetica-CdS biohybrid, the first artificial photosynthetic microbial system, has gained a wide variety of scientific attention. Proteomic and metabolomic results indicate that a number of electron carriers and enzymes in the Wood-Ljungdahl pathway play very important roles in electron transfer from CdS to cytoplasm and CO 2 fixation. Targeted metabolomics together with proteome data reveal that glycolysis and the TCA cycle are involved in ATP production in the biohybrid system.
Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses (“stats” module), generating informative plots (“plotting” module), identifying lipid species at different confidence levels (“identification” module), and carrying out all functions using a user-friendly text-based interface (“interactive” module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.
The rate-determining step in free radical lipid peroxidation is the propagation of the peroxyl radical, where generally two types of reactions occur: (a) hydrogen-atom transfer (HAT) from a donor to the peroxyl radical; (b) peroxyl radical addition (PRA) to a "CC" double bond. Peroxyl radical clocks have been used to determine the rate constants of HAT reactions (k H ), but no radical clock is available to measure the rate constants of PRA reactions (k add ). In this work, we modified the analytical approach on the linoleate-based peroxyl radical clock to enable the simultaneous measurement of both k H and k add . Compared to the original approach, this new approach involves the use of a strong reducing agent, LiAlH 4 , to completely reduce both HAT and PRAderived products and the relative quantitation of total linoleate oxidation products with or without reduction. The new approach was then applied to measuring the k H and k add values for several series of organic substrates, including para-and meta-substituted styrenes, substituted conjugated dienes, and cyclic alkenes. Furthermore, the k H and k add values for a variety of biologically important lipids were determined for the first time, including conjugated fatty acids, sterols, coenzyme Q10, and lipophilic vitamins, such as vitamins D 3 and A.
Antimicrobial resistance (AMR) is one of the most serious problems affecting public health and safety. It is crucial to understand antimicrobial resistance from the molecular level. In this work, TiO-assisted laser desorption/ionization (LDI) mass spectrometry (MS) was used for the fast metabolites analysis from intact bacterial cells to discriminate different strains of bacteria and to detect AMR. With the mass spectra of bacterial metabolites by TiO-LDI MS, multivariable analysis was performed for bacterial identification to determine distinctive metabolites as the potential biomarkers. The most statistically significant metabolites were screened out by the method and further identified using liquid-chromatography (LC) tandem MS (MS/MS). Robustness of our developed methods in bacterial taxonomy was demonstrated by iterative validation using 48 clinical samples. The strategy was further illustrated with three clinical strains of ESBL (extended-spectrum β-lactamase-resistant)-positive Escherichia. coli and four clinical strains of ESBL-negative ones. Eleven key metabolites were identified as potential biomarkers of ESBL-positive E. coli. We also implemented the pathway and network analysis on the key metabolites to prove the feasibility of our method in executing metabolomics analysis. Compared to the most prevalent techniques in a metabolomics study, such as LC-MS, gas chromatography MS, and nuclear magnetic resonance spectroscopy, the current method has advantages in its simple sample preparation and short analysis time, thereby fitting especially into clinical usages and fast analyses.
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