The reproduction process of Gram-positive protocells.

Protocells are believed to have existed on early Earth prior to the emergence of prokaryotes. Due to their rudimentary nature, it is widely accepted that these protocells lacked intracellular mechanisms to regulate their reproduction, thereby relying heavily on environmental conditions. To understand protocell reproduction, we adopted a top-down approach of transforming a Gram-positive bacterium into a lipid-vesicle-like state. In this state, cells lacked intrinsic mechanisms to regulate their morphology or reproduction, resembling theoretical propositions on protocells. Subsequently, we grew these proxy-protocells under the environmental conditions of early Earth to understand their impact on protocell reproduction. Despite the lack of molecular biological coordination, cells in our study underwent reproduction in an organized manner. The method and the efficiency of their reproduction can be explained by an interplay between the physicochemical properties of cell constituents and environmental conditions. While the overall reproductive efficiency in these top-down modified cells was lower than their counterparts with a cell wall, the process always resulted in viable daughter cells. Given the simplicity and suitability of this reproduction method to early Earth environmental conditions, we propose that primitive protocells likely reproduced by a process like the one we described below.

In-vivo tracking of deuterium metabolism in mouse organs using LC-MS/MS.

Mass spectrometry-based metabolomics with stable isotope labeling (SIL) is an established tool for sensitive and precise analyses of tissue metabolism, its flux, and pathway activities in diverse models of physiology and disease. Despite the simplicity and broad applicability of deuterium (2H)-labeled precursors for tracing metabolic pathways with minimal biological perturbations, they are rarely employed in LC-MS/MS-guided metabolomics. In this study, we have developed a LC-MS/MS-guided workflow to trace deuterium metabolism in mouse organs following 2H7 -glucose infusion. The workflow includes isotopically labeled glucose infusion, mouse organ isolation and metabolite extraction, zwitterion-based hydrophilic interaction liquid chromatography (HILIC) coupled to high-resolution tandem mass spectrometry, targeted data acquisition for sensitive detection of deuterated metabolites, a spectral library of over 400 metabolite standards, and multivariate data analysis with pathway mapping. The optimized method was validated for matrix effects, normalization, and quantification to provide both tissue metabolomics and tracking the in-vivo metabolic fate of deuterated glucose through key metabolic pathways. We quantified more than 100 metabolites in five major mouse organ tissues (liver, kidney, brain, brown adipose tissue, and heart). Furthermore, we mapped isotopologues of deuterated metabolites from glycolysis, tricarboxylic acid (TCA) cycle, and amino acid pathways, which are significant for studying both health and various diseases. This study will open new avenues in LC-MS based analysis of 2H-labeled tissue metabolism research in animal models and clinical settings.

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update.

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella.

Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells.

Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics.

Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.

Quantification of Dolichyl Phosphates Using Phosphate Methylation and Reverse-Phase Liquid Chromatography-High Resolution Mass Spectrometry.

Dolichyl monophosphates (DolPs) are essential lipids in glycosylation pathways that are highly conserved across almost all domains of life. The availability of DolP is critical for all glycosylation processes, as these lipids serve as membrane-anchored building blocks used by various types of glycosyltransferases to generate complex post-translational modifications of proteins and lipids. The analysis of DolP species by reverse-phase liquid chromatography-mass spectrometry (RPLC-MS) remains a challenge due to their very low abundance and wide range of lipophilicities. Until now, a method for the simultaneous qualitative and quantitative assessment of DolP species from biological membranes has been lacking. Here, we describe a novel approach based on simple sample preparation, rapid and efficient trimethylsilyl diazomethane-dependent phosphate methylation, and RPLC-MS analysis for quantification of DolP species with different isoprene chain lengths. We used this workflow to selectively quantify DolP species from lipid extracts derived of Saccharomyces cerevisiae, HeLa, and human skin fibroblasts from steroid 5-α-reductase 3- congenital disorders of glycosylation (SRD5A3-CDG) patients and healthy controls. Integration of this workflow with global lipidomics analyses will be a powerful tool to expand our understanding of the role of DolPs in pathophysiological alterations of metabolic pathways downstream of HMG-CoA reductase, associated with CDGs, hypercholesterolemia, neurodegeneration, and cancer.

Identification of herbal teas and their compounds eliciting antiviral activity against SARS-CoV-2 in vitro.

BACKGROUND: The SARS-CoV-2/COVID-19 pandemic has inflicted medical and socioeconomic havoc, and despite the current availability of vaccines and broad implementation of vaccination programs, more easily accessible and cost-effective acute treatment options preventing morbidity and mortality are urgently needed. Herbal teas have historically and recurrently been applied as self-medication for prophylaxis, therapy, and symptom alleviation in diverse diseases, including those caused by respiratory viruses, and have provided sources of natural products as basis for the development of therapeutic agents. To identify affordable, ubiquitously available, and effective treatments, we tested herbs consumed worldwide as herbal teas regarding their antiviral activity against SARS-CoV-2. RESULTS: Aqueous infusions prepared by boiling leaves of the Lamiaceae perilla and sage elicit potent and sustained antiviral activity against SARS-CoV-2 when applied after infection as well as prior to infection of cells. The herbal infusions exerted in vitro antiviral effects comparable to interferon-ß and remdesivir but outperformed convalescent sera and interferon-α2 upon short-term treatment early after infection. Based on protein fractionation analyses, we identified caffeic acid, perilla aldehyde, and perillyl alcohol as antiviral compounds. Global mass spectrometry (MS) analyses performed comparatively in two different cell culture infection models revealed changes of the proteome upon treatment with herbal infusions and provided insights into the mode of action. As inferred by the MS data, induction of heme oxygenase 1 (HMOX-1) was confirmed as effector mechanism by the antiviral activity of the HMOX-1-inducing compounds sulforaphane and fraxetin. CONCLUSIONS: In conclusion, herbal teas based on perilla and sage exhibit antiviral activity against SARS-CoV-2 including variants of concern such as Alpha, Beta, Delta, and Omicron, and we identified HMOX-1 as potential therapeutic target. Given that perilla and sage have been suggested as treatment options for various diseases, our dataset may constitute a valuable resource also for future research beyond virology.

Quantification of Duloxetine in the Bacterial Culture and Medium to Study Drug-gut Microbiome Interactions.

Expanding our understanding of drug-gut bacteria interactions requires high-throughput drug measurements in complex bacterial cultures. Quantification of drugs in the cultures, media, and cell pellets is prone to strong matrix effects. We have developed a liquid chromatography-high resolution mass spectrometry (LC-HRMS) method for quantifying duloxetine from high-throughput gut-drug interaction experiments. The method is partially validated for its reproducibility, sensitivity, and accuracy, which makes it suitable for largescale drug screens. We extensively used this method to study biotransformation and bioaccumulation of duloxetine and other drugs in several species of gut bacteria.

Bioaccumulation of therapeutic drugs by human gut bacteria.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

Coupling proteomics and metabolomics for the unsupervised identification of protein-metabolite interactions in Chaetomium thermophilum.

Protein-metabolite interactions play an important role in the cell's metabolism and many methods have been developed to screen them in vitro. However, few methods can be applied at a large scale and not alter biological state. Here we describe a proteometabolomic approach, using chromatography to generate cell fractions which are then analyzed with mass spectrometry for both protein and metabolite identification. Integrating the proteomic and metabolomic analyses makes it possible to identify protein-bound metabolites. Applying the concept to the thermophilic fungus Chaetomium thermophilum, we predict 461 likely protein-metabolite interactions, most of them novel. As a proof of principle, we experimentally validate a predicted interaction between the ribosome and isopentenyl adenine.

SpaceM reveals metabolic states of single cells.

A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

Public LC-Orbitrap Tandem Mass Spectral Library for Metabolite Identification.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies require high-quality spectral libraries for reliable metabolite identification. We have constructed EMBL-MCF (European Molecular Biology Laboratory-Metabolomics Core Facility), an open LC-MS/MS spectral library that currently contains over 1600 fragmentation spectra from 435 authentic standards of endogenous metabolites and lipids. The unique features of the library include the presence of chromatographic profiles acquired with different LC-MS methods and coverage of different adduct ions. The library covers many biologically important metabolites with some unique metabolites and lipids as compared with other public libraries. The EMBL-MCF spectral library is created and shared using an in-house-developed web application at https://curatr.mcf.embl.de/. The library is freely available online and also integrated with other mass spectral repositories.

Pharmaco-metabolomics opportunities in drug development and clinical research.

Pharmaco-metabolomics uses metabolic phenotypes for the prediction of inter-individual variations in drug response and helps in understanding the mechanisms of drug action. The field has made significant progress over the last 14 years with numerous studies providing clinical evidence for personalised medicine. However, discovered pharmaco-metabolomic biomarkers are not yet translated into clinics due to a lack of large-scale validation. Integration of targeted and untargeted metabolomics workflows into pharmacokinetic analysis and drug development can advance the field from bench to bedside. Also, Indian pharmaceutical research and its bioanalytical infrastructure are in a position to take on these opportunities by addressing challenges such as appropriate training and regulatory compliance.

Untargeted Metabolomics Workshop Report: Quality Control Considerations from Sample Preparation to Data Analysis.

The Proteomics Society, India (PSI), hosted the Metabolomics workshop on experimental and data analysis training for untargeted metabolomics in December 2019. The workshop included six tutorial lectures and hands-on data analysis training sessions presented by seven speakers from across the globe. The tutorials and hands-on data analysis sessions focused on workflows for liquid chromatography-mass spectrometry (LC-MS) based on untargeted metabolomics. We review here three main topics from the workshop, which were uniquely identified as bottlenecks for new researchers: (a) experimental design, (b) quality controls during sample preparation and instrumental analysis, and (c) data quality evaluation using open source tools. Our objective here is to present common challenges faced by novice researchers and present guidelines to address them. We provide resources and good practices for researchers who are at the initial stage of setting up metabolomics workflows in their laboratories.

GPD1 Specifically Marks Dormant Glioma Stem Cells with a Distinct Metabolic Profile.

Brain tumor stem cells (BTSCs) are a chemoresistant population that can drive tumor growth and relapse, but the lack of BTSC-specific markers prevents selective targeting that spares resident stem cells. Through a ribosome-profiling analysis of mouse neural stem cells (NSCs) and BTSCs, we find glycerol-3-phosphate dehydrogenase 1 (GPD1) expression specifically in BTSCs and not in NSCs. GPD1 expression is present in the dormant BTSC population, which is enriched at tumor borders and drives tumor relapse after chemotherapy. GPD1 inhibition prolongs survival in mouse models of glioblastoma in part through altering cellular metabolism and protein translation, compromising BTSC maintenance. Metabolomic and lipidomic analyses confirm that GPD1+ BTSCs have a profile distinct from that of NSCs, which is dependent on GPD1 expression. Similar GPD1 expression patterns and prognostic associations are observed in human gliomas. This study provides an attractive therapeutic target for treating brain tumors and new insights into mechanisms regulating BTSC dormancy.

Endogenous Fatty Acids Are Essential Signaling Factors of Pancreatic ß-Cells and Insulin Secretion.

The secretion of insulin from ß-cells depends on extracellular factors, in particular glucose and other small molecules, some of which act on G-protein-coupled receptors. Fatty acids (FAs) have been discussed as exogenous secretagogues of insulin for decades, especially after the FA receptor GPR40 (G-protein-coupled receptor 40) was discovered. However, the role of FAs as endogenous signaling factors has not been investigated until now. In the present work, we demonstrate that lowering endogenous FA levels in ß-cell medium by stringent washing or by the application of FA-free (FAF) BSA immediately reduced glucose-induced oscillations of cytosolic Ca2+ ([Ca2+]i oscillations) in MIN6 cells and mouse primary ß-cells, as well as insulin secretion. Mass spectrometry confirmed BSA-mediated removal of FAs, with palmitic, stearic, oleic, and elaidic acid being the most abundant species. [Ca2+]i oscillations in MIN6 cells recovered when BSA was replaced by buffer or as FA levels in the supernatant were restored. This was achieved by recombinant lipase-mediated FA liberation from membrane lipids, by the addition of FA-preloaded FAF-BSA, or by the photolysis of cell-impermeant caged FAs. Our combined data support the hypothesis of FAs as essential endogenous signaling factors for ß-cell activity and insulin secretion.

Curatr: a web application for creating, curating and sharing a mass spectral library.

Summary: We have developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries from liquid-chromatography mass spectrometry datasets. Curatr handles datasets from single or multiplexed standards and extracts chromatographic profiles and potential fragmentation spectra for multiple adducts. An intuitive interface helps users to select high quality spectra that are stored along with searchable molecular information, the providence of each standard and experimental metadata. Curatr supports exports to several standard formats for use with third party software or submission to repositories. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library http://curatr.mcf.embl.de. Availability and implementation: Source code and example data are at http://github.com/alexandrovteam/curatr/. Contact: palmer@embl.de. Supplementary information: Supplementary data are available at Bioinformatics online.

3D molecular cartography using LC-MS facilitated by Optimus and 'ili software.

Our skin, our belongings, the world surrounding us, and the environment we live in are covered with molecular traces. Detecting and characterizing these molecular traces is necessary to understand the environmental impact on human health and disease, and to decipher complex molecular interactions between humans and other species, particularly microbiota. We recently introduced 3D molecular cartography for mapping small organic molecules (including metabolites, lipids, and environmental molecules) found on various surfaces, including the human body. Here, we provide a protocol and open-source software for 3D molecular cartography. The protocol includes step-by-step procedures for sample collection and processing, liquid chromatography-mass spectrometry (LC-MS)-based metabolomics, quality control (QC), molecular identification using MS/MS, data processing, and visualization with 3D models of the sampled environment. The LC-MS method was optimized for a broad range of small organic molecules. We enable scientists to reproduce our previously obtained results, and illustrate the broad utility of our approach with molecular maps of a rosemary plant and an ATM keypad after a PIN code was entered. To promote reproducibility, we introduce cartographical snapshots: files that describe a particular map and visualization settings, and that can be shared and loaded to reproduce the visualization. The protocol enables molecular cartography to be performed in any mass spectrometry laboratory and, in principle, for any spatially mapped data. We anticipate applications, in particular, in medicine, ecology, agriculture, biotechnology, and forensics. The protocol takes 78 h for a molecular map of 100 spots, excluding the reagent setup.

FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry.

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.

Meeting Report: "Proteomics from Discovery to Function:" 6th Annual Meeting of Proteomics Society, India and International Conference-A Milestone for the Indian Proteomics Community.

Proteomics is at the epicenter of post-genomics biotechnologies that are currently driving the next generation system science. Moreover, proteomics is a truly global science. The 6(th) Annual Meeting of Proteomics Society, India (PSI) and International Conference on "Proteomics from Discovery to Function" held from December 7-9, 2014, was a transformative endeavor for global proteomics, bringing together the luminaries in the field of proteomics for the very first time in India. This meeting report presents the lessons learned and the highlights of this international scientific conference that was comprised of nine thematic sessions, pre- and post-conference workshops, and an opportunity to cultivate enduring collaborations for proteomics science to benefit both India and global society. The conference had an unforgettable impression on the participants: for the first time, India hosted past and present President and Council members from the Human Proteome Organization (HUPO), along with eminent scientists and young scholars from India and abroad in the field of proteomics at such a large scale, a major highlight of this international event. In all, the PSI 2014 was a milestone conference that has firmly poised the Indian life sciences community as a leading contributor to post-genomics life sciences, thus cultivating crucial trans-generational capacity and inspiration by recognizing the emerging scholars and omics systems scientists who can think and conduct science from cell to society.