An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines

A Perspective on How Fibrinaloid Microclots and Platelet Pathology May be Applied in Clinical Investigations

Microscopy imaging has enabled us to establish the presence of fibrin(ogen) amyloid (fibrinaloid) microclots in a range of chronic, inflammatory diseases. Microclots may also be induced by a variety of purified substances, often at very low concentrations. These molecules include bacterial inflammagens, serum amyloid A, and the S1 spike protein of severe acute respiratory syndrome coronavirus 2. Here, we explore which of the properties of these microclots might be used to contribute to differential clinical diagnoses and prognoses of the various diseases with which they may be associated. Such properties include distributions in their size and number before and after the addition of exogenous thrombin, their spectral properties, the diameter of the fibers of which they are made, their resistance to proteolysis by various proteases, their cross-seeding ability, and the concentration dependence of their ability to bind small molecules including fluorogenic amyloid stains. Measuring these microclot parameters, together with microscopy imaging itself, along with methodologies like proteomics and imaging flow cytometry, as well as more conventional assays such as those for cytokines, might open up the possibility of a much finer use of these microclot properties in generative methods for a future where personalized medicine will be standard procedures in all clotting pathology disease diagnoses.

A rapid and simple non-radioactive assay for measuring uptake by solute carrier transporters

Introduction: Solute carrier (SLC) transport proteins play a crucial role in maintaining cellular nutrient and metabolite homeostasis and are implicated in various human diseases, making them potential targets for therapeutic interventions. However, the study of SLCs has been limited due to the lack of suitable tools, particularly cell-based substrate uptake assays, necessary for understanding their biological functions and for drug discovery purposes. Methods: In this study, a cell-based uptake assay was developed using a stable isotope-labeled compound as the substrate for SLCs, with detection facilitated by liquid chromatography-tandem mass spectrometry (LC-MS/MS). This assay aimed to address the limitations of existing assays, such as reliance on hazardous radiolabeled substrates and limited availability of fluorescent biosensors. Results: The developed assay was successfully applied to detect substrate uptakes by two specific SLCs: L-type amino acid transporter 1 (LAT1) and sodium taurocholate co-transporting polypeptide (NTCP). Importantly, the assay demonstrated comparable results to the radioactive method, indicating its reliability and accuracy. Furthermore, the assay was utilized to screen for novel inhibitors of NTCP, leading to the identification of a potential NTCP inhibitor compound. Discussion: The findings highlight the utility of the developed cell-based uptake assay as a rapid, simple, and environmentally friendly tool for investigating SLCs' biological roles and for drug discovery purposes. This assay offers a safer alternative to traditional methods and has the potential to contribute significantly to advancing our understanding of SLC function and identifying therapeutic agents targeting SLC-mediated pathways.

Deorphanizing solute carriers in Saccharomyces cerevisiae for secondary uptake of xenobiotic compounds

The exchange of small molecules between the cell and the environment happens through transporter proteins. Besides nutrients and native metabolic products, xenobiotic molecules are also transported, however it is not well understood which transporters are involved. In this study, by combining exo-metabolome screening in yeast with transporter characterization in Xenopus oocytes, we mapped the activity of 30 yeast transporters toward six small non-toxic substrates. Firstly, using LC-MS, we determined 385 compounds from a chemical library that were imported and exported by S. cerevisiae. Of the 385 compounds transported by yeast, we selected six compounds (viz. sn-glycero-3-phosphocholine, 2,5-furandicarboxylic acid, 2-methylpyrazine, cefadroxil, acrylic acid, 2-benzoxazolol) for characterization against 30 S. cerevisiae xenobiotic transport proteins expressed in Xenopus oocytes. The compounds were selected to represent a diverse set of chemicals with a broad interest in applied microbiology. Twenty transporters showed activity toward one or more of the compounds. The tested transporter proteins were mostly promiscuous in equilibrative transport (i.e., facilitated diffusion). The compounds 2,5-furandicarboxylic acid, 2-methylpyrazine, cefadroxil, and sn-glycero-3-phosphocholine were transported equilibratively by transporters that could transport up to three of the compounds. In contrast, the compounds acrylic acid and 2-benzoxazolol, were strictly transported by dedicated transporters. The prevalence of promiscuous equilibrative transporters of non-native substrates has significant implications for strain development in biotechnology and offers an explanation as to why transporter engineering has been a challenge in metabolic engineering. The method described here can be generally applied to study the transport of other small non-toxic molecules. The yeast transporter library is available at AddGene (ID 79999).

A 70% Ethanol Neorhodomela munita Extract Attenuates RANKL-Induced Osteoclast Activation and H2O2-Induced Osteoblast Apoptosis In Vitro

The rapid aging of the population worldwide presents a significant social and economic challenge, particularly due to osteoporotic fractures, primarily resulting from an imbalance between osteoclast-mediated bone resorption and osteoblast-mediated bone formation. While conventional therapies offer benefits, they also present limitations and a range of adverse effects. This study explores the protective impact of Neorhodomela munita ethanol extract (EN) on osteoporosis by modulating critical pathways in osteoclastogenesis and apoptosis. Raw264.7 cells and Saos-2 cells were used for in vitro osteoclast and osteoblast models, respectively. By utilizing various in vitro methods to detect osteoclast differentiation/activation and osteoblast death, it was demonstrated that the EN's potential to inhibit RANKL induced osteoclast formation and activation by targeting the MAPKs-NFATc1/c-Fos pathway and reducing H2O2-induced cell death through the downregulation of apoptotic signals. This study highlights the potential benefits of EN for osteoporosis and suggests that EN is a promising natural alternative to traditional treatments.

Legume rhizodeposition promotes nitrogen fixation by soil microbiota under crop diversification

Biological nitrogen fixation by free-living bacteria and rhizobial symbiosis with legumes plays a key role in sustainable crop production. Here, we study how different crop combinations influence the interaction between peanut plants and their rhizosphere microbiota via metabolite deposition and functional responses of free-living and symbiotic nitrogen-fixing bacteria. Based on a long-term (8 year) diversified cropping field experiment, we find that peanut co-cultured with maize and oilseed rape lead to specific changes in peanut rhizosphere metabolite profiles and bacterial functions and nodulation. Flavonoids and coumarins accumulate due to the activation of phenylpropanoid biosynthesis pathways in peanuts. These changes enhance the growth and nitrogen fixation activity of free-living bacterial isolates, and root nodulation by symbiotic Bradyrhizobium isolates. Peanut plant root metabolites interact with Bradyrhizobium isolates contributing to initiate nodulation. Our findings demonstrate that tailored intercropping could be used to improve soil nitrogen availability through changes in the rhizosphere microbiome and its functions.

Comparative putative metabolites profiling of Tachypleus gigas and Carcinoscorpius rotundicauda hemocytes stimulated with lipopolysaccharide

Horseshoe crabs are among the most studied invertebrates due to their unique, innate immune system and biological processes. The metabolomics study was conducted on lipopolysaccharide (LPS)-stimulated and non-stimulated hemocytes isolated from the Malaysian Tachypleus gigas and Carcinoscorpius rotundicauda. LC-TOF-MS, multivariate analyses, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) were included in this study to profile the metabolites. A total of 37 metabolites were identified to be differentially abundant and were selected based on VIP > 1. However, of the 37 putative metabolites, only 23 were found to be significant with ANOVA at p < 0.05. The metabolites were identified using several databases, and the literature review of the metabolites was reported in the manuscript. Thus, this study has provided further insights into the putative metabolites' presence in the hemocytes of horseshoe crabs that are stimulated and non-stimulated with LPS and their abundance in each species. Several putative metabolites showed they have medicinal values from previous studies.

Screening of Saccharomyces cerevisiae metabolite transporters by 13C isotope substrate labeling

The transportome of Saccharomyces cerevisiae comprises approximately 340 membrane-bound proteins, of which very few are well-characterized. Elucidating transporter proteins' function is essential not only for understanding central cellular processes in metabolite exchange with the external milieu but also for optimizing the production of value-added compounds in microbial cell factories. Here, we describe the application of 13C-labeled stable isotopes and detection by targeted LC-MS/MS as a screening tool for identifying Saccharomyces cerevisiae metabolite transporters. We compare the transport assay's sensitivity, reproducibility, and accuracy in yeast transporter mutant cell lines and Xenopus oocytes. As proof of principle, we analyzed the transport profiles of five yeast amino acid transporters. We first cultured yeast transporter deletion or overexpression mutants on uniformly labeled 13C-glucose and then screened their ability to facilitate the uptake or export of an unlabeled pool of amino acids. Individual transporters were further studied by heterologous expression in Xenopus oocytes, followed by an uptake assay with 13C labeled yeast extract. Uptake assays in Xenopus oocytes showed higher reproducibility and accuracy. Although having lower accuracy, the results from S. cerevisiae indicated the system's potential for initial high-throughput screening for native metabolite transporters. We partially confirmed previously reported substrates for all five amino acid transporters. In addition, we propose broader substrate specificity for two of the transporter proteins. The method presented here demonstrates the application of a comprehensive screening platform for the knowledge expansion of the transporter-substrate relationship for native metabolites in S. cerevisiae.

Metabolomic identification of biochemical changes induced by fluoxetine in an insulinoma cell line (MIN6)

Background and purpose

The use of fluoxetine raises the risk of pancreatic beta-cell dysfunction. However, the specific mechanism behind its mechanism of action in beta cells is unknown. This study investigated the cellular response of MIN6 cells to fluoxetine using untargeted cell-based metabolomics.

Experimental approach

Metabolic profiling of MIN6 cells was performed using liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis on samples prepared under optimized conditions, followed by principal component analysis, partial least squares-discriminant analysis, and pair-wise orthogonal projections to latent structures discriminant analyses.

Findings/Results

Sixty-six metabolites that had been differentially expressed between the control and fluoxetine-treated groups demonstrated that the citric acid cycle is mainly perturbed by fluoxetine treatment.

Conclusion and implications

The current study provides insights into the molecular mechanisms of fluoxetine effects in MIN6 cells.

Multi-OMICs landscape of SARS-CoV-2-induced host responses in human lung epithelial cells

COVID-19 pandemic continues to remain a global health concern owing to the emergence of newer variants. Several multi-Omics studies have produced extensive evidence on host-pathogen interactions and potential therapeutic targets. Nonetheless, an increased understanding of host signaling networks regulated by post-translational modifications and their ensuing effect on the cellular dynamics is critical to expanding the current knowledge on SARS-CoV-2 infections. Through an unbiased transcriptomics, proteomics, acetylomics, phosphoproteomics, and exometabolome analysis of a lung-derived human cell line, we show that SARS-CoV-2 Norway/Trondheim-S15 strain induces time-dependent alterations in the induction of type I IFN response, activation of DNA damage response, dysregulated Hippo signaling, among others. We identified interplay of phosphorylation and acetylation dynamics on host proteins and its effect on the altered release of metabolites, especially organic acids and ketone bodies. Together, our findings serve as a resource of potential targets that can aid in designing novel host-directed therapeutic strategies.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes – the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system—the Microbial Containment Device (MCD)—that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Lactogenic effect of Polyscias scutellaria extract to maintain postpartum prolactin and oxytocin in lactating rats

BACKGROUND

Hypogalactia remains a challenge for mother to complete exclusive breastfeeding. Shield aralia (Polysicias scutellaria) has been used by Indonesian society to increase breast milk production.

OBJECTIVE

This study aimed to elucidate the novel galactopoietic effect of P. scutellaria extract (PSE) in lactating rats for three weeks.

MATERIALS AND METHODS

PSE were assayed for total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity. The secondary metabolites of PSE were determined by liquid chromatography-high-resolution mass spectrometry (LC-HRMS). After parturition, dams were randomly divided into four groups: lactating rats (LR) + distilled water (LRO), LR + Asifit, and LR + PSE at doses of 250 and 500 mg/kg body weight (BW). Virgin rats served as the control group. The treatments were given for 14 days.

RESULTS

There were no significant differences (p > 0.05) in TPC, TFC, and antioxidant capacity among the three solvents. LC-HRMS revealed 22 compounds greater than 70 of the best-match, which were categorised as flavonoids, terpenoids, phenolic acids, and lignan. PSE increased the weight gain and survival rate of pups from our 3-week observation. Serum Prl was significantly higher (p < 0.05) in lactating rats treated with PSE 250 mg/kg BW at the first and third weeks than in the LRO group. Oxt serum was significantly higher (p < 0.05) in lactating rats treated with PSE (500 mg/kg BW) in the first and second weeks than in the LRO group.

CONCLUSION

PSE-rich polyphenols are promising galactopoietics by orchestrating Prl and Oxt and reducing the mortality of pups.

Harmonizing Labeling and Analytical Strategies to Obtain Protein Turnover Rates in Intact Adult Animals

Changes in the abundance of individual proteins in the proteome can be elicited by modulation of protein synthesis (the rate of input of newly synthesized proteins into the protein pool) or degradation (the rate of removal of protein molecules from the pool). A full understanding of proteome changes therefore requires a definition of the roles of these two processes in proteostasis, collectively known as protein turnover. Because protein turnover occurs even in the absence of overt changes in pool abundance, turnover measurements necessitate monitoring the flux of stable isotope–labeled precursors through the protein pool such as labeled amino acids or metabolic precursors such as ammonium chloride or heavy water. In cells in culture, the ability to manipulate precursor pools by rapid medium changes is simple, but for more complex systems such as intact animals, the approach becomes more convoluted. Individual methods bring specific complications, and the suitability of different methods has not been comprehensively explored. In this study, we compare the turnover rates of proteins across four mouse tissues, obtained from the same inbred mouse strain maintained under identical husbandry conditions, measured using either [¹³C₆]lysine or [²H₂]O as the labeling precursor. We show that for long-lived proteins, the two approaches yield essentially identical measures of the first-order rate constant for degradation. For short-lived proteins, there is a need to compensate for the slower equilibration of lysine through the precursor pools. We evaluate different approaches to provide that compensation. We conclude that both labels are suitable, but careful determination of precursor enrichment kinetics in amino acid labeling is critical and has a considerable influence on the numerical values of the derived protein turnover rates.

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Controlled comparison of heavy water or amino acid labeling for protein turnover.

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Delays in amino acid precursor labeling mostly affect high turnover proteins

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Both methods produced similar turnover rates after adjustment of precursor kinetics.

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Recommendations for analytical workflows for protein turnover studies in animals.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

Chemical Components of Aqueous Extracts of Melia azedarach Fruits and Their Effects on The Transcriptome of Staphylococcus aureus

Staphylococcus aureus is the causative agent of numerous and varied clinical infections. Crude aqueous extracts of Melia azedarach fruits inhibit the planktonic growth and initial biofilm formation of S. aureus in a dose-dependent manner. Moreover, the biofilm topologies became sparse and decreased as the concentration of the aqueous extracts increased. RNA-Seq analyses revealed 532 differentially expressed genes (DEGs) after S. aureus exposure to 0.25 g/ml extracts; 319 of them were upregulated, and 213 were downregulated. The majority of DEGs were categorized into abundant sub-groups in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Finally, untargeted UHPLC-MS/MS analyses of the aqueous extracts of M. azedarach fruits demonstrated a highly complex profile in positive and negative electrospray ionization modes. The extracts primarily consisted of lipids and lipid-like molecules, organic acids and their derivatives, phenylpropanoids, polyketides, organoheterocyclic compounds, and benzenoids annotated by abundant lipid maps and KEGG pathways. Overall, this study provides evidences that the aqueous extracts of M. azedarach fruits can control S. aureus infections and sought to understand the mode of action of these extracts on S. aureus.

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

INTRODUCTION

The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.

OBJECTIVES

Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased).

METHODS

High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created.

RESULTS

The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86).

CONCLUSION

Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra

The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

In vitro toxic synergistic effects of exogenous pollutants-trimethylamine and its metabolites on human respiratory tract cells

The wide presence of volatile organic amines in atmosphere has been clarified to relate to adverse effects on human respiratory health. However, toxic effects of them on human respiratory tract and their metabiotic mechanism of in vivo transformation have not been elucidated yet. Herein, cell viability and production of reactive oxygen species (ROSs) were first investigated during acute exposure of trimethylamine (TMA) to bronchial epithelial cells (16HBE), along with identification of toxic metabolites and metabolic mechanisms of TMA from headspace atmosphere and cell culture. Results showed that cell activity decreased and ROS production increased with raising exposure TMA concentration. Toxic effects may be induced not only by TMA itself, but also more likely by its cellular metabolites. Increased dimethylamine identified in headspace atmosphere and cell solution was the main metabolite of TMA, and methylamine was also confirmed to be a further metabolite. In addition, TMA can also be oxygenated to generate N,N-dimethylformamide and N,N'-Bis(2-hydroxyethyl)-1,2-ethanediaminium by N-formylation or hydroxylation, which was considered to be the participation of cytochrome P450 (CYP) enzymes. Overall, we can conclude that respiratory tract cells may produce more toxic metabolites during exposure of toxic organic amines, which together further induce cellular oxidative stress and necrosis. Hence, the environment and health impact of metabolites as well as original parent atmospheric organic amines should be paid more attention in further researches and disease risk assessments.

An Overview of Cell-Based Assay Platforms for the Solute Carrier Family of Transporters

The solute carrier (SLC) superfamily represents the biggest family of transporters with important roles in health and disease. Despite being attractive and druggable targets, the majority of SLCs remains understudied. One major hurdle in research on SLCs is the lack of tools, such as cell-based assays to investigate their biological role and for drug discovery. Another challenge is the disperse and anecdotal information on assay strategies that are suitable for SLCs. This review provides a comprehensive overview of state-of-the-art cellular assay technologies for SLC research and discusses relevant SLC characteristics enabling the choice of an optimal assay technology. The Innovative Medicines Initiative consortium RESOLUTE intends to accelerate research on SLCs by providing the scientific community with high-quality reagents, assay technologies and data sets, and to ultimately unlock SLCs for drug discovery.

Comparison of Osteosarcoma Aggregated Tumour Models with Human Tissue by Multimodal Mass Spectrometry Imaging

Osteosarcoma (OS) is the most common primary bone malignancy and largely effects adolescents and young adults, with 60% of patients under the age of 25. There are multiple cell models of OS described in vitro that express the specific genetic alterations of the sarcoma. In the work reported here, multiple mass spectrometry imaging (MSI) modalities were employed to characterise two aggregated cellular models of OS models formed using the MG63 and SAOS-2 cell lines. Phenotyping of the metabolite activity within the two OS aggregoid models was achieved and a comparison of the metabolite data with OS human tissue samples revealed relevant fatty acid and phospholipid markers. Although, annotations of these species require MS/MS analysis for confident identification of the metabolites. From the putative assignments however, it was suggested that the MG63 aggregoids are an aggressive tumour model that exhibited metastatic-like potential. Alternatively, the SAOS-2 aggregoids are more mature osteoblast-like phenotype that expressed characteristics of cellular differentiation and bone development. It was determined the two OS aggregoid models shared similarities of metabolic behaviour with different regions of OS human tissues, specifically of the higher metastatic grade.

Changes in the chemical and sensory profile of ripened Italian salami following the addition of different microbial starters

In this work, Italian salami were produced using microbial starters (Pediococcus pentosaceus, Lactobacillus sakei, and Staphylococcus xylosus) and compared to a control sample (without starter). Metabolomics in combination with microbiological and sensory analyses were used to investigate the overall quality. Samples were analyzed immediately after stuffing, following 7, 30, and 45 days of ripening. Each microbial starter imposed distinctive metabolomic signatures at the end of ripening. The accumulated discriminant compounds were mainly related to lipid oxidation (including hydroxy- and epoxy derivatives of fatty acids) following the inoculation with L. sakei. However, the inoculation with P. pentosaceus resulted in the accumulation of γ-glutamyl peptides, compounds driving a kokumi-related taste. Noteworthy, our findings supported the involvement of the chemical compounds profiled in the definition of final taste and aroma. This information paves the way towards the definition of more objective and tailored starters-related flavours enhancement approaches in the sector of cured meat.

Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study

Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies.