Best-Matched Internal Standard Normalization in Liquid Chromatography-Mass Spectrometry Metabolomics Applied to Environmental Samples

Direct extraction of ten estrogens from milk samples with DVB/NVP-modified magnetic solid-phase extraction adsorbent followed by pre-column derivatization-UHPLC-MS/MS

Estrogens and their analogues can cause harm to human health through the food chain. Ten estrogens in different milk samples were directly extracted by amphiphilic divinylbenzene/N-vinyl-2-pyrrolidone (DVB/NVP)-Fe3O4@SiO2-based magnetic solid-phase extraction (MSPE) followed by pre-column derivatization and ultra-high performance liquid chromatography tandem mass-spectrometry (UHPLC-MS/MS) detection. Under the optimal conditions, the limits of detection for ten analytes were in the range of 0.05-0.38 ng mL-1 in whole liquid milk matrix and 0.04-3.00 ng g-1 in milk powder matrix. The intra-/inter-day accuracy ranged in 83.4-113.8%, with RSDs in 2.5-15.0%. A total of 15 brands of liquid milk and milk powder samples were analyzed, and only estradiol was detected in three brands of boxed liquid milk within safe range. The proposed sample pretreatment eliminated the common protein precipitation process, improved the sample throughput, and has the potential for routine testing of estrogens and their analogues in market-sale milk samples.

Isotope dilution with isotopically labeled biomass: An effective alternative for quantitative metabolomics

BACKGROUND

State-of-the-art quantitative metabolomics relies on isotope dilution using internal standards (IS) derived from fully 13C labeled biomass. By spiking samples and external standards with known amounts of IS, the spike characterization demands are kept to a minimum. In fact, it is sufficient to experimentally assess the isotopic enrichment of the IS. This study develops the yeast derived IS toolbox further, (1) by characterizing the concentration levels of hydrophilic metabolites in a yeast fermentation batch and (2) by exploring the analytical figures of merit of one-point IS versus multipoint external calibration using IS, the established gold-standard for quantitative metabolomics.

RESULTS

Independent reverse isotope dilution experiments using different chromatographic methods over a period of several months, delivered a list of 83 13C-labeled metabolites with fully characterized concentration and their uncertainty, covering 5 orders of magnitude, from the nanomolar to the low millimolar range. The 13C-labeled yeast-derived IS showed excellent intermediate stability with 92 % of molecules showing inter-method RSDs ≤30 % (75 % of molecules showed RSDs ≤15 %) over a timeframe of five months. One-point internal standardization with the characterized labeled biomass achieved figures of merit equivalent to multipoint calibrations for the majority of metabolites.

SIGNIFICANCE

The proposed calibration workflow rationalizes time and standard expenditure and is particularly beneficial for laboratories dealing with wide-target assays and small analysis batches. The present assessment serves as a seminal study for further developments of the concept towards absolute quantification from archive high-resolution MS data of U13C-biomass-spiked samples and the implementation of quick biomass recalibration with each experiment, promising seamless transition between internal standards derived from different fermentation batches.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.

Relating Molecular Properties to the Persistence of Marine Dissolved Organic Matter with Liquid Chromatography-Ultrahigh-Resolution Mass Spectrometry

Marine dissolved organic matter (DOM) contains a complex mixture of small molecules that eludes rapid biological degradation. Spatial and temporal variations in the abundance of DOM reflect the existence of fractions that are removed from the ocean over different time scales, ranging from seconds to millennia. However, it remains unknown whether the intrinsic chemical properties of these organic components relate to their persistence. Here, we elucidate and compare the molecular compositions of distinct DOM fractions with different lability along a water column in the North Atlantic Gyre. Our analysis utilized ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry at 21 T coupled to liquid chromatography and a novel data pipeline developed in CoreMS that generates molecular formula assignments and metrics of isomeric complexity. Clustering analysis binned 14 857 distinct molecular components into groups that correspond to the depth distribution of semilabile, semirefractory, and refractory fractions of DOM. The more labile fractions were concentrated near the ocean surface and contained more aliphatic, hydrophobic, and reduced molecules than the refractory fraction, which occurred uniformly throughout the water column. These findings suggest that processes that selectively remove hydrophobic compounds, such as aggregation and particle sorption, contribute to variable removal rates of marine DOM.

Application of Nontarget High-Resolution Mass Spectrometry Fingerprints for Qualitative and Quantitative Source Apportionment: A Real Case Study

High-resolution mass spectrometry (HRMS) provides extensive chemical data, facilitating the differentiation and quantification of contaminants of emerging concerns (CECs) in aquatic environments. This study utilizes liquid chromatography-HRMS for source apportionment in Chebei Stream, an urban water stream in Guangzhou, South China. Initially, 254 features were identified as potential CECs by the nontarget screening (NTS) method. We then established 1689, 1317, and 15,759 source-specific HRMS fingerprints for three distinct sources, the mainstream (C3), the tributary (T2), and the rain runoff (R1), qualitatively assessing the contribution from each source downstream. Subsequently, 32, 55, and 3142 quantitative fingerprints were isolated for sites C3, T2, and R1, respectively, employing dilution curve screening for source attribution. The final contribution estimates downstream from sites C3, T2, and R1 span 32-96, 12-23, and 8-23%, respectively. Cumulative contributions from these sources accurately mirrored actual conditions, fluctuating between 103 and 114% across C6 to C8 sites. Yet, with further tributary integration, the overall source contribution dipped to 52%. The findings from this research present a pioneering instance of applying HRMS fingerprints for qualitative and quantitative source tracking in real-world scenarios, which empowers the development of more effective strategies for environmental protection.

Microbial metabolomic responses to changes in temperature and salinity along the western Antarctic Peninsula

Seasonal cycles within the marginal ice zones in polar regions include large shifts in temperature and salinity that strongly influence microbial abundance and physiology. However, the combined effects of concurrent temperature and salinity change on microbial community structure and biochemical composition during transitions between seawater and sea ice are not well understood. Coastal marine communities along the western Antarctic Peninsula were sampled and surface seawater was incubated at combinations of temperature and salinity mimicking the formation (cold, salty) and melting (warm, fresh) of sea ice to evaluate how these factors may shape community composition and particulate metabolite pools during seasonal transitions. Bacterial and algal community structures were tightly coupled to each other and distinct across sea-ice, seawater, and sea-ice-meltwater field samples, with unique metabolite profiles in each habitat. During short-term (approximately 10-day) incubations of seawater microbial communities under different temperature and salinity conditions, community compositions changed minimally while metabolite pools shifted greatly, strongly accumulating compatible solutes like proline and glycine betaine under cold and salty conditions. Lower salinities reduced total metabolite concentrations in particulate matter, which may indicate a release of metabolites into the labile dissolved organic matter pool. Low salinity also increased acylcarnitine concentrations in particulate matter, suggesting a potential for fatty acid degradation and reduced nutritional value at the base of the food web during freshening. Our findings have consequences for food web dynamics, microbial interactions, and carbon cycling as polar regions undergo rapid climate change.

Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics

BACKGROUND

Chromatographic peakpicking continues to represent a significant bottleneck in automated LC-MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality.

RESULTS

Here, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram, and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70-80% down to 1-5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone.

CONCLUSIONS

We conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation.

Use and detection of a vitamin B1 degradation product yields new views of the marine B1 cycle and plankton metabolite exchange

Vitamin B1 (thiamin) is a vital nutrient for most cells in nature, including marine plankton. Early and recent experiments show that B1 degradation products instead of B1 can support the growth of marine bacterioplankton and phytoplankton. However, the use and occurrence of some degradation products remains uninvestigated, namely N-formyl-4-amino-5-aminomethyl-2-methylpyrimidine (FAMP), which has been a focus of plant oxidative stress research. We investigated the relevance of FAMP in the ocean. Experiments and global ocean meta-omic data indicate that eukaryotic phytoplankton, including picoeukaryotes and harmful algal bloom species, use FAMP while bacterioplankton appear more likely to use deformylated FAMP, 4-amino-5-aminomethyl-2-methylpyrimidine. Measurements of FAMP in seawater and biomass revealed that it occurs at picomolar concentrations in the surface ocean, heterotrophic bacterial cultures produce FAMP in the dark-indicating non-photodegradation of B1 by cells, and B1-requiring (auxotrophic) picoeukaryotic phytoplankton produce intracellular FAMP. Our results require an expansion of thinking about vitamin degradation in the sea, but also the marine B1 cycle where it is now crucial to consider a new B1-related compound pool (FAMP), as well as generation (dark degradation-likely via oxidation), turnover (plankton uptake), and exchange of the compound within the networks of plankton. IMPORTANCE Results of this collaborative study newly show that a vitamin B1 degradation product, N-formyl-4-amino-5-aminomethyl-2-methylpyrimidine (FAMP), can be used by diverse marine microbes (bacteria and phytoplankton) to meet their vitamin B1 demands instead of B1 and that FAMP occurs in the surface ocean. FAMP has not yet been accounted for in the ocean and its use likely enables cells to avoid B1 growth deficiency. Additionally, we show FAMP is formed in and out of cells without solar irradiance-a commonly considered route of vitamin degradation in the sea and nature. Altogether, the results expand thinking about oceanic vitamin degradation, but also the marine B1 cycle where it is now crucial to consider a new B1-related compound pool (FAMP), as well as its generation (dark degradation-likely via oxidation), turnover (plankton uptake), and exchange within networks of plankton.

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics

For a large-scale metabolomics study, sample collection, preparation, and analysis may last several days, months, or even (intermittently) over years. This may lead to apparent batch effects in the acquired metabolomics data due to variability in instrument status, environmental conditions, or experimental operators. Batch effects may confound the true biological relationships among metabolites and thus obscure real metabolic changes. At present, most of the commonly used batch effect correction (BEC) methods are based on quality control (QC) samples, which require sufficient and stable QC samples. However, the quality of the QC samples may deteriorate if the experiment lasts for a long time. Alternatively, isotope-labeled internal standards have been used, but they generally do not provide good coverage of the metabolome. On the other hand, BEC can also be conducted through a data-driven method, in which no QC sample is needed. Here, we propose a novel data-driven BEC method, namely, CordBat, to achieve concordance between each batch of samples. In the proposed CordBat method, a reference batch is first selected from all batches of data, and the remaining batches are referred to as "other batches." The reference batch serves as the baseline for the batch adjustment by providing a coordinate of correlation between metabolites. Next, a Gaussian graphical model is built on the combined dataset of reference and other batches, and finally, BEC is achieved by optimizing the correction coefficients in the other batches so that the correlation between metabolites of each batch and their combinations are in concordance with that of the reference batch. Three real-world metabolomics datasets are used to evaluate the performance of CordBat by comparing it with five commonly used BEC methods. The present experimental results showed the effectiveness of CordBat in batch effect removal and the concordance of correlation between metabolites after BEC. CordBat was found to be comparable to the QC-based methods and achieved better performance in the preservation of biological effects. The proposed CordBat method may serve as an alternative BEC method for large-scale metabolomics that lack proper QC samples.

LC-MS-Based Targeted Metabolomics for FACS-Purified Rare Cells

Metabolism plays a fundamental role in regulating cellular functions and fate decisions. Liquid chromatography-mass spectrometry (LC-MS)-based targeted metabolomic approaches provide high-resolution insights into the metabolic state of a cell. However, the typical sample size is in the order of 10⁵-10⁷ cells and thus not compatible with rare cell populations, especially in the case of a prior flow cytometry-based purification step. Here, we present a comprehensively optimized protocol for targeted metabolomics on rare cell types, such as hematopoietic stem cells and mast cells. Only 5000 cells per sample are required to detect up to 80 metabolites above background. The use of regular-flow liquid chromatography allows for robust data acquisition, and the omission of drying or chemical derivatization avoids potential sources of error. Cell-type-specific differences are preserved while the addition of internal standards, generation of relevant background control samples, and targeted metabolite with quantifiers and qualifiers ensure high data quality. This protocol could help numerous studies to gain thorough insights into cellular metabolic profiles and simultaneously reduce the number of laboratory animals and the time-consuming and costly experiments associated with rare cell-type purification.

A Complete Pipeline for Untargeted Urinary Volatolomic Profiling with Sorptive Extraction and Dual Polar and Nonpolar Column Methodologies Coupled with Gas Chromatography Time-of-Flight Mass Spectrometry

Volatolomics offers an opportunity for noninvasive detection and monitoring of human disease. While gas chromatography-mass spectrometry (GC-MS) remains the technique of choice for analyzing volatile organic compounds (VOCs), barriers to wider adoption in clinical practice still exist, including: sample preparation and introduction techniques, VOC extraction, throughput, volatolome coverage, biological interpretation, and quality control (QC). Therefore, we developed a complete pipeline for untargeted urinary volatolomic profiling. We optimized a novel extraction technique using HiSorb sorptive extraction, which exhibited high analytical performance and throughput. We achieved a broader VOC coverage by using HiSorb coupled with a set of complementary chromatographic methods and time-of-flight mass spectrometry. Furthermore, we developed a data preprocessing strategy by evaluating internal standard normalization, batch correction, and we adopted strict QC measures including removal of nonlinearly responding, irreproducible, or contaminated metabolic features, ensuring the acquisition of high-quality data. The applicability of this pipeline was evaluated in a clinical cohort consisting of pancreatic ductal adenocarcinoma (PDAC) patients (n = 28) and controls (n = 33), identifying four urinary candidate biomarkers (2-pentanone, hexanal, 3-hexanone, and p-cymene), which can successfully discriminate the cancer and noncancer subjects. This study presents an optimized, high-throughput, and quality-controlled pipeline for untargeted urinary volatolomic profiling. Use of the pipeline to discriminate PDAC from control subjects provides proof of principal of its clinical utility and potential for application in future biomarker discovery studies.

Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis

In this chapter, we review the cutting-edge statistical and machine learning methods for missing value imputation, normalization, and downstream analyses in mass spectrometry metabolomics studies, with illustration by example datasets. The missing peak recovery includes simple imputation by zero or limit of detection, regression-based or distribution-based imputation, and prediction by random forest. The batch effect can be removed by data-driven methods, internal standard-based, and quality control sample-based normalization. We also summarize different types of statistical analysis for metabolomics and clinical outcomes, such as inference on metabolic biomarkers, clustering of metabolomic profiles, metabolite module building, and integrative analysis with transcriptome.

Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system

Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month's samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch.

Inhibition of mitochondrial complex I leading to NAD+/NADH imbalance in type 2 diabetic patients who developed late stent thrombosis: Evidence from an integrative analysis of platelet bioenergetics and metabolomics

Type 2 diabetes mellitus (T2DM) is a strong indicator of late stent thrombosis (LST). Platelet bioenergetic dysfunction, although critical to the pathogenesis of diabetic macrovascular complications, remains uncharacterized in T2DM patients who developed LST. Here, we explored the mechanistic link between the alterations in platelet bioenergetics and LST in the setting of T2DM. Platelet bioenergetics, metabolomics, and their interactomes were analyzed in a nested case-control study including 15 T2DM patients who developed LST and 15 matched T2DM patients who did not develop LST (non-LST). Overall, we identified a bioenergetic alteration in T2DM patients with LST characterized by an imbalanced NAD+/NADH redox state resulting from deficient mitochondrial complex I (NADH: ubiquinone oxidoreductase) activity, which led to reduced ATP-linked and maximal mitochondrial respiration, increased glycolytic flux, and platelet hyperactivation compared with non-LST patients. Congruently, platelets from LST patients exhibited downregulation of tricarboxylic acid cycle and NAD+ biosynthetic pathways as well as upregulation of the proximal glycolytic pathway, a metabolomic change that was primarily attributed to compromised mitochondrial respiration rather than increased glycolytic flux as evidenced by the integrative analysis of bioenergetics and metabolomics. Importantly, both bioenergetic and metabolomic aberrancies in LST platelets could be recapitulated ex vivo by exposing the non-LST platelets to a low dose of rotenone, a complex I inhibitor. In contrast, normalization of the NAD+/NADH redox state, either by increasing NAD+ biosynthesis or by inhibiting NAD+ consumption, was able to improve mitochondrial respiration, inhibit mitochondrial oxidant generation, and consequently attenuate platelet aggregation in both LST platelets and non-LST platelets pretreated with low-dose rotenone. These data, for the first time, delineate the specific patterns of bioenergetic and metabolomic alterations for T2DM patients who suffer from LST, and establish the deficiency of complex I-derived NAD+ as a potential pathogenic mechanism in platelet abnormalities.

Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods

Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders’ abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders.

A novel 6-metabolite signature for prediction of clinical outcomes in type 2 diabetic patients undergoing percutaneous coronary intervention

Background

Outcome prediction tools for patients with type 2 diabetes mellitus (T2DM) undergoing percutaneous coronary intervention (PCI) are lacking. Here, we developed a machine learning-based metabolite classifier for predicting 1-year major adverse cardiovascular events (MACEs) after PCI among patients with T2DM.

Methods

Serum metabolomic profiling was performed in a nested case–control study of 108 matched pairs of patients with T2DM occurring and not occurring MACEs at 1 year after PCI, then the matched pairs were 1:1 assigned into the discovery and internal validation sets. External validation was conducted using targeted metabolite analyses in an independent prospective cohort of 301 patients with T2DM receiving PCI. The function of candidate metabolites was explored in high glucose-cultured human aortic smooth muscle cells (HASMCs).

Results

Overall, serum metabolome profiles differed between diabetic patients with and without 1-year MACEs after PCI. Through VSURF, a machine learning approach for feature selection, we identified the 6 most important metabolic predictors, which mainly targeted the nicotinamide adenine dinucleotide (NAD⁺) metabolism. The 6-metabolite model based on random forest and XGBoost algorithms yielded an area under the curve (AUC) of ≥ 0.90 for predicting MACEs in both discovery and internal validation sets. External validation of the 6-metabolite classifier also showed good accuracy in predicting MACEs (AUC 0.94, 95% CI 0.91–0.97) and target lesion failure (AUC 0.89, 95% CI 0.83–0.95). In vitro, there were significant impacts of altering NAD⁺ biosynthesis on bioenergetic profiles, inflammation and proliferation of HASMCs.

Conclusion

The 6-metabolite model may help for noninvasive prediction of 1-year MACEs following PCI among patients with T2DM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01561-1.

Measuring Vitamin D3 Metabolic Status, Comparison between Vitamin D Deficient and Sufficient Individuals

The main branch of vitamin D3 metabolism involves several hydroxylation reactions to obtain mono-, di- and trihydroxylated metabolites, including the circulating and active forms—25(OH)D3 and 1,25(OH)2D3, respectively. However, most clinical trials strictly target the determination of 25(OH)D3 to offer a view of the metabolic status of vitamin D3. Due to the growing interest in expanding this restricted view, we have developed a method for measuring vitamin D3 metabolism by determination of vitamin D3, 25(OH)D3, 24,25(OH)2D3, 1,25(OH)2D3 and 1,24,25(OH)3D3 in human plasma. The method was based on SPE–LC–MS/MS with a large volume injection of human plasma (240 µL). Detection of di- and trihydroxymetabolites, found at the picogram per milliliter level, was attained by the combined action of high preconcentration and clean-up effects. The method allows obtaining information about ratios such as the known vitamin D metabolite ratio (24,25(OH)2D3/25(OH)D3), which can provide complementary views of vitamin D3 metabolic status. The method was applied to a cohort of obese patients and a reference cohort of healthy volunteers to find metabolic correlations between target analytes as well as differences as a function of vitamin D levels within and between cohorts.

Norm ISWSVR: A Data Integration and Normalization Approach for Large-Scale Metabolomics

Large-scale and long-period metabolomics study is more susceptible to various sources of systematic errors, resulting in nonreproducibility and poor data quality. A reliable and robust batch correction method removes unwanted systematic variations and improves the statistical power of metabolomics data, which undeniably becomes an important issue for the quality control of metabolomics. This study proposed a novel data normalization and integration method, Norm ISWSVR. It is a two-step approach via combining the best-performance internal standard correction with support vector regression normalization, comprehensively removing the systematic and random errors and matrix effects. This method was investigated in three untargeted lipidomics or metabolomics datasets, and the performance was further evaluated systematically in comparison with that of 11 other normalization methods. As a result, Norm ISWSVR decreased the data's median cross-validated relative standard deviation (cvRSD), increased the correlation between QCs, improved the classification accuracy of biomarkers, and was well-compatible with quantitative data. More importantly, Norm ISWSVR also allows a low frequency of QCs, which could significantly decrease the burden of a large-scale experiment. Correspondingly, Norm ISWSVR favorably improves the data quality of large-scale metabolomics data.

Early Metabolomic Markers of Acute Low-Dose Exposure to Uranium in Rats

Changes in metabolomics over time were studied in rats to identify early biomarkers and highlight the main metabolic pathways that are significantly altered in the period immediately following acute low-dose uranium exposure. A dose response relationship study was established from urine and plasma samples collected periodically over 9 months after the exposure of young adult male rats to uranyl nitrate. LC-MS and biostatistical analysis were used to identify early discriminant metabolites. As expected, low doses of uranium lead to time-based non-toxic biological effects, which can be used to identify early and delayed markers of exposure in both urine and plasma samples. A combination of surrogate markers for uranium exposure was validated from the most discriminant early markers for making effective predictions. N-methyl-nicotinamide, kynurenic acid, serotonin, tryptophan, tryptamine, and indole acetic acid associated with the nicotinate–nicotinamide and tryptophan pathway seem to be one of the main biological targets, as shown previously for chronic contaminations and completed, among others, by betaine metabolism. This study can be considered as a proof of concept for the relevance of metabolomics in the field of low-dose internal contamination by uranium, for the development of predictive diagnostic tests usable for radiotoxicological monitoring.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Glycine betaine uptake and metabolism in marine microbial communities

Glycine betaine (GBT) is a compatible solute in high concentrations in marine microorganisms. As a component of labile organic matter, GBT has complex biochemical potential as a substrate for microbial use that is unconstrained in the environment. Here we determine the uptake kinetics and metabolic fate of GBT in two natural microbial communities in the North Pacific characterized by different nitrate concentrations. Dissolved GBT had maximum uptake rates of 0.36 and 0.56 nM h⁻¹ with half‐saturation constants of 79 and 11 nM in the high nitrate and low nitrate stations respectively. During multiday incubations, most GBT taken into cells was retained as a compatible solute. Stable isotopes derived from the added GBT were also observed in other metabolites, including choline, carnitine and sarcosine, suggesting that GBT was used for biosynthesis and for catabolism to pyruvate and ammonium. Where nitrate was scarce, GBT was primarily metabolized via demethylation to glycine. Gene transcript data were consistent with SAR11 using GBT as a source of methyl groups to fuel the methionine cycle. Where nitrate concentrations were higher, more GBT was partitioned for lipid biosynthesis by both bacteria and eukaryotic phytoplankton. Our data highlight unexpected metabolic pathways and potential routes of microbial metabolite exchange.

Applying a Set of Potential Methods for the Integrated Assessment of the Marine Eco-Environmental Carrying Capacity in Coastal Areas

The accelerated socioeconomic development has placed the coastal ecosystems under stress, which influences the sustainable development of coastal areas. Marine eco-environmental carrying capacity assessment (MECCA) can provide a scientific basis for coordinating coastal socioeconomic development and eco-environmental protection, ensuring a more effective marine ecosystem-based management approach toward sustainability. However, accurate assessment methods are still in the exploratory stage, as there has been a lack of systematic research and applications combining integrated MECCA with a unified method to underpin coastal management processes. In light of this issue, this study applied the marine eco-environmental carrying capacity in coastal waters (MECCCW) conceptual framework to support the establishment of an assessment indicator system for MECCA and used the regularization method and entropy method to determine weights. This study also applied the simplified state space model to comprehensively evaluate and analyze the marine eco-environmental carrying capacity (MECC) of coastal areas. Focusing on the coastal area of Sanya Bay, southern China, as the study area, we assessed the MECC for the period from 2015 to 2020. The state of the MECC was divided into three grades: load capacity, full-load capacity, and overload capacity. The results showed that (1) the MECCA indicator system in Sanya Bay included a total of three criteria and eight assessment indicators and (2) the weights of the environmental carrying capacity (ECC) and human activities (HA) were both relatively higher than that of ecological resilience (ER). The latter result indicates that either ECC or HA could play a more predominant role in the changes of the MECC state in Sanya Bay. The results also indicated that (3) for each criterion, ECC, ER, and HA were at load capacity from 2015 to 2020. In this instance, ECC and HA presented similar change trends in relation to the MECC state of Sanya Bay. Finally, (4) the overall Sanya Bay’s MECC was also at load capacity and weakened, fluctuating between 2015 and 2020. These findings indicate that the coastal area of Sanya Bay is capable of sustainable development, but that there is a need for further eco-environmental improvement. The results of this study can serve as a reference when decisions have to be made about coastal management from an environmental and ecological perspective. Furthermore, this method may provide a feasible approach for integrated MECCA in other coastal areas.

Linkages Among Dissolved Organic Matter Export, Dissolved Metabolites, and Associated Microbial Community Structure Response in the Northwestern Sargasso Sea on a Seasonal Scale

Deep convective mixing of dissolved and suspended organic matter from the surface to depth can represent an important export pathway of the biological carbon pump. The seasonally oligotrophic Sargasso Sea experiences annual winter convective mixing to as deep as 300 m, providing a unique model system to examine dissolved organic matter (DOM) export and its subsequent compositional transformation by microbial oxidation. We analyzed biogeochemical and microbial parameters collected from the northwestern Sargasso Sea, including bulk dissolved organic carbon (DOC), total dissolved amino acids (TDAA), dissolved metabolites, bacterial abundance and production, and bacterial community structure, to assess the fate and compositional transformation of DOM by microbes on a seasonal time-scale in 2016-2017. DOM dynamics at the Bermuda Atlantic Time-series Study site followed a general annual trend of DOC accumulation in the surface during stratified periods followed by downward flux during winter convective mixing. Changes in the amino acid concentrations and compositions provide useful indices of diagenetic alteration of DOM. TDAA concentrations and degradation indices increased in the mesopelagic zone during mixing, indicating the export of a relatively less diagenetically altered (i.e., more labile) DOM. During periods of deep mixing, a unique subset of dissolved metabolites, such as amino acids, vitamins, and benzoic acids, was produced or lost. DOM export and compositional change were accompanied by mesopelagic bacterial growth and response of specific bacterial lineages in the SAR11, SAR202, and SAR86 clades, Acidimicrobiales, and Flavobacteria, during and shortly following deep mixing. Complementary DOM biogeochemistry and microbial measurements revealed seasonal changes in DOM composition and diagenetic state, highlighting microbial alteration of the quantity and quality of DOM in the ocean.

Assessing Reliability of Non-targeted High-Resolution Mass Spectrometry Fingerprints for Quantitative Source Apportionment in Complex Matrices

Effective management of contaminated sites requires differentiating and deconvoluting contaminant source impacts in complex environmental systems. The existing source apportionment approaches that use targeted analyses of preselected indicator chemicals are limited whenever target analytes are below the detection limits or derived from multiple sources. However, non-targeted analyses that leverage high-resolution mass spectrometry (HRMS) yield rich datasets that deeply characterize sample-specific chemical compositions, providing additional potential end-members for source differentiation and apportionment. Previous work demonstrated that HRMS fingerprints can define sample uniqueness and support accurate, quantitative source concentration estimates. Here, using two aqueous film-forming foams as representative complex sources, we assessed the qualitative fidelity and quantitative accuracy of HRMS source fingerprints in increasingly complex background matrices. Across all matrices, HRMS-derived source concentration estimates were 0.81 ± 0.11-fold and 0.64 ± 0.24-fold of actual in samples impacted solely by analytical matrix effects (MEs) or by sample processing recovery and analytical MEs, respectively. Isotopic internal standards were not easily paired to individual unidentified non-target features, but bulk internal standard-based abundance corrections improved apportionment accuracy in higher matrix samples (to 0.90 ± 0.12-fold of actual) and/or informed concentration estimate relative errors. HRMS fingerprint mining could identify, based on the dilution behavior, effective individual chemical end-members across 16 homologous series. Although method development is needed, the results further demonstrate the potential applications of non-targeted HRMS data for source apportionment and other quantitative outcomes.

Cysteinolic Acid Is a Widely Distributed Compatible Solute of Marine Microalgae

Phytoplankton rely on bioactive zwitterionic and highly polar small metabolites with osmoregulatory properties to compensate changes in the salinity of the surrounding seawater. Dimethylsulfoniopropionate (DMSP) is a main representative of this class of metabolites. Salinity-dependent DMSP biosynthesis and turnover contribute significantly to the global sulfur cycle. Using advanced chromatographic and mass spectrometric techniques that enable the detection of highly polar metabolites, we identified cysteinolic acid as an additional widely distributed polar metabolite in phytoplankton. Cysteinolic acid belongs to the class of marine sulfonates, metabolites that are commonly produced by algae and consumed by bacteria. It was detected in all dinoflagellates, haptophytes, diatoms and prymnesiophytes that were surveyed. We quantified the metabolite in different phytoplankton taxa and revealed that the cellular content can reach even higher concentrations than the ubiquitous DMSP. The cysteinolic acid concentration in the cells of the diatom Thalassiosira weissflogii increases significantly when grown in a medium with elevated salinity. In contrast to the compatible solute ectoine, cysteinolic acid is also found in high concentrations in axenic algae, indicating biosynthesis by the algae and not the associated bacteria. Therefore, we add this metabolite to the family of highly polar metabolites with osmoregulatory characteristics produced by phytoplankton.

Prochlorococcus extracellular vesicles: molecular composition and adsorption to diverse microbes

Extracellular vesicles are small (~50–200 nm diameter) membrane‐bound structures released by cells from all domains of life. While vesicles are abundant in the oceans, their functions, both for cells themselves and the emergent ecosystem, remain a mystery. To better characterize these particles – a prerequisite for determining function – we analysed the lipid, protein, and metabolite content of vesicles produced by the marine cyanobacterium Prochlorococcus. We show that Prochlorococcus exports a diverse array of cellular compounds into the surrounding seawater enclosed within discrete vesicles. Vesicles produced by two different strains contain some materials in common, but also display numerous strain‐specific differences, reflecting functional complexity within vesicle populations. The vesicles contain active enzymes, indicating that they can mediate extracellular biogeochemical reactions in the ocean. We further demonstrate that vesicles from Prochlorococcus and other bacteria associate with diverse microbes including the most abundant marine bacterium, Pelagibacter. Together, our data point toward hypotheses concerning the functional roles of vesicles in marine ecosystems including, but not limited to, possibly mediating energy and nutrient transfers, catalysing extracellular biochemical reactions, and mitigating toxicity of reactive oxygen species.

Identification of Effector Metabolites Using Exometabolite Profiling of Diverse Microalgae

Dissolved exometabolites mediate algal interactions in aquatic ecosystems, but microalgal exometabolomes remain understudied. We conducted an untargeted metabolomic analysis of nonpolar exometabolites exuded from four phylogenetically and ecologically diverse eukaryotic microalgal strains grown in the laboratory, freshwater Chlamydomonas reinhardtii, brackish Desmodesmus sp., marine Phaeodactylum tricornutum, and marine Microchloropsis salina, to identify released metabolites based on relative enrichment in the exometabolomes compared to cell pellet metabolomes. Exudates from the different taxa were distinct, but we did not observe clear phylogenetic patterns. We used feature-based molecular networking to explore the identities of these metabolites, revealing several distinct di- and tripeptides secreted by each of the algae, lumichrome, a compound that is known to be involved in plant growth and bacterial quorum sensing, and novel prostaglandin-like compounds. We further investigated the impacts of exogenous additions of eight compounds selected based on exometabolome enrichment on algal growth. Of these compounds, five (lumichrome, 5′-S-methyl-5′-thioadenosine, 17-phenyl trinor prostaglandin A2, dodecanedioic acid, and aleuritic acid) impacted growth in at least one of the algal cultures. Two of these compounds (dodecanedioic acid and aleuritic acid) produced contrasting results, increasing growth in some algae and decreasing growth in others. Together, our results reveal new groups of microalgal exometabolites, some of which could alter algal growth when provided exogenously, suggesting potential roles in allelopathy and algal interactions.

IMPORTANCE Microalgae are responsible for nearly half of primary production on earth and play an important role in global biogeochemical cycling as well as in a range of industrial applications. Algal exometabolites are important mediators of algal-algal and algal-bacterial interactions that ultimately affect algal growth and physiology. In this study, we characterize exometabolomes across marine and freshwater algae to gain insights into the diverse metabolites they release into their environments (“exudates”). We observe that while phylogeny can play a role in exometabolome content, environmental conditions or habitat origin (freshwater versus marine) are also important. We also find that several of these compounds can influence algal growth (as measured by chlorophyll production) when provided exogenously, highlighting the importance of characterization of these novel compounds and their role in microalgal ecophysiology.

Particulate Metabolites and Transcripts Reflect Diel Oscillations of Microbial Activity in the Surface Ocean

Light fuels photosynthesis and organic matter production by primary producers in the sunlit ocean. The quantity and quality of the organic matter produced influence community function, yet in situ measurements of metabolites, the products of cellular metabolism, over the diel cycle are lacking. We evaluated community-level biochemical consequences of oscillations of light in the North Pacific Subtropical Gyre by quantifying 79 metabolites in particulate organic matter from 15 m every 4 h over 8 days. Total particulate metabolite concentration peaked at dusk and represented up to 2% of total particulate organic carbon (POC). The concentrations of 55/79 (70%) individual metabolites exhibited significant 24-h periodicity, with daily fold changes from 1.6 to 12.8, often greater than those of POC and flow cytometry-resolvable biomass, which ranged from 1.2 to 2.8. Paired metatranscriptome analysis revealed the taxa involved in production and consumption of a subset of metabolites. Primary metabolites involved in anabolism and redox maintenance had significant 24-h periodicity and diverse organisms exhibited diel periodicity in transcript abundance associated with these metabolites. Compounds with osmotic properties displayed the largest oscillations in concentration, implying rapid turnover and supporting prior evidence of functions beyond cell turgor maintenance. The large daily oscillation of trehalose paired with metatranscriptome and culture data showed that trehalose is produced by the nitrogen-fixing cyanobacterium Crocosphaera, likely to store energy for nighttime metabolism. Together, paired measurements of particulate metabolites and transcripts resolve strategies that microbes use to manage daily energy and redox oscillations and highlight dynamic metabolites with cryptic roles in marine microbial ecosystems.IMPORTANCE Fueled by light, phytoplankton produce the organic matter that supports ocean ecosystems and carbon sequestration. Ocean change impacts microbial metabolism with repercussions for biogeochemical cycling. As the small molecule products of cellular metabolism, metabolites often change rapidly in response to environmental conditions and form the basis of energy and nutrient management and storage within cells. By pairing measurements of metabolites and gene expression in the stratified surface ocean, we reveal strategies of microbial energy management over the day-night cycle and hypothesize that oscillating metabolites are important substrates for dark respiration by phytoplankton. These high-resolution diel measurements of in situ metabolite concentrations form the basis for future work into the specific roles these compounds play in marine microbial communities.

Marine Community Metabolomes Carry Fingerprints of Phytoplankton Community Composition

Phytoplankton transform inorganic carbon into thousands of biomolecules that represent an important pool of fixed carbon, nitrogen, and sulfur in the surface ocean. Metabolite production differs between phytoplankton, and the flux of these molecules through the microbial food web depends on compound-specific bioavailability to members of a wider microbial community. Yet relatively little is known about the diversity or concentration of metabolites within marine plankton. Here, we compare 313 polar metabolites in 21 cultured phytoplankton species and in natural planktonic communities across environmental gradients to show that bulk community metabolomes reflect the chemical composition of the phytoplankton community. We also show that groups of compounds have similar patterns across space and taxonomy, suggesting that the concentrations of these compounds in the environment are controlled by similar sources and sinks. We quantify several compounds in the surface ocean that represent substantial understudied pools of labile carbon. For example, the N-containing metabolite homarine was up to 3% of particulate carbon and is produced in high concentrations by cultured Synechococcus, and S-containing gonyol accumulated up to 2.5 nM in surface particles and likely originates from dinoflagellates or haptophytes. Our results show that phytoplankton composition directly shapes the carbon composition of the surface ocean. Our findings suggest that in order to access these pools of bioavailable carbon, the wider microbial community must be adapted to phytoplankton community composition.

IMPORTANCE Microscopic phytoplankton transform 100 million tons of inorganic carbon into thousands of different organic compounds each day. The structure of each chemical is critical to its biological and ecosystem function, yet the diversity of biomolecules produced by marine microbial communities remained mainly unexplored, especially small polar molecules which are often considered the currency of the microbial loop. Here, we explore the abundance and diversity of small biomolecules in planktonic communities across ecological gradients in the North Pacific and within 21 cultured phytoplankton species. Our work demonstrates that phytoplankton diversity is an important determinant of the chemical composition of the highly bioavailable pool of organic carbon in the ocean, and we highlight understudied yet abundant compounds in both the environment and cultured organisms. These findings add to understanding of how the chemical makeup of phytoplankton shapes marine microbial communities where the ability to sense and use biomolecules depends on the chemical structure.

HIGH RESOLUTION MASS SPECTROMETRY IN LIPIDOMICS

The boost of research output in lipidomics during the last decade is tightly linked to improved instrumentation in mass spectrometry. Associated with this trend is the shift from low resolution-toward high-resolution lipidomics platforms. This review article summarizes the state of the art in the lipidomics field with a particular focus on the merits of high mass resolution. Following some theoretical considerations on the benefits of high mass resolution in lipidomics, it starts with a historical perspective on lipid analysis by sector instruments and moves further to today's instrumental approaches, including shotgun lipidomics, liquid chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time-of-flight, and imaging lipidomics. Subsequently, several data processing and data analysis software packages are critically evaluated with all their pros and cons. Finally, this article emphasizes the importance and necessity of quality standards as the field evolves from its pioneering phase into a mature and robust omics technology and lists various initiatives for improving the applicability of lipidomics. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

Quantification of Amine- and Alcohol-Containing Metabolites in Saline Samples Using Pre-extraction Benzoyl Chloride Derivatization and Ultrahigh Performance Liquid Chromatography Tandem Mass Spectrometry (UHPLC MS/MS)

Dissolved metabolites serve as nutrition, energy, and chemical signals for microbial systems. However, the full scope and magnitude of these processes in marine systems are unknown, largely due to insufficient methods, including poor extraction of small, polar compounds using common solid-phase extraction resins. Here, we utilized pre-extraction derivatization and ultrahigh performance liquid chromatography electrospray ionization tandem mass spectrometry (UHPLC-ESI-MS/MS) to detect and quantify targeted dissolved metabolites in seawater and saline culture media. Metabolites were derivatized with benzoyl chloride by their primary and secondary amine and alcohol functionalities and quantified using stable isotope-labeled internal standards (SIL-ISs) produced from ¹³C₆-labeled benzoyl chloride. We optimized derivatization, extraction, and sample preparation for field and culture samples and evaluated matrix-derived biases. We have optimized this quantitative method for 73 common metabolites, of which 50 cannot be quantified without derivatization due to low extraction efficiencies. Of the 73 metabolites, 66 were identified in either culture media or seawater and 45 of those were quantified. This derivatization method is sensitive (detection limits = pM to nM), rapid (∼5 min per sample), and high throughput.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the 'thorough' removal of unwanted variations, the collective consideration of multiple criteria ('intragroup variation', 'marker stability' and 'classification capability') was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were 'first' discovered to be non-CWP. 'Then', 21 new strategies that combined the 'sample'-based method with the 'metabolite'-based one were found to be CWP. 'Finally', a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.

Enhanced Metabolome Coverage and Evaluation of Matrix Effects by the Use of Experimental-Condition-Matched 13C-Labeled Biological Samples in Isotope-Assisted LC-HRMS Metabolomics

Stable isotope-assisted approaches can improve untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally ¹³C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve the detection of treatment-relevant metabolites and can aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly ¹³C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched ¹³C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

Large Diversity in Nitrogen- and Sulfur-Containing Compatible Solute Profiles in Polar and Temperate Diatoms

Intense bottom-ice algal blooms, often dominated by diatoms, are an important source of food for grazers, organic matter for export during sea ice melt, and dissolved organic carbon. Sea-ice diatoms have a number of adaptations, including accumulation of compatible solutes, that allows them to inhabit this highly variable environment characterized by extremes in temperature, salinity, and light. In addition to protecting them from extreme conditions, these compounds present a labile, nutrient-rich source of organic matter, and include precursors to climate active compounds (e.g., dimethyl sulfide [DMS]), which are likely regulated with environmental change. Here, intracellular concentrations of 45 metabolites were quantified in three sea-ice diatom species and were compared to two temperate diatom species, with a focus on compatible solutes and free amino acid pools. There was a large diversity of metabolite concentrations between diatoms with no clear pattern identifiable for sea-ice species. Concentrations of some compatible solutes (isethionic acid, homarine) approached 1 M in the sea-ice diatoms, Fragilariopsis cylindrus and Navicula cf. perminuta, but not in the larger sea-ice diatom, Nitzschia lecointei or in the temperate diatom species. The differential use of compatible solutes in sea-ice diatoms suggests different adaptive strategies and highlights which small organic compounds may be important in polar biogeochemical cycles.

Metabolomic insights into the lasting impacts of early-life exposure to BDE-47 in mice

Early-life exposure to toxicants may have lasting effects that adversely impact later development. Thus, although the production and use of a toxicant have been banned, the risk to previously exposed individuals may continue. BDE-47, a component of commercial penta-BDEs, is a persistent organic pollutant with demonstrated neurotoxicity. To investigate the persistent effects of BDE-47 and the mechanisms thereof, we employed a metabolomics approach to analyze the brain, blood and urine of mice exposed to BDE-47 for 28 days and then 3 months post-exposure. In the brain, BDE-47 was detectable just after exposure but was below the limit of detection (LOD) 3 months later. However, the metabolomic alterations caused by early-life exposure to BDE-47 persisted. Potential biomarkers related to these alterations included phosphatidylcholine, lysophosphatidylcholine, sphingomyelin and several amino acids and biogenic amines. The metabolic pathways involved in the response to BDE-47 in the brain were mainly those related to glycerophospholipid metabolism, sphingomyelin metabolism and neurotransmitter regulation. Thus, our study demonstrates the utility of metabolomics, as the omics most closely reflecting the phenotype, in exploring the mechanisms underlying the lasting effects induced by early-life BDE-47 exposure.

Reference Standardization for Quantification and Harmonization of Large-Scale Metabolomics

Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

Lipidomic profiling of non-mineralized dental plaque and biofilm by untargeted UHPLC-QTOF-MS/MS and SWATH acquisition

Dental plaque is a structurally organized biofilm which consists of diverse microbial colonies and extracellular matrix. Its composition may change when pathogenic microorganisms become dominating. Therefore, dental biofilm or plaque has been frequently investigated in the context of oral health and disease. Furthermore, its potential as an alternative matrix for analytical purposes has also been recognized in other disciplines like archeology, food sciences, and forensics. Thus, a careful in-depth characterization of dental plaque is worthwhile. Most of the conducted studies focused on the screening of microbial populations in dental plaque. Their lipid membranes, on the other hand, may significantly impact substance (metabolite) exchange within microbial colonies as well as xenobiotics uptake and incorporation into teeth. Under this umbrella, a comprehensive lipidomic profiling for determination of lipid compositions of in vivo dental plaque samples and of in vitro cultivated biofilm as surrogate matrix to be used for analytical purposes has been performed in this work. An untargeted lipidomics workflow utilizing a ultra-high-performance liquid chromatography (UHPLC)-quadrupole-time-of-flight (QTOF) platform together with comprehensive SWATH (sequential window acquisition of all theoretical fragment ion mass spectra) acquisition and compatible software (MS-DIAL) that comprises a vast lipid library has been adopted to establish an extensive lipidomic fingerprint of dental plaque. The main lipid components in dental plaque were identified as triacylglycerols, followed by cholesterol, cholesteryl esters as well as diacylglycerols, and various phospholipid classes. In vivo plaque is a rare matrix which is usually available in very low amounts. When higher quantities for specific research assays are required, efficient ways to produce an appropriate surrogate matrix are mandatory. A potential surrogate matrix substituting dental plaque was prepared by cultivation of in vitro biofilm from saliva and similarities and differences in the lipidomics profile to in vivo plaque were mapped by statistical evaluation post-analysis. It was discovered that most lipid classes were highly elevated in the in vitro biofilm samples, in particular diacylglycerols, phosphatidylglycerols, and phosphatidylethanolamines (PEs). Furthermore, an overall shift from even-chain lipid species to odd-chain lipids was observed in the cultivated biofilms. On the other hand, even-chain phosphatidylcholines (PCs), lysoPCs, cholesteryl esters, and cholesterol-sulfate were shown to be specifically increased in plaque samples.

Nanoparticle Conjugation of Ginsenoside Rg3 Inhibits Hepatocellular Carcinoma Development and Metastasis

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. The prognosis of HCC remains very poor; thus, an effective treatment remains urgent. Herein, a type of nanomedicine is developed by conjugating Fe@Fe₃ O₄ nanoparticles with ginsenoside Rg3 (NpRg3), which achieves an excellent coupling effect. In the dimethylnitrosamine-induced HCC model, NpRg3 application significantly prolongs the survival of HCC mice. Further research indicates that NpRg3 application significantly inhibits HCC development and eliminates HCC metastasis to the lung. Notably, NpRg3 application delays HCC-induced ileocecal morphology and gut microbial alterations more than 12 weeks during HCC progression. NpRg3 administration elevates the abundance of Bacteroidetes and Verrucomicrobia, but decreases Firmicutes. Twenty-nine predicted microbial gene functions are enriched, while seven gene functions are reduced after NpRg3 administration. Moreover, the metabolomics profile presents a significant progression during HCC development, but NpRg3 administration corrects tumor-dominant metabolomics. NpRg3 administration decreases 3-indolepropionic acid and urea, but elevates free fatty acids. Importantly, NpRg3 application remodels the unbalanced correlation networks between gut microbiota and metabolism during HCC therapy. In conclusion, nanoparticle conjugation of ginsenoside Rg3 inhibits HCC development and metastasis via the remodeling of unbalanced gut microbiota and metabolism in vivo, providing an antitumor therapy strategy.

Lipidomics from sample preparation to data analysis: a primer

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment

High resolution mass spectrometry (HRMS) analyses provide expansive chemical characterizations of environmental samples. To date, most research efforts have developed tools to expedite labor- and time-intensive contaminant identification efforts. However, even without chemical identity, the richness of nontarget HRMS data sets represents a significant opportunity to chemically differentiate samples and delineate source contributions. To develop this potential, we evaluated the use of unidentified HRMS detections to define sample uniqueness and provide additional statistical resolution for quantitative source apportionment, overcoming a critical limitation of existing approaches based on targeted contaminants. By creating a series of sample mixtures that mimic pollution sources in a representative watershed, we assessed the fidelity of HRMS source fingerprints during dilution and mixing. This approach isolated 8-447 nontarget compounds per sample for source apportionment and yielded accurate source concentration estimates (between 0.82 and 1.4-fold of actual values), even in multisource systems with <1% source contributions. Furthermore, we mined the nontarget data to identify five source-specific chemical end-members amenable to apportionment. While additional development studies are needed to fully evaluate the myriad factors affecting method accuracy and capabilities, this study provides a conceptual foundation for novel applications of nontarget HRMS data to confidently distinguish and quantify source impacts in complex systems.

Sulfur metabolites in the pelagic ocean

Marine microorganisms play crucial roles in Earth's element cycles through the production and consumption of organic matter. One of the elements whose fate is governed by microbial activities is sulfur, an essential constituent of biomass and a crucial player in climate processes. With sulfur already being well studied in the ocean in its inorganic forms, organic sulfur compounds are emerging as important chemical links between marine phytoplankton and bacteria. The high concentration of inorganic sulfur in seawater, which can readily be reduced by phytoplankton, provides a freely available source of sulfur for biomolecule synthesis. Mechanisms such as exudation and cell lysis release these phytoplankton-derived sulfur metabolites into seawater, from which they are rapidly assimilated by marine bacteria and archaea. Energy-limited bacteria use scavenged sulfur metabolites as substrates or for the synthesis of vitamins, cofactors, signalling compounds and antibiotics. In this Review, we examine the current knowledge of sulfur metabolites released into and taken up from the marine dissolved organic matter pool by microorganisms, and the ecological links facilitated by their diversity in structures, oxidation states and chemistry.

Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean

In the surface ocean, phytoplankton transform inorganic substrates into organic matter that fuels the activity of heterotrophic microorganisms, creating intricate metabolic networks that determine the extent of carbon recycling and storage in the ocean. Yet, the diversity of organic molecules and interacting organisms has hindered detection of specific relationships that mediate this large flux of energy and matter. Here, we show that a tightly coupled microbial network based on organic sulfur compounds (sulfonates) exists among key lineages of eukaryotic phytoplankton producers and heterotrophic bacterial consumers in the North Pacific Subtropical Gyre. We find that cultured eukaryotic phytoplankton taxa produce sulfonates, often at millimolar internal concentrations. These same phytoplankton-derived sulfonates support growth requirements of an open-ocean isolate of the SAR11 clade, the most abundant group of marine heterotrophic bacteria. Expression of putative sulfonate biosynthesis genes and sulfonate abundances in natural plankton communities over the diel cycle link sulfonate production to light availability. Contemporaneous expression of sulfonate catabolism genes in heterotrophic bacteria highlights active cycling of sulfonates in situ. Our study provides evidence that sulfonates serve as an ecologically important currency for nutrient and energy exchange between microbial autotrophs and heterotrophs, highlighting the importance of organic sulfur compounds in regulating ecosystem function.