Processing methods for differential analysis of LC/MS profile data

Non-targeted mass spectrometry and feature-based molecular networking for determination of branched fatty acid esters of hydroxy fatty acids in milk

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) represent trace lipids with significant natural biological functions. While exogenous FAHFAs have been extensively studied, research on FAHFAs in milk remains limited, constraining our grasp of their nutritional roles. This study introduces a non-targeted mass spectrometry approach combined with chemical networking of spectral fragmentation patterns to uncover FAHFAs. Through meticulous sample handling and comparisons of various data acquisition and processing modes, we validate the method's superiority, identifying twice as many FAHFAs compared to alternative techniques. This validated method was then applied to different milk samples, revealing 45 chemical signals associated with known and potential FAHFAs, alongside findings of 66 ceramide/hexosylceramide (Cer/HexCer), 48 phosphatidyl ethanolamine/lyso phosphatidyl ethanolamine (PE/LPE), 21 phosphatidylcholine/lysophosphatidylcholine (PC/LPC), 16 phosphatidylinositol (PI), 7 phosphatidylserine (PS), and 11 sphingomyelin (SM) compounds. This study expands our understanding of the FAHFA family in milk and provides a fast and convenient method for identifying FAHFAs.

Metabolomics for dental caries diagnosis: Past, present, and future

Dental caries, a prevalent global infectious condition affecting over 95% of adults, remains elusive in its precise etiology. Addressing the complex dynamics of caries demands a thorough exploration of taxonomic, potential, active, and encoded functions within the oral ecosystem. Metabolomic profiling emerges as a crucial tool, offering immediate insights into microecosystem physiology and linking directly to the phenotype. Identified metabolites, indicative of caries status, play a pivotal role in unraveling the metabolic processes underlying the disease. Despite challenges in metabolite variability, the use of metabolomics, particularly via mass spectrometry and nuclear magnetic resonance spectroscopy, holds promise in caries research. This review comprehensively examines metabolomics in caries prevention, diagnosis, and treatment, highlighting distinct metabolite expression patterns and their associations with disease-related bacterial communities. Pioneering in approach, it integrates singular and combinatory metabolomics methodologies, diverse biofluids, and study designs, critically evaluating prior limitations while offering expert insights for future investigations. By synthesizing existing knowledge, this review significantly advances our comprehension of caries, providing a foundation for improved prevention and treatment strategies.

HtrA, fatty acids, and membrane protein interplay in Chlamydia trachomatis to impact stress response and trigger early cellular exit

Chlamydia trachomatis is an intracellular bacterial pathogen that undergoes a biphasic developmental cycle, consisting of intracellular reticulate bodies and extracellular infectious elementary bodies. A conserved bacterial protease, HtrA, was shown previously to be essential for Chlamydia during the reticulate body phase, using a novel inhibitor (JO146). In this study, isolates selected for the survival of JO146 treatment were found to have polymorphisms in the acyl-acyl carrier protein synthetase gene (aasC). AasC encodes the enzyme responsible for activating fatty acids from the host cell or synthesis to be incorporated into lipid bilayers. The isolates had distinct lipidomes with varied fatty acid compositions. A reduction in the lipid compositions that HtrA prefers to bind to was detected, yet HtrA and MOMP (a key outer membrane protein) were present at higher levels in the variants. Reduced progeny production and an earlier cellular exit were observed. Transcriptome analysis identified that multiple genes were downregulated in the variants especially stress and DNA processing factors. Here, we have shown that the fatty acid composition of chlamydial lipids, HtrA, and membrane proteins interplay and, when disrupted, impact chlamydial stress response that could trigger early cellular exit.

IMPORTANCE

Chlamydia trachomatis is an important obligate intracellular pathogen that has a unique biphasic developmental cycle. HtrA is an essential stress or virulence protease in many bacteria, with many different functions. Previously, we demonstrated that HtrA is critical for Chlamydia using a novel inhibitor. In the present study, we characterized genetic variants of Chlamydia trachomatis with reduced susceptibility to the HtrA inhibitor. The variants were changed in membrane fatty acid composition, outer membrane proteins, and transcription of stress genes. Earlier and more synchronous cellular exit was observed. Combined, this links stress response to fatty acids, membrane proteins, and HtrA interplay with the outcome of disrupted timing of chlamydial cellular exit.

Unveil the sugar diet and associated environmental compounds in the crop of the mosquito Culex pipiens

Culex pipiens (Linnaeus, 1758) mosquitoes search plant sources of sugars to cope with the energetic demand of various physiological processes. The crop as part of the digestive system is devoted to the storage of sugar-based meal obtained from various nectars sources. The profiling of sugars and metabolites in the Culex pipiens' crop is scarce, and only few studies used Liquid Chromatography - Mass Spectrometry (LC-MS), which provides broad detection for biomonitoring environmental substances and even contaminants in the sugar diet of mosquitoes populations. Therefore, sugar and metabolite profiling were performed on crops obtained from mosquitoes exposed to plant nectar under laboratory or natural conditions by Ultra High-Performance LC-MS (UHPLC-MS). This method allowed us a precise quantitative and qualitative identification of sugar diet and associated environmental compounds in the crop of the mosquito C. pipiens. Under laboratory condition, mosquitoes were allowed to feed on either glucose solution, commercially-available flowers or field collected flowers. In addition, we collected mosquitoes from the field to compare those crop metabolomes with metabolome patterns occurring after nectar feeding in the lab. The sugar quantities and quality obtained from the crops of mosquitoes collected in the field were similar to those crops obtained from mosquitoes that fed on commercially-available flowers and from field collected flowers with a limit of detection of 10 μg/L for sucrose, glucose and sucrose. Next to sugar compounds, we identified 2 types of amino acids, 12 natural products, and 9 pesticides. Next to the diversity of sugar compounds, we could confirm that secondary metabolites and environmental pollutants are typically up taken from floral nectar sources by C. pipiens. The in-depth knowledge on mosquito-plant interactions may inspire the development and further optimization of mosquito trap systems and arboviral surveillance systems.

Advances in high-resolution mass spectrometry techniques for analysis of high mass-to-charge ions

A major challenge in modern mass spectrometry (MS) is achieving high mass resolving power and accuracy for precision analyses in high mass-to-charge (m/z) regions. To advance the capability of MS for increasingly demanding applications, understanding limitations of state-of-the-art techniques and their status in applied sciences is essential. This review summarizes important instruments in high-resolution mass spectrometry (HRMS) and related advances to extend their working range to high m/z regions. It starts with an overview of HRMS techniques that provide adequate performance for macromolecular analysis, including Fourier-transform, time-of-flight (TOF), quadrupole-TOF, and related data-processing techniques. Methodologies and applications of HRMS for characterizing macromolecules in biochemistry and material sciences are summarized, such as top-down proteomics, native MS, drug discovery, structural virology, and polymer analyses.

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.

Antioxidant and anticancer potential of ethyl acetate extract of bark and flower of Tecoma stans (Linn) and In Silico studies on phytoligands against Bcl2 and VEGFR2 factors

This study was designed to appraise the antioxidant and anticancer competence of solvent extracts of Tecoma stans (Linn) and analyze the phytoligands interaction against Bcl2 VEGFR2 through in silico studies. The phytochemical analysis revealed that the ethyl acetate extract contains more number of pharmaceutically valuable phytochemicals than other solvent extracts. Among the various phytochemicals, flavonoid was found as a predominant component, and UV-Vis- spectrophotometer analysis initially confirmed it. Hence, the column chromatogram was performed to purify the flavonoid, and High-performance liquid chromatography (HPLC) was performed. It revealed that the flavonoid enriched fraction by compared with standard flavonoid molecules. About 84.69% and 80.43% of antioxidant activity were found from ethyl acetate extract of bark and flower at the dosage of 80 μg mL^-1 with the IC₅₀ value of 47.24 and 43.40 μg mL^-1, respectively. In a dose-dependent mode, the ethyl acetate extract of bark and flower showed cytotoxicity against breast cancer cell line MCF 7 (Michigan Cancer Foundation-7) as up to 81.38% and 80.94% of cytotoxicity respectively. Furthermore, the IC₅₀ was found as 208.507 μg mL^-1 and 207.38 μg mL^-1 for bark and flower extract correspondingly. About 10 medicinal valued flavonoid components were identified from bark (6) and flower (4) ethyl acetate extract through LC-MS analysis. Out of 10 components, the 3,5-O-dicaffeoylquinic acid (ΔG -8.8) and Isorhamnetin-3-O-rutinoside (ΔG -8.3) had the competence to interact with Bcl2 (B-Cell Lymphoma 2) and VEGFR2 (Vascular Endothelial Growth Factor Receptor 2) respectively with more energy. Hence, these results confirm that the ethyl acetate extract of bark and flower of T. stans has significant medicinal potential and could be used as antioxidant and anticancer agent after some animal performance study.

Metabolomic Signatures of Scarff-Bloom-Richardson (SBR) Grade in Non-Metastatic Breast Cancer

PURPOSE

Identification of metabolomic biomarkers of high SBR grade in non-metastatic breast cancer.

METHODS

This retrospective bicentric metabolomic analysis included a training set (n = 51) and a validation set (n = 49) of breast cancer tumors, all classified as high-grade (grade III) or low-grade (grade I-II). Metabolomes of tissue samples were studied by liquid chromatography coupled with mass spectrometry.

RESULTS

A molecular signature of the top 12 metabolites was identified from a database of 602 frequently predicted metabolites. Partial least squares discriminant analyses showed that accuracies were 0.81 and 0.82, the R2 scores were 0.57 and 0.55, and the Q2 scores were 0.44431 and 0.40147 for the training set and validation set, respectively; areas under the curve for the Receiver Operating Characteristic Curve were 0.882 and 0.886. The most relevant metabolite was diacetylspermine. Metabolite set enrichment analyses and metabolic pathway analyses highlighted the tryptophan metabolism pathway, but the concentration of individual metabolites varied between tumor samples.

CONCLUSIONS

This study indicates that high-grade invasive tumors are related to diacetylspermine and tryptophan metabolism, both involved in the inhibition of the immune response. Targeting these pathways could restore anti-tumor immunity and have a synergistic effect with immunotherapy. Recent studies could not demonstrate the effectiveness of this strategy, but the use of theragnostic metabolomic signatures should allow better selection of patients.

Chemical derivatization strategy for mass spectrometry-based lipidomics

Lipids, serving as the structural components of cellular membranes, energy storage, and signaling molecules, play the essential and multiple roles in biological functions of mammals. Mass spectrometry (MS) is widely accepted as the first choice for lipid analysis, offering good performance in sensitivity, accuracy, and structural characterization. However, the untargeted qualitative profiling and absolute quantitation of lipids are still challenged by great structural diversity and high structural similarity. In recent decade, chemical derivatization mainly targeting carboxyl group and carbon-carbon double bond of lipids have been developed for lipidomic analysis with diverse advantages: (i) offering more characteristic structural information; (ii) improving the analytical performance, including chromatographic separation and MS sensitivity; (iii) providing one-to-one chemical isotope labeling internal standards based on the isotope derivatization regent in quantitative analysis. Moreover, the chemical derivatization strategy has shown great potential in combination with ion mobility mass spectrometry and ambient mass spectrometry. Herein, we summarized the current states and advances in chemical derivatization-assisted MS techniques for lipidomic analysis, and their strengths and challenges are also given. In summary, the chemical derivatization-based lipidomic approach has become a promising and reliable technique for the analysis of lipidome in complex biological samples.

Untargeted Fecal Metabolomic Analyses across an Industrialization Gradient Reveal Shared Metabolites and Impact of Industrialization on Fecal Microbiome-Metabolome Interactions

The metabolome is a central determinant of human phenotypes and includes the plethora of small molecules produced by host and microbiome or taken up from exogenous sources. However, studies of the metabolome have so far focused predominantly on urban, industrialized populations. Through an untargeted metabolomic analysis of 90 fecal samples from human individuals from Africa and the Americas-the birthplace and the last continental expansion of our species, respectively-we characterized a shared human fecal metabolome. The majority of detected metabolite features were ubiquitous across populations, despite any geographic, dietary, or behavioral differences. Such shared metabolite features included hyocholic acid and cholesterol. However, any characterization of the shared human fecal metabolome is insufficient without exploring the influence of industrialization. Here, we show chemical differences along an industrialization gradient, where the degree of industrialization correlates with metabolomic changes. We identified differential metabolite features such as amino acid-conjugated bile acids and urobilin as major metabolic correlates of these behavioral shifts. Additionally, coanalyses with over 5,000 publicly available human fecal samples and cooccurrence probability analyses with the gut microbiome highlight connections between the human fecal metabolome and gut microbiome. Our results indicate that industrialization significantly influences the human fecal metabolome, but diverse human lifestyles and behavior still maintain a shared human fecal metabolome. This study represents the first characterization of the shared human fecal metabolome through untargeted analyses of populations along an industrialization gradient. IMPORTANCE As the world becomes increasingly industrialized, understanding the biological consequences of these lifestyle shifts and what it means for past, present, and future human health is critical. Indeed, industrialization is associated with rises in allergic and autoimmune health conditions and reduced microbial diversity. Exploring these health effects on a chemical level requires consideration of human lifestyle diversity, but understanding the significance of any differences also requires knowledge of what molecular components are shared between human groups. Our study reveals the key chemistry of the human gut as defined by varied industrialization-based differences and ubiquitous shared features. Ultimately, these novel findings extend our knowledge of human molecular biology, especially as it is influenced by lifestyle and behavior, and provide steps toward understanding how human biology has changed over our species' history.

Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts

Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.

Micro-analytical and molecular approaches for understanding the distribution, biochemistry, and molecular biology of selenium in (hyperaccumulator) plants

Micro-analytical techniques to untangle Se distribution and chemical speciation in plants coupled with molecular biology analysis enable the deciphering of metabolic pathways responsible for Se tolerance and accumulation. Selenium (Se) is not essential for plants and is toxic at high concentrations. However, Se hyperaccumulator plants have evolved strategies to both tolerate and accumulate > 1000 µg Se g^-1 DW in their living above-ground tissues. Given the complexity of the biochemistry of Se, various approaches have been adopted to study Se metabolism in plants. These include X-ray-based techniques for assessing distribution and chemical speciation of Se, and molecular biology techniques to identify genes implicated in Se uptake, transport, and assimilation. This review presents these techniques, synthesises the current state of knowledge on Se metabolism in plants, and highlights future directions for research into Se (hyper)accumulation and tolerance. We conclude that powerful insights may be gained from coupling information on the distribution and chemical speciation of Se to genome-scale studies to identify gene functions and molecular mechanisms that underpin Se tolerance and accumulation in these ecologically and biotechnologically important plants species. The study of Se metabolism is challenging and is a useful testbed for developing novel analytical approaches that are potentially more widely applicable to the study of the regulation of a wide range of metal(loid)s in hyperaccumulator plants.

Alignstein: Optimal transport for improved LC-MS retention time alignment

BACKGROUND

Reproducibility of liquid chromatography separation is limited by retention time drift. As a result, measured signals lack correspondence over replicates of the liquid chromatography-mass spectrometry (LC-MS) experiments. Correction of these errors is named retention time alignment and needs to be performed before further quantitative analysis. Despite the availability of numerous alignment algorithms, their accuracy is limited (e.g., for retention time drift that swaps analytes' elution order).

RESULTS

We present the Alignstein, an algorithm for LC-MS retention time alignment. It correctly finds correspondence even for swapped signals. To achieve this, we implemented the generalization of the Wasserstein distance to compare multidimensional features without any reduction of the information or dimension of the analyzed data. Moreover, Alignstein by design requires neither a reference sample nor prior signal identification. We validate the algorithm on publicly available benchmark datasets obtaining competitive results. Finally, we show that it can detect the information contained in the tandem mass spectrum by the spatial properties of chromatograms.

CONCLUSIONS

We show that the use of optimal transport effectively overcomes the limitations of existing algorithms for statistical analysis of mass spectrometry datasets. The algorithm's source code is available at https://github.com/grzsko/Alignstein.

Role of lipidomics in assessing the functional lipid composition in breast milk

Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2–5% of the total breast milk composition and are a major energy source providing 50% of an infant’s energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.

Characterizing Powdered Activated Carbon Treatment of Surface Water Samples Using Polarity-Extended Non-Target Screening Analysis

Advanced wastewater treatment such as powdered activated carbon (PAC) reduces the load of organic micropollutants entering the aquatic environment. Since mobile and persistent compounds accumulate in water cycles, treatment strategies need to be evaluated for the removal of (very) polar compounds. Thereby, non-targeted analysis gives a global picture of the molecular fingerprint (including these very polar molecules) of water samples. Target and non-target screening were conducted using polarity-extended chromatography hyphenated with mass spectrometry. Samples treated with different types and concentrations of PAC were compared to untreated samples. Molecular features were extracted from the analytical data to determine fold changes, perform a principal component analysis and for significance testing. The results suggest that a part of the polar target analytes was adsorbed but also some byproducts might be formed or desorbed from the PAC.

Trends in environmental monitoring of high explosives present in soil/sediment/groundwater using LC-MS/MS

Environmental contamination by explosives occurs due to improper handling and disposal procedures. Explosives and their transformation products pose threat to human health and the ecosystem. Trace level detection of explosives present in different environmental matrices is a challenge, due to the interference caused by matrix components and the presence of cocontaminants. Liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) is an advanced analytical tool, which is ideal for quantitative and qualitative detection of explosives and its metabolites at trace levels. This review aims to showcase the current trends in the application of LC-MS/MS for detecting explosives present in soil, sediment, and groundwater with detection limits ranging from nano to femtogram levels. Specificity and advantages of using LC-MS/MS over conventional analytical methods and various processing methods and techniques used for sample preparation are discussed in this article. Important application aspects of LC-MS/MS on environmental monitoring include site characterization and degradation evaluation. Studies on qualitative and quantitative LC-MS/MS analysis in determining the efficiency of treatment processes and contamination mapping, optimized conditions of LC and MS/MS adopted, role of different ionization techniques and mass analyzers in detection of explosives and its metabolites, relative abundance of various product ions formed on dissociation and the levels of detection achieved are reviewed. Ionization suppression, matrix effect, additive selection are some of the major factors which influence MS/MS detection. A summary of challenges and future research insights for effective utilization of this technique in the environmental monitoring of explosives are presented.

Comparative Metabolic Analysis Reveals a Metabolic Switch in Mature, Hydrated, and Germinated Pollen in Arabidopsis thaliana

Pollen germination is an essential process for pollen tube growth, pollination, and therefore seed production in flowering plants, and it requires energy either from remobilization of stored carbon sources, such as lipids and starches, or from secreted exudates from the stigma. Transcriptome analysis from in vitro pollen germination previously showed that 14 GO terms, including metabolism and energy, were overrepresented in Arabidopsis. However, little is understood about global changes in carbohydrate and energy-related metabolites during the transition from mature pollen grain to hydrated pollen, a prerequisite to pollen germination, in most plants, including Arabidopsis. In this study, we investigated differential metabolic pathway enrichment among mature, hydrated, and germinated pollen using an untargeted metabolomic approach. Integration of publicly available transcriptome data with metabolomic data generated as a part of this study revealed starch and sucrose metabolism increased significantly during pollen hydration and germination. We analyzed in detail alterations in central metabolism, focusing on soluble carbohydrates, non-esterified fatty acids, glycerophospholipids, and glycerolipids. We found that several metabolites, including palmitic acid, oleic acid, linolenic acid, quercetin, luteolin/kaempferol, and γ-aminobutyric acid (GABA), were elevated in hydrated pollen, suggesting a potential role in activating pollen tube emergence. The metabolite levels of mature, hydrated, and germinated pollen, presented in this work provide insights on the molecular basis of pollen germination.

Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated Myocardial Infarctions

Background

Postoperative risk stratification is challenging in patients with ST-segment elevation myocardial infarction (STEMI) who undergo percutaneous coronary intervention. This study aimed to characterize the metabolic fingerprints of patients with STEMI with different inhospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model.

Methods

Plasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6, 2020 to April 20, 2021. Cohort 1 consisted of 48 survivors and 48 non-survivors. Cohort 2 included 48 patients with unstable angina pectoris, 48 patients with STEMI, and 48 age- and sex-matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. The comprehensive metabolomic data analysis was performed using MetaboAnalyst version 5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves.

Results

Metabonomic analysis from the two cohorts showed that patients with STEMI with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting inhospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC = 0.998, 95% CI 0.983–1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969–0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981–0.998).

Conclusion

A survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for patients with STEMI. The validation of internal and external cohorts is required.

Human Plasma Metabolomics Identify 9-cis-retinoic Acid and Dehydrophytosphingosine Levels as Novel biomarkers for Early Ventricular Fibrillation after ST-elevated Myocardial Infarction

The relevant metabolite biomarkers for risk prediction of early onset of ventricular fibrillation (VF) after ST-segment elevation myocardial infarction (STEMI) remain unstudied. Here, we aimed to identify these imetabolites and the important metabolic pathways involved, and explore whether these metabolites could be used as predictors for the phenotype. Plasma samples were obtained retrospectively from a propensity-score matched cohort including 42 STEMI patients (21 consecutive VF and 21 non-VF). Ultra-performance liquid chromatography and mass spectrometry in combination with a comprehensive analysis of metabolomic data using Metaboanalyst 5.0 version were performed. As a result, the retinal metabolism pathway proved to be the most discriminative for the VF phenotype. Furthermore, 9-cis-Retinoic acid (9cRA) and dehydrophytosphingosine proved to be the most discriminative biomarkers. Biomarker analysis through receiver operating characteristic (ROC) curve showed the 2-metabolite biomarker panel yielding an area under the curve (AUC) of 0.836. The model based on Monte Carlo cross-validation found that 9cRA had the greatest probability of appearing in the predictive panel of biomarkers in the model. Validation of model efficiency based on an ROC curve showed that the combination model constructed by 9cRA and dehydrophytosphingosine had a good predictive value for early-onset VF after STEMI, and the AUC was 0.884 (95% CI 0.714–1). Conclusively, the retinol metabolism pathway was the most powerful pathway for differentiating the post-STEMI VF phenotype. 9cRA was the most important predictive biomarker of VF, and a plasma biomarker panel made up of two metabolites, may help to build a potent predictive model for VF.

Cocultivation of Anaerobic Fungi with Rumen Bacteria Establishes an Antagonistic Relationship

Anaerobic gut fungi (Neocallimastigomycetes) live in the digestive tract of large herbivores, where they are vastly outnumbered by bacteria. It has been suggested that anaerobic fungi challenge growth of bacteria owing to the wealth of biosynthetic genes in fungal genomes, although this relationship has not been experimentally tested. Here, we cocultivated the rumen bacteria Fibrobacter succinogenes strain UWB7 with the anaerobic gut fungi Anaeromyces robustus or Caecomyces churrovis on a range of carbon substrates and quantified the bacterial and fungal transcriptomic response. Synthetic cocultures were established for at least 24 h, as verified by active fungal and bacterial transcription. A. robustus upregulated components of its secondary metabolism in the presence of Fibrobacter succinogenes strain UWB7, including six nonribosomal peptide synthetases, one polyketide synthase-like enzyme, and five polyketide synthesis O-type methyltransferases. Both A. robustus and C. churrovis cocultures upregulated S-adenosyl-l-methionine (SAM)-dependent methyltransferases, histone methyltransferases, and an acetyltransferase. Fungal histone 3 lysine 27 trimethylation marks were more abundant in coculture, and heterochromatin protein-1 was downregulated. Together, these findings suggest that fungal chromatin remodeling occurs when bacteria are present. F. succinogenes strain UWB7 upregulated four genes in coculture encoding drug efflux pumps, which likely protect the cell against toxins. Furthermore, untargeted nonpolar metabolomics data revealed at least one novel fungal metabolite enriched in coculture, which may be a defense compound. Taken together, these data suggest that A. robustus and C. churrovis produce antimicrobials when exposed to rumen bacteria and, more broadly, that anaerobic gut fungi are a source of novel antibiotics.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Antidiabetic, Antimicrobial, and Molecular Profiling of Selected Medicinal Plants

Natural products have been the center of attraction ever since they were discovered. Among them, plant-based natural products were popular as analgesics, anti-inflammatory, antidiabetic, and cosmetics and possess widespread biotechnological applications. The use of plant products as cosmetics and therapeutics is deep-rooted in Nepalese society. Although there are few ethnobotanical studies conducted, extensive research of these valuable medicinal plants has not been a priority due to the limitation of technology and infrastructure. Here, we selected 4 traditionally used medicinal plants to examine their bioactive properties and their enzyme inhibition potential. α-Glucosidase and α-amylase inhibitory activities were investigated using an in vitro model followed up by antioxidant and antimicrobial activities. The present study shows that ethyl acetate fraction of Melastoma melabathrium (IC₅₀ 9.1 ± 0.3 µg/mL) and water fraction Acacia catechu (IC₅₀ 9.0 ± 0.6 µg/mL) exhibit strong α-glucosidase inhibition. Likewise, the highest α-amylase inhibition was shown by crude extracts of Ficus religiosa (IC₅₀ 29.2 ± 1.2 µg/mL) and ethyl acetate fractions of Shorea robusta (IC₅₀ 69.3 ± 1.1 µg/mL), and the highest radical scavenging activity was shown by F. religiosa with an IC₅₀ 67.4 ± 0.6 µg/mL. Furthermore, to identify the metabolites within the fractions, we employed high-resolution mass spectrometry (LC-HRMS) and annotated 17 known metabolites which justify our assumption on activity. Of 4 medicinal plants examined, ethyl acetate fraction of S. robusta, ethyl acetate fraction of M. melabathrium, and water or ethyl acetate fraction of A. catechu extracts illustrated the best activities. With our study, we set up a foundation that provides authentic evidence to the community for use of these traditional plants. The annotated metabolites in this study support earlier experimental evidence towards the inhibition of enzymes. Further study is necessary to explore the clinical efficacy of these secondary molecules, which might be alternatives for the treatment of diabetes and pathogens.

TaNAC032 transcription factor regulates lignin-biosynthetic genes to combat Fusarium head blight in wheat

Fusarium head blight (FHB) is a destructive disease affecting cereal crops globally due to mycotoxin contamination of grains that reduce yield and quality. Among hundreds of QTLs identified for resistance, the QTL-Fhb1 is of significant interest even today, for its major contribution to FHB resistance. Previously, QTL-Fhb1 dissection based on a combined metabolo-genomics approach, identified a few potential resistance genes, including a NAC like transcription factor for FHB resistance. Sequencing and phylogenetic analysis confirmed NAC to be the wheat TaNAC032. Also, the quantitative RT-PCR studies revealed a greater induced expression of TaNAC032 in resistant NIL in comparison to susceptible NIL upon Fusarium graminearum (Fg) infection. The virus-induced gene silencing (VIGS) based functional validation of TaNAC032 in resistant NIL confirmed increased disease severity and fungal biomass. Metabolic profiling revealed low abundances of resistance-related (RR) metabolites in TaNAC032 silenced NIL-R compared to non-silenced. Silenced plants showed decreased transcript abundances of RR metabolite biosynthetic genes associated with a reduction in total lignin content in rachis, confirming the regulatory role of TaNAC032 in wheat in response to Fg infection. If TaNA032 is mutated in an FHB susceptible cultivar, it can be edited to enhance FHB resistance.

Ester-mediated peptide formation promoted by deep eutectic solvents: a facile pathway to proto-peptides

The ester-amide exchange reaction enables spontaneous formation of prebiotic proto-peptides under mild conditions. However, this reaction also leads to oligomers with a vast sequence diversity of ester and amide linkages. Here, we demonstrate using deep eutectic solvents as a universal strategy to regulate the reaction pathways and promote the formation of amino acid-enriched oligomers with peptide backbones.

Targeted and non-targeted unexpected food contaminants analysis by LC/HRMS: Feasibility study on rice

A widely applicable analytical LC/HRMS method based on ion source optimization, data treatment optimization on rice matrix was developed. The effects of key parameters of ion source, and their interactions on ESI response were studied on HPLC-QTOF. Compared with center points, 40% and 20% increase of response factors in the positive and negative mode can be achieved by ion source optimization, respectively. Data processing strategies inspired from metabolomics and multi-targeted analysis were compared and developed using case and control rice samples. Highly automated workflow using XCMS achieved highest mass accuracy, highest detection rate of 96% for 5 μg/kg in a non-targeted way. A clear distinction between the control and contaminated samples by PCA and PLS-DA was also achieved by this workflow using XCMS, even for the concentration of 5 μg/kg.

Role of laccase gene in wheat NILs differing at QTL-Fhb1 for resistance against Fusarium head blight

Fusarium head blight (FHB), caused mainly by Fusarium graminearum (Fg), is one of the most severe diseases of wheat. It affects grain yield and quality due to mycotoxin contamination, which is harmful for both human and livestock consumption. Cell wall lignification, following pathogen invasion, is one of the innate defense responses. Plant laccases are known to lignify the secondary cell walls. A metabolo-genomics study identified laccase as one of the candidate genes in QTL-Fhb1 of wheat NILs derived from Sumai 3*5/Thatcher cross. Based on phylogenetics, it was named as TaLAC4. Real-time qPCR revealed a strongly induced expression of TaLAC4 in NIL-R. The VIGS based transient silencing of TaLAC4 in NIL-R resulted in an increased susceptibility leading to Fg spread within the entire spike in 15dpi, contrasting to non-silenced where the infection was limited to inoculated spikelets. Histopathology revealed thickened cell walls, mainly due to G-lignin, in non-silenced NIL-R, relative to silenced, in conjunction with higher total lignin content. Metabolic profiling of TaLAC4 silenced NILs identified the accumulation of several precursor metabolites higher in abundances upstream TaLAC4. These results confirm that the resistance function of TaLAC4 in NIL-R is due to pathogen-induced lignification of secondary cell walls in the rachis.

Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

HvWRKY23 regulates flavonoid glycoside and hydroxycinnamic acid amide biosynthetic genes in barley to combat Fusarium head blight

Crop plant resistance against pathogens is governed by dynamic molecular and biochemical responses driven by complex transcriptional networks. However, the underlying mechanisms are largely unclear. Here we report an interesting role of HvWRKY23 transcription factor (TF) in modulating defense response against Fusarium head blight (FHB) in barley. The combined approach of gene silencing, metabolomics, real time expression analysis and ab initio bioinformatics tools led to the identification of the HvWRKY23 role in FHB resistance. The knock-down of HvWRKY23 gene in the FHB resistant barley genotype CI9831, followed by inoculation with Fusarium graminearum, led to the down regulation of key flavonoid and hydroxycinnamic acid amide biosynthetic genes resulting in reduced accumulation of resistant related (RR) secondary metabolites such as pelargonidin 3-rutinoside, peonidin 3-rhamnoside-5-glucoside, kaempferol 3-O-arabinoside and other flavonoid glycosides. Reduced abundances of RR metabolites in TF silenced plants were also associated with an increased proportion of spikelets diseased and amount of fungal biomass in spikelets, depicting the role of HvWRKY23 in disease resistance. The luciferase reporter assay demonstrated binding of HvWRKY23 protein to promoters of key flavonoid and hydroxycinnamic acid amides (HCAA) biosynthetic genes, such as HvPAL2, HvCHS1, HvHCT, HvLAC15 and HvUDPGT. The accumulation of high abundances of HCAAs and flavonoid glycosides reinforce cell walls to contain the pathogen to initial infection area. This gene in commercial cultivars can be edited, if non-functional, to enhance resistance against FHB.

Metabolomics Profiling before, during, and after the Beijing Olympics: A Panel Study of Within-Individual Differences during Periods of High and Low Air Pollution

BACKGROUND

The metabolome is a collection of exogenous chemicals and metabolites from cellular processes that may reflect the body's response to environmental exposures. Studies of air pollution and metabolomics are limited.

OBJECTIVES

To explore changes in the human metabolome before, during, and after the 2008 Beijing Olympics Games, when air pollution was high, low, and high, respectively.

METHODS

Serum samples were collected before, during, and after the Olympics from 26 participants in an existing panel study. Gas and ultra-high performance liquid chromatography/mass spectrometry were used in metabolomics analysis. Repeated measures ANOVA, network analysis, and enrichment analysis methods were employed to identify metabolites and classes associated with air pollution changes.

RESULTS

A total of 886 molecules were measured in our metabolomics analysis. Network partitioning identified four modules with 65 known metabolites that significantly changed across the three time points. All known molecules in the first module ([Formula: see text]) were lipids (e.g., eicosapentaenoic acid, stearic acid). The second module consisted primarily of dipeptides ([Formula: see text], e.g., isoleucylglycine) plus 8 metabolites from four other classes (e.g., hypoxanthine, 12-hydroxyeicosatetraenoic acid). Most of the metabolites in Modules 3 (19 of 23) and 4 (5 of 5) were unknown. Enrichment analysis of module-identified metabolites indicted significantly overrepresented pathways, including long- and medium-chain fatty acids, polyunsaturated fatty acids (n3 and n6), eicosanoids, lysolipid, dipeptides, fatty acid metabolism, and purine metabolism [(hypo) xanthine/inosine-containing pathways].

CONCLUSIONS

We identified two major metabolic signatures: one consisting of lipids, and a second that included dipeptides, polyunsaturated fatty acids, taurine, and xanthine. Metabolites in both groups decreased during the 2008 Beijing Olympics, when air pollution was low, and increased after the Olympics, when air pollution returned to normal (high) levels. https://doi.org/10.1289/EHP3705.

A matching algorithm with isotope distribution pattern in LC-MS based on support vector machine (SVM) learning model

In proteomics, it is important to detect, analyze, and quantify complex peptide components and differences.

Metabolomics-Driven Discovery of Meroterpenoids from a Mussel-Derived Penicillium ubiquetum

Penicillium ubiquetum MMS330 isolated from the blue mussel Mytilus edulis collected on the Loire estuary in France was here investigated. As very few secondary metabolites have been documented for this species, its metabolome was studied following the OSMAC approach to enhance as many biosynthetic pathways as possible. Interestingly, HPLC-HRMS based hierarchical clustering analysis together with MS/MS molecular networking highlighted the selective overproduction of some structurally related compounds when the culture was performed on seawater CYA (Czapek Yeast extract Agar) medium. Mass-guided purification from large scale cultivation on this medium led to the isolation of nine meroterpenoids including two new analogues, 22-deoxyminiolutelide A (1) and 4-hydroxy-22-deoxyminiolutelide B (2), together with seven known compounds (3-9). The structures of 1 and 2 were elucidated on the basis of HR-ESIMS and NMR spectroscopic data analysis. Furthermore, NMR signals of 22-deoxyminiolutelide B (3) were reassigned.

Metabolomics of Hydrazine-Induced Hepatotoxicity in Rats for Discovering Potential Biomarkers

Metabolic pathway disturbances associated with drug-induced liver injury remain unsatisfactorily characterized. Diagnostic biomarkers for hepatotoxicity have been used to minimize drug-induced liver injury and to increase the clinical safety. A metabolomics strategy using rapid-resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS) analyses and multivariate statistics was implemented to identify potential biomarkers for hydrazine-induced hepatotoxicity. The global serum and urine metabolomics of 30 hydrazine-treated rats at 24 or 48 h postdosing and 24 healthy rats were characterized by a metabolomics approach. Multivariate statistical data analyses and receiver operating characteristic (ROC) curves were performed to identify the most significantly altered metabolites. The 16 most significant potential biomarkers were identified to be closely related to hydrazine-induced liver injury. The combination of these biomarkers had an area under the curve (AUC) > 0.85, with 100% specificity and sensitivity, respectively. This high-quality classification group included amino acids and their derivatives, glutathione metabolites, vitamins, fatty acids, intermediates of pyrimidine metabolism, and lipids. Additionally, metabolomics pathway analyses confirmed that phenylalanine, tyrosine, and tryptophan biosynthesis as well as tyrosine metabolism had great interactions with hydrazine-induced liver injury in rats. These discriminating metabolites might be useful in understanding the pathogenesis mechanisms of liver injury and provide good prospects for drug-induced liver injury diagnosis clinically.

Metabolomic analysis of 92 pulmonary embolism patients from a nested case-control study identifies metabolites associated with adverse clinical outcomes

Essentials Risk-stratification often fails to predict clinical deterioration in pulmonary embolism (PE). First-ever high-throughput metabolomics analysis of risk-stratified PE patients. Changes in circulating metabolites reflect a compromised energy metabolism in PE. Metabolites play a key role in the pathophysiology and risk stratification of PE.

SUMMARY

Background Patients with acute pulmonary embolism (PE) exhibit wide variation in clinical presentation and outcomes. Our understanding of the pathophysiologic mechanisms differentiating low-risk and high-risk PE is limited, so current risk-stratification efforts often fail to predict clinical deterioration and are insufficient to guide management. Objectives To improve our understanding of the physiology differentiating low-risk from high-risk PE, we conducted the first-ever high-throughput metabolomics analysis (843 named metabolites) comparing PE patients across risk strata within a nested case-control study. Patients/methods We enrolled 92 patients diagnosed with acute PE and collected plasma within 24 h of PE diagnosis. We used linear regression and pathway analysis to identify metabolites and pathways associated with PE risk-strata. Results When we compared 46 low-risk with 46 intermediate/high-risk PEs, 50 metabolites were significantly different after multiple testing correction. These metabolites were enriched in the following pathways: tricarboxylic acid (TCA) cycle, fatty acid metabolism (acyl carnitine) and purine metabolism, (hypo)xanthine/inosine containing. Additionally, energy, nucleotide and amino acid pathways were downregulated in intermediate/high-risk PE patients. When we compared 28 intermediate-risk with 18 high-risk PE patients, 41 metabolites differed at a nominal P-value level. These metabolites were enriched in fatty acid metabolism (acyl cholines), and hemoglobin and porphyrin metabolism. Conclusion Our results suggest that high-throughput metabolomics can provide insight into the pathophysiology of PE. Specifically, changes in circulating metabolites reflect compromised energy metabolism in intermediate/high-risk PE patients. These findings demonstrate the important role metabolites play in the pathophysiology of PE and highlight metabolomics as a potential tool for risk stratification of PE.

Potato native and wound periderms are differently affected by down-regulation of FHT, a suberin feruloyl transferase

Potato native and wound healing periderms contain an external multilayered phellem tissue (potato skin) consisting of dead cells whose cell walls are impregnated with suberin polymers. The phellem provides physical and chemical barriers to tuber dehydration, heat transfer, and pathogenic infection. Previous RNAi-mediated gene silencing studies in native periderm have demonstrated a role for a feruloyl transferase (FHT) in suberin biosynthesis and revealed how its down-regulation affects both chemical composition and physiology. To complement these prior analyses and to investigate the impact of FHT deficiency in wound periderms, a bottom-up methodology has been used to analyze soluble tissue extracts and solid polymers concurrently. Multivariate statistical analysis of LC-MS and GC-MS data, augmented by solid-state NMR and thioacidolysis, yields two types of new insights: the chemical compounds responsible for contrasting metabolic profiles of native and wound periderms, and the impact of FHT deficiency in each of these plant tissues. In the current report, we confirm a role for FHT in developing wound periderm and highlight its distinctive features as compared to the corresponding native potato periderm.

The serum metabolic profiles of different Barcelona stages hepatocellular carcinoma associated with hepatitis B virus

The present study aimed to explore the characteristic ions distinguishing different Barcelona stages in patients with hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) using the ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) platform, and to evaluate their value in diagnosing and monitoring the progress of HCC. The serum was sampled from 20 healthy volunteers, 20 patients with HBV-induced cirrhosis and 75 patients with HBV-associated HCC of different BCLC stages. Samples were all examined using UPLC-MS. Principal components analysis (PCA) and the orthogonal partial least squares discriminant analysis (OPLS-DA) model were constructed to determine potential biomarkers. Then, the independent sample-nonparametric test was used to perform the final screening for ion identification. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of these ions. Serum metabolomic PCA and OPLS-DA models were established to diagnose different BCLC stages of HCC associated with HBV, with OPLS-DA model parameters (R2X=67.2%, R2Y=82%, Q2Y=61.1%). A total of 20 metabolites with statistically significant differences among groups were identified, primarily including amino acids, bile acid, fatty acid and phosphatidate. The area under the curve (AUC) of LysoPC [18:2 (9Z,12Z)], LysoPC (P-16:0), asparaginyl-proline and vaccenic acid in the comparison between HCC and cirrhosis were all increased compared with that of AFP, indicating a more improved diagnosis ability. Furthermore, the AUC of L-aspartyl-4-phosphate and LysoPC [20:5 (5Z,8Z,11Z,14Z,17Z)] in the stage A vs. B comparison were increased compared with that of AFP, but were decreased in the comparison between stage B and C. The present study succeeded in screening metabolic ions that reflect the progress of HCC with high diagnostic value. Thus, the identified ions may serve a role in clinically diagnosing HBV-associated HCC and monitoring the development of the disease.

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

The goal of metabolomics is to measure the entire range of small organic molecules in biological samples. In liquid chromatography-mass spectrometry-based metabolomics, formidable analytical challenges remain in removing the nonbiological factors that affect chromatographic peak areas. These factors include sample matrix-induced ion suppression, chromatographic quality, and analytical drift. The combination of these factors is referred to as obscuring variation. Some metabolomics samples can exhibit intense obscuring variation due to matrix-induced ion suppression, rendering large amounts of data unreliable and difficult to interpret. Existing normalization techniques have limited applicability to these sample types. Here we present a data normalization method to minimize the effects of obscuring variation. We normalize peak areas using a batch-specific normalization process, which matches measured metabolites with isotope-labeled internal standards that behave similarly during the analysis. This method, called best-matched internal standard (B-MIS) normalization, can be applied to targeted or untargeted metabolomics data sets and yields relative concentrations. We evaluate and demonstrate the utility of B-MIS normalization using marine environmental samples and laboratory grown cultures of phytoplankton. In untargeted analyses, B-MIS normalization allowed for inclusion of mass features in downstream analyses that would have been considered unreliable without normalization due to obscuring variation. B-MIS normalization for targeted or untargeted metabolomics is freely available at https://github.com/IngallsLabUW/B-MIS-normalization .

Comparative assessment of software for non-targeted data analysis in the study of volatile fingerprint changes during storage of a strawberry beverage

Five free software packages were compared to assess their utility for the non-targeted study of changes in the volatile profile during the storage of a novel strawberry beverage. AMDIS coupled to Gavin software turned out to be easy to use, required the minimum handling for subsequent data treatment and its results were the most similar to those obtained by manual integration. However, AMDIS coupled to SpectConnect software provided more information for the study of volatile profile changes during the storage of strawberry beverage. During storage, volatile profile changed producing the differentiation among the strawberry beverage stored at different temperatures, and this difference increases as time passes; these results were also supported by PCA. As expected, it seems that cold temperature is the best way of preservation for this product during long time storage. Variable Importance in the Projection (VIP) and correlation scores pointed out four volatile compounds as potential markers for shelf-life of our strawberry beverage: 2-phenylethyl acetate, decanoic acid, γ-decalactone and furfural.

Global Analysis of Plasma Lipids Identifies Liver-Derived Acylcarnitines as a Fuel Source for Brown Fat Thermogenesis

Cold-induced thermogenesis is an energy-demanding process that protects endotherms against a reduction in ambient temperature. Using non-targeted liquid chromatography-mass spectrometry-based lipidomics, we identified elevated levels of plasma acylcarnitines in response to the cold. We found that the liver undergoes a metabolic switch to provide fuel for brown fat thermogenesis by producing acylcarnitines. Cold stimulates white adipocytes to release free fatty acids that activate the nuclear receptor HNF4α, which is required for acylcarnitine production in the liver and adaptive thermogenesis. Once in circulation, acylcarnitines are transported to brown adipose tissue, while uptake into white adipose tissue and liver is blocked. Finally, a bolus of L-carnitine or palmitoylcarnitine rescues the cold sensitivity seen with aging. Our data highlight an elegant mechanism whereby white adipose tissue provides long-chain fatty acids for hepatic carnitilation to generate plasma acylcarnitines as a fuel source for peripheral tissues in mice.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.