IntroductionColorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Alteration in lipid metabolism and chemokine expression are considered hallmark characteristics of malignant progression and metastasis of CRC. Validated diagnostic and prognostic biomarkers are urgently needed to define molecular heterogeneous CRC clinical stages and subtypes, as liver dominant metastasis has poor survival outcomes.ObjectivesThe aim of this study was to integrate lipid changes, concentrations of chemokines, such as platelet factor 4 and interleukin 8, and gene marker status measured in plasma samples, with clinical features from patients at different CRC stages or who had progressed to stage-IV colorectal liver metastasis (CLM).MethodsHigh-resolution liquid chromatography-mass spectrometry (HR-LC-MS) was used to determine the levels of candidate lipid biomarkers in each CRC patient’s preoperative plasma samples and combined with chemokine, gene and clinical data. Machine learning models were then trained using known clinical outcomes to select biomarker combinations that best classify CRC stage and group.ResultsBayesian neural net and multilinear regression-machine learning identified candidate biomarkers that classify CRC (stages I-III), CLM patients and control subjects (cancer-free or patients with polyps/diverticulitis), showing that integrating specific lipid signatures and chemokines (platelet factor-4 and interluken-8; IL-8) can improve prognostic accuracy. Gene marker status could contribute to disease prediction, but requires ubiquitous testing in clinical cohorts.ConclusionOur findings demonstrate that correlating multiple disease related features with lipid changes could improve CRC prognosis. The identified signatures could be used as reference biomarkers to predict CRC prognosis and classify stages, and monitor therapeutic intervention.

IntroductionThe objective of this study was to explore potential novel biomarkers for moderate to severe lower urinary tract symptoms (LUTS) using a metabolomics-based approach, and statistical methods with significant different features than previous reported.Materials and MethodsThe patients and the controls were selected to participate in the study according to inclusion/exclusion criteria (n = 82). We recorded the following variables: International prostatic symptom score (IPSS), prostate volume, comorbidities, PSA, height, weight, triglycerides, glycemia, HDL cholesterol, and blood pressure. The study of 41 plasma metabolites was done using the nuclear magnetic resonance spectroscopy technique. First, the correlations between the metabolites and the IPSS were done using Pearson. Second, significant biomarkers of LUTS from metabolites were further analysed using a multiple linear regression model. Finally, we validated the findings using partial least square regression (PLS).ResultsSmall to moderate correlations were found between IPSS and methionine (-0.301), threonine (-0.320), lactic acid (0.294), pyruvic acid (0.207) and 2-aminobutyric-acid (0.229). The multiple linear regression model revealed that only threonine (p = 0.022) was significantly associated with IPSS, whereas methionine (p = 0.103), lactic acid (p = 0.093), pyruvic acid (p = 0.847) and 2-aminobutyric-acid (p = 0.244) lost their significance. However, all metabolites lost their significance in the PLS model.ConclusionWhen using the robust PLS-regression method, none of the metabolites in our analysis had a significant association with lower urinary tract symptoms. This highlights the importance of using appropriate statistical methods when exploring new biomarkers in urology.

BackgroundBees provide essential pollination services for many food crops and are critical in supporting wild plant diversity. However, the dietary landscape of pollen food sources for social and solitary bees has changed because of agricultural intensification and habitat loss. For this reason, understanding the basic nutrient metabolism and meeting the nutritional needs of bees is becoming an urgent requirement for agriculture and conservation. We know that pollen is the principal source of dietary fat and sterols for pollinators, but a precise understanding of what the essential nutrients are and how much is needed is not yet clear. Sterols are key for producing the hormones that control development and may be present in cell membranes, where fatty-acid-containing species are important structural and signalling molecules (phospholipids) or to supply, store and distribute energy (glycerides).Aim of the reviewIn this critical review, we examine the current general understanding of sterol and lipid metabolism of social and solitary bees from a variety of literature sources and discuss implications for bee health.Key scientific concepts of reviewWe found that while eusocial bees are resilient to some dietary variation in sterol supply the scope for this is limited. The evidence of both de novo lipogenesis and a dietary need for particular fatty acids (FAs) shows that FA metabolism in insects is analogous to mammals but with distinct features. Bees rely on their dietary intake for essential sterols and lipids in a way that is dependent upon pollen availability.

ContextInsulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming.ObjectiveDevelop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data.DesignInsulin Resistance Atherosclerosis Family Study (IRASFS).SettingCommunity based.ParticipantsMexican Americans (MA) and African Americans (AA).Main outcomeEstimation models for measures of insulin resistance, i.e. SI and HOMA-IR.ResultsLeast Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r2 = 0.77) and AA (r2 = 0.74) testing datasets. Further, estimated and empiric SI were consistently associated with BMI, low-density lipoprotein cholesterol (LDL), and triglycerides. We applied similar approaches to estimate HOMA-IR with similar results.ConclusionsWe have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions.

IntroductionThis study has investigated the temporal disruptive effects of tributyltin (TBT) on lipid homeostasis in Daphnia magna. To achieve this, the study used Liquid Chromatography–Mass Spectrometry (LC–MS) analysis to analyze biological samples of Daphnia magna treated with TBT over time. The resulting data sets were multivariate and three-way, and were modeled using bilinear and trilinear non-negative factor decomposition chemometric methods. These methods allowed for the identification of specific patterns in the data and provided insight into the effects of TBT on lipid homeostasis in Daphnia magna.ObjectivesInvestigation of how are the changes in the lipid concentrations of Daphnia magna pools when they were exposed with TBT and over time using non-targeted LC–MS and advanced chemometric analysis.MethodsThe simultaneous analysis of LC–MS data sets of Daphnia magna samples under different experimental conditions (TBT dose and time) were analyzed using the ROIMCR method, which allows the resolution of the elution and mass spectra profiles of a large number of endogenous lipids. Changes obtained in the peak areas of the elution profiles of these lipids caused by the dose of TBT treatment and the time after its exposure are analyzed by principal component analysis, multivariate curve resolution-alternative least square, two-way ANOVA and ANOVA-simultaneous component analysis.Results87 lipids were identified. Some of these lipids are proposed as Daphnia magna lipidomic biomarkers of the effects produced by the two considered factors (time and dose) and by their interaction. A reproducible multiplicative effect between these two factors is confirmed and the optimal approach to model this dataset resulted to be the application of the trilinear factor decomposition model.ConclusionThe proposed non-targeted LC–MS lipidomics approach resulted to be a powerful tool to investigate the effects of the two factors on the Daphnia magna lipidome using chemometric methods based on bilinear and trilinear factor decomposition models, according to the type of interaction between the design factors.

IntroductionMetabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging.ObjectivesThis study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation.MethodsWe designed a preclinical, untargeted metabolomics procedure, that focuses on the determination of central metabolites relevant to substance use disorders that are (a) associated with changes in behavior produced by acute drug exposure and (b) impacted by repeated drug exposure. Untargeted metabolomics analysis was carried out on liquid chromatography-mass spectrometry data obtained from 336 microdialysis samples. Samples were collected from the medial striatum of male Sprague-Dawley (N = 21) rats whilst behavioral data were simultaneously collected as part of a (±)-3,4-methylenedioxymethamphetamine (MDMA)-induced behavioral sensitization experiment. Analysis was conducted by orthogonal partial least squares, where the Y variable was the behavioral data, and the X variables were the relative concentrations of the 737 detected features.ResultsMDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the greatest predictors of acute MDMA-produced behavior. Subsequent univariate analyses showed that repeated MDMA exposure produced significant changes in MDMA metabolism, which may contribute to the increased abuse liability of the drug as a function of repeated exposure.ConclusionThese findings highlight how the inclusion of behavioral data can guide metabolomics data analysis and increase the relevance of the results to the phenotype of interest.

BackgroundAnalysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.AimThis review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.Key scientific conceptsGiven its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

IntroductionHelminths are parasitic worms that infect millions of people worldwide and secrete a variety of excretory-secretory products (ESPs), including proteins, peptides, and small molecules. Despite this, there is currently no comprehensive review article on cataloging small molecules from helminths, particularly focusing on the different classes of metabolites (polar and lipid molecules) identified from the ESP and somatic tissue extracts of helminths that were studied in isolation from their hosts.ObjectiveThis review aims to provide a comprehensive assessment of the metabolomics and lipidomics studies of parasitic helminths using all available analytical platforms.MethodTo achieve this objective, we conducted a meta-analysis of the identification and characterization tools, metabolomics approaches, metabolomics standard initiative (MSI) levels, software, and databases commonly applied in helminth metabolomics studies published until November 2021.ResultThis review analyzed 29 studies reporting the metabolomic assessment of ESPs and somatic tissue extracts of 17 helminth species grown under ex vivo/in vitro culture conditions. Of these 29 studies, 19 achieved the highest level of metabolite identification (MSI level-1), while the remaining studies reported MSI level-2 identification. Only 155 small molecule metabolites, including polar and lipids, were identified using MSI level-1 characterization protocols from various helminth species. Despite the significant advances made possible by the ‘omics’ technology, standardized software and helminth-specific metabolomics databases remain significant challenges in this field. Overall, this review highlights the potential for future studies to better understand the diverse range of small molecules that helminths produce and leverage their unique metabolomic features to develop novel treatment options.

IntroductionAssessing intraspecific variation in plant volatile organic compounds (VOCs) involves pitfalls that may bias biological interpretation, particularly when several laboratories collaborate on joint projects. Comparative, inter-laboratory ring trials can inform on the reproducibility of such analyses.ObjectivesIn a ring trial involving five laboratories, we investigated the reproducibility of VOC collections with polydimethylsiloxane (PDMS) and analyses by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). As model plant we used Tanacetum vulgare, which shows a remarkable diversity in terpenoids, forming so-called chemotypes. We performed our ring-trial with two chemotypes to examine the sources of technical variation in plant VOC measurements during pre-analytical, analytical, and post-analytical steps.MethodsMonoclonal root cuttings were generated in one laboratory and distributed to five laboratories, in which plants were grown under laboratory-specific conditions. VOCs were collected on PDMS tubes from all plants before and after a jasmonic acid (JA) treatment. Thereafter, each laboratory (donors) sent a subset of tubes to four of the other laboratories (recipients), which performed TD-GC-MS with their own established procedures.ResultsChemotype-specific differences in VOC profiles were detected but with an overall high variation both across donor and recipient laboratories. JA-induced changes in VOC profiles were not reproducible. Laboratory-specific growth conditions led to phenotypic variation that affected the resulting VOC profiles.ConclusionOur ring trial shows that despite large efforts to standardise each VOC measurement step, the outcomes differed both qualitatively and quantitatively. Our results reveal sources of variation in plant VOC research and may help to avoid systematic errors in similar experiments.