Development, characterization and comparisons of targeted and non-targeted metabolomics methods

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

The Application of Untargeted Metabolomic Approaches for the Search of Common Bioavailable Metabolites in Human Plasma Samples from Lippia citriodora and Olea europaea Extracts

Lippia citriodora and Olea europaea are known for their shared common bioactivities. Although both matrices are rich in similar families of bioactive compounds, their specific phytochemical compounds are mostly different. Since these compounds can be metabolized in the organism, this study hypothesized that common bioavailable metabolites may contribute to their similar bioactive effects. To test this, an acute double-blind intervention study in humans was conducted with blood samples collected at multiple time points. Using an untargeted metabolomic approach based on HPLC-ESI-QTOF-MS, 66 circulating metabolites were detected, including 9 common to both extracts, such as homovanillic acid sulfate and glucuronide derivates, hydroxytyrosol sulfate, etc. These common metabolites displayed significantly different Tmax values depending on the source, suggesting distinct metabolization pathways for each extract. The study highlights how shared bioavailable metabolites may underlie similar bioactivities observed between these two plant sources.

Potential Antitumor Mechanism of Propolis Against Skin Squamous Cell Carcinoma A431 Cells Based on Untargeted Metabolomics

Propolis is a sticky substance produced by honeybees (Apis mellifera) through the collection of plant resins, which they mix with secretions from their palate and wax glands. Propolis can inhibit tumor invasion and metastasis, thereby reducing the proliferation of tumor cells and inducing cell apoptosis. Previous research has shown that propolis has an inhibitory effect on skin squamous cell carcinoma A431 cells. Nevertheless, its inhibitory mechanism is unclear because of many significantly different Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the ethanol extract of the propolis (EEP) group and the control group of cells. In this study, the main components of EEP and the antitumor mechanism at an IC50 of 29.04 μg/mL EEP were determined via untargeted metabolomics determined using ultra high-performance liquid chromatography tandem mass spectrometry (UHPLC‒MS/MS), respectively. The results revealed 43 polyphenolic components in the EEP and 1052 metabolites, with 160 significantly upregulated and 143 significantly downregulated metabolites between cells treated with EEP and solvent. The KEGG enrichment results revealed that EEP significantly inhibited A431 cell proliferation via the steroid hormone biosynthesis and linoleic acid metabolism pathways. These findings may provide valuable insights for the development of targeted therapies for the treatment of cutaneous squamous cell carcinoma.

Unravelling the metabolomic signatures of migrant and non-migrant glass eels (Anguilla anguilla) and their response to diazepam exposure

Understanding the migratory cycle of the European eel is crucial for implementing effective conservation measures. The reasons why some glass eels settle in lower estuaries rather than migrating upriver remain unclear. This study aims to identify metabolomic signatures that distinguish active (migrant) from inactive (non-migrant) glass eels. Using a combination of target and non-target screening (NTS) approaches, the metabolite profile of glass eels was studied, and a PLS-DA classification model was applied to find differences between behavioural phenotypes. This model highlighted methionine, glutaryl-L-carnitine, and palmitoylcarnitine as key metabolites, with methionine being significantly different between groups. Glutaryl-L-carnitine strongly correlated with activity, suggesting it might be a more sensitive indicator of glass eel activity than previously studied parameters such as weight loss and oxygen consumption. The findings suggest that differences between active and inactive eels result from both swimming activity and intrinsic metabolic differences, with methionine linked to both factors. We also explored potential differences in how diazepam affects active and inactive glass eels. However, our metabolomic approach lacked the sensitivity to detect significant variations. Overall, this study provides valuable insights into the metabolomic distinctions between active and inactive glass eels, establishing a foundation for future research in this field.

Metabolomics and histopathological analysis of two tomato cultivars after co-infection with soil-borne pathogens (Southern root-knot nematode and Fusarium wilt fungus)

Southern root-knot nematode (Meloidogyne incognita) and Fusarium wilt fungus (Fusarium oxysporum) are one of the most predominant pathogens responsible for substantial agricultural yield reduction of tomato. The current study planned to assess the effects of M. incognita (Mi) and F. oxysporum (Fo) and their co-infection on two tomato cultivars, Zhongza 09 (ZZ09) and Gailing Maofen 802 (GLM802). The present study examined the effects of co-infection on leaf morphology, chlorophyll content, leaf area, and histopathology. The present study used metabolomics to evaluate plant-pathogen interactions. The outcomes of the current study revealed that chlorophyll content and leaf area decreased more in GLM802 during co-infection. In co-infection (Fo + Mi), the chlorophyll content reduction in ZZ09 was 11%, while in GLM802 the reduction reached up to 31% as compared to control. Moreover, the reduction in leaf are in ZZ09 was 31%, however, in the GLM802 reduction was observed 54% as compared to control plants. Similarly, GLM802 stems exhibited larger brown patches on their vascular bundles than ZZ09 stems. The rate of browning of GLM802 stems was 247% more than ZZ09, during co-infection. Moreover, GLM802 roots exhibited a higher abundance of hyphae and larger galls than ZZ09 roots. In metabolic studies, glutathione, succinic acid, and 2-isopropylmalic acid decreased, whereas spermine and fumaric acid increased in GLM802 co-infected stems. It indicates that GLM802 is weakly resistant; therefore, F. oxysporum and other pathogens readily damage tissue. In the co-infected stem of ZZ09, L-asparagine and shikimic acid increased, but pipecolic acid, L-saccharine, and 2-isopropylmalic acid declined. L-asparagine was crucial in preserving the stability of nitrogen metabolism, chlorophyll synthesis, and leaf growth in ZZ09. Shikimic acid's substantial accumulation could explain the limited extent of browning observed in the vascular bundles of ZZ09. Thus, the present study provides insight into M. incognita and F. oxysporum co-infection in two tomato cultivars, which may aid breeding efforts to generate commercially viable resistant cultivars. However, further research on the relationship between M. incognita and F. oxysporum in different host plants is required in the future.

Prediction of storage years of Wuyi rock tea Shuixian by metabolites analysis

Wuyi rock teas of different storage duration have different flavor, bioactivity, and market value, Shuixian is a main variety of Wuyi rock tea. In this study, metabolites composition of Shuixian with different storage years were analyzed using Ultrahigh Performance Liquid Chromatography-Quadrupole-Time of Flight-Mass Spectrometry (UPLC-Q-TOF-MS). A total of 1201 compounds were identified, and 104 differential compounds (VIP > 1.5) were determined. Furthermore, the results showed that five compounds exhibited a positive correlation with storage time, such as alpha-terpineol formate, carnosol, 2-phenethyl-D-glucopyranoside, Ellagic acid, and D-ribosyl nicotinic acid, while 24 compounds showed a negative correlation, such as Ethyl linoleate, leucocyanidin, cis-3-hexenyl acetate. In total, 29 signature compounds significantly correlated with storage time. These findings shed light on the patterns and mechanisms of changes in the composition of Wuyi rock tea during storage and provide a theoretical foundation for distinguishing the storage years.

Association of per- and polyfluoroalkyl substances with the antioxidant bilirubin across pregnancy

BACKGROUND

In mechanistic and preliminary human studies, prenatal exposure to per- and polyfluoroalkyl substances (PFAS) is associated with oxidative stress, a potential contributor to maternal liver disease. Bilirubin is an endogenous antioxidant abundant in the liver that may serve as a physiological modulator of oxidative stress in pregnant people. Hence, our objective was to estimate the association between repeated measures of PFAS and bilirubin during pregnancy.

METHODS

The study population included 332 participants in the Atlanta African American Maternal-Child Cohort between 2014 and 2020. Serum samples were collected up to two times (early pregnancy: 6-18 gestational weeks; late pregnancy: 21-36 gestational weeks) for the measurement of perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and total bilirubin. We analyzed single PFAS with linear mixed effect regression and a mixture of the four PFAS with quantile g-computation. Models were repeated with a multiplicative interaction term to explore effect modification by study visit.

RESULTS

Overall, PFHxS was positively associated with bilirubin (β = 0.08, 95 % CI = 0.01, 0.15). We also found during late pregnancy, there was a positive association of PFHxS and the PFAS mixture with bilirubin (β = 0.12, 95 % CI = 0.02, 0.22; ψ = 0.19, 95 % CI = 0.03, 0.34, respectively). Finally, study visit modified the PFOA-bilirubin association (interaction p-value = 0.09), which was greater during early pregnancy (β = 0.08, 95 % CI = 0.01, 0.15).

CONCLUSION

In a prospective cohort of pregnant African Americans, an increase in PFOA, PFHxS, and the PFAS mixture was associated with an increase in bilirubin. Our results suggest that, depending on pregnancy stage, prenatal PFAS exposure disrupts the maternal liver antioxidant capacity.

Gut microbiota and metabolomic profile changes play critical roles in tacrolimus-induced diabetes in rats

Aims

Hyperglycemia is one of the adverse effects of tacrolimus (TAC), but the underlying mechanism is not fully identified. We used multi-omics analysis to evaluate the changes in the gut microbiota and metabolic profile of rats with TAC-induced diabetes.

Methods

To establish a diabetic animal model, Sprague Dawley rats were divided randomly into two groups. Those in the TAC group received intraperitoneal injections of TAC (3 mg/kg) for 8 weeks, and those in the CON group served as the control. 16S rRNA sequencing was used to analyze fecal microbiota. The metabolites of the two groups were detected and analyzed by nontargeted and targeted metabolomics, including amino acids (AAs), bile acids (BAs), and short-chain fatty acids (SCFAs).

Results

The rats treated with TAC exhibited hyperglycemia as well as changes in the gut microbiota and metabolites. Specifically, their gut microbiota had significantly higher abundances of Escherichia-Shigella, Enterococcus, and Allobaculum, and significantly lower abundances of Ruminococcus, Akkermansia, and Roseburia. In addition, they had significantly reduced serum levels of AAs including asparagine, aspartic acid, glutamic acid, and methionine. With respect to BAs, they had significantly higher serum levels of taurocholic acid (TCA), and glycochenodeoxycholic acid (GCDCA), but significantly lower levels of taurodeoxycholic acid (TDCA) and tauroursodeoxycholic acid (TUDCA). There were no differences in the levels of SCFAs between the two groups. Correlations existed among glucose metabolism indexes (fasting blood glucose and fasting insulin), gut microbiota (Ruminococcus and Akkermansia), and metabolites (glutamic acid, hydroxyproline, GCDCA, TDCA, and TUDCA).

Conclusions

Both AAs and BAs may play crucial roles as signaling molecules in the regulation of TAC-induced diabetes.

An untargeted metabolomics approach applied to the study of the bioavailability and metabolism of three different bioactive plant extracts in human blood samples

Advances in the understanding of bioavailability and metabolism of bioactive compounds have been achieved primarily through targeted or semi-targeted metabolomics approaches using the hypothesis of potential metabolized compounds. The recent development of untargeted metabolomics approaches can present great advantages in this field, such as in the discovery of new metabolized compounds or to study the metabolism of compounds from multiple matrices simultaneously. Thus, this study proposes the use of an untargeted metabolomics strategy based on HPLC-ESI-QTOF-MS for the study of bioavailability and metabolism of bioactive compounds from different vegetal sources. Specifically, this study has been applied to plasma samples collected in an acute human intervention study using three matrices (Hibiscus sabdariffa, Silybum marianum and Theobroma cacao). This approach allowed the selection of those significant variables associated with exogenous metabolites derived from the consumption of bioactive compounds for their subsequent identification. As a result, 14, 25 and 3 potential metabolites associated with supplement intake were significantly detected in the plasma samples from volunteers who ingested the H. sabdariffa (HS), S. marianum (SM) and T. cacao (TC) extracts. Furthermore, Tmax values have been computed for each detected compound. The results highlight the potential of untargeted metabolomics for rapid and comprehensive analysis when working with a wide range of exogenous metabolites from different plant sources in biological samples.

Techniques, Databases and Software Used for Studying Polar Metabolites and Lipids of Gastrointestinal Parasites

Gastrointestinal parasites (GIPs) are organisms known to have coevolved for millennia with their mammalian hosts. These parasites produce small molecules, peptides, and proteins to evade or fight their hosts' immune systems and also to protect their host for their own survival/coexistence. The small molecules include polar compounds, amino acids, lipids, and carbohydrates. Metabolomics and lipidomics are emerging fields of research that have recently been applied to study helminth infections, host-parasite interactions and biochemicals of GIPs. This review comprehensively discusses metabolomics and lipidomics studies of the small molecules of GIPs, providing insights into the available tools and techniques, databases, and analytical software. Most metabolomics and lipidomics investigations employed LC-MS, MS or MS/MS, NMR, or a combination thereof. Recent advancements in artificial intelligence (AI)-assisted software tools and databases have propelled parasitomics forward, offering new avenues to explore host-parasite interactions, immunomodulation, and the intricacies of parasitism. As our understanding of AI technologies and their utilisation continue to expand, it promises to unveil novel perspectives and enrich the knowledge of these complex host-parasite relationships.

Metabolomic profile of muscles from tilapia cultured in recirculating aquaculture systems and traditional aquaculture in ponds and protein stability during freeze-thaw cycles

Muscle protein stability during freeze-thaw (F-T) cycles was investigated with tilapia cultured in recirculating aquaculture systems (RAS) and traditional aquaculture in ponds (TAP). This study found that fatty acids (eg., palmitic acid) were enriched in TAP, while antioxidants (eg., glutathione) were enriched in RAS. Generally, proteins in the RAS group exhibited greater stability against denaturation during the F-T cycle, suggested by a less decrease in haem protein content (77% in RAS and 86% in TAP) and a less increase in surface hydrophobicity of sarcoplasmic protein (63% in RAS and 101% in TAP). There was no significant difference in oxidative stability of myofibrillar protein between the two groups. This study provides a theoretical guide for the quality control of tilapia cultured in RAS during frozen storage.

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Metabolomic profiles predict clinical severity in patients with obstructive sleep apnea-hypopnea syndrome

STUDY OBJECTIVES

Obstructive sleep apnea-hypopnea syndrome (OSAHS) poses a significant health hazard, intermittent hypoxia inflicts damage throughout the body and is considered a critical risk factor for metabolic disorders. The aim of this study was to establish a metabolic profile for patients with OSAHS using nontargeted metabolomics detection techniques, providing a basis for OSAHS diagnosis and novel biological marker identification.

METHODS

45 patients with OSAHS composed the OSAHS group, and 44 healthy volunteers composed the control group. Nontargeted metabolomics technology was used to analyze participants' urinary metabolites. Differentially abundant metabolites were screened and correlated through hierarchical clustering analysis. We constructed a composite metabolite diagnostic model using a random forest model. Simultaneously, we analyzed the relationships between 20 metabolites involved in model construction and OSAHS severity.

RESULTS

The urinary metabolomics pattern of the OSAHS group exhibited significant changes, demonstrating noticeable differences in metabolic products. Urinary metabolite analysis revealed differences between the mild-to-moderate OSAHS and severe OSAHS groups. The composite metabolite model constructed in this study demonstrated excellent diagnostic performance not only in distinguishing healthy control participants from patients with mild-to-moderate OSAHS (area under the curve = 0.78) and patients with severe OSAHS (area under the curve = 0.78), but also in discriminating between patients with mild-to-moderate and severe OSAHS (area under the curve = 0.71).

CONCLUSIONS

This study comprehensively analyzed the urinary metabolomic characteristics of patients with OSAHS. The established composite metabolite model provides robust support for OSAHS diagnosis and severity assessment. Twenty metabolites associated with OSAHS disease severity offer a new perspective for diagnosis.

CITATION

Wang X, Zhao J, Wang X, Zhang L. Metabolomic profiles predict clinical severity in patients with obstructive sleep apnea-hypopnea syndrome. J Clin Sleep Med. 2024;20(9):1445-1453.

Deciphering Blood Flow Restriction Training to Aid Lipid Lowering in Obese College Students through Untargeted Metabolomics

(1) Objective: The aim of this study was to observe the lipid-lowering effects of blood flow restriction training (BFR) combined with moderate-intensity continuous training (MICT) in obese college students by observing lipid-lowering hormones and untargeted metabolomics. (2) Methods: In this study, 14 obese college students were convened into three groups-MICT, MICT+BFR, and high-intensity interval training (HIIT)-for a crossover experiment. Blood was drawn before and after exercise for the analysis of lipolytic agents and untargeted metabolomics. The study used a paired t-test and ANOVA for statistical analyses. (3) Results: The lipolytic agent results showed that MICT+BFR was superior to the other two groups in terms of two agents (p = 0.000 and p = 0.003), namely, GH and IL-6 (difference between before and after testing: 10,986.51 ± 5601.84 and 2.42 ± 2.49, respectively), and HIIT was superior to the other two groups in terms of one agent (p = 0.000), i.e., EPI (22.81 ± 16.12). No advantage was observed for MICT. The metabolomics results showed that, compared to MICT, MICT+BFR was associated with the upregulated expression of xanthine, succinate, lactate, N-lactoylphenylalanine, citrate, ureido acid, and myristic acid after exercise, with the possibility of the involvement of the citric acid cycle, alanine, aspartic acid, glutamate metabolism, butyric acid metabolism, and the histidylate metabolism pathway. (4) Conclusions: The superior lipid-lowering effect of MICT+BFR over MICT in a group of obese college students may be due to the stronger activation of GH and IL-6 agents, with the citric acid cycle and alanine, aspartate, and glutamate metabolic pathways being associated with this type of exercise.

Detection of phthalate esters and targeted metabolome analysis in Franciscana dolphin (Pontoporia blainvillei) blubber in the coast of Santa Catarina, southern Brazil

The concerning of plastic pollution in different ecosystems has been worsened by the widespread presence. Phthalate esters (PAEs), plasticizers found in everyday products, can migrate into the environment, especially into the oceans. Researches on their effects on cetaceans are still rare. Metabolomics helps assess perturbations induced by exposure to PAEs, which act as persistent endocrine disruptors. Four PAEs (dimethyl phthalate - DMP, diethyl phthalate - DEP, dibutyl phthalate - DBP, and di(2-ethylhexyl phthalate - DEHP) were analyzed, along with cholesterol and fatty acid profiles of P. blainvillei's blubber samples collected in southern Brazil. The study reveals pervasive contamination by PAEs - especially DEHP, present in all samples - with positive correlations between DEP content and animal size and weight, as well as between the DEHP amount and the C17:1 fatty acid. These findings will be relevant to conservation efforts aimed at this threatened species and overall marine ecosystems.

Proteomic and metabolomic revealed the effect of shading treatment on cigar tobacco

Shading or low light conditions are essential cultivation techniques for cigar wrapper tobacco leaves production, yet their impact on protein and metabolic regulatory networks is not well understood. In this study, we integrated proteomic and metabolomic analyses to uncover the potential molecular mechanisms affecting cigar tobacco leaves under shading treatment. Our findings include: (1) Identification of 780 significantly differentially expressed proteins (DEPs) in the cigar wrapper tobacco leaves, comprising 560 up-regulated and 220 down-regulated proteins, predominantly located in the chloroplast, cytoplasm, and nucleus, collectively accounting for 50.01%. (2) Discovery of 254 significantly differentially expressed metabolites (DEMs), including 148 up-regulated and 106 down-regulated metabolites. (3) KEGG pathway enrichment analysis revealed that the mevalonate (MVA) pathway within 'Terpenoid backbone biosynthesis' was inhibited, leading to a down-regulation of 'Sesquiterpenoid and triterpenoid biosynthesis'. Conversely, the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway was enhanced, resulting in an up-regulation of 'Monoterpenoid biosynthesis', 'Diterpenoid biosynthesis', and 'Carotenoid biosynthesis', thereby promoting the synthesis of terpenoids such as carotenoids and chlorophylls. Simultaneously, the Calvin cycle in 'Carbon fixation in photosynthetic organisms' was amplified, increasing photosynthetic efficiency. These results suggest that under low light conditions, cigar tobacco optimizes photosynthetic efficiency by reconfiguring its energy metabolism and terpenoid biosynthesis. This study contributes valuable insights into protein and metabolic analyses, paving the way for future functional studies on plant responses to low light.

Recent advances and challenges in the analysis of natural toxins

Natural toxins (NTs) are poisonous secondary metabolites produced by living organisms developed to ward off predators. Especially low molecular weight NTs (MW<∼1 kDa), such as mycotoxins, phycotoxins, and plant toxins, are considered an important and growing food safety concern. Therefore, accurate risk assessment of food and feed for the presence of NTs is crucial. Currently, the analysis of NTs is predominantly performed with targeted high pressure liquid chromatography tandem mass spectrometry (HPLC-MS/MS) methods. Although these methods are highly sensitive and accurate, they are relatively expensive and time-consuming, while unknown or unexpected NTs will be missed. To overcome this, novel on-site screening methods and non-targeted HPLC high resolution mass spectrometry (HRMS) methods have been developed. On-site screening methods can give non-specialists the possibility for broad "scanning" of potential geographical regions of interest, while also providing sensitive and specific analysis at the point-of-need. Non-targeted chromatography-HRMS methods can detect unexpected as well as unknown NTs and their metabolites in a lab-based approach. The aim of this chapter is to provide an insight in the recent advances, challenges, and perspectives in the field of NTs analysis both from the on-site and the laboratory perspective.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Metabolomics of human umbilical vein endothelial cell-based analysis of the relationship between hyperuricemia and dyslipidemia

BACKGROUND AND AIMS

Hyperuricemia frequently accompanies dyslipidemia, yet the precise mechanism remains elusive. Leveraging cellular metabolomics analyses, this research probes the potential mechanisms wherein hyperuricemia provokes endothelial cell abnormalities, inducing disordered bile metabolism and resultant lipid anomalies.

METHODS AND RESULTS

We aimed to identify the differential metabolite associated with lipid metabolism through adopting metabolomics approach, and thereafter adequately validating its protective function on HUVECs by using diverse assays to measure cellular viability, reactive oxygen species, migration potential, apoptosis and gene and protein levels of inflammatory factors. Taurochenodeoxycholic acid (TCDCA) (the differential metabolite of HUVECs) and the TCDCA-involved primary bile acid synthesis pathway were found to be negatively correlated with high UA levels based on the results of metabolomics analysis. It was noted that compared to the outcomes observed in UA-treated HUVECs, TCDCA could protect against UA-induced cellular damage and oxidative stress, increase proliferation as well as migration, and decreases apoptosis. In addition, it was observed that TCDCA might protect HUVECs by inhibiting UA-induced p38 mitogen-activated protein kinase/nuclear factor kappa-B p65 (p38MAPK/NF-κB p65) pathway gene and protein levels, as well as the levels of downstream inflammatory factors.

CONCLUSION

The pathogenesis of hyperuricemia accompanying dyslipidemia may involve high uric acid levels eliciting inflammatory reactions and cellular damage in human umbilical vein endothelial cells (HUVECs), mediated through the p38MAPK/NF-κB signaling pathway, subsequently impinging on cellular bile acid synthesis and reducing bile acid production.

An at-leg pellet and associated Penicillium sp. provide multiple protections to mealybugs

Beneficial fungi are well known for their contribution to insects' adaptation to diverse habitats. However, where insect-associated fungi reside and the underlying mechanisms of insect-fungi interaction are not well understood. Here, we show a pellet-like structure on the legs of mealybugs, a group of economically important insect pests. This at-leg pellet, formed by mealybugs feeding on tomato but not by those on cotton, potato, or eggplant, originates jointly from host secretions and mealybug waxy filaments. A fungal strain, Penicillium citrinum, is present in the pellets and it colonizes honeydew. P. citrinum can inhibit mealybug fungal pathogens and is highly competitive in honeydew. Compounds within the pellets also have inhibitory activity against mealybug pathogens. Further bioassays suggest that at-leg pellets can improve the survival rate of Phenacoccus solenopsis under pathogen pressure, increase their sucking frequency, and decrease the defense response of host plants. Our study presents evidences on how a fungi-associated at-leg pellet provides multiple protections for mealybugs through suppressing pathogens and host defense, providing new insights into complex insect × fungi × plant interactions and their coevolution.

Nuclear Magnetic Resonance (NMR) Metabolomics: Current Applications in Equine Health Assessment

Metabolomics can allow for the comprehensive identification of metabolites within biological systems, at given time points, in physiological and pathological conditions. In the last few years, metabolomic analysis has gained popularity both in human and in veterinary medicine, showing great potential for novel applications in clinical activity. The aim of applying metabolomics in clinical practice is understanding the mechanisms underlying pathological conditions and the influence of certain stimuli (i.e., drugs, nutrition, exercise) on body systems, in the attempt of identifying biomarkers that can help in the diagnosis of diseases. Proton Nuclear Magnetic Resonance spectroscopy (1H-NMR) is well tailored to be used as an analytical platform for metabolites' detection at the base of metabolomics studies, due to minimal sample preparation and high reproducibility. In this mini-review article, the scientific production of NMR metabolomic applications to equine medicine is examined. The research works are very different in methodology and difficult to compare. Studies are mainly focused on exercise, reproduction, and nutrition, other than respiratory and musculoskeletal diseases. The available information on this topic is still scant, but a greater collection of data could allow researchers to define new reliable markers to be used in clinical practice for diagnostic and therapeutical purposes.

Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

Metabolomics for the diagnosis of bladder cancer: A systematic review

Objective

Metabolomics has been extensively utilized in bladder cancer (BCa) research, employing mass spectrometry and nuclear magnetic resonance spectroscopy to compare various variables (tissues, serum, blood, and urine). This study aimed to identify potential biomarkers for early BCa diagnosis.

Methods

A search strategy was designed to identify clinical trials, descriptive and analytical observational studies from databases such as Medline, Embase, Cochrane Central Register of Controlled Trials, and Latin American and Caribbean Literature in Health Sciences. Inclusion criteria comprised studies involving BCa tissue, serum, blood, or urine profiling using widely adopted metabolomics techniques like mass spectrometry and nuclear magnetic resonance. Primary outcomes included description of metabolites and metabolomics profiling in BCa patients and the association of metabolites and metabolomics profiling with BCa diagnosis compared to control patients. The risk of bias was assessed using the Quality Assessment of Studies of Diagnostic Accuracy.

Results

The search strategy yielded 2832 studies, of which 30 case-control studies were included. Urine was predominantly used as the primary sample for metabolite identification. Risk of bias was often unclear inpatient selection, blinding of the index test, and reference standard assessment, but no applicability concerns were observed. Metabolites and metabolomics profiles associated with BCa diagnosis were identified in glucose, amino acids, nucleotides, lipids, and aldehydes metabolism.

Conclusion

The identified metabolites in urine included citric acid, valine, tryptophan, taurine, aspartic acid, uridine, ribose, phosphocholine, and carnitine. Tissue samples exhibited elevated levels of lactic acid, amino acids, and lipids. Consistent findings across tissue, urine, and serum samples revealed downregulation of citric acid and upregulation of lactic acid, valine, tryptophan, taurine, glutamine, aspartic acid, uridine, ribose, and phosphocholine.

Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

New perspectives on 'Breathomics': metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. "Breathomics" as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

Exploring applications of non-targeted analysis in the characterization of the prenatal exposome

Capturing the breadth of chemical exposures in utero is critical in understanding their long-term health effects for mother and child. We explored methodological adaptations in a Non-Targeted Analysis (NTA) pipeline and evaluated the effects on chemical annotation and discovery for maternal and infant exposure. We focus on lesser-known/underreported chemicals in maternal and umbilical cord serum analyzed with liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). The samples were collected from a demographically diverse cohort of 296 maternal-cord pairs (n = 592) recruited in San Francisco Bay area. We developed and evaluated two data processing pipelines, primarily differing by detection frequency cut-off, to extract chemical features from non-targeted analysis (NTA). We annotated the detected chemical features by matching with EPA CompTox Chemicals Dashboard (n = 860,000 chemicals) and Human Metabolome Database (n = 3140 chemicals) and applied a Kendrick Mass Defect filter to detect homologous series. We collected fragmentation spectra (MS/MS) on a subset of serum samples and matched to an experimental MS/MS database within the MS-Dial website and other experimental MS/MS spectra collected from standards in our lab. We annotated ~72 % of the features (total features = 32,197, levels 1-4). We confirmed 22 compounds with analytical standards, tentatively identified 88 compounds with MS/MS spectra, and annotated 4862 exogenous chemicals with an in-house developed annotation algorithm. We detected 36 chemicals that appear to not have been previously reported in human blood and 9 chemicals that were reported in less than five studies. Our findings underline the importance of NTA in the discovery of lesser-known/unreported chemicals important to characterize human exposures.

Thyroid cancer cell metabolism: A glance into cell culture system-based metabolomics approaches

Thyroid cancer is the most common malignancy of the endocrine system and the seventh most prevalent cancer in women worldwide. It is a complex and diverse disease characterized by heterogeneity, underscoring the importance of understanding the underlying metabolic alterations within tumor cells. Metabolomics technologies offer a powerful toolset to explore and identify endogenous and exogenous biochemical reaction products, providing crucial insights into the intricate metabolic pathways and processes within living cells. Metabolism plays a central role in cell function, making metabolomics a valuable reflection of a cell's phenotype. In the OMICs era, metabolomics analysis of cells brings numerous advantages over existing methods, propelling cell metabolomics as an emerging field with vast potential for investigating metabolic pathways and their perturbation in pathophysiological conditions. This review article aims to look into recent developments in applying metabolomics for characterizing and interpreting the cellular metabolome in thyroid cancer cell lines, exploring their unique metabolic characteristics. Understanding the metabolic alterations in tumor cells can lead to the identification of critical nodes in the metabolic network that could be targeted for therapeutic intervention.

Study on the accumulation pattern of anthocyanins, sugars and organic acids in medicinal Vitis vinifera 'SuoSuo' during ripening

In this study, targeted metabolomics technology was used to accurately and quantitatively analyze the metabolic pathways of anthocyanin, sugars and organic acid metabolites during the ripening of 'SuoSuo' grape berries. Results, 33, 10 and 36 metabolites of anthocyanins, sugars and organic acids, respectively, were detected. The anthocyanin with the highest content was cyanidin-3-O-glucoside (136.343 ng/g), which reached a maximum at 135 days after full bloom. The highest fructose content in sugar was 167.69 ng/g (135 days after full bloom). Among the organic acids, tartaric acid exhibited the highest content (37,196.67 mg/kg, 105 days after full bloom). The content of oleanolic acid (230.064 mg/kg, 135 days after full bloom) was higher in organic acids. These results clarify how anthocyanin, sugar and organic acid metabolites accumulate and change as 'SuoSuo' grapes ripen and provide a reference for the development and utilization of 'SuoSuo'.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.

The effect of lipid metabolism disorder on patients with hyperuricemia using Multi-Omics analysis

A multiomics study was conducted to investigate how lipid metabolism disorders affect the immune system in Xinjiang patients with hyperuricemia. The serum of 60 healthy individuals and 60 patients with hyperuricemia was collected. This study used LC-MS and HPLC to analyze differential lipid metabolites and enrichment pathways. It measured levels of immune factors tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), carnitine palmitoyltransferase-1 (CPT1), transforming growth factor-β1 (TGF-β1), glucose (Glu), lactic acid (LD), interleukin 10 (IL-10), and selenoprotein 1 (SEP1) using ELISA, as well as to confirm dysregulation of lipid metabolism in hyperuricemia. 33 differential lipid metabolites were significantly upregulated in patients with hyperuricemia. These lipid metabolites were involved in arachidonic acid metabolism, glycerophospholipid metabolism, linoleic acid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and alpha-Linolenic acid metabolism pathways. Moreover, IL-10, CPT1, IL-6, SEP1, TGF-β1, Glu, TNF-α, and LD were associated with glycerophospholipid metabolism. In patients with hyperuricemia of Han and Uyghur nationalities, along with healthy individuals, significant differences in CPT1, TGF-β1, Glu, and LD were demonstrated by ELISA (P < 0.05). Furthermore, the levels of SEP1, IL-6, TGF-β1, Glu, and LD differed considerably between groups of the same ethnicity (P < 0.05). It was found that 33 kinds of lipid metabolites were significantly different in patients with hyperuricemia, which mainly involved 5 metabolic pathways. According to the results of further studies, it is speculated that CPT1, TGF-β1, SEP1, IL-6, Glu and LD may increase fatty acid oxidation and mitochondrial oxidative phosphorylation in patients through glycerophospholipid pathway, reduce the rate of glycolysis, and other pathways to change metabolic patterns, promote different cellular functions, and thus affect the disease progression in patients with hyperuricemia.

Exploratory optimisation of a LC-HRMS based analytical method for untargeted metabolomic screening of Cannabis Sativa L. through Data Mining

BACKGROUND

Recent increase in public acceptance of cannabis as a natural medical alternative for certain neurological pathologies has led to its approval in different regions of the world. However, due to its previous illegal background, little research has been conducted around its biochemical insights. Therefore, in the current framework, metabolomics may be a suitable approach for deepening the knowledge around this plant species. Nevertheless, experimental methods in metabolomics must be carefully handled, as slight modifications can lead to metabolomic coverage loss. Hence, the main objective of this work was to optimise an analytical method for appropriate untargeted metabolomic screening of cannabis.

RESULTS

We present an empirically optimised experimental procedure through which the broadest metabolomic coverage was obtained, in which extraction solvents for metabolite isolation, chromatographic columns for LC-qOrbitrap analysis and plant-representative biological tissues were compared. By exploratory means, it was determined that the solvent combination composed of CHCl3:H2O:CH3OH (2:1:1, v/v) provided the highest number of features from diverse chemical classes, as it was a two-phase extractant. In addition, a reverse phase 2.6 μm C18 100 Å (150 × 3 mm) chromatographic column was determined as the appropriate choice for adequate separation and further detection of the diverse metabolite classes. Apart from that, overall chromatographic peak quality provided by each column was observed and the need for batch correction methods through quality control (QC) samples was confirmed. At last, leaf and flower tissues resulted to provide complementary metabolic information of the plant, to the detriment of stem tissue, which resulted to be negligible.

SIGNIFICANCE

It was concluded that the optimised experimental procedure could significantly ease the path for future research works related to cannabis metabolomics by LC-HRMS means, as the work was based on previous plant metabolomics literature. Furthermore, it is crucial to highlight that an optimal analytical method can vary depending on the main objective of the research, as changes in the experimental factors can lead to different outcomes, regardless of whether the results are better or worse.

Mass spectrometry-based metabolic profiling for identification of biomarkers related to footpad dermatitis in ducks

1. A new assessment method for duck footpad dermatitis (FPD) evaluation was developed, combining visual and histological characters using the images and sections of 400 ducks' feet at 340 d of age. All ducks were graded as G0 (healthy), G1 (mild), G2 (moderate) and G3 (severe) according to the degree of FPD.2. To reveal the potential biomarkers in serum related to duck FPD, non-targeted metabolomics and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to explore differential metabolites in each group.3. There were 57, 91 and 210 annotated differential metabolites in groups G1, G2 and G3 compared with G0, which meant that the severity of FPD increased in line with the number of metabolites. Four metabolites, L-phenylalanine, L-arginine, L-leucine and L-lysine, were considered potential biomarkers related to FPD.4. KEGG enrichment analysis showed that the FPD was mainly involved in glycolysis, the tricarboxylic acid (TCA) cycle, the pentose phosphate pathway and amino acid metabolism. These are related to production metabolism and can affect the physiological activities of ducks, which might explain the decrease in production performance.

The Impact of Pigment-Epithelium-Derived Factor on MCF-7 Cell Metabolism in the Context of Glycaemic Condition

Studies have demonstrated that pigment-epithelium-derived factor (PEDF) is a robust inhibitor of tumour growth and development, implying that this may serve as a promising target for therapeutic intervention. However, the precise impact of PEDF on cancerous cell metabolic pathways remains uncertain despite ongoing research. In this light, this study aimed to employ a metabolomics approach for understanding the metabolic reprogramming events in breast cancer across different glycaemic loads and their response to PEDF. Gas chromatography-quadrupole mass spectrometry (GC/Q-MS) analysis revealed metabolic alterations in ER+ human cell line MCF-7 cells treated with PEDF under varying glycaemic conditions. The identification of significantly altered metabolites was accomplished through MetaboAnalyst (v.5.0) and R packages, which enabled both multivariate and univariate analyses. Out of the 48 metabolites identified, 14 were chosen based on their significant alterations in MCF-7 cells under different glycaemic conditions and PEDF treatment (p < 0.05, VIP > 0.8). Dysregulation in pathways associated with amino acid metabolism, intermediates of the TCA cycle, nucleotide metabolism, and lipid metabolism were detected, and they exhibited different responses to PEDF. Our results suggest that PEDF has a diverse influence on the metabolism of MCF-7 cells in both normo- and hyperglycaemic environments, thereby warranting studies using patient samples to correlate our findings with clinical response in the future.

Metabolomics in archaeological science: A review of their advances and present requirements

Metabolomics, the study of metabolites (small molecules of <1500 daltons), has been posited as a potential tool to explore the past in a comparable manner to other omics, e.g., genomics or proteomics. Archaeologists have used metabolomic approaches for a decade or so, mainly applied to organic residues adhering to archaeological materials. Because of advances in sensitivity, resolution, and the increased availability of different analytical platforms, combined with the low mass/volume required for analysis, metabolomics is now becoming a more feasible choice in the archaeological sector. Additional approaches, as presented by our group, show the versatility of metabolomics as a source of knowledge about the human past when using human osteoarchaeological remains. There is tremendous potential for metabolomics within archaeology, but further efforts are required to position it as a routine technique.

Dysregulated lipid metabolism in TMZ-resistant glioblastoma: pathways, proteins, metabolites and therapeutic opportunities

Glioblastoma (GBM) is a highly aggressive and lethal brain tumor with limited treatment options, such as the chemotherapeutic agent, temozolomide (TMZ). However, many GBM tumors develop resistance to TMZ, which is a major obstacle to effective therapy. Recently, dysregulated lipid metabolism has emerged as an important factor contributing to TMZ resistance in GBM. The dysregulation of lipid metabolism is a hallmark of cancer and alterations in lipid metabolism have been linked to multiple aspects of tumor biology, including proliferation, migration, and resistance to therapy. In this review, we aimed to summarize current knowledge on lipid metabolism in TMZ-resistant GBM, including key metabolites and proteins involved in lipid synthesis, uptake, and utilization, and recent advances in the application of metabolomics to study lipid metabolism in GBM. We also discussed the potential of lipid metabolism as a target for novel therapeutic interventions. Finally, we highlighted the challenges and opportunities associated with developing these interventions for clinical use, and the need for further research to fully understand the role of lipid metabolism in TMZ resistance in GBM. Our review suggests that targeting dysregulated lipid metabolism may be a promising approach to overcome TMZ resistance and improve outcomes in patients with GBM.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis 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.

AIM

This 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 CONCEPTS

Given 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.

Maize kernel metabolome involved in resistance to fusarium ear rot and fumonisin contamination

Fusarium verticillioides poses a threat to worldwide maize production due to its ability to infect maize kernel and synthesize fumonisins that can be accumulated above safety levels for humans and animals. Maize breeding has been proposed as key tool to decrease kernel contamination with fumonisins, but metabolic studies complementary to genomic approaches are necessary to disclose the complexity of maize resistance. An untargeted metabolomic study was proposed using inbreds genetically related but with contrasting levels of resistance in order to uncover pathways implicated in resistance to Fusarium ear rot (FER) and fumonisin contamination in the maize kernel and to look for possible biomarkers. Metabolite determinations were performed in kernels collected at 3 and 10 days after inoculation with F. verticillioides (dat). Discriminant metabolites between resistant and susceptible RILs were rather found at 10 than 3 dat, although metabolite differences at later stages of colonization could be driven by subtle variations at earlier stages of infection. Within this context, differences for membrane lipid homeostasis, methionine metabolism, and indolacetic acid conjugation seemed highly relevant to distinguish between resistant and susceptible inbreds, confirming the polygenic nature of resistance to FER and fumonisin contamination in the maize kernels. Nevertheless, some specific metabolites such as the polyamine spermidine and/or the alkaloid isoquinoline seemed to be promising indirect selection traits to improve resistance to FER and reduce fumonisin accumulation. Therefore, in vitro and in vivo experiments will be necessary to validate the inhibitory effects of these compounds on fumonisins biosynthesis.

Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction

The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.

Metabolomics to study the sublethal effects of diazepam and irbesartan on glass eels (Anguilla anguilla)

Since glass eels are continuously exposed to contamination throughout their migratory journey in estuaries, to a certain extent the fall in the population of this endangered species might be attributed to this exposure, which is especially acute in estuaries under high urban pressure. In this work, metabolomics was used to address the main objective of this study, to evaluate the effects of two pharmaceuticals previously identified as potential concerning chemicals for fish (diazepam and irbesartan) on glass eels. An exposure experiment to diazepam, irbesartan and their mixture was carried out over 7 days followed by 7 days of depuration phase. After exposure, glass eels were individually sacrificed using a lethal bath of anesthesia, and then an unbiased sample extraction method was used to extract separately the polar metabolome and the lipidome. The polar metabolome was submitted to targeted and non-targeted analysis, whereas for the lipidome only the non-targeted analysis was carried out. A combined strategy using partial least squares discriminant analysis and univariate and multivariate statistical analysis (ANOVA, ASCA, t-test, and fold-change analysis) was used to identify the metabolites altered in the exposed groups with respect to the control group. The results of the polar metabolome analysis revealed that glass eels exposed to the diazepam-irbesartan mixture were the most impacted ones, with altered levels for 11 metabolites, some of them belonging to the energetic metabolism, which was confirmed to be sensitive to these contaminants. Additionally, the dysregulation of the levels of twelve lipids, most of them with energetic and structural functions, was also found after exposure to the mixture, which might be related to oxidative stress, inflammation, or alteration of the energetic metabolism.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

Comparison of muscle metabolomics between two Chinese horse breeds

With their enormous muscle mass and athletic ability, horses are well-positioned as model organisms for understanding muscle metabolism. There are two different types of horse breeds-Guanzhong (GZ) horses, an athletic breed with a larger body height (~148.7 cm), and the Ningqiang pony (NQ) horses, a lower height breed generally used for ornamental purposes-both inhabited in the same region of China with obvious differences in muscle content. The main objective of this study was to evaluate the breed-specific mechanisms controlling muscle metabolism. In this study, we observed muscle glycogen, enzyme activities, and LC-MS/MS untargeted metabolomics in the gluteus medius muscle of six, each of GZ and NQ horses, to explore differentiated metabolites that are related to the development of two muscles. As expected, the glycogen content, citrate synthase, and hexokinase activity of muscle were significantly higher in GZ horses. To alleviate the false positive rate, we used both MS1 and MS2 ions for metabolite classification and differential analysis. As a result, a total of 51,535 MS1 and 541 MS2 metabolites were identified, and these metabolites can separate these two groups from each other. Notably, 40% of these metabolites were clustered into lipids and lipid-like molecules. Furthermore, 13 significant metabolites were differentially detected between GZ and NQ horses (fold change [FC] value ≥ 2, variable important in projection value ≥1, and Q value ≤ 0.05). They are primarily clustered into glutathione metabolism (GSH, p = 0.01), taurine, and hypotaurine metabolism (p < 0.05) pathways. Seven of the 13 metabolites were also found in thoroughbred racing horses, suggesting that metabolites related to antioxidants, amino acids, and lipids played a key role in the development of skeleton muscle in horses. Those metabolites related to muscle development shed a light on racing horses' routine maintenance and improvement of athletic performance.

Comparison of serum metabolomics in women with breast Cancer Prior to Chemotherapy and at 1 year: cardiometabolic implications

OBJECTIVE

Early-stage breast cancer (BC) is the second most common malignancy in women, worldwide. Early-detection and treatment advances have led to 5-year survival rates of 90% for early-stage breast cancer. However, the long-term morbidity of breast cancer remains high, with a majority of survivors facing increased risk of cardiometabolic conditions as well as secondary cancers. In particular, African American women with breast cancer experience higher morbidity and mortality than other women. Metabolomics is the comprehensive study of metabolites in biological samples to elucidate the role of monosaccharides, amino acids, and their respective metabolic pathways. Although some studies have found differential metabolites in women with breast cancer compared to normal controls, there has been little study of women with breast cancer across time and the active treatment trajectory. This study examines and compares the serum metabolomic profile of women with BC, prior to initial chemotherapy and at 1 year after inception of chemotherapy.

METHODS

This study examined serum metabolites through a secondary analysis of a longitudinal parent study (EPIGEN) of women diagnosed with early-stage BC. Participants were evaluated across 5 time points: prior to their receipt of chemotherapy (T1), at the time of their 4th chemotherapy treatment (T2), 6 months after the initiation of chemotherapy (T3), one year after the initiation of chemotherapy (T4) and two years after the initiation of chemotherapy (T5). This analysis focused on the metabolomic data from 70 participants from T1 to T4. Using ultra high-pressure liquid chromatography high resolution mass spectrometry (UHPLC-HRMS), we performed Friedman Rank Sum Test followed by Nemenyi post-hoc pairwise tests to identify which metabolite levels differed between time points, focusing on metabolites with a Benjamini-Hochberg false discovery rate (FDR) from the overall Friedman test < 0.05 and then specifically examined the p-values from the T1 vs. T4 pairwise comparison.

RESULTS

The untargeted serum metabolomics yielded a total of 2,395 metabolites identified on the basis of the accurate mass and MS/MS fragmentation, 1,264 of which were significant after Friedman's test (FDR < 0.05). The analysis then focused on the levels of 124 metabolites from the T1 vs. T4 post-hoc comparison that had a combined FDR < 0.05 and fold change (FC) > 2.0. Metabolite set enrichment analysis (MSEA) as part of Metaboanalyst 3.0 was performed to identify pathways that were significantly altered. The known metabolites identified from the functional analysis were used to evaluate the up and down regulated pathways. The 40metabolites from the Functional Analysis were mainly attributed to amino acids (specifically lysine regulation), fatty acids (particularly unsaturated) and steroid hormone synthesis (lysophosphatidic acid).

CONCLUSION

There were multiple significant changes in the serum metabolomic profile of women with breast cancer at one-year post inception of chemotherapy compared to pre-chemotherapy, most notably associated with lysine degradation, branched-chain amino acid synthesis, linoleic acid metabolism, tyrosine metabolism and biosynthesis of unsaturated fatty acids as the top 5 metabolic pathways. Some of these changes could be associated with metabolic perturbations that are consistent with heightened risk of cardiometabolic morbidity. Our results provide new insights into the mechanisms underlying potential heightened cardiovascular health risks in this population.

A State-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: Current Progress, Analytical Challenges, and Recommendations for Future Direction

BACKGROUND

Understanding the mechanistic basis of air pollution toxicity is dependent on accurately characterizing both exposure and biological responses. Untargeted metabolomics, an analysis of small-molecule metabolic phenotypes, may offer improved estimation of exposures and corresponding health responses to complex environmental mixtures such as air pollution. The field remains nascent, however, with questions concerning the coherence and generalizability of findings across studies, study designs and analytical platforms.

OBJECTIVES

We aimed to review the state of air pollution research from studies using untargeted high-resolution metabolomics (HRM), highlight the areas of concordance and dissimilarity in methodological approaches and reported findings, and discuss a path forward for future use of this analytical platform in air pollution research.

METHODS

We conducted a state-of-the-science review to a) summarize recent research of air pollution studies using untargeted metabolomics and b) identify gaps in the peer-reviewed literature and opportunities for addressing these gaps in future designs. We screened articles published within Pubmed and Web of Science between 1 January 2005 and 31 March 2022. Two reviewers independently screened 2,065 abstracts, with discrepancies resolved by a third reviewer.

RESULTS

We identified 47 articles that applied untargeted metabolomics on serum, plasma, whole blood, urine, saliva, or other biospecimens to investigate the impact of air pollution exposures on the human metabolome. Eight hundred sixteen unique features confirmed with level-1 or -2 evidence were reported to be associated with at least one or more air pollutants. Hypoxanthine, histidine, serine, aspartate, and glutamate were among the 35 metabolites consistently exhibiting associations with multiple air pollutants in at least 5 independent studies. Oxidative stress and inflammation-related pathways-including glycerophospholipid metabolism, pyrimidine metabolism, methionine and cysteine metabolism, tyrosine metabolism, and tryptophan metabolism-were the most commonly perturbed pathways reported in > 70 % of studies. More than 80% of the reported features were not chemically annotated, limiting the interpretability and generalizability of the findings.

CONCLUSIONS

Numerous investigations have demonstrated the feasibility of using untargeted metabolomics as a platform linking exposure to internal dose and biological response. Our review of the 47 existing untargeted HRM-air pollution studies points to an underlying coherence and consistency across a range of sample analytical quantitation methods, extraction algorithms, and statistical modeling approaches. Future directions should focus on validation of these findings via hypothesis-driven protocols and technical advances in metabolic annotation and quantification. https://doi.org/10.1289/EHP11851.

Pregnancy-related hemodynamic biomarkers in relation to trimester-specific maternal per - and polyfluoroalkyl substances exposures and adverse birth outcomes

The fate of environmental chemicals in maternal and fetal tissues might be affected by pregnancy-related hemodynamic changes that occur across gestation. Specifically, hemodilution and renal function are hypothesized to confound associations between per- and polyfluoroalkyl substances (PFAS) exposure measures in late pregnancy with gestational length and fetal growth. We sought to analyze two pregnancy-related hemodynamic biomarkers, creatinine and estimated glomerular filtration rate (eGFR), as confounders of the trimester-specific relationships between maternal serum PFAS concentrations and adverse birth outcomes. Participants were enrolled in the Atlanta African American Maternal-Child Cohort between 2014 and 2020. Biospecimens were collected at up to two timepoints, which were categorized into the 1st trimester (N = 278; 11 mean weeks gestation), 2nd trimester (N = 162; 24 mean weeks gestation), and 3rd trimester (N = 110; 29 mean weeks gestation). We quantified six PFAS in serum, creatinine in serum and urine, and eGFR using the Cockroft-Gault equation. Multivariable regression models estimated the associations between single PFAS and their sum with gestational age at delivery (weeks), preterm birth (PTB, <37 gestational weeks), birthweight z-scores, and small for gestational age (SGA). Primary models were adjusted for sociodemographics. We additionally adjusted for serum creatinine, urinary creatinine, or eGFR in the confounding assessments. An interquartile range increase in perfluorooctanoic acid (PFOA) produced a non-significant reduction in birthweight z-score during the 1st and 2nd trimesters (β = -0.01 g [95% CI = -0.14, 0.12] and β = -0.07 g [95% CI = -0.19, 0.06], respectively) whereas the relationship was significant and positive during the 3rd trimester (β = 0.15 g; 95% CI = 0.01, 0.29). Trimester-specific effects were similar for the other PFAS and adverse birth outcomes, which persisted after adjusting for creatinine or eGFR. The relationships between prenatal PFAS exposure and adverse birth outcomes were not strongly confounded by renal function or hemodilution. However, 3rd trimester samples consistently exhibited different effects than those collected during the 1st and 2nd trimesters.

Combined toxicity of acetamiprid and cadmium to larval zebrafish (Danio rerio) based on metabolomic analysis

Emerging contaminants, such as neonicotinoid pesticide acetamiprid (Ace), are frequently detected in the water environment, which can interact with existing heavy metal cadmium (Cd) to produce unpredicted influence. Limited studies have evaluated the effects of multiple pollutant exposures on aquatic animals. Here, we characterized the joint toxicity of Ace and Cd exposure to zebrafish (Danio rerio). The results revealed that Cd and its combined exposure with Ace had an inhibitory effect on the growth of larval zebrafish and induced morphological defects. Combined exposure to high doses of Ace and Cd could significantly reduce the levels of TG, glucose, and pyruvate in larval zebrafish. Untargeted metabolomics revealed that Cd treatment (285) produced more differentially expressed metabolites (DEMs) than Ace treatment (115), and combined treatment produced the most DEMs (294). The KEGG pathway enrichment analysis showed that they could disrupt riboflavin metabolism, amino acid metabolism, and glycolipid metabolism in the larvae of D. rerio. ELISA showed that VB2, FMN, and FAD levels were significantly increased. In addition, gene expression analysis exhibited that the mRNA levels of essential genes related to glycolipid metabolism were substantially affected, such as PK, PEPckc, PPAR-α, and FABP6. Furthermore, targeted amino acid metabolomics confirmed that both single exposure to Cd and combined exposure to Ace and Cd altered the levels of amino acids in larvae, including ALA, ARG, MET, PRO, TYR, VAL, GLY, ORN, and PHE. Taken together, exposure to Ace and Cd, alone or in combination, exerted harmful effects on the individual development, riboflavin metabolism, glycolipid metabolism, and amino acid metabolism disorder of D. rerio. These findings highlighted that more attention should be paid to the compound toxicity of chemical mixtures to aquatic organisms.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK Consortium on Metabolic Phenotyping (MAP/UK)

Targeted metabolite assays that measure tens or hundreds of pre-selected metabolites, typically using liquid chromatography-mass spectrometry, are increasingly being developed and applied to metabolic phenotyping studies. These are used both as standalone phenotyping methods and for the validation of putative metabolic biomarkers obtained from untargeted metabolomics studies. However, there are no widely accepted standards in the scientific community for ensuring reliability of the development and validation of targeted metabolite assays (referred to here as 'targeted metabolomics'). Most current practices attempt to adopt, with modifications, the strict guidance provided by drug regulatory authorities for analytical methods designed largely for measuring drugs and other xenobiotic analytes. Here, the regulatory guidance provided by the European Medicines Agency, US Food and Drug Administration and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use are summarized. In this Perspective, we have adapted these guidelines and propose a less onerous 'tiered' approach to evaluate the reliability of a wide range of metabolomics analyses, addressing the need for community-accepted, harmonized guidelines for tiers other than full validation. This 'fit-for-purpose' tiered approach comprises four levels-discovery, screening, qualification and validation-and is discussed in the context of a range of targeted and untargeted metabolomics assays. Issues arising with targeted multiplexed metabolomics assays, and how these might be addressed, are considered. Furthermore, guidance is provided to assist the community with selecting the appropriate degree of reliability for a series of well-defined applications of metabolomics.

Missing data in multi-omics integration: Recent advances through artificial intelligence

Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.