Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses

Metabolomics-based estimation of activated sludge microbial composition and prediction of filamentous bulking

Understanding the structure and activity of activated sludge (AS) microbiome is key to ensuring optimal operation of wastewater treatment processes. While high-throughput metagenomics offers a comprehensive view of AS microbiome, its cost and time demands warrant alternative approaches. This study employed machine learning methods to integrate metabolomic and metagenomic data, enabling predictions of selected microbial abundances from metabolite profiling. Model training relied on rich microbial and metabolite abundance data collected in an intensively sampled AS system, including a period of filamentous bulking, as well as a few other AS systems. Multiple linear regression out-competed other three algorithms in achieving relatively high prediction accuracy (R2 = 0.70±0.02) for the abundances of 10 selected, either keystone or core metagenome-assembled genomes (MAGs). The model predicted the abundances of filamentous Microtrichaceae and Thiotrichaceae during bulking with an error range of 14-17.8 %. This predictive power extends beyond the specific system studied, showcasing potentials for broader applications across other AS systems. Aspartate, glycine, and folate were the most influential metabolite features contributing to model performance, which were also effective indicators for filamentous bulking, with up to one week of early warning potential. This study pioneers the application of metabolomics for fast, relatively accurate and cost-effective prediction of AS community composition, enabling proactive management of AS systems towards improved efficiency and stability.

Production of Polyphenolic Natural Products by Bract-Derived Tissue Cultures of Three Medicinal Tilia spp.: A Comparative Untargeted Metabolomics Study

Medicinal plant tissue cultures are potential sources of bioactive compounds. In this study, we report the chemical characterization of the callus cultures of three medicinal Tilia spp. (Tilia cordata, Tilia vulgaris and Tilia tomentosa), along with the comparison to bracts and flowers of the same species. Our aim was to show that calli of Tilia spp. are good alternatives to the calli of T. americana for the production of polyphenols and are better sources of a subset of polyphenolic metabolites, compared to the original organs. Calli were initiated from young bracts and grown on woody plant medium containing 1 mg L-1 2,4-D and 0.1 mg L-1 BAP. For chemical characterization, a quality-controlled untargeted metabolomics approach and the quantification of several bioactive compounds was performed with the use of LC-ESI-MS/MS. While bracts and flowers contained flavonoid glycosides (astragalin, isoquercitrin) as major polyphenols, calli of all species contained catechins, coumarins (fraxin, esculin and scopoletin) and flavane aglyca. T. tomentosa calli contained 5397 µg g DW-1 catechin, 201 µg g DW-1 esculin, 218 µg g DW-1 taxifolin and 273 µg g DW-1 eriodictyol, while calli from other species contained lower amounts. T. cordata and T. tomentosa flowers were rich in isoquercitrin, containing 8134 and 6385 µg g DW-1, respectively. The currently tested species contained many of the bioactive metabolites described from T. americana. The production of catechin was shown to be comparable to the most efficient tissue cultures reported. Flowers and bracts contained flavonoid glycosides, including tiliroside, resembling bioactive fractions of T. americana. In addition, untargeted metabolomics has shown fingerprint-like differences among species, highlighting possible chemotaxonomic and quality control applications, especially for bracts.

Comparative Metabolomic Profiling of Eggs from 3 Diverse Chicken Breeds Using GC-MS Analysis

Eggs, as a crucial source of essential nutrients for consumers, possess a high nutritional value owing to their rich composition of vital components essential for human health. While previous research has extensively investigated genetic factors influencing egg quality, there has been a limited focus on exploring the impact of specific strains, particularly within the African context, on the polar metabolite profile of eggs. In this extensive study, we conducted an untargeted analysis of the chemical composition of both albumen and yolk from 3 distinct strains of hens-Blue Holland, Sasso, and Wassache-raised under identical feeding conditions. Utilizing gas chromatography coupled with mass spectrometry (GC-MS), we meticulously examined amino acids, carbohydrates, fatty acids, and other small polar metabolites. In total, 38 and 44 metabolites were identified in the whites and yolk, respectively, of the 3 studied strains. The application of chemometric analysis revealed notable differences in metabolite profiles with 8 relevant metabolites in each egg part. These metabolites include amino acids (N-α-Acetyl-L-lysine, lysine, L-valine, L-Tryptophan), fatty acids (oleic acid, linoleic acid, palmitic acid and stearic acid), and carbohydrates (d-glucose, maltose, lactose). These findings shed light on strain-specific metabolic nuances within eggs, emphasizing potential nutritional implications. The ensuing discussion delves into the diverse metabolic pathways influenced by the identified metabolites, offering insights that contribute to a broader understanding of egg composition and its significance in tailoring nutritional strategies for diverse populations.

Untargeted metabolomics for lifestyle biomarker discovery in human hair

There is a risk of crimes remaining unsolved when no matching DNA profiles or fingermarks are found. If this is the case, forensic investigations are faced with a significant shortage of evidence and information regarding the unknown perpetrator and/or victim as well as any missing persons. However, a rather commonly found biological trace encountered at crime scenes is human hair. As hair acts as a biochemical reservoir, it may contain valuable information regarding one's characteristics and habits. This study aimed to build an analytical method capable of determining a marker set of relevant metabolites in hair, eventually building up a profile of its donor. To find potential markers, an untargeted metabolomics approach was developed to select and identify statistically significant features. For that purpose, a total of 68 hair samples were collected at several hairdresser shops in varying neighbourhoods. Compound extraction was achieved via methanolic incubation overnight and analysis performed using a high-resolution mass spectrometry (HRMS) Orbitrap Q Exactive Focus. The acquired data was uploaded and statistically evaluated using two free online software/libraries, where a total of eight compounds have given a match on both tools. Their presumptive identity was confirmed using reference standards and consequently added to a dynamic target donor profiling list. These results show the potential of using untargeted metabolomics for the search for lifestyle biomarkers capable of differentiating individuals. Such tools are of paramount importance in a forensic setting with little or no evidence available and no clear tactical leads.

Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study

Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.

Biochemical characterization of Euphorbia resinifera floral cyathia

Euphorbia resinifera O. Berg is a plant endemic to the Northern and Central regions of Morocco known since the ancient Roman and Greek times for secreting a poisonous latex containing resiniferatoxin. However, E. resinifera pseudo-inflorescences called cyathia are devoid of laticifers and, therefore, do not secrete latex. Instead, they exudate nectar that local honey bees collect and craft into honey. Honey and cyathium water extracts find a broad range of applications in the traditional medicine of Northern Africa as ointments and water decoctions. Moreover, E. resinifera monofloral honey has received the Protected Geographic Indication certification for its outstanding qualities. Given the relevance of E. resinifera cyathia for bee nutrition, honey production, and the health benefit of cyathium-derived products, this study aimed to screen metabolites synthesized and accumulated in its pseudo-inflorescences. Our analyses revealed that E. resinifera cyathia accumulate primary metabolites in considerable abundance, including hexoses, amino acids and vitamins that honey bees may collect from nectar and craft into honey. Cyathia also synthesize volatile organic compounds of the class of benzenoids and terpenes, which are emitted by flowers pollinated by honey bees and bumblebees. Many specialized metabolites, including carotenoids, flavonoids, and polyamines, were also detected, which, while protecting the reproductive organs against abiotic stresses, also confer antioxidant properties to water decoctions. In conclusion, our analyses revealed that E. resinifera cyathia are a great source of antioxidant molecules and a good food source for the local foraging honeybees, revealing the central role of the flowers from this species in mediating interactions with local pollinators and the conferral of medicinal properties to plant extracts.

Identification of trans-genus biomarkers for early diagnosis of intestinal schistosomiasis and progression of gut pathology in a mouse model using metabolomics

Schistosomiasis is one of the most devastating human diseases worldwide. The disease is caused by six species of Schistosoma blood fluke; five of which cause intestinal granulomatous inflammation and bleeding. The current diagnostic method is inaccurate and delayed, hence, biomarker identification using metabolomics has been applied. However, previous studies only investigated infection caused by one Schistosoma spp., leaving a gap in the use of biomarkers for other species. No study focused on understanding the progression of intestinal disease. Therefore, we aimed to identify early gut biomarkers of infection with three Schistosoma spp. and progression of intestinal pathology. We infected 3 groups of mice, 3 mice each, with Schistosoma mansoni, Schistosoma japonicum or Schistosoma mekongi and collected their feces before and 1, 2, 4 and 8 weeks after infection. Metabolites in feces were extracted and identified using mass spectrometer-based metabolomics. Metabolites were annotated and analyzed with XCMS bioinformatics tool and Metaboanalyst platform. From >36,000 features in all conditions, multivariate analysis found a distinct pattern at each time point for all species. Pathway analysis reported alteration of several lipid metabolism pathways as infection progressed. Disturbance of the glycosaminoglycan degradation pathway was found with the presence of parasite eggs, indicating involvement of this pathway in disease progression. Biomarkers were discovered using a combination of variable importance for projection score cut-off and receiver operating characteristic curve analysis. Five molecules met our criteria and were present in all three species: 25-hydroxyvitamin D2, 1α-hydroxy-2β-(3-hydroxypropoxy) vitamin D3, Ganoderic acid Md, unidentified feature with m/z 455.3483, and unidentified feature with m/z 456.3516. These molecules were proposed as trans-genus biomarkers of early schistosomiasis. Our findings provide evidence for disease progression in intestinal schistosomiasis and potential biomarkers, which could be beneficial for early detection of this disease.

Effect of Cimetidine on Metformin Pharmacokinetics and Endogenous Metabolite Levels in Rats

Tubular secretion is a primary mechanism along with glomerular filtration for renal elimination of drugs and toxicants into urine. Organic cation transporters (OCTs) and multidrug and toxic extrusion (MATE) transporters facilitate the active secretion of cationic substrates, including drugs such as metformin and endogenous cations. We hypothesized that administration of cimetidine, an Oct/Mate inhibitor, will result in increased plasma levels and decreased renal clearance of metformin and endogenous Oct/Mate substrates in rats. A paired rat pharmacokinetic study was carried out in which metformin (5 mg/kg, intravenous) was administered as an exogenous substrate of Oct/Mate transporters to six Sprague-Dawley rats with and without cimetidine (100 mg/kg, intraperitoneal). When co-administered with cimetidine, metformin area under the curve increased significantly by 3.2-fold, and its renal clearance reduced significantly by 73%. Untargeted metabolomics was performed to investigate the effect of cimetidine on endogenous metabolome in the blood and urine samples. Over 8,000 features (metabolites) were detected in the blood, which were shortlisted using optimized criteria, i.e., a significant increase (P value < 0.05) in metabolite peak intensity in the cimetidine-treated group, reproducible retention time, and quality of chromatogram peak. The metabolite hits were classified into three groups that can potentially distinguish inhibition of i) extra-renal uptake transport or catabolism, ii) renal Octs, and iii) renal efflux transporters or metabolite formation. The metabolomics approach identified novel putative endogenous substrates of cationic transporters that could be tested as potential biomarkers to predict Oct/Mate transporter mediated drug-drug interactions in the preclinical stages. SIGNIFICANCE STATEMENT: Endogenous substrates of renal transporters in animal models could be used as potential biomarkers to predict renal drug-drug interactions in early drug development. Here we demonstrated that cimetidine, an inhibitor of organic cation transporters (Oct/Mate), could alter the pharmacokinetics of metformin and endogenous cationic substrates in rats. Several putative endogenous metabolites of Oct/Mate transporters were identified using metabolomics approach, which could be tested as potential transporter biomarkers to predict renal drug-drug interaction of Oct/Mate substrates.

Dose-response technique combined with stable isotope tracing for drug metabolite profiling by using high-resolution mass spectrometry

Background: Mass spectrometry metabolomics-based data-processing approaches have been developed for drug metabolite profiling. However, existing approaches cannot be used to comprehensively identify drug metabolites with high efficacy. Methods: Herein, we propose a two-stage data-processing approach for effective and comprehensive drug metabolite identification. The approach combines dose-response experiments with stable isotope tracing (SIT). Rosiglitazone (ROS), commonly used to treat type 2 diabetes, was employed as a model drug. Results: In the first stage of data processing, 1,071 features exhibited a dose-response relationship among 22,597 features investigated. In the second stage, these 1,071 features were screened for isotope pairs, and 200 features with isotope pairs were identified. In time-course experiments, a large proportion of the identified features (69.5%: 137 out of 200 features) were confirmed to be possible ROS metabolites. We compared the validated features identified using our approach with those identified using a previously reported approach [the mass defect filter (MDF) combined with SIT] and discovered that most of the validated features (37 out of 42) identified using the MDF-SIT combination were also successfully identified using our approach. Of the 143 validated features identified by both approaches, 74 had a proposed structure of an ROS-structure-related metabolite; the other 34 features that contained a specific fragment of ROS metabolites were considered possible ROS metabolites. Interestingly, numerous ROS-structure-related metabolites were identified in this study, most of which were novel. Conclusion: The results reveal that the proposed approach can effectively and comprehensively identify ROS metabolites.

Heterologous expression of PtAAS1 reveals the metabolic potential of the common plant metabolite phenylacetaldehyde for auxin synthesis in planta

Aromatic aldehydes and amines are common plant metabolites involved in several specialized metabolite biosynthesis pathways. Recently, we showed that the aromatic aldehyde synthase PtAAS1 and the aromatic amino acid decarboxylase PtAADC1 contribute to the herbivory-induced formation of volatile 2-phenylethanol and its glucoside 2-phenylethyl-β-D-glucopyranoside in Populus trichocarpa. To unravel alternative metabolic fates of phenylacetaldehyde and 2-phenylethylamine beyond alcohol and alcohol glucoside formation, we heterologously expressed PtAAS1 and PtAADC1 in Nicotiana benthamiana and analyzed plant extracts using untargeted LC-qTOF-MS and targeted LC-MS/MS analysis. While the metabolomes of PtAADC1-expressing plants did not significantly differ from those of control plants, expression of PtAAS1 resulted in the accumulation of phenylacetic acid (PAA) and PAA-amino acid conjugates, identified as PAA-aspartate and PAA-glutamate. Herbivory-damaged poplar leaves revealed significantly induced accumulation of PAA-Asp, while levels of PAA remained unaltered upon herbivory. Transcriptome analysis showed that members of auxin-amido synthetase GH3 genes involved in the conjugation of auxins with amino acids were significantly upregulated upon herbivory in P. trichocarpa leaves. Overall, our data indicates that phenylacetaldehyde generated by poplar PtAAS1 serves as a hub metabolite linking the biosynthesis of volatile, non-volatile herbivory-induced specialized metabolites, and phytohormones, suggesting that plant growth and defense can be balanced on a metabolic level.

Molecular Interplay between Non-Host Resistance, Pathogens and Basal Immunity as a Background for Fatal Yellowing in Oil Palm (Elaeis guineensis Jacq.) Plants

An oil palm (Elaeis guineensis Jacq.) bud rod disorder of unknown etiology, named Fatal Yellowing (FY) disease, is regarded as one of the top constraints with respect to the growth of the palm oil industry in Brazil. FY etiology has been a challenge embraced by several research groups in plant pathology throughout the last 50 years in Brazil, with no success in completing Koch's postulates. Most recently, the hypothesis of having an abiotic stressor as the initial cause of FY has gained ground, and oxygen deficiency (hypoxia) damaging the root system has become a candidate for stress. Here, a comprehensive, large-scale, single- and multi-omics integration analysis of the metabolome and transcriptome profiles on the leaves of oil palm plants contrasting in terms of FY symptomatology-asymptomatic and symptomatic-and collected in two distinct seasons-dry and rainy-is reported. The changes observed in the physicochemical attributes of the soil and the chemical attributes and metabolome profiles of the leaves did not allow the discrimination of plants which were asymptomatic or symptomatic for this disease, not even in the rainy season, when the soil became waterlogged. However, the multi-omics integration analysis of enzymes and metabolites differentially expressed in asymptomatic and/or symptomatic plants in the rainy season compared to the dry season allowed the identification of the metabolic pathways most affected by the changes in the environment, opening an opportunity for additional characterization of the role of hypoxia in FY symptom intensification. Finally, the initial analysis of a set of 56 proteins/genes differentially expressed in symptomatic plants compared to the asymptomatic ones, independent of the season, has presented pieces of evidence suggesting that breaks in the non-host resistance to non-adapted pathogens and the basal immunity to adapted pathogens, caused by the anaerobic conditions experienced by the plants, might be linked to the onset of this disease. This set of genes might offer the opportunity to develop biomarkers for selecting oil palm plants resistant to this disease and to help pave the way to employing strategies to keep the safety barriers raised and strong.

Exploring the Mechanisms behind the Anti-Tumoral Effects of Model C-Scorpionate Complexes

The growing worldwide cancer incidence, coupled to the increasing occurrence of multidrug cancer resistance, requires a continuous effort towards the identification of new leads for cancer management. In this work, two C-scorpionate complexes, [FeCl₂(κ³-Tpm)] (1) and [Co(κ³-Tpm^OH)₂](NO₃)₂ (2), (Tpm = hydrotris(pyrazol-1-yl)methane and Tpm^OH = 2,2,2-tris(pyrazol-1-yl)ethanol), were studied as potential scaffolds for future anticancer drug development. Their cytotoxicity and cell migration inhibitory activity were analyzed, and an untargeted metabolomics approach was employed to elucidate the biological processes significantly affected by these two complexes, using two tumoral cell lines (B16 and HCT116) and a non-tumoral cell line (HaCaT). While [FeCl₂(κ³-Tpm)] did not display a significant cytotoxicity, [Co(κ³-Tpm^OH)₂](NO₃)₂ was particularly cytotoxic against the HCT116 cell line. While [Co(κ³-Tpm^OH)₂](NO₃)₂ significantly inhibited cell migration in all tested cell lines, [FeCl₂(κ³-Tpm)] displayed a mixed activity. From a metabolomics perspective, exposure to [FeCl₂(κ³-Tpm)] was associated with changes in various metabolic pathways involving tyrosine, where iron-dependent enzymes are particularly relevant. On the other hand, [Co(κ³-Tpm^OH)₂](NO₃)₂ was associated with dysregulation of cell adhesion and membrane structural pathways, suggesting that its antiproliferative and anti-migration properties could be due to changes in the overall cellular adhesion mechanisms.

The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy

Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient's survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.

Osmoprotectants play a major role in the Portulaca oleracea resistance to high levels of salinity stress-insights from a metabolomics and proteomics integrated approach

Introduction

Purslane (Portulaca oleracea L.) is a non-conventional food plant used extensively in folk medicine and classified as a multipurpose plant species, serving as a source of features of direct importance to the agricultural and agri-industrial sectors. This species is considered a suitable model to study the mechanisms behind resistance to several abiotic stresses including salinity. The recently achieved technological developments in high-throughput biology opened a new window of opportunity to gain additional insights on purslane resistance to salinity stress-a complex, multigenic, and still not well-understood trait. Only a few reports on single-omics analysis (SOA) of purslane are available, and only one multi-omics integration (MOI) analysis exists so far integrating distinct omics platforms (transcriptomics and metabolomics) to characterize the response of purslane plants to salinity stress.

Methods

The present study is a second step in building a robust database on the morpho-physiological and molecular responses purslane to salinity stress and its subsequent use in attempting to decode the genetics behind its resistance to this abiotic stress. Here, the characterization of the morpho-physiological responses of adult purslane plants to salinity stress and a metabolomics and proteomics integrative approach to study the changes at the molecular level in their leaves and roots is presented.

Results and discussion

Adult plants of the B1 purslane accession lost approximately 50% of the fresh and dry weight (from shoots and roots) whensubmitted to very high salinity stress (2.0 g of NaCl/100 g of the substrate). The resistance to very high levels of salinity stress increases as the purslane plant matures, and most of the absorbed sodium remains in the roots, with only a part (~12%) reaching the shoots. Crystal-like structures, constituted mainly by Na⁺, Cl^-, and K⁺, were found in the leaf veins and intercellular space near the stoma, indicating that this species has a mechanism of salt exclusion operating on the leaves, which has its role in salt tolerance. The MOI approach showed that 41 metabolites were statistically significant on the leaves and 65 metabolites on the roots of adult purslane plants. The combination of the mummichog algorithm and metabolomics database comparison revealed that the glycine, serine, and threonine, amino sugar and nucleotide sugar, and glycolysis/gluconeogenesis pathways were the most significantly enriched pathways when considering the total number of occurrences in the leaves (with 14, 13, and 13, respectively) and roots (all with eight) of adult plants; and that purslane plants employ the adaptive mechanism of osmoprotection to mitigate the negative effect of very high levels of salinity stress; and that this mechanism is prevalent in the leaves. The multi-omics database built by our group underwent a screen for salt-responsive genes, which are now under further characterization for their potential to promote resistance to salinity stress when heterologously overexpressed in salt-sensitive plants.

Chemoselective bicyclobutane-based mass spectrometric detection of biological thiols uncovers human and bacterial metabolites

Sulfur is an essential element of life. Thiol-containing metabolites in all organisms are involved in the regulation of diverse biological processes. Especially, the microbiome produces bioactive metabolites or biological intermediates of this compound class. The analysis of thiol-containing metabolites is challenging due to the lack of specific tools, making these compounds difficult to investigate selectively. We have now developed a new methodology comprising bicyclobutane for chemoselective and irreversible capturing of this metabolite class. We utilized this new chemical biology tool immobilized onto magnetic beads for the investigation of human plasma, fecal samples, and bacterial cultures. Our mass spectrometric investigation detected a broad range of human, dietary and bacterial thiol-containing metabolites and we even captured the reactive sulfur species cysteine persulfide in both fecal and bacterial samples. The described comprehensive methodology represents a new mass spectrometric strategy for the discovery of bioactive thiol-containing metabolites in humans and the microbiome.

Application of UPLC-Triple TOF-MS/MS metabolomics strategy to reveal the dynamic changes of triterpenoid saponins during the decocting process of Asian ginseng and American ginseng

Triterpenoid saponins are the main bioactive components contributed to the nutritional value of ginseng, and different process conditions will affect their content and quality. To study the holistic characterization and dynamic changes of triterpenoid saponins in Asian ginseng (ASG) and American ginseng (AMG) during soaking and decoction, a UPLC-Triple TOF-MS/MS-based metabolomics strategy was used to characterize and discover differential saponin markers. In total, 739 triterpenoid saponins (including 225 potential new saponins) were identified from ASG and AMG in untargeted metabolomics. Based on PCA and OPLS-DA, 51 and 48 saponin markers were screened from soaked and decocted ASG and AMG, respectively. Additionally, targeted metabolomics analysis and HCA of 22 ginsenoside markers suggested that decoction of ASG and AMG for 2 h to 4 h could significantly increase the contents of rare ginsenosides (G), such as G-Rg₃, G-Rg₅, G-F₄. This study provides a scientific insight that high boiling combined with simmering enriches ASG and AMG extracts with rich rare ginsenosides that are more beneficial to human health.

Effects of extended transition milk feeding on blood metabolites of female Holstein dairy calves at 3 weeks of age: a liquid chromatography with tandem mass spectrometry-based metabolomics approach

Transition milk (TRM) is a rich source of bioactive components that promotes intestinal development and growth, and reduces the susceptibility to diarrhoea in calves. The objective of this study was to characterise the effects of replacing pasteurised waste milk (none-saleable milk containing antibiotic and/or drug residues) with pasteurised TRM for 3 wk on blood metabolites of dairy calves at 21 d of age. A total of 84 healthy newborn female Holstein calves was blocked by birth order and assigned randomly to four treatment groups with partial replacement of pasteurised waste milk by TRM (second milking after parturition) at 0 (0 L/day TRM + 6 L/day milk), 0.5 (0.5 L/day TRM + 5.5 L/day milk), 1 (1 L/day TRM + 5 L/day milk), or 2 L (2 L/day TRM + 4 L/day milk) for a 21-day period. Serum metabolome was determined by liquid chromatography with tandem mass spectrometry-based metabolomics analysis on a subset of 26 randomly selected individuals from calves fed pasteurised waste milk (CON, 6 L/d milk; n = 13) or TRM (2 L/d TRM + 4 L/d milk; n = 13) at 21 d of age. The identified metabolites (194 out of 265) were categorised according to chemical class and the number of metabolites per class in the serum, amongst which glycerophospholipids 16% (n = 43), fatty acyls 7% (n = 19), organic acids 7% (n = 18), organic heterocyclic compounds 5% (n = 13), benzenoids 5% (n = 12), sphingolipids 5% (n = 12), organic oxygen compounds 4% (n = 11), and nucleic acids 3% (n = 9), were the predominant types. Significant differences in metabolites were determined by the volcano plot. Applying the volcano plot, only two metabolites (ceramide and phosphatidylserine) were significantly different between CON and TRM. Overall, our results suggested that prolonged TRM feeding for 3 wk had little effect on the serum metabolome of the dairy calves. We speculate that the potential effects of feeding TRM for 3 wk compared with waste milk were spatially limited to affect the composition of the local gut microbial community and the growth or function of the intestinal epithelium, not allowing detection of the likely effects in the serum through a metabolomic approach.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

SWD1 epigenetically chords fungal morphogenesis, aflatoxin biosynthesis, metabolism, and virulence of Aspergillus flavus

As the main producer of aflatoxins, Aspergillus flavus is also one of the most important causes of invasive and non-invasive aspergillosis. Therefore, it is crucial to unravel the regulatory mechanisms of growth, metabolism, and pathogenicity of A. flavus. SWD1 is highly conserved across species for maintaining COMPASS methyltransferase activity, but the bio-function of SWD1 in A. flavus has not been explored. Through genetic analysis, this study revealed that SWD1 is involved in fungal morphogenesis and AFB1 biosynthesis by regulating the orthodox pathways through H3K4me1-3. Stresses sensitivity and crop models analysis revealed that SWD1 is a key regulator for the resistance of A. flavus to adapt to extreme adverse environments and to colonize crop kernels. It also revealed that the WD40 domain and 25 aa highly conserved sequence are indispensable for SWD1 in the regulation of mycotoxin bio-synthesis and fungal virulence. Metabolomic analysis inferred that SWD1 is crucial for the biosynthesis of numerous primary and secondary metabolites, regulates biological functions by reshaping the whole metabolic process, and may inhibit fungal virulence by inducing the apoptosis of mycelia through the inducer sphingosine. This study elucidates the epigenetic mechanism of SWD1 in regulating fungal pathogenicity and mycotoxin biosynthesis, and provides a potential novel target for controlling the virulence of A. flavus.

The hindgut microbiome contributes to host oxidative stress in postpartum dairy cows by affecting glutathione synthesis process

BACKGROUND

Dairy cows are susceptible to postpartum systemic oxidative stress (OS), which leads to significant production loss and metabolic disorders. The gut microbiota has been linked to host health and stress levels. However, to what extent the gut microbiota is associated with postpartum OS remains unknown. In this study, the contribution of the fecal microbiota to postpartum systemic OS and its underlying mechanisms were investigated by integrating 16S rRNA gene sequencing, metagenomics, and metabolomics in postpartum dairy cattle and by transplanting fecal microbiota from cattle to mice.

RESULTS

A strong link was found between fecal microbial composition and postpartum OS, with an explainability of 43.1%. A total of 17 significantly differential bacterial genera and 19 species were identified between cows with high (HOS) and low OS (LOS). Among them, 9 genera and 16 species showed significant negative correlations with OS, and Marasmitruncus and Ruminococcus_sp._CAG:724 had the strongest correlations. The microbial functional analysis showed that the fecal microbial metabolism of glutamine, glutamate, glycine, and cysteine involved in glutathione synthesis was lower in HOS cows. Moreover, 58 significantly different metabolites were identified between HOS and LOS cows, and of these metabolites, 19 were produced from microbiota or cometabolism of microbiota and host. Furthermore, these microbial metabolites were enriched in the metabolism of glutamine, glutamate, glycine, and cysteine. The mice gavaged with HOS fecal microbiota had significantly higher OS and lower plasma glutathione peroxidase and glutathione content than those orally administered saline or LOS fecal microbiota.

CONCLUSIONS

Integrated results suggest that the fecal microbiota is responsible for OS and that lower glutathione production plays a causative role in HOS. These findings provide novel insights into the mechanisms of postpartum OS and potential regulatory strategies to alleviate OS in dairy cows. Video Abstract.

Analysis of the urinary metabolic profiles in irradiated rats treated with Activated Protein C (APC), a potential mitigator of radiation toxicity

PURPOSE

The goal of the current study was to identify longitudinal changes in urinary metabolites following IR exposure and to determine potential alleviation of radiation toxicities by administration of recombinant APC formulations.

MATERIALS AND METHODS

Female adult WAG/RijCmcr rats were irradiated with 13.0 Gy leg-out partial body X-rays; longitudinally collected urine samples were subject to LC-MS based metabolomic profiling. Sub-cohorts of rats were treated with three variants of recombinant APC namely, rat wildtype (WT) APC, rat 3K3A mutant form of APC, and human WT APC as two bolus injections at 24 and 48 hours post IR.

RESULTS

Radiation induced robust changes in the urinary profiles leading to oxidative stress, severe dyslipidemia, and altered biosynthesis of PUFAs, glycerophospholipids, sphingolipids, and steroids. Alterations were observed in multiple metabolic pathways related to energy metabolism, nucleotide biosynthesis and metabolism that were indicative of disrupted mitochondrial function and DNA damage. On the other hand, sub-cohorts of rats that were treated with rat wildtype-APC showed alleviation of radiation toxicities, in part, at the 90-day time point, while rat 3K3A-APC showed partial alleviation of radiation induced metabolic alterations 14 days after irradiation.

CONCLUSIONS

Taken together, these results show that augmenting the Protein C pathway and activity via administration of recombinant APC may be an effective approach for mitigation of radiation induced normal tissue toxicity.

Untargeted serum metabolomics analysis of Trichinella spiralis-infected mouse

BACKGROUND

Trichinellosis, caused by a parasitic nematode of the genus Trichinella, is a zoonosis that affects people worldwide. After ingesting raw meat containing Trichinella spp. larvae, patients show signs of myalgia, headaches, and facial and periorbital edema, and severe cases may die from myocarditis and heart failure. The molecular mechanisms of trichinellosis are unclear, and the sensitivity of the diagnostic methods used for this disease are unsatisfactory. Metabolomics is an excellent tool for studying disease progression and biomarkers; however, it has never been applied to trichinellosis. We aimed to elucidate the impacts of Trichinella infection on the host body and identify potential biomarkers using metabolomics.

METHODOLOGY/PRINCIPAL FINDINGS

Mice were infected with T. spiralis larvae, and sera were collected before and 2, 4, and 8 weeks after infection. Metabolites in the sera were extracted and identified using untargeted mass spectrometry. Metabolomic data were annotated via the XCMS online platform and analyzed with Metaboanalyst version 5.0. A total of 10,221 metabolomic features were identified, and the levels of 566, 330, and 418 features were significantly changed at 2-, 4-, and 8-weeks post-infection, respectively. The altered metabolites were used for further pathway analysis and biomarker selection. A major pathway affected by Trichinella infection was glycerophospholipid metabolism, and glycerophospholipids comprised the main metabolite class identified. Receiver operating characteristic revealed 244 molecules with diagnostic power for trichinellosis, with phosphatidylserines (PS) being the primary lipid class. Some lipid molecules, e.g., PS (18:0/19:0)[U] and PA (O-16:0/21:0), were not present in metabolome databases of humans and mice, thus they may have been secreted by the parasites.

CONCLUSIONS/SIGNIFICANCE

Our study highlighted glycerophospholipid metabolism as the major pathway affected by trichinellosis, hence glycerophospholipid species are potential markers of trichinellosis. The findings of this study represent the initial steps in biomarker discovery that may benefit future trichinellosis diagnosis.

Divergence in metabolomic profile in clownfish and damselfish skin mucus

Introduction

The clownfish - sea anemone mutualism was suggested to have triggered the adaptive radiation of clownfishes, but the origin of clownfish resistance to stinging tentacles of host anemones remains unclear. The presence of specific compounds in the mucus of clownfishes conferring them the unique ability to prevent nematocyst discharge from their hosts has been the most supported hypothesis. Yet the mystery regarding the types of compounds found in clownfish skin mucus remains unsolved.

Methods

We analyzed the chemical composition of clownfish and damselfish mucus using an untargeted metabolomics (HILIC-HRMS) and lipidomics (RPLC-HRMS) approach.

Results and Discussion

The polar and lipid metabolome signatures were highly specific and allowed to discriminate between the clownfish and damselfish clades. The most discriminative part of the signature was the sphingolipid profile, displaying a broader diversity of ceramides present in significantly higher levels in clownfish mucus. Importantly, the inter-specific variability of metabolic signature was significantly higher in clownfishes, although their diversification is evolutionarily more recent, thus implying the impact of symbiosis on metabolic variability and adaptation. Furthermore, specialists and generalists clownfish species displayed distinctive metabolite signature. Two strict clownfish specialists, which are phylogenetically distant but share the same host species, clustered together based on their molecular signature, suggesting a link with their mutualistic nature. Overall, comparative analyses of metabolic signatures highlight differences in chemical composition of clownfish mucus and provide insight into biochemical pathways potentially implicated in clownfish adaptation to inhabit sea anemones and consequently diversify.

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

Quality-controlled LC-ESI-MS food metabolomics of fenugreek (Trigonella foenum-graecum) sprouts: Insights into changes in primary and specialized metabolites

Fenugreek (Trigonella foenum-graecum L.) is an important food and spice with bioactive compounds against diabetes. In this study, fenugreek seeds germinating in darkness for 72 h were studied using quantification of trigonelline and 4-hydroxyisoleucine and an LC-ESI-MS/MS-based metabolomic approach capable of accurately estimating 237 features from various primary and specialized compound classes. During germination, the concentrations of trigonelline and 4-hydroxyisoleucine rose by 33.5% and 33.3%, respectively. At the same time, untargeted metabolomics revealed 9 putative flavonoids increasing 1.19- to 2.77-fold compared to the dormant seeds. A set of 19 steroid saponins rose by 1.08- to 31.86-fold. Primary metabolites however showed much more variability: abundance changes in amino acid derivatives, peptides and saccharides fell in the 0.09- to 22.25-fold, 0.93- to 478.79-fold and 0.36- to 941.58-fold ranges, respectively. To increase biosynthesis of specialized metabolites during germination, sprouts were exposed to 1-100 mM methyl jasmonate (MeJA) and methyl salicylate (MeSA). The hormone treatments affected normal metabolism: 67.1-83.1 % and 64.1-83.5 % of compounds showed a reduction compared to the controls in 100 mM MeJA and MeSA treatments at different sampling time points. Contrary to expectations, the abundance of flavonoids decreased, compared to the control sprouts (0.75- and 0.68-fold change medians, respectively). The same was observed for most, but not all steroid saponins. The quality-controlled untargeted metabolomics approach proved to yield excellent insight into the metabolic changes during germination of fenugreek. The results suggest that although fenugreek germination causes major shifts in plant metabolism, there are no major qualitative changes in bioactive specialized metabolites during the first three days. This stability likely translates into good bioactivity that is similar to that of the seeds. Because the large changes in the primary metabolites likely alter the nutritive value of the seed, further studies are warranted.

Multi-omics Analysis of Young Portulaca oleracea L. Plants' Responses to High NaCl Doses Reveals Insights into Pathways and Genes Responsive to Salinity Stress in this Halophyte Species

Soil salinity is among the abiotic stressors that threaten agriculture the most, and purslane (Portulaca oleracea L.) is a dicot species adapted to inland salt desert and saline habitats that hyper accumulates salt and has high phytoremediation potential. Many researchers consider purslane a suitable model species to study the mechanisms of plant tolerance to drought and salt stresses. Here, a robust salinity stress protocol was developed and used to characterize the morphophysiological responses of young purslane plants to salinity stress; then, leaf tissue underwent characterization by distinct omics platforms to gain further insights into its response to very high salinity stress. The salinity stress protocol did generate different levels of stress by gradients of electrical conductivity at field capacity and water potential in the saturation extract of the substrate, and the morphological parameters indicated three distinct stress levels. As expected from a halophyte species, these plants remained alive under very high levels of salinity stress, showing salt crystal-like structures constituted mainly by Na+, Cl-, and K+ on and around closed stomata. A comprehensive and large-scale metabolome and transcriptome single and integrated analyses were then employed using leaf samples. The multi-omics integration (MOI) system analysis led to a data-set of 51 metabolic pathways with at least one enzyme and one metabolite differentially expressed due to salinity stress. These data sets (of genes and metabolites) are valuable for future studies aimed to deepen our knowledge on the mechanisms behind the high tolerance of this species to salinity stress. In conclusion, besides showing that this species applies salt exclusion already in young plants to support very high levels of salinity stress, the initial analysis of metabolites and transcripts data sets already give some insights into other salt tolerance mechanisms used by this species to support high levels of salinity stress.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00061-2.

Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC-MS

Region of interest (ROI) extraction is a fundamental step in analyzing metabolomic datasets acquired by liquid chromatography-mass spectrometry (LC-MS). However, noises and backgrounds in LC-MS data often affect the quality of extracted ROIs. Therefore, developing effective ROI evaluation algorithms is necessary to eliminate false positives meanwhile keep the false-negative rate as low as possible. In this study, a deep fused filter of ROIs (dffROI) was proposed to improve the accuracy of ROI extraction by combining the handcrafted evaluation metrics with convolutional neural network (CNN)-learned representations. To evaluate the performance of dffROI, dffROI was compared with peakonly (CNN-learned representation) and five handcrafted metrics on three LC-MS datasets and a gas chromatography-mass spectrometry (GC-MS) dataset. Results show that dffROI can achieve higher accuracy, better true-positive rate, and lower false-positive rate. Its accuracy, true-positive rate, and false-positive rate are 0.9841, 0.9869, and 0.0186 on the test set, respectively. The classification error rate of dffROI (1.59%) is significantly reduced compared with peakonly (2.73%). The model-agnostic feature importance demonstrates the necessity of fusing handcrafted evaluation metrics with the convolutional neural network representations. dffROI is an automatic, robust, and universal method for ROI filtering by virtue of information fusion and end-to-end learning. It is implemented in Python programming language and open-sourced at https://github.com/zhanghailiangcsu/dffROI under BSD License. Furthermore, it has been integrated into the KPIC2 framework previously proposed by our group to facilitate real metabolomic LC-MS dataset analysis.

Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data

In untargeted liquid chromatography‒mass spectrometry (LC‒MS) metabolomics studies, data preprocessing and metabolic pathway recognition are crucial for screening important pathways that are disturbed by diseases or restored by drugs. Here, we collected high-resolution mass spectrometry data of serum samples from 221 coronary heart disease (CHD) patients under two different chromatographic columns (BEH amide and C₁₈ column) and evaluated the three commonly used software programs (XCMS, Progenesis QI, MarkerView) from four aspects (including signal drift, peak number, metabolite annotation and metabolic pathway enrichment). The results showed that the data preprocessed by the three software programs have different degrees of signal drift, but the StatTarget could improve the data quality to meet the data analysis requirement after correction. In addition, XCMS surpassed other software in detection of real chromatographic peaks and Progenesis QI was the best performer in terms of the number of metabolite annotation. XCMS and Progenesis QI showed different performance in pathway enrichment. However, metabolic pathways based on the combination of XCMS and Progenesis QI had a high coincidence with Progenesis QI. In addition, we also reported that C₁₈ and amide columns were highly complementary and have great potential for cooperation in the context of metabolic pathways. In this study, the effects of different chromatographic columns and software pretreatments on metabolomics data were evaluated based on clinical large cohort samples, which will provide a reference for the metabolomics of clinical samples and guide subsequent mechanistic research.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

Metabolomics Applied to Cyanobacterial Toxins and Natural Products

The biological and chemical diversity of Cyanobacteria is remarkable. These ancient prokaryotes are widespread in nature and can be found in virtually every habitat on Earth where there is light and water. They are producers of an array of secondary metabolites with important ecological roles, toxic effects, and biotechnological applications. The investigation of cyanobacterial metabolites has benefited from advances in analytical tools and bioinformatics that are employed in metabolomic analyses. In this chapter, we review selected articles highlighting the use of targeted and untargeted metabolomics in the analyses of secondary metabolites produced by cyanobacteria. Here, cyanobacterial secondary metabolites have been didactically divided into toxins and natural products according to their relevance to toxicological studies and drug discovery, respectively. This review illustrates how metabolomics has improved the chemical analysis of cyanobacteria in terms of speed, sensitivity, selectivity, and/or coverage, allowing for broader and more complex scientific questions.

Comparison of Phytochemical Profiles of Wild and Cultivated American Ginseng Using Metabolomics by Ultra-High Performance Liquid Chromatography-High-Resolution Mass Spectrometry

American ginseng (Panax quinquefolius L.) has been recognized as a valuable herb medicine, and ginsenosides are the most important components responsible for the health-beneficial effects. This study investigated the secondary metabolites responsible for the differentiation of wild and cultivated American ginsengs with ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomic approach. An in-house ginsenoside library was developed to facilitate data processing and metabolite identification. Data visualization methods, such as heatmaps and volcano plots, were utilized to extract discriminated ion features. The results suggested that the ginsenoside profiles of wild and cultivated ginsengs were significantly different. The octillol (OT)-type ginsenosides were present in greater abundance and diversity in wild American ginsengs; however, a wider distribution of the protopanaxadiol (PPD)-and oleanolic acid (OA)-type ginsenosides were found in cultivated American ginseng. Based on the tentative identification and semi-quantification, the amounts of five ginsenosides (i.e., notoginsenoside H, glucoginsenoside Rf, notoginsenoside R1, pseudoginsenoside RT2, and ginsenoside Rc) were 2.3-54.5 fold greater in wild ginseng in comparison to those in their cultivated counterparts, and the content of six ginsenosides (chicusetsusaponin IVa, malonylginsenoside Rd, pseudoginsenoside Rc1, malonylfloralginsenoside Rd6, Ginsenoside Rd, and malonylginsenoside Rb1) was 2.6-14.4 fold greater in cultivated ginseng compared to wild ginseng. The results suggested that the in-house metabolite library can significantly reduce the complexity of the data processing for ginseng samples, and UHPLC-HRMS is effective and robust for identifying characteristic components (marker compounds) for distinguishing wild and cultivated American ginseng.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Gender differences contribute to variability of serum lipid biomarkers for Alzheimer's disease

Background: Alzheimer's disease (AD) cannot currently be diagnosed by a blood test. One reason may be gender differences. Another may be the statistical methods used. The authors evaluate these possibilities. Objective: The authors applied serum lipidomics to find AD biomarkers in men and women. They hypothesized that AD biomarkers would differ between genders and that machine-learning algorithms would improve diagnostic performance. Methods: Serum lipids were analyzed by mass spectrometry for a training set of AD cases and controls and in a blinded test set. Statistical analyses considered gender differences. Results: Lipids best classifying AD subjects differed significantly between men and women. Robust statistical algorithms did not improve diagnostic performance. Conclusion: Poor performance of AD biomarkers appears to be due primarily to inherent variability in AD patients.

Comparative Analysis of Host Metabolic Alterations in Murine Malaria Models with Uncomplicated or Severe Malaria

Malaria varies in severity, with complications ranging from uncomplicated to severe malaria. Severe malaria could be attributed to peripheral hyperparasitemia or cerebral malaria. The metabolic interactions between the host and Plasmodium species are yet to be understood during these infections of varied pathology and severity. An untargeted metabolomics approach utilizing the liquid chromatography-mass spectrometry platform has been used to identify the affected host metabolic pathways and associated metabolites in the serum of murine malaria models with uncomplicated malaria, hyperparasitemia, and experimental cerebral malaria. We report that mice with malaria share similar metabolic attributes like higher levels of bile acids, bile pigments, and steroid hormones that have been reported for human malaria infections. Moreover, in severe malaria, upregulated levels of metabolites like phenylalanine, histidine, valine, pipecolate, ornithine, and pantothenate, with decreased levels of arginine and hippurate, were observed. Metabolites of sphingolipid metabolism were upregulated in experimental cerebral malaria. Higher levels of 20-hydroxy-leukotriene B₄ and epoxyoctadecamonoenoic acids were found in uncomplicated malaria, with lower levels observed for experimental cerebral malaria. Our study provides insights into host biology during different pathological stages of malaria disease and would be useful for the selection of animal models for evaluating diagnostic and therapeutic interventions against malaria. The raw data files are available via MetaboLights with the identifier MTBLS4387.

Analysis of Streptomyces Volatilomes Using Global Molecular Networking Reveals the Presence of Metabolites with Diverse Biological Activities

Streptomyces species produce a wide variety of specialized metabolites, some of which are used for communication or competition for resources in their natural environments. In addition, many natural products used in medicine and industry are derived from Streptomyces, and there has been interest in their capacity to produce volatile organic compounds (VOCs) for different industrial and agricultural applications. Recently, a machine-learning workflow called MSHub/GNPS was developed, which enables auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data, molecular networking, and library search capabilities, but it has not been applied to Streptomyces volatilomes. In this study, 131 Streptomyces isolates from the island of Newfoundland were phylogenetically typed, and 37 were selected based on their phylogeny and growth characteristics for VOC analysis using both a user-guided (conventional) and an MSHub/GNPS-based approach. More VOCs were annotated by MSHub/GNPS than by the conventional method. The number of unknown VOCs detected by the two methods was higher than those annotated, suggesting that many novel compounds remain to be identified. The molecular network generated by GNPS can be used to guide the annotation of such unknown VOCs in future studies. However, the number of overlapping VOCs annotated by the two methods is relatively small, suggesting that a combination of analysis methods might be required for robust volatilome analysis. More than half of the VOCs annotated with high confidence by the two approaches are plant-associated, many with reported bioactivities such as insect behavior modulation. Details regarding the properties and reported functions of such VOCs are described. IMPORTANCE This study represents the first detailed analysis of Streptomyces volatilomes using MSHub/GNPS, which in combination with a routinely used conventional method led to many annotations. More VOCs could be annotated using MSHub/GNPS as compared to the conventional method, many of which have known antimicrobial, anticancer, and insect behavior-modulating activities. The identification of numerous plant-associated VOCs by both approaches in the current study suggests that their production could be a more widespread phenomenon by members of the genus, highlighting opportunities for their large-scale production using Streptomyces. Plant-associated VOCs with antimicrobial activities, such as 1-octen-3-ol, octanol, and phenylethyl alcohol, have potential applications as fumigants. Furthermore, many of the annotated VOCs are reported to influence insect behavior, alluding to a possible explanation for their production based on the functions of other recently described Streptomyces VOCs in dispersal and nutrient acquisition.

Enhancing Genome-Scale Model by Integrative Exometabolome and Transcriptome: Unveiling Carbon Assimilation towards Sphingolipid Biosynthetic Capability of Cordyceps militaris

Cordyceps militaris is an industrially important fungus, which is often used in Asia as traditional medicine. There has been a published genome-scale metabolic model (GSMM) of C. militaris useful for predicting its growth behaviors; however, lipid metabolism, which plays a vital role in cellular functions, remains incomplete in the GSMM of C. militaris. A comprehensive study on C. militaris was thus performed by enhancing GSMM through integrative analysis of metabolic footprint and transcriptome data. Through the enhanced GSMM of C. militaris (called iPC1469), it contained 1469 genes, 1904 metabolic reactions and 1229 metabolites. After model evaluation, in silico growth simulation results agreed well with the experimental data of the fungal growths on different carbon sources. Beyond the model-driven integrative data analysis, interestingly, we found key metabolic responses in alteration of lipid metabolism in C. militaris upon different carbon sources. The sphingoid bases (e.g., sphinganine, sphingosine, and phytosphingosine) and ceramide were statistically significant accumulated in the xylose culture when compared with other cultures; this study suggests that the sphingolipid biosynthetic capability in C. militaris was dependent on the carbon source assimilated for cell growth; this finding provides a comprehensive basis for the sphingolipid biosynthesis in C. militaris that can help to further redesign its metabolic control for medicinal and functional food applications.

Identification and quantification of bioactive compounds suppressing SARS-CoV-2 signals in wastewater-based epidemiology surveillance

Recent SARS-CoV-2 wastewater-based epidemiology (WBE) surveillance have documented a positive correlation between the number of COVID-19 patients in a sewershed and the level of viral genetic material in the wastewater. Efforts have been made to use the wastewater SARS-CoV-2 viral load to predict the infected population within each sewershed using a multivariable regression approach. However, reported clear and sustained variability in SARS-CoV-2 viral load among treatment facilities receiving industrial wastewater have made clinical prediction challenging. Several classes of molecules released by regional industries and manufacturing facilities, particularly the food processing industry, can significantly suppress the SARS-CoV-2 signals in wastewater by breaking down the lipid-bilayer of the membranes. Therefore, a systematic ranking process in conjugation with metabolomic analysis was developed to identify the wastewater treatment facilities exhibiting SARS-CoV-2 suppression and identify and quantify the chemicals suppressing the SARS-COV-2 signals. By ranking the viral load per diagnosed case among the sewersheds, we successfully identified the wastewater treatment facilities in Missouri, USA that exhibit SARS-CoV-2 suppression (significantly lower than 5 × 1011 gene copies/reported case) and determined their suppression rates. Through both untargeted global chemical profiling and targeted analysis of wastewater samples, 40 compounds were identified as candidates of SARS-CoV-2 signal suppressors. Among these compounds, 14 had higher concentrations in wastewater treatment facilities that exhibited SARS-CoV-2 signal suppression compared to the unsuppressed control facilities. Stepwise regression analyses indicated that 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethylene glycol dioleate are positively correlated with SARS-CoV-2 signal suppression rates. Suppression activities were further confirmed by incubation studies, and the suppression kinetics for each bioactive compound were determined. According to the results of these experiments, bioactive molecules in wastewater can significantly reduce the stability of SARS-CoV-2 genetic marker signals. Based on the concentrations of these chemical suppressors, a correction factor could be developed to achieve more reliable and unbiased surveillance results for wastewater treatment facilities that receive wastewater from similar industries.

mTORC1 and mTORC2 Complexes Regulate the Untargeted Metabolomics and Amino Acid Metabolites Profile through Mitochondrial Bioenergetic Functions in Pancreatic Beta Cells

Background: Pancreatic beta cells regulate bioenergetics efficiency and secret insulin in response to glucose and nutrient availability. The mechanistic Target of Rapamycin (mTOR) network orchestrates pancreatic progenitor cell growth and metabolism by nucleating two complexes, mTORC1 and mTORC2. Objective: To determine the impact of mTORC1/mTORC2 inhibition on amino acid metabolism in mouse pancreatic beta cells (Beta-TC-6 cells, ATCC-CRL-11506) using high-resolution metabolomics (HRM) and live-mitochondrial functions. Methods: Pancreatic beta TC-6 cells were incubated for 24 h with either: RapaLink-1 (RL); Torin-2 (T); rapamycin (R); metformin (M); a combination of RapaLink-1 and metformin (RLM); Torin-2 and metformin (TM); compared to the control. We applied high-resolution mass spectrometry (HRMS) LC-MS/MS untargeted metabolomics to compare the twenty natural amino acid profiles to the control. In addition, we quantified the bioenergetics dynamics and cellular metabolism by live-cell imaging and the MitoStress Test XF24 (Agilent, Seahorse). The real-time, live-cell approach simultaneously measures the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) to determine cellular respiration and metabolism. Statistical significance was assessed using ANOVA on Ranks and post-hoc Welch t-Tests. Results: RapaLink-1, Torin-2, and rapamycin decreased L-aspartate levels compared to the control (p = 0.006). Metformin alone did not affect L-aspartate levels. However, L-asparagine levels decreased with all treatment groups compared to the control (p = 0.03). On the contrary, L-glutamate and glycine levels were reduced only by mTORC1/mTORC2 inhibitors RapaLink-1 and Torin-2, but not by rapamycin or metformin. The metabolic activity network model predicted that L-aspartate and AMP interact within the same activity network. Live-cell bioenergetics revealed that ATP production was significantly reduced in RapaLink-1 (122.23 ± 33.19), Torin-2 (72.37 ± 17.33) treated cells, compared to rapamycin (250.45 ± 9.41) and the vehicle control (274.23 ± 38.17), p < 0.01. However, non-mitochondrial oxygen consumption was not statistically different between RapaLink-1 (67.17 ± 3.52), Torin-2 (55.93 ± 8.76), or rapamycin (80.01 ± 4.36, p = 0.006). Conclusions: Dual mTORC1/mTORC2 inhibition by RapaLink-1 and Torin-2 differentially altered the amino acid profile and decreased mitochondrial respiration compared to rapamycin treatment which only blocks the FRB domain on mTOR. Third-generation mTOR inhibitors may alter the mitochondrial dynamics and reveal a bioenergetics profile that could be targeted to reduce mitochondrial stress.

Multi-omics study identifies novel signatures of DNA/RNA, amino acid, peptide, and lipid metabolism by simulated diabetes on coronary endothelial cells

Coronary artery endothelial cells (CAEC) exert an important role in the development of cardiovascular disease. Dysfunction of CAEC is associated with cardiovascular disease in subjects with type 2 diabetes mellitus (T2DM). However, comprehensive studies of the effects that a diabetic environment exerts on this cellular type are scarce. The present study characterized the molecular perturbations occurring on cultured bovine CAEC subjected to a prolonged diabetic environment (high glucose and high insulin). Changes at the metabolite and peptide level were assessed by Liquid Chromatography–Mass Spectrometry (LC–MS²) and chemoinformatics. The results were integrated with published LC–MS²-based quantitative proteomics on the same in vitro model. Our findings were consistent with reports on other endothelial cell types and identified novel signatures of DNA/RNA, amino acid, peptide, and lipid metabolism in cells under a diabetic environment. Manual data inspection revealed disturbances on tryptophan catabolism and biosynthesis of phenylalanine-based, glutathione-based, and proline-based peptide metabolites. Fluorescence microscopy detected an increase in binucleation in cells under treatment that also occurred when human CAEC were used. This multi-omics study identified particular molecular perturbations in an induced diabetic environment that could help unravel the mechanisms underlying the development of cardiovascular disease in subjects with T2DM.

Dissecting Enhanced Carbohydrate and Pigment Productivity in Mutants of Nannochloropsis oculata Using Metabolomics and Lipidomics

Random mutagenesis is an effective strategy for enhancing cellular traits. In this study, we used the mutagen ethyl methanesulfonate to create fast-growing Nannochloropsis oculata mutants. When cultivated in a photobioreactor with a diel cycle, two mutants exhibited 2.2-fold higher carbohydrate productivity and 3.5-4.0-fold higher pigment productivity than the wild type, while one of them also showed 2.5-fold higher lipid productivity. A comprehensive physiological, metabolomic, and lipidomic study showed that the mutants had high levels of glucose-, galactose-, and xylose-based carbohydrates. Their high growth rate was attributed to increased chlorophyll a content, improved nitrogen assimilation, storage, and recycling, and low monogalactosyldiacyl glycerol/digalactosyldiacyl glycerol ratio, which was responsible for higher biomass productivity. The investigation revealed upregulation of lipid precursors, shedding light on high lipid accumulation. The derived algae strains are capable of increasing the biosynthesis of value-added storage molecules without impairing growth, rendering them promising candidates for commercial development in future biorefineries.

Insights from a Multi-Omics Integration (MOI) Study in Oil Palm (Elaeis guineensis Jacq.) Response to Abiotic Stresses: Part One—Salinity

Oil palm (Elaeis guineensis Jacq.) is the number one source of consumed vegetable oil nowadays. It is cultivated in areas of tropical rainforest, where it meets its natural condition of high rainfall throughout the year. The palm oil industry faces criticism due to a series of practices that was considered not environmentally sustainable, and it finds itself under pressure to adopt new and innovative procedures to reverse this negative public perception. Cultivating this oilseed crop outside the rainforest zone is only possible using artificial irrigation. Close to 30% of the world’s irrigated agricultural lands also face problems due to salinity stress. Consequently, the research community must consider drought and salinity together when studying to empower breeding programs in order to develop superior genotypes adapted to those potential new areas for oil palm cultivation. Multi-Omics Integration (MOI) offers a new window of opportunity for the non-trivial challenge of unraveling the mechanisms behind multigenic traits, such as drought and salinity tolerance. The current study carried out a comprehensive, large-scale, single-omics analysis (SOA), and MOI study on the leaves of young oil palm plants submitted to very high salinity stress. Taken together, a total of 1239 proteins were positively regulated, and 1660 were negatively regulated in transcriptomics and proteomics analyses. Meanwhile, the metabolomics analysis revealed 37 metabolites that were upregulated and 92 that were downregulated. After performing SOA, 436 differentially expressed (DE) full-length transcripts, 74 DE proteins, and 19 DE metabolites underwent MOI analysis, revealing several pathways affected by this stress, with at least one DE molecule in all three omics platforms used. The Cysteine and methionine metabolism (map00270) and Glycolysis/Gluconeogenesis (map00010) pathways were the most affected ones, each one with 20 DE molecules.

Molecular Mechanisms behind Safranal's Toxicity to HepG2 Cells from Dual Omics

The spice saffron (Crocus sativus) has anticancer activity in several human tissues, but the molecular mechanisms underlying its potential therapeutic effects are poorly understood. We investigated the impact of safranal, a small molecule secondary metabolite from saffron, on the HCC cell line HepG2 using untargeted metabolomics (HPLC–MS) and transcriptomics (RNAseq). Increases in glutathione disulfide and other biomarkers for oxidative damage contrasted with lower levels of the antioxidants biliverdin IX (139-fold decrease, p = 5.3 × 10⁵), the ubiquinol precursor 3-4-dihydroxy-5-all-trans-decaprenylbenzoate (3-fold decrease, p = 1.9 × 10⁻⁵), and resolvin E1 (−3282-fold decrease, p = 4⁵), which indicates sensitization to reactive oxygen species. We observed a significant increase in intracellular hypoxanthine (538-fold increase, p = 7.7 × 10⁻⁶) that may be primarily responsible for oxidative damage in HCC after safranal treatment. The accumulation of free fatty acids and other biomarkers, such as S-methyl-5′-thioadenosine, are consistent with safranal-induced mitochondrial de-uncoupling and explains the sharp increase in hypoxanthine we observed. Overall, the dual omics datasets describe routes to widespread protein destabilization and DNA damage from safranal-induced oxidative stress in HCC cells.

Identifying congeners and transformation products of organic contaminants within complex chemical mixtures in impacted surface waters with a top-down non-targeted screening workflow

Over 350,000 compounds are registered for production and use including a high number of congeners found in complex chemical mixtures (CCMs). With such a high number of chemicals being released in the environment and degraded into transformation products (TPs), the challenge of identifying contaminants by non-targeted screening (NTS) is massive. "Bottom-up" studies, where compounds are subjected to conditions simulating environmental degradation to identify new TPs, are time consuming and cannot be relied upon to study the TPs of hundreds of thousands of compounds. Therefore, the development of "top-down" workflows, where the structural elucidation of unknown compounds is carried directly on the sample, is of interest. In this study, a top-down NTS workflow was developed using molecular networking and clustering (MNC). A total of 438 compounds were identified including 176 congeners of consumer product additives and 106 TPs. Reference standards were used to confirm the identification of 53 contaminants among them lesser-known pharmaceuticals (aliskiren, sitagliptin) and consumer product additives (lauramidopropyl betaine, 2,2,4-trimethyl-1,2-dihydroquinoline). The MNC tools allowed to group similar TPs and congeners together. As such, several previously unknown TPs of pesticides (metolachlor) and pharmaceuticals (gliclazide, irbesartan) were identified as tentative candidates or probable structures. Moreover, some congeners that had no entry on global repositories (PubChem, ChemSpider) were identified as probable structures. The workflow worked efficiently with oligomers containing ethylene oxide moieties, and with TPs structurally related to their parent compounds. The top-down approach shown in this study addresses several issues with the identification of congeners of industrial compounds from CCMs. Furthermore, it allows elucidating the structure of TPs directly from samples without relying on bottom-up studies under conditions discussed herein. The top-down workflow and the MNC tools show great potential for data mining and retrospective analysis of previous NTS studies.

Identification of covalent inhibitors that disrupt M. tuberculosis growth by targeting multiple serine hydrolases involved in lipid metabolism

The increasing incidence of antibiotic-resistant Mycobacterium tuberculosis infections is a global health threat necessitating the development of new antibiotics. Serine hydrolases (SHs) are a promising class of targets because of their importance for the synthesis of the mycobacterial cell envelope. We screen a library of small molecules containing serine-reactive electrophiles and identify narrow-spectrum inhibitors of M. tuberculosis growth. Using these lead molecules, we perform competitive activity-based protein profiling and identify multiple SH targets, including enzymes with uncharacterized functions. Lipidomic analyses of compound-treated cultures reveal an accumulation of free lipids and a substantial decrease in lipooligosaccharides, linking SH inhibition to defects in cell envelope biogenesis. Mutant analysis reveals a path to resistance via the synthesis of mycocerates, but not through mutations to SH targets. Our results suggest that simultaneous inhibition of multiple SH enzymes is likely to be an effective therapeutic strategy for the treatment of M. tuberculosis infections.

Deep Untargeted Metabolomics Analysis to Further Characterize the Adaptation Response of Gliricidia sepium (Jacq.) Walp. to Very High Salinity Stress

The multipurpose tree Gliricidia sepium (Jacq.) Walp. adapts to a very high level of salt stress (≥20 dS m^-1) and resumes the production of new leaves around 2 weeks after losing all leaves due to abrupt salinity stress. The integration of metabolome and transcriptome profiles from gliricidia leaves points to a central role of the phenylpropanoid biosynthesis pathway in the short-term response to salinity stress. In this study, a deeper untargeted metabolomics analysis of the leaves and roots of young gliricidia plants was conducted to characterize the mechanism(s) behind this adaptation response. The polar and lipidic fractions from leaf and root samples were extracted and analyzed on a UHPLC.ESI.Q-TOF.HRMS system. Acquired data were analyzed using the XCMS Online, and MetaboAnalyst platforms, via three distinct and complementary strategies. Together, the results obtained first led us to postulate that these plants are salt-excluding plants, which adapted to high salinity stress via two salt-excluding mechanisms, starting in the canopy-severe defoliation-and concluding in the roots-limited entry of Na. Besides that, it was possible to show that the phenylpropanoid biosynthesis pathway plays a role throughout the entire adaptation response, starting in the short term and continuing in the long one. The roots metabolome analysis revealed 11 distinct metabolic pathways affected by salt stress, and the initial analysis of the two most affected ones-steroid biosynthesis and lysine biosynthesis-led us also to postulate that the accumulation of lignin and some phytosterols, as well as lysine biosynthesis-but not degradation, play a role in promoting the adaptation response. However, additional studies are necessary to investigate these hypotheses.

Steroid hormones of the octopus self-destruct system

Among all invertebrates, soft-bodied cephalopods have the largest central nervous systems and the greatest brain-to-body mass ratios, yet unlike other big-brained animals, cephalopods are unusually short lived.^1-5 Primates and corvids survive for many decades, but shallow-water octopuses, such as the California two-spot octopus (Octopus bimaculoides), typically live for only 1 year.^6,7 Lifespan and reproduction are controlled by the principal neuroendocrine center of the octopus: the optic glands, which are functional analogs to the vertebrate pituitary gland.^8-10 After mating, females steadfastly brood their eggs, begin fasting, and undergo rapid physiological decline, featuring repeated self-injury and leading to death.¹¹ Removal of the optic glands completely reverses this life history trajectory,¹⁰ but the signaling factors underlying this major life transition are unknown. Here, we characterize the major secretions and steroidogenic pathways of the female optic gland using mass spectrometry techniques. We find that at least three pathways are mobilized to increase synthesis of select sterol hormones after reproduction. One pathway generates pregnane steroids, known in other animals to support reproduction.^12-16 Two other pathways produce 7-dehydrocholesterol and bile acid intermediates, neither of which were previously known to be involved in semelparity. Our results provide insight into invertebrate cholesterol pathways and confirm a remarkable unity of steroid hormone biology in life history processes across Bilateria.

Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species

A metabolomic ratio rule-based classification method was developed and programmed for automated metabolite profiling and differentiation of four major cinnamon species using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). The computational program identifies key cinnamon metabolites, including proanthocyanidins, cinnamaldehyde, and coumarin, from test samples through LC-MS data processing and assigns cinnamon species by critical metabolite ratios using a stepwise classification strategy. Further, 100% classification accuracy was achieved on the training sample set through critical ratio optimization, and over 95% accuracy was achieved on the validation sample set. The proposed cinnamon classification method exhibited superior accuracy compared to the metabolomic-based PLS-DA modeling method and offered great value for the authentication of cinnamon samples and evaluation of their potential health benefits.

Computational Workflow to Study the Diversity of Secondary Metabolites in Fourteen Different Isatis Species

The screening of real features among thousands of ions remains a great challenge in the study of metabolomics. In this research, a workflow designed based on the MetaboFR tool and “feature-rating” rule was developed to screen the real features in large-scale data analyses. Seventy-four reference standards were used to test the feasibility, with 83.21% of real features being obtained after MetaboFR processing. Moreover, the full workflow was applied for systematic characterization of 14 species of the genus Isatis, with the result that 87.72% of real features were retained and 69.19% of the in-source fragments were removed. To gain insights into metabolite diversity within this plant family, 1697 real features were tentatively identified, including lipids, phenylpropanoids, organic acids, indole derivatives, etc. Indole derivatives were demonstrated to be the best chemical markers with which to differentiate different species. The rare existence of indole derivatives in Isatis cappadocica (cap) and Isatis cappadocica subsp. Steveniana (capS) indicates that the biosynthesis of indole derivatives could play a key role in driving the chemical diversity and evolution of genus Isatis. Our workflow provides the foundations for the exploration of real features in metabolomics, and has the potential to reveal the chemical composition and marker metabolites of secondary metabolites in plant fields.