METLIN: A Technology Platform for Identifying Knowns and Unknowns

Amelioration of AlCl3-induced Memory Loss in the Rats by an Aqueous Extract of Guduchi, a Medhya Rasayana

Background

In ayurvedic practice, the Guduchi ( Tinospora cordifolia (Willd.) Miers) stem is used as a Medhya drug for its beneficial effects on memory improvement.

Objectives

The current study was planned to explore the Medhya properties of the Guduchi stem extract by observing its ameliorating effect on AlCl3-induced neurotoxicity in rats that acted as a chronic model of memory loss.

Materials and Methods

The aqueous extract of the Guduchi stem was prepared per the Ayurvedic Pharmacopoeia of India and administered to the AlCl3-treated Wistar rats for 42 days. The biochemical assessment of the brain tissues of the treated animals was done by the acetylcholinesterase (AChE) inhibition assay, protein expression, and oxidative stress assays, namely lipid peroxidation, reduced glutathione, superoxide dismutase, and catalase assay. The neurobehavioral assessment was done using the elevated plus maze (EPM) test.

Results

The EPM test revealed that treatment with Guduchi extract showed marked improvement of memory status in rats along with reduced oxidative stress, and a marked modulation of the AChE inhibition and expression of AChE tubulin proteins.

Conclusion

The results substantiate the Medhya properties of the Guduchi. Detailed investigations are required to be carried out to explore the precise mechanism of the neuroprotective action of the Guduchi stem extract against the AlCl3-induced neurotoxicity in rats.

Gut microbiome lipid metabolism and its impact on host physiology

Metabolites produced by commensal gut microbes impact host health through their recognition by the immune system and their influence on numerous metabolic pathways. Notably, the gut microbiota can both transform and synthesize lipids as well as break down dietary lipids to generate secondary metabolites with host modulatory properties. Although lipids have largely been consigned to structural roles, particularly in cell membranes, recent research has led to an increased appreciation of their signaling activities, with potential impacts on host health and physiology. This review focuses on studies that highlight the functions of bioactive lipids in mammalian physiology, with a special emphasis on immunity and metabolism.

Investigating the Resistance Mechanism of Wheat Varieties to Fusarium Head Blight Using Comparative Metabolomics

Fusarium head blight (FHB) is primarily caused by Fusarium graminearum and severely reduces wheat yield, causing mycotoxin contamination in grains and derived products. F. graminearum-secreted chemical toxins stably accumulate in plant cells, disturbing host metabolic homeostasis. We determined the potential mechanisms underlying FHB resistance and susceptibility in wheat. Three representative wheat varieties (Sumai 3, Yangmai 158, and Annong 8455) were inoculated with F. graminearum and their metabolite changes were assessed and compared. In total, 365 differentiated metabolites were successfully identified. Amino acids and derivatives, carbohydrates, flavonoids, hydroxycinnamate derivatives, lipids, and nucleotides constituted the major changes in response to fungal infection. Changes in defense-associated metabolites, such as flavonoids and hydroxycinnamate derivatives, were dynamic and differed among the varieties. Nucleotide and amino acid metabolism and the tricarboxylic acid cycle were more active in the highly and moderately resistant varieties than in the highly susceptible variety. We demonstrated that two plant-derived metabolites, phenylalanine and malate, significantly suppressed F. graminearum growth. The genes encoding the biosynthetic enzymes for these two metabolites were upregulated in wheat spike during F. graminearum infection. Thus, our findings uncovered the metabolic basis of resistance and susceptibility of wheat to F. graminearum and provided insights into engineering metabolic pathways to enhance FHB resistance in wheat.

Metabolomics Profiling of Age-Associated Metabolites in Malay Population

Aging is a complex process characterized by progressive loss of functional abilities due to the accumulation of molecular damages. Metabolomics could offer novel insights into the predictors and mechanisms of aging. This cross-sectional study is aimed at identifying age-associated plasma metabolome in a Malay population. A total of 146 (90 females) healthy participants aged 28-69 were selected for the study. Untargeted metabolomics profiling was performed using liquid chromatography-tandem mass spectrometry. Association analysis was based on the general linear model. Gender-associated metabolites were adjusted for age, while age-associated metabolites were adjusted for gender or analyzed in a gender-stratified manner. Gender-associated metabolites such as 4-hydroxyphenyllactic acid, carnitine, cortisol, and testosterone sulfate showed higher levels in males than females. Deoxycholic acid and hippuric acid were among the metabolites with a positive association with age after being adjusted for gender, while 9(E),11(E)-conjugated linoleic acid, cortisol, and nicotinamide were negatively associated with age. In gender-stratified analysis, glutamine was one of the common metabolites that showed a direct association with age in both genders, while metabolites such as 11-deoxy prostaglandin F2β, guanosine monophosphate, and testosterone sulfate were inversely associated with age in males and females. This study reveals several age-associated metabolites in Malays that could reflect the changes in metabolisms during aging and may be used to discern the risk of geriatric syndromes and disorders later. Further studies are required to determine the interplay between these metabolites and environmental factors on the functional outcomes during aging.

Reconstruction of Glutathione Metabolism in the Neuronal Model of Rotenone-Induced Neurodegeneration Using Mass Isotopologue Analysis with Hydrophilic Interaction Liquid Chromatography-Zeno High-Resolution Multiple Reaction Monitoring

Accurate reconstruction of metabolic pathways is an important prerequisite for interpreting metabolomics changes and understanding the diverse biological processes in disease models. A tracer-based metabolomics strategy utilizes stable isotope-labeled precursors to resolve complex pathways by tracing the labeled atom(s) to downstream metabolites through enzymatic reactions. Isotope enrichment analysis is informative and achieved by counting total labeled atoms and acquiring the mass isotopologue distribution (MID) of the intact metabolite. However, quantitative analysis of labeled metabolite substructures/moieties (MS² fragments) can offer more valuable insights into the reaction connections through measuring metabolite transformation. In order to acquire the isotopic labeling information at the intact metabolite and moiety level simultaneously, we developed a method that couples hydrophilic interaction liquid chromatography (HILIC) with Zeno trap-enabled high-resolution multiple reaction monitoring (MRM^HR). The method enabled accurate and reproducible MID quantification for intact metabolites as well as their fragmented moieties, with notably high sensitivity in the MS² fragmentation mode based on the measurement of ¹³C- or ¹⁵N-labeled cellular samples. The method was applied to human-induced pluripotent stem cell-derived neurons to trace the fate of ¹³C/¹⁵N atoms from D-¹³C₆-glucose/L-¹⁵N₂-glutamine added to the media. With the MID analysis of both intact metabolites and fragmented moieties, we validated the pathway reconstruction of de novo glutathione synthesis in mid-brain neurons. We discovered increased glutathione oxidization from both basal and newly synthesized glutathione pools under neuronal oxidative stress. Furthermore, the significantly decreased de novo glutathione synthesis was investigated and associated with altered activities of several key enzymes, as evidenced by suppressed glutamate supply via glucose metabolism and a diminished flux of glutathione synthetic reaction in the neuronal model of rotenone-induced neurodegeneration.

Elevated 18:0 lysophosphatidylcholine contributes to the development of pain in tissue injury

ABSTRACT

Tissue injuries, including burns, are major causes of death and morbidity worldwide. These injuries result in the release of intracellular molecules and subsequent inflammatory reactions, changing the tissues' chemical milieu and leading to the development of persistent pain through activating pain-sensing primary sensory neurons. However, the majority of pain-inducing agents in injured tissues are unknown. Here, we report that, amongst other important metabolite changes, lysophosphatidylcholines (LPCs) including 18:0 LPC exhibit significant and consistent local burn injury-induced changes in concentration. 18:0 LPC induces immediate pain and the development of hypersensitivities to mechanical and heat stimuli through molecules including the transient receptor potential ion channel, vanilloid subfamily, member 1, and member 2 at least partly via increasing lateral pressure in the membrane. As levels of LPCs including 18:0 LPC increase in other tissue injuries, our data reveal a novel role for these lipids in injury-associated pain. These findings have high potential to improve patient care.

Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets

Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.

Strength in Numbers: Density-Dependent Volatile-Induced Antimicrobial Activity by Xanthomonas perforans

For most of the 20th century, Xanthomonas euvesicatoria was the only known bacterium associated with bacterial spot of tomato in Florida. X. perforans quickly replaced X. euvesicatoria, mainly because of production of three bacteriocins (BCNs) against X. euvesicatoria; however, X. perforans outcompeted X. euvesicatoria even when the three known BCNs were deleted. Surprisingly, we observed antimicrobial activity against X. euvesicatoria in the BCN triple mutant when the triple mutant was grown in Petri plates containing multiple spots but not in Petri plates containing only one spot. We determined that changes in the headspace composition (i.e., volatiles) rather than a diffusible signal in the agar were required for induction of the antimicrobial activity. Other Xanthomonas species also produced volatile-induced antimicrobial compounds against X. euvesicatoria and elicited antimicrobial activity by X. perforans. A wide range of plant pathogenic bacteria, including Clavibacter michiganensis subsp. michiganensis, Pantoea stewartii, and Pseudomonas cichorii, also elicited antimicrobial activity by X. perforans when multiple spots of the species were present. To identify potential antimicrobial compounds, we performed liquid chromatography with high-resolution mass spectrometry of the agar surrounding the spot in the high cell density Petri plates where the antimicrobial activity was present compared with agar surrounding the spot in Petri plates with one spot where antimicrobial activity was not observed. Among the compounds identified in the zone of inhibition were N-butanoyl-L-homoserine lactone and N-(3-hydroxy-butanoyl)-homoserine lactone, which are known quorum-sensing metabolites in other bacteria.

Metabolite fingerprinting: A powerful metabolomics approach for marker identification and functional gene annotation

Non-targeted metabolome approaches aim to detect metabolite markers related to stress, disease, developmental or genetic perturbation. In the later context, it is also a powerful means for functional gene annotation. A prerequisite for non-targeted metabolome analyses are methods for comprehensive metabolite extraction. We present three extraction protocols for a highly efficient extraction of metabolites from plant material with a very broad metabolite coverage. The presented metabolite fingerprinting workflow is based on liquid chromatography high resolution accurate mass spectrometry (LC-HRAM-MS), which provides suitable separation of the complex sample matrix for the analysis of compounds of different polarity by positive and negative electrospray ionization and mass spectrometry. The resulting data sets are then analyzed with the software suite MarVis and the web-based interface MetaboAnalyst. MarVis offers a straightforward workflow for statistical analysis, data merging as well as visualization of multivariate data, while MetaboAnalyst is used in our hands as complementary software for statistics, correlation networks and figure generation. Finally, MarVis provides access to species-specific metabolite and pathway data bases like KEGG and BioCyc and to custom data bases tailored by the user to connect the identified markers or features with metabolites. In addition, identified marker candidates can be interactively visualized and inspected in metabolic pathway maps by KEGG pathways for a more detailed functional annotation and confirmed by mass spectrometry fragmentation experiments or coelution with authentic standards. Together this workflow is a valuable toolbox to identify novel metabolites, metabolic steps or regulatory principles and pathways.

Efficacy of bile acid profiles in diagnosing and staging of alcoholic liver disease

AIM

The diagnosis of alcoholic liver disease (ALD) is still a great challenge. Therefore, the purpose of this study is to identify and characterize new metabolomic biomarkers for the diagnosis and staging of ALD.

METHODS

A total of 127 patients with early liver injury, 40 patients with alcoholic cirrhosis (ALC) and 40 healthy controls were included in this study. Patients with early liver injury included 45 patients with alcoholic liver disease (ALD), 40 patients with non-alcoholic fatty liver disease (NAFLD) and 40 patients with viral liver disease (VLD). The differential metabolites in serum samples were analyzed using ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry, and partial metabolites in the differential metabolic pathway were identified by liquid chromatography- tandem mass spectrometry.

RESULTS

A total of 40 differential metabolites and five differential metabolic pathways in the four groups of patients with early liver disease and healthy controls were found, and the metabolic pathway of primary bile acid (BA) biosynthesis was the pathway that included the most differential metabolites. Therefore, 22 BA profiles were detected. The results revealed that the changes of BA profiles were most pronounced in patients with ALD compared with patients with NAFLD and VLD, in whom 12 differential BAs were diagnostic markers of ALD (AUC = 0.883). The 19 differential BAs in ALC and ALD were diagnostic markers of the stage of alcoholic hepatic fibrosis (AUC = 0.868).

CONCLUSION

BA profiles are potential indicators in the diagnosis of ALD and evaluation of different stages.

Circulating Metabolites Associated with Albuminuria in a Hispanic/Latino Population

BACKGROUND

Albuminuria is associated with metabolic abnormalities, but these relationships are not well understood. We studied the association of metabolites with albuminuria in Hispanic/Latino people, a population with high risk for metabolic disease.

METHODS

We used data from 3736 participants from the Hispanic Community Health Study/Study of Latinos, of which 16% had diabetes and 9% had an increased urine albumin-to-creatinine ratio (UACR). Metabolites were quantified in fasting serum through nontargeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Spot UACR was inverse normally transformed and tested for the association with each metabolite or combined, correlated metabolites, in covariate-adjusted models that accounted for the study design. In total, 132 metabolites were available for replication in the Hypertension Genetic Epidemiology Network study ( n =300), and 29 metabolites were available for replication in the Malmö Offspring Study ( n =999).

RESULTS

Among 640 named metabolites, we identified 148 metabolites significantly associated with UACR, including 18 novel associations that replicated in independent samples. These metabolites showed enrichment for D-glutamine and D-glutamate metabolism and arginine biosynthesis, pathways previously reported for diabetes and insulin resistance. In correlated metabolite analyses, we identified two modules significantly associated with UACR, including a module composed of lipid metabolites related to the biosynthesis of unsaturated fatty acids and alpha linolenic acid and linoleic acid metabolism.

CONCLUSIONS

Our study identified associations of albuminuria with metabolites involved in glucose dysregulation, and essential fatty acids and precursors of arachidonic acid in Hispanic/Latino population.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_02_08_CJN09070822.mp3.

Chemical Triggering Cyanobacterial Glycogen Accumulation: Methyl Viologen Treatment Increases Synechocystis sp. PCC 6803 Glycogen Storage by Enhancing Levels of Gene Transcript and Substrates in Glycogen Synthesis

Two-stage cultivation is effective for glycogen production by cyanobacteria. Cells were first grown under adequate nitrate supply (BG11) to increase biomass and subsequently transferred to nitrogen deprivation (-N) to stimulate glycogen accumulation. However, the two-stage method is time-consuming and requires extensive energy. Thus, one-stage cultivation that enables both cell growth and glycogen accumulation is advantageous. Such one-stage method could be achieved using a chemical triggering glycogen storage. However, there is a limited study on such chemicals. Here, nine compounds previously reported to affect cyanobacterial cellular functions were examined in Synechocystis sp. PCC 6803. 2-Phenylethanol, phenoxyethanol, 3-(3,4-dichlorophenyl)-1,1-dimethylurea and methyl viologen can stimulate glycogen accumulation. The oxidative stress agent, methyl viologen significantly increased glycogen levels up to 57% and 69% [w/w dry weight (DW)] under BG11 and -N cultivation, respectively. One-stage cultivation where methyl viologen was directly added to the pre-grown culture enhanced glycogen storage to 53% (w/w DW), compared to the 10% (w/w DW) glycogen level of the control cells without methyl viologen. Methyl viologen treatment reduced the contents of total proteins (including phycobiliproteins) but caused increased transcript levels of glycogen synthetic genes and elevated levels of metabolite substrates for glycogen synthesis. Metabolomic results suggested that upon methyl viologen treatment, proteins degraded to amino acids, some of which could be used as a carbon source for glycogen synthesis. Results of oxygen evolution and metabolomic analysis suggested that photosynthesis and carbon fixation were not completely inhibited upon methyl viologen treatment, and these two processes may partially generate upstream metabolites required for glycogen synthesis.

Bioactive compounds as an alternative for the sugarcane industry: Towards an integrative approach

Here, a comprehensive review of sugarcane industrialization and its relationship with bioactive compounds (BCs) detected in various products and by-products generated during its processing is presented. Furthermore, it is discussed how these compounds have revealed important antioxidant, antineoplastic, antidiabetic, and antimicrobial activities. From this bibliographic research highlights the significance of two types of BCs of natural origin (phenolic compounds (PCs) and terpenoids) and a group of compounds synthesized during industrial transformation processes (Maillard reaction products (MRPs)). It was found that most of the studies about the BCs from sugarcane have been conducted by identifying, isolating, and analyzing ones or a few compounds at a specific period, this being a conventional approach. However, given the complexity of the synthesis processes of all these BCs and the biological activities they can manifest in a specific biological context, novel approaches are needed to address these analyses holistically. To overcome this challenge, integrating massive and multiscale methods, such as omics sciences, seems necessary to enrich these studies. This work is intended to contribute to the state of the art that could support future research about the exploration, characterization, or evaluation of different bioactive molecules from sugarcane and its derivatives.

Supplementation with Tribulus Terrestris Extract Exhibits Protective Effects on MCAO Rats via Modulating Inflammation-Related Metabolic and Signaling Pathways

The extract of Tribulus terrestris (TT) has been used as a component of several nutritional supplements for enhancing human vitality. However, its protective effect on ischemic stroke has yet to be fully investigated. In this study, the middle cerebral artery occlusion (MCAO) rat model was established and treated with gross saponin of TT fruit (GSTTF) by gavage to explore its anti-ischemic stroke efficacy. Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics was applied to profile the brain tissue metabolite changes and further obtain the metabolic pathways that were greatly involved in the efficacy of GSTTF. Subsequent molecular biology experiments were applied to validate the findings from metabolomics analysis. The results showed that GSTTF administration remarkably decreased the infarction volume of brain tissue and improved the neurobehavioral scores of MCAO rats. Metabolomics analysis revealed that pathways, including glycerophospholipid metabolism, sphingolipid metabolism, and arachidonic acid metabolism, were considered associated with the protective effect of GSTTF against MCAO, which were greatly involved in the inflammatory responses. The results of the biochemistry analysis showed that GSTTF treatment significantly reduced the levels of TNF-α and IL-6 in brain tissue after MCAO. The anti-inflammatory mechanism of GSTTF was further investigated, which revealed that GSTTF could inhibit the TLR4/MyD88/NF-κB signaling pathway to exert protective effects on MCAO. This study provides the underlying anti-inflammatory mechanism of GSTTF for ischemic stroke protection, which has important implications for the development of GSTTF-related functional foods or food supplements.

An exposome connectivity paradigm for the mechanistic assessment of the effects of prenatal and early life exposure to metals on neurodevelopment

The exposome paradigm through an integrated approach to investigating the impact of perinatal exposure to metals on child neurodevelopment in two cohorts carried out in Slovenia (PHIME cohort) and Greece (HERACLES cohort) respectively, is presented herein. Heavy metals are well-known neurotoxicants with well-established links to impaired neurodevelopment. The links between in utero and early-life exposure to metals, metabolic pathway dysregulation, and neurodevelopmental disorders were drawn through urinary and plasma untargeted metabolomics analysis, followed by the combined application of in silico and biostatistical methods. Heavy metal prenatal and postnatal exposure was evaluated, including parameters indirectly related to exposure and health adversities, such as sociodemographic and anthropometric parameters and dietary factors. The primary outcome of the study was that the identified perturbations related to the TCA cycle are mainly associated with impaired mitochondrial respiration, which is detrimental to cellular homeostasis and functionality; this is further potentiated by the capacity of heavy metals to induce oxidative stress. Insufficient production of energy from the mitochondria during the perinatal period is associated with developmental disorders in children. The HERACLES cohort included more detailed data regarding diet and sociodemographic status of the studied population, allowing the identification of a broader spectrum of effect modifiers, such as the beneficial role of a diet rich in antioxidants such as lycopene and ω-3 fatty acids, the negative effect the consumption of food items such as pork and chicken meat has or the multiple impacts of fish consumption. Beyond diet, several other factors have been proven influential for child neurodevelopment, such as the proximity to pollution sources (e.g., waste treatment site) and the broader living environment, including socioeconomic and demographic characteristics. Overall, our results demonstrate the utility of exposome-wide association studies (EWAS) toward understanding the relationships among the multiple factors that determine human exposure and the underlying biology, reflected as omics markers of effect on neurodevelopment during childhood.

Molecular insights into sex-specific metabolic alterations in Alzheimer's mouse brain using multi-omics approach

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized by altered cellular metabolism in the brain. Several of these alterations have been found to be exacerbated in females, known to be disproportionately affected by AD. We aimed to unravel metabolic alterations in AD at the metabolic pathway level and evaluate whether they are sex-specific through integrative metabolomic, lipidomic, and proteomic analysis of mouse brain tissue.

METHODS

We analyzed male and female triple-transgenic mouse whole brain tissue by untargeted mass spectrometry-based methods to obtain a molecular signature consisting of polar metabolite, complex lipid, and protein data. These data were analyzed using multi-omics factor analysis. Pathway-level alterations were identified through joint pathway enrichment analysis or by separately evaluating lipid ontology and known proteins related to lipid metabolism.

RESULTS

Our analysis revealed significant AD-associated and in part sex-specific alterations across the molecular signature. Sex-dependent alterations were identified in GABA synthesis, arginine biosynthesis, and in alanine, aspartate, and glutamate metabolism. AD-associated alterations involving lipids were also found in the fatty acid elongation pathway and lysophospholipid metabolism, with a significant sex-specific effect for the latter.

CONCLUSIONS

Through multi-omics analysis, we report AD-associated and sex-specific metabolic alterations in the AD brain involving lysophospholipid and amino acid metabolism. These findings contribute to the characterization of the AD phenotype at the molecular level while considering the effect of sex, an overlooked yet determinant metabolic variable.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Metabolomics analysis of antiquitin deficiency in cultured human cells and plasma: Relevance to pyridoxine-dependent epilepsy

Deficiency of antiquitin (α-aminoadipic semialdehyde dehydrogenase), an enzyme involved in lysine degradation and encoded by ALDH7A1, is the major cause of vitamin B₆ -dependent epilepsy (PDE-ALDH7A1). Despite seizure control with high dose pyridoxine (PN), developmental delay still occurs in approximately 70% of patients. We aimed to investigate metabolic perturbations due to possible previously unidentified roles of antiquitin, which may contribute to developmental delay, as well as metabolic effects of high dose pyridoxine supplementation reflecting the high doses used for seizure control in patients with PDE-ALDH7A1. Untargeted metabolomics by high resolution mass spectrometry (HRMS) was used to analyze plasma of patients with PDE-ALDH7A1 and two independently generated lines of cultured ReNcell CX human neuronal progenitor cells (NPCs) with CRISPR/Cas mediated antiquitin deficiency. Accumulation of lysine pathway metabolites in antiquitin-deficient NPCs and western-blot analysis confirmed knockdown of ALDH7A1. Metabolomics analysis of antiquitin-deficient NPCs in conditions of lysine restriction and PN supplementation identified changes in metabolites related to the transmethylation and transsulfuration pathways and osmolytes, indicating a possible unrecognized role of antiquitin outside the lysine degradation pathway. Analysis of plasma samples of PN treated patients with PDE-ALDH7A1 and antiquitin-deficient NPCs cultured in conditions comparable to the patient plasma samples demonstrated perturbation of metabolites of the gamma-glutamyl cycle, suggesting potential oxidative stress-related effects in PN-treated patients with PDE-ALDH7A1. We postulate that a model of human NPCs with CRISPR/Cas mediated antiquitin deficiency is well suited to characterize previously unreported roles of antiquitin, relevant to this most prevalent form of pyridoxine-dependent epilepsy.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

"Candidatus Hydrogenisulfobacillus filiaventi" strain R50 gen. nov. sp. nov., a highly efficient producer of extracellular organic compounds from H2 and CO2

Production of organic molecules is largely depending on fossil fuels. A sustainable alternative would be the synthesis of these compounds from CO₂ and a cheap energy source, such as H₂, CH₄, NH₃, CO, sulfur compounds or iron(II). Volcanic and geothermal areas are rich in CO₂ and reduced inorganic gasses and therefore habitats where novel chemolithoautotrophic microorganisms for the synthesis of organic compounds could be discovered. Here we describe "Candidatus Hydrogenisulfobacillus filiaventi" R50 gen. nov., sp. nov., a thermoacidophilic, autotrophic H₂-oxidizing microorganism, that fixed CO₂ and excreted no less than 0.54 mol organic carbon per mole fixed CO₂. Extensive metabolomics and NMR analyses revealed that Val, Ala and Ile are the most dominant form of excreted organic carbon while the aromatic amino acids Tyr and Phe, and Glu and Lys were present at much lower concentrations. In addition to these proteinogenic amino acids, the excreted carbon consisted of homoserine lactone, homoserine and an unidentified amino acid. The biological role of the excretion remains uncertain. In the laboratory, we noticed the production under high growth rates (0.034 h^-1, doubling time of 20 h) in combination with O₂-limitation, which will most likely not occur in the natural habitat of this strain. Nevertheless, this large production of extracellular organic molecules from CO₂ may open possibilities to use chemolithoautotrophic microorganisms for the sustainable production of important biomolecules.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Blood Plasma Metabolome Profiling at Different Stages of Renal Cell Carcinoma

Early diagnostics significantly improves the survival of patients with renal cell carcinoma (RCC), which is the prevailing type of adult kidney cancer. However, the absence of clinically obvious symptoms and effective screening strategies at the early stages result to disease progression and survival rate reducing. The study was focused on revealing of potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from 51 non-cancer volunteers (control) and 78 patients with different RCC subtypes and stages (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC) and advanced stages of ccRCC) was performed. Comparative analysis of the blood plasma metabolites between the control and cancer groups provided the detection of metabolites associated with different tumor stages. The designed model based on the revealed metabolites demonstrated high diagnostic power and accuracy. Overall, using the metabolomics approach the study revealed the metabolites demonstrating a high value for design of plasma-based test to improve early ccRCC diagnosis.

Drug discovery inspired by bioactive small molecules from nature

Natural products (NPs) have greatly contributed to the development of novel treatments for human diseases such as cancer, metabolic disorders, and infections. Compared to synthetic chemical compounds, primary and secondary metabolites from medicinal plants, fungi, microorganisms, and our bodies are promising resources with immense chemical diversity and favorable properties for drug development. In addition to the well-validated significance of secondary metabolites, endogenous small molecules derived from central metabolism and signaling events have shown great potential as drug candidates due to their unique metabolite-protein interactions. In this short review, we highlight the values of NPs, discuss recent scientific and technological advances including metabolomics tools, chemoproteomics approaches, and artificial intelligence-based computation platforms, and explore potential strategies to overcome the current challenges in NP-driven drug discovery.

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.

New metabolic signature for Chagas disease reveals sex steroid perturbation in humans and mice

The causative agent of Chagas disease (CD), Trypanosoma cruzi, claims thousands of lives each year. Current diagnostic tools are insufficient to ensure parasitological detection in chronically infected patients has been achieved. A host-derived metabolic signature able to distinguish CD patients from uninfected individuals and assess antiparasitic treatment efficiency is introduced. Serum samples were collected from chronic CD patients, prior to and three years after treatment, and subjected to untargeted metabolomics analysis against demographically matched CD-negative controls. Five metabolites were confirmed by high-resolution tandem mass spectrometry. Several database matches for sex steroids were significantly altered in CD patients. A murine experiment corroborated sex steroid perturbation in T. cruzi-infected mice, particularly in male animals. Proteomics analysis also found increased steroidogenesis in the testes of infected mice. Metabolic alterations identified in this study shed light on the pathogenesis and provide the basis for developing novel assays for the diagnosis and screening of CD patients.

VPS34-dependent control of apical membrane function of proximal tubule cells and nutrient recovery by the kidney

The lipid kinase VPS34 orchestrates autophagy, endocytosis, and metabolism and is implicated in cancer and metabolic disease. The proximal tubule in the kidney is a key metabolic organ that controls reabsorption of nutrients such as fatty acids, amino acids, sugars, and proteins. Here, by combining metabolomics, proteomics, and phosphoproteomics analyses with functional and superresolution imaging assays of mice with an inducible deficiency in proximal tubular cells, we revealed that VPS34 controlled the metabolome of the proximal tubule. In addition to inhibiting pinocytosis and autophagy, VPS34 depletion induced membrane exocytosis and reduced the abundance of the retromer complex necessary for proper membrane recycling and lipid retention, leading to a loss of fuel and biomass. Integration of omics data into a kidney cell metabolomic model demonstrated that VPS34 deficiency increased β-oxidation, reduced gluconeogenesis, and enhanced the use of glutamine for energy consumption. Furthermore, the omics datasets revealed that VPS34 depletion triggered an antiviral response that included a decrease in the abundance of apically localized virus receptors such as ACE2. VPS34 inhibition abrogated SARS-CoV-2 infection in human kidney organoids and cultured proximal tubule cells in a glutamine-dependent manner. Thus, our results demonstrate that VPS34 adjusts endocytosis, nutrient transport, autophagy, and antiviral responses in proximal tubule cells in the kidney.

Flavor Profiling Using Comprehensive Mass Spectrometry Analysis of Metabolites in Tomato Soups

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having "garlic-like" and "onion-like" attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix.

Enhanced polyglucan contents in divergent cyanobacteria under nutrient-deprived photoautotrophy: transcriptional and metabolic changes in response to increased glycogen accumulation in nitrogen-deprived Synechocystis sp. PCC 6803

Cyanobacteria accumulate polyglucan as main carbohydrate storage. Here, the cellular polyglucan content was determined in 27 cyanobacterial strains from 25 genera. The polyglucan contents were significantly enhanced in 20 and 23 strains under nitrogen (-N) and phosphate (-P) deprivation, respectively. High polyglucan accumulation was not associated with particular evolutionary groups but was strain specific. The highest polyglucan accumulations of 46.2% and 52.5% (w/w dry weight; DW) were obtained under -N in Synechocystis sp. PCC 6803 (hereafter Synechocystis) and Chroococcus limneticus, respectively. In Synechocystis, 80-97% (w/w) of the polyglucan was glycogen. Transcriptome and metabolome analyses during glycogen accumulation under -N were determined in Synechocystis. The genes responsible for the supply of the substrates for glycogen synthesis: glycerate-1,3-phosphate and fructose-1,6-phosphate, were significantly up-regulated. The genes encoding the enzymes converting succinate to malate in TCA cycle, were significantly down-regulated. The genes encoding the regulator proteins which inhibits metabolism at lower part of glycolysis pathway, were also significantly up-regulated. The transcript levels of PII protein and the level of 2-oxoglutarate, which form a complex that inhibits lower part of glycolysis pathway, were significantly increased. Thus, the increased Synechocystis glycogen accumulation under -N was likely to be mediated by the increased supply of glycogen synthesis substrates and metabolic inhibitions at lower part of glycolysis pathway and TCA cycle.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion

MOTIVATION

Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe-metabolite-disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed.

RESULTS

In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe-metabolite, metabolite-disease and disease-microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease-microbe-metabolite graph, we used the bipartite graph attention network on the disease-microbe and metabolite-microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease-metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson's disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases.Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git.

Multi-Bioactivity of Protein Digests and Peptides from Oat (Avena sativa L.) Kernels in the Prevention of the Cardiometabolic Syndrome

The aim of this study was to characterize the digests and peptides derived from oat kernel proteins in terms of their major enzyme inhibitory activities related to the prevention of cardiometabolic syndrome. It also entailed the characteristics of antioxidant bioactivity of the analyzed material. The study was carried out using coupled in silico and in vitro methods. The additional goal was to investigate whether identified peptides can pervade Caco-2 cells. Based on the results of bioinformatic analysis, it was found that the selected oat proteins may be a potential source of 107 peptides with DPP-IV and/or ACE inhibitory and/or antioxidant activity. The duodenal digest of oat kernels revealed multiple activities. It inhibited the activities of the following enzymes: DPP-IV (IC₅₀ = 0.51 vs. 10.82 mg/mL of the intact protein), α-glucosidase (IC₅₀ = 1.55 vs. 25.20 mg/mL), and ACE (IC₅₀ = 0.82 vs. 34.52 mg/mL). The DPPH^• scavenging activity was 35.7% vs. 7.93% that of the intact protein. After in silico digestion of oat proteins, 24 peptides were selected for identification using LC-Q-TOF-MS/MS. Among them, 13 sequences were successfully identified. One of them, i.e., VW peptide, exhibited triple activities, i.e., DPP-IV and ACE inhibitory and DPPH^• scavenging activity. The multifunctional peptides: PW, TF, VF, and VW, were identified in the basolateral samples after transport experiments. Both in silico and in vitro analyses demonstrated that oat kernel proteins were the abundant sources of bioactive digests and peptides to be used in a diet for patients suffering from cardiometabolic syndrome.

Metabolite Profiling of Microwave-Assisted Sargassum fusiforme Extracts with Improved Antioxidant Activity Using Hybrid Response Surface Methodology and Artificial Neural Networking-Genetic Algorithm

Sargassum fusiforme (SF) is a popular edible brown macroalga found in Korea, Japan, and China and is known for its health-promoting properties. In this study, we used two sophisticated models to obtain optimized conditions for high antioxidant activity and metabolite profiling using high-resolution mass spectrometry. A four-factor central composite design was used to optimize the microwave-assisted extraction and achieve the maximum antioxidant activities of DPPH (Y1: 28.01 % inhibition), ABTS (Y2: 36.07 % inhibition), TPC (Y3: 43.65 mg GAE/g), and TFC (Y4: 17.67 mg CAE/g), which were achieved under the optimized extraction conditions of X1: 47.67 %, X2: 2.96 min, X3: 139.54 °C, and X4: 600.00 W. Moreover, over 79 secondary metabolites were tentatively identified, of which 12 compounds were reported for the first time in SF, including five phenolic (isopropyl 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate, 3,4-dihydroxyphenylglycol, scopoletin, caffeic acid 4-sulfate, and cinnamoyl glucose), two flavonoids (4',7-dihydroxyisoflavone and naringenin), three phlorotannins (diphlorethohydroxycarmalol, dibenzodioxin-1,3,6,8-tetraol, and fucophlorethol), and two other compounds (dihydroxyphenylalanine and 5-hydroxybenzofuran-2(3H)-one) being identified for the first time in optimized SF extract. These compounds may also be involved in improving the antioxidant potential of the extract. Therefore, optimized models can provide better estimates and predictive capabilities that would assist in finding new bioactive compounds with improved biological activities that can be further applied at a commercial level.

Chemoinformatics-driven classification of Angiosperms using sulfur-containing compounds and machine learning algorithm

BACKGROUND

Phytochemicals or secondary metabolites are low molecular weight organic compounds with little function in plant growth and development. Nevertheless, the metabolite diversity govern not only the phenetics of an organism but may also inform the evolutionary pattern and adaptation of green plants to the changing environment. Plant chemoinformatics analyzes the chemical system of natural products using computational tools and robust mathematical algorithms. It has been a powerful approach for species-level differentiation and is widely employed for species classifications and reinforcement of previous classifications.

RESULTS

This study attempts to classify Angiosperms using plant sulfur-containing compound (SCC) or sulphated compound information. The SCC dataset of 692 plant species were collected from the comprehensive species-metabolite relationship family (KNApSAck) database. The structural similarity score of metabolite pairs under all possible combinations (plant species-metabolite) were determined and metabolite pairs with a Tanimoto coefficient value > 0.85 were selected for clustering using machine learning algorithm. Metabolite clustering showed association between the similar structural metabolite clusters and metabolite content among the plant species. Phylogenetic tree construction of Angiosperms displayed three major clades, of which, clade 1 and clade 2 represented the eudicots only, and clade 3, a mixture of both eudicots and monocots. The SCC-based construction of Angiosperm phylogeny is a subset of the existing monocot-dicot classification. The majority of eudicots present in clade 1 and 2 were represented by glucosinolate compounds. These clades with SCC may have been a mixture of ancestral species whilst the combinatorial presence of monocot-dicot in clade 3 suggests sulphated-chemical structure diversification in the event of adaptation during evolutionary change.

CONCLUSIONS

Sulphated chemoinformatics informs classification of Angiosperms via machine learning technique.

The association between plasma metabolites and future risk of all-cause mortality

BACKGROUND

Metabolite profiles provide snapshots of the overall effect of numerous exposures accumulated over life courses, which may lead to health outcomes in the future.

OBJECTIVE

We hypothesized that the risk of all-cause mortality is linked to alterations in metabolism earlier in life, which are reflected in plasma metabolite profiles. We aimed to identify plasma metabolites associated with future risk of all-cause mortality.

METHODS

Through metabolomics, 110 metabolites were measured in 3833 individuals from the Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC). A total of 1574 deaths occurred within an average follow-up time of 22.2 years. Metabolites that were significantly associated with all-cause mortality in MDC-CC were replicated in 1500 individuals from Malmö Preventive Project re-examination (MPP), among whom 715 deaths occurred within an average follow-up time of 11.3 years.

RESULTS

Twenty two metabolites were significantly associated with all-cause mortality in MDC-CC, of which 13 were replicated in MPP. Levels of trigonelline, glutamate, dimethylglycine, C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, and 1-methyladenosine were associated with an increased risk, while levels of valine, tryptophan, lysine, leucine, histidine, and 2-aminoisobutyrate were associated with a decreased risk of all-cause mortality.

CONCLUSION

We used metabolomics in two Swedish prospective cohorts and identified replicable associations between 13 metabolites and future risk of all-cause mortality. Novel associations between five metabolites-C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, trigonelline, and 2-aminoisobutyrate-and all-cause mortality were discovered. These findings suggest potential new biomarkers for the prediction of mortality and provide insights for understanding the biochemical pathways that lead to mortality.

Metabolome Profiling in Aging Studies

Organism aging is closely related to systemic metabolic changes. However, due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, scientists are trying to solve this problem using one of the main approaches of metabolomics-untargeted metabolome profiling. The purpose of this publication is to review metabolomic studies based on such profiling, both in animal models and in humans. This review describes metabolites that vary significantly across age groups and include carbohydrates, amino acids, carnitines, biogenic amines, and lipids. Metabolic pathways associated with the aging process are also shown, including those associated with amino acid, lipid, and energy metabolism. The presented data reveal the mechanisms of aging and can be used as a basis for monitoring biological age and predicting age-related diseases in the early stages of their development.

Molecular Classification of Genes Associated with Hypoxic Lipid Metabolism in Pancreatic Cancer

Abnormal lipid metabolism often occurs under hypoxic microenvironment, which is an important energy supplement for cancer cell proliferation and metastasis. We aimed to explore the lipid metabolism characteristics and gene expression features of pancreatic ductal adenocarcinoma (PDAC) related to hypoxia and identify biomarkers for molecular classification based on hypoxic lipid metabolism that are evaluable for PDAC prognosis and therapy. The multiple datasets were analyzed integratively, including corresponding clinical information of samples. PDAC possesses a distinct metabolic profile and oxygen level compared with normal pancreatic tissues, according to the bioinformatics methods. In addition, a study on untargeted metabolomics using Ultra Performance Liquid Chromatography Tandem Mass Spectrometry(UPLC-MS) revealed lipid metabolites differences affected by oxygen. Analysis of PDAC gene expression profiling in The Cancer Genome Atlas (TCGA) revealed that the sphingolipid process correlates closely with HIF1α. According to the characters of HIF-1 and sphingolipid, samples can be clustered into three subgroups using non-negative matrix factorization clustering. In cluster2, patients had an increased survival time. Relatively high MUC16 mutation arises in cluster2 and may positively influence the cancer survival rates. This study explored the expression pattern of lipid metabolism under hypoxia microenvironment in PDAC. On the basis of metabolic signatures, we identified the prognosis subtypes linking lipid metabolism to hypoxia. The classifications may be conducive to developing personalized treatment programs targeting metabolic profiles.

Microbiome-metabolome analysis reveals cervical lesion alterations

Cervical cancer (CC) continues to be one of the most common cancers among females worldwide. It takes a few years or even decades for CC to arise in a minority of women with cervical precancers. An increasing corpus of studies today indicates that local microecology and carcinogenesis are intimately related. To investigate the changes in cericovaginal microecology with the development of cervical cancer, we performed 16S rDNA sequencing and metabolomic analysis in cericovaginal fluid from 10 LSIL patients, 10 HSIL patients, 10 CC patients and 10 healthy controls to reveal the differential flora and metabolites during cervical carcinogenesis. Carcinogenesis is associated with alterations in microbiome diversity, individual taxa, and functions with notable changes in Lactobacillus, Prevotella and Aquabacterium, as well as in cervicovaginal metabolites that correlate with cervicovaginal microbial patterns. Increased bacterial diversity and a decline in the relative abundance of Lactobacillus, the dominant species in the cericovaginal flora, are observed when cervical lesions advance. According to KEGG pathway enrichment analysis, lipids and organic acids change as cervical cancer progresses, and the phenylalanine, tyrosine, and tryptophan biosynthesis pathway is essential for the development of cervical cancer. Our results reveal that microbic and metabolomic profiling is capable of distinguishing CC from precancer and highlights potential biomarkers for the early detection of cervical dysplasia. These differential microorganisms and metabolites are expected to become a potential tool to assist in the diagnosis of cervical cancer.

Omics profiling identifies the regulatory functions of the MAPK/ERK pathway in nephron progenitor metabolism

Nephron endowment is defined by fetal kidney growth and crucially dictates renal health in adults. Defects in the molecular regulation of nephron progenitors contribute to only a fraction of reduced nephron mass cases, suggesting alternative causative mechanisms. The importance of MAPK/ERK activation in nephron progenitor maintenance has been previously demonstrated, and here, we characterized the metabolic consequences of MAPK/ERK deficiency. Liquid chromatography/mass spectrometry-based metabolomics profiling identified 42 reduced metabolites, of which 26 were supported by in vivo transcriptional changes in MAPK/ERK-deficient nephron progenitors. Among these, mitochondria, ribosome and amino acid metabolism, together with diminished pyruvate and proline metabolism, were the most affected pathways. In vitro cultures of mouse kidneys demonstrated a dosage-specific function for pyruvate in controlling the shape of the ureteric bud tip, a regulatory niche for nephron progenitors. In vivo disruption of proline metabolism caused premature nephron progenitor exhaustion through their accelerated differentiation in pyrroline-5-carboxylate reductases 1 (Pycr1) and 2 (Pycr2) double-knockout kidneys. Pycr1/Pycr2-deficient progenitors showed normal cell survival, indicating no changes in cellular stress. Our results suggest that MAPK/ERK-dependent metabolism functionally participates in nephron progenitor maintenance by monitoring pyruvate and proline biogenesis in developing kidneys.

In Vitro Gene Expression Responses of Bovine Rumen Epithelial Cells to Different pH Stresses

Ruminal acidosis often occurs in production, which greatly affects animal health and production efficiency. Subacute rumen acidosis (SARA) occurs when rumen pH drops rapidly to 5.5−5.8, and acute rumen acidosis (ARA) occurs when rumen pH drops below 5.0, but the molecular regulation mechanism of the rumen epithelium after the rapid decrease in pH is still unclear. Bovine rumen epithelial cells (BRECs) were cultured at pH = 7.4 (control), 5.5 (SARA), and 4.5 (ARA). Transcriptome and metabolomic methods were used to obtain the molecular-based response of BRECs to different pH treatments; pH = 4.5 can significantly induce apoptosis of BRECs. The RNA-seq experiments revealed 1381 differently expressed genes (DEGs) in the control vs. SARA groups (p < 0.05). Fibroblast growth factor (FGF) and tumor necrosis factor (TNF) were upregulated 4.25 and 6.86 fold, respectively, and TLR4 was downregulated 0.58 fold. In addition, 283 DEGs were identified in the control vs. ARA comparison (p < 0.05), and prostaglandin-endoperoxide synthase 2 (PSTG2) was downregulated 0.54 fold. Our research reveals that the MAPK/TNF signaling pathway regulates the inflammatory response of BRECs. Metabolomics identified 35 biochemical compounds that were significantly affected (p < 0.05) in control vs. SARA and 51 in control vs. ARA. Bioinformatics analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database revealed that drug metabolism-cytochrome P450 metabolic and alpha-linolenic acid metabolism changes occurred. These transcriptional and metabolic changes are related to the adaptation of BRECs to low-pH stresses. In conclusion, the combined data analyses presented a worthy strategy to characterize the cellular, transcriptomic, and metabonomic adaptation of BRECs to pH in vitro. We demonstrated transcriptional expression changes in BRECs under pH stress and activation of the molecular mechanisms controlling inflammation.

Integrative analysis of the mouse fecal microbiome and metabolome reveal dynamic phenotypes in the development of colorectal cancer

The gut microbiome and its interaction with host have been implicated as the causes and regulators of colorectal cancer (CRC) pathogenesis. However, few studies comprehensively investigate the compositions of gut bacteria and their interactions with host at the early inflammatory and cancerous stages of CRC. In this study, mouse fecal samples collected at inflammation and CRC were subjected to microbiome and metabolome analyses. The datasets were analyzed individually and integratedly using various bioinformatics approaches. Great variations in gut microbiota abundance and composition were observed in inflammation and CRC. The abundances of Bacteroides, S24-7_group_unidifineted, and Allobaculum were significantly changed in inflammation and CRC. The abundances of Bacteroides and Allobaculum were significantly different between inflammation and CRC. Furthermore, strong excluding and appealing microbial interactions were found in the gut microbiota. CRC and inflammation presented specific fecal metabolome profiling. Fecal metabolomic analysis led to the identification and quantification of 1,138 metabolites with 32 metabolites significantly changed in CRC and inflammation. 1,17-Heptadecanediol and 24,25,26,27-Tetranor-23-oxo-hydroxyvitamin D3 were potential biomarkers for CRC. 3α,7β,12α-Trihydroxy-6-oxo-5α-cholan-24-oic Acid and NNAL-N-glucuronide were potential biomarkers for inflammation. The significantly changed bacterial species and metabolites contribute to inflammation and CRC diagnosis. Integrated microbiome and metabolomic analysis correlated microbes with host metabolites, and the variated microbe-metabolite association in inflammation and CRC suggest that microbes facilitate tumorigenesis of CRC through interfering host metabolism.

An online atlas of human plasma metabolite signatures of gut microbiome composition

Human gut microbiota produce a variety of molecules, some of which enter the bloodstream and impact health. Conversely, dietary or pharmacological compounds may affect the microbiota before entering the circulation. Characterization of these interactions is an important step towards understanding the effects of the gut microbiota on health. In this cross-sectional study, we used deep metagenomic sequencing and ultra-high-performance liquid chromatography linked to mass spectrometry for a detailed characterization of the gut microbiota and plasma metabolome, respectively, of 8583 participants invited at age 50 to 64 from the population-based Swedish CArdioPulmonary bioImage Study. Here, we find that the gut microbiota explain up to 58% of the variance of individual plasma metabolites and we present 997 associations between alpha diversity and plasma metabolites and 546,819 associations between specific gut metagenomic species and plasma metabolites in an online atlas (https://gutsyatlas.serve.scilifelab.se/). We exemplify the potential of this resource by presenting novel associations between dietary factors and oral medication with the gut microbiome, and microbial species strongly associated with the uremic toxin p-cresol sulfate. This resource can be used as the basis for targeted studies of perturbation of specific metabolites and for identification of candidate plasma biomarkers of gut microbiota composition.

Here, Dekkers et al. characterize associations of 1528 gut metagenomic species with the plasma metabolome in 8583 participants of the SCAPIS Study, and find that gut microbiota explain up to 58% of the variance of individual plasma metabolites.

Chemicals Affecting Cyanobacterial Poly(3-hydroxybutyrate) Accumulation: 2-Phenylethanol Treatment Combined with Nitrogen Deprivation Synergistically Enhanced Poly(3-hydroxybutyrate) Storage in Synechocystis sp. PCC6803 and Anabaena sp. TISTR8076

Various photoautotrophic cyanobacteria increase the accumulation of bioplastic poly(3-hydroxybutyrate) (PHB) under nitrogen deprivation (-N) for energy storage. Several metabolic engineering enhanced cyanobacterial PHB accumulation, but these strategies are not applicable in non-gene-transformable strains. Alternatively, stimulating PHB levels by chemical exposure is desirable because it might be applied to various cyanobacterial strains. However, the study of such chemicals is still limited. Here, 19 compounds previously reported to affect bacterial cellular processes were evaluated for their effect on PHB accumulation in Synechocystis sp. PCC6803, where 3-(3,4-dichlorophenyl)-1,1-dimethylurea, methyl viologen, arsenite, phenoxyethanol and 2-phenylethanol were found to increase PHB accumulation. When cultivated with optimal nitrate supply, Synechocystis contained less than 0.5% [w/w dry weight (DW)] PHB, while cultivation under -N conditions increased the PHB content to 7% (w/w DW). Interestingly, the -N cultivation combined with 2-phenylethanol exposure reduced the Synechocystis protein content by 27% (w/w DW) but significantly increased PHB levels up to 33% (w/w DW), the highest ever reported photoautotrophic cyanobacterial PHB accumulation in a wild-type strain. Results from transcriptomic and metabolomic analysis suggested that under 2-phenylethanol treatment, Synechocystis proteins were degraded to amino acids, which might be subsequently utilized as the source of carbon and energy for PHB biosynthesis. 2-Phenylethanol treatment also increased the levels of metabolites required for Synechocystis PHB synthesis (acetyl-CoA, acetoacetyl-CoA, 3-hydroxybutyryl-CoA and NADPH). Additionally, under -N, the exposure to phenoxyethanol and 2-phenylethanol increased the PHB levels of Anabaena sp. from 0.4% to 4.1% and 6.6% (w/w DW), respectively. The chemicals identified in this study might be applicable for enhancing PHB accumulation in other cyanobacteria.

Machine learning for identification of silylated derivatives from mass spectra

Motivation

Compound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Compound Structure Identification:Input Output Kernel Regression (CSI:IOKR) is an excellent example, have been used to elucidate compound structure from mass spectral (MS) data with significant accuracy, confidence and speed. They have, however, largely focused on data coming from liquid chromatography coupled to tandem mass spectrometry (LC–MS).

Gas chromatography coupled to mass spectrometry (GC–MS) is an alternative which offers several advantages as compared to LC–MS, including higher data reproducibility. Of special importance is the substantial compound coverage offered by GC–MS, further expanded by derivatization procedures, such as silylation, which can improve the volatility, thermal stability and chromatographic peak shape of semi-volatile analytes. Despite these advantages and the increasing size of compound databases and MS libraries, GC–MS data have not yet been used by machine learning approaches to compound structure identification.

Results

This study presents a successful application of the CSI:IOKR machine learning method for the identification of environmental contaminants from GC–MS spectra. We use CSI:IOKR as an alternative to exhaustive search of MS libraries, independent of instrumental platform and data processing software. We use a comprehensive dataset of GC–MS spectra of trimethylsilyl derivatives and their molecular structures, derived from a large commercially available MS library, to train a model that maps between spectra and molecular structures. We test the learned model on a different dataset of GC–MS spectra of trimethylsilyl derivatives of environmental contaminants, generated in-house and made publicly available. The results show that 37% (resp. 50%) of the tested compounds are correctly ranked among the top 10 (resp. 20) candidate compounds suggested by the model. Even though spectral comparisons with reference standards or de novo structural elucidations are neccessary to validate the predictions, machine learning provides efficient candidate prioritization and reduction of the time spent for compound annotation.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

Targeted and Untargeted Metabolomic Analyses Reveal Organ Specificity of Specialized Metabolites in the Model Grass Brachypodium distachyon

Brachypodium distachyon, because of its fully sequenced genome, is frequently used as a model grass species. However, its metabolome, which constitutes an indispensable element of complex biological systems, remains poorly characterized. In this study, we conducted comprehensive, liquid chromatography-mass spectrometry (LC-MS)-based metabolomic examination of roots, leaves and spikes of Brachypodium Bd21 and Bd3-1 lines. Our pathway enrichment analysis emphasised the accumulation of specialized metabolites representing the flavonoid biosynthetic pathway in parallel with processes related to nucleotide, sugar and amino acid metabolism. Similarities in metabolite profiles between both lines were relatively high in roots and leaves while spikes showed higher metabolic variance within both accessions. In roots, differences between Bd21 and Bd3-1 lines were manifested primarily in diterpenoid metabolism, while differences within spikes and leaves concerned nucleotide metabolism and nitrogen management. Additionally, sulphate-containing metabolites differentiated Bd21 and Bd3-1 lines in spikes. Structural analysis based on MS fragmentation spectra enabled identification of 93 specialized metabolites. Among them phenylpropanoids and flavonoids derivatives were mainly determined. As compared with closely related barley and wheat species, metabolic profile of Brachypodium is characterized with presence of threonate derivatives of hydroxycinnamic acids.

Metabolomic profiling of intrauterine growth-restricted preterm infants: a matched case-control study

BACKGROUND

The biochemical variations occurring in intrauterine growth restriction (IUGR), when a fetus is unable to achieve its genetically determined potential, are not fully understood. The aim of this study is to compare the urinary metabolomic profile between IUGR and non-IUGR very preterm infants to investigate the biochemical adaptations of neonates affected by early-onset-restricted intrauterine growth.

METHODS

Neonates born <32 weeks of gestation admitted to neonatal intensive care unit (NICU) were enrolled in this prospective matched case-control study. IUGR was diagnosed by an obstetric ultra-sonographer and all relevant clinical data during NICU stay were captured. For each subject, a urine sample was collected within 48 h of life and underwent untargeted metabolomic analysis using mass spectrometry ultra-performance liquid chromatography. Data were analyzed using multivariate and univariate statistical analyses.

RESULTS

Among 83 enrolled infants, 15 IUGR neonates were matched with 19 non-IUGR controls. Untargeted metabolomic revealed evident clustering of IUGR neonates versus controls showing derangements of pathways related to tryptophan and histidine metabolism and aminoacyl-tRNA and steroid hormones biosynthesis.

CONCLUSIONS

Neonates with IUGR showed a distinctive urinary metabolic profile at birth. Although results are preliminary, metabolomics is proving to be a promising tool to explore biochemical pathways involved in this disease.

IMPACT

Very preterm infants with intrauterine growth restriction (IUGR) have a distinctive urinary metabolic profile at birth. Metabolism of glucocorticoids, sexual hormones biosynthesis, tryptophan-kynurenine, and methionine-cysteine pathways seem to operate differently in this sub-group of neonates. This is the first metabolomic study investigating adaptations exclusively in extremely and very preterm infants affected by early-onset IUGR. New knowledge on metabolic derangements in IUGR may pave the ways to further, more tailored research from a perspective of personalized medicine.

Indoor microbiome and allergic diseases: From theoretical advances to prevention strategies

The prevalence of allergic diseases, such as asthma, rhinitis, eczema, and sick building syndrome (SBS), has increased drastically in the past few decades. Current medications can only relieve the symptoms but not cure these diseases whose development is suggested to be greatly impacted by the indoor microbiome. However, no study comprehensively summarizes the progress and general rules in the field, impeding subsequent translational application. To close knowledge gaps between theoretical research and practical application, we conducted a comprehensive literature review to summarize the epidemiological, environmental, and molecular evidence of indoor microbiome studies. Epidemiological evidence shows that the potential protective indoor microorganisms for asthma are mainly from the phyla Actinobacteria and Proteobacteria, and the risk microorganisms are mainly from Bacilli, Clostridia, and Bacteroidia. Due to extremely high microbial diversity and geographic variation, different health-associated species/genera are detected in different regions. Compared with indoor microbial composition, indoor metabolites show more consistent associations with health, including microbial volatile organic compounds (MVOCs), lipopolysaccharides (LPS), indole derivatives, and flavonoids. Therefore, indoor metabolites could be a better indicator than indoor microbial taxa for environmental assessments and health outcome prediction. The interaction between the indoor microbiome and environmental characteristics (surrounding greenness, relative humidity, building confinement, and CO2 concentration) and immunology effects of indoor microorganisms (inflammatory cytokines and pattern recognition receptors) are briefly reviewed to provide new insights for disease prevention and treatment. Widely used tools in indoor microbiome studies are introduced to facilitate standard practice and the precise identification of health-related targets.