FELLA: an R package to enrich metabolomics data

Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

Tissue-specific metabolomic signatures for a doublesex model of reduced sexual dimorphism

Sex has a major effect on the metabolome. However, we do not yet understand the degree to which these quantitative sex differences in metabolism are associated with anatomical dimorphism and modulated by sex-specific tissues. In the fruit fly, Drosophila melanogaster, knocking out the doublesex (dsx) gene gives rise to adults with intermediate sex characteristics. Here we sought to determine the degree to which this key node in sexual development leads to sex differences in the fly metabolome. We measured 91 metabolites across head, thorax and abdomen in Drosophila, comparing the differences between distinctly sex-dimorphic flies with those of reduced sexual dimorphism: dsx null flies. Notably, in the reduced dimorphism flies, we observed a sex difference in only 1 of 91 metabolites, kynurenate, whereas 51% of metabolites (46/91) were significantly different between wildtype XX and XY flies in at least one tissue, suggesting that dsx plays a major role in sex differences in fly metabolism. Kynurenate was consistently higher in XX flies in both the presence and absence of functioning dsx. We observed tissue-specific consequences of knocking out dsx. Metabolites affected by sex were significantly enriched in branched chain amino acid metabolism and the mTOR pathway. This highlights the importance of considering variation in genes that cause anatomical sexual dimorphism when analyzing sex differences in metabolic profiles and interpreting their biological significance.

Wide-ranging genetic variation in sensitivity to rapamycin in Drosophila melanogaster

The progress made in aging research using laboratory organisms is undeniable. Yet, with few exceptions, these studies are conducted in a limited number of isogenic strains. The path from laboratory discoveries to treatment in human populations is complicated by the reality of genetic variation in nature. To model the effect of genetic variation on the action of the drug rapamycin, here we use the growth of Drosophila melanogaster larvae. We screened 140 lines from the Drosophila Genetic References Panel for the extent of developmental delay and found wide-ranging variation in their response, from lines whose development time is nearly doubled by rapamycin, to those that appear to be completely resistant. Sensitivity did not associate with any single genetic marker, nor with any gene. However, variation at the level of genetic pathways was associated with rapamycin sensitivity and might provide insight into sensitivity. In contrast to the genetic analysis, metabolomic analysis showed a strong response of the metabolome to rapamycin, but only among the sensitive larvae. In particular, we found that rapamycin altered levels of amino acids in sensitive larvae, and in a direction strikingly similar to the metabolome response to nutrient deprivation. This work demonstrates the need to evaluate interventions across genetic backgrounds and highlights the potential of omic approaches to reveal biomarkers of drug efficacy and to shed light on mechanisms underlying sensitivity to interventions aimed at increasing lifespan.

HuR controls glutaminase RNA metabolism

Glutaminase (GLS) is directly related to cell growth and tumor progression, making it a target for cancer treatment. The RNA-binding protein HuR (encoded by the ELAVL1 gene) influences mRNA stability and alternative splicing. Overexpression of ELAVL1 is common in several cancers, including breast cancer. Here we show that HuR regulates GLS mRNA alternative splicing and isoform translation/stability in breast cancer. Elevated ELAVL1 expression correlates with high levels of the glutaminase isoforms C (GAC) and kidney-type (KGA), which are associated with poor patient prognosis. Knocking down ELAVL1 reduces KGA and increases GAC levels, enhances glutamine anaplerosis into the TCA cycle, and drives cells towards glutamine dependence. Furthermore, we show that combining chemical inhibition of GLS with ELAVL1 silencing synergistically decreases breast cancer cell growth and invasion. These findings suggest that dual inhibition of GLS and HuR offers a therapeutic strategy for breast cancer treatment.

Mitochondrial DNA variants and microbiota: An experimental strategy to identify novel therapeutic potential in chronic inflammatory diseases

We previously demonstrated that mice carrying natural mtDNA variants of the FVB/NJ strain (m.7778 G>T in the mt-Atp8 gene in mitochondrial complex V), namely C57BL/6 J-mtFVB/NJ (B6-mtFVB), exhibited (i) partial protection from experimental skin inflammatory diseases in an anti-murine type VII collagen antibody-induced skin inflammation model and psoriasiform dermatitis model; (ii) significantly altered metabolites, including short-chain fatty acids, according to targeted metabolomics of liver, skin and lymph node samples; and (iii) a differential composition of the gut microbiota according to bacterial 16 S rRNA gene sequencing of stool samples compared to wild-type C57BL/6 J (B6) mice. To further dissect these disease-contributing factors, we induced an experimental antibody-induced skin inflammatory disease in gnotobiotic mice. We performed shotgun metagenomic sequencing of caecum contents and untargeted metabolomics of liver, CD4+ T cell, and caecum content samples from conventional B6-mtFVB and B6 mice. We identified D-glucosamine as a candidate mediator that ameliorated disease severity in experimental antibody-induced skin inflammation by modulating immune cell function in T cells, neutrophils and macrophages. Because mice carrying mtDNA variants of the FVB/NJ strain show differential disease susceptibility to a wide range of experimental diseases, including diet-induced atherosclerosis in low-density lipoprotein receptor knockout mice and collagen antibody-induced arthritis in DBA/1 J mice, this experimental approach is valuable for identifying novel therapeutic options for skin inflammatory conditions and other chronic inflammatory diseases to which mice carrying specific mtDNA variants show differential susceptibility.

Plasma acylcarnitines and amino acids in dyslipidemia: An integrated metabolomics and machine learning approach

Purpose

The Discovery of underlying intermediates associated with the development of dyslipidemia results in a better understanding of pathophysiology of dyslipidemia and their modification will be a promising preventive and therapeutic strategy for the management of dyslipidemia.

Methods

The entire dataset was selected from the Surveillance of Risk Factors of Noncommunicable Diseases (NCDs) in 30 provinces of Iran (STEPs 2016 Country report in Iran) that included 1200 subjects and was stratified into four binary classes with normal and abnormal cases based on their levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.Plasma concentrations of 20 amino acids and 30 acylcarnitines in each class of dyslipidemia were evaluated using Tandem mass spectrometry. Then, these attributes, along with baseline characteristics data, were used to check whether machine learning (ML) algorithms could classify cases and controls.

Results

Our ML framework accurately predicts TG binary classes. Among the models tested, the SVM model stood out, performing slightly better with an AUC of 0.81 and a standard deviation of test accuracy at 0.04. Consequently, it was chosen as the optimal model for TG classification. Moreover, the findings showed that alanine, phenylalanine, methionine, C3, C14:2, and C16 had great power in differentiating patients with high TG from normal TG controls. Conclusions: The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01384-9.

Metabolite accumulation from oral NMN supplementation drives aging-specific kidney inflammation

The mitochondrial-rich renal tubule cells are key regulators of blood homeostasis via excretion and reabsorption of metabolic waste. With age, tubules are subject to increasing mitochondrial dysfunction and declining nicotinamide adenine dinucleotide (NAD+) levels, both hampering ATP production efficiency. We tested two mitochondrial interventions in young (6-mo) and aged (26-mo) adult male mice: elamipretide (ELAM), a tetrapeptide in clinical trials that improves mitochondrial structure and function, and nicotinamide mononucleotide (NMN), an NAD+ intermediate and commercially available oral supplement. Kidneys were analyzed from young and aged mice after eight weeks of treatment with ELAM (3 mg/kg/day), NMN (300 mg/kg/day), or from aged mice treated with the two interventions combined (ELAM+NMN). We hypothesized that combining pharmacologic treatments to ameliorate mitochondrial dysfunction and boost NAD+ levels, would more effectively reduce kidney aging than either intervention alone. Unexpectedly, in aged kidneys, NMN increased expression of genetic markers of inflammation (IL-1-beta; and Ccl2) and tubule injury (Kim-1). Metabolomics of endpoint sera showed that NMN-treated aged mice had higher circulating levels of uremic toxins than either aged controls or young NMN-treated mice. ELAM+NMN-treated aged mice accumulated uremic toxins like NMN-only aged mice, but reduced IL-1-beta; and Ccl2 kidney mRNA. This suggests that pre-existing mitochondrial dysfunction in aged kidney underlies susceptibility to inflammatory signaling with NMN supplementation in aged, but not young, mice. These findings demonstrate age and tissue dependent effects on downstream metabolic accumulation from NMN and highlight the need for targeted analysis of aged kidneys to assess the safety of anti-aging supplements in older populations.

Using a metabolomics approach to investigate the sensitivity of a potential Arctic-invader and its Arctic sister-species to marine heatwaves and traditional harvesting disturbances

Coastal species are threatened by fishing practices and changing environmental conditions, such as marine heatwaves (MHW). The mechanisms that confer tolerance to such stressors in marine invertebrates are poorly understood. However, differences in tolerance among different species may be attributed to their geographical distribution. To test the tolerance of species occupying different thermal ranges, we used two closely related bivalves the softshell clam Mya arenaria (Linnaeus, 1758), a cold-temperate invader with demonstrated potential for establishment in the Arctic, and the blunt gaper Mya truncata (Linnaeus, 1758), a native polar species. Clams were subjected to a thermal stress, mimicking a MHW, and harvesting stress in a controlled environment. Seven acute temperature changes (2, 7, 12, 17, 22, 27, and 32 °C) were tested at two harvesting disturbance intensities (with, without). Survival was measured after 12 days and three tissues (gills, mantle, and posterior adductor muscle) collected from surviving individuals for targeted metabolomic profiling. MHW tolerance differed significantly between species: 26.9 °C for M. arenaria and 17.8 °C for M. truncata, with a negligeable effect of harvesting. At the upper thermal limit, M. arenaria displayed a more profound metabolomic remodelling when compared to M. truncata, and this varied greatly between tissue types. Network analysis revealed differences in pathway utilization at the upper MHW limit, with M. arenaria displaying a greater reliance on multiple DNA repair and expression and cell signalling pathways, while M. truncata was limited to fewer pathways. This suggests that M. truncata is ill equipped to cope with warming environments. MHW patterning in the Northwest Atlantic may be a strong predictor of population survival and future range shifts in these two clam species. As polar environments undergo faster rates of warming compared to the global average, M. truncata may be outcompeted by M. arenaria expanding into its native range.

Modulation of intestinal metabolites by calorie restriction and its association with gut microbiota in a xenograft model of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a common malignant tumor, and its occurrence and development are closely related to dysbiosis of gut microbes. Previously, we found calorie restriction altered the composition of the microbial community in a colorectal cancer mouse model and inhibited in vivo growth of CRC cells. Here, we aim to further investigate alteration in the intestinal metabolites and explore the interplay between gut microbiota and intestinal metabolites upon calorie restriction.

METHODS

Human colorectal cancer HCT116 cells were used to establish a colorectal cancer xenograft mouse model. The changes of intestinal metabolites in the ad libitum group and calorie restriction group were investigated through untargeted metabolomics analysis. The integrative analysis of gut microbiota and metabolites to elucidate the associations between gut microbiota and intestinal metabolites.

RESULTS

Compared with the mice in the ad libitum group, mice upon calorie restriction exhibited downregulation of Isoleucyl-Valine, and upregulation of D-Proline, 1-Palmitoylphosphatidylcholine, and 4-Trimethylammoniobutanoic acid. Additionally, an integrative analysis of gut microbiota and metabolites revealed that Lactobacillus, Parabacteroides and rC4-4 genus were upregulated in the calorie restriction group and positively correlated with D-Proline, 4-Trimethylammoniobutanoic acid or 1-Palmitoylphosphatidylcholine, while negatively correlated with Isoleucyl-Valine. In contrast, the Nitrospirae and Deferribacteres phylum exhibited opposite trends.

CONCLUSION

Calorie restriction affects the abundance of gut microbes such as Nitrospirae phylum and Lactobacillus genus in mouse model of colorectal cancer, leading to changes in the metabolites such as D-Proline、Isoleucyl-Valine, which contributes to the suppression of in vivo growth of CRC by calorie restriction.

Interactive effects of multiple antibiotic residues and ocean acidification on physiology and metabolome of the bay scallops Argopecten irradians irradians

Coastal areas are confronted with compounding threats arising from both climatic and non-climatic stressors. Antibiotic pollution and ocean acidification are two prevalently concurrent environmental stressors. Yet their interactive effects on marine biota have not been investigated adequately and the compound hazard remain obscure. In this study, bay scallops Argopecten irradians irradians were exposed to multiple antibiotics (sulfamethoxazole, tetracycline, oxytetracycline, norfloxacin, and erythromycin, each at a concentration of 1 μg/L) combined with/without acidic seawater (pH 7.6) for 35 days. The single and interactive effects of the two stressors on A. irradians irradians were determined from multidimensional bio-responses, including energetic physiological traits as well as the molecular underpinning (metabolome and expressions of key genes). Results showed that multiple antibiotics predominantly enhanced the process of DNA repair and replication via disturbing the purine metabolism pathway. This alternation is perhaps to cope with the DNA damage induced by oxidative stress. Ocean acidification mainly disrupted energy metabolism and ammonia metabolism of the scallops, as evidenced by the increased ammonia excretion rate, the decreased O:N ratio, and perturbations in amino acid metabolism pathways. Moreover, the antagonistic effects of multiple antibiotics and ocean acidification caused alternations in the relative abundance of neurotransmitter and gene expression of neurotransmitter receptors, which may lead to neurological disorders in scallops. Overall, the revealed alternations in physiological traits, metabolites and gene expressions provide insightful information for the health status of bivalves in a natural environmental condition under the climate change scenarios.

Induced responses to the wheat pathogen: Tan Spot-(Pyrenophora tritici-repentis) in wheat (Triticum aestivum) focus on changes in defence associated and sugar metabolism

INTRODUCTION

Tan Spot (TS) disease of wheat is caused by Pyrenophora tritici-repentis (Ptr), where most of the yield loss is linked to diseased flag leaves. As there are no fully resistant cultivars available, elucidating the responses of wheat to Ptr could inform the derivation of new resistant genotypes.

OBJECTIVES

The study aimed to characterise the flag-leaf metabolomes of two spring wheat cultivars (Triticum aestivum L. cv. PF 080719 [PF] and cv. Fundacep Horizonte [FH]) following challenge with Ptr to gain insights into TS disease development.

METHODS

PF and FH plants were inoculated with a Ptr strain that produces the necrotrophic toxin ToxA. The metabolic changes in flag leaves following challenge (24, 48, 72, and 96 h post-inoculation [hpi]) with Ptr were investigated using untargeted flow infusion ionisation-high resolution mass spectroscopy (FIE-HRMS).

RESULTS

Both cultivars were susceptible to Ptr at the flag-leaf stage. Comparisons of Ptr- and mock-inoculated plants indicated that a major metabolic shift occurred at 24 hpi in FH, and at 48 hpi in PF. Although most altered metabolites were genotype specific, they were linked to common pathways; phenylpropanoid and flavonoid metabolism. Alterations in sugar metabolism as well as in glycolysis and glucogenesis pathways were also observed. Pathway enrichment analysis suggested that Ptr-triggered alterations in chloroplast and photosynthetic machinery in both cultivars, especially in FH at 96 hpi. In a wheat-Ptr interactome in integrative network analysis, "flavone and flavonol biosynthesis" and "starch and sucrose metabolism" were targeted as the key metabolic processes underlying PF-FH-Ptr interactions.

CONCLUSION

These observations suggest the potential importance of flavone and flavonol biosynthesis as well as bioenergetic shifts in susceptibility to Ptr. This work highlights the value of metabolomic approaches to provide novel insights into wheat pathosystems.

The biomarkers' landscape of post-COVID-19 patients can suggest selective clinical interventions

In COVID-19 clinical symptoms can persist even after negativization also in individuals who have had mild or moderate disease. We here investigated the biomarkers that define the post-COVID-19 clinical state analyzing the exhaled breath condensate (EBC) of 38 post COVID-19 patients and 38 sex and age-matched healthy controls via nuclear magnetic resonance (NMR)-based metabolomics. Predicted gene-modulated microRNAs (miRNAs) related to COVID-19 were quantified from EBC of 10 patients and 10 controls. Finally, clinical parameters from all post-COVID-19 patients were correlated with metabolomic data. Post-COVID-19 patients and controls showed different metabolic phenotype ("metabotype"). From the metabolites, by using enrichment analysis we identified miRNAs that resulted up-regulated (hsa-miR146a-5p) and down-regulated (hsa-miR-126-3p and hsa-miR-223-3p) in post-COVID-19. Taken together, our multiomics data indicate that post-COVID-19 patients before rehabilitation are characterized by persistent inflammation, dysregulation of liver, endovascular thrombotic and pulmonary processes, and physical impairment, which should be the primary clinical targets to contrast the post-acute sequelae of COVID-19.

Epinephrine Stimulates Mycobacterium tuberculosis Growth and Biofilm Formation

The human stress hormones catecholamines play a critical role in communication between human microbiota and their hosts and influence the outcomes of bacterial infections. However, it is unclear how M. tuberculosis senses and responds to certain types of human stress hormones. In this study, we screened several human catecholamine stress hormones (epinephrine, norepinephrine, and dopamine) for their effects on Mycobacterium growth. Our results showed that epinephrine significantly stimulated the growth of M. tuberculosis in the serum-based medium as well as macrophages. In silico analysis and molecular docking suggested that the extra-cytoplasmic domain of the MprB might be the putative adrenergic sensor. Furthermore, we showed that epinephrine significantly enhances M. tuberculosis biofilm formation, which has distinct texture composition, antibiotic resistance, and stress tolerance. Together, our data revealed the effect and mechanism of epinephrine on the growth and biofilm formation of M. tuberculosis, which contributes to the understanding of the environmental perception and antibiotic resistance of M. tuberculosis and provides important clues for the understanding of bacterial pathogenesis and the development of novel antibacterial therapeutics.

Identification of potential biomarkers in malnutrition children with severity by 1H-NMR-based metabolomics: a preliminary study in the Chinese population

PURPOSE

Child malnutrition is a global public health problem, but the underlying pathophysiologic mechanisms with severity remain poorly understood, and the potential biomarkers served to the clinical diagnosis are still not available. This study aimed to identify the serum metabolic characteristics of malnourished children with severity.

METHODS

Fasted overnight serum samples were collected following clinical standard procedures among 275 malnourished and 199 healthy children from the Women and Children's Hospital, Xiamen University Child Health Department from July 2020 to May 2022. Nuclear magnetic resonance (NMR)-based metabolomics strategy was applied to identify the potential serum biomarkers of malnutrition from 275 malnourished children aged 4 to 84 months with mild (Mil, 199 cases), moderate (Mod, 101 cases), and severe (Sev, 7 cases) malnutrition.

RESULTS

Ten, fifteen, and fifteen differential metabolites were identified from the Mil, Mod, and Sev malnutrition groups, respectively. Eight common metabolites, including increased acetoacetate, acetone, ethanol, succinate, 3-hydroxybutyrate, and decreased alanine, methionine, and N-acetyl-glycoprotein, could be the potential biomarkers for malnourished children. The altered metabolic pathways were mainly related to energy metabolism and amino acid metabolism via the network-based pathway enrichment.

CONCLUSION

Eight potential biomarkers, including acetoacetate, acetone, ethanol, succinate, 3-hydroxybutyrate, alanine, methionine, and N-acetyl-glycoprotein, could characterize the child malnutrition. Child malnutrition-induced abnormal energy metabolism, impaired nutrition utilization and the reduced nutrient availability, and more metabolic disturbance will appear with the severity. Our results are valuable for further studies on the etiology and pathogenesis of malnutrition for clinical intervention and improvement.

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares

OBJECTIVE

The objective of this study was to discover differential metabolites and pathways underlying infrequent gout flares (InGF) and frequent gout flares (FrGF) using metabolomics and to establish a predictive model by machine learning (ML) algorithms.

METHODS

Serum samples from a discovery cohort of 163 patients with InGF and 239 patients with FrGF were analyzed by mass spectrometry-based untargeted metabolomics to profile differential metabolites and explore dysregulated metabolic pathways using pathway enrichment analysis and network propagation-based algorithms. ML algorithms were performed to establish a predictive model based on selected metabolites, which was further optimized by a quantitative targeted metabolomics method and validated in an independent validation cohort with 97 participants with InGF and 139 participants with FrGF.

RESULTS

A total of 439 differential metabolites between InGF and FrGF groups were identified. Top dysregulated pathways included carbohydrates, amino acids, bile acids, and nucleotide metabolism. Subnetworks with maximum disturbances in the global metabolic networks featured cross-talk between purine metabolism and caffeine metabolism, as well as interactions among pathways involving primary bile acid biosynthesis, taurine and hypotaurine metabolism, alanine, aspartate, and glutamate metabolism, suggesting epigenetic modifications and gut microbiome in metabolic alterations underlying InGF and FrGF. Potential metabolite biomarkers were identified using ML-based multivariable selection and further validated by targeted metabolomics. Area under receiver operating characteristics curve for differentiating InGF and FrGF achieved 0.88 and 0.67 for the discovery and validation cohorts, respectively.

CONCLUSION

Systematic metabolic alterations underlie InGF and FrGF, and distinct profiles are associated with differences in gout flare frequencies. Predictive modeling based on selected metabolites from metabolomics can differentiate InGF and FrGF.

Jute (Corchorus olitorius L.) Nanocrystalline Cellulose Inhibits Insect Virus via Gut Microbiota and Metabolism

Natural plant nanocrystalline cellulose (NCC), exhibiting a number of exceptional performance characteristics, is widely used in food fields. However, little is known about the relationship between NCC and the antiviral effect in animals. Here, we tested the function of NCC in antiviral methods utilizing honey bees as the model organism employing Israeli acute paralysis virus (IAPV), a typical RNA virus of honey bees. In both the lab and the field, we fed the IAPV-infected bees various doses of jute NCC (JNCC) under carefully controlled conditions. We found that JNCC can reduce IAPV proliferation and improve gut health. The metagenome profiling suggested that IAPV infection significantly decreased the abundance of gut core bacteria, while JNCC therapy considerably increased the abundance of the gut core bacteria Snodgrassella alvi and Lactobacillus Firm-4. Subsequent metabolome analysis further revealed that JNCC promoted the biosynthesis of fatty acids and unsaturated fatty acids, accelerated the purine metabolism, and then increased the expression of antimicrobial peptides (AMPs) and the genes involved in the Wnt and apoptosis signaling pathways against IAPV infection. Our results highlighted that JNCC could be considered as a prospective candidate agent against a viral infection.

Uncovering Interactions between Plant Metabolism and Plant-Associated Bacteria in Huanglongbing-Affected Citrus Cultivars Using Multiomics Analysis and Machine Learning

Huanglongbing (HLB) is a highly destructive disease that inflicts significant economic losses on the citrus industry worldwide but with no cure available. However, microbiomes formulated by citrus plants may serve as disease antagonists, increasing the level of HLB tolerance. This study established an integrated analysis of untargeted metabolomics and microbiomics data for different citrus cultivars, providing critical insights into the interactions between plant metabolism and plant-associated bacteria in the development of HLB. Machine learning models were applied to screen important metabolites and bacteria in multiple citrus materials, and the selected metabolites were then analyzed to identify essential pathways enriched in the plant and to correlate with the selected bacteria. Results demonstrated that the regulation of plant pathways, especially ABC transporters and ubiquinone and other terpene-ubiquinone biosynthesis pathways, could affect the microbial community structure, indicating potential solutions for controlling HLB by modulating bacteria in citrus plants or breeding tolerant citrus cultivars.

Integration of Volatilomics and Metabolomics Unveils Key Flavor-Related Biological Pathways in Different Carambola Cultivars

Carambola is a tropical fruit that is highly sought after by consumers due to its unique flavor, star shape, and nutritional value. Enhancing the flavor quality of this fruit can increase the consumer acceptance and market demand. However, flavor is an intrinsic characteristic of fruits. Its decoding requires in-depth knowledge based on recognizing key biological pathways relevant to flavor formation and development. In this study, the volatile and non-volatile metabolites contributing to the flavor variation of five carambola cultivars were investigated by a novel strategy combining GC-MS/O-based volatilomics with LC-MS-based metabolomics. Several significant flavor-related pathways, involving biosynthesis or metabolism of amino acids, terpenoids, fatty acids, sugar and organic acid, and flavonoids were identified based on the enrichment analysis of important volatile and non-volatile metabolites. The results indicated that there were metabolites in the flavor-related pathways being up- or downregulated, leading to the differences in flavor traits of different carambola cultivars. This study could provide a valuable reference for breeders and researchers of interest in the mechanisms underlying the regulation of flavor, which would ultimately lead to the creation of carambola cultivars with more attractive flavor profiles and pleasurable consuming experiences.

Degradation performance and potential protection mechanism of the anammox consortia in response to capecitabine

The potential risks of anti-cancer drugs such as capecitabine have attracted considerable attention due to their continuous release. Understanding the response of removal performance and protective mechanism to the presence of emerging contaminants is crucial for the application of anammox techniques in wastewater treatment. Capecitabine affected the nitrogen removal performance slightly in the activity experiment. Due to bio-adsorption and biodegradation, up to 64-70% of the capecitabine can be removed effectively. However, 10 mg/L of capecitabine significantly decreased the removal efficiency of capecitabine and total nitrogen at repeated load of capecitabine. Metabolomic analysis revealed the metabolites 5'-deoxy-5-fluorocytidine and alpha-fluoro-beta-alanine, while metagenomic analysis confirmed the biodegradation pathway and underlying gene distribution. The potentially protective mechanisms of the system against capecitabine were the increased heterotrophic bacteria and secretion of sialic acid. Blast analysis confirmed the presence of potential genes involved in the complete biosynthesis pathway of sialic acid in anammox bacteria, some of which are also found in Nitrosomonas, Thauera, and Candidatus Promineofilum.

High-throughput metabolomics for the design and validation of a diauxic shift model

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

Multi-Omics Analysis of Lung Tissue Demonstrates Changes to Lipid Metabolism during Allergic Sensitization in Mice

Allergy and asthma pathogenesis are associated with the dysregulation of metabolic pathways. To understand the effects of allergen sensitization on metabolic pathways, we conducted a multi-omics study using BALB/cJ mice sensitized to house dust mite (HDM) extract or saline. Lung tissue was used to perform untargeted metabolomics and transcriptomics while both lung tissue and plasma were used for targeted lipidomics. Following statistical comparisons, an integrated pathway analysis was conducted. Histopathological changes demonstrated an allergic response in HDM-sensitized mice. Untargeted metabolomics showed 391 lung tissue compounds were significantly different between HDM and control mice (adjusted p < 0.05); with most compounds mapping to glycerophospholipid and sphingolipid pathways. Several lung oxylipins, including 14-HDHA, 8-HETE, 15-HETE, 6-keto-PGF1α, and PGE2 were significantly elevated in HDM-sensitized mice (p < 0.05). Global gene expression analysis showed upregulated calcium channel, G protein–signaling, and mTORC1 signaling pathways. Genes related to oxylipin metabolism such as Cox, Cyp450s, and cPla2 trended upwards. Joint analysis of metabolomics and transcriptomics supported a role for glycerophospholipid and sphingolipid metabolism following HDM sensitization. Collectively, our multi-omics results linked decreased glycerophospholipid and sphingolipid compounds and increased oxylipins with allergic sensitization; concurrent upregulation of associated gene pathways supports a role for bioactive lipids in the pathogenesis of allergy and asthma.

Untargeted profiling of secondary metabolites and phytotoxins associated with stemphylium blight of lentil

Stemphylium botryosum alters lentil secondary metabolism and differentially affects resistant and susceptible genotypes. Untargeted metabolomics identifies metabolites and their potential biosynthetic pathways that play a crucial role in resistance to S. botryosum. The molecular and metabolic processes that mediate resistance to stemphylium blight caused by Stemphylium botryosum Wallr. in lentil are largely unknown. Identifying metabolites and pathways associated with Stemphylium infection may provide valuable insights and novel targets to breed for enhanced resistance. The metabolic changes following infection of four lentil genotypes by S. botryosum were investigated by comprehensive untargeted metabolic profiling employing reversed-phase or hydrophilic interaction liquid chromatography (HILIC) coupled to a Q-Exactive mass spectrometer. At the pre-flowering stage, plants were inoculated with S. botryosum isolate SB19 spore suspension and leaf samples were collected at 24, 96 and 144 h post-inoculation (hpi). Mock-inoculated plants were used as negative controls. After analyte separation, high-resolution mass spectrometry data was acquired in positive and negative ionization modes. Multivariate modeling revealed significant treatment, genotype and hpi effects on metabolic profile changes that reflect lentil response to Stemphylium infection. In addition, univariate analyses highlighted numerous differentially accumulated metabolites. By contrasting the metabolic profiles of SB19-inoculated and mock-inoculated plants and among lentil genotypes, 840 pathogenesis-related metabolites were detected including seven S. botryosum phytotoxins. These metabolites included amino acids, sugars, fatty acids and flavonoids in primary and secondary metabolism. Metabolic pathway analysis revealed 11 significant pathways including flavonoid and phenylpropanoid biosynthesis, which were affected upon S. botryosum infection. This research contributes to ongoing efforts toward a comprehensive understanding of the regulation and reprogramming of lentil metabolism under biotic stress, which will provide targets for potential applications in breeding for enhanced disease resistance.

Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview

The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.

Divergent Metabolomic Signatures of TGFβ2 and TNFα in the Induction of Retinal Epithelial-Mesenchymal Transition

Epithelial-mesenchymal transition (EMT) is a dedifferentiation program in which polarized, differentiated epithelial cells lose their cell-cell adhesions and transform into matrix-producing mesenchymal cells. EMT of retinal pigment epithelial (RPE) cells plays a crucial role in many retinal diseases, including age-related macular degeneration, proliferative vitreoretinopathy, and diabetic retinopathy. This dynamic process requires complex metabolic reprogramming to accommodate the demands of this dramatic cellular transformation. Both transforming growth factor-beta 2 (TGFβ2) and tumor necrosis factor-alpha (TNFα) have the capacity to induce EMT in RPE cells; however, little is known about their impact on the RPE metabolome. Untargeted metabolomics using high-resolution mass spectrometry was performed to reveal the metabolomic signatures of cellular and secreted metabolites of primary human fetal RPE cells treated with either TGFβ2 or TNFα for 5 days. A total of 638 metabolites were detected in both samples; 188 were annotated as primary metabolites. Metabolomics profiling showed distinct metabolomic signatures associated with TGFβ2 and TNFα treatment. Enrichment pathway network analysis revealed alterations in the pentose phosphate pathway, galactose metabolism, nucleotide and pyrimidine metabolism, purine metabolism, and arginine and proline metabolism in TNFα-treated cells compared to untreated control cells, whereas TGFβ2 treatment induced perturbations in fatty acid biosynthesis metabolism, the linoleic acid pathway, and the Notch signaling pathway. These results provide a broad metabolic understanding of the bioenergetic rewiring processes governing TGFβ2- and TNFα-dependent induction of EMT. Elucidating the contributions of TGFβ2 and TNFα and their mechanistic differences in promoting EMT of RPE will enable the identification of novel biomarkers for diagnosis, management, and tailored drug development for retinal fibrotic diseases.

From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology

The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels.

Analysis of the oral microbiome during hormonal cycle and its alterations in menopausal women: the "AMICA" project

The maintenance of human health is dependent on a symbiotic relationship between humans and associated bacteria. The diversity and abundance of each habitat's signature microbes vary widely among body areas and among them the oral microbiome plays a key role. Significant changes in the oral cavity, predominantly at salivary and periodontal level, have been associated with changes in estrogen levels. However, whether the oral microbiome is affected by hormonal level alterations is understudied. Hence the main objective pursued by AMICA project was to characterize the oral microbiome (saliva) in healthy women through: profiling studies using "omics" technologies (NMR-based metabolomics, targeted lipidomics by LC-MS, metagenomics by NGS); SinglePlex ELISA assays; glycosidase activity analyses and bioinformatic analysis. For this purpose, thirty-nine medically healthy women aged 26-77 years (19 with menstrual cycle and 20 in menopause) were recruited. Participants completed questionnaires assessing detailed medical and medication history and demographic characteristics. Plasmatic and salivary levels of sexual hormones were assessed (FSH, estradiol, LH and progesteron) at day 3 and 14 for women with menstrual cycle and only once for women in menopause. Salivary microbiome composition was assessed through meta-taxonomic 16S sequencing and overall, the salivary microbiome of most women remained relatively stable throughout the menstrual cycle and in menopause. Targeted lipidomics and untargeted metabolomics profiling were assessed through the use of LC-MS and NMR spectroscopy technologies, respectively and significant changes in terms of metabolites were identified in saliva of post-menopausal women in comparison to cycle. Moreover, glycosyl hydrolase activities were screened and showed that the β-D-hexosaminidase activity was the most present among those analyzed. Although this study has not identified significant alterations in the composition of the oral microbiome, multiomics analysis have revealed a strong correlation between 2-AG and α-mannosidase. In conclusion, the use of a multidisciplinary approach to investigate the oral microbiome of healthy women provided some indication about microbiome-derived predictive biomarkers that could be used in the future for developing new strategies to help to re-establish the correct hormonal balance in post-menopausal women.

Responses of the Macroalga Ulva prolifera Müller to Ocean Acidification Revealed by Complementary NMR- and MS-Based Omics Approaches

Ocean acidification (OA) is a dramatic perturbation of seawater environments due to increasing anthropogenic emissions of CO₂. Several studies indicated that OA frequently induces marine biota stress and a reduction of biodiversity. Here, we adopted the macroalga Ulva prolifera as a model and applied a complementary multi-omics approach to investigate the metabolic profiles under normal and acidified conditions. Our results show that U. prolifera grows at higher rates in acidified environments. Consistently, we observed lower sucrose and phosphocreatine concentrations in response to a higher demand of energy for growth and a higher availability of essential amino acids, likely related to increased protein biosynthesis. In addition, pathways leading to signaling and deterrent compounds appeared perturbed. Finally, a remarkable shift was observed here for the first time in the fatty acid composition of triglycerides, with a decrease in the relative abundance of PUFAs towards an appreciable increase of palmitic acid, thus suggesting a remodeling in lipid biosynthesis. Overall, our studies revealed modulation of several biosynthetic pathways under OA conditions in which, besides the possible effects on the marine ecosystem, the metabolic changes of the alga should be taken into account considering its potential nutraceutical applications.

Evolution and regulation of microbial secondary metabolism

Microbes have disproportionate impacts on the macroscopic world. This is in part due to their ability to grow to large populations that collectively secrete massive amounts of secondary metabolites and alter their environment. Yet, the conditions favoring secondary metabolism despite the potential costs for primary metabolism remain unclear. Here we investigated the biosurfactants that the bacterium Pseudomonas aeruginosa makes and secretes to decrease the surface tension of surrounding liquid. Using a combination of genomics, metabolomics, transcriptomics, and mathematical modeling we show that the ability to make surfactants from glycerol varies inconsistently across the phylogenetic tree; instead, lineages that lost this ability are also worse at reducing the oxidative stress of primary metabolism on glycerol. Experiments with different carbon sources support a link with oxidative stress that explains the inconsistent distribution across the P. aeruginosa phylogeny and suggests a general principle: P. aeruginosa lineages produce surfactants if they can reduce the oxidative stress produced by primary metabolism and have excess resources, beyond their primary needs, to afford secondary metabolism. These results add a new layer to the regulation of a secondary metabolite unessential for primary metabolism but important to change physical properties of the environments surrounding bacterial populations.

Maternal Physiological Variations Induced by Chronic Gestational Hypoxia: 1H NMR-Based Metabolomics Study

Metabolomics have been widely used in pregnancy-related diseases. However, physiological variations induced by chronic hypoxia during pregnancy are not well characterized. We aimed to investigate physiological variations induced by chronic hypoxia during pregnancy. A Sprague-Dawley (SD) pregnant rat model of chronic hypoxia was established. Plasma and urine metabolite profiles at different stages of the pregnancy were detected by ¹H NMR (nuclear magnetic resonance). Multivariate statistical analysis was used to analyze changes in plasma and urine metabolic trajectories at different time-points. We identified hypoxia-induced changes in the levels of 30 metabolites in plasma and 29 metabolites in urine during different stages of pregnancy; the prominently affected metabolites included acetic acid, acetone, choline, citric acid, glutamine, isoleucine, lysine, and serine. Most significant hypoxia-induced changes in plasma and urine sample metabolites were observed on the 11th day of gestation. In summary, chronic hypoxia has a significant effect on pregnant rats, and may cause metabolic disorders involving glucose, lipids, amino acids, and tricarboxylic acid cycle. Metabolomics study of the effect of hypoxia during pregnancy may provide insights into the pathogenesis of obstetric disorders.

Interpretable machine learning methods for predictions in systems biology from omics data

Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design “interpretable” models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: “What is interpretability?” We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.

Insight into Lotusine and Puerarin in Repairing Alcohol-Induced Metabolic Disorder Based on UPLC-MS/MS

Alcohol is an essential element in human culture. However, alcoholism has contributed to numerous health issues, including alcoholic fatty liver and sudden death. We found that the alkaloid lotusine possessed hepato- and neuroprotection against alcohol injuries. Lotusine showed comparable protective effects to puerarin, a widely recognized antagonist against alcohol damage. To better understand the metabolic response to alcohol injury and antagonist molecules, we applied sensitive zebrafish and LC-ESI-MS to collect metabolites related to alcohol, puerarin and lotusine exposure. LC-MS identified 119 metabolites with important physiological roles. Differential metabolomic analysis showed that alcohol caused abnormal expression of 82 metabolites (60 up-regulated and 22 down-regulated). These differential metabolites involved 18 metabolic pathways and modules, including apoptosis, necroptosis, nucleotide and fatty acid metabolism. Puerarin reversed seven metabolite variations induced by alcohol, which were related to necroptosis and sphingolipid metabolism. Lotusine was found to repair five metabolites disorders invoked by alcohol, mainly through nucleotide metabolism and glutathione metabolism. In phenotypic bioassay, lotusine showed similar activities to puerarin in alleviating behavioral abnormalities, neuroapoptosis and hepatic lipid accumulation induced by alcohol exposure. Our findings provided a new antagonist, lotusine, for alcohol-induced damage and explored the roles in repairing abnormal metabolism.

LC-MS/MS Insight into Vitamin C Restoration to Metabolic Disorder Evoked by Amyloid β in Caenorhabditis elegans CL2006

The transitional expression and aggregation of amyloid β (Aβ) are the most important causative factors leading to the deterioration of Alzheimer’s disease (AD), a commonly occurring metabolic disease among older people. Antioxidant agents such as vitamin C (Vc) have shown potential effects against AD and aging. We applied an liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) method and differential metabolites strategy to explore the metabolic disorders and Vc restoration in a human Aβ transgenic (Punc-54::Aβ1–42) nematode model CL2006. We combined the LC-MS/MS investigation with the KEGG and HMDB databases and the CFM-ID machine-learning model to identify and qualify the metabolites with important physiological roles. The differential metabolites responding to Aβ activation and Vc treatment were filtered out and submitted to enrichment analysis. The enrichment showed that Aβ mainly caused abnormal biosynthesis and metabolism pathways of phenylalanine, tyrosine and tryptophan biosynthesis, as well as arginine and proline metabolism. Vc reversed the abnormally changed metabolites tryptophan, anthranilate, indole and indole-3-acetaldehyde. Vc restoration affected the tryptophan metabolism and the biosynthesis of phenylalanine, tyrosine and tryptophan. Our findings provide supporting evidence for understanding the metabolic abnormalities in neurodegenerative diseases and the repairing effect of drug interventions.

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

Noise exposure can lead to various kinds of disorders. Noise-induced hearing loss (NIHL) is one of the leading disorders confusing the noise-exposed workers. It is essential to identify NIHL markers for its early diagnosis and new therapeutic targets for its treatment. In this study, a total of 90 plasma samples from 60 noise-exposed steel factory male workers (the noise group) with (NIHL group, n = 30) and without NIHL (non-NIHL group, n = 30) and 30 male controls without noise exposure (control group) were collected. Untargeted human plasma metabolomic profiles were determined with HPLC-MS/MS. The levels of the metabolites in the samples were normalized to total peak intensity, and the processed data were subjected to multivariate data analysis. The Wilcoxon test and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed. With the threshold of p &lt; 0.05 and the variable importance of projection (VIP) value &gt;1, 469 differential plasma metabolites associated with noise exposure (DMs-NE) were identified, and their associated 58 KEGG pathways were indicated. In total, 33 differential metabolites associated with NIHL (DMs-NIHL) and their associated 12 KEGG pathways were identified. There were six common pathways associated with both noise exposure and NIHL. Through multiple comparisons, seven metabolites were shown to be dysregulated in the NIHL group compared with the other two groups. Through LASSO regression analysis, two risk models were constructed for NIHL status predication which could discriminate NIHL from non-NIHL workers with the area under the curve (AUC) values of 0.840 and 0.872, respectively, indicating their efficiency in NIHL diagnosis. To validate the results of the metabolomics, cochlear gene expression comparisons between susceptible and resistant mice in the GSE8342 dataset from Gene Expression Omnibus (GEO) were performed. The immune response and cell death-related processes were highlighted for their close relations with noise exposure, indicating their critical roles in noise-induced disorders. We concluded that there was a significant difference between the metabolite’s profiles between NIHL cases and non-NIHL individuals. Noise exposure could lead to dysregulations of a variety of biological pathways, especially immune response and cell death-related processes. Our results might provide new clues for noise exposure studies and NIHL diagnosis.

Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing

Early accurate detection of crop disease is extremely important for timely disease management. Huanglongbing (HLB), one of the most destructive citrus diseases, has brought about severe economic losses for the global citrus industry. The direct strategies for HLB identification, such as quantitative real-time polymerase chain reaction (qPCR) and chemical staining, are robust for the symptomatic plants but powerless for the asymptomatic ones at the early stage of affection. Thus, it is very necessary to develop a practical method used for the early detection of HLB. In this study, a novel method combining ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS)-based nontargeted metabolomics and machine learning (ML) was developed for conducting the early detection of HLB for the first time. Six ML algorithms were selected to build the classifiers. Regularized logistic regression (LR-L2) and gradient-boosted decision tree (GBDT) outperformed with the highest average accuracy of 95.83% to not only classify healthy and infected plants but identify significant features. The proposed method proved to be practical for early detection of HLB, which tackled the shortcomings of low sensitivity in the conventional methods and avoid the problems such as lighting condition interference in spectrum/image recognition-based ML methods. Additionally, the discovered biomarkers were verified by the metabolic pathway analysis and content change analysis, which was remarkably consistent with the previous reports.

High-throughput plant phenotyping: a role for metabolomics?

High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.

Toxicity of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) in early development: A wide-scope metabolomics assay in zebrafish embryos

The tobacco-specific nitrosamine 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is a carcinogenic and ubiquitous environmental pollutant for which toxic activity has been thoroughly investigated in murine models and human tissues. However, its potential deleterious effects on vertebrate early development are yet poorly understood. In this work, we characterized the impact of NNK exposure during early developmental stages of zebrafish embryos, a known alternative model for mammalian toxicity studies. Embryos exposed to different NNK concentrations were monitored for lethality and for the appearance of malformations during the first five days after fertilization. LC-MS based untargeted metabolomics was subsequently performed for a wide-scope assay of NNK-related metabolic alterations. Our results revealed the presence of not only the parental compound, but also of two known NNK metabolites, 4-Hydroxy-4-(3-pyridyl)-butyric acid (HPBA) and 4-(Methylnitrosamino)-1-(3-pyridyl-N-oxide)-1-butanol (NNAL-N-oxide) in exposed embryos likely resulting from active CYP450-mediated α-hydroxylation and NNK detoxification pathways, respectively. This was paralleled by a disruption in purine and pyrimidine metabolisms and the activation of the base excision repair pathway. Our results confirm NNK as a harmful embryonic agent and demonstrate zebrafish embryos to be a suitable early development model to monitor NNK toxicity.

Metabolomics and integrated network analysis reveal roles of endocannabinoids and large neutral amino acid balance in the ayahuasca experience

There has been a renewed interest in the potential use of psychedelics for the treatment of psychiatric conditions. Nevertheless, little is known about the mechanism of action and molecular pathways influenced by ayahuasca use in humans. Therefore, for the first time, our study aims to investigate the human metabolomics signature after consumption of a psychedelic, ayahuasca, and its connection with both the psychedelic-induced subjective effects and the plasma concentrations of ayahuasca alkaloids. Plasma samples of 23 individuals were collected both before and after ayahuasca consumption. Samples were analysed through targeted metabolomics and further integrated with subjective ratings of the ayahuasca experience (i.e., using the 5-Dimension Altered States of Consciousness Rating Scale [ASC]), and plasma ayahuasca-alkaloids using integrated network analysis. Metabolic pathways enrichment analysis using diffusion algorithms for specific KEGG modules was performed on the metabolic output. Compared to baseline, the consumption of ayahuasca increased N-acyl-ethanolamine endocannabinoids, decreased 2-acyl-glycerol endocannabinoids, and altered several large-neutral amino acids (LNAAs). Integrated network results indicated that most of the LNAAs were inversely associated with 9 out of the 11 subscales of the ASC, except for tryptophan which was positively associated. Several endocannabinoids and hexosylceramides were directly associated with the ayahuasca alkaloids. Enrichment analysis confirmed dysregulation in several pathways involved in neurotransmission such as serotonin and dopamine synthesis. In conclusion, a crosstalk between the circulating LNAAs and the subjective effects is suggested, which is independent of the alkaloid concentrations and provides insights into the specific metabolic fingerprint and mechanism of action underlying ayahuasca experiences.

Cross-comparative metabolomics reveal sex-age specific metabolic fingerprints and metabolic interactions in acute myocardial infarction

The elucidation of metabolic perturbations and gender-age-specific metabolic characteristics associated with acute myocardial infarction (AMI) is essential for clinical risk stratification and disease management. A comprehensive cross-comparative metabolomics analysis was performed on the sera from 445 healthy controls, 347 AMI patients without cardiovascular disease (CVD), 79 AMI with CVD (AMICVD) patients including 27 deaths. Machine-learning-based integrated biomarker profiling and global network analysis were used to create a multi-biomarker for distinguishing the different AMI outcomes. The changes of most metabolites were dependent on AMI, but gender and age also give additional contributions to the changes of histidine, malonate, O-acetyl-glycoprotein and trimethylamine N-oxide. The altered metabolic pathways included gut dysbiosis, increased amino acid metabolism, glucose metabolism and ketone metabolism, and inactivation of tricarboxylic acid cycle. Enhanced histidine metabolism and microbiota dysbiosis may be one of the key factors during the developing of AMI into AMICVD. For the differential diagnosis of AMI events, three sets of specific multi-biomarkers provided relatively high accuracy with the areas under the curve more than 0.8 and hazard ratio more than 1 in the discovery set, and the results were reproduced and confirmed by the validation set. First use of cross-comparative metabolomics and machine-learning-based integrated biomarker analysis gives great capability to discriminate the different AMI outcomes. Also, the multi-biomarkers seem to be a valid and accurate auxiliary diagnosis biomarker in addition to standard stratification based on clinical parameters.

Preparation, Antibacterial Potential, and Antibacterial Components of Fermented Compound Chinese Medicine Feed Additives

This experiment was conducted to compare the antibacterial ability and to identify the antibacterial components of different fermented compound Chinese medicine feed additives in order to develop one fermented compound Chinese medicine feed additive product that can effectively alleviate metritis, vaginitis, and mastitis of sows. The Oxford cup method and double dilution method were used to compare the antibacterial ability of three fermented compound Chinese medicine feed additives (A, B, and C). UHPLC-QE-MS-based untargeted metabolomics was used to identify the antibacterial components of fermented compound Chinese medicine feed additives. Results showed that among fermented compound Chinese medicine feed additives A, B, and C, additive A had the strongest ability to inhibit the growth of Staphylococcus aureus, Salmonella cholerae suis, Escherichia coli, and Streptococcus agalactiae. The MIC and MBC of additive A were the lowest for Staphylococcus aureus compared to that for the other three pathogens. The concentrations of 23 Chinese medicine ingredients (ellagic acid, guanine, camphor, L-valine, sinapine, dipropylphthalate, 3-hydroxy-5-isopropylidene-3,8-dimethyl-2,3,3a,4,5,8a-hexahydro-6(1H)-azulenone, 7-dihydroxy-2-(4-hydroxyphenyl)-8-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]-6-(3,4,5-trihydroxyoxan-2-yl)chromen-4-one, acetylcholine, farrerol, pyrogallol, ethyl gallate, demethylwedelolactone, methyl gallate, kaempferide, gallic acid, eriodictyol, threonic acid, inositol, 3′,4′,7-trihydroxyflavanone, taxifolin, asiatic acid, and isorhamnetin) in additive A were significantly (p &lt; 0.05 or p &lt; 0.01) higher than those in additive B, respectively. It is concluded that the mixture composed of 23 active components in fermented compound Chinese medicine feed additive A plays an important role in inhibiting the growth of Staphylococcus aureus, Salmonella cholerae suis, Escherichia coli, and Streptococcus agalactiae.

A Multilevel Assessment of Plasticity in Response to High-Altitude Environment for Agama Lizards

Upslope range shifting has been documented in diverse species in response to global warming. Plasticity, which refers to the ability of organisms to alter their phenotypes in changing environments, is crucial for the survival of those that newly migrated to a high-altitude environment. The scope and mechanisms of plasticity across biological levels, however, have rarely been examined. We used two agama lizards (genus Phrynocephalus) as model systems and a transplant experiment to comprehensively assess their plasticity on multiple organization levels. Two low-altitude (934 m) agama species, Phrynocephalus axillaris (oviparous) and P. forsythii (viviparous), were transplanted to a high-altitude site (3,400 m). After acclimation for 6 weeks in seminatural enclosures, plasticity was measured from bite force, tail display behavior, gene expression, and metabolome. Both lizards were capable of acclimating to the high-altitude environment without sacrificing their performance in bite force, but they also showed high plasticity in tail display behavior by either decreasing the intensity of a specific display component (P. forsythii) or by the trade-off between display components (P. axillaris). Genes and metabolites associated with lipids, especially fatty acid metabolism, exhibited significant differentiation in expression, compared to individuals from their native habitats. Improved fatty acid storage and metabolism appeared to be a common response among animals at high altitudes. Despite distinct reproductive modes that may differ in response to physiological pressure, the two lizards demonstrated high concordance in plasticity when they faced a novel environment at high altitudes. Taken together, lizards likely acclimate to high-altitude environments by reducing behavioral activity and increasing energy efficiency after range shifting. Our results provide new insights into our understanding of phenotypic plasticity and its importance in today’s changing climate.

Metabolomics Mechanism and Lignin Response to Laxogenin C, a Natural Regulator of Plants Growth

Laxogenin C (LGC) is a natural spirostanol deriving from plant hormone which has shown growing regulation similar to those of brassinosteroids. In the present study, LGC showed a promoting effect on tomato seed germination and seedling growth in a dose-dependent manner. We applied LC-MS/MS to investigate metabolome variations in the tomato treated with LGC, which revealed 10 differential metabolites (DMs) related to KEGG metabolites, associated with low and high doses of LGC. Enrichment and pathway mapping based on the KEGG database indicated that LGC regulated expressions of 2-hydroxycinnamic acid and l-phenylalanine to interfere with phenylalanine metabolism and phenylpropanoids biosynthesis. The two pathways are closely related to plant growth and lignin formation. In our further phenotypic verification, LGC was confirmed to affect seedling lignification and related phenylpropanoids, trans-ferulic acid and l-phenylalanine levels. These findings provided a metabolomic aspect on the plant hormone derivates and revealed the affected metabolites. Elucidating their regulation mechanisms can contribute to the development of sustainable agriculture. Further studies on agrichemical development would provide eco-friendly and efficient regulators for plant growth control and quality improvement.

Modular Evolution of the Drosophila Metabolome

Comparative phylogenetic studies offer a powerful approach to study the evolution of complex traits. Although much effort has been devoted to the evolution of the genome and to organismal phenotypes, until now relatively little work has been done on the evolution of the metabolome, despite the fact that it is composed of the basic structural and functional building blocks of all organisms. Here we explore variation in metabolite levels across 50 My of evolution in the genus Drosophila, employing a common garden design to measure the metabolome within and among 11 species of Drosophila. We find that both sex and age have dramatic and evolutionarily conserved effects on the metabolome. We also find substantial evidence that many metabolite pairs covary after phylogenetic correction, and that such metabolome coevolution is modular. Some of these modules are enriched for specific biochemical pathways and show different evolutionary trajectories, with some showing signs of stabilizing selection. Both observations suggest that functional relationships may ultimately cause such modularity. These coevolutionary patterns also differ between sexes and are affected by age. We explore the relevance of modular evolution to fitness by associating modules with lifespan variation measured in the same common garden. We find several modules associated with lifespan, particularly in the metabolome of older flies. Oxaloacetate levels in older females appear to coevolve with lifespan, and a lifespan-associated module in older females suggests that metabolic associations could underlie 50 My of lifespan evolution.

Comparisons of the immunological landscape of COVID-19 patients based on sex and disease severity by multi-omics analysis

Objective

To determine the differences in the immune response against SARS-CoV-2 infection of patients based on sex and disease severity.

Methods

We used an analytical framework of 382 transcriptional modules and multi-omics analyses to discriminate COVID-19 patients based on sex and disease severity.

Results

Male and female patients overexpressed modules related to the innate immune response. The expression of modules related to the adaptive immune response showed lower enrichment levels in males than females. Inflammation modules showed ascending overexpression in male and female patients, while a higher level was observed in severe female patients. Moderate female patients demonstrated significant overexpression to interferon, cytolytic lymphocyte, T & B cells, and erythrocytes modules. Moderate female patients showed a higher adaptive immune response than males matched group. Pathways involved in metabolism dysregulation and Hippo signaling were upregulated in females than in male patients. Females and moderate cases showed higher levels of metabolic dysregulation.

Conclusions

The immune landscape in COVID-19 patients was noticeably different between the sexes, and these differences may highlight disease vulnerability in males. This study suggested that certain treatments that increase or decrease the immune responses to SARS-CoV-2 might be necessary for male and female patients at certain disease stages.

Mapping the metabolomic and lipidomic changes in the Bleomycin model of pulmonary fibrosis in young and aged mice

Alterations in metabolic pathways were recently recognized as potential underlying drivers of idiopathic pulmonary fibrosis (IPF), translating into novel therapeutic targets. However, knowledge of metabolic and lipid regulation in fibrotic lungs is limited. To comprehensively characterize metabolic perturbations in the bleomycin mouse model of IPF, we analyzed the metabolome and lipidome by mass spectrometry. We identified increased tissue turnover and repair, evident by enhanced breakdown of proteins, nucleic acids and lipids and extracellular matrix turnover. Energy production was upregulated, including glycolysis, the tricarboxylic acid cycle, glutaminolysis, lactate production and fatty acid oxidation. Higher eicosanoid synthesis indicated inflammatory processes. Because the risk of IPF increases with age, we investigated how age influences metabolomic and lipidomic changes in the bleomycin-induced pulmonary fibrosis model. Surprisingly, except for cytidine, we did not detect any significantly differential metabolites or lipids between old and young bleomycin-treated lungs. Together, we identified metabolomic and lipidomic changes in fibrosis that reflect higher energy demand, proliferation, tissue remodeling, collagen deposition and inflammation, which might serve to improve diagnostic and therapeutic options for fibrotic lung diseases in the future.

Editor's choice: Using bleomycin-induced lung injury as a mouse model for idiopathic pulmonary fibrosis, this study identifies metabolomic and lipidomic changes in fibrosis reflecting higher energy demand, proliferation, tissue remodeling, collagen deposition and inflammation.

Metabolomics Comparison of Drug-Resistant and Drug-Susceptible Pseudomonas aeruginosa Strain (Intra- and Extracellular Analysis)

Pseudomonas aeruginosa is a common human pathogen belonging to the ESKAPE group. The multidrug resistance of bacteria is a considerable problem in treating patients and may lead to increased morbidity and mortality rate. The natural resistance in these organisms is caused by the production of specific enzymes and biofilm formation, while acquired resistance is multifactorial. Precise recognition of potential antibiotic resistance on different molecular levels is essential. Metabolomics tools may aid in the observation of the flux of low molecular weight compounds in biochemical pathways yielding additional information about drug-resistant bacteria. In this study, the metabolisms of two P. aeruginosa strains were compared-antibiotic susceptible vs. resistant. Analysis was performed on both intra- and extracellular metabolites. The ¹H NMR method was used together with multivariate and univariate data analysis, additionally analysis of the metabolic pathways with the FELLA package was performed. The results revealed the differences in P. aeruginosa metabolism of drug-resistant and drug-susceptible strains and provided direct molecular information about P. aeruginosa response for different types of antibiotics. The most significant differences were found in the turnover of amino acids. This study can be a valuable source of information to complement research on drug resistance in P. aeruginosa.

A novel strategy for D-psicose and lipase co-production using a co-culture system of engineered Bacillus subtilis and Escherichia coli and bioprocess analysis using metabolomics

To develop an economically feasible fermentation process, this study designed a novel bioprocess based on the co-culture of engineered Bacillus subtilis and Escherichia coli for the co-production of extracellular D-psicose and intracellular lipase. After optimizing the co-culture bioprocess, 11.70 g/L of D-psicose along with 16.03 U/mg of lipase was obtained; the glucose and fructose were completely utilized. Hence, the conversion rate of D-psicose reached 69.54%. Compared with mono-culture, lipase activity increased by 58.24%, and D-psicose production increased by 7.08%. In addition, the co-culture bioprocess was explored through metabolomics analysis, which included 168 carboxylic acids and derivatives, 70 organooxygen compounds, 34 diazines, 32 pyridines and derivatives, 30 benzene and substituted derivatives, and other compounds. It also could be found that the relative abundance of differential metabolites in the co-culture system was significantly higher than that in the mono-culture system. Pathway analysis revealed that, tryptophan metabolism and β-alanine metabolism had the highest correlation and played an important role in the co-culture system; among them, tryptophan metabolism regulates protein synthesis and β-alanine metabolism, which is related to the formation of metabolic by-products. These results confirm that the co-cultivation of B. subtilis and E. coli can provide a novel idea for D-psicose and lipase biorefinery, and are beneficial for the discovery of valuable secondary metabolites such as turanose and morusin.

mWISE: An Algorithm for Context-Based Annotation of Liquid Chromatography-Mass Spectrometry Features through Diffusion in Graphs

Untargeted metabolomics using liquid chromatography coupled to mass spectrometry (LC-MS) allows the detection of thousands of metabolites in biological samples. However, LC-MS data annotation is still considered a major bottleneck in the metabolomics pipeline since only a small fraction of the metabolites present in the sample can be annotated with the required confidence level. Here, we introduce mWISE (metabolomics wise inference of speck entities), an R package for context-based annotation of LC-MS data. The algorithm consists of three main steps aimed at (i) matching mass-to-charge ratio values to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, (ii) clustering and filtering the potential KEGG candidates, and (iii) building a final prioritized list using diffusion in graphs. The algorithm performance is evaluated with three publicly available studies using both positive and negative ionization modes. We have also compared mWISE to other available annotation algorithms in terms of their performance and computation time. In particular, we explored four different configurations for mWISE, and all four of them outperform xMSannotator (a state-of-the-art annotator) in terms of both performance and computation time. Using a diffusion configuration that combines the biological network obtained from the FELLA R package and raw scores, mWISE shows a sensitivity mean (standard deviation) across data sets of 0.63 (0.07), while xMSannotator achieves a sensitivity of 0.55 (0.19). We have also shown that the chemical structures of the compounds proposed by mWISE are closer to the original compounds than those proposed by xMSannotator. Finally, we explore the diffusion prioritization separately, showing its key role in the annotation process. mWISE is freely available on GitHub (https://github.com/b2slab/mWISE) under a GPL license.

Plasma Metabolome Profiling Identifies Metabolic Subtypes of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Developing biomarkers for early detection and chemotherapeutic response prediction is crucial to improve the dismal prognosis of PDAC patients. However, molecular cancer signatures based on transcriptome analysis do not reflect intratumoral heterogeneity. To explore a more accurate stratification of PDAC phenotypes in an easily accessible matrix, plasma metabolome analysis using MxP® Global Profiling and MxP® Lipidomics was performed in 361 PDAC patients. We identified three metabolic PDAC subtypes associated with distinct complex lipid patterns. Subtype 1 was associated with reduced ceramide levels and a strong enrichment of triacylglycerols. Subtype 2 demonstrated increased abundance of ceramides, sphingomyelin and other complex sphingolipids, whereas subtype 3 showed decreased levels of sphingolipid metabolites in plasma. Pathway enrichment analysis revealed that sphingolipid-related pathways differ most among subtypes. Weighted correlation network analysis (WGCNA) implied PDAC subtypes differed in their metabolic programs. Interestingly, a reduced expression among related pathway genes in tumor tissue was associated with the lowest survival rate. However, our metabolic PDAC subtypes did not show any correlation to the described molecular PDAC subtypes. Our findings pave the way for further studies investigating sphingolipids metabolisms in PDAC.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

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