MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity

Exploring machine learning for untargeted metabolomics using molecular fingerprints

BACKGROUND

Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways.

METHODS

This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups.

RESULTS

The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study.

CONCLUSION

In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models.

Gut microbial metabolite targets HDAC3-FOXK1-interferon axis in fibroblast-like synoviocytes to ameliorate rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease. Early studies hold an opinion that gut microbiota is environmentally acquired and associated with RA susceptibility. However, accumulating evidence demonstrates that genetics also shape the gut microbiota. It is known that some strains of inbred laboratory mice are highly susceptible to collagen-induced arthritis (CIA), while the others are resistant to CIA. Here, we show that transplantation of fecal microbiota of CIA-resistant C57BL/6J mice to CIA-susceptible DBA/1J mice confer CIA resistance in DBA/1J mice. C57BL/6J mice and healthy human individuals have enriched B. fragilis than DBA/1J mice and RA patients. Transplantation of B. fragilis prevents CIA in DBA/1J mice. We identify that B. fragilis mainly produces propionate and C57BL/6J mice and healthy human individuals have higher level of propionate. Fibroblast-like synoviocytes (FLSs) in RA are activated to undergo tumor-like transformation. Propionate disrupts HDAC3-FOXK1 interaction to increase acetylation of FOXK1, resulting in reduced FOXK1 stability, blocked interferon signaling and deactivation of RA-FLSs. We treat CIA mice with propionate and show that propionate attenuates CIA. Moreover, a combination of propionate with anti-TNF etanercept synergistically relieves CIA. These results suggest that B. fragilis or propionate could be an alternative or complementary approach to the current therapies.

Metabolic Responses to an Acute Glucose Challenge: The Differential Effects of Eight Weeks of Almond vs. Cracker Consumption in Young Adults

This study investigated the dynamic responses to an acute glucose challenge following chronic almond versus cracker consumption for 8 weeks (clinicaltrials.gov ID: NCT03084003). Seventy-three young adults (age: 18-19 years, BMI: 18-41 kg/m2) participated in an 8-week randomized, controlled, parallel-arm intervention and were randomly assigned to consume either almonds (2 oz/d, n=38) or an isocaloric control snack of graham crackers (325 kcal/d, n=35) daily for 8 weeks. Twenty participants from each group underwent a 2-hour oral glucose tolerance test (oGTT) at the end of the 8-week intervention. Metabolite abundances in the oGTT serum samples were quantified using untargeted metabolomics, and targeted analyses for free PUFAs, total fatty acids, oxylipins, and endocannabinoids. Multivariate, univariate, and chemical enrichment analyses were conducted to identify significant metabolic shifts. Findings exhibit a biphasic lipid response distinguished by higher levels of unsaturated triglycerides in the earlier periods of the oGTT followed by lower levels in the latter period in the almond versus cracker group (p-value<0.05, chemical enrichment analyses). Almond (vs. cracker) consumption was also associated with higher AUC120 min of aminomalonate, and oxylipins (p-value<0.05), but lower AUC120 min of L-cystine, N-acetylmannosamine, and isoheptadecanoic acid (p-value<0.05). Additionally, the Matsuda Index in the almond group correlated with AUC120 min of CE 22:6 (r=-0.46; p-value<0.05) and 12,13 DiHOME (r=0.45; p-value<0.05). Almond consumption for 8 weeks leads to dynamic, differential shifts in response to an acute glucose challenge, marked by alterations in lipid and amino acid mediators involved in metabolic and physiological pathways.

Mass spectrometry-based pseudotargeted metabolomics reveals metabolic variations in a2-induced gastric cancer cell

Gastric cancer (GC) is one of the most malignant tumors with high morbidity and mortality in the world. Compound a2, a Jiyuan oridonin derivative, exhibited excellent anti-proliferative activity against GC cells. To investigate the gastric cellular response to a2 therapy as a novel drug candidate, we adopted a pseudotargeted metabolomics method to explore metabolic variation in a2-induced MGC-803 gastric cells using liquid chromatography tandem mass spectrometry combined with multivariate statistical analysis. The results showed that a2 treatment induced significant metabolic changes in the levels of aminoacyl-tRNA biosynthesis, alanine, aspartate and glutamate metabolism, pyrimidine metabolism, and tricarboxylic acid cycle, approximately 80% of the metabolites were down-regulated in the low-dose and high-dose groups including aspartate, tryptophan, sedoheptulose 7-phosphate, succinate, 2'-deoxyadenosine, uridine, cytidine, etc. which can provide evidence for a new therapy of GC.

Tracking Dynamic Chemical Reactivity Networks with High-Resolution Mass Spectrometry: A Case of Microplastic-Derived Dissolved Organic Carbon

Chemical degradation testing often involves monitoring the loss of a chemical or the evolution of a single diagnostic product through time. Here, we demonstrate a novel approach to tracing complex degradation networks using mass-spectrometry-based methods and open cheminformatics tools. Ester- and ether-based thermoplastic polyurethane (TPU_Ester and TPU_Ether) microplastics (350 μm) and microplastics-derived dissolved organic carbon (MP-DOC) were photoweathered in a simulated marine environment and subsequently analyzed by liquid chromatography coupled to high-resolution mass spectrometry. We formula-annotated 1342 and 2344 unique features in the MP-DOC of TPU_Ester and TPU_Ether, respectively. From these, we extracted 199 and 568 plausible parent-transformation product pairs via matching of features (a) with complementary increasing and decreasing trends (Spearman's correlation coefficient between normalized intensity and time), (b) spectral similarities of at least three accurate mass MS2 fragments, and (c) at least 3 ppm agreement between the theoretical and measured change in m/z between the parent-transformation product formula. Molecular network analysis revealed that both chain scission and cross-linking reactions occur dynamically rather than degradation proceeding in a monotonic progression to smaller or more oxygenated structures. Network nodes with the highest degree of centrality were tentatively identified using in silico fragmentation and can be prioritized for toxicity screening or other physicochemical properties of interest. This work has important implications for chemical transformation tracking in complex mixtures and may someday enable improved elucidation of environmental transformation rules (i.e., structure-reactivity relationships) and fate modeling.

Insights into serum metabolic biomarkers for early detection of incident diabetic kidney disease in Chinese patients with type 2 diabetes by random forest

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD) worldwide. Early detection is critical for the risk stratification and early intervention of progressive DKD. Serum creatinine (sCr) and urine output are used to assess kidney function, but these markers are limited by their delayed changes following kidney pathology, and lacking of both sensitivity and accuracy. Hence, it is essential to illustrate potential diagnostic indicators to enhance the precise prediction of early DKD. A total of 194 Chinese individuals include 30 healthy participants (Stage 0) and 164 incidents with type 2 diabetes (T2D) spanning from DKD's Stage 1a to 4 were recruited and their serums were subjected for untargeted metabolomic analysis. Random forest (RF), a machine learning approach, together with univariate linear regression (ULR) and multivariate linear regression (MvLR) analysis were applied to characterize the features of untargeted metabolites of DKD patients and to identify candidate DKD biomarkers. Our results indicate that 2-(α-D-mannopyranosyl)-L-tryptophan (ADT), succinyladenosine (SAdo), pseudouridine and N,N,N-trimethyl-L-alanyl-L-proline betaine (L-L-TMAP) were associated with the development of DKD, in particular, the latter three that were significantly elevated in Stage 2-4 T2D incidents. Each of the four metabolites in combination with sCr achieves better performance than sCr alone with area under the receiver operating characteristic curve (AUC) of 0.81-0.91 in predicting DKD stages. An average of 3.9 years follow-up study of another cohort including 106 Stage 2-3 patients suggested that "urinary albumin-to-creatinine ratio (UACR) + ADT + SAdo" can be utilized for better prognosis evaluation of early DKD (average AUC = 0.9502) than UACR without sexual difference.

Strategy for Comprehensive Detection and Annotation of Gut Microbiota-Related Metabolites Based on Liquid Chromatography-High-Resolution Mass Spectrometry

Gut microbiota, widely populating the mammalian gastrointestinal tract, plays an important role in regulating diverse pathophysiological processes by producing bioactive molecules. Extensive detection of these molecules contributes to probing microbiota function but is limited by insufficient identification of existing analytical methods. In this study, a systematic strategy was proposed to detect and annotate gut microbiota-related metabolites on a large scale. A pentafluorophenyl (PFP) column-based liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method was first developed for high-coverage analysis of gut microbiota-related metabolites, which was verified to be stable and robust with a wide linearity range, high sensitivity, satisfactory recovery, and repeatability. Then, an informative database integrating 968 knowledge-based microbiota-related metabolites and 282 sample-sourced ones defined by germ-free (GF)/antibiotic-treated (ABX) models was constructed and subsequently used for targeted extraction and annotation in biological samples. Using pooled feces, plasma, and urine of mice for demonstration application, 672 microbiota-related metabolites were annotated, including 21% neglected by routine nontargeted peak detection. This strategy serves as a useful tool for the comprehensive capture of the intestinal flora metabolome, contributing to our deeper understanding of microbe-host interactions.

Comprehensive phytochemical profiles and antioxidant activity of Korean local cultivars of red chili pepper (Capsicum annuum L.)

Red chili pepper (Capsicum annuum L.), which belongs to the Solanaceae family, contains a variety of phytochemicals with health-promoting properties including capsaicinoids, phenolics and fatty acids. Red chili pepper is one of the most consumed vegetables in Korea and occupies the largest cultivated area among spices. In this study, the ethanolic extracts from two Korean local cultivars, namely Subicho and Eumseong, were analyzed using a hybrid trapped ion mobility Q-TOF mass spectrometer equipped with a UPLC system, and their phytochemical profiles were then compared with those of a common phytophthora disease-resistant cultivar called Dokbulwang, which is extensively used for red chili pepper powder in public spaces across Korea. Utilizing high-resolution ion-mobility Q-TOF MS analysis, 458 and 192 compounds were identified from the three different red chili peppers in positive and negative ion modes, respectively, by matching with a reference spectral library. Principal component analysis revealed clear distinctions among the three cultivars, allowing us to identify key phytochemical components responsible for discriminating the local cultivars from the public cultivar. Furthermore, the assessment of total flavonoid, phenolic, and antioxidant activity in the red pepper extracts, highlighted their diverse molecular and chemical profiles. Despite the higher total flavonoid and phenolic content values observed in the public cultivar, the radical scavenging rate was higher in the local cultivars, particularly in Subicho. This suggest the presence of stronger antioxidant compounds in the local cultivar, indicating their potential health benefits due to their rich content of bioactive compounds. Notably, the local cultivars exhibited significantly higher proportions of organic compounds (more than four times) and terpenoids (more than two times) compared to the public cultivar. Specifically, higher levels of five major capsaicinoid compounds were found in the local cultivars when compared to the public cultivar. The observed disparities in phytochemical composition and antioxidant activities indicate the molecular diversity present among these cultivars. Further exploration of the bioactive compounds in these local cultivars could prove invaluable for the development of native crops, potentially leading to the discovery of novel sources of bioactive molecules for various applications in health and agriculture.

IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ .

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.

The Carbon Flow Shifts from Primary to Secondary Metabolism during Xylem Vessel Cell Differentiation in Arabidopsis thaliana

Xylem vessel cell differentiation is characterized by the deposition of a secondary cell wall (SCW) containing cellulose, hemicellulose and lignin. VASCULAR-RELATED NAC-DOMAIN7 (VND7), a plant-specific NAC (NAM, ATAF1/2, and CUC2) transcription factor, is a master regulator of xylem vessel cell differentiation in Arabidopsis (Arabidopsis thaliana). Previous metabolome analysis using the VND7-inducible system in tobacco BY-2 cells successfully revealed significant quantitative changes in primary metabolites during xylem vessel cell differentiation. However, the flow of primary metabolites is not yet well understood. Here, we performed a metabolomic analysis of VND7-inducible Arabidopsis T87 suspension cells. Capillary electrophoresis-time-of-flight mass spectrometry quantified 57 metabolites, and subsequent data analysis highlighted active changes in the levels of UDP-glucose and phenylalanine, which are building blocks of cellulose and lignin, respectively. In a metabolic flow analysis using stable carbon 13 (13C) isotope, the 13C-labeling ratio specifically increased in 3-phosphoglycerate after 12 h of VND7 induction, followed by an increase in shikimate after 24 h of induction, while the inflow of 13C into lactate from pyruvate was significantly inhibited, indicating an active shift of carbon flow from glycolysis to the shikimate pathway during xylem vessel cell differentiation. In support of this notion, most glycolytic genes involved in the downstream of glyceraldehyde 3-phosphate were downregulated following the induction of xylem vessel cell differentiation, whereas genes for the shikimate pathway and phenylalanine biosynthesis were upregulated. These findings provide evidence for the active shift of carbon flow from primary metabolic pathways to the SCW polymer biosynthetic pathway at specific points during xylem vessel cell differentiation.

Metabolomics analysis of pathways underlying radiation-induced salivary gland dysfunction stages

Salivary gland hypofunction is an adverse side effect associated with radiotherapy for head and neck cancer patients. This study delineated metabolic changes at acute, intermediate, and chronic radiation damage response stages in mouse salivary glands following a single 5 Gy dose. Ultra-high performance liquid chromatography-mass spectrometry was performed on parotid salivary gland tissue collected at 3, 14, and 30 days following radiation (IR). Pathway enrichment analysis, network analysis based on metabolite structural similarity, and network analysis based on metabolite abundance correlations were used to incorporate both metabolite levels and structural annotation. The greatest number of enriched pathways are observed at 3 days and the lowest at 30 days following radiation. Amino acid metabolism pathways, glutathione metabolism, and central carbon metabolism in cancer are enriched at all radiation time points across different analytical methods. This study suggests that glutathione and central carbon metabolism in cancer may be important pathways in the unresolved effect of radiation treatment.

Sleep Promotion by 3-Hydroxy-4-Iminobutyric Acid in Walnut Diaphragma juglandis Fructus

Insufficient sleep can produce a multitude of deleterious repercussions on various domains of human well-being. Concomitantly, the walnut (Juglans mandshurica) confers numerous salutary biological activities pertaining to sleep. Nevertheless, the sedative and hypnotic capacities of walnut's functional constituents remain obscure. In this investigation, we analyzed the sedative and hypnotic components of the walnut Diaphragma juglandis fructus and innovatively discovered a compound, defined as 3-hydroxy-4-iminobutyric acid (HIBA), which disrupts motor activity and enhances sleep duration by regulating the neurotransmitters (GABA, DA, etc.) within the brain and serum of mice. Subsequently, a metabolomics approach of the serum, basal ganglia, hypothalamus, and hippocampus as well as the gut microbiota was undertaken to unravel the underlying molecular mechanisms of sleep promotion. Our data reveal that HIBA can regulate the metabolism of basal ganglia (sphingolipids, acylcarnitines, etc.), possibly in relation to HIBA's influence on the gut microbiome (Muribaculum, Bacteroides, Lactobacillus, etc.). Therefore, we introduce a novel natural product, HIBA, and explicate the modulation of sleep promotion in mice based on the microbiota-gut-brain axis. This study contributes fresh insights toward natural product-based sleep research.

Metabolomic Reconfiguration in Primed Barley (Hordeum vulgare) Plants in Response to Pyrenophora teres f. teres Infection

Necrotrophic fungi affect a wide range of plants and cause significant crop losses. For the activation of multi-layered innate immune defences, plants can be primed or pre-conditioned to rapidly and more efficiently counteract this pathogen. Untargeted and targeted metabolomics analyses were applied to elucidate the biochemical processes involved in the response of 3,5-dichloroanthranilic acid (3,5-DCAA) primed barley plants to Pyrenophora teres f. teres (Ptt). A susceptible barley cultivar ('Hessekwa') at the third leaf growth stage was treated with 3,5-DCAA 24 h prior to infection using a Ptt conidia suspension. The infection was monitored over 2, 4, and 6 days post-inoculation. For untargeted studies, ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) was used to analyse methanolic plant extracts. Acquired data were processed to generate the data matrices utilised in chemometric modelling and multi-dimensional data mining. For targeted studies, selected metabolites from the amino acids, phenolic acids, and alkaloids classes were quantified using multiple reaction monitoring (MRM) mass spectrometry. 3,5-DCAA was effective as a priming agent in delaying the onset and intensity of symptoms but could not prevent the progression of the disease. Unsupervised learning methods revealed clear differences between the sample extracts from the control plants and the infected plants. Both orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and 'shared and unique structures' (SUS) plots allowed for the extraction of potential markers of the primed and naïve plant responses to Ptt. These include classes of organic acids, fatty acids, amino acids, phenolic acids, and derivatives and flavonoids. Among these, 5-oxo-proline and citric acid were notable as priming response-related metabolites. Metabolites from the tricarboxylic acid pathway were only discriminant in the primed plant infected with Ptt. Furthermore, the quantification of targeted metabolites revealed that hydroxycinnamic acids were significantly more prominent in the primed infected plants, especially at 2 d.p.i. Our research advances efforts to better understand regulated and reprogrammed metabolic responses that constitute defence priming in barley against Ptt.

Metabolic reprogramming of the glutathione biosynthesis modulates the resistance of Salmonella Derby to ceftriaxone

Salmonella, a foodborne pathogen, has become a major public health concern because of its widespread drug resistance, including resistance to multiple drugs such as third-generation cephalosporin, ceftriaxone (CRO). However, the metabolic profile changes and associated mechanisms engendered by cephalosporin-resistant mutations remain uncharted. In this study, we have employed the LC-MS/MS metabolomics platform to determine the metabolic profiles of 138 strains of Salmonella. Our results show that metabolic profiles correspond to specific serotypes, sources, processing stages, and antibiotic resistance patterns. Notably, we observed that Salmonella Derby (S. Derby) with drug resistance to CRO has a different metabolic status with changes in glutathione biosynthesis. Specifically, glutathione oxidized (GSSG) and citrulline abundances are greatly suppressed in CRO-resistant S. Derby. Furthermore, exogenous GSSG or citrulline, but not glutathione reduced (GSH), restored the susceptibility of multidrug-resistant S. Derby to CRO. This study establishes a strategy based on functional metabolomics to manage the survival of antibiotic-resistant bacteria.

An integrated deep learning framework for the interpretation of untargeted metabolomics data

Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.

Long-term percutaneous triclosan exposure induces thyroid damage in mice: Interpretation of toxicity mechanism from metabolic and proteomic perspectives

Triclosan (TCS) is an antiseptic incorporated in consumer goods and personal care products that can be absorbed via the skin, raising public health concerns for its continuous detection in human biofluids and tissues. Epidemiology has associated TCS exposure with thyroid function disturbances and decreasing serum thyroid hormone (TH) levels, but the underlying mechanism remains unclear. In this study, we revealed hypothyroidism and histological alternation in the thyroid of mice with chronic percutaneous exposure to TCS, indicating a TCS-caused thyroid impairment. Subsequently, multi-omics approaches were performed to investigate the molecular mechanism of the thyroid in response to long-term dermal TCS exposure. We discovered that TCS interfered with the TH synthesis as indicated by the changes in the levels of the synthetic materials for TH (iodide, Tg, and H2O2) and affected TH release by the downregulation of lysosomal enzymes. The upregulation of glycolysis, tricarboxylic acid cycle, fatty acid, amino acid metabolism, and adenine salvage in the thyroid was also observed after TCS exposure. All these changes led to the elevation of ATP, serving as a rescue for the decreasing thyroid functions. Together, our study demonstrated TCS-induced thyroid damage and identified the interrupted pathways, providing meaningful insight into the molecular mechanisms underpinning the potential health influence of TCS in humans.

Serum, Urine, and Fecal Metabolome Alterations in the Gut Microbiota in Response to Lifestyle Interventions in Pediatric Obesity: A Non-Randomized Clinical Trial

Pediatric obesity is associated with alterations in the gut microbiota and its metabolites. However, how they influence obesity and the effect of lifestyle interventions remains unknown.. In this non-randomized clinical trial, we analyzed metabolomes and microbial features to understand the associated metabolic pathways and the effect of lifestyle interventions on pediatric obesity. Anthropometric/biochemical data and fasting serum, urine, and fecal samples were collected at baseline and after an eight-week, weight-reduction lifestyle modification program. Post-intervention, children with obesity were classified into responder and non-responder groups based on changes in total body fat. At baseline, serum L-isoleucine and uric acid levels were significantly higher in children with obesity compared with those in normal-weight children and were positively correlated with obesogenic genera. Taurodeoxycholic and tauromuricholic α + β acid levels decreased significantly with obesity and were negatively correlated with obesogenic genera. Branched-chain amino acid and purine metabolisms were distinguished metabolic pathways in the obese group. Post-intervention, urinary myristic acid levels decreased significantly in the responder group, showing a significant positive correlation with Bacteroides. Fatty acid biosynthesis decreased significantly in the responder group. Thus, lifestyle intervention with weight loss is associated with changes in fatty acid biosynthesis, and myristic acid is a possible therapeutic target for pediatric obesity.

Untargeted Metabolomics to Characterize the Urinary Chemical Landscape of E-Cigarette Users

The health and safety of using e-cigarette products (vaping) have been challenging to assess and further regulate due to their complexity. Inhaled e-cigarette aerosols contain chemicals with under-recognized toxicological profiles, which could influence endogenous processes once inhaled. We urgently need more understanding on the metabolic effects of e-cigarette exposure and how they compare to combustible cigarettes. To date, the metabolic landscape of inhaled e-cigarette aerosols, including chemicals originated from vaping and perturbed endogenous metabolites in vapers, is poorly characterized. To better understand the metabolic landscape and potential health consequences of vaping, we applied liquid chromatography-mass spectrometry (LC-MS) based nontargeted metabolomics to analyze compounds in the urine of vapers, cigarette smokers, and nonusers. Urine from vapers (n = 34), smokers (n = 38), and nonusers (n = 45) was collected for verified LC-HRMS nontargeted chemical analysis. The altered features (839, 396, and 426 when compared smoker and control, vaper and control, and smoker and vaper, respectively) among exposure groups were deciphered for their structural identities, chemical similarities, and biochemical relationships. Chemicals originating from e-cigarettes and altered endogenous metabolites were characterized. There were similar levels of nicotine biomarkers of exposure among vapers and smokers. Vapers had higher urinary levels of diethyl phthalate and flavoring agents (e.g., delta-decalactone). The metabolic profiles featured clusters of acylcarnitines and fatty acid derivatives. More consistent trends of elevated acylcarnitines and acylglycines in vapers were observed, which may suggest higher lipid peroxidation. Our approach in monitoring shifts of the urinary chemical landscape captured distinctive alterations resulting from vaping. Our results suggest similar nicotine metabolites in vapers and cigarette smokers. Acylcarnitines are biomarkers of inflammatory status and fatty acid oxidation, which were dysregulated in vapers. With higher lipid peroxidation, radical-forming flavoring, and higher level of specific nitrosamine, we observed a trend of elevated cancer-related biomarkers in vapers as well. Together, these data present a comprehensive profiling of urinary biochemicals that were dysregulated due to vaping.

Metabolomic and cultivation insights into the tolerance of the spacecraft-associated Acinetobacter toward Kleenol 30, a cleanroom floor detergent

Introduction

Stringent cleaning procedures during spacecraft assembly are critical to maintaining the integrity of life-detection missions. To ensure cleanliness, NASA spacecraft are assembled in cleanroom facilities, where floors are routinely cleansed with Kleenol 30 (K30), an alkaline detergent.

Methods

Through metabolomic and cultivation approaches, we show that cultures of spacecraft-associated Acinetobacter tolerate up to 1% v/v K30 and are fully inhibited at ≥2%; in comparison, NASA cleanrooms are cleansed with ~0.8-1.6% K30.

Results

For A. johnsonii 2P08AA (isolated from a cleanroom floor), cultivations with 0.1% v/v K30 yield (1) no changes in cell density at late-log phase, (2) modest decreases in growth rate (~17%), (3) negligible lag phase times, (4) limited changes in the intracellular metabolome, and (5) increases in extracellular sugar acids, monosaccharides, organic acids, and fatty acids. For A. radioresistens 50v1 (isolated from a spacecraft surface), cultivations yield (1) ~50% survivals, (2) no changes in growth rate, (3) ~70% decreases in the lag phase time, (4) differential changes in intracellular amino acids, compatible solutes, nucleotide-related metabolites, dicarboxylic acids, and saturated fatty acids, and (5) substantial yet differential impacts to extracellular sugar acids, monosaccharides, and organic acids.

Discussion

These combined results suggest that (1) K30 manifests strain-dependent impacts on the intracellular metabolomes, cultivation kinetics, and survivals, (2) K30 influences extracellular trace element acquisition in both strains, and (3) K30 is better tolerated by the floor-associated strain. Hence, this work lends support towards the hypothesis that repeated cleansing during spacecraft assembly serve as selective pressures that promote tolerances towards the cleaning conditions.

Almond Consumption for 8 Weeks Altered Host and Microbial Metabolism in Comparison to a Control Snack in Young Adults

Almond consumption can improve cardiometabolic (CM) health. However, the mechanisms underlying those benefits are not well characterized. This study explored the effects of consuming a snack of almonds vs. crackers for 8 weeks on changes in metabolomic profiles in young adults (clinicaltrials.gov ID: NCT03084003).

Participants (n = 73, age: 18-19 years, BMI: 18-41 kg/m2) were randomly assigned to consume either almonds (2 oz/d, n = 38) or an isocaloric control snack of graham crackers (325 kcal/d, n = 35) daily for 8 weeks. Blood samples were collected at baseline prior to and at 4 and 8 weeks after the intervention. Metabolite abundances in the serum were quantified by hydrophilic interaction chromatography quadrupole (Q) time-of-flight (TOF) mass spectrometry (MS/MS), gas chromatography (GC) TOF MS, CSH-ESI (electrospray) QTOF MS/MS, and targeted analyses for free PUFAs, total fatty acids, oxylipins and endocannabinoids. Linear mixed model analyses with baseline-adjustment were conducted, and those results were used for enrichment and network analyses. Microbial community pathway predictions from 16S rRNA sequencing of fecal samples was done using PICRUST2.

Almond consumption enriched unsaturated triglycerides, unsaturated phosphatidylcholines, saturated and unsaturated lysophosphatidylcholines, tricarboxylic acids, and tocopherol clusters (p < 0.05). Targeted analyses reveal lower levels of omega-3 total fatty acids (TFAs) overall in the almond group compared to the cracker group (p < 0.05). Microbial amino acid biosynthesis, and amino sugar and nucleotide sugar metabolism pathways were also differentially enriched at the end of the intervention (p < 0.05).

The study demonstrates the differential effects of almonds on host tocopherol, lipid, and TCA cycle metabolism with potential changes in microbial metabolism, which may interact with host metabolism to facilitate the CM benefits.

Developed network between taxoid and phenylpropanoid pathways in Cryptosporiopsis tarraconensis, taxan-producing endophytic fungus by Debiased Sparse Partial Correlation (DSPC) algorithm

Although bioproduction of Paclitaxel by endophytic fungi is highly considered as an alternative promising source, but its yield is usually very low in comparison with other taxoids. Different strategies i.e., chemical and physical elicitations have been developed in order to overcome the shortage of Paclitaxel production. Paclitaxel biosynthesis is started with terpenoid pathway followed by phenylpropanoid metabolism where a benzoylphenylisoserine moiety is attached to C13 of baccatin III skeleton. This point which is catalyzed by the function of PAM seems to be a bottleneck that limits the rate of Paclitaxel production. Whether phenylpropanoids pathway regulates the taxanes biosynthesis in Cryptosporiopsis tarraconensis endophytic fungus elicited with benzoic acid (BA) was hypothesized in the present paper. The involvement of certain signal molecules and key enzymes of terpenoid and phenylpropanoid metabolism were investigated. According to the results, application of BA promoted a signaling pathway which was started with increase of H2O2 and ABA and continued by increase of NO and MJ, and finally resulted in increase of both phenylpropanoids and taxanes. However, again the rate of Paclitaxel production was lower than other taxoids, and the latter was much lower than phenolics. Therefore, supplying benzoic acid provided the precursor for the common taxan ring production. It is unlikely that Paclitaxel production is merely controlled by side chain production stage. It is more likely that in C. tarraconensis endophytic fungus, similar to Taxus sp., the competition between phenylpropanoid and taxoid pathways for substrate ended in favor of the former. The interaction network which was constructed based on DSPC algorithm confirmed that most compounds with close proximity have shared metabolic pathway relationships. Therefore, it is unlikely that the feeding with a given precursor directly result in increase of a desired metabolite which is composed of different merits.

Serum metabolomic profiling revealed potential diagnostic biomarkers in patients with panic disorder

BACKGROUND

Currently, specific metabolites and diagnostic biomarkers of panic disorder (PD) patients have not been identified in clinical practice. The aim of this study was to explore metabolites and metabolic pathways in serum through a metabolomics method.

METHODS

Fifty-five PD patients who completed 2 weeks of inpatient treatment and 55 healthy control subjects (HCs) matched for age, sex and BMI were recruited. Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was used to detect metabolites in serum. Multivariate Statistical Analysis was used to identify differential metabolites. The relevant biometabolic pathways were further identified by the online tool MetaboAnalyst 5.0.

RESULTS

43 different metabolites in PD patients compared to HCs (P < 0.05) were screened. Pathway analysis showed that these small molecules were mainly associated with amino acid metabolism. 14 metabolites were significantly changed after 2 weeks of drug treatment (P < 0.05), which were mainly associated with tryptophan metabolism.

CONCLUSION

In conclusion, our analysis of metabolomics of PD patients at baseline and two weeks after treatment screened for differential metabolites that could be potential diagnostic biomarkers involved in PD pathogenesis and influence some biometabolic pathways such as phenylalanine metabolism and tryptophan metabolism. In the future, we can summarize and observe the dynamic changes of differential metabolites that appear more frequently in similar studies to further explore the underlying mechanisms of PD evolution.

Subjective and objective burden on providers from a multicenter app-based study of patients with cirrhosis and caregivers

BACKGROUND

App-based technologies could enhance patient and caregiver communication and provide alerts that potentially reducing readmissions. However, the burden of App alerts needs to be optimized to reduce provider burnout.

AIM

The purpose of this study was to determine subjective and objective burden of using the Patient Buddy App, a health information technology (HIT) on providers in a randomized multicenter trial, who completed a semi-quantitative Likert scale survey regarding training procedures, data and privacy concerns, follow-up details, and technical support. This randomized multicenter trial recruits cirrhosis inpatients and their caregivers, and randomizes them into standard-of-care, HIT (communication only via App) and HIT+visits (App+phone calls/visits) for 30 days after discharge. The alerts are monitored by providers through a central iPad. The reason(s) and number of alerts were recorded as the objective burden. A total of 1442 messages were sent as alerts from the 103 dyads (patient + caregiver) (n=206) randomized to HIT arms. The most common messages related to Hepatic Encephalopathy (HE) (high or low bowel movement=50% or orientation tests=37%). Twelve providers completed the surveys reflecting the following themes-92% and 100%, felt adequately trained and confident about educating the patients and caregivers before roll out of App and had no concerns related to data and privacy; 70%, felt that appropriate time was spent on pursuing reason for data not being logged; 60% each, had issues with availability of adequate technical support and connectivity.

CONCLUSION

The Patient Buddy App randomized multicenter trial till date shows an overall favorable rating regarding training procedures/education, privacy concerns, and ease of message follow-up, from providers. However, it is important to gauge and address subjective and objective burdens of monitoring human resources in current and future HIT studies to avoid burnout and to ensure successful study completion.

The activity of the aryl hydrocarbon receptor in T cells tunes the gut microenvironment to sustain autoimmunity and neuroinflammation

Multiple sclerosis (MS) is a T cell-driven autoimmune disease that attacks the myelin of the central nervous system (CNS) and currently has no cure. MS etiology is linked to both the gut flora and external environmental factors but this connection is not well understood. One immune system regulator responsive to nonpathogenic external stimuli is the aryl hydrocarbon receptor (AHR). The AHR, which binds diverse molecules present in the environment in barrier tissues, is a therapeutic target for MS. However, AHR's precise function in T lymphocytes, the orchestrators of MS, has not been described. Here, we show that in a mouse model of MS, T cell-specific Ahr knockout leads to recovery driven by a decrease in T cell fitness. At the mechanistic level, we demonstrate that the absence of AHR changes the gut microenvironment composition to generate metabolites that impact T cell viability, such as bile salts and short chain fatty acids. Our study demonstrates a newly emerging role for AHR in mediating the interdependence between T lymphocytes and the microbiota, while simultaneously identifying new potential molecular targets for the treatment of MS and other autoimmune diseases.

Metabolite alterations in zebrafish embryos exposed to hydroxylated polybrominated diphenyl ethers

Hydroxylated polybrominated diphenyl ethers (OH-PBDEs) are formed by metabolism from the flame retardants polybrominated diphenyl ethers (PBDEs). In the aquatic environment, they are also produced naturally. OH-PBDEs are known for their potential to disrupt energy metabolism, the endocrine system, and the nervous system. This is the first study focusing on the effects of OH-PBDEs at the metabolite level in vivo. The aim of the current study was to investigate the metabolic effects of exposure to OH-PBDEs using metabolomics, and to identify potential biomarker(s) for energy disruption of OH-PBDEs. Zebrafish (Danio rerio) embryos were exposed to two different concentrations of 6-OH-BDE47 and 6-OH-BDE85 and a mixture of these two compounds. In total, 342 metabolites were annotated and 79 metabolites were affected in at least one exposure. Several affected metabolites, e.g. succinic acid, glutamic acid, glutamine, tyrosine, tryptophan, adenine, and several fatty acids, could be connected to known toxic mechanisms of OH-PBDEs. Several phospholipids were strongly up-regulated with up to a six-fold increase after exposure to 6-OH-BDE47, a scarcely described effect of OH-PBDEs. Based on the observed metabolic effects, a possible connection between disruption of the energy metabolism, neurotoxicity and potential immunotoxicity of OH-PBDEs was suggested. Single compound exposures to 6-OH-BDE47 and 6-OH-BDE85 showed little overlap in the affected metabolites. This shows that compounds of similar chemical structure can induce different metabolic effects, possibly relating to their different toxic mechanisms. There were inter-concentration differences in the metabolic profiles, indicating that the metabolic effects were concentration dependent. After exposure to the mixture of 6-OH-BDE47 and 6-OH-BDE85, a new metabolic profile distinct from the profiles obtained from the single compounds was observed. Succinic acid was up-regulated at the highest, but still environmentally relevant, concentration of 6-OH-BDE47, 6-OH-BDE85, and the mixture. Therefore, succinic acid is suggested as a potential biomarker for energy disruption of OH-PBDEs.

Cell-Based Metabolomics Approach for Anticipating and Investigating Cytotoxicity of Gold Nanorods

Despite the increasing application of gold nanoparticles, there has been little assessment of biological system toxicity to evaluate their potential impact on human health. In this study, the human hepatoma cell line (Hep G2) was used in a metabolomics approach to study the effects of shape, time, and dose of gold nanorods (GNRs). Using optimized parameters for chromatography and mass spectrometry, the metabolites detected by GC-MS were processed with MS DIAL and identified with Fiehnlib. Key metabolic pathways affected by GNRs were identified by endo-metabolic profiling of cells mixed with GNRs of varying shape while varying the dose and time of exposure. The shape of GNRs affected cytotoxicity, and short GNR (GNR-S) triggered disorder of cell metabolism. High concentrations of GNRs caused more significant toxicity. The cytotoxicity and bioTEM results illustrated that the mitochondria toxicity, as the main cytotoxicity of GNRs, caused declining cytoprotective ability. The mitochondrial dysfunction disrupted alanine, aspartate, glutamate, arginine, and proline metabolism, with amino acid synthesis generally downregulated. However, the efflux function of cells can exclude GNRs extracellularly within 24 h, resulting in reduced cell mitochondrial metabolic toxicity and allowing metabolic disorders to recover to normal function.

Development of LC-HRMS untargeted analysis methods for nasal epithelial lining fluid exposomics

BACKGROUND

The nasal mucosa, as a primary site of entry for inhaled substances, contains both inhaled xenobiotic and endogenous biomarkers. Nasal mucosa can be non-invasively sampled (nasal epithelial lining fluid "NELF") and analyzed for biological mediators. However, methods for untargeted analysis of compounds inhaled and/or retained in the nasal mucosa are needed.

OBJECTIVES

This study aimed to develop a high resolution LC-MS untargeted method to analyze collected NELF. Profiling of compounds in NELF samples will also provide baseline data for future comparative studies to reference.

METHODS

Extracted NELF analytes were injected to LC-ESI-MS. After spectrum processing, an in-house library provided annotations with high confidence, while more tentative annotation proposals were obtained via ChemSpider database matching.

RESULTS

The established method successfully detected unique molecular signatures within NELF. Baseline profiling of 27 samples detected 2002 unknown molecules, with 77 and 463 proposed structures by our in-house library and Chemspider matching. High confidence annotations revealed common metabolites and tentative annotations implied various environmental exposure biomarkers are also present in NELF.

SIGNIFICANCE

The experimental pipeline for analyzing NELF samples serves as simple and robust method applicable for future studies to characterize identities/effects of inhaled substances and metabolites retained in the nasal mucosa.

IMPACT STATEMENT

The nasal mucosa contains exogenous and endogenous compounds. The development of an untargeted analysis is necessary to characterize the nasal exposome by deciphering the identity and influence of inhaled compounds on nasal mucosal biology. This study established a high resolution LC-MS based untargeted analysis of non-invasively collected nasal epithelial lining fluid. Baseline profiling of the nasal mucosa (n = 27) suggests the presence of environmental pollutants, along with detection of endogenous metabolites. Our results show high potential for the analytical pipeline to facilitate future respiratory health studies involving inhaled pollutants or pharmaceutical compounds and their effects on respiratory biology.

Dual effects of zearalenone on aflatoxin B1-induced liver and mammary gland toxicity in pregnant and lactating rats

Food and feed are frequently co-contaminated with aflatoxin B1 (AFB1) and zearalenone (ZEN). This study investigated the effects of ZEN on the AFB1-induced liver and mammary gland toxicity in pregnant and lactating rats. AFB1 and ZEN co-exposure inhibited the growth of rats and caused oxidative stress and inflammatory responses in the liver and mammary gland. Compared with the AFB1-only group, damage was aggravated in the AFB1 + 10 mg/kg ZEN group, and the AFB1 + 1 mg/kg ZEN group showed a reduction in some metrics. The metabolomic results of the mammary gland showed that metabolite changes were mainly in lipid, amino acid, and glucose metabolism. Compared with the AFB1 + 0 mg/kg ZEN group, the AFB1 + 1 mg/kg ZEN group had the most metabolite changes. Moreover, AFB1 and ZEN co-exposure reduced the levels of sex hormones and RNA m⁶A methylation in the mammary gland. We speculate that ZEN affects the toxicity of AFB1 to the liver and mammary gland by interfering with the function of sex hormones, regulating cell proliferation and metabolic processes.

Association of Metabolic Signatures with Nonalcoholic Fatty Liver Disease in Pediatric Population

Several adult omics studies have been conducted to understand the pathophysiology of nonalcoholic fatty liver disease (NAFLD). However, the histological features of children are different from those of adults, and the onset and progression of pediatric NAFLD are not fully understood. In this study, we aimed to evaluate the metabolome profile and metabolic pathway changes associated with pediatric NAFLD to elucidate its pathophysiology and to develop machine learning-based NAFLD diagnostic models. We analyzed the metabolic profiles of healthy control, lean NAFLD, overweight control, and overweight NAFLD groups of children and adolescent participants (N = 165) by assessing plasma samples. Additionally, we constructed diagnostic models by applying three machine learning methods (ElasticNet, random forest, and XGBoost) and multiple logistic regression by using NAFLD-specific metabolic features, genetic variants, and clinical data. We identified 18 NAFLD-specific metabolic features and metabolic changes in lipid, glutathione-related amino acid, and branched-chain amino acid metabolism by comparing the control and NAFLD groups in the overweight pediatric population. Additionally, we successfully developed and cross-validated diagnostic models that showed excellent diagnostic performance (ElasticNet and random forest model: area under the receiver operating characteristic curve, 0.95). Metabolome changes in the plasma of pediatric patients with NAFLD are associated with the pathophysiology of the disease and can be utilized as a less-invasive approach to diagnosing the disease.

Early Life Short-Term Exposure to Polychlorinated Biphenyl 126 in Mice Leads to Metabolic Dysfunction and Microbiota Changes in Adulthood

Early life exposure to environmental pollutants may have long-term consequences and harmful impacts on health later in life. Here, we investigated the short- and long-term impact of early life 3,3′,4,4′,5-pentacholorobiphenyl (PCB 126) exposure (24 μg/kg body weight for five days) in mice on the host and gut microbiota using 16S rRNA gene sequencing, metagenomics, and ¹H NMR- and mass spectrometry-based metabolomics. Induction of Cyp1a1, an aryl hydrocarbon receptor (AHR)-responsive gene, was observed at 6 days and 13 weeks after PCB 126 exposure consistent with the long half-life of PCB 126. Early life, Short-Term PCB 126 exposure resulted in metabolic abnormalities in adulthood including changes in liver amino acid and nucleotide metabolism as well as bile acid metabolism and increased hepatic lipogenesis. Interestingly, early life PCB 126 exposure had a greater impact on bacteria in adulthood at the community structure, metabolic, and functional levels. This study provides evidence for an association between early life environmental pollutant exposure and increased risk of metabolic disorders later in life and suggests the microbiome is a key target of environmental chemical exposure.

Information enhanced model selection for Gaussian graphical model with application to metabolomic data

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

Molecular mechanisms associated with microbial biostimulant-mediated growth enhancement, priming and drought stress tolerance in maize plants

Microbial-based biostimulants are emerging as effective strategies to improve agricultural productivity; however, the modes of action of such formulations are still largely unknown. Thus, herein we report elucidated metabolic reconfigurations in maize (Zea mays) leaves associated with growth promotion and drought stress tolerance induced by a microbial-based biostimulant, a Bacillus consortium. Morphophysiological measurements revealed that the biostimulant induced a significant increase in biomass and enzymatic regulators of oxidative stress. Furthermore, the targeted metabolomics approach revealed differential quantitative profiles in amino acid-, phytohormone-, flavonoid- and phenolic acid levels in plants treated with the biostimulant under well-watered, mild, and severe drought stress conditions. These metabolic alterations were complemented with gene expression and global DNA methylation profiles. Thus, the postulated framework, describing biostimulant-induced metabolic events in maize plants, provides actionable knowledge necessary for industries and farmers to confidently and innovatively explore, design and fully implement microbial-based formulations and strategies into agronomic practices for sustainable agriculture and food production.

The Metabolic Signatures of Surviving Cotwins in Cases of Single Intrauterine Fetal Death During Monochorionic Diamniotic Pregnancy: A Prospective Case-Control Study

Introduction: Single intrauterine fetal death (sIUFD) in monochorionic diamniotic (MCDA) twin pregnancy may be associated with adverse clinical outcomes and possible metabolic changes in the surviving co-twin. Metabolomic profiling has not been undertaken before in these complex twin pregnancies.

Methods: In this prospectively collected case-control study, three cross-cohort comparisons were made between sIUFD MCDA (n = 16), uncomplicated MCDA (n = 16, eight pairs), and uncomplicated singleton pregnancies (n = 8). To identify major sources of variation within the sIUFD MCDA cohort, a secondary comparison was conducted between spontaneous sIUFD (n = 8) and sIUFD in MCDA twins due to selective termination of a single abnormal fetus by radiofrequency ablation (RFA) (n = 8). Metabolomics analysis of placental tissue and umbilical cord plasma was performed using LC-MS profiling. The underlying metabolic networks and pathways were analyzed by web-based platforms. Associations and statistical correlations of all identified differential metabolites with neonatal birthweight and birth length were assessed by multivariable linear regression, adjusted for maternal age and gestation.

Results: Across four comparisons, 131 and 111 differential metabolites were identified in placental tissue and cord plasma, respectively, with the highest variation seen between the spontaneous vs. single-induced IUFD in MCDA twins by RFA in the cord plasma. Conversely, the number of viable fetuses and the presence of sIUFD in MCDA twins had the highest impact on metabolite variation in placental tissue. Compounds correlated with fetal growth including placental acylcarnitines and gangliosides, along with specific amino acids (e.g., histidinyl-hydroxyproline), xenobiotics and biliverdin in cord plasma.

Conclusion: sIUFD in MCDA twin pregnancy correlates with distinctive metabolic signatures, mostly in fatty acyls and complex lipids, in placental tissue and cord plasma of the surviving cotwin. Some metabolites are also associated with fetal growth.

High-Coverage Serum Metabolomics Reveals Metabolic Pathway Dysregulation in Diabetic Retinopathy: A Propensity Score-Matched Study

Background: Diabetic retinopathy (DR) is a major diabetes-related disease linked to metabolism. However, the cognition of metabolic pathway alterations in DR remains scarce. We aimed to corroborate alterations of metabolic pathways identified in prior studies and investigate novel metabolic dysregulations that may lead to new prevention and treatment strategies for DR.

Methods: In this case-control study, we tested 613 serum metabolites in 69 pairs of type 2 diabetic patients (T2DM) with DR and propensity score-matched T2DM without DR via ultra-performance liquid chromatography-tandem mass spectrometry system. Metabolic pathway dysregulation in DR was thoroughly investigated by metabolic pathway analysis, chemical similarity enrichment analysis (ChemRICH), and integrated pathway analysis. The associations of ChemRICH-screened key metabolites with DR were further estimated with restricted cubic spline analyses.

Results: A total of 89 differentially expressed metabolites were identified by paired univariate analysis and partial least squares discriminant analysis. We corroborated biosynthesis of unsaturated fatty acids, glycine, serine and threonine metabolism, glutamate and cysteine-related pathways, and nucleotide-related pathways were significantly perturbed in DR, which were identified in prior studies. We also found some novel metabolic alterations associated with DR, including the disturbance of thiamine metabolism and tryptophan metabolism, decreased trehalose, and increased choline and indole derivatives in DR.

Conclusions: The results suggest that the metabolism disorder in DR can be better understood through integrating multiple biological knowledge databases. The progression of DR is associated with the disturbance of thiamine metabolism and tryptophan metabolism, decreased trehalose, and increased choline and indole derivatives.

Metabolic Circuits in Sap Extracts Reflect the Effects of a Microbial Biostimulant on Maize Metabolism under Drought Conditions

The use of microbial biostimulants in the agricultural sector is increasingly gaining momentum and drawing scientific attention to decode the molecular interactions between the biostimulants and plants. Although these biostimulants have been shown to improve plant health and development, the underlying molecular phenomenology remains enigmatic. Thus, this study is a metabolomics work to unravel metabolic circuits in sap extracts from maize plants treated with a microbial biostimulant, under normal and drought conditions. The biostimulant, which was a consortium of different Bacilli strains, was applied at the planting stage, followed by drought stress application. The maize sap extracts were collected at 5 weeks after emergence, and the extracted metabolites were analyzed on liquid chromatography-mass spectrometry platforms. The acquired data were mined using chemometrics and bioinformatics tools. The results showed that under both well-watered and drought stress conditions, the application of the biostimulant led to differential changes in the profiles of amino acids, hormones, TCA intermediates, phenolics, steviol glycosides and oxylipins. These metabolic changes spanned several biological pathways and involved a high correlation of the biochemical as well as structural metabolic relationships that coordinate the maize metabolism. The hypothetical model, postulated from this study, describes metabolic events induced by the microbial biostimulant for growth promotion and enhanced defences. Such understanding of biostimulant-induced changes in maize sap pinpoints to the biochemistry and molecular mechanisms that govern the biostimulant-plant interactions, which contribute to ongoing efforts to generate actionable knowledge of the molecular and physiological mechanisms that define modes of action of biostimulants.

Metabolomics study of COVID-19 patients in four different clinical stages

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the coronavirus strain causing the respiratory pandemic COVID-19 (coronavirus disease 2019). To understand the pathobiology of SARS-CoV-2 in humans it is necessary to unravel the metabolic changes that are produced in the individuals once the infection has taken place. The goal of this work is to provide new information about the altered biomolecule profile and with that the altered biological pathways of patients in different clinical situations due to SARS-CoV-2 infection. This is done via metabolomics using HPLC-QTOF-MS analysis of plasma samples at COVID-diagnose from a total of 145 adult patients, divided into different clinical stages based on their subsequent clinical outcome (25 negative controls (non-COVID); 28 positive patients with asymptomatic disease not requiring hospitalization; 27 positive patients with mild disease defined by a total time in hospital lower than 10 days; 36 positive patients with severe disease defined by a total time in hospital over 20 days and/or admission at the ICU; and 29 positive patients with fatal outcome or deceased). Moreover, follow up samples between 2 and 3 months after hospital discharge were also obtained from the hospitalized patients with mild prognosis. The final goal of this work is to provide biomarkers that can help to better understand how the COVID-19 illness evolves and to predict how a patient could progress based on the metabolites profile of plasma obtained at an early stage of the infection. In the present work, several metabolites were found as potential biomarkers to distinguish between the end-stage and the early-stage (or non-COVID) disease groups. These metabolites are mainly involved in the metabolism of carnitines, ketone bodies, fatty acids, lysophosphatidylcholines/phosphatidylcholines, tryptophan, bile acids and purines, but also omeprazole. In addition, the levels of several of these metabolites decreased to "normal" values at hospital discharge, suggesting some of them as early prognosis biomarkers in COVID-19 at diagnose.

Activation of ectopic olfactory receptor 544 induces GLP-1 secretion and regulates gut inflammation

Olfactory receptors are ectopically expressed in extra-nasal tissues. The gut is constantly exposed to high levels of odorants where ectopic olfactory receptors may play critical roles. Activation of ectopic olfactory receptor 544 (Olfr544) by azelaic acid (AzA), an Olfr544 ligand, reduces adiposity in mice fed a high-fat diet (HFD) by regulating fuel preference to fats. Herein, we investigated the novel function of Olfr544 in the gut. In GLUTag cells, AzA induces the cAMP-PKA-CREB signaling axis and increases the secretion of GLP-1, an enteroendocrine hormone with anti-obesity effects. In mice fed a HFD and orally administered AzA, GLP-1 plasma levels were elevated in mice. The induction of GLP-1 secretion was negated in cells with Olfr544 gene knockdown and in Olfr544-deficient mice. Gut microbiome analysis revealed that AzA increased the levels of Bacteroides acidifaciens and microbiota associated with antioxidant pathways. In fecal metabolomics analysis, the levels of succinate and trehalose, metabolites correlated with a lean phenotype, were elevated by AzA. The function of Olfr544 in gut inflammation, a key feature in obesity, was further investigated. In RNA sequencing analysis, AzA suppressed LPS-induced activation of inflammatory pathways and reduced TNF-α and IL-6 expression, thereby improving intestinal permeability. The effects of AzA on the gut metabolome, microbiome, and colon inflammation were abrogated in Olfr544-KO mice. These results collectively demonstrated that activation of Olfr544 by AzA in the gut exerts multiple effects by regulating GLP-1 secretion, gut microbiome and metabolites, and colonic inflammation in anti-obesogenic phenotypes and, thus, may be applied for obesity therapeutics.

Metabolic responses in the cortex and hippocampus induced by Il-15rα mutation

Metabolomics showed distinct metabolic phenotypes of the different brain regions related to the IL-15 system, enhancing our understanding of the IL-15 system and its interactions with neuropsychiatric disorders.

Eusociality is linked to caste-specific differences in metabolism, immune system, and somatic maintenance-related processes in an ant species

The social organization of many primate, bird and rodent species and the role of individuals within that organization are associated with specific individual physiological traits. However, this association is perhaps most pronounced in eusocial insects (e.g., termites, ants). In such species, genetically close individuals show significant differences in behavior, physiology, and life expectancy. Studies addressing the metabolic changes according to the social role are still lacking. We aimed at understanding how sociality could influence essential molecular processes in a eusocial insect, the black garden ant (Lasius niger) where queens can live up to ten times longer than workers. Using mass spectrometry-based analysis, we explored the whole metabolome of queens, nest-workers and foraging workers. A former proteomics study done in the same species allowed us to compare the findings of both approaches. Confirming the former results at the proteome level, we showed that queens had fewer metabolites related to immunity. Contrary to our predictions, we did not find any metabolite linked to reproduction in queens. Among the workers, foragers had a metabolic signature reflecting a more stressful environment and a more highly stimulated immune system. We also found that nest-workers had more digestion-related metabolites. Hence, we showed that specific metabolic signatures match specific social roles. Besides, we identified metabolites differently expressed among behavioral castes and involved in nutrient sensing and longevity pathways (e.g., sirtuins, FOXO). The links between such molecular pathways and aging being found in an increasing number of taxa, our results confirm and strengthen their potential universality.

Metabolomic Signatures for the Effects of Weight Loss Interventions on Severe Obesity in Children and Adolescents

Childhood obesity has increased worldwide, and many clinical and public interventions have attempted to reduce morbidity. We aimed to determine the metabolomic signatures associated with weight control interventions in children with obesity. Forty children from the “Intervention for Children and Adolescent Obesity via Activity and Nutrition (ICAAN)” cohort were selected according to intervention responses. Based on changes in body mass index z-scores, 20 were responders and the remaining non-responders. Their serum metabolites were quantitatively analyzed using capillary electrophoresis time-of-flight mass spectrometry at baseline and after 6 and 18 months of intervention. After 18 months of intervention, the metabolite cluster changes in the responders and non-responders showed a difference on the heatmap, but significant metabolites were not clear. However, regardless of the responses, 13 and 49 metabolites were significant in the group of children with obesity intervention at 6 months and 18 months post-intervention compared to baseline. In addition, the top five metabolic pathways (D-glutamine and D-glutamate metabolism; arginine biosynthesis; alanine, aspartate, and glutamate metabolism; TCA cycle (tricarboxylic acid cycle); valine, leucine, and isoleucine biosynthesis) including several amino acids in the metabolites of obese children after 18 months were significantly changed. Our study showed significantly different metabolomic profiles based on time post obesity-related intervention. Through this study, we can better understand and predict childhood obesity through metabolite analysis and monitoring.

Metabolomic Profiling Identifies Exogenous and Microbiota-Derived Metabolites as Markers of Methotrexate Efficacy in Juvenile Idiopathic Arthritis

Variability in methotrexate (MTX) efficacy represents a barrier to early and effective disease control in the treatment of juvenile idiopathic arthritis (JIA). This work seeks to understand the impact of MTX on the plasma metabolome and to identify metabolic biomarkers of MTX efficacy in a prospective cohort of children with JIA. Plasma samples from a cohort of children with JIA (n = 30) collected prior to the initiation of MTX and after 3 months of therapy were analyzed using a semi-targeted global metabolomic platform detecting 673 metabolites across a diversity of biochemical classes. Disease activity was measured using the 71-joint count juvenile arthritis disease activity score (JADAS-71) and clinical response to MTX was based on achievement of ACR Pedi 70 response. Metabolomic analysis identified 50 metabolites from diverse biochemical classes that were altered following the initiation of MTX (p < 0.05) with 15 metabolites reaching a false-discovery rate adjusted p-value (q-value) of less than 0.05. Enrichment analysis identified a class-wide reduction in unsaturated triglycerides following initiation of MTX (q = 0.0009). Twelve of the identified metabolites were significantly associated with disease activity by JADAS-71. Reductions in three metabolites were found to be associated with clinical response by ACR Pedi 70 response criteria and represented several microbiota and exogenously derived metabolites including: dehydrocholic acid, biotin, and 4-picoline. These findings support diverse metabolic changes following initiation of MTX in children with JIA and identify metabolites associated with microbial metabolism and exogenous sources associated with MTX efficacy.

Mass recovery following caloric restriction reverses lipolysis and proteolysis, but not gluconeogenesis, in insulin resistant OLETF rats

Caloric restriction (CR) is one of the most important behavioral interventions to reduce excessive abdominal adiposity, which is a risk factor for the development of insulin resistance. Previous metabolomics studies have characterized substrate metabolism during healthy conditions; however, the effects of CR and subsequent mass recovery on shifts in substrate metabolism during insulin resistance (IR) have not been widely investigated. To assess the effects of acute CR and the subsequent mass recovery on shifts in substrate metabolism, a cohort of 15-week old Long Evans Tokushima Otsuka (LETO) and Otsuka Long Evans Tokushima Fatty (OLETF) rats were calorie restricted (CR: 50% × 10 days) with or without partial body mass recovery (PR; 73% x 7 days), along with their respective ad libitum controls. End-of-study plasma samples were analyzed for primary carbon metabolites by gas chromatography (GC) time-of-flight (TOF) mass spectrometry (MS) data acquisition. Data analysis included PCA, Pearson correlation vs previously reported variables (adipose and body masses, and insulin resistance index, IRI), and metabolomics maps (MetaMapp) generated for the most significant group comparisons. All treatments elicited a significant group differentiation in at least one principal component. CR improved TCA cycle in OLETF, and increased lipolysis and proteolysis. These changes were reversed after PR except for gluconeogenesis. Plasma lipid concentrations were inversely correlated to IRI in LETO, but not OLETF. These shifts in substrate metabolism suggest that the CR-induced decreases in adipose may not be sufficient to more permanently alter substrate metabolism to improve IR status during metabolic syndrome.

Prioritizing cancer hazard assessments for IARC Monographs using an integrated approach of database fusion and text mining

BACKGROUND

Systematic evaluation of literature data on the cancer hazards of human exposures is an essential process underlying cancer prevention strategies. The scope and volume of evidence for suspected carcinogens can range from very few to thousands of publications, requiring a complex, systematically planned, and critical procedure to nominate, prioritize and evaluate carcinogenic agents. To aid in this process, database fusion, cheminformatics and text mining techniques can be combined into an integrated approach to inform agent prioritization, selection, and grouping.

RESULTS

We have applied these techniques to agents recommended for the IARC Monographs evaluations during 2020-2024. An integration of PubMed filters to cover cancer epidemiology, key characteristics of carcinogens, chemical lists from 34 databases relevant for cancer research, chemical structure grouping and a literature data-based clustering was applied in an innovative approach to 119 agents recommended by an advisory group for future IARC Monographs evaluations. The approach also facilitated a rational grouping of these agents and aids in understanding the volume and complexity of relevant information, as well as important gaps in coverage of the available studies on cancer etiology and carcinogenesis.

CONCLUSION

A new data-science approach has been applied to diverse agents recommended for cancer hazard assessments, and its applications for the IARC Monographs are demonstrated. The prioritization approach has been made available at www.cancer.idsl.me site for ranking cancer agents.

Effects of Acute 2,3,7,8-Tetrachlorodibenzo-p-Dioxin Exposure on the Circulating and Cecal Metabolome Profile

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a polyhalogenated planar hydrocarbon belonging to a group of highly toxic and persistent environmental contaminants known as "dioxins". TCDD is an animal teratogen and carcinogen that is well characterized for causing immunosuppression through activation of aryl hydrocarbon receptor (AHR). In this study, we investigated the effect of exposure of mice to an acute dose of TCDD on the metabolic profile within the serum and cecal contents to better define the effects of TCDD on host physiology. Our findings demonstrated that within the circulating metabolome following acute TCDD exposure, there was significant dysregulation in the metabolism of bioactive lipids, amino acids, and carbohydrates when compared with the vehicle (VEH)-treated mice. These widespread changes in metabolite abundance were identified to regulate host immunity via modulating nuclear factor-kappa B (NF-κB) and extracellular signal-regulated protein kinase (ERK1/2) activity and work as biomarkers for a variety of organ injuries and dysfunctions that follow TCDD exposure. Within the cecal content of mice exposed to TCDD, we were able to detect changes in inflammatory markers that regulate NF-κB, markers of injury-related inflammation, and changes in lysine degradation, nicotinamide metabolism, and butanoate metabolism, which collectively suggested an immediate suppression of broad-scale metabolic processes in the gastrointestinal tract. Collectively, these results demonstrate that acute TCDD exposure results in immediate irregularities in the circulating and intestinal metabolome, which likely contribute to TCDD toxicity and can be used as biomarkers for the early detection of individual exposure.

High-coverage metabolomics uncovers microbiota-driven biochemical landscape of interorgan transport and gut-brain communication in mice

The mammalian gut harbors a complex and dynamic microbial ecosystem: the microbiota. While emerging studies support that microbiota regulates brain function with a few molecular cues suggested, the overall biochemical landscape of the “microbiota-gut-brain axis” remains largely unclear. Here we use high-coverage metabolomics to comparatively profile feces, blood sera, and cerebral cortical brain tissues of germ-free C57BL/6 mice and their age-matched conventionally raised counterparts. Results revealed for all three matrices metabolomic signatures owing to microbiota, yielding hundreds of identified metabolites including 533 altered for feces, 231 for sera, and 58 for brain with numerous significantly enriched pathways involving aromatic amino acids and neurotransmitters. Multicompartmental comparative analyses single out microbiota-derived metabolites potentially implicated in interorgan transport and the gut-brain axis, as exemplified by indoxyl sulfate and trimethylamine-N-oxide. Gender-specific characteristics of these landscapes are discussed. Our findings may be valuable for future research probing microbial influences on host metabolism and gut-brain communication.

The gut microbiota harbours neuroactive potential with links to neurological disorders. Here, the authors apply global metabolomics with an integrated annotation strategy to comparatively profile fecal, blood serum and cerebral cortical brain tissues of eight-week-old germ-free mice vs. age-matched specific-pathogen-free mice, providing a snapshot of the metabolome status linked to the gut-brain axis.

Changes in the gut microbiome influence the hypoglycemic effect of metformin through the altered metabolism of branched-chain and nonessential amino acids

AIMS

Although metformin has been reported to affect the gut microbiome, the mechanism has not been fully determined. We explained the potential underlying mechanisms of metformin through a multiomics approach.

METHODS

An open-label and single-arm clinical trial involving 20 healthy Korean was conducted. Serum glucose and insulin concentrations were measured, and stool samples were collected to analyze the microbiome. Untargeted metabolomic profiling of plasma, urine, and stool samples was performed by GC-TOF-MS. Network analysis was applied to infer the mechanism of the hypoglycemic effect of metformin.

RESULTS

The relative abundances of Escherichia, Romboutsia, Intestinibacter, and Clostridium were changed by metformin treatment. Additionally, the relative abundances of metabolites, including carbohydrates, amino acids, and fatty acids, were changed. These changes were correlated with energy metabolism, gluconeogenesis, and branched-chain amino acid metabolism, which are major metabolic pathways related to the hypoglycemic effect.

CONCLUSIONS

We observed that specific changes in metabolites may affect hypoglycemic effects through both pathways related to AMPK activation and microbial changes. Energy metabolism was mainly related to hypoglycemic effects. In particular, branched-chain amino acid metabolism and gluconeogenesis were related to microbial metabolites. Our results will help uncover the potential underlying mechanisms of metformin through AMPK and the microbiome.

Function of ORFC of the polyketide synthase gene cluster on fatty acid accumulation in Schizochytrium limacinum SR21

BACKGROUND

As a potential source of polyunsaturated fatty acids (PUFA), Schizochytrium sp. has been widely used in industry for PUFA production. Polyketide synthase (PKS) cluster is supposed to be the primary way of PUFA synthesis in Schizochytrium sp. As one of three open reading frames (ORF) in the PKS cluster, ORFC plays an essential role in fatty acid biosynthesis. However, the function of domains in ORFC in the fatty acid synthesis of Schizochytrium sp. remained unclear.

RESULTS

In this study, heterologous expression and overexpression were carried out to study the role of ORFC and its domains in fatty acid accumulation. Firstly, ORFC was heterologously expressed in yeast which increased the PUFA content significantly. Then, the dehydratase (DH) and enoyl reductase (ER) domains located on ORFC were overexpressed in Schizochytrium limacinum SR21, respectively. Fatty acids profile analysis showed that the contents of PUFA and saturated fatty acid were increased in the DH and ER overexpression strains, respectively. This indicated that the DH and ER domains played distinct roles in lipid accumulation. Metabolic and transcriptomic analysis revealed that the pentose phosphate pathway and triacylglycerol biosynthesis were enhanced, while the tricarboxylic acid cycle and fatty acids oxidation were weakened in DH-overexpression strain. However, the opposite effect was found in the ER-overexpression strain.

CONCLUSION

Therefore, ORFC was required for the biosynthesis of fatty acid. The DH domain played a crucial role in PUFA synthesis, whereas the ER domain might be related to saturated fatty acids (SFA) synthesis in Schizochytrium limacinum SR21. This research explored the role of ORFC in the PKS gene cluster in Schizochytrium limacinum and provided potential genetic modification strategies for improving lipid production and regulating PUFA and SFA content.