Multivariate Analysis in Metabolomics

Edaphoclimatic variation and harvest seasonality as determining factors of multidimensional quality in avocado cv. hass grown in the tropics

The increase in cultivated areas in tropical zones such as Colombia for avocado cv. Hass and the lack of knowledge on edaphoclimatic relationships with factors associated with quality led to the present research. The aim of this research was to establish the relationship of soil, climatic, spatial factors (plot location), and harvest seasonality (principal and transitory) with the multidimensional quality of avocado cv. Hass planted under tropical conditions. This research was carried out on eight farms located in three producing subregions. Soil, environmental and harvest data were recorded for three years (2015-2017) in each plot. Avocado fruit samples were used to determine the parameters of macronutrient, fatty acids, minerals, and vitamin E. Descriptive, inferential statistics, multivariate analysis, effect size, second-order exponential model, and causal relationships were used to determine variables associated with soil, climate, harvest seasonality, and spatial location, and to determine quality parameters. The results established a relationship between nutritional quality and the origin region. Similarly, it was possible to identify parameters associated with differential quality with a robust statistical methodology to propose origin as a differentiating factor for quality. This study provided useful information for the value chain that selected the best areas for avocado crops according to market expectations and nutritional quality criteria.

Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes

The hydrophobic cuticle is the first line of defense between aerial portions of plants and the external environment. On maize (Zea mays L.) silks, the cuticular cutin matrix is infused with cuticular waxes, consisting of a homologous series of very long-chain fatty acids (VLCFAs), aldehydes, and hydrocarbons. Together with VLC fatty-acyl-CoAs (VLCFA-CoAs), these metabolites serve as precursors, intermediates, and end-products of the cuticular wax biosynthetic pathway. To deconvolute the potentially confounding impacts of the change in silk microenvironment and silk development on this pathway, we profiled cuticular waxes on the silks of the inbreds B73 and Mo17, and their reciprocal hybrids. Multivariate interrogation of these metabolite abundance data demonstrates that VLCFA-CoAs and total free VLCFAs are positively correlated with the cuticular wax metabolome, and this metabolome is primarily affected by changes in the silk microenvironment and plant genotype. Moreover, the genotype effect on the pathway explains the increased accumulation of cuticular hydrocarbons with a concomitant reduction in cuticular VLCFA accumulation on B73 silks, suggesting that the conversion of VLCFA-CoAs to hydrocarbons is more effective in B73 than Mo17. Statistical modeling of the ratios between cuticular hydrocarbons and cuticular VLCFAs reveals a significant role of precursor chain length in determining this ratio. This study establishes the complexity of the product-precursor relationships within the silk cuticular wax-producing network by dissecting both the impact of genotype and the allocation of VLCFA-CoA precursors to different biological processes and demonstrates that longer chain VLCFA-CoAs are preferentially utilized for hydrocarbon biosynthesis.

Identification of antioxidant and flavour marker compounds in Kalosi-Enrekang Arabica brewed coffee processed using different postharvest treatment methods

This research aims to predict the presence of marker compounds that differentiate tubruk brew from coffee beans with different postharvest processing. This research also aims to predict compounds correlating with antioxidant activity and sensory flavour attributes. This research used Kalosi-Enrekang Arabica coffee beans, which were processed with three different postharvest processing (honey, full-washed and natural), roasted at medium level, and brewed using the tubruk method. Each brew was analyzed for chemical profiles using LC-MS and GC-MS, antioxidant analysis using the DPPH IC50 and FRAP methods, and sensory analysis for flavour using the QDA and SCAA methods for cupping scores. OPLS-DA analysis revealed the presence of marker compounds from each brew, and the dried fruit flavour attribute was to be an inter-process marker. After that, OPLS analysis showed marker compounds that correlate to antioxidant activity and flavour attributes. Rhaponticin is thought to be one of the marker compounds in natural coffee brews and is one of the compounds that correlates to the antioxidant activity of the DPPH method (IC50); prunin is thought to be one of the marker compounds for full-washed coffee brews and is one of the compounds that correlates to the activity antioxidants of FRAP method. Triacetin, which is thought to be a marker compound in natural brewed coffee, correlates with fruity flavour. 3-acetylpyridine, as a marker in honey-brewed coffee, correlates with nutty flavour. Even though there are differences in dominant flavours, the cupping score shows the brew is categorized as a specialty. This research shows that different post-harvest processing processes influence the compound profile, antioxidant activity and flavour attributes of Tubruk brewed coffee.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-024-05948-8.

Differences of Typical Wuyi Rock Tea in Taste and Nonvolatiles Profile Revealed by Multisensory Analysis and LC-MS-Based Metabolomics

Wuyi Rock tea, specifically Shuixian and Rougui, exhibits distinct sensory characteristics. In this study, we investigated the sensory and metabolite differences between Shuixian and Rougui. Quantitative description analysis revealed that Rougui exhibited higher intensity in bitter, thick, harsh, and numb tastes, while Shuixian had stronger salty and umami tastes. Nontargeted metabolomics identified 151 compounds with 66 compounds identified as key differential metabolites responsible for metabolic discrimination. Most of the catechins and flavonoids were enriched in Rougui tea, while epigallocatechin-3,3'-di-O-gallate, epigallocatechin-3,5-di-O-gallate, gallocatechin-3,5-di-O-gallate, isovitexin, and theaflavanoside I were enriched in Shuixian tea. Catechins, kaempferol, quercetin, and myricetin derivatives were positively correlated with bitter taste and numb sensation. Sour taste was positively correlated to organic acids. Amino acids potentially contributed to salty and umami tastes. These results provide further insights into the taste characteristics and the relationship between taste attributes and specific metabolites in Wuyi Rock tea.

UV-A radiation increases biomass yield by enhancing energy flow and carbon assimilation in the edible cyanobacterium Nostoc sphaeroides

Ultraviolet (UV) A radiation (315-400 nm) is the predominant component of solar UV radiation that reaches the Earth's surface. However, the underlying mechanisms of the positive effects of UV-A on photosynthetic organisms have not yet been elucidated. In this study, we investigated the effects of UV-A radiation on the growth, photosynthetic ability, and metabolome of the edible cyanobacterium Nostoc sphaeroides. Exposures to 5-15 W m-2 (15-46 µmol photons m-2 s-1) UV-A and 4.35 W m-2 (20 μmol photons m-2 s-1) visible light for 16 days significantly increased the growth rate and biomass production of N. sphaeroides cells by 18%-30% and 15%-56%, respectively, compared to the non-UV-A-acclimated cells. Additionally, the UV-A-acclimated cells exhibited a 1.8-fold increase in the cellular nicotinamide adenine dinucleotide phosphate (NADP) pool with an increase in photosynthetic capacity (58%), photosynthetic efficiency (24%), QA re-oxidation, photosystem I abundance, and cyclic electron flow (87%), which further led to an increase in light-induced NADPH generation (31%) and ATP content (83%). Moreover, the UV-A-acclimated cells showed a 2.3-fold increase in ribulose-1,5-bisphosphate carboxylase/oxygenase activity, indicating an increase in their carbon-fixing capacity. Gas chromatography-mass spectrometry-based metabolomics further revealed that UV-A radiation upregulated the energy-storing carbon metabolism, as evidenced by the enhanced accumulation of sugars, fatty acids, and citrate in the UV-A-acclimated cells. Therefore, our results demonstrate that UV-A radiation enhances energy flow and carbon assimilation in the cyanobacterium N. sphaeroides.IMPORTANCEUltraviolet (UV) radiation exerts harmful effects on photo-autotrophs; however, several studies demonstrated the positive effects of UV radiation, especially UV-A radiation (315-400 nm), on primary productivity. Therefore, understanding the underlying mechanisms associated with the promotive effects of UV-A radiation on primary productivity can facilitate the application of UV-A for CO2 sequestration and lead to the advancement of photobiological sciences. In this study, we used the cyanobacterium Nostoc sphaeroides, which has an over 1,700-year history of human use as food and medicine, to explore its photosynthetic acclimation response to UV-A radiation. As per our knowledge, this is the first study to demonstrate that UV-A radiation increases the biomass yield of N. sphaeroides by enhancing energy flow and carbon assimilation. Our findings provide novel insights into UV-A-mediated photosynthetic acclimation and provide a scientific basis for the application of UV-A radiation for optimizing light absorption capacity and enhancing CO2 sequestration in the frame of a future CO2 neutral, circular, and sustainable bioeconomy.

Effects of Hypoxia and Reoxygenation on Metabolic Profiles of Cardiomyocytes

In vitro cellular models provide valuable insights into the adaptive biochemical mechanisms triggered by cells to cope with the stress situation induced by hypoxia and reoxygenation cycles. The first biological data generated in studies based on this micrometric life-scale has the potential to provide us a global overview about the main biochemical phenomena presented in some reported preconditioning therapies in life-scale of higher dimensions. Thus, in this study, a cell incubator was designed and manufactured to produce a cellular model of heart hypoxia followed by reoxygenation (HfR) through consecutive repetitions of hypoxia-normoxia gas exchange. Samples of cellular extracts and culture media were obtained from non-proliferative cardiomyocytes (CMs) cultivated under challenging HfR (stressed CMs) and regular cultivation (unstressed CMs) in rounds of four days for each case. Metabolomic based on proton magnetic resonance spectroscopy (1H-MRS) was used as an analytical approach to identify and quantify the metabolomes of these samples, the endo- and exo-metabolome. Despite the stressed CMs presented over 90% higher cellular death rate compared to the unstressed CMs, the metabolic profiles indicates that the surviving cells up-regulate their amino acid metabolism either by active protein degradation or by the consumption of culture media components to increase coenzyme A-dependent metabolic pathways. This cell auto-regulation mechanism could be well characterized in the first two days when the difference smears off under once the metabolomes become similar. The metabolic adaptations of stressed CMs identified the relevance of the cyclic oxidation/reduction reactions of nicotinamide adenine dinucleotide phosphate molecules, NADP+/NADPH, and the increased tricarboxylic acid cycle activity in an environment overloaded with such a powerful antioxidant agent to survive an extreme HfR challenge. Thus, the combination of cellular models based on CMs, investigative methods, such as metabolomic and 1H-MRS, and the instrumental development of hypoxia incubator shown in this work were able to provide the first biochemical evidences behind therapies of gaseous exchanges paving the way to future assays.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging

BACKGROUND

The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.

AIM OF REVIEW

The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Amino acid profile alteration in age-related atrial fibrillation

BACKGROUND

Amino acids (AAs) are one of the primary metabolic substrates for cardiac work. The correlation between AAs and both atrial fibrillation (AF) and aging has been documented. However, the relationship between AAs and age-related AF remains unclear.

METHODS

Initially, the plasma AA levels of persistent AF patients and control subjects were assessed, and the correlations between AA levels, age, and other clinical indicators were explored. Subsequently, the age-related AF mouse model was constructed and the untargeted myocardial metabolomics was conducted to detect the level of AAs and related metabolites. Additionally, the gut microbiota composition associated with age-related AF was detected by a 16S rDNA amplicon sequencing analysis on mouse fecal samples.

RESULTS

Higher circulation levels of lysine (Student's t-test, P = 0.001), tyrosine (P = 0.002), glutamic acid (P = 0.008), methionine (P = 0.008), and isoleucine (P = 0.014), while a lower level of glycine (P = 0.003) were observed in persistent AF patients. The feature AAs identified by machine learning algorithms were glutamic acid and methionine. The association between AAs and age differs between AF and control subjects. Distinct patterns of AA metabolic profiles were observed in the myocardial metabolites of aged AF mice. Aged AF mice had lower levels of Betaine, L-histidine, L-alanine, L-arginine, L-Pyroglutamic acid, and L-Citrulline compared with adult AF mice. Aged AF mice also presented a different gut microbiota pattern, and its functional prediction analysis showed AA metabolism alteration.

CONCLUSION

This study provided a comprehensive network of AA disturbances in age-related AF from multiple dimensions, including plasma, myocardium, and gut microbiota. Disturbances of AAs may serve as AF biomarkers, and restoring their homeostasis may have potential benefits for the management of age-related AF.

FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

Comparative transcriptome and metabolome profiles of the leaf and fruits of a Xianjinfeng litchi budding mutant and its mother plant

Background: Litchi (Litchi chinensis) is an important sub-tropical fruit in the horticulture market in China. Breeding for improved fruit characteristics is needed for satisfying consumer demands. Budding is a sustainable method for its propagation. During our ongoing breeding program, we observed a litchi mutant with flat leaves and sharp fruit peel cracking in comparison to the curled leaves and blunt fruit peel cracking fruits of the mother plant. Methods: To understand the possible molecular pathways involved, we performed a combined metabolome and transcriptome analysis. Results: We identified 1,060 metabolites in litchi leaves and fruits, of which 106 and 101 were differentially accumulated between the leaves and fruits, respectively. The mutant leaves were richer in carbohydrates, nucleotides, and phenolic acids, while the mother plant was rich in most of the amino acids and derivatives, flavonoids, lipids and organic acids and derivatives, and vitamins. Contrastingly, mutant fruits had higher levels of amino acids and derivatives, carbohydrates and derivatives, and organic acids and derivatives. However, the mother plant's fruits contained higher levels of flavonoids, scopoletin, amines, some amino acids and derivatives, benzamidine, carbohydrates and derivatives, and some organic acids and derivatives. The number of differentially expressed genes was consistent with the metabolome profiles. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enriched gene expressions showed consistent profiles as of metabolome analysis. Conclusion: These results provide the groundwork for breeding litchi for fruit and leaf traits that are useful for its taste and yield.

Phenylpropane biosynthesis and alkaloid metabolism pathways involved in resistance of Amorphophallus spp. against soft rot disease

Soft rot of konjac (Amorphophallus spp.) is a devastating disease caused by the bacterium Pectobacterium carotovorum subsp. carotovorum (Pcc) with serious adverse effects on plantation development, corm quality and crop yield due to the current lack of effective control measures. The main objective of the present study was to elucidate the mechanisms underlying plant resistance to soft rot disease. A combination of transcriptomic and metabolomic analyses demonstrated significant enrichment of differentially expressed genes (DEG) and differentially accumulated metabolites (DAM) associated with plant hormones, phenylpropanoid biosynthesis and, in particular, alkaloid metabolism, in Amorphophallus muelleri following Pcc infection compared with A. konjac, these data implicate alkaloid metabolism as the dominant mechanism underlying disease resistance of A. muelleri. Quantitative real-time polymerase chain reaction analysis further revealed involvement of PAL, CYP73A16, CCOAOMT1, RBOHD and CDPK20 genes in the response of konjac to Pcc. Analysis of the bacteriostatic activities of total alkaloid from A. muelleri validated the assumption that alkaloid metabolism positively regulates disease resistance of konjac. Our collective results provide a foundation for further research on the resistance mechanisms of konjac against soft rot disease.

Plasma metabolomics for diagnostic biomarkers on ectopic pregnancy

Metabolomics is a relatively novel omics tool to provide potential biomarkers for early diagnosis of the diseases and to insight the pathophysiology not having discussed ever before. In the present study, an ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was employed to the plasma samples of Group T1: Patients with ectopic pregnancy diagnosed using ultrasound, and followed-up with beta-hCG level (n = 40), Group T2: Patients with ectopic pregnancy diagnosed using ultrasound, underwent surgical treatment and confirmed using histopathology (n = 40), Group P: Healthy pregnant women (n = 40) in the first prenatal visit of pregnancy, Group C: Healthy volunteers (n = 40) scheduling a routine gynecological examination. Metabolite extraction was performed using 3 kDa pores - Amicon® Ultra 0.5 mL Centrifugal Filters. A gradient elution program (mobile phase composition was water and acetonitrile consisting of 0.1% formic acid) was applied using a C18 column (Agilent Zorbax 1.8 μM, 100 x 2.1 mm). Total analysis time was 25 min when the flow rate was 0.2 mL/min. The raw data was processed through XCMS - R program language edition where the optimum parameters detected using Isotopologue Parameter Optimization (IPO). The potential metabolites were identified using MetaboAnalyst 5.0 and finally 27 metabolites were evaluated to be proposed as potential biomarkers to be used for the diagnosis of ectopic pregnancy.

A Genome-Wide Association Study of Serum Metabolite Profiles in Septic Shock Patients

OBJECTIVES

We sought to assess whether genetic associations with metabolite concentrations in septic shock patients could be used to identify pathways of potential importance for understanding sepsis pathophysiology.

DESIGN

Retrospective multicenter cohort studies of septic shock patients.

SETTING

All participants who were admitted to 27 participating hospital sites in three countries (Australia, New Zealand, and the United Kingdom) were eligible for inclusion.

PATIENTS

Adult, critically ill, mechanically ventilated patients with septic shock (n = 230) who were a subset of the Adjunctive Corticosteroid Treatment in Critically Ill Patients with Septic Shock trial (ClinicalTrials.gov number: NCT01448109).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A genome-wide association study was conducted for a range of serum metabolite levels for participants. Genome-wide significant associations (p ≤ 5 × 10-8) were found for the two major ketone bodies (3-hydroxybutyrate [rs2456680] and acetoacetate [rs2213037] and creatinine (rs6851961). One of these single-nucleotide polymorphisms (SNPs) (rs2213037) was located in the alcohol dehydrogenase cluster of genes, which code for enzymes related to the metabolism of acetoacetate and, therefore, presents a plausible association for this metabolite. None of the three SNPs showed strong associations with risk of sepsis, 28- or 90-day mortality, or Acute Physiology and Chronic Health Evaluation score (a measure of sepsis severity).

CONCLUSIONS

We suggest that the genetic associations with metabolites may reflect a starvation response rather than processes involved in sepsis pathophysiology. However, our results require further investigation and replication in both healthy and diseased cohorts including those of different ancestry.

Maternal and newborn metabolomic changes associated with urinary polycyclic aromatic hydrocarbon metabolite concentrations at delivery: an untargeted approach

INTRODUCTION

Prenatal exposure to polycyclic aromatic hydrocarbons (PAHs) has been associated with adverse human health outcomes. To explore the plausible associations between maternal PAH exposure and maternal/newborn metabolomic outcomes, we conducted a cross-sectional study among 75 pregnant people from Cincinnati, Ohio.

METHOD

We quantified 8 monohydroxylated PAH metabolites in maternal urine samples collected at delivery. We then used an untargeted high-resolution mass spectrometry approach to examine alterations in the maternal (n = 72) and newborn (n = 63) serum metabolome associated with PAH metabolites. Associations between individual maternal urinary PAH metabolites and maternal/newborn metabolome were assessed using linear regression adjusted for maternal and newborn factors while accounting for multiple testing with the Benjamini-Hochberg method. We then conducted functional analysis to identify potential biological pathways.

RESULTS

Our results from the metabolome-wide associations (MWAS) indicated that an average of 1% newborn metabolome features and 2% maternal metabolome features were associated with maternal urinary PAH metabolites. Individual PAH metabolite concentrations in maternal urine were associated with maternal/newborn metabolome related to metabolism of vitamins, amino acids, fatty acids, lipids, carbohydrates, nucleotides, energy, xenobiotics, glycan, and organic compounds.

CONCLUSION

In this cross-sectional study, we identified associations between urinary PAH concentrations during late pregnancy and metabolic features associated with several metabolic pathways among pregnant women and newborns. Further studies are needed to explore the mediating role of the metabolome in the relationship between PAHs and adverse pregnancy outcomes.

Feature-Based Molecular Networking Combined with Multivariate Analysis for the Characterization of Glutathione Adducts as a Smoking Gun of Bioactivation

Feature-based molecular networking (FBMN) is a powerful analytical tool for mass spectrometry (MS)-based untargeted metabolomics data analysis. FBMN plays an important role in drug metabolism studies, enabling the visualization of complex metabolomics data to achieve metabolite characterization. In this study, we propose a strategy for the characterization of glutathione (GSH) adducts formed via in vitro metabolic activation using FBMN assisted by multivariate analysis (MVA). Acetaminophen was used as a model substrate for method development, and the practical potential of the method was investigated by its application to 2-aminophenol (2-AP) and 2,4-dinitrochlorobenzene (DNCB). Two 2-AP GSH adducts and one DNCB GSH adduct were successfully characterized by forming networks with GSH even though the mass spectral information obtained for the parent compound was deficient. False positives were effectively filtered out by the variable influence on projection cutoff criteria obtained from orthogonal partial least-squares-discriminant analysis. The GSH adducts formed by enzymatic or nonenzymatic reactions were intuitively distinguished by the pie chart of FBMN results. In summary, our approach effectively characterizes GSH adducts, which serve as compelling evidence of bioactivation. It can be widely utilized to enhance risk assessment in the context of drug metabolism.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Characterisation of Sclerocarya birrea (marula) seed oil and investigation of the geographical origin by applying similarity calculations, differential NMR and hierarchical cluster analysis

INTRODUCTION

The marula fruit is an important indigenous African fruit since various commercial products are produced from the pulp and the seed oil. The increased demand requires methods for authentication, quality control and determination of geographical origin.

OBJECTIVE

The study aimed to establish a fast and reliable method for characterisation and authentication of marula seed oil. Furthermore, to identify marker compounds that can distinguish marula seed oils from other commercial oils and indicate regional differences.

MATERIALS AND METHODS

Metabolic profiling of 44 commercial marula seed oils was performed using proton nuclear magnetic resonance (1 H NMR). For rapid classification similarity calculations were compared with principal component analysis. Differential NMR was used to determine marker compounds.

RESULTS

Marula seed oil was found to be similar to macadamia and olive oils and was distinguished from these oils by the detection of minor components. Marula seed oil is differentiated from the other two oils by the absence of α-linolenic acid, relatively high levels of monoglycerides and diglycerides, and an approximately 1:1 ratio of 1,2- and 1,3-diglycerides. When comparing marula seed oils from various regions using hierarchical cluster analysis, clustering of the marula seed oils from Namibia and Zimbabwe was observed and was related to the quantities of linoleic acid and monoglycerides and diglycerides. Some samples displayed deviations in their composition which might indicate adulteration or contamination during the production process.

CONCLUSION

The study demonstrates the potential of NMR as a tool in the quality control of marula seed oil. This technique requires very little sample preparation, circumvents derivatisation of the oil components with fast run-times. In addition, samples with chemical profiles that differ from the general signature profile can easily be identified.

Multi-omics analysis of hospital-acquired diarrhoeal patients reveals biomarkers of enterococcal proliferation and Clostridioides difficile infection

Hospital-acquired diarrhoea (HAD) is common, and often associated with gut microbiota and metabolome dysbiosis following antibiotic administration. Clostridioides difficile is the most significant antibiotic-associated diarrhoeal (AAD) pathogen, but less is known about the microbiota and metabolome associated with AAD and C. difficile infection (CDI) with contrasting antibiotic treatment. We characterised faecal microbiota and metabolome for 169 HAD patients (33 with CDI and 133 non-CDI) to determine dysbiosis biomarkers and gain insights into metabolic strategies C. difficile might use for gut colonisation. The specimen microbial community was analysed using 16 S rRNA gene amplicon sequencing, coupled with untargeted metabolite profiling using gas chromatography-mass spectrometry (GC-MS), and short-chain fatty acid (SCFA) profiling using GC-MS. AAD and CDI patients were associated with a spectrum of dysbiosis reflecting non-antibiotic, short-term, and extended-antibiotic treatment. Notably, extended antibiotic treatment was associated with enterococcal proliferation (mostly vancomycin-resistant Enterococcus faecium) coupled with putative biomarkers of enterococcal tyrosine decarboxylation. We also uncovered unrecognised metabolome dynamics associated with concomitant enterococcal proliferation and CDI, including biomarkers of Stickland fermentation and amino acid competition that could distinguish CDI from non-CDI patients. Here we show, candidate metabolic biomarkers for diagnostic development with possible implications for CDI and vancomycin-resistant enterococci (VRE) treatment.

Metabolomics of Personalized Body Elements in Thai Traditional Medicine Response to Herbal Medicine for Body Elements Balancing in Healthy Volunteers

Background

In Thai traditional medicine (TTM), the dominant body element called "Dhat Chao Ruean" (DCR) is an integral part in the diagnostic process of Thai traditional medicine. TTM practitioners usually use Thai herbal Benjakul formula (BKF) for adjusting and balancing the body elements. However, the effects of BKF on metabolism and individual response to it have not been studied yet.

Methods

This study proposed to investigate the metabolic profiling in 24 volunteers categorized by their types of birth month DCR (bDCR) after the administration of BKF (450 mg, three tablets three times a day before meals) for seven days. Differences in metabolic profiling between bDCR groups were investigated by using liquid chromatography coupled with mass spectrometry for untargeted analysis, and in addition, the safety was assessed by testing the plasma biochemical level.

Results

This study identified 57 biomarkers in positive ESI and 12 in negative ESI. Piperine was found in varying amount among the participants but it was the highest in the earth group. In addition, this study found that elemicin, phenylpropionic acid, ricinoleic acid, and β-sitosterol are important substances in a single herb of BKF. Regarding biochemical tests, the results indicated that BKF can decrease the lipid profile and it has no toxic effects on liver and kidney functions.

Conclusion

The findings indicated that it is safe to use BKF which can help to improve health in chronic diseases by adjusting abnormality of the elements of the body. In addition, the information gathered from this study is valuable for further study in the field of Thai traditional medicine.

Metabolomic signatures distinguish extracellular vesicles from osteoclasts and odontoclasts

AIMS

Pathological dental root resorption and alveolar bone loss are often detected only after irreversible damage. Biomarkers in the gingival crevicular fluid or saliva could provide a means for early detection; however, such biomarkers have proven elusive. We hypothesize that a multiomic approach might yield reliable diagnostic signatures for root resorption and alveolar bone loss. Previously, we showed that extracellular vesicles (EVs) from osteoclasts and odontoclasts differ in their protein composition. In this study, we investigated the metabolome of EVs from osteoclasts, odontoclasts and clasts (non-resorbing clastic cells).

MATERIALS AND METHODS

Mouse haematopoietic precursors were cultured on dentine, bone or plastic, in the presence of recombinant RANKL and CSF-1 to trigger differentiation along the clastic line. On Day 7, the cells were fixed and the differentiation state and resorptive status of the clastic cells were confirmed. EVs were isolated from the conditioned media on Day 7 and characterized by nanoparticle tracking and electron microscopy to ensure quality. Global metabolomic profiling was performed using a Thermo Q-Exactive Orbitrap mass spectrometer with a Dionex UHPLC and autosampler.

RESULTS

We identified 978 metabolites in clastic EVs. Of those, 79 are potential biomarkers with Variable Interdependent Parameters scores of 2 or greater. Known metabolites cytidine, isocytosine, thymine, succinate and citrulline were found at statistically higher levels in EVs from odontoclasts compared with osteoclasts.

CONCLUSION

We conclude that numerous metabolites found in odontoclast EVs differ from those in osteoclast EVs, and thus represent potential biomarkers for root resorption and periodontal tissue destruction.

Aroma-based discrimination of Egyptian versus Indian guava fruits and in response to probiotics as analyzed via SPME/GC-MS and chemometric tools

Guava tree (Psidium guajava L., Myrtaceae) is an economic grown worldwide, particularly in tropical and subtropical regions. Guavas encompass numerous cultivars (cvs.) that were discriminated in previous studies based on leaf morphological features and profile of volatile organic compounds (VOCs). Nevertheless, fruit VOCs have also shown outstanding potential for discrimination of other plant taxa, which has not been utilized in guava. Hence, the current study investigates the various guava cvs. harvested from India and Egypt. A total of 5 samples were analyzed by solid phase microextraction coupled to gas chromatography/mass spectrometry. Results led to the detection of 42 VOCs belonging to aldehydes, alcohols, esters, ketones, aliphatic and aromatic hydrocarbons, in addition to monoterpene and sesquiterpene hydrocarbons. Butylated hydroxytoluene and β-caryophyllene were predominant reaching 77% and 41% in Egyptian and Indian guava, respectively. The impact of probiotic fermentation, i.e., Lactobacillus acidophilus and L. plantarum on aroma profile was not significantly different (p > 0.05). Multivariate data analyses were further applied for samples classification and markers determination, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). PCA score plot showed clear segregation of Egyptian from Indian specimens, whereas OPLS-DA revealed that β-caryophyllene was associated with white fruit versus 3-butenyl isothiocyanate and muurolol in red fruit type in the case of Indian guava. The richness of Egyptian guava in butylated hydroxytoluene in addition to the presence of vitamin C may potentiate its antioxidant activity, to be followed in subsequent studies regarding its health effects.

Identification of potential quality markers in Indonesia's Arabica specialty coffee using GC/MS-based metabolomics approach

INTRODUCTION

The cupping test is a widely used method for quality assessment of Arabica coffee. However, the cupping test is limited by the low number of certified panelists and the low throughput. Therefore, an analytical-based quality assessment may be a promising tool to complement the cupping test. A present, there is no report investigating quality marker candidates, focusing only on "specialty" grade Arabica coffee from Indonesia.

OBJECTIVE

This study identified the potential quality marker(s) in Arabica Specialty coffee at different stages (green beans, roasted beans, and brewed coffee.

METHODS

The metabolite profiles of ten different Arabica specialty-grade coffees were analyzed with different cup scores using gas chromatography-mass spectrometry (GC/MS). From the ten samples, green coffee beans, roasted coffee beans, and brewed coffee were selected. In addition, an orthogonal projection to latent structure (OPLS) regression analysis was conducted to obtain a potential quality marker based on the variable importance in projection (VIP). The potential quality marker(s) were validated by GC/MS metabolome profiling and OPLS analysis of different sets of samples consisting of 35 Arabica specialty-grade coffee samples.

RESULTS

In Arabica coffee samples, the OPLS model of the three stages showed galactinol to have a high VIP score. Galactinol showed a consistent positive correlation with cup scores at all stages of coffee production (green beans, roasted beans, and brewed coffee). The correlation suggests galactinol is a potential quality marker after further validation using different samples.

CONCLUSION

GC/MS combined with OPLS regression analysis suggested galactinol as a quality marker and provide an early screening method for Arabica coffee quality that complements the cupping test performed by certified panelists.

Light effects on Lasiodiplodia theobromae metabolome cultured in vitro

INTRODUCTION

The present work identified and compared intracellular metabolites and metabolic networks in mycelial cultures of Lasiodiplodia theobromae grown under 12 natural light and 24 hours' dark using a 1 H NMR-based metabolomics approach.

MATERIALS AND METHODS

Fungal cultures were grown in potato dextrose media, and metabolites were extracted by sonication with sodium phosphate-buffered saline (pH = 6.0, 10% D2O, 0.1 mM TSP) from mycelium samples collected every week over four weeks.

RESULTS

Multivariate analyses revealed that the light exposure group showed a positive correlation within beta-hydroxybutyrate, acetoacetate, acetone, betaine, choline, glycerol, and phosphocholine. On the other hand, phenyl acetate, leucine, isoleucine, valine, and tyrosine were positively correlated with dark conditions. Light favored the oxidative degradation of valine, leucine, and isoleucine, leading to the accumulation of choline, phosphocholine, betaine, and ketone bodies (ketogenesis). Ketogenesis, gluconeogenesis, and the biosynthesis of choline, phosphocholine, and betaine, were considered discriminatory routes for light conditions. The light-sensing pathways were interlinked with fungal development, as verified by the increased production of mycelia biomass without fruiting bodies and stress signaling, as demonstrated by the increased production of pigments.

Lipopolysaccharides from Ralstonia solanacearum induce a broad metabolomic response in Solanum lycopersicum

Ralstonia solanacearum, one of the most destructive crop pathogens worldwide, causes bacterial wilt disease in a wide range of host plants. The major component of the outer membrane of Gram-negative bacteria, lipopolysaccharides (LPS), has been shown to function as elicitors of plant defense leading to the activation of signaling and defense pathways in several plant species. LPS from a R. solanacearum strain virulent on tomato (LPSR. sol.), were purified, chemically characterized, and structurally elucidated. The lipid A moiety consisted of tetra- to hexa-acylated bis-phosphorylated disaccharide backbone, also decorated by aminoarabinose residues in minor species, while the O-polysaccharide chain consisted of either linear tetrasaccharide or branched pentasaccharide repeating units containing α-L-rhamnose, N-acetyl-β-D-glucosamine, and β-L-xylose. These properties might be associated with the evasion of host surveillance, aiding the establishment of the infection. Using untargeted metabolomics, the effect of LPSR. sol. elicitation on the metabolome of Solanum lycopersicum leaves was investigated across three incubation time intervals with the application of UHPLC-MS for metabolic profiling. The results revealed the production of oxylipins, e.g., trihydroxy octadecenoic acid and trihydroxy octadecadienoic acid, as well as several hydroxycinnamic acid amide derivatives, e.g., coumaroyl tyramine and feruloyl tyramine, as phytochemicals that exhibit a positive correlation to LPSR. sol. treatment. Although the chemical properties of these metabolite classes have been studied, the functional roles of these compounds have not been fully elucidated. Overall, the results suggest that the features of the LPSR. sol. chemotype aid in limiting or attenuating the full deployment of small molecular host defenses and contribute to the understanding of the perturbation and reprogramming of host metabolism during biotic immune responses.

Hyperfructosemia in sleep disordered breathing: metabolome analysis of Nagahama study

Sleep disordered breathing (SDB), mainly obstructive sleep apnea (OSA), constitutes a major health problem due to the large number of patients. Intermittent hypoxia caused by SDB induces alterations in metabolic function. Nevertheless, metabolites characteristic for SDB are largely unknown. In this study, we performed gas chromatography-mass spectrometry-based targeted metabolome analysis using data from The Nagahama Study (n = 6373). SDB-related metabolites were defined based on their variable importance score in orthogonal partial least squares discriminant analysis and fold changes in normalized peak-intensity levels between moderate-severe SDB patients and participants without SDB. We identified 20 metabolites as SDB-related, and interestingly, these metabolites were frequently included in pathways related to fructose. Multivariate analysis revealed that moderate-severe SDB was a significant factor for increased plasma fructose levels (β = 0.210, P = 0.006, generalized linear model) even after the adjustment of confounding factors. We further investigated changes in plasma fructose levels after continuous positive airway pressure (CPAP) treatment using samples from patients with OSA (n = 60) diagnosed by polysomnography at Kyoto University Hospital, and found that patients with marked hypoxemia exhibited prominent hyperfructosemia and their plasma fructose levels lowered after CPAP treatment. These data suggest that hyperfructosemia is the abnormality characteristic to SDB, which can be reduced by CPAP treatment.

Behavioral metabolomics: how behavioral data can guide metabolomics research on neuropsychiatric disorders

INTRODUCTION

Metabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging.

OBJECTIVES

This study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation.

METHODS

We designed a preclinical, untargeted metabolomics procedure, that focuses on the determination of central metabolites relevant to substance use disorders that are (a) associated with changes in behavior produced by acute drug exposure and (b) impacted by repeated drug exposure. Untargeted metabolomics analysis was carried out on liquid chromatography-mass spectrometry data obtained from 336 microdialysis samples. Samples were collected from the medial striatum of male Sprague-Dawley (N = 21) rats whilst behavioral data were simultaneously collected as part of a (±)-3,4-methylenedioxymethamphetamine (MDMA)-induced behavioral sensitization experiment. Analysis was conducted by orthogonal partial least squares, where the Y variable was the behavioral data, and the X variables were the relative concentrations of the 737 detected features.

RESULTS

MDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the greatest predictors of acute MDMA-produced behavior. Subsequent univariate analyses showed that repeated MDMA exposure produced significant changes in MDMA metabolism, which may contribute to the increased abuse liability of the drug as a function of repeated exposure.

CONCLUSION

These findings highlight how the inclusion of behavioral data can guide metabolomics data analysis and increase the relevance of the results to the phenotype of interest.

The potential of metabolomics in assessing global compositional changes resulting from the application of CRISPR/Cas9 technologies

Exhaustive analysis of genetically modified crops over multiple decades has increased societal confidence in the technology. New Plant Breeding Techniques are now emerging with improved precision and the ability to generate products containing no foreign DNA and mimic/replicate conventionally bred varieties. In the present study, metabolomic analysis was used to compare (i) tobacco genotypes with and without the CRISPR associated protein 9 (Cas9), (ii) tobacco lines with the edited and non-edited DE-ETIOLATED-1 gene without phenotype and (iii) leaf and fruit tissue from stable non-edited tomato progeny with and without the Cas9. In all cases, multivariate analysis based on the difference test using LC-HRMS/MS and GC-MS data indicated no significant difference in their metabolomes. The variations in metabolome composition that were evident could be associated with the processes of tissue culture regeneration and/or transformation (e.g. interaction with Agrobacterium). Metabolites responsible for the variance included quantitative changes of abundant, well characterised metabolites such as phenolics (e.g. chlorogenic acid) and several common sugars such as fructose. This study provides fundamental data on the characterisation of gene edited crops, that are important for the evaluation of the technology and its assessment. The approach also suggests that metabolomics could contribute to routine product-based analysis of crops/foods generated from New Plant Breeding approaches.

Application of novel AI-based algorithms to biobank data: uncovering of new features and linear relationships

We independently analyzed two large public domain datasets that contain ¹H-NMR spectral data from lung cancer and sex studies. The biobanks were sourced from the Karlsruhe Metabolomics and Nutrition (KarMeN) study and Bayesian Automated Metabolite Analyzer for NMR data (BATMAN) study. Our approach of applying novel artificial intelligence (AI)-based algorithms to NMR is an attempt to globalize metabolomics and demonstrate its clinical applications. The intention of this study was to analyze the resulting spectra in the biobanks via AI application to demonstrate its clinical applications. This technique enables metabolite mapping in areas of localized enrichment as a measure of true activity while also allowing for the accurate categorization of phenotypes.

Statistical considerations and database limitations in NMR-based metabolic profiling studies

INTRODUCTION

Interpretation and analysis of NMR-based metabolic profiling studies is limited by substantially incomplete commercial and academic databases. Statistical significance tests, including p-values, VIP scores, AUC values and FC values, can be largely inconsistent. Data normalization prior to statistical analysis can cause erroneous outcomes.

OBJECTIVES

The objectives were (1) to quantitatively assess consistency among p-values, VIP scores, AUC values and FC values in representative NMR-based metabolic profiling datasets, (2) to assess how data normalization can impact statistical significance outcomes, (3) to determine resonance peak assignment completion potential using commonly used databases and (4) to analyze intersection and uniqueness of metabolite space in these databases.

METHODS

P-values, VIP scores, AUC values and FC values, and their dependence on data normalization, were determined in orthotopic mouse model of pancreatic cancer and two human pancreatic cancer cell lines. Completeness of resonance assignments were evaluated using Chenomx, the human metabolite database (HMDB) and the COLMAR database. The intersection and uniqueness of the databases was quantified.

RESULTS

P-values and AUC values were strongly correlated compared to VIP or FC values. Distributions of statistically significant bins depended strongly on whether or not datasets were normalized. 40-45% of peaks had either no or ambiguous database matches. 9-22% of metabolites were unique to each database.

CONCLUSIONS

Lack of consistency in statistical analyses of metabolomics data can lead to misleading or inconsistent interpretation. Data normalization can have large effects on statistical analysis and should be justified. About 40% of peak assignments remain ambiguous or impossible with current databases. 1D and 2D databases should be made consistent to maximize metabolite assignment confidence and validation.

Learning from Seed Microbes: Trichoderma Coating Intervenes in Rhizosphere Microbiome Assembly

Seed-associated microbiomes can impact the later colonization of a plant rhizosphere microbiome. However, there remains little insight into the underlying mechanisms concerning how alterations in the composition of the seed microbiome may intervene in the assembly of a rhizosphere microbiome. In this study, the fungus Trichoderma guizhouense NJAU4742 was introduced to both maize and watermelon seed microbiomes by seed coating. Application was found to significantly promote seed germination and improve plant growth and rhizosphere soil quality. The activities of acid phosphatase, cellulase, peroxidase, sucrase, and α-glucosidase increased significantly in two crops. The introduction of Trichoderma guizhouense NJAU4742 also led to a decrease in the occurrence of disease. Coating with T. guizhouense NJAU4742 did not alter the alpha diversities of the bacterial and fungal communities but formed a key network module that contained both Trichoderma and Mortierella. This key network module comprised of these potentially beneficial microorganisms was positively linked with the belowground biomass and activities of rhizosphere soil enzymes but negatively correlated with disease incidence. Overall, this study provides insights into plant growth promotion and plant health maintenance via seed coating in order to influence the rhizosphere microbiome. IMPORTANCE Seed-associated microbiomes can impact the rhizosphere microbiome assembly and function display. However, there remains little insight into the underlying mechanisms concerning how alterations in the composition of the seed microbiome with the beneficial microbes may intervene in the assembly of a rhizosphere microbiome. Here, we introduced T. guizhouense NJAU4742 to the seed microbiome by seed coating. This introduction led to a decrease in the occurrence of disease and an increase in plant growth; furthermore, it formed a key network module that contained both Trichoderma and Mortierella. Our study provides insights into plant growth promotion and plant health maintenance via seed coating in order to influence the rhizosphere microbiome.

Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson's Disease

The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy of ML and extent of information obtained from metabolomics can be limited owing to challenges associated with interpreting disease prediction models and analyzing many chemical features with abundances that are correlated and "noisy". Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole metabolomics data sets without a priori feature selection. The performance of the NN approach for predicting Parkinson's disease (PD) from blood plasma metabolomics data is significantly higher than other ML methods with a mean area under the curve of >0.995. PD-specific markers that predate clinical PD diagnosis and contribute significantly to early disease prediction were identified including an exogenous polyfluoroalkyl substance. It is anticipated that this accurate and interpretable NN-based approach can improve diagnostic performance for many diseases using metabolomics and other untargeted 'omics methods.

AMPK is a mechano-metabolic sensor linking cell adhesion and mitochondrial dynamics to Myosin-dependent cell migration

Cell migration is crucial for cancer dissemination. We find that AMP-activated protein kinase (AMPK) controls cell migration by acting as an adhesion sensing molecular hub. In 3-dimensional matrices, fast-migrating amoeboid cancer cells exert low adhesion/low traction linked to low ATP/AMP, leading to AMPK activation. In turn, AMPK plays a dual role controlling mitochondrial dynamics and cytoskeletal remodelling. High AMPK activity in low adhering migratory cells, induces mitochondrial fission, resulting in lower oxidative phosphorylation and lower mitochondrial ATP. Concurrently, AMPK inactivates Myosin Phosphatase, increasing Myosin II-dependent amoeboid migration. Reducing adhesion or mitochondrial fusion or activating AMPK induces efficient rounded-amoeboid migration. AMPK inhibition suppresses metastatic potential of amoeboid cancer cells in vivo, while a mitochondrial/AMPK-driven switch is observed in regions of human tumours where amoeboid cells are disseminating. We unveil how mitochondrial dynamics control cell migration and suggest that AMPK is a mechano-metabolic sensor linking energetics and the cytoskeleton.

Multivariate analysis and model building for classifying patients in the peroxisomal disorders X-linked adrenoleukodystrophy and Zellweger syndrome in Chinese pediatric patients

BACKGROUND

The peroxisome is a ubiquitous single membrane-enclosed organelle with an important metabolic role. Peroxisomal disorders represent a class of medical conditions caused by deficiencies in peroxisome function and are segmented into enzyme-and-transporter defects (defects in single peroxisomal proteins) and peroxisome biogenesis disorders (defects in the peroxin proteins, critical for normal peroxisome assembly and biogenesis). In this study, we employed multivariate supervised and non-supervised statistical methods and utilized mass spectrometry data of neurological patients, peroxisomal disorder patients (X-linked adrenoleukodystrophy and Zellweger syndrome), and healthy controls to analyze the role of common metabolites in peroxisomal disorders, to develop and refine a classification models of X-linked adrenoleukodystrophy and Zellweger syndrome, and to explore analytes with utility in rapid screening and diagnostics.

RESULTS

T-SNE, PCA, and (sparse) PLS-DA, operated on mass spectrometry data of patients and healthy controls were utilized in this study. The performance of exploratory PLS-DA models was assessed to determine a suitable number of latent components and variables to retain for sparse PLS-DA models. Reduced-features (sparse) PLS-DA models achieved excellent classification performance of X-linked adrenoleukodystrophy and Zellweger syndrome patients.

CONCLUSIONS

Our study demonstrated metabolic differences between healthy controls, neurological patients, and peroxisomal disorder (X-linked adrenoleukodystrophy and Zellweger syndrome) patients, refined classification models and showed the potential utility of hexacosanoylcarnitine (C26:0-carnitine) as a screening analyte for Chinese patients in the context of a multivariate discriminant model predictive of peroxisomal disorders.

Maternal metabolomic profiling and congenital heart disease risk in offspring: A systematic review of observational studies

Aetiological understanding and screening methods for congenital heart disease (CHD) are limited. Maternal metabolomic assessment offers the potential to identify risk factors and biomarkers. We performed a systematic review (PROSPERO CRD42022308452) investigating the association between fetal/childhood CHD and endogenous maternal metabolites. Ovid-MEDLINE, Ovid-EMBASE and Cochrane Library were searched between inception and 06/09/2022. Case control studies included analysing maternal blood or urine metabolites in pregnancy or postpartum where there was foetal/childhood CHD. Risk of bias assessment utilised the Scottish Intercollegiate Guidelines Network methodology checklist and narrative synthesis was performed. A total of 134 records were screened with eight eligible studies (n = 3242 pregnancies, n = 842 CHD-affected offspring). Five studies performed metabolomic analysis in pregnancy. Metabolites distinguishing case and control groups spanned lipid, glucose and amino-acid pathways, with the development of sensitive risk prediction models. No single metabolite consistently distinguished cases and controls across studies. Three studies performed targeted analysis postnatally with altered lipid and amino acid metabolites and raised homocysteine and markers of oxidative stress identified in cases. Included studies reported small sample sizes, analysing different biosamples at variable time points using differing techniques. At present, there is not enough evidence to confidently associate maternal metabolomic profiles with offspring CHD risk. However, several identified pathways warrant further investigation.

High-Resolution Liquid Chromatography-Mass Spectrometry-Based Metabolomics for the Classification of Chuquiraga (Barnadesioideae, Asteraceae): New Phenylpropanoid Derivatives as Chemical Markers for Chuquiraga spinosa

Despite their relatively poorly investigated phytochemistry, species of the genus Chuquiraga are widely commercialized. The present study reports the use of a high-resolution liquid chromatography-mass spectrometry-based metabolomics approach coupled with exploratory and supervised multivariate statistical analyses for species classification and chemical marker identification of four species of Chuquiraga (C. jussieui, C. weberbaueri, C. spinosa, and Chuquiraga sp.) from Ecuador and Peru. Based on these analyses, a high percentage of correct classifications (87% to 100%) allowed the prediction of the taxonomic identity of Chuquiraga species. Through the metabolite selection process, several key constituents with the potential to be chemical markers were identified. Samples of C. jussieui displayed alkyl glycosides and triterpenoid glycosides as discriminating metabolites, while Chuquiraga sp. displayed high concentrations of p-hydroxyacetophenone, p-hydroxyacetophenone 4-O-glucoside, p-hydroxyacetophenone 4-O-(6-O-apiosyl)-glucoside, and quinic acid ester derivatives as the main metabolites. Caffeic acid was characteristic for C. weberbaueri samples, whereas C. spinosa displayed higher concentrations of the following new phenylpropanoid ester derivatives: 2-O-caffeoyl-4-hydroxypentanedioic acid (24), 2-O-p-coumaroyl-4-hydroxypentanedioic acid (34), 2-O-feruloyl-4-hydroxypentanedioic acid (46), 2,4-O-dicaffeoylpentanedioic acid (71), and 2-O-caffeoyl-4-O-feruloylpentanedioic acid (77).

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a "toxicity index" for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration.

Performance comparison of three scaling algorithms in NMR-based metabolomics analysis

Unit variance (UV) scaling, mean centering (CTR) scaling, and Pareto (Par) scaling are three commonly used algorithms in the preprocessing of metabolomics data. Based on our NMR-based metabolomics studies, we found that the clustering identification performances of these three scaling methods were dramatically different as tested by the spectra data of 48 young athletes’ urine samples, spleen tissue (from mice), serum (from mice), and cell (from Staphylococcus aureus) samples. Our data suggested that for the extraction of clustering information, UV scaling could serve as a robust approach for NMR metabolomics data for the identification of clustering analysis even with the existence of technical errors. However, for the purpose of discriminative metabolite identification, UV scaling, CTR scaling, and Par scaling could equally extract discriminative metabolites efficiently based on the coefficient values. Based on the data presented in this study, we propose an optimal working pipeline for the selection of scaling algorithms in NMR-based metabolomics analysis, which has the potential to serve as guidance for junior researchers working in the NMR-based metabolomics research field.

Ebola virus-like particles reprogram cellular metabolism

Ebola virus can trigger a release of pro-inflammatory cytokines with subsequent vascular leakage and impairment of clotting finally leading to multiorgan failure and shock after entering and infecting patients. Ebola virus is known to directly target endothelial cells and macrophages, even without infecting them, through direct interactions with viral proteins. These interactions affect cellular mechanics and immune processes, which are tightly linked to other key cellular functions such as metabolism. However, research regarding metabolic activity of these cells upon viral exposure remains limited, hampering our understanding of its pathophysiology and progression. Therefore, in the present study, an untargeted cellular metabolomic approach was performed to investigate the metabolic alterations of primary human endothelial cells and M1 and M2 macrophages upon exposure to Ebola virus-like particles (VLP). The results show that Ebola VLP led to metabolic changes among endothelial, M1, and M2 cells. Differential metabolite abundance and perturbed signaling pathway analysis further identified specific metabolic features, mainly in fatty acid-, steroid-, and amino acid-related metabolism pathways for all the three cell types, in a host cell specific manner. Taken together, this work characterized for the first time the metabolic alternations of endothelial cells and two primary human macrophage subtypes after Ebola VLP exposure, and identified the potential metabolites and pathways differentially affected, highlighting the important role of those host cells in disease development and progression. KEY MESSAGES: • Ebola VLP can lead to metabolic alternations in endothelial cells and M1 and M2 macrophages. • Differential abundance of metabolites, mainly including fatty acids and sterol lipids, was observed after Ebola VLP exposure. • Multiple fatty acid-, steroid-, and amino acid-related metabolism pathways were observed perturbed.

Micafungin effect on Pseudomonas aeruginosa metabolome, virulence and biofilm: potential quorum sensing inhibitor

The prevalence of antibiotic resistance in Pseudomonas aeruginosa places a heavy burden on the health care sectors urging the need to find alternative, non-antibiotic strategies. The interference with the P. aeruginosa quorum sensing (QS) system represents a promising alternative strategy to attenuate the bacterial virulency and its ability to form biofilms. Micafungin has been reported to impede the pseudomonal biofilm formation. However, the influences of micafungin on the biochemical composition and metabolites levels of P. aeruginosa have not been explored. In this study, the effect of micafungin (100 µg/mL) on the virulence factors, QS signal molecules and the metabolome of P. aeruginosa was studied using exofactor assay and mass spectrometry-based metabolomics approaches. Furthermore, confocal laser scanning microscopy (CLSM) using the fluorescent dyes ConA-FITC and SYPRO® Ruby was used to visualize micafungin disturbing effects on the pseudomonal glycocalyx and protein biofilm-constituents, respectively. Our findings showed that micafungin significantly decreased the production of various QS-controlled virulence factors (pyocyanin, pyoverdine, pyochelin and rhamnolipid), along with a dysregulation in the level of various metabolites involved in QS system, lysine degradation, tryptophan biosynthesis, TCA cycle, and biotin metabolism. In addition, the CLSM examination showed an altered matrix distribution. The presented findings highlight the promising role of micafungin as a potential quorum sensing inhibitor (QSI) and anti-biofilm agent to attenuate P. aeruginosa pathogenicity. In addition, they point to the promising role of metabolomics study in investigating the altered biochemical pathways in P. aeruginosa.

NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)

Each year, bovine respiratory disease (BRD) results in significant economic loss in the cattle sector, and novel metabolic profiling for early diagnosis represents a promising tool for developing effective measures for disease management. Here, ¹H-nuclear magnetic resonance (¹H-NMR) spectra were used to characterize metabolites from blood plasma collected from male dairy calves (n = 10) intentionally infected with two of the main BRD causal agents, bovine respiratory syncytial virus (BRSV) and Mannheimia haemolytica (MH), to generate a well-defined metabolomic profile under controlled conditions. In response to infection, 46 metabolites (BRSV = 32, MH = 33) changed in concentration compared to the uninfected state. Fuel substrates and products exhibited a particularly strong effect, reflecting imbalances that occur during the immune response. Furthermore, ¹H-NMR spectra from samples from the uninfected and infected stages were discriminated with an accuracy, sensitivity, and specificity ≥ 95% using chemometrics to model the changes associated with disease, suggesting that metabolic profiles can be used for further development, understanding, and validation of novel diagnostic tools.

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling

INTRODUCTION

This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation.

METHOD

A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure.

CONCLUSIONS

The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

Metabolite Profiling to Evaluate Metabolic Changes in Genetically Modified Protopanaxadiol-Enriched Rice

Event DS rice producing protopanaxadiol (PPD) has been previously developed by inserting Panax ginseng dammarenediol-II synthase gene (PgDDS) and PPD synthase gene (CYP716A47). We performed a gas chromatography-mass spectrometry (GC-MS)-based metabolomics of the DS rice to identify metabolic alterations as the effects of genetic engineering by measuring the contents of 65 metabolites in seeds and 63 metabolites in leaves. Multivariate analysis and one-way analysis of variance between DS and non-genetically modified (GM) rice showed that DS rice accumulated fewer tocotrienols, tocopherols, and phytosterols than non-GM rice. These results may be due to competition for the same precursors because PPDs in DS rice are synthesized from the same precursors as those of phytosterols. In addition, multivariate analysis of metabolic data from rice leaves revealed that composition differed by growth stage rather than genetic modifications. Our results demonstrate the potential of metabolomics for identifying metabolic alterations in response to genetic modifications.

Cyprinid herpesvirus 2 infection changes microbiota and metabolites in the gibel carp (Carassius auratus gibelio) midgut

Cyprinid herpesvirus 2 (CyHV-2) infects gibel carp (Carassius auratus gibelio) and causes severe losses. Microbiota in animal guts involves nutrition intake, development, immunity, and disease resistance. However, the relationship between gibel carp gut microbiota and CyHV-2 infection is not well known. Herein, we analyzed the gut microbiota composition and metabolite profiles in CyHV-2-infected and -uninfected fish using high-throughput sequencing and gas chromatography/mass spectrometry. Results showed that CyHV-2 infection significantly changed gut microbiota and metabolite profiles (p < 0.05). High-throughput sequencing demonstrated that the relative abundance of Aeromonas in the midgut increased dramatically while Cetobacterium decreased. Time-course analysis showed that the number of Aeromonas in the midgut of infected fish increased more than 1,000 times within 5 days post infection. Metabolome analysis illustrated that CyHV-2 infection significantly altered 24 metabolites in the midgut of gibel carp, annotating to the anomaly of digestion and metabolisms of amino acids, carbohydrates, and lipids, such as tryptophan (Trp) metabolism. The Mantel test demonstrated that gut microbiota and metabolite profiles were well related (r = 0.89). Furthermore, Trp metabolism responded to CyHV-2 infection closely was taken as one example to prove the correlation among CyHV-2 infection, metabolites and microbiota in the midgut, and host immunity. Results showed that modulating Trp metabolism could affect the relative abundance of Aeromonas in the midgut of fish, transcription of antiviral cytokines, and CyHV-2 infection. Therefore, we can conclude that CyHV-2 infection significantly perturbed the gut microbiome, disrupted its' metabolic functions, and caused the proliferation of the opportunistic pathogen Aeromonas. This study also suggests that modulation of the gut microbiome will open a therapeutic opportunity to control CyHV-2 infection in gibel carp.

Is substrate choice an overlooked variable in ecotoxicology experiments?

It is crucial to understand the effects caused by experimental parameters such as temperature, light, and food type on lab and field-based ecotoxicology experiments, as these variables, and combinations thereof, can affect results. The type of substrate used in exposure experiments, however, is generally assumed to have no effect. This may not always be correct. The metabolic changes in the freshwater crustacean, Austrochiltonia subtenuis exposed to copper, using three common substrates, gauze; toilet paper; and cellulose were investigated. Substrate alone did not affect survival, but each substrate elicited a different metabolic response and adult and juvenile amphipods had different substrate preferences. Several classes of metabolites were shown to change in response to different substrates and toxicant. These included disaccharides, monosaccharides, fatty acids, and tricarboxylic acid cycle intermediates. The results illustrate that metabolomic responses can differ in response to experimental factors that were previously thought not to be significant. In fact, our data indicate that substrate should be viewed as an experimental factor as important to control for as more well-known confounders such as temperature or food, thus challenging the current paradigm. Assuming substrate type has no effect on the experiment could potentially lead to errors in contaminant toxicity assessments. We propose that ideal good practise would be that all experimental factors should be evaluated for their potential influence on metabolomic profiles prior to contaminant response experiments being undertaken.

Metabolic rewiring in MYC-driven medulloblastoma by BET-bromodomain inhibition

Medulloblastoma (MB) is the most common malignant brain tumour in children. High-risk MB patients harbouring MYC amplification or overexpression exhibit a very poor prognosis. Aberrant activation of MYC markedly reprograms cell metabolism to sustain tumorigenesis, yet how metabolism is dysregulated in MYC-driven MB is not well understood. Growing evidence unveiled the potential of BET-bromodomain inhibitors (BETis) as next generation agents for treating MYC-driven MB, but whether and how BETis may affect tumour cell metabolism to exert their anticancer activities remains unknown. In this study, we explore the metabolic features characterising MYC-driven MB and examine how these are altered by BET-bromodomain inhibition. To this end, we employed an NMR-based metabolomics approach applied to the MYC-driven MB D283 and D458 cell lines before and after the treatment with the BETi OTX-015. We found that OTX-015 triggers a metabolic shift in both cell lines resulting in increased levels of myo-inositol, glycerophosphocholine, UDP-N-acetylglucosamine, glycine, serine, pantothenate and phosphocholine. Moreover, we show that OTX-015 alters ascorbate and aldarate metabolism, inositol phosphate metabolism, phosphatidylinositol signalling system, glycerophospholipid metabolism, ether lipid metabolism, aminoacyl-tRNA biosynthesis, and glycine, serine and threonine metabolism pathways in both cell lines. These insights provide a metabolic characterisation of MYC-driven childhood MB cell lines, which could pave the way for the discovery of novel druggable pathways. Importantly, these findings will also contribute to understand the downstream effects of BETis on MYC-driven MB, potentially aiding the development of new therapeutic strategies to combat medulloblastoma.

Effects of Different Rootstocks on the Metabolites of Huanglongbing-Affected Sweet Orange Juices Using a Novel Combined Strategy of Untargeted Metabolomics and Machine Learning

Huanglongbing (HLB) is one of the most destructive citrus diseases, mainly caused by the Gram-negative bacteria Candidatus Liberibacter asiaticus. Aiming at unraveling the mechanisms of different scion/rootstock combinations on improving HLB-affected orange juice quality, the effects of rootstocks on the metabolites of HLB-affected sweet orange juices were investigated using a combined strategy of untargeted metabolomics and machine learning. A total of 2531 ion features were detected using UHPLC-Q-Orbitrap high-resolution electrospray ionization mass spectrometry, and 54 metabolites including amino acids, amines, flavonoids, coumarins, fatty acids, and glycosides were definitely or tentatively identified as the differential markers based on the random forest algorithm. Furthermore, 24 metabolites were verified and semi-quantified using authentic standards. Notably, the presence of specific amino acids and amines, especially polyamines, indicated that different rootstocks might affect glutamate, aspartate, proline, and arginine metabolism to regulate the physiological response against HLB. Meanwhile, the production of flavonoids and prenylated coumarins suggested that rootstocks influenced phenylalanine and phenylpropanoid metabolism. The possible metabolic pathways were proposed, and the important intermediates were verified by authentic standards. These results provide new insights on the effects of rootstocks on the metabolites of HLB-affected sweet orange juices.

Biomarker discovery in galactosemia: Metabolomics with UPLC/HRMS in dried blood spots

Introduction:Galactosemia (GAL) is a genetic disorder that results in disturbances in galactose metabolism and can lead to life-threatening complications. However, the underlying pathophysiology of long-term complications in GAL remains poorly understood. Methods: In this study, a metabolomics approach using ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to investigate metabolomic changes in dried blood spots of 15 patients with GAL and 39 healthy individuals. Results: The study found that 2,819 metabolites underwent significant changes in patients with GAL compared to the control group. 480 human endogenous metabolites were identified, of which 209 and 271 were upregulated and downregulated, respectively. PA (8:0/LTE4) and ganglioside GT1c (d18:0/20:0) metabolites showed the most significant difference between GAL and the healthy group, with an area under the curve of 1 and 0.995, respectively. Additionally, the study identified potential biomarkers for GAL, such as 17-alpha-estradiol-3-glucuronide and 16-alpha-hydroxy DHEA 3-sulfatediphosphate. Conclusion: This metabolomics study deepened the understanding of the pathophysiology of GAL and presented potential biomarkers that might serve as prognostic biomarkers to monitor the progression or support the clinical diagnosis of GAL.

Detection of Markers in Green Beans and Roasted Beans of Kalosi-Enrekang Arabica Coffee with Different Postharvest Processing Using LC-MS/MS

Our study is aimed at evaluating the effect of postharvest processing (natural, honey, and fully washed) on the compounds profile in green beans and roasted beans of Kalosi-Enrekang Arabica coffee and determining the marker compounds for each process. These beans were extracted using boiling water, and the extract was analyzed using LC-MS/MS. The results of this work confirmed the significant impact of postharvest processing on compounds in the coffee beans, and each process has a marker compound. Green beans by natural processing have 3 marker compounds, honey processing has 6 marker compounds, and fully washed processing has 2 marker compounds. Meanwhile, roasted beans by natural processing have 4 marker compounds, honey processing has 5 marker compounds, and fully washed processing has 7 marker compounds. In addition, our research identified caffeoyl tyrosine in green beans from natural and honey processing, which was previously only identified in Robusta coffee. These marker compounds can differentiate postharvest processing (natural, honey, and fully washed). These results can also help understand the effect of postharvest processing on the chemical composition of green and roasted beans.