A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding

PLS-DA vs sparse PLS-DA in food traceability. A case study: Authentication of avocado samples

Conventional and sparse partial least squares-discriminant analysis (PLS-DA and sPLS-DA) have been successfully tested in order to authenticate avocado samples in terms of three different geographical origins and six kinds of cultivar. For this, lipid chromatographic fingerprints of different avocado fruits have been acquired using gas chromatography coupled with flame ionization detector (GC-FID) and employed for building classification models. In addition, classification models concatenating strategy has been applied, which has proved to be successful to resolve multiclass problems in food authentication. Finally, fine performance metrics around of 0.95 were obtained for both multivariate classification methods.

Relaxometric learning: a pattern recognition method for T2 relaxation curves based on machine learning supported by an analytical framework

Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T₂ relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

Cerebral arterial architectonics and CFD simulation in mice with type 1 diabetes mellitus of different duration

Type 1 diabetes is a chronic autoimmune disease that affects tens of millions of people. Diabetes mellitus is one of the strongest factors in the development of cerebrovascular diseases. In this study we used NOD.CB17 Prkdcscid mice and the pharmacological model of type 1 diabetes mellitus of different duration to study changes in the cerebral vasculature. We used two combined approaches using magnetic resonance angiography both steady and transient CFD blood flow modeling. We identified the influence of type 1 diabetes on the architectonics and hemodynamics of the large blood vessels of the brain as the disease progresses. For the first time, we detected a statistically significant change in angioarchitectonics (the angles between the vessels of the circle of Willis, cross-sections areas of vessels) and hemodynamic (maximum blood flow rate, hydraulic resistance) in animals with diabetes duration of 2 months, that is manifested by the development of asymmetry of cerebral blood flow. The result shows the negative effect of diabetes on cerebral circulation as well as the practicability of CFD modeling. This may be of extensive interest, in pharmacological and preclinical studies.

Discrimination of the Geographical Origin of Soybeans Using NMR-Based Metabolomics

With the increase in soybean trade between countries, the intentional mislabeling of the origin of soybeans has become a serious problem worldwide. In this study, metabolic profiling of soybeans from the Republic of Korea and China was performed by nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to predict the geographical origin of soybeans. The optimal orthogonal partial least squares-discriminant analysis (OPLS-DA) model was obtained using total area normalization and unit variance (UV) scaling, without applying the variable influences on projection (VIP) cut-off value, resulting in 96.9% sensitivity, 94.4% specificity, and 95.6% accuracy in the leave-one-out cross validation (LOO-CV) test for discriminating between Korean and Chinese soybeans. Soybeans from the northeastern, middle, and southern regions of China were successfully differentiated by standardized area normalization and UV scaling with a VIP cut-off value of 1.0, resulting in 100% sensitivity, 91.7%–100% specificity, and 94.4%–100% accuracy in a LOO-CV test. The methods employed in this study can be used to obtain essential information for the authentication of soybean samples from diverse geographical locations in future studies.

Specificity and Plasticity of the Functional Ionome of Brassica napus and Triticum aestivum Exposed to Micronutrient or Beneficial Nutrient Deprivation and Predictive Sensitivity of the Ionomic Signatures

The specific variation in the functional ionome was studied in Brassica napus and Triticum aestivum plants subjected to micronutrient or beneficial mineral nutrient deprivation. Effects of these deprivations were compared to those of macronutrient deprivation. In order to identify early events, plants were harvested after 22 days, i.e., before any significant reduction in growth relative to control plants. Root uptake, tissue concentrations and relative root nutrient contents were analyzed revealing numerous interactions with respect to the 20 elements quantified. The assessment of the functional ionome under individual mineral nutrient deficiency allows the identification of a large number of interactions between elements, although it is not totally exhaustive, and gives access to specific ionomic signatures that discriminate among deficiencies in N, P, S, K, Ca, Mn, Fe, Zn, Na, Si, and Se in both species, plus Mg, Cl, Cu, and Mo in wheat. Ionome modifications and components of ionomic signatures are discussed in relation to well-known mechanisms that may explain crosstalks between mineral nutrients, such as between Na and K, V, Se, Mo and S or Fe, Zn and Cu. More surprisingly, when deprived of beneficial nutrients such as Na, Si, Co, or Se, the plant ionome was strongly modified while these beneficial nutrients contributed greatly to the leaf ionomic signature of most mineral deficiencies.

Metabolomic profiling, biological evaluation of Aspergillus awamori, the river Nile-derived fungus using epigenetic and OSMAC approaches

LC-HRMS-based metabolomics approach was applied to the river Nile-derived fungus Aspergillus awamori after its fermentation on four different media and using four epigenetic modifiers as elicitors. Thereafter, a comprehensive multivariate statistical analysis such as PCA, PLS-DA and OPLS-DA were employed to explain the generated metabolomic data (1587 features). PCA showed that the fungus displayed a unique chemical profile in each medium or elicitor. Additionally, PLS-DA results revealed the upregulated metabolites under each of these conditions. Results indicated that both rice and malt dextrose agar were recognized as the best media in terms of secondary metabolites diversity and showed better profiles than the four applied epigenetic modifiers, of which nicotinamide was the best secondary metabolite elicitor. Testing the antibacterial and cytotoxic effects of all A. awamori-derived extracts revealed that using epigenetic modifiers can induce antimicrobial metabolites against S. aureus and E. coli, whereas using rice, malt dextrose or nicotinamide can induce groups of cytotoxic metabolites. OPLS-DA results assisted in the putative identification of the induced metabolites that could be responsible for these observed inhibitory activities. This study highlighted how powerful the OSMAC approach in maximizing of the chemical diversity of a single organism. Furthermore, it revealed the power of metabolomics in tracing, profiling and categorizing such chemical diversity and even targeting the possible bioactive candidates which require further scaling up studies in the future.

LC-HRMS-based metabolomics approach was applied to the river Nile-derived fungus Aspergillus awamori after its fermentation on four different media and using four epigenetic modifiers as elicitors.

Fast Screening and Primary Diagnosis of COVID-19 by ATR-FT-IR

The outbreak of coronavirus disease 2019 (COVID-19) has led to substantial infections and mortality around the world. Fast screening and diagnosis are thus crucial for quick isolation and clinical intervention. In this work, we showed that attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FT-IR) can be a primary diagnostic tool for COVID-19 as a supplement to in-use techniques. It requires only a small volume (∼3 μL) of the serum sample and a shorter detection time (several minutes). The distinct spectral differences and the separability between normal control and COVID-19 were investigated using multivariate and statistical analysis. Results showed that ATR-FT-IR coupled with partial least squares discriminant analysis was effective to differentiate COVID-19 from normal controls and some common respiratory viral infections or inflammation, with the area under the receiver operating characteristic curve (AUROC) of 0.9561 (95% CI: 0.9071-0.9774). Several serum constituents including, but not just, antibodies and serum phospholipids could be reflected on the infrared spectra, serving as "chemical fingerprints" and accounting for good model performances.

Fast Screening and Primary Diagnosis of COVID-19 by ATR-FT-IR

The outbreak of coronavirus disease 2019 (COVID-19) has led to substantial infections and mortality around the world. Fast screening and diagnosis are thus crucial for quick isolation and clinical intervention. In this work, we showed that attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FT-IR) can be a primary diagnostic tool for COVID-19 as a supplement to in-use techniques. It requires only a small volume (∼3 μL) of the serum sample and a shorter detection time (several minutes). The distinct spectral differences and the separability between normal control and COVID-19 were investigated using multivariate and statistical analysis. Results showed that ATR-FT-IR coupled with partial least squares discriminant analysis was effective to differentiate COVID-19 from normal controls and some common respiratory viral infections or inflammation, with the area under the receiver operating characteristic curve (AUROC) of 0.9561 (95% CI: 0.9071-0.9774). Several serum constituents including, but not just, antibodies and serum phospholipids could be reflected on the infrared spectra, serving as "chemical fingerprints" and accounting for good model performances.

A shift from glycolytic and fatty acid derivatives toward one-carbon metabolites in the developing lung during transitions of the early postnatal period

During postnatal lung development, metabolic changes that coincide with stages of alveolar formation are poorly understood. Responding to developmental and environmental factors, metabolic changes can be rapidly and adaptively altered. The objective of the present study was to determine biological and technical determinants of metabolic changes during postnatal lung development. Over 118 metabolic features were identified by liquid chromatography with tandem mass spectrometry (LC-MS/MS, Sciex QTRAP 5500 Triple Quadrupole). Biological determinants of metabolic changes were the transition from the postnatal saccular to alveolar stages and exposure to 85% hyperoxia, an environmental insult. Technical determinants of metabolic identification were brevity and temperature of harvesting, both of which improved metabolic preservation within samples. Multivariate statistical analyses revealed the transition between stages of lung development as the period of major metabolic alteration. Of three distinctive groups that clustered by age, the saccular stage was identified by its enrichment of both glycolytic and fatty acid derivatives. The critical transition between stages of development were denoted by changes in amino acid derivatives. Of the amino acid derivatives that significantly changed, a majority were linked to metabolites of the one-carbon metabolic pathway. The enrichment of one-carbon metabolites was independent of age and environmental insult. Temperature was also found to significantly influence the metabolic levels within the postmortem sampled lung, which underscored the importance of methodology. Collectively, these data support not only distinctive stages of metabolic change but also highlight amino acid metabolism, in particular one-carbon metabolites as metabolic signatures of the early postnatal lung.

Application of UPLC-QTOF-MS Based Untargeted Metabolomics in Identification of Metabolites Induced in Pathogen-Infected Rice

Metabolomics is a useful tool for comparing metabolite changes in plants. Because of its high sensitivity, metabolomics combined with high-resolution mass spectrometry (HR-MS) is the most widely accepted metabolomics tools. In this study, we compared the metabolites of pathogen-infected rice (Oryza sativa) with control rice using an untargeted metabolomics approach. We profiled the mass features of two rice groups using a liquid chromatography quadrupole time-of-flight mass spectrometry (QTOF-MS) system. Twelve of the most differentially induced metabolites in infected rice were selected through multivariate data analysis and identified through a mass spectral database search. The role of these compounds in metabolic pathways was finally investigated using pathway analysis. Our study showed that the most frequently induced secondary metabolites are prostanoids, a subclass of eicosanoids, which are associated with plant defense metabolism against pathogen infection. Herein, we propose a new untargeted metabolomics approach for understanding plant defense system at the metabolic level.

A novel experimental workflow to determine the impact of storage parameters on the mass spectrometric profiling and assessment of representative phosphatidylethanolamine lipids in mouse tissues

Evaluation of signaling lipids is essential for measuring biological processes. There is a lack of experimental data regarding the proper storage of extracts for signaling lipid analysis, potentially impacting the procedures that can lead to accurate and reproducible evaluation. In this study, the importance of pre-analytical conditions for analyzing ion transitions for phosphatidylethanolamines (PEs), an abundant signaling phospholipid, was systematically assessed. A novel workflow was utilized involving an MRM-based experimental approach followed by statistical analysis. Specifically, lipids were extracted from the brain, heart, lungs, and serum of C57BL/6 mice. Extract subsets were resuspended in organic solvents prior to storage in various temperature conditions. Mass spectrometry analysis by multiple reaction monitoring (MRM) profiling was performed at four time points (1 day, 2 weeks, 2 months, or 6 months) to measure relative amounts of PEs in distinct lipid extract aliquots. We introduce an innovative statistical workflow to measure the changes in relative amounts of PEs in the profiles over time to determine lipid extract storage conditions in which fewer profile changes occur. Results demonstrated that time is the most significant factor affecting the changes in lipid samples, with temperature and solvent having comparatively minor effects. We conclude that for lipid extracts obtained by Bligh & Dyer extraction, storage at - 80.0 °C without solvent for less than 2 weeks before analysis is ideal. By considering the data generated by this study, lipid extract storage practices may be optimized and standardized, enhancing the validity and reproducibility of lipid assessments.

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

Multiple reaction monitoring profiling as an analytical strategy to investigate lipids in extracellular vesicles

Extracellular vesicles (EVs) convey information used in cell-to-cell interactions. Lipid analysis of EVs remains challenging because of small sample amounts available. Lipid discovery using traditional mass spectrometry platforms based on liquid chromatography and high mass resolution typically employs milligram sample amounts. We report a simple workflow for lipid profiling of EVs based on multiple reaction monitoring (MRM) profiling that uses microgram amounts of sample. After liquid-liquid extraction, individual EV samples were injected directly into the electrospray ionization (ESI) ion source at low flow rates (10 μl/min) and screened for 197 MRM transitions chosen to be a characteristic of several classes of lipids. This choice was based on a discovery experiment, which applied 1,419 MRMs associated with multiple lipid classes to a representative pooled sample. EVs isolated from 12 samples of human lymphocytes and 16 replicates from six different rat cells lines contained an estimated amount of total lipids of 326 to 805 μg. Samples showed profiles that included phosphatidylcholine (PC), sphingomyelin (SM), cholesteryl ester (CE), and ceramide (Cer) lipids, as well as acylcarnitines. The lipid profiles of human lymphocyte EVs were distinguishable using principal component and cluster analysis in terms of prior antibody and drug exposure. Lipid profiles of rat cell lines EV's were distinguishable by their tissue of origin.

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

This review compares and contrasts MALDI-MS, FT-IR spectroscopy and Raman spectroscopy for whole organism fingerprinting and bacterial typing.

Systems and synthetic biology to elucidate secondary metabolite biosynthetic gene clusters encoded in Streptomyces genomes

Covering: 2010 to 2020 Over the last few decades, Streptomyces have been extensively investigated for their ability to produce diverse bioactive secondary metabolites. Recent advances in Streptomyces research have been largely supported by improvements in high-throughput technology 'omics'. From genomics, numerous secondary metabolite biosynthetic gene clusters were predicted, increasing their genomic potential for novel bioactive compound discovery. Additional omics, including transcriptomics, translatomics, interactomics, proteomics and metabolomics, have been applied to obtain a system-level understanding spanning entire bioprocesses of Streptomyces, revealing highly interconnected and multi-layered regulatory networks for secondary metabolism. The comprehensive understanding derived from this systematic information accelerates the rational engineering of Streptomyces to enhance secondary metabolite production, integrated with the exploitation of the highly efficient 'Design-Build-Test-Learn' cycle in synthetic biology. In this review, we describe the current status of omics applications in Streptomyces research to better understand the organism and exploit its genetic potential for higher production of valuable secondary metabolites and novel secondary metabolite discovery.

Practical Considerations in Method Development for Gas Chromatography-Based Metabolomic Profiling

This chapter discusses the fundamentals of gas chromatography (GC) to improve method development for metabolic profiling of complex biological samples. The selection of column geometry and phase ratio impacts analyte mass transfer, which must be carefully optimized for fast analysis. Stationary phase selection is critical to obtain baseline resolution of critical pairs, but such selection must consider important aspects of metabolomic protocols, such as derivatization and dependence of analyte identification on existing databases. Sample preparation methods are also addressed depending on the sample matrix, including liquid-liquid extraction and solid-phase microextraction.

1H NMR-based metabolomics study of the dynamic effect of Xue-Fu-Zhu-Yu capsules on coronary heart disease rats induced by high-fat diet, coronary artery ligation

An ¹H NMR-based metabolomics approach was conducted to holisticly explore the effect of Xue Fu Zhu Yu (XFZY) capsule (a well-known Chinese herbal medicine) on high-fat diets combined with coronary artery ligation induced coronary heart disease (CHD) model rats. 1H NMR-based metabolomics approach combined with multivariate analysis was performed to explore potential biomarkers, a total of 20 metabolites were confirmed as contributors to the discrimination of model group and sham group. We investigated the dynamic metabolic characteristics of XFZY capsule on CHD rats, lactate, glutamine, pyruvate, citrate, choline and taurine were potential biomarkers of early effect. More potential biomarkers changed after 28 days of medication, XFZY capsules primarily influenced the taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, purine metabolism, glycolysis/gluconeogenesis, amino sugar and nucleotide sugar metabolism, primary bile acid biosynthesis.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

Secreted Peptides for Diagnostic Trajectory Assessments in Brain Injury Rehabilitation

BACKGROUND

Rehabilitation following traumatic brain injury (TBI) significantly improves outcomes; yet TBI heterogeneity raises the need for molecular evidence of brain recovery processes to better track patient progress, evaluate therapeutic efficacy, and provide prognostication.

OBJECTIVE

Here, we assessed whether the trajectory of TBI-responsive peptides secreted into urine can produce a predictive model of functional recovery during TBI rehabilitation.

METHODS

The multivariate urinary peptidome of 12 individuals with TBI was examined using quantitative peptidomics. Measures were assessed upon admission and discharge from inpatient rehabilitation. A combination of Pavlidis template matching and partial least-squares discriminant analysis was used to build models on Disability Rating Scale (DRS) and Functional Independence Measure (FIM) scores, with participants bifurcated into more or less functional improvement groups.

RESULTS

The produced models exhibited high sensitivity and specificity with the area under the receiver operator curve being 0.99 for DRS- and 0.95 for FIM-based models using the top 20 discriminant peptides. Predictive ability for each model was assessed using robust leave-one-out cross-validation with Q² statistics of 0.64 (P = .00012) and 0.62 (P = .011) for DRS- and FIM-based models, respectively, both with a high predictive accuracy of 0.875. Identified peptides that discriminated improved functional recovery reflected heightened neuroplasticity and synaptic refinement and diminished cell death and neuroinflammation, consistent with postacute TBI pathobiology.

CONCLUSIONS

Produced models of urine-based peptide measures reflective of ongoing recovery pathobiology can inform on rehabilitation progress after TBI, warranting further study to assess refined stratification across a larger population and efficacy in assessing therapeutic interventions.

Comprehensive exploratory autoantibody profiling in patients with early rheumatoid arthritis treated with methotrexate or tocilizumab

Background

We sought to identify immunoglobin G autoantibodies predictive of early treatment response to methotrexate, the recommended first-line therapy for patients with newly diagnosed rheumatoid arthritis, and to the interleukin-6 receptor inhibitor biologic tocilizumab, initiated as the first disease-modifying anti-rheumatic drug.

Materials and methods

In baseline sera of a subset of patients with newly diagnosed rheumatoid arthritis in the U-Act-Early study, selected based on specific responder/non-responder criteria using the Disease Activity Score assessing 28 joints (DAS28) within the first 20 weeks, we measured immunoglobin G antibody reactivity against 463 protein antigens and performed supervised cluster analysis to identify predictive autoantibodies for treatment response. The analysis subset comprised 56 patients in the methotrexate arm (22 responders, 34 non-responders) and 50 patients in the tocilizumab arm (34 responders, 16 non-responders). For comparison, these analyses were also performed in 50 age- and gender-matched healthy controls.

Results

Increased reactivity in responders versus non-responders was found in the methotrexate arm against two antigens—DOT1-like histone lysine methyltransferase (p = 0.009) and tropomyosin (p = 0.003)—and in the tocilizumab arm against one antigen—neuro-oncological ventral antigen 2 (p = 0.039). Decreased reactivity was detected against two antigens in the methotrexate arm—G₁ to S phase transition 2 (p = 0.023) and the zinc finger protein ZPR1 (p = 0.021). Reactivity against the identified antigens was not statistically significant in either treatment arm for patients with rheumatoid factor–positive versus–negative or anti-cyclic citrullinated test–positive versus test–negative rheumatoid arthritis (p ≥ 0.06).

Conclusions

Comprehensive profiling of baseline sera revealed several novel immunoglobin G autoantibodies associated with early treatment response to methotrexate and to tocilizumab in disease-modifying anti-rheumatic drug-naive patients with rheumatoid arthritis. These findings could eventually yield clinically relevant predictive markers, if corroborated in different patient cohorts, and may facilitate future benefit in personalised healthcare.

Determination of the Geographical Origin of Asparagus officinalis L. by 1H NMR Spectroscopy

Food authenticity concerning the geographical origin becomes increasingly important for consumers, food industries, and food authorities. In this study, nontargeted ¹H NMR metabolomics combined with machine learning methodologies was applied to successfully distinguish the geographical origin of 237 samples of white asparagus from Germany, Poland, The Netherlands, Spain, Greece, and Peru. Support vector classification of the geographical origin achieved an accuracy of 91.5% for the entire sample set and 87.8% after undersampling the majority class. Important regions of the spectra could be identified and assigned to potential chemical markers. A subset of samples was compared to isotope-ratio mass spectrometry (IRMS), an established method for the determination of origin of white asparagus in Germany. Here, SVM classification led to accuracies of 79.4% for NMR and 70.9% for IRMS. Finally, the classification of asparagus from different German regions was evaluated, and the influence of year and variety was analyzed.

Metabolomic Profiling of Drought-Tolerant and Susceptible Peanut (Arachis hypogaea L.) Genotypes in Response to Drought Stress

Peanut is frequently constrained by extreme environmental conditions such as drought. To reveal the involvement of metabolites, TAG 24 (drought-tolerant) and JL 24 (drought-sensitive) peanut genotypes were investigated under control and 20% PEG 6000-mediated water scarcity conditions at the seedling stage. Samples were analyzed by gas chromatography-mass spectrometry (GC-MS) to identify untargeted metabolites and targeted metabolites, i.e., polyamines and polyphenols by high-performance liquid chromatography (HPLC) and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), respectively. The principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), heat map, and cluster analysis were applied to the metabolomics data obtained by the GC-MS technique to determine the important metabolites for drought tolerance. Among 46 resulting metabolites, pentitol, phytol, xylonic acid, d-xylopyranose, stearic acid, and d-ribose were important drought-responsive metabolites. Agmatine and cadaverine were present in TAG 24 leaves and roots, respectively, during water-deficit conditions and believed to be the potential polyamines for drought tolerance. Polyphenols such as syringic acid and vanillic acid were produced more in the leaves of TAG 24, while catechin production was high in JL 24 during stress conditions. Seven metabolic pathways, namely, galactose metabolism, starch and sucrose metabolism, fructose and mannose metabolism, pentose and glucuronate interconversion, propanoate metabolism, amino sugar and nucleotide sugar metabolism, and biosynthesis of unsaturated fatty acids were significantly affected by water-deficit conditions. This study provides valuable information about the metabolic response of peanut to drought stress and metabolites identified, which encourages further study by transcriptome and proteomics to improve drought tolerance in peanut.

Spoilage potential of Bacillus subtilis in a neutral-pH dairy dessert

The objective of this study was the characterization of the microbiota associated with spoilage of vanilla cream pudding during storage at different temperatures. Commercial cream samples were stored aerobically at 4, 8, 12 and 15 °C for a maximum time period of 40 days. At appropriate time intervals, cream samples were subjected to: (i) microbiological analyses, and (ii) high-performance liquid chromatography (HPLC). Furthermore, the spoilage microbiota was identified through repetitive extragenic palindrome-PCR, while selected isolates were further characterized based on sequencing of the V1-V3 region of the 16S rRNA gene. Microbial growth was observed only during storage of cream samples at 12 and 15 °C, with the applied genotypic analysis demonstrating that Bacillus subtilis subsp. subtilis was the dominant spoilage microorganism of this product. Based on the HPLC analysis results, citric acid and sucrose were the most abundant organic acid and sugar, respectively throughout storage of cream pudding, whereas notable changes mainly included: (i) increase in the concentration of lactic acid and to a lesser extent of formic and acetic acids, and (ii) increase in the concentration of glucose and fructose at the expense of sucrose and lactose. The results of this study should be useful for the dairy industry in detecting and controlling microbiological spoilage in cream pudding and other chilled, neutral-pH dairy desserts.

Identification of Novel Biomarkers and Pathways for Coronary Artery Calcification in Nondiabetic Patients on Hemodialysis Using Metabolomic Profiling

BACKGROUND

A better understanding of the pathophysiology involving coronary artery calcification (CAC) in patients on hemodialysis (HD) will help to develop new therapies. We sought to identify the differences in metabolomics profiles between patients on HD with and without CAC.

METHODS

In this case-control study, nested within a cohort of 568 incident patients on HD, the cases were patients without diabetes with a CAC score >100 (n=51), and controls were patients without diabetes with a CAC score of zero (n=48). We measured 452 serum metabolites in each participant. Metabolites and pathway scores were compared using Mann-Whitney U tests, partial least squares-discriminant analyses, and pathway enrichment analyses.

RESULTS

Compared with controls, cases were older (64±13 versus 42±12 years) and were less likely to be Black (51% versus 94%). We identified three metabolites in bile-acid synthesis (chenodeoxycholic, deoxycholic, and glycolithocholic acids) and one pathway (arginine/proline metabolism). After adjusting for demographics, higher levels of chenodeoxycholic, deoxycholic, and glycolithocholic acids were associated with higher odds of having CAC; comparing the third with the first tertile of each bile acid, the OR was 6.34 (95% CI, 1.12 to 36.06), 6.73 (95% CI, 1.20 to 37.82), and 8.53 (95% CI, 1.50 to 48.49), respectively. These associations were no longer significant after further adjustment for coronary artery disease and medication use. Per 1 unit higher in the first principal component score, arginine/proline metabolism was associated with CAC after adjusting for demographics (OR, 1.83; 95% CI, 1.06 to 3.15), and the association remained significant with additional adjustments for statin use (OR, 1.84; 95% CI, 1.04 to 3.27).

CONCLUSIONS

Among patients on HD without diabetes mellitus, chenodeoxycholic, deoxycholic, and glycolithocholic acids may be potential biomarkers for CAC, and arginine/proline metabolism is a plausible mechanism to study for CAC. These findings need to be confirmed in future studies.

Association between the metabolome and bone mineral density in a Chinese population

BACKGROUND

Osteoporosis is a common metabolic bone disease, which always leads to osteoporotic fractures. Biomarkers of bone mineral density (BMD) are helpful for prevention and early diagnosis of osteoporosis. This study aims to identify metabolomic biomarkers of low BMD.

METHODS

We included 701 participants who had BMD measures by dual-energy X-ray absorptiometry scans and donated fasting plasma samples from three clinical centres as a discovery set and another 278 participants from the fourth centre as an independent replication set. We used a liquid chromatography-mass spectrometry-based metabolomics approach to profile the global metabolites of fasting plasma.

FINDINGS

Among the 265 named metabolites identified in our study, six were associated with low BMD (FDR-adjusted P<0.05) in the discovery set and were successfully validated in the independent replication set. The circulating levels of five metabolites, i.e., inosine, hypoxanthine, PC (O-18:0/22:6), SM (d18:1/21:0) and isoleucyl-proline were associated with decreased odds of low BMD, and PC (16:0/18:3) level was associated with increased odds of low BMD. Per 1-SD increase in a composite metabolite score of these six metabolites was associated with about half decreased odds of low BMD (odds ratio 0.59, 95% confidence interval: 0.52-0.68). Furthermore, introduction of a panel of metabolites selected by elastic net regression to a prediction model of classical risk factors and plasma biomarker of bone resorption substantially improved the prediction performance for low BMD (AUCs: 0.782 vs. 0.698, P=0.002).

INTERPRETATION

Metabolomics profiling may help identify novel biomarkers of low BMD and be helpful for early diagnosis of osteoporosis beyond the current clinical index.

FUNDING

This study was supported by the National Key R&D Program of China [2018YFC2001500 to J.S.], Shanghai Municipal Science and Technology Major Project [2017SHZDZX01], the National Natural Science Foundation of China [Key Program, 91749204 to J.S.], the National Natural Science Foundation of China [General Program, 81771491 to J.S.], the Project of Shanghai Subject Chief Scientist [2017BR011 to J.S.], Grants from the TCM Supported Project [18431902300 to J.S.] from the Science and Technology Commission of Shanghai Municipality, and the National Natural Science Foundation of China [General Program, 81972089 to Z.X.]. Y.Z. was supported by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and the National Natural Science Foundation of China [81973032].

Salivary metabolomics for cancer detection

Introduction: Saliva is an ideal biofluid that can be collected in a noninvasive manner, enabling safe and frequent screening of various diseases. Recent studies have revealed that salivary metabolomics analysis has the potential to detect both oral and systemic cancers. Area covered: We reviewed the technical aspects, as well as applications, of salivary metabolomics for cancer detection. The topics include the effects of preconditioning and the method of sample collection, sample storage, processing, measurement, data analysis, and validation of the results. We also examined the rational relationship between salivary biomarkers and tumors distant from the oral cavity. A strategy to establish standard operating protocols for obtaining reproducible quantification data is also discussed Expert opinion: Salivary metabolomics reflects oral and systematic health status, which potently enables cancer detection. The sensitivity and specificity of each marker and their combinations have been well evaluated, but a validation study is required. Further, the standard operating protocol for each procedure should be established to obtain reproducible data before clinical usage.

Chemometrics-based LC-UV-ESIMS analyses of 50 Salvia species for detecting their antioxidant constituents

Iran is one of the main hubs for sage's diversity in Asia. Generally, prediction and characterization of the chemical structures of the specialized metabolites that have significant role in the bioactivity of a plant remains a significant challenge. In this study, 50 different accessions of 32 Salvia species in 2015 and 2017 were collected and extracted for their phenolics, using 80 % methanol. LC-PDA-ESIMS analyses was coupled with multivariate analysis to identify the specific metabolites in the plant extracts, responsible for the antioxidant activity. Based on the variable importance in projection (VIP) method on the Genetic algorithm combined with PLS (GA-PLS) models, eighteen peaks were detected as multifunctional compounds. The putative phenolic compounds were attributed to apigenin-O-diglucoside, rutin, cynaroside, luteolin-7-O-glucoronide, apigenin-7-O-glucoside, apigenin-7-O-glucuronide, hispidulin-7-O-glucuronide, hispidulin-7-O-glucoside, rosmarinic acid, salvianolic acid B derivative, cirsimaritin, eupatorin, and carnosol. This investigation showed that the Salvia's extract can facilitate an opportunity for rapid detection, isolation and development of antioxidant agents.

Complex Chemical Data Classification and Discrimination Using Locality Preserving Partial Least Squares Discriminant Analysis

Partial least squares discriminant analysis (PLS-DA) is a well-known technique for feature extraction and discriminant analysis in chemometrics. Despite its popularity, it has been observed that PLS-DA does not automatically lead to extraction of relevant features. Feature learning and extraction depends on how well the discriminant subspace is captured. In this paper, discriminant subspace learning of chemical data is discussed from the perspective of PLS-DA and a recent extension of PLS-DA, which is known as the locality preserving partial least squares discriminant analysis (LPPLS-DA). The objective is twofold: (a) to introduce the LPPLS-DA algorithm to the chemometrics community and (b) to demonstrate the superior discrimination capabilities of LPPLS-DA and how it can be a powerful alternative to PLS-DA. Four chemical data sets are used: three spectroscopic data sets and one that contains compositional data. Comparative performances are measured based on discrimination and classification of these data sets. To compare the classification performances, the data samples are projected onto the PLS-DA and LPPLS-DA subspaces, and classification of the projected samples into one of the different groups (classes) is done using the nearest-neighbor classifier. We also compare the two techniques in data visualization (discrimination) task. The ability of LPPLS-DA to group samples from the same class while at the same time maximizing the between-class separation is clearly shown in our results. In comparison with PLS-DA, separation of data in the projected LPPLS-DA subspace is more well defined.

Targeting Methionine Synthase in a Fungal Pathogen Causes a Metabolic Imbalance That Impacts Cell Energetics, Growth, and Virulence

Fungal pathogens are responsible for millions of life-threatening infections on an annual basis worldwide. The current repertoire of antifungal drugs is very limited and, worryingly, resistance has emerged and already become a serious threat to our capacity to treat fungal diseases. The first step to develop new drugs is often to identify molecular targets in the pathogen whose inhibition during infection can prevent its growth. However, the current models are not suitable to validate targets in established infections. Here, we have characterized the promising antifungal target methionine synthase in great detail, using the prominent fungal pathogen Aspergillus fumigatus as a model. We have uncovered the underlying reason for its essentiality and confirmed its druggability. Furthermore, we have optimized the use of a genetic system to show a beneficial effect of targeting methionine synthase in established infections. Therefore, we believe that antifungal drugs to target methionine synthase should be pursued and additionally, we provide a model that permits gaining information about the validity of antifungal targets in established infections.

There is an urgent need to develop novel antifungals to tackle the threat fungal pathogens pose to human health. Here, we have performed a comprehensive characterization and validation of the promising target methionine synthase (MetH). We show that in Aspergillus fumigatus the absence of this enzymatic activity triggers a metabolic imbalance that causes a reduction in intracellular ATP, which prevents fungal growth even in the presence of methionine. Interestingly, growth can be recovered in the presence of certain metabolites, which shows that metH is a conditionally essential gene and consequently should be targeted in established infections for a more comprehensive validation. Accordingly, we have validated the use of the tetOFF genetic model in fungal research and improved its performance in vivo to achieve initial validation of targets in models of established infection. We show that repression of metH in growing hyphae halts growth in vitro, which translates into a beneficial effect when targeting established infections using this model in vivo. Finally, a structure-based virtual screening of methionine synthases reveals key differences between the human and fungal structures and unravels features in the fungal enzyme that can guide the design of novel specific inhibitors. Therefore, methionine synthase is a valuable target for the development of new antifungals.

Effect of Baseline Characteristics and Tumor Burden on Vaspin Expression and Progressive Disease in Operable Colorectal Cancer

Colorectal cancer is a highly heterogeneous malignancy in the Asian population, and it is considered an important prognostic factor for baseline characteristics, tumor burden, and tumor markers. This study investigated the effect of baseline characteristics and tumor burden on tumor marker expression and progressive disease in colorectal cancer by using partial least squares variance-based path modeling (PLS-PM). PLS-PM can be used to evaluate the complex relationship between prognostic variables and progressive disease status with a small sample of measurements and structural models. A total of 89 tissue samples of colorectal cancer were analyzed. Our results suggested that the expression of visceral adipose tissue-derived serpin (vaspin) is a potential indicator of colorectal cancer progression and may be affected by baseline characteristics such as age, sex, body mass index, and diabetes mellitus. Moreover, according to the characteristics of tumor burden, the expression of vaspin was generally higher in each progressive disease patient. The overall findings suggest that vaspin is a potential indicator of the progressive disease and may be affected by the baseline characteristics of patients.

Metabolomics Based Comparison on the Biomarkers between Panax Notoginseng and its Counterfeit Gynura Segetum in Rats

Background:

Because of the similar appearance of Gynura segetum and panax notoginseng, the patients often mistakenly use Gynura segetum as Panax notoginseng, which causes serious liver damage. There is no comparative study on the metabolism of Gynura segetum and Panax notoginseng in the literature. This study was conducted to compare the difference between Panax notoginseng and its counterfeit Gynura segetum by using metabolomics method.

Methods:

In this paper, an ultra performance liquid chromatography coupled to quadrupole time-offlight mass spectrometric(UPLC-Q/TOF/MS) were used to detect the type of endogenous metabolites in urine and plasma of three groups (normal group, ethanol extract of panax notoginseng, decoction of Gynura segetum respectively, and different multivariate statistical analysis methods were established.

Results:

In this experiment, main urine biomarkers were L-glutamate, L-methionine, cytidine, and Ltyrosine in the Panax notoginseng group, which are phytosphingosine, creatine and sphinganine in the Gynura segetum group. The plasma biomarkers identified in the Panax notoginseng group were arachidonic acid, L-tyrosine, linoleic acid, alpha-linolenoyl ethanolamide and lysoPC (15:0), and in the Gynura segetum group are L-arginine, L-valine, arachidonic acid and LysoPC(18:2(9Z,12Z)).

Conclusion:

There are significant difference between Panax notoginseng and Gynura segetum in biomarkers from the perspective of metabolomics in the body.

Deep learning meets metabolomics: a methodological perspective

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

Differentiation of black and white pitch (Burseraceae) oleoresins: A mass spectrometry-based chemoethnotaxonomic study

ETHNOPHARMACOLOGICAL RELEVANCE

In the quilombola communities of the municipality of Oriximiná (Pará State, Brazil), Protium spp. (Burseraceae) oleoresins are distinguished in black and white pitch. White pitch oleoresins may be superior to black pitch in terms of quality, but the criteria used for their differentiation are fairly subjective.

AIM OF THE STUDY

This study was designed to provide a scientific rationale for the traditional differentiation of black and white pitch oleoresins based on their non-volatile fraction.

MATERIALS AND METHODS

Black and white pitch oleoresin samples collected in quilombola territories in Oriximiná were analysed by GC-EI-MS and UPLC-APCI-MS. The feasibility of EI and APCI mass spectrometry-based pattern recognition methods PLS-DA and Random Forest Analysis (RFA) for black and white pitch oleoresins differentiation was demonstrated.

RESULTS

The UPLC-APCI-MS method allowed the separation of 43 triterpenoids. Assessment of the triterpenoid fingerprints by GC-EI-MS led to the tentative identification of ursa-9(11),12-dien-3-ol as a potential marker for black pitch oleoresins. PLS-DA and RFA applied to the APCI-MS and EI-MS data gave good models for black and white pitch oleoresins classification. The most important ions for the classifications of black pitch oleoresins by APCI-MS/PLS-DA and APCI-MS/RFA likely represented triterpenoid acids.

CONCLUSIONS

The triterpenoid pattern differs between black and white pitch oleoresins. The characteristic presence of ursa-9(11),12-dien-3-ol and triterpenoids acids in black pitch oleoresins, along with other field observations, suggest that black pitch oleoresins are actually aged white pitch oleoresins.

1H NMR-based metabonomics for infertility diagnosis in men with varicocele

PURPOSE

"Omics" techniques have been used to understand and to identify biomarkers of male infertility. We report on the first metabonomics models created to diagnose varicocele and infertility among men with varicocele.

METHODS

We recruited 35 infertile men with varicocele (VI group), 21 fertile men with varicocele (VF group) and 24 fertile men without varicocele (C group). All men underwent standard semen analysis, scrotal duplex ultrasonography, and sexual hormone level measurement. Hydrogen-1 nuclear magnetic resonance (1H NMR) spectra of seminal plasma were used to create metabonomics models to discriminate between men with and without varicocele, and between fertile and infertile men with varicocele.

RESULTS

Using the statistical formalisms partial least square discriminants analysis and genetic algorithm-based linear discriminant analysis (GA-LDA), we created two models that discriminated the three groups from each other with accuracy of 92.17%. We also created metabonomics models using orthogonal partial least square discriminants analysis and GA-LDA that discriminated VF group from VI group, with an accuracy of 94.64% and 100% respectively. We identified 19 metabolites that were important in group segregation: caprate, 2-hydroxy-3-methylvalerate, leucine, valine, 3-hydroxybutyrate, lactate, alanine, 4-aminobutyrate, isoleucine, citrate, methanol, glucose, glycosides, glycerol-3-phosphocoline, n-acetyltyrosine, glutamine, tyrosine, arginine, and uridine.

CONCLUSIONS

1HNMR-based metabonomics of seminal plasma can be used to create metabonomics models to discriminate between men with varicocele from those without varicocele, and between fertile men with varicocele from those infertile with varicocele. Furthermore, the most important metabolites for group segregation are involved in the oxidative stress caused by varicocele.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Microbial Diversity of Fermented Greek Table Olives of Halkidiki and Konservolia Varieties from Different Regions as Revealed by Metagenomic Analysis

Current information from conventional microbiological methods on the microbial diversity of table olives is insufficient. Next-generation sequencing (NGS) technologies allow comprehensive analysis of their microbial community, providing microbial identity of table olive varieties and their designation of origin. The purpose of this study was to evaluate the bacterial and yeast diversity of fermented olives of two main Greek varieties collected from different regions-green olives, cv. Halkidiki, from Kavala and Halkidiki and black olives, cv. Konservolia, from Magnesia and Fthiotida-via conventional microbiological methods and NGS. Total viable counts (TVC), lactic acid bacteria (LAB), yeast and molds, and Enterobacteriaceae were enumerated. Microbial genomic DNA was directly extracted from the olives' surface and subjected to NGS for the identification of bacteria and yeast communities. Lactobacillaceae was the most abundant family in all samples. In relation to yeast diversity, Phaffomycetaceae was the most abundant yeast family in Konservolia olives from the Magnesia region, while Pichiaceae dominated the yeast microbiota in Konservolia olives from Fthiotida and in Halkidiki olives from both regions. Further analysis of the data employing multivariate analysis allowed for the first time the discrimination of cv. Konservolia and cv. Halkidiki table olives according to their geographical origin.

Molecular fluorescence spectroscopy with multi-way analysis techniques detects spectral variations distinguishing uninfected serum versus dengue or chikungunya viral infected samples

Significant attempts are being made worldwide in an attempt to develop a tool that, with a simple analysis, is capable of distinguishing between different arboviruses. Herein, we employ molecular fluorescence spectroscopy as a sensitive and specific rapid tool, with simple methodology response, capable of identifying spectral variations between serum samples with or without the dengue or chikungunya viruses. Towards this, excitation emission matrices (EEM) of clinical samples from patients with dengue or chikungunya, in addition to uninfected controls, were separated into a training or test set and analysed using multi-way classification models such as n-PLSDA, PARAFAC-LDA and PARAFAC-QDA. Results were evaluated based on calculations of accuracy, sensitivity, specificity and F score; the most efficient model was identified to be PARAFAC-QDA, whereby 100% was obtained for all figures of merit. QDA was able to predict all samples in the test set based on the scores present in the factors selected by PARAFAC. The loadings obtained by PARAFAC can be used in future studies to prove the direct or indirect relationship of spectral changes caused by the presence of these viruses. This study demonstrates that molecular fluorescence spectroscopy has a greater capacity to detect spectral variations related to the presence of such viruses when compared to more conventional techniques.

The Sample, the Spectra and the Maths-The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

The last two decades have witnessed an increasing interest in the use of the so-called rapid analytical methods or high throughput techniques. Most of these applications reported the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman) in a wide range of samples (e.g., food ingredients and natural products). In these applications, the analytical method is integrated with a wide range of multivariate data analysis (MVA) techniques (e.g., pattern recognition, modelling techniques, calibration, etc.) to develop the target application. The availability of modern and inexpensive instrumentation together with the access to easy to use software is determining a steady growth in the number of uses of these technologies. This paper underlines and briefly discusses the three critical pillars-the sample (e.g., sampling, variability, etc.), the spectra and the mathematics (e.g., algorithms, pre-processing, data interpretation, etc.)-that support the development and implementation of vibrational spectroscopy applications.

Changes in Amino Acid and Acylcarnitine Plasma Profiles for Distinguishing Patients with Multiple Sclerosis from Healthy Controls

McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n = 16) and control (n = 12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.

Exploring the protective effects of schizandrol A in acute myocardial ischemia mice by comprehensive metabolomics profiling integrated with molecular mechanism studies

Schizandrol A (SA) is an bioactive component isolated from the Schisandra chinensis (Turcz.) Baill., which has been used as a remedy to prevent oxidative injury. However, whether the cardioprotective effect of SA is associated with regulating endogenous metabolites remains unclear, thus we performed comprehensive metabolomics profiling in acute myocardial ischemia (AMI) mice following SA treatment. AMI was induced in ICR mice by coronary artery ligation, then SA (6 mg·kg-1·d-1, ip) was administered. SA treatment significantly decreased the infarct size, preserved the cardiac function, and improved the biochemical indicators and cardiac pathological alterations. Moreover, SA (10, 100 M) significantly decreased the apoptotic index in OGD-treated H8c2 cardiomycytes in vitro. By using HPLC-Q-TOF/MS, we conducted metabonomics analysis to screen the significantly changed endogenous metabolites and construct the network in both serum and urine. The results revealed that SA regulated the pathways of glycine, serine and threonine metabolism, lysine biosynthesis, pyrimidine metabolism, arginine and proline metabolism, cysteine and methionine metabolism, valine, leucine and isoleucine biosynthesis under the pathological conditions of AMI. Furthermore, we selected the regulatory enzymes related to heart disease, including ecto-5'-nucleotidase (NT5E), guanidinoacetate N-methyltransferase (GAMT), platelet-derived endothelial cell growth factor (PD-ECGF) and methionine synthase (MTR), for validation. In addition, SA was found to facilitate PI3K/Akt activation and inhibit the expression of NOX2 in AMI mice and OGD-treated H9c2 cells. In conclusion, we have elucidated SA-regulated endogenous metabolic pathways and constructed a regulatory metabolic network map. Furthermore, we have validated the new potential therapeutic targets and underlying molecular mechanisms of SA against AMI, which might provide a reference for its future application in cardiovascular diseases.