Novel technologies for metabolomics: More for less

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Metabolomic discoveries for early diagnosis and traditional Chinese medicine efficacy in ischemic stroke

Ischemic stroke (IS), a devastating cerebrovascular accident, presents with high mortality and morbidity. Following IS onset, a cascade of pathological changes, including excitotoxicity, inflammatory damage, and blood-brain barrier disruption, significantly impacts prognosis. However, current clinical practices struggle with early diagnosis and identifying these alterations. Metabolomics, a powerful tool in systems biology, offers a promising avenue for uncovering early diagnostic biomarkers for IS. By analyzing dynamic metabolic profiles, metabolomics can not only aid in identifying early IS biomarkers but also evaluate Traditional Chinese Medicine (TCM) efficacy and explore its mechanisms of action in IS treatment. Animal studies demonstrate that TCM interventions modulate specific metabolite levels, potentially reflecting their therapeutic effects. Identifying relevant metabolites in cerebral ischemia patients holds immense potential for early diagnosis and improved outcomes. This review focuses on recent metabolomic discoveries of potential early diagnostic biomarkers for IS. We explore variations in metabolites observed across different ages, genders, disease severity, and stages. Additionally, the review examines how specific TCM extracts influence IS development through metabolic changes, potentially revealing their mechanisms of action. Finally, we emphasize the importance of integrating metabolomics with other omics approaches for a comprehensive understanding of IS pathophysiology and TCM efficacy, paving the way for precision medicine in IS management.

Structure-Assisted Boronic Acid Implanted Mesoporous Metal-Organic Frameworks for Specific Extraction of cis-Diol Molecules

cis-Diol-containing molecules, an essential type of compounds in living organisms, have attracted intensive research interest from various fields. The analysis of cis-diol-containing molecules is still suffering from some drawbacks, including low abundance and abundant interference. Metal-organic frameworks (MOFs) have proven to be an ideal sorbent for sample preparation. However, most of the reported MOFs are mainly restricted to a microporous regime (pore size <2 nm), which greatly limits the application. Herein, a facile strategy is established to construction of boronate affinity MOFs via the postsynthetic ligand-exchange process. Owing to the fact that the ligand-exchange process was assisted by the structural integrity of the primitive metal-organic framework and the great compatibility of click chemistry, the obtained EPBA-PCN-333(Fe) is able to realize the maximum maintaining the porosity and crystallinity of the parent material. Several intriguing features of EPBA-PCN-333(Fe) (e.g., excellent selectivity, efficient diffusion, good accessibility, and size exclusion effect) are experimentally demonstrated via a series of cis-diol-containing molecules with different molecular sizes (small molecules, glycopeptides, and glycoproteins). The binding performance of EPBA-PCN-333(Fe) is evaluated by employing catechol as the test molecule (binding capacity: 0.25 mmol/g, LOD: 200 ng/mL). Finally, the real-world applications of EPBA-PCN-333(Fe) were demonstrated by the detection of nucleosides of human urine samples.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

A host-microbial metabolite interaction gut-on-a-chip model of the adult human intestine demonstrates beneficial effects upon inulin treatment of gut microbiome

Background: The gut and its microbiome have a major impact on many aspects of health and are therefore also an attractive target for drug- or food-based therapies. Here, we report on the added value of combining a microbiome screening model, the i-screen, with fresh intestinal tissue explants in a microfluidic gut-on-a-chip model, the Intestinal Explant Barrier Chip (IEBC). Methods: Adult human gut microbiome (fecal pool of 6 healthy donors) was cultured anaerobically in the i-screen platform for 24 h, without and with exposure to 4 mg/mL inulin. The i-screen cell-free culture supernatant was subsequently applied to the luminal side of adult human colon tissue explants (n = 3 donors), fixed in the IEBC, for 24 h and effects were evaluated. Results: The supplementation of the media with inulin promoted the growth of Anaerostipes, Bifidobacterium, Blautia, and Collinsella in the in vitro i-screen, and triggered an elevated production of butyrate by the microbiota. Human colon tissue exposed to inulin-treated i-screen cell-free culture supernatant or control i-screen cell-free culture supernatant with added short-chain fatty acids (SCFAs) showed improved tissue barrier integrity measured by a 28.2%-34.2% reduction in FITC-dextran 4000 (FD4) leakage and 1.3 times lower transport of antipyrine. Furthermore, the release of pro-inflammatory cytokines IL-1β, IL-6, IL-8, and TNF-α was reduced under these circumstances. Gene expression profiles confirmed these findings, but showed more profound effects for inulin-treated supernatant compared to SCFA-supplemented supernatant. Conclusion: The combination of i-screen and IEBC facilitates the study of complex intestinal processes such as host-microbial metabolite interaction and gut health.

Microfluidics enabled multi-omics triple-shot mass spectrometry for cell-based therapies

In recent years, cell-based therapies have transformed medical treatment. These therapies present a multitude of challenges associated with identifying the mechanism of action, developing accurate safety and potency assays, and achieving low-cost product manufacturing at scale. The complexity of the problem can be attributed to the intricate composition of the therapeutic products: living cells with complex biochemical compositions. Identifying and measuring critical quality attributes (CQAs) that impact therapy success is crucial for both the therapy development and its manufacturing. Unfortunately, current analytical methods and tools for identifying and measuring CQAs are limited in both scope and speed. This Perspective explores the potential for microfluidic-enabled mass spectrometry (MS) systems to comprehensively characterize CQAs for cell-based therapies, focusing on secretome, intracellular metabolome, and surfaceome biomarkers. Powerful microfluidic sampling and processing platforms have been recently presented for the secretome and intracellular metabolome, which could be implemented with MS for fast, locally sampled screening of the cell culture. However, surfaceome analysis remains limited by the lack of rapid isolation and enrichment methods. Developing innovative microfluidic approaches for surface marker analysis and integrating them with secretome and metabolome measurements using a common analytical platform hold the promise of enhancing our understanding of CQAs across all "omes," potentially revolutionizing cell-based therapy development and manufacturing for improved efficacy and patient accessibility.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

Polarity-extended liquid chromatography-triple quadrupole mass spectrometry for simultaneous hydrophilic and hydrophobic metabolite analysis

Although various metabolomic methods have been reported in recent years, simultaneous detection of hydrophilic and hydrophobic metabolites in a single analysis remains a technical challenge. In this study, based on the combination of hydrophilic interaction liquid chromatography (HILIC) and reversed phase liquid chromatography (RPLC), an online two-dimensional liquid chromatography/triple quadrupole mass spectrometry method (2D-LC/TQMS) was developed for the simultaneous analysis of hydrophilic and hydrophobic metabolites of various biological samples. The method can measure 417 biologically important metabolites (e.g., amino acids and peptides, pyrimidines, purines, monosaccharides, fatty acids and conjugates, organic dicarboxylic acids, and others) with logP values ranging from -10.3 to 21.9. The metabolites are involved in a variety of metabolic pathways (e.g., purine metabolism, pyrimidine metabolism, tyrosine metabolism, galactose metabolism, gluconeogenesis, and TCA cycle). The developed method has good intra- and inter-day reproducibility (RSD of retention time <2%, RSD of peak area <30%), good linearity (R2 > 0.9) and wide linear range (from 0.0025 μg/mL to 5 μg/mL). The applicability of the method was tested using different biological samples (i.e., plasma, serum, urine, fecal, seminal plasma and liver) and it was found that 208 (out of 417) identical metabolites were detected in all biological samples. Furthermore, the metabolomic method was applied to a case/control study of urinary of bladder cancer. Thirty differential metabolites were identified that were involved in carbohydrate and amino acid metabolism.

Automatization of metabolite extraction for high-throughput metabolomics: case study on transgenic isoprene-emitting birch

Metabolomics studies are becoming increasingly common for understanding how plant metabolism responds to changes in environmental conditions, genetic manipulations and treatments. Despite the recent advances in metabolomics workflow, the sample preparation process still limits the high-throughput analysis in large-scale studies. Here, we present a highly flexible robotic system that integrates liquid handling, sonication, centrifugation, solvent evaporation and sample transfer processed in 96-well plates to automatize the metabolite extraction from leaf samples. We transferred an established manual extraction protocol performed to a robotic system, and with this, we show the optimization steps required to improve reproducibility and obtain comparable results in terms of extraction efficiency and accuracy. We then tested the robotic system to analyze the metabolomes of wild-type and four transgenic silver birch (Betula pendula Roth) lines under unstressed conditions. Birch trees were engineered to overexpress the poplar (Populus × canescens) isoprene synthase and to emit various amounts of isoprene. By fitting the different isoprene emission capacities of the transgenic trees with their leaf metabolomes, we observed an isoprene-dependent upregulation of some flavonoids and other secondary metabolites as well as carbohydrates, amino acid and lipid metabolites. By contrast, the disaccharide sucrose was found to be strongly negatively correlated to isoprene emission. The presented study illustrates the power of integrating robotics to increase the sample throughput, reduce human errors and labor time, and to ensure a fully controlled, monitored and standardized sample preparation procedure. Due to its modular and flexible structure, the robotic system can be easily adapted to other extraction protocols for the analysis of various tissues or plant species to achieve high-throughput metabolomics in plant research.

Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids

Small molecules derived from gut microbiota have been increasingly investigated to better understand the functional roles of the human gut microbiome. Microbial metabolites of aromatic amino acids (AAA) have been linked to many diseases, such as metabolic disorders, chronic kidney diseases, inflammatory bowel disease, diabetes, and cancer. Important microbial AAA metabolites are often discovered via global metabolite profiling of biological specimens collected from humans or animal models. Subsequent metabolite identity confirmation and absolute quantification using targeted analysis enable comparisons across different studies, which can lead to the establishment of threshold concentrations of potential metabolite biomarkers. Owing to their excellent selectivity and sensitivity, hyphenated mass spectrometry (MS) techniques are often employed to identify and quantify AAA metabolites in various biological matrices. Here, we summarize the developments over the past five years in MS-based methodology for analyzing gut microbiota-derived AAA. Sample preparation, method validation, analytical performance, and statistical methods for correlation analysis are discussed, along with future perspectives.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Metabolomics in archaeological science: A review of their advances and present requirements

Metabolomics, the study of metabolites (small molecules of <1500 daltons), has been posited as a potential tool to explore the past in a comparable manner to other omics, e.g., genomics or proteomics. Archaeologists have used metabolomic approaches for a decade or so, mainly applied to organic residues adhering to archaeological materials. Because of advances in sensitivity, resolution, and the increased availability of different analytical platforms, combined with the low mass/volume required for analysis, metabolomics is now becoming a more feasible choice in the archaeological sector. Additional approaches, as presented by our group, show the versatility of metabolomics as a source of knowledge about the human past when using human osteoarchaeological remains. There is tremendous potential for metabolomics within archaeology, but further efforts are required to position it as a routine technique.

Recent advances in analysis of capsaicin and its effects on metabolic pathways by mass spectrometry

Capsaicin is the main food active component in Capsicum that has gained considerable attention due to its broad biological activities, including antioxidation, anti-inflammation, anti-tumor, weight regulation, cardiac protection, anti-calculi, and diurnal-circadian regulation. The potent biological effects of capsaicin are intimately related to metabolic pathways such as lipid metabolism, energy metabolism, and antioxidant stress. Mass spectrometry (MS) has emerged as an effective tool for deciphering the mechanisms underlying capsaicin metabolism and its biological impacts. However, it remains challenging to accurately identify and quantify capsaicin and its self-metabolites in complex food and biological samples, and to integrate multi-omics data generated from MS. In this work, we summarized recent advances in the detection of capsaicin and its self-metabolites using MS and discussed the relevant MS-based studies of metabolic pathways. Furthermore, we discussed current issues and future directions in this field. In-depth studies of capsaicin metabolism and its physiological functions based on MS is anticipated to yield new insights and methods for preventing and treating a wide range of diseases.

Blood microsampling technologies: Innovations and applications in 2022

With the development of highly sensitive bioanalytical techniques, the volume of samples necessary for accurate analysis has reduced. Microsampling, the process of obtaining small amounts of blood, has thus gained popularity as it offers minimal-invasiveness, reduced logistical costs and biohazard risks while simultaneously showing increased sample stability and a potential for the decentralization of the approach and at-home self-sampling. Although the benefits of microsampling have been recognised, its adoption in clinical practice has been slow. Several microsampling technologies and devices are currently available and employed in research studies for various biomedical applications. This review provides an overview of the state-of-the-art in microsampling technology with a focus on the latest developments and advancements in the field of microsampling. Research published in the year 2022, including studies (i) developing strategies for the quantitation of analytes in microsamples and (ii) bridging and comparing the interchangeability between matrices and choice of technology for a given application, is reviewed to assess the advantages, challenges and limitations of the current state of microsampling. Successful implementation of microsampling in routine clinical care requires continued efforts for standardization and harmonization. Microsampling has been shown to facilitate data-rich studies and a patient-centric approach to healthcare and is foreseen to play a central role in the future digital revolution of healthcare through continuous monitoring to improve the quality of life.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

Plasma 1H-NMR metabolic and amino acid profiles of newborn piglets from two lines divergently selected for residual feed intake

Together with environmental factors, physiological maturity at birth is a major determinant for neonatal survival and postnatal development in mammalian species. Maturity at birth is the outcome of complex mechanisms of intra-uterine development and maturation during the end of gestation. In pig production, piglet preweaning mortality averages 20% of the litter and thus, maturity is a major welfare and economic concern. Here, we used both targeted and untargeted metabolomic approaches to provide a deeper understanding of the maturity in a model of lines of pigs divergently selected on residual feed intake (RFI), previously shown to have contrasted signs of maturity at birth. Analyses were conducted on plasma metabolome of piglets at birth and integrated with other phenotypic characteristics associated to maturity. We confirmed proline and myo-inositol, previously described for their association with delayed growth, as potential markers of maturity. Urea cycle and energy metabolism were found more regulated in piglets from high and low RFI lines, respectively, suggesting a better thermoregulation ability for the low RFI (with higher feed efficiency) piglets.

1H nuclear magnetic resonance-based metabolomics study of serum and pectoralis major for different commercial chicken breeds

This study aimed to characterize the metabolic composition of four types of commercially available chicken breeds [village chicken, colored broiler (Hubbard), broiler (Cobb), and spent layers (Dekalb)] by ¹H NMR coupling and discriminate them using multivariate analysis. Five chickens were collected for each chicken breed based on the marketing age from the respective commercial farms. The orthogonal partial least squares discriminant analysis (OPLS-DA) results showed an obvious separation of local village chickens from the other breeds based on the metabolites present in their serum and meat (pectoralis major). The cumulative values of Q ², R ² X, and R ² Y of the OPLS-DA model for chicken serum were 0.722, 0.877, and 0.841. For the pectoralis major muscle, the cumulative values of Q ², R ² X, and R ² Y of the OPLS-DA model were reported as 0.684, 0.781, and 0.786, respectively. The quality of both OPLS-DA models was accepted by the cumulative values of Q ² ≥ 0.5 and R ² ≥ 0.65. The ¹H NMR result with multivariate analysis has successfully distinguished local village chicken from the other three commercial chicken breeds based on serum and pectoralis major muscle. Nonetheless, colored broiler (Hubbard) was not distinguished from broiler (Cobb) and spent layers (Dekalb) in serum and pectoralis major, respectively. The OPLS-DA assessment in this study identified 19 and 15 potential metabolites for discriminating different chicken breeds in serum and pectoralis major muscle, respectively. Some of the prominent metabolites identified include amino acids (betaine, glycine, glutamine, guanidoacetate, phenylalanine, and valine), nucleotides (IMP and NAD+), organic acids (lactate, malate, and succinate), peptide (anserine), and sugar alcohol (myo-inositol).

Metabolomics of Osteoporosis in Humans: A Systematic Review

PURPOSE OF REVIEW

To systematically review recent studies investigating the association between metabolites and bone mineral density (BMD) in humans.

METHODS

Using predefined keywords, we searched literature published from Jan 1, 2019 to Feb 20, 2022 in PubMed, Web of Science, Embase, and Scopus. Studies that met the predefined exclusion criteria were excluded. Among the included studies, we identified metabolites that were reported to be associated with BMD by at least three independent studies.

RECENT FINDINGS

A total of 170 studies were retrieved from the databases. After excluding studies that did not meet our predefined inclusion criteria, 16 articles were used in this review. More than 400 unique metabolites in blood were shown to be significantly associated with BMD. Of these, three metabolites were reported by ≥ 3 studies, namely valine, leucine and glycine. Glycine was consistently shown to be inversely associated with BMD, while valine was consistently observed to be positively associated with BMD. Inconsistent associations with BMD was observed for leucine. With advances in metabolomics technology, an increasing number of metabolites associated with BMD have been identified. Two of these metabolites, namely valine and glycine, were consistently associated with BMD, highlighting their potential for clinical application in osteoporosis. International collaboration with a larger population to conduct clinical studies on these metabolites is warranted. On the other hand, given that metabolomics could be affected by genetics and environmental factors, whether the inconsistent association of the metabolites with BMD is due to the interaction between metabolites and genes and/or lifestyle warrants further study.

Metabolite fingerprinting of the ripening process in Pixian douban using a feature-based molecular network and metabolomics analysis

The unique flavor of Pixian douban (PXDB) is widely acknowledged to be associated with its maturation process. However, there is limited knowledge about the non-volatile metabolites that contribute to this flavor. To bridge this gap, this study employed a metabolomics approach and a feature-based molecular network (FBMN) analysis to investigate the non-volatile metabolite fingerprints of PXDB during its two-year maturation process. Specifically, the FBMN tool was utilized to annotate the flavonoid, amide derivatives, and lipid components of PXDB for the first time. Subsequently, the MolNetEnhancer tool was employed to complement the FBMN annotation and identify eight substructural components. Finally, metabolomics analysis was carried out to identify 45 key metabolites involved in flavor formation across 10 major metabolic pathways (p < 0.05). Overall, the findings of this study have significantly expanded our understanding of the non-volatile metabolite fingerprinting and flavor formation mechanisms.

Immunometabolomics provides a new perspective for studying systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic multi-organ autoimmune disease characterized by clinical heterogeneity, unpredictable progression, and flare ups. Due to the heterogeneous nature of lupus, it has been challenging to identify sensitive and specific biomarkers for its diagnosis and monitoring. Despite the fact that the mechanism of SLE remains unknown, impressive progress has been made over the last decade towards understanding how different immune cells contribute to its pathogenesis. Research suggests that cellular metabolic programs could affect the immune response by regulating the activation, proliferation, and differentiation of innate and adaptive immune cells. Many studies have shown that the dysregulation of the immune system is associated with changes to metabolite profiles. The study of metabolite profiling may provide a means for mechanism exploration and novel biomarker discovery for disease diagnostic, classification, and monitoring. Here we review the latest advancements in understanding the role of immunometabolism in SLE, as well as the systemic metabolite profiling of this disease along with possible clinical application.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review

Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.

The Biological Hierarchy, Time, and Temporal 'Omics in Evolutionary Biology: A Perspective

Sequencing data-genomics, transcriptomics, epigenomics, proteomics, and metabolomics-have revolutionized biological research, enabling a more detailed study of processes, ranging from subcellular to evolutionary, that drive biological organization. These processes, collectively, are responsible for generating patterns of phenotypic variation and can operate over dramatically different timescales (milliseconds to billions of years). While researchers often study phenotypic variation at specific levels of biological organization to isolate processes operating at that particular scale, the varying types of sequence data, or 'omics, can also provide complementary inferences to link molecular and phenotypic variation to produce an integrated view of evolutionary biology, ranging from molecular pathways to speciation. We briefly describe how 'omics has been used across biological levels and then demonstrate the utility of integrating different types of sequencing data across multiple biological levels within the same study to better understand biological phenomena. However, single-time-point studies cannot evaluate the temporal dynamics of these biological processes. Therefore, we put forward temporal 'omics as a framework that can better enable researchers to study the temporal dynamics of target processes. Temporal 'omics is not infallible, as the temporal sampling regime directly impacts inferential ability. Thus, we also discuss the role the temporal sampling regime plays in deriving inferences about the environmental conditions driving biological processes and provide examples that demonstrate the impact of the sampling regime on biological inference. Finally, we forecast the future of temporal 'omics by highlighting current methodological advancements that will enable temporal 'omics to be extended across species and timescales. We extend this discussion to using temporal multi-omics to integrate across the biological hierarchy to evaluate and link the temporal dynamics of processes that generate phenotypic variation.

MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

Therapeutic Potentials of Immunometabolomic Modulations Induced by Tuberculosis Vaccination

Metabolomics is emerging as a promising tool to understand the effect of immunometabolism for the development of novel host-directed alternative therapies. Immunometabolism can modulate both innate and adaptive immunity in response to pathogens and vaccinations. For instance, infections can affect lipid and amino acid metabolism while vaccines can trigger bile acid and carbohydrate pathways. Metabolomics as a vaccinomics tool, can provide a broader picture of vaccine-induced biochemical changes and pave a path to potentiate the vaccine efficacy. Its integration with other systems biology tools or treatment modes can enhance the cure, response rate, and control over the emergence of drug-resistant strains. Mycobacterium tuberculosis (Mtb) infection can remodel the host metabolism for its survival, while there are many biochemical pathways that the host adjusts to combat the infection. Similarly, the anti-TB vaccine, Bacillus Calmette-Guerin (BCG), was also found to affect the host metabolic pathways thus modulating immune responses. In this review, we highlight the metabolomic schema of the anti-TB vaccine and its therapeutic applications. Rewiring of immune metabolism upon BCG vaccination induces different signaling pathways which lead to epigenetic modifications underlying trained immunity. Metabolic pathways such as glycolysis, central carbon metabolism, and cholesterol synthesis play an important role in these aspects of immunity. Trained immunity and its applications are increasing day by day and it can be used to develop the next generation of vaccines to treat various other infections and orphan diseases. Our goal is to provide fresh insight into this direction and connect various dots to develop a conceptual framework.

High-throughput interspecies profiling of acidic plant hormones using miniaturised sample processing

BACKGROUND

Acidic phytohormones are small molecules controlling many physiological functions in plants. A comprehensive picture of their profiles including the active forms, precursors and metabolites provides an important insight into ongoing physiological processes and is essential for many biological studies performed on plants.

RESULTS

A high-throughput sample preparation method for liquid chromatography-tandem mass spectrometry determination of 25 acidic phytohormones classed as auxins, jasmonates, abscisates and salicylic acid was optimised. The method uses a small amount of plant tissue (less than 10 mg fresh weight) and acidic extraction in 1 mol/L formic acid in 10% aqueous methanol followed by miniaturised purification on reverse phase sorbent accommodated in pipette tips organised in a 3D printed 96-place interface, capable of processing 192 samples in one run. The method was evaluated in terms of process efficiency, recovery and matrix effects as well as establishing validation parameters such as accuracy and precision. The applicability of the method in relation to the amounts of sample collected from distantly related plant species was evaluated and the results for phytohormone profiles are discussed in the context of literature reports.

CONCLUSION

The method developed enables high-throughput profiling of acidic phytohormones with minute amounts of plant material, and it is suitable for large scale interspecies studies.

Bile Acids: Physiological Activity and Perspectives of Using in Clinical and Laboratory Diagnostics

Bile acids play a significant role in the digestion of nutrients. In addition, bile acids perform a signaling function through their blood-circulating fraction. They regulate the activity of nuclear and membrane receptors, located in many tissues. The gut microbiota is an important factor influencing the effects of bile acids via enzymatic modification. Depending on the rate of healthy and pathogenic microbiota, a number of bile acids may support lipid and glucose homeostasis as well as shift to more toxic compounds participating in many pathological conditions. Thus, bile acids can be possible biomarkers of human pathology. However, the chemical structure of bile acids is similar and their analysis requires sensitive and specific methods of analysis. In this review, we provide information on the chemical structure and the biosynthesis of bile acids, their regulation, and their physiological role. In addition, the review describes the involvement of bile acids in various diseases of the digestive system, the approaches and challenges in the analysis of bile acids, and the prospects of their use in omics technologies.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

Bioactive Compounds from Marine Sponges and Algae: Effects on Cancer Cell Metabolome and Chemical Structures

Metabolomics represent the set of small organic molecules generally called metabolites, which are located within cells, tissues or organisms. This new “omic” technology, together with other similar technologies (genomics, transcriptomics and proteomics) is becoming a widely used tool in cancer research, aiming at the understanding of global biology systems in their physiologic or altered conditions. Cancer is among the most alarming human diseases and it causes a considerable number of deaths each year. Cancer research is one of the most important fields in life sciences. In fact, several scientific advances have been made in recent years, aiming to illuminate the metabolism of cancer cells, which is different from that of healthy cells, as suggested by Otto Warburg in the 1950s. Studies on sponges and algae revealed that these organisms are the main sources of the marine bioactive compounds involved in drug discovery for cancer treatment and prevention. In this review, we analyzed these two promising groups of marine organisms to focus on new metabolomics approaches for the study of metabolic changes in cancer cell lines treated with chemical extracts from sponges and algae, and for the classification of the chemical structures of bioactive compounds that may potentially prove useful for specific biotechnological applications.

Nutrigenomics in livestock sector and its human-animal interface-a review

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Nutrigenomics unfolds the link between nutrition and gene expression for productivity.expression profile of intramuscular.

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Nutrigenomics helps scientists discover genes and DNA in each animal's cell or tissue by assisting them in selecting nutrients.

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It brings out the importance of micronutrition for increasing animal production.

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Nutrigenomics integrates nutrition, molecular biology, genomics, bioinformatics, molecular medicine, and epidemiology.

Noncommunicable diseases such as cardiovascular disease, obesity, diabetes, and cancer now outnumber all other health ailments in humans globally due to abrupt changes in lifestyle following the industrial revolution. The industrial revolution has also intensified livestock farming, resulting in an increased demand for productivity and stressed animals. The livestock industry faces significant challenges from a projected sharp increase in global food and high animal protein demand. Nutrition genomics holds great promise for the future as its advances have opened up a whole new world of disease understanding and prevention. Nutrigenomics is the study of the interactions between genes and diet. It investigates molecular relationships between nutrients and genes to identify how even minor modifications could potentially alter animal and human health/performance by using techniques like proteomics, transcriptomics, metabolomics, and lipidomics. Dietary modifications mostly studied in livestock focus mainly on health and production traits through protein, fat, mineral, and vitamin supplementation changes. Nutrigenomics meticulously selects nutrients for fine-tuning the expression of genes that match animal/human genotypes for better health, productivity, and the environment. As a step forward, nutrigenomics integrates nutrition, molecular biology, genomics, bioinformatics, molecular medicine, and epidemiology to better understand the role of food as an epigenetic factor in the occurrence of these diseases. This review aims to provide a comprehensive overview of the fundamental concepts, latest advances, and studies in the field of nutrigenomics, emphasizing the interaction of diet with gene expression, and how it relates to human and animal health along with its human-animal interphase.

Multi-Omics Approach in Amelioration of Food Products

Determination of the quality of food products is an essential key factor needed for safe-guarding the quality of food for the interest of the consumers, along with the nutritional and sensory improvements that are necessary for delivering better quality products. Bacteriocins are a group of ribosomally synthesized antimicrobial peptides that help in maintaining the quality of food. The implementation of multi-omics approach has been important for the overall enhancement of the quality of the food. This review uses various recent technologies like proteomics, transcriptomics, and metabolomics for the overall enhancement of the quality of food products. The matrix associated with the food products requires the use of sophisticated technologies that help in the extraction of a large amount of information necessary for the amelioration of the food products. This review would provide a wholesome view of how various recent technologies can be used for improving the quality food products and for enhancing their shelf-life.

Advanced Development of Supercritical Fluid Chromatography in Herbal Medicine Analysis

The greatest challenge in the analysis of herbal components lies in their variety and complexity. Therefore, efficient analytical tools for the separation and qualitative and quantitative analysis of multi-components are essential. In recent years, various emerging analytical techniques have offered significant support for complicated component analysis, with breakthroughs in selectivity, sensitivity, and rapid analysis. Among these techniques, supercritical fluid chromatography (SFC) has attracted much attention because of its high column efficiency and environmental protection. SFC can be used to analyze a wide range of compounds, including non-polar and polar compounds, making it a prominent analytical platform. The applicability of SFC for the separation and determination of natural products in herbal medicines is overviewed in this article. The range of applications was expanded through the selection and optimization of stationary phases and mobile phases. We also focus on the two-dimensional SFC analysis. This paper provides new insight into SFC method development for herbal medicine analysis.

Introduction of Octadecyl-Bonded Porous Particles in 3D-Printed Transparent Housings with Multiple Outlets

Microfluidic devices for comprehensive three-dimensional spatial liquid chromatography will ultimately require a body of stationary phase with multiple in- and outlets. In the present work, 3D printing with a transparent polymer resin was used to create a simplified device that can be seen as a unit cell for an eventual three-dimensional separation system. Complete packing of the device with 5-μm C18 particles was achieved, with reasonable permeability. The packing process could be elegantly monitored from the pressure profile, which implies that optical transparency may not be required for future devices. The effluent flow was different for each of the four outlets of the device, but all flows were highly repeatable, suggesting that correction for flow-rate variations is possible. The investigation into flow patterns through the device was supported by computational-fluid-dynamics simulations. A proof-of-principle separation of four standard peptides is described, with mass-spectrometric detection for each of the four channels separately.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

metaboprep: an R package for preanalysis data description and processing

MOTIVATION

Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures.

RESULTS

Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data are generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds.

AVAILABILITY AND IMPLEMENTATION

metaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition

Reviewing the metabolomics literature is becoming increasingly difficult because of the rapid expansion of relevant journal literature. Text-mining technologies are therefore needed to facilitate more efficient literature reviews. Here we contribute a standardised corpus of full-text publications from metabolomics studies and describe the development of two metabolite named entity recognition (NER) methods. These methods are based on Bidirectional Long Short-Term Memory (BiLSTM) networks and each incorporate different transfer learning techniques (for tokenisation and word embedding). Our first model (MetaboListem) follows prior methodology using GloVe word embeddings. Our second model exploits BERT and BioBERT for embedding and is named TABoLiSTM (Transformer-Affixed BiLSTM). The methods are trained on a novel corpus annotated using rule-based methods, and evaluated on manually annotated metabolomics articles. MetaboListem (F1-score 0.890, precision 0.892, recall 0.888) and TABoLiSTM (BioBERT version: F1-score 0.909, precision 0.926, recall 0.893) have achieved state-of-the-art performance on metabolite NER. A training corpus with full-text sentences from >1000 full-text Open Access metabolomics publications with 105,335 annotated metabolites was created, as well as a manually annotated test corpus (19,138 annotations). This work demonstrates that deep learning algorithms are capable of identifying metabolite names accurately and efficiently in text. The proposed corpus and NER algorithms can be used for metabolomics text-mining tasks such as information retrieval, document classification and literature-based discovery and are available from the omicsNLP GitHub repository.

Increased inosine levels in drug-free individuals with at-risk mental state: A serum metabolomics study

AIM

At-risk mental state (ARMS) has been recently attracting attention with respect to the improvement of the management and outcome of psychiatric diseases, such as schizophrenia. Since only a few studies have reported on biological alterations in ARMS, serum metabolomics was carried out in ARMS subjects and healthy controls using liquid chromatography with high-resolution mass spectrometry.

METHODS

Serum samples were collected from ARMS subjects (n = 24; male: 12; female 12) and age- and sex-matched healthy controls (n = 23 male: 11, female: 12). After serum pre-treatment, liquid chromatography with high-resolution mass spectrometry was performed. Multivariate analyses, such as orthogonal partial least-squares discriminant and volcano plot analyses, were performed.

RESULTS

Serum inosine, lactate, taurine, 2,3-dihydroxypropanoate and glutamate levels differed between the two groups. A significant increase in inosine levels was detected in the positive- and negative-ion modes; however, significant differences were not observed in the levels of other purine-related metabolites (hypoxanthine, xanthine and urate) between the two groups.

CONCLUSION

Increased inosine levels may serve as biological markers for ARMS, in addition to alterations in the levels of lactate and certain amino acids.

Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics

Regulatory bodies have started to recognise the value of in vitro screening and metabolomics as two types of new approach methodologies (NAMs) for chemical risk assessments, yet few high-throughput in vitro toxicometabolomics studies have been reported. A significant challenge is to implement automated sample preparation of the low biomass samples typically used for in vitro screening. Building on previous work, we have developed, characterised and demonstrated an automated sample preparation and analysis workflow for in vitro metabolomics of HepaRG cells in 96-well microplates using a Biomek i7 Hybrid Workstation (Beckman Coulter) and Orbitrap Elite (Thermo Scientific) high-resolution nanoelectrospray direct infusion mass spectrometry (nESI-DIMS), across polar metabolites and lipids. The experimental conditions evaluated included the day of metabolite extraction, order of extraction of samples in 96-well microplates, position of the 96-well microplate on the instrument's deck and well location within a microplate. By using the median relative standard deviation (mRSD (%)) of spectral features, we have demonstrated good repeatability of the workflow (final mRSD < 30%) with a low percentage of features outside the threshold applied for statistical analysis. To improve the quality of the automated workflow further, small method modifications were made and then applied to a large cohort study (4860 sample infusions across three nESI-DIMS assays), which confirmed very high repeatability of the whole workflow from cell culturing to metabolite measurements, whilst providing a significant improvement in sample throughput. It is envisioned that the automated in vitro metabolomics workflow will help to advance the application of metabolomics (as a part of NAMs) in chemical safety, primarily as an approach for high throughput screening and prioritisation.

Comparison of Microflow and Analytical Flow Liquid Chromatography Coupled to Mass Spectrometry Global Metabolomics Methods Using a Urea Cycle Disorder Mouse Model

Microscale-based separations are increasingly being applied in the field of metabolomics for the analysis of small-molecule metabolites. These methods have the potential to provide improved sensitivity, less solvent waste, and reduced sample-size requirements. Ion-pair free microflow-based global metabolomics methods, which we recently reported, were further compared to analytical flow ion-pairing reagent containing methods using a sample set from a urea cycle disorder (UCD) mouse model. Mouse urine and brain homogenate samples representing healthy, diseased, and disease-treated animals were analyzed by both methods. Data processing was performed using univariate and multivariate techniques followed by analyte trend analysis. The microflow methods performed comparably to the analytical flow ion-pairing methods with the ability to separate the three sample groups when analyzed by partial least-squares analysis. The number of detected metabolic features present after each data processing step was similar between the microflow-based methods and the ion-pairing methods in the negative ionization mode. The observed analyte trend and coverage of known UCD biomarkers were the same for both evaluated approaches. The 12.5-fold reduction in sample injection volume required for the microflow-based separations highlights the potential of this method to support studies with sample-size limitations.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.

A Global Metabolic Map Defines the Effects of a Si-Based Biostimulant on Tomato Plants under Normal and Saline Conditions

The ongoing unpredictability of climate changes is exponentially exerting a negative impact on crop production, further aggravating detrimental abiotic stress effects. Several research studies have been focused on the genetic modification of crop plants to achieve more crop resilience against such stress factors; however, there has been a paradigm shift in modern agriculture focusing on more organic, eco-friendly and long-lasting systems to improve crop yield. As such, extensive research into the use of microbial and nonmicrobial biostimulants has been at the core of agricultural studies to improve crop growth and development, as well as to attain tolerance against several biotic and abiotic stresses. However, the molecular mechanisms underlying the biostimulant activity remain enigmatic. Thus, this study is a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics approach to unravel the hypothetical biochemical framework underlying effects of a nonmicrobial biostimulant (a silicon-based formulation) on tomato plants (Solanum lycopersium) under salinity stress conditions. This metabolomics study postulates that Si-based biostimulants could alleviate salinity stress in tomato plants through modulation of the primary metabolism involving changes in the tricarboxylic acid cycle, fatty acid and numerous amino acid biosynthesis pathways, with further reprogramming of several secondary metabolism pathways such as the phenylpropanoid pathway, flavonoid biosynthesis pathways including flavone and flavanol biosynthesis. Thus, the postulated hypothetical framework, describing biostimulant-induced metabolic events in tomato plants, provides actionable knowledge necessary for industries and farmers to, confidently and innovatively, explore, design, and fully implement Si-based formulations and strategies into agronomic practices for sustainable agriculture and food production.