Applied metabolomics in drug discovery

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Tinosporae Radix attenuates acute pharyngitis by regulating glycerophospholipid metabolism and inflammatory responses through PI3K-Akt signaling pathway

Introduction

With the onset of the COVID-19 pandemic, the incidence and prevalence of acute pharyngitis (AP) have increased significantly. Tinosporae Radix (TR) is a vital medication utilized in the treatment of pharyngeal and laryngeal ailments, especially AP. The study endeavors to explore unclear molecular mechanisms of TR in addressing AP.

Methods

Network pharmacology and metabolomics analyses of effect of TR on AP were conducted, and apossible pathway was validated both in vivo using the acute pharyngitis rat model and in vitro using the LPS-induced RAW264.7 cells model, through techniques such as histopathological examinations, immunohistochemical technology, ELISA, RT-qPCR, and Western blotting to systematically explore the possible mechanisms underlying the inhibition of AP by TR.

Results and discussion

Network pharmacology analysis identified several key targets, including PIK3CA, IL6, AKT1, TNF, and PTGS2, alongside pivotal signaling pathways such as IL-17, TNF, Hepatitis B, nuclear factor kappa B (NF-κB), Influenza A, and the PI3K-Akt pathway. Most of them are closely associated with inflammation. Then, wide-target metabolomics analysis showed that TR downregulated substances within the glycerophospholipid metabolic pathway, and modulated the PI3K-Akt pathway. The integrated findings from network pharmacology and metabolomics underscored the pivotal role of the PI3K-Akt signaling pathway and the attenuation of inflammatory responses. Finally, in vitro and in vivo experiments have shown that TR can inhibit inflammatory factors such as IL-6, TNF - α, and COX-2, downregulate targets such as PI3K and AKT on the PI3K-Akt signaling pathway, and thereby alleviate the inflammatory response of AP. Our study demonstrated that TR exerts an anti-AP effect through suppression of release of inflammatory factors and modulation of glycerophospholipid metabolism via suppressing the PI3K-Akt signaling pathway.

Integrated metagenomic and metabonomic mechanisms for the therapeutic effects of Duhuo Jisheng decoction on intervertebral disc degeneration

Intervertebral disc degeneration (IVDD) is a prevalent orthopedic condition with lower back pain as the predominant clinical presentation that challenges clinical treatment with few therapeutic options. Duhuo Jisheng Decoction (DHJSD) has been proven effective in the therapy of IVDD, but the precise underlying mechanisms remain not fully elucidated. The current study was designed to test our hypothesis that DHJSD may systematically correct the phenotypic disruption of the gut microbiota and changes in the serum metabolome linked to IVDD. Analysis of the active ingredients of DHJSD by ultra high performance liquid chromatography. An integrated metagenomic and metabonomic approach was used to analyze feces and blood samples from normal and IVDD rats. Compared to the control group, fiber ring pinning on the caudal 3 to caudal 5 segments of the rats caused IVDD and significantly altered the compositions of the intestinal microbiota and serum metabolites. Integrated analysis revealed commonly-altered metabolic pathways shared by both intestinal microbiota and serum metabolome of the IVDD rats. DHJSD inhibited the degenerative process and restored the compositions of the perturbed gut microbiota, particularly the relative abundance of commensal microbes of the Prevotellaceae family. DHJSD also corrected the altered metabolic pathways involved in the metabolism of glycine, serine, threonine, valine, the citric acid cycle, and biosynthesis of leucine and isoleucine. DHJSD inhibited the disc degeneration process by an integrated metagenomic and metabonomic mechanism to restore the microbiome profile and normalize the metabonomic pathways.

NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm

We introduce NMR-Onion, an open-source, computationally efficient algorithm based on Python and PyTorch, designed to facilitate the automatic deconvolution of 1D NMR spectra. NMR-Onion features two innovative time-domain models capable of handling asymmetric non-Lorentzian line shapes. Its core components for resolution-enhanced peak detection and digital filtering of user-specified key regions ensure precise peak prediction and efficient computation. The NMR-Onion framework includes three built-in statistical models, with automatic selection via the BIC criterion. Additionally, NMR-Onion assesses the repeatability of results by evaluating post-modeling uncertainty. Using the NMR-Onion algorithm helps to minimize excessive peak detection.

Deciphering chemical diversity among five variants of Abeliophyllum distichum flowers through metabolomics analysis

Abeliophyllum distichum (Oleaceae), endemic to the Korean Peninsula and the sole member of its genus and species, possesses high scarcity value, escalating its importance under the Nagoya Protocol. Despite its significance, their metabolites and activities of A. distichum flowers remain unexplored. This study employs an integrated metabolomic approach utilizing NMR, LC/MS, GC/MS, and FTIR techniques to comprehensively analyze the metabolite profile of A. distichum flowers. By combining these methods, we identified 35 metabolites, 43 secondary metabolites, and 108 hydrophobic primary metabolites. Notably, distinct concentration patterns of these compounds were observed across five variants, classified based on morphological characteristics. Correlation analyses of primary and secondary metabolites unveiled varietal metabolic flux, providing insights into A. distichum flower metabolism. Additionally, the reconstruction of metabolic pathways based on dissimilarities in morphological traits elucidates variant-specific metabolic signatures. These findings not only enhance our understanding of chemical differences between varieties but also underscore the importance of considering varietal differences in future research and conservation efforts.

The Art of Finding the Right Drug Target: Emerging Methods and Strategies

Drug targets are specific molecules in biological tissues and body fluids that interact with drugs. Drug target discovery is a key component of drug discovery and is essential for the development of new drugs in areas such as cancer therapy and precision medicine. Traditional in vitro or in vivo target discovery methods are time-consuming and labor-intensive, limiting the pace of drug discovery. With the development of modern discovery methods, the discovery and application of various emerging technologies have greatly improved the efficiency of drug discovery, shortened the cycle time, and reduced the cost. This review provides a comprehensive overview of various emerging drug target discovery strategies, including computer-assisted approaches, drug affinity response target stability, multiomics analysis, gene editing, and nonsense-mediated mRNA degradation, and discusses the effectiveness and limitations of the various approaches, as well as their application in real cases. Through the review of the aforementioned contents, a general overview of the development of novel drug targets and disease treatment strategies will be provided, and a theoretical basis will be provided for those who are engaged in pharmaceutical science research. SIGNIFICANCE STATEMENT: Target-based drug discovery has been the main approach to drug discovery in the pharmaceutical industry for the past three decades. Traditional drug target discovery methods based on in vivo or in vitro validation are time-consuming and costly, greatly limiting the development of new drugs. Therefore, the development and selection of new methods in the drug target discovery process is crucial.

Untargeted metabolomics uncovers prime pathways linked to antibacterial action of citral against bacterial vaginosis-causing Gardnerella vaginalis: An in vitro and in vivo study

Global increase in recurrence of bacterial vaginosis (BV) and worrisome rise in antimicrobial resistance pose an urgent call for new/novel antibacterial agents. In light of the circumstance, the present study demonstrates the in vitro and in vivo antibacterial activity of a phytochemical citral, with a particular emphasis to elucidate its mechanistic action against Gardnerella vaginalis -a potential cause of BV. Out of 21 phytochemicals screened initially against G. vaginalis, citral was envisaged to be a phenomenal antibacterial agent showing MIC and MBC at 128 μg/mL. Citral's rapid killing ability was revealed by a time-killing kinetics assay supported by CFU, signifying that it completely killed the given inoculum of planktonic G. vaginalis cells within 60 min. Further, citral was found to exhibit 1 min contact-killing efficacy together with mature-biofilm disintegrating ability at increasing MICs. To further understand the molecular action of citral, in vitro investigations such as ROS estimation, PI staining and intracellular protein release assay were performed, which demonstrated that citral deteriorated the membrane integrity of G. vaginalis. Galleria mellonella, a simple invertebrate model used to evaluate citral's non-toxic and antibacterial activity in vivo, demonstrates that citral completely restored the larvae from G. vaginalis infection. The metabolite level investigation using LC-MS revealed that citral had negative impact on biotin metabolism (via., biotin), spermidine metabolism (via., 5'-methylthioadenosine and spermidine) and nucleotide metabolism (via., guanine, adenine and uridine). Since that biotin is associated with seven different metabolic pathways, it is conceivable that citral could target biotin biosynthesis or its metabolism and as a result, disrupt other metabolic pathways, such as lipid and fatty acid synthesis, which is essential for the creation of cell membranes. Thus, the current study is the first of its kind to delineate the promising in vitro and in vivo antibacterial efficacy of citral and decipher its plausible antibacterial action mechanism through metabolomic approach, which concomitantly emphasizes citral as a viable natural therapeutic alternative to manage and control BV.

Targeted metabolomics in human and animal biofluids and tissues using liquid chromatography coupled with tandem mass spectrometry

Here, we present a targeted polar metabolomics protocol for the analysis of biofluids and frozen tissue biopsies using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We describe steps for sample pretreatment, liquid-liquid extraction, and isolation of polar metabolites. We then detail procedures for target LC-MS/MS analysis. In this protocol, we focus on the analysis of plasma and serum samples. We also provide brief instructions on how to process other biological matrices as supplemental information. For complete details on the use and execution of this protocol, please refer to Coskun et al. (2022).1.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections

Metabolomics is an analytical approach that involves profiling and comparing the metabolites present in biological samples. This scoping review article offers an overview of current metabolomics approaches and their utilization in evaluating metabolic changes in biological fluids that occur in response to viral infections. Here, we provide an overview of metabolomics methods including high-throughput analytical chemistry and multivariate data analysis to identify the specific metabolites associated with viral infections. This review also focuses on data interpretation and applications designed to improve our understanding of the pathogenesis of these viral diseases.

Glutaminase 1 isoform up-regulation associated with lipid metabolism disorder induced by methyl tertiary-butyl ether in male rats

Methyl tertiary-butyl ether (MTBE) is a new unleaded gasoline additive, which is considered to be associated with abnormal lipid metabolism in many studies, but the metabolic characteristics and mechanism are still unclear. To observe the characteristics of lipid metabolism induced by MTBE and possible pathways, 21 male Wistar rats got intragastric administration for 24 weeks. The serum lipid metabolism indexes and metabolites were analyzed separately by a biochemical analyzer and untargeted metabolomics. And found that serum high-density lipoprotein cholesterol (HDL-C) levels in the exposure group were significantly reduced, and serum very low-density lipoprotein (VLDL) levels were significantly increased. In untargeted metabolomics, 190 differential metabolites were obtained. Among them, 23 metabolites were found to show the same trend in MTBE exposure groups, which might play a key role in systemic energy metabolism. Further metabolic pathways analysis showed that D-Glutamine, D-glutamate metabolism, and the other three pathways were affected by MTBE significantly. Therefore, we evaluated serum glutamine and glutamate levels and found that MTBE exposure significantly reduced glutamine levels and increased glutamate levels in rat serum and L-02 cells. Further, the key regulatory gene of glutamine metabolism, glutaminase 1 isoform (GLS1), was significantly up-regulated in rat liver and L-02 cells exposed to MTBE. While the effect of glutamine and glutamate metabolism induced by MTBE could be weakened by BPTES, an antagonist of GLS1. In conclusion, our results indicated that MTBE exposure could change the level of glutamine metabolism by promoting GLS1 expression and ultimately lead to abnormal lipid metabolism.

Investigation on acquired palbociclib resistance by LC-MS based multi-omics analysis

Palbociclib is a specific CDK4/6 inhibitor that has been widely applied in multiple types of tumors. Different from cytotoxic drugs, the anticancer mechanism of palbociclib mainly depends on cell cycle inhibition. Therefore, the resistance mechanism is different. For clinical cancer patients, drug resistance is inevitable for almost all cancer therapies including palbociclib. We have trained palbociclib resistant cells in vitro to simulate the clinical situation and applied LC-MS multi-omics analysis methods including proteomic, metabolomic, and glycoproteomic techniques, to deeply understand the underly mechanism behind the resistance. As a result of proteomic analysis, the resistant cells were found to rely on altered metabolic pathways to keep proliferation. Metabolic processes related to carbohydrates, lipids, DNA, cellular proteins, glucose, and amino acids were observed to be upregulated. Most dramatically, the protein expressions of COX-1 and NDUFB8 have been detected to be significantly overexpressed by proteomic analysis. When a COX-1 inhibitor was hired to combine with palbociclib, a synergistic effect could be obtained, suggesting the altered COX-1 involved metabolic pathway is an important reason for the acquired palbociclib resistance. The KEGG pathway of N-glycan biosynthesis was identified through metabolomics analysis. N-glycoproteomic analysis was therefore included and the global glycosylation was found to be elevated in the palbociclib-resistant cells. Moreover, integration analysis of glycoproteomic data allowed us to detect a lot more proteins that have been glycosylated with low abundances, these proteins were considered to be overwhelmed by those highly abundant proteins during regular proteomic LC-MS detection. These low-abundant proteins are mainly involved in the cellular biology processes of cell migration, the regulation of chemotaxis, as well as the glycoprotein metabolic process which offered us great more details on the roles played by N-glycosylation in drug resistance. Our result also verified that N-glycosylation inhibitors could enhance the cell growth inhibition of palbociclib in resistant cells. The high efficiency of the integrated multi-omics analysis workflow in discovering drug resistance mechanisms paves a new way for drug development. With a clear understanding of the resistance mechanism, new drug targets and drug combinations could be designed to resensitize the resistant tumors.

Natural Products Drug Discovery: On Silica or In-Silico?

Natural products have been the most important source for drug development throughout the human history. Over time, the formulation of drugs has evolved from crude drugs to refined chemicals. In modern drug discovery, conventional natural products lead-finding usually uses a top-down approach, namely bio-guided fractionation. In this approach, the crude extracts are separated by chromatography and resulting fractions are tested for activity. Subsequently, active fractions are further refined until a single active compound is obtained. However, this is a painstakingly slow and expensive process. Among the alternatives that have been developed to improve this situation, metabolomics has proved to yield interesting results having been applied successfully to drug discovery in the last two decades. The metabolomics-based approach in lead-finding comprises two steps: (1) in-depth chemical profiling of target samples, e.g. plant extracts, and bioactivity assessment, (2) correlation of the chemical and biological data by chemometrics. In the first step of this approach, the target samples are chemically profiled in an untargeted manner to detect as many compounds as possible. So far, NMR spectroscopy, LC-MS, GC-MS, and MS/MS spectrometry are the most common profiling tools. The profile data are correlated with the biological activity with the help of various chemometric methods such as multivariate data analysis. This in-silico analysis has a high potential to replace or complement conventional on-silica bioassay-guided fractionation as it will greatly reduce the number of bioassays, and thus time and costs. Moreover, it may reveal synergistic mechanisms, when present, something for which the classical top-down approach is clearly not suited. This chapter aims to give an overview of successful approaches based on the application of chemical profiling with chemometrics in natural products drug discovery.

Serum metabolomics analysis in patients with alcohol dependence

Objective

Alcohol dependence (AD) is a chronic recurrent mental disease caused by long-term drinking. It is one of the most prevalent public health problems. However, AD diagnosis lacks objective biomarkers. This study was aimed to shed some light on potential biomarkers of AD patients by investigating the serum metabolomics profiles of AD patients and the controls.

Methods

Liquid chromatography-mass spectrometry (LC-MS) was used to detect the serum metabolites of 29 AD patients (AD) and 28 controls. Six samples were set aside as the validation set (Control: n = 3; AD group: n = 3), and the remaining were used as the training set (Control: n = 26; AD group: n = 25). Principal component analysis (PCA) and partial least squares discriminant analysis (PCA-DA) were performed to analyze the training set samples. The metabolic pathways were analyzed using the MetPA database. The signal pathways with pathway impact >0.2, value of p <0.05, and FDR < 0.05 were selected. From the screened pathways, the metabolites whose levels changed by at least 3-fold were screened. The metabolites with no numerical overlap in their concentrations in the AD and the control groups were screened out and verified with the validation set.

Results

The serum metabolomic profiles of the control and the AD groups were significantly different. We identified six significantly altered metabolic signal pathways, including protein digestion and absorption; alanine, aspartate, and glutamate metabolism; arginine biosynthesis; linoleic acid metabolism; butanoate metabolism; and GABAergic synapse. In these six signal pathways, the levels of 28 metabolites were found to be significantly altered. Of these, the alterations of 11 metabolites changed by at least 3-fold compared to the control group. Of these 11 metabolites, those with no numerical overlap in their concentrations between the AD and the control groups were GABA, 4-hydroxybutanoic acid, L-glutamic acid, citric acid and L-glutamine.

Conclusion

The metabolite profile of the AD group was significantly different from that of the control group. GABA, 4-hydroxybutanoic acid, L-glutamic acid, citric acid, and L-glutamine could be used as potential diagnostic markers for AD.

Metabolic profiling of lysophosphatidylcholines in chlorpromazine hydrochloride- and N-acetyl-p-amino-phenoltriptolide-induced liver injured rats based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry

Chlorpromazine hydrochloride (CH) and N-acetyl-p-amino-phenoltriptolide (APAP) are typical acentral dopamine receptor antagonists and antipyretic analgesics in clinical applications, respectively. However, it has been reported that these 2 drugs could cause liver damage. Lysophosphatidylcholines (LPCs) have multiple physiological functions and are metabolized primarily in the liver, where it undergoes significant changes when the liver is damaged. In the study, 15 LPCs in the rat serum with CH- and APAP-induced liver injury were quantified based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry, and multivariate statistical analyses including principal component analysis (PCA) and orthogonal partial least squares discriminate analysis (OPLS-DA) were combined to understand CH- and APAP-induced liver injury from the perspective of LPC metabolic profiling. The quantitative results showed that there were significant changes in 10 LPCs and 5 LPCs after CH- and APAP-administration, separately. The results of PCA and OPLS-DA indicated that CH- and APAP-induced liver injury could be well distinguished by the LPC metabolic profiling, and 7 LPCs and 1 LPC biomarkers that could characterize CH- and APAP-induced liver damage in turn had been screened. This study will not only provide a new perspective for the clinical diagnosis of CH- and APAP-induced liver injury, but also offer a reference for further study of their hepatotoxicity mechanisms.

Using MVDA with stoichiometric balances to optimize amino acid concentrations in chemically defined CHO cell culture medium for improved culture performance

Chemically defined (CD) media are routinely used in the production of biologics in Chinese hamster ovary (CHO) cell culture and provide enhanced raw material control. Nutrient optimized CD media is an important path to increase cell growth and monoclonal antibody (mAb) productivity in recombinant CHO cell lines. However, nutrient optimization efforts for CD media typically rely on multifactorial and experimental design of experiment approaches or complex mathematical models of cellular metabolism or gene expression systems. Moreover, the majority of these efforts are aimed at amino acids since they constitute essential nutrients in CD media as they directly contribute to biomass and protein production. In this study, we demonstrate the utilization of multivariate data analytics (MVDA) coupled with amino acid stoichiometric balances (SBs) to increased cell growth and mAb productivity in efforts to support CD media development efforts. SBs measure the difference between theoretical demand of amino acids and the empirically measured fluxes to identify various catabolic or anabolic states of the cell. When coupled with MVDA, the statistical models were not only able to highlight key amino acids toward cell growth or productivity, but also provided direction on metabolic favorability of the amino acid. Experimental validation of our approach resulted in a 55% increase in total cell growth and about an 80% increase in total mAb productivity. Increased specific consumption of stoichiometrically balanced amino acids and decreased specific consumption of glucose was also observed in optimized CD media suggesting favorable consumption of desired nutrients and a potential for energy redistribution toward increased cellular growth and mAb productivity.

The isolation of water-soluble natural products - challenges, strategies and perspectives

Covering period: up to 2019Water-soluble natural products constitute a relevant group of secondary metabolites notably known for presenting potent biological activities. Examples are aminoglycosides, β-lactam antibiotics, saponins of both terrestrial and marine origin, and marine toxins. Although extensively investigated in the past, particularly during the golden age of antibiotics, hydrophilic fractions have been less scrutinized during the last few decades. This review addresses the possible reasons on why water-soluble metabolites are now under investigated and describes approaches and strategies for the isolation of these natural compounds. It presents examples of several classes of hydrosoluble natural products and how they have been isolated. Novel stationary phases and chromatography techniques are also reviewed, providing a perspective towards a renaissance in the investigation of water-soluble natural products.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Metabolomic characteristics of hepatotoxicity in rats induced by silica nanoparticles

Silica nanoparticles (SiNPs) have become one of the most widely studied nanoparticles in nanotechnology for environmental health and safety. Although many studies have devoted to evaluating the hepatotoxicity of SiNPs, it is currently impossible to predict the extent of liver lipid metabolism disorder by identifying changes in metabolites. In the present study, 40 male Sprague-Dawley (SD) rats were randomly divided into control group and 3 groups with different doses (1.8 mg/kg body weight (bw), 5.4 mg/kg bw, 16.2 mg/kg bw), receiving intratracheal instillation of SiNPs. Liver tissue was taken for lipid level analysis, and serum was used for blood biochemical analysis. Then, the metabolites changes of liver tissue in rats were systematically analyzed using ¹H nuclear magnetic resonance (¹H NMR) techniques in combination with multivariate statistical analysis. SiNPs induced serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) and triglyceride (TG) elevation in treated groups; TG and low-density lipoprotein cholesterol (LDL-C) were significantly higher in SiNPs-treated groups of high-dose, however high-density lipoprotein cholesterol (HDL-C) showed a declining trend in liver tissue. The orthogonal partial least squares discriminant analysis (OPLS-DA) scores plots revealed different metabolic profiles between control and high-dose group (Q² =0.495, R²Y=0.802, p = 0.037), and a total of 11 differential metabolites. Pathway analysis indicated that SiNPs treatment mainly affected 10 metabolic pathways including purine metabolism, glucose-alanine cycle and metabolism of various amino acids such as glutamate, cysteine and aspartate (impact value>0.1, false discovery rate (FDR)< 0.05). The result indicated that exposure to SiNPs caused liver lipid metabolism disorder in rats, the biochemical criterions related to lipid metabolism changed significantly. The obviously changed metabolomics in SiNPs-treated rats mostly occurred in amino acids, organic acids and nucleosides.

Metabolomics in the study of spontaneous animal diseases

Using analytical chemistry techniques such as nuclear magnetic resonance (NMR) spectroscopy and liquid or gas chromatography-mass spectrometry (LC/GC-MS), metabolomics allows detection of most endogenous and exogenous metabolites in a biological sample. Metabolomics has a wide range of applications, and has been employed in nutrition science, toxicology, environmental studies, and systems biology. Metabolomics is particularly useful in biomedical science, and has been used for diagnostic laboratory testing, identifying targets for drug development, and monitoring drug metabolism, mode of action, and toxicity. Despite its immense potential, metabolomics remains underutilized in the study of spontaneous animal diseases. Our aim was to comprehensively review the existing literature on the use of metabolomics in spontaneous veterinary diseases. Three databases were used to find journal articles that applied metabolomics in veterinary medicine. A screening process was then conducted to eliminate references that did not meet the eligibility criteria; only primary research studies investigating spontaneous animal disease were included; 38 studies met the inclusion criteria. The main techniques used were NMR and MS. All studies detected metabolite alterations in diseased animals compared with non-diseased animals. Metabolomics was mainly used to study diseases of the digestive, reproductive, and musculoskeletal systems. Inflammatory conditions made up the largest proportion of studies when articles were categorized by disease process. Following a comprehensive analysis of the literature on metabolomics in spontaneous veterinary diseases, we concluded that metabolomics, although in its early stages in veterinary research, is a promising tool regarding diagnosis, biomarker discovery, and in uncovering new insights into disease pathophysiology.

Omics for Bioprospecting and Drug Discovery from Bacteria and Microalgae

"Omics" represent a combinatorial approach to high-throughput analysis of biological entities for various purposes. It broadly encompasses genomics, transcriptomics, proteomics, lipidomics, and metabolomics. Bacteria and microalgae exhibit a wide range of genetic, biochemical and concomitantly, physiological variations owing to their exposure to biotic and abiotic dynamics in their ecosystem conditions. Consequently, optimal conditions for adequate growth and production of useful bacterial or microalgal metabolites are critically unpredictable. Traditional methods employ microbe isolation and 'blind'-culture optimization with numerous chemical analyses making the bioprospecting process laborious, strenuous, and costly. Advances in the next generation sequencing (NGS) technologies have offered a platform for the pan-genomic analysis of microbes from community and strain downstream to the gene level. Changing conditions in nature or laboratory accompany epigenetic modulation, variation in gene expression, and subsequent biochemical profiles defining an organism's inherent metabolic repertoire. Proteome and metabolome analysis could further our understanding of the molecular and biochemical attributes of the microbes under research. This review provides an overview of recent studies that have employed omics as a robust, broad-spectrum approach for screening bacteria and microalgae to exploit their potential as sources of drug leads by focusing on their genomes, secondary metabolite biosynthetic pathway genes, transcriptomes, and metabolomes. We also highlight how recent studies have combined molecular biology with analytical chemistry methods, which further underscore the need for advances in bioinformatics and chemoinformatics as vital instruments in the discovery of novel bacterial and microalgal strains as well as new drug leads.

Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics

Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine. Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.Areas covered: This review illustrates the progressive introduction of pharmacometabolomics as an innovative solution for enhancing the discovery of novel drugs and improving research and development (R&D) productivity of the pharmaceutical industry. An extended analysis on published pharmacometabolomics studies both in animal models and humans includes results obtained in several areas such as hepatology, gastroenterology, nephrology, neuropsychiatry, oncology, drug addiction, embryonic cells, neonatology, and microbiomics.Expert opinion: a tailored, individualized therapy based on the optimization of pharmacokinetics and pharmacodynamics, the improvement of drug efficacy, and the abolition of drug toxicity and adverse drug reactions is a key issue in precision medicine. Genetics alone has become insufficient for deciphring intra- and inter-individual variations in drug-response, since they originate both from genetic and environmental factors, including human microbiota composition. The association between pharmacogenomics and pharmacometabolomics may be considered the new strategy for an in-deep knowledge on changes and alterations in human and microbial metabolic pathways due to the action of a drug.

Review of recent developments in GC-MS approaches to metabolomics-based research

BACKGROUND

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available.

AIM OF REVIEW

This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

A Study of NMR-Based Hepatic and Serum Metabolomics in a Liver Injury Sprague-Dawley Rat Model Induced by Psoralen

Psoralen is the main active component of Psoralea corylifolia and is used as a marker to assess its quality. The effects of psoralen on animals have been well characterized. However, the molecular pathway of its toxicity is not fully understood. In this study, the toxic effects of psoralen administration (60 mg/kg) for 7 days in Sprague-Dawley rats were observed. Serum biochemistry and liver histopathology were further investigated. Proton nuclear magnetic resonance was applied to characterize the metabolic profile of liver toxicity induced by psoralen and to find changed metabolites in rat serum and liver. It was revealed that visceral coefficients and serum biochemistry indexes were significantly changed in rats with psoralen-induced liver injury. Furthermore, the histopathological examination exhibited that the liver might be the target organ for psoralen. Metabolic analysis of both serum and liver samples further proved the liver was the target of toxicity of psoralen. Multivariate analysis identified 7 metabolites in serum samples and 15 in liver samples as potential biomarkers in liver injury induced by psoralen. In addition, our results suggest that psoralen can cause a disturbance in amino acid metabolism, especially valine, leucine, and isoleucine biosynthesis in both serum and liver samples. In conclusion, we combined the results of toxicity and metabolomics induced by psoralen and provide useful information about the safety and potential risks of psoralen and Psoralea corylifolia.

Comparative metabolomic responses to gibberellic acid and 6-benzylaminopurine in Cunila menthoides Benth. (Lamiaceae): a contribution to understand the metabolic pathways

Gibberellic acid elicited synthesis of many phenols from different classes and enhanced production of sesquiterpenoids, polyterpenoids, steroids and monoterpenoids compared to control and 6-benzylaminopurine. Little is known about the effects of 6-benzylaminopurine (BA) and gibberellic acid (GA3) on the synthesis of secondary metabolites in species of Lamiaceae. In this study, for the first time, the profile of secondary metabolites in plantlets of Cunila menthoides was characterized, using UPLC-ESI-Qq-oaTOF-MS. Ninety metabolites were identified, including polyphenols and terpenes. BA down-regulated most of the identified molecules in relation to GA3 and MS0 (control). The results showed that GA3 elicited synthesis of many phenols from different classes, and seemed to play a major role in the shikimate pathway in relation to BA. GA3 enhanced production of sesquiterpenoids, polyterpenoids, steroids and monoterpenoids compared to MS0 and BA, and also seemed to positively influence the MEP/DOXP and MVA pathways. These data show the most comprehensive metabolomic profile of Cunila menthoides to date, and the effects of BA and GA3 on the synthesis of secondary metabolites, modulating quantitative aspects of metabolism in Lamiaceae.

Targeted metabolomic analysis of 33 amino acids and biogenic amines in human urine by ion-pairing HPLC-MS/MS: Biomarkers for tacrolimus nephrotoxicity after renal transplantation

Calcineurin inhibitor nephrotoxicity, especially for the widely used tacrolimus, has become a major concern in post-transplant immunosuppression. Multiparametric amino acid metabolomics is useful for biomarker identification of tacrolimus nephrotoxicity, for which specific quantitative methods are highlighted as a premise. This article presents a targeted metabolomic assay to quantify 33 amino acids and biogenic amines in human urine by high-performance liquid chromatography coupled with tandem mass spectrometry. Chromatographic separation was carried out on an Agilent Zorbax SB-C₁₈ column (3.0 × 150 mm, 5 μm) with addition of an ion-pairing agent in the mobile phase, and MS/MS detection was achieved in both the positive and negative multiple reaction monitoring modes. Good correlation coefficients (r² > 0.98) were obtained for most analytes. Intra- and inter-day precision, stability, carryover and incurred sample reanalysis met with the acceptance criteria of the guidance of the US Food and Drug Administration. Analysis on urine from healthy volunteers and renal transplantation patients with tacrolimus nephrotoxicity confirmed symmetric dimethylarginine and serine as biomarkers for kidney injury, with AUC values of 0.95 and 0.81 in receiver operating characteristic analysis, respectively. Additionally, symmetric dimethylarginine exhibited a tight correlation with serum creatinine, and was therefore indicative of renal function. The targeted metabolomic assay was time and cost prohibitive for amino acid analysis in human urine, facilitating the biomarker identification of tacrolimus nephrotoxicity.

Screening and identification of novel biologically active natural compounds

With the advent of very rapid and cheap genome analyses and the linkage of these plus microbial metabolomics to potential compound structures came the realization that there was an immense sea of novel agents to be mined and tested. In addition, it is now recognized that there is significant microbial involvement in many natural products isolated from “nominally non-microbial sources”.

This short review covers the current screening methods that have evolved and one might even be tempted to say “devolved” in light of the realization that target-based screens had problems when the products entered clinical testing, with off-target effects being the major ones. Modern systems include, but are not limited to, screening in cell lines utilizing very modern techniques (a high content screen) that are designed to show interactions within cells when treated with an “agent”. The underlying principle(s) used in such systems dated back to unpublished attempts in the very early 1980s by the pharmaceutical industry to show toxic interactions within animal cells by using automated light microscopy. Though somewhat successful, the technology was not adequate for any significant commercialization. Somewhat later, mammalian cell lines that were “genetically modified” to alter signal transduction cascades, either up or down, and frequently linked to luciferase readouts, were then employed in a 96-well format. In the case of microbes, specific resistance parameters were induced in isogenic cell lines from approximately the mid-1970s. In the latter two cases, comparisons against parent and sibling cell lines were used in order that a rapid determination of potential natural product “hits” could be made. Obviously, all of these assay systems could also be, and were, used for synthetic molecules.

These methods and their results have led to a change in what the term “screening for bioactivity” means. In practice, versions of phenotypic screening are returning, but in a dramatically different scientific environment from the 1970s, as I hope to demonstrate in the short article that follows.

Muddled mechanisms: recent progress towards antimalarial target identification

In the past decade, malaria rates have plummeted as a result of aggressive infection control measures and the adoption of artemisinin-based combination therapies (ACTs). However, a potential crisis looms ahead. Treatment failures to standard antimalarial regimens have been reported in Southeast Asia, and devastating consequences are expected if resistance spreads to the African continent. To prevent a potential public health emergency, the antimalarial arsenal must contain therapeutics with novel mechanisms of action (MOA). An impressive number of high-throughput screening (HTS) campaigns have since been launched, identifying thousands of compounds with activity against one of the causative agents of malaria, Plasmodium falciparum. Now begins the difficult task of target identification, for which studies are often tedious, labor intensive, and difficult to interpret. In this review, we highlight approaches that have been instrumental in tackling the challenges of target assignment and elucidation of the MOA for hit compounds. Studies that apply these innovative techniques to antimalarial target identification are described, as well as the impact of the data in the field.