Metabolomics technology and bioinformatics

NMR metabolomic analysis of bacterial resistance pathways using multivalent quaternary ammonium functionalized macromolecules

INTRODUCTION

Multivalent antimicrobial dendrimers are an exciting new system that is being developed to address the growing problem of drug resistant bacteria. Nuclear Magnetic Resonance (NMR) metabolomics is a quantitative and reproducible method for the determination of bacterial response to environmental stressors and for visualization of perturbations to biochemical pathways.

OBJECTIVES

NMR metabolomics is used to elucidate metabolite differences between wild type and antimicrobially mutated Escherichia coli (E. coli) samples.

METHODS

Proton (¹H) NMR hydrophilic metabolite analysis was conducted on samples of E. coli after 33 growth cycles of a minimum inhibitory challenge to E. coli by poly(amidoamine) dendrimers functionalized with mannose and with C₁₆-DABCO quaternary ammonium endgroups and compared to the metabolic profile of wild type E. coli.

RESULTS

The wild type and mutated E. coli samples were separated into distinct sample sets by hierarchical clustering, principal component analysis (PCA) and sparse partial least squares discriminate analysis (sPLS-DA). Metabolite components of membrane fortification and energy related pathways had a significant p value and fold change between the wild type and mutated E. coli. Amino acids commonly associated with membrane fortification from cationic antimicrobials, such as lysine, were found to have a higher concentration in the mutated E. coli than in the wild type E. coli. N-acetylglucosamine, a major component of peptidoglycan synthesis, was found to have a 25-fold higher concentration in the mid log phase of the mutated E. coli than in the mid log phase of the wild type.

CONCLUSION

The metabolic profile suggests that E. coli change their peptidoglycan composition in order to garner protection from the highly positively charged and multivalent C₁₆-DABCO and mannose functionalized dendrimer.

Study on Urinary Candidate Metabolome for the Early Detection of Breast Cancer

A metabolomic study for determination of certain urinary metabolomes, 1-methyladenosine (1-MA), 1-methylguanosine (1-MG), and 8-hydroxy-2' deoxyguanosine (8-OHdG) in urine specimens of breast cancer patients. The accuracy of these metabolites and their combined score with cancer antigen 15-3 (CA15-3) was developed to improve the early detection of breast cancer. This study recruited 52 healthy individuals, 47 benign breast tumors, and 167 malignant breast tumor patients. Urine samples were handled to adjust the creatinine concentrations to 8 mg/dL (0.7 mmol/L) and analyzed using GC-MS to detect and quantify the selected urinary metabolomes in urine samples of all participants. The accuracy of individual urinary metabolomes and their combination with CA15-3 were evaluated using multivariate statistical analysis. The cutoff value of CA15-3 was 32.5 U/mL. Cutoff values of 1-MA, 1-MG, and 8-OHdG were 2.19, 2.1, and 7.3 µmol/mmol creatinine, respectively. The concentrations of 1-MA, 1-MG, and 8-OHdG were significantly higher in breast cancer patients, especially in the early-stage. The combination of three urinary metabolomes with CA15-3 improves the diagnostic sensitivity of breast cancer. For the combined score, the area under the curve (AUC) value of combined score ranged from 0.820 to 0.950, with high accuracy, ranged from 77.0 to 95.5%. The most significant AUC (0.973), sensitivity (90.1%), selectivity (94.0%) was recorded at comparing the healthy control with the early-stage of malignant breast cancer. In conclusion, the combination of three urinary metabolomes with serum CA15-3 improves the diagnostic sensitivity of breast cancer.

Novel biomarkers for lysosomal storage disorders: Metabolomic and proteomic approaches

Lysosomal storage disorders (LSDs) are characterized by the accumulation of specific disease substrates inside the lysosomes of various cells, eventually leading to the deterioration of cellular function and multisystem organ damage. With the continuous discovery and validation of novel and advanced therapies for most LSDs, there is an urgent need to discover more versatile and clinically relevant biomarkers. The utility of these biomarkers should ideally extend beyond the screening and diagnosis of LSDs to the evaluation of disease severity and monitoring of therapy. Metabolomic and proteomic approaches provide the means to the discovery and validation of such novel biomarkers. This is achieved mainly through the application of various mass spectrometric techniques to common and easily accessible biological samples, such as plasma, urine and dried blood spots. In this review, we tried to summarize the complexity of the lysosomal disorders phenotypes, their current diagnostic and therapeutic approaches, the various techniques supporting metabolomic and proteomic studies and finally we tried to explore the newly discovered biomarkers for most LSDs and their reported clinical values.

Progression-Dependent Altered Metabolism in Osteosarcoma Resulting in Different Nutrient Source Dependencies

Osteosarcoma (OS) is a primary malignant bone tumor and OS metastases are mostly found in the lung. The limited understanding of the biology of metastatic processes in OS limits the ability for effective treatment. Alterations to the metabolome and its transformation during metastasis aids the understanding of the mechanism and provides information on treatment and prognosis. The current study intended to identify metabolic alterations during OS progression by using a targeted gas chromatography mass spectrometry approach. Using a female OS cell line model, malignant and metastatic cells increased their energy metabolism compared to benign OS cells. The metastatic cell line showed a faster metabolic flux compared to the malignant cell line, leading to reduced metabolite pools. However, inhibiting both glycolysis and glutaminolysis resulted in a reduced proliferation. In contrast, malignant but non-metastatic OS cells showed a resistance to glycolytic inhibition but a strong dependency on glutamine as an energy source. Our in vivo metabolic approach hinted at a potential sex-dependent metabolic alteration in OS patients with lung metastases (LM), although this will require validation with larger sample sizes. In line with the in vitro results, we found that female LM patients showed a decreased central carbon metabolism compared to metastases from male patients.

Metabolomics for Evaluating Flavor-Associated Metabolites in Plant-Based Products

Plant-based diets (PBDs) are associated with environmental benefits, human health promotion and animal welfare. There is a worldwide shift towards PBDs, evident from the increased global demand for fresh plant-based products (PBPs). Such shifts in dietary preferences accompanied by evolving food palates, create opportunities to leverage technological advancements and strict quality controls in developing PBPs that can drive consumer acceptance. Flavor, color and texture are important sensory attributes of a food product and, have the largest influence on consumer appeal and acceptance. Among these, flavor is considered the most dominating quality attribute that significantly affects overall eating experience. Current state-of-art technologies rely on physicochemical estimations and sensory-based tests to assess flavor-related attributes in fresh PBPs. However, these methodologies often do not provide any indication about the metabolic features associated with unique flavor profiles and, consequently, can be used in a limited way to define the quality attributes of PBPs. To this end, a systematic understanding of metabolites that contribute to the flavor profiles of PBPs is warranted to complement the existing methodologies. This review will discuss the use of metabolomics for evaluating flavor-associated metabolites in fresh PBPs at post-harvest stage, alongside its applications for quality assessment and grading. We will summarize the current research in this area, discuss technical challenges and considerations pertaining to sampling and analytical techniques, as well as s provide future perspectives and directions for government organizations, industries and other stakeholders associated with the quality assessment of fresh PBPs.

Untargeted metabolomic profiling of accessory sex gland fluid from Morada Nova rams

The present study was conducted to characterize the metabolome of accessory gland fluid (AGF) of locally adapted Morada Nova rams, raised in the Brazilian Northeast. AGF was collected by an artificial vagina from five vasectomized rams. Metabolites were identified by gas chromatography-mass spectrometry (GC/MS) and high-performance liquid chromatography-mass spectrometry (LC/MS), with the support of Human Metabolome Database, PubChem, LIPID Metabolites, Pathways Strategy databases, and MetaboAnalyst platforms. There were 182 and 190 metabolites detected by GC/MS and LC/MS, respectively, with an overlap of one molecule. Lipids and lipid-like molecules were the most abundant class of metabolites in the ram AGF (127 compounds), followed by amino acids, peptides, and analogs(103 metabolites). Considering all GC/MS and LC/MS, fructose, glycerol, citric acid, d-mannitol, d-glucose, and l-(+)-lactic acid were the most abundant single metabolites present in the ram AGF. Meaningful pathways associated with AGF metabolites included glycine, serine and threonine metabolism; pantothenate and CoA biosynthesis; galactose metabolism; glutamate metabolism and phenylalanine metabolism, and so forth. In conclusion, the combined use of LC/MS and GC/MS was essential for getting a holistic view of the compounds embedded in the ram AGF. Chemical analysis of the accessory sex gland secretion is relevant for understanding sperm function and fertilization.

Foliar spray of TiO2 nanoparticles prevails over root application in reducing Cd accumulation and mitigating Cd-induced phytotoxicity in maize (Zea mays L.)

Cadmium (Cd) pollution is considered one of the global environmental issues due to its adverse effects on plant and human health. With the rapid development of nanotechnology and the practical application of engineered nanoparticles (ENPs) in agriculture, the mechanisms underlying the interactions between NPs and heavy metal on their uptake, accumulation, and phytotoxicity in crops are still not fully understood. Therefore, the impact of TiO₂ NPs (0, 100, 250 mg/L) and Cd (0, 50 μM) co-exposure on hydroponic maize (Zea mays L.) was determined under two exposure modes. Results showed that root co-exposure to TiO₂ NPs and 100 mg/L Cd significantly enhanced Cd uptake and produced greater phytotoxicity in maize than foliar exposure to TiO₂ NPs. Meanwhile, plant dry weight and chlorophyll content showed a reduction of 45.3% and 50.5%, respectively, when compared with single Cd treatment. In addition, the accumulation of Ti in shoots and roots increased by 1.61 and 4.29 times, respectively when root exposure to 250 mg/L TiO₂ NPs. By contrast, foliar exposure of TiO₂ NPs could markedly decrease shoot Cd contents from 15.2% to 17.8% and had a stronger influence on alleviating Cd-induced toxicity via increasing superoxide dismutase (SOD) and glutathione S-transferase (GST) activities and upregulating several metabolic pathways, including galactose metabolism and citrate cycle, alanine, aspartate and glutamate metabolism, as well as glycine, serine and threonine metabolism. This study provides a new strategy for the application of TiO₂ NPs in crop safety production in Cd contaminated soils.

Insights into the Chemical Biology of Childhood Embryonal Solid Tumors by NMR-Based Metabolomics

Most childhood cancers occur as isolated cases and show very different biological behavior when compared with cancers in adults. There are some solid tumors that occur almost exclusively in children among which stand out the embryonal solid tumors. These cancers main types are neuroblastoma, nephroblastoma (Wilms tumors), retinoblastoma and hepatoblastomas and tumors of the central nervous system (CNS). Embryonal solid tumors represent a heterogeneous group of cancers supposedly derived from undifferentiated cells, with histological features that resemble tissues of origin during embryogenesis. This key observation suggests that tumorigenesis might begin during early fetal or child life due to the errors in growth or pathways differentiation. There are not many literature data on genomic, transcriptomic, epigenetic, proteomic, or metabolomic differences in these types of cancers when compared to the omics- used in adult cancer research. Still, metabolomics by nuclear magnetic resonance (NMR) in childhood embryonal solid tumors research can contribute greatly to understand better metabolic pathways alterations and biology of the embryonal solid tumors and potential to be used in clinical applications. Different types of samples, such as tissues, cells, biofluids, mostly blood plasma and serum, can be analyzed by NMR to detect and identify cancer metabolic signatures and validated biomarkers using enlarged group of samples. The literature search for biomarkers points to around 20-30 compounds that could be associated with pediatric cancer as well as metastasis.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Mucosal Metabolomic Profiling and Pathway Analysis Reveal the Metabolic Signature of Ulcerative Colitis

The onset of ulcerative colitis (UC) is characterized by a dysregulated mucosal immune response triggered by several genetic and environmental factors in the context of host–microbe interaction. This complexity makes UC ideal for metabolomic studies to unravel the disease pathobiology and to improve the patient stratification strategies. This study aims to explore the mucosal metabolomic profile in UC patients, and to define the UC metabolic signature. Treatment- naïve UC patients (n = 18), UC patients in deep remission (n = 10), and healthy volunteers (n = 14) were recruited. Mucosa biopsies were collected during colonoscopies. Metabolomic analysis was performed by combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). In total, 177 metabolites from 50 metabolic pathways were identified. The most prominent metabolome changes among the study groups were in lysophosphatidylcholine, acyl carnitine, and amino acid profiles. Several pathways were found perturbed according to the integrated pathway analysis. These pathways ranged from amino acid metabolism (such as tryptophan metabolism) to fatty acid metabolism, namely linoleic and butyrate. These metabolic changes during UC reflect the homeostatic disturbance in the gut, and highlight the importance of system biology approaches to identify key drivers of pathogenesis which prerequisite personalized medicine.

R-MetaboList 2: A Flexible Tool for Metabolite Annotation from High-Resolution Data-Independent Acquisition Mass Spectrometry Analysis

Technological advancements have permitted the development of innovative multiplexing strategies for data independent acquisition (DIA) mass spectrometry (MS). Software solutions and extensive compound libraries facilitate the efficient analysis of MS¹ data, regardless of the analytical platform. However, the development of comparable tools for DIA data analysis has significantly lagged. This research introduces an update to the former MetaboList R package and a workflow for full-scan MS¹ and MS/MS DIA processing of metabolomic data from multiplexed liquid chromatography high-resolution mass spectrometry (LC-HRMS) experiments. When compared to the former version, new functions have been added to address isolated MS¹ and MS/MS workflows, processing of MS/MS data from stepped collision energies, performance scoring of metabolite annotations, and batch job analysis were incorporated into the update. The flexibility and efficiency of this strategy were assessed through the study of the metabolite profiles of human urine, leukemia cell culture, and medium samples analyzed by either liquid chromatography quadrupole time-of-flight (q-TOF) or quadrupole orbital (q-Orbitrap) instruments. This open-source alternative was designed to promote global metabolomic strategies based on recursive retrospective research of multiplexed DIA analysis.

Metabonomic Analysis of the Therapeutic Effects of Chinese Medicine Sanqi Oral Solution on Rats With Exhaustive Exercise

Exhaustive exercise has emerged as an important health issue nowadays. This study was designed to assess the metabolite abnormalities of rats after exhaustive exercise and the holistic efficacy of Chinese medicine Sanqi oral solution (SQ). Through exhaustive swimming, the exhaustive exercise model in rats was established. Thirty male Sprague–Dawley rats were randomly divided into control, model, and treatment groups. SQ (12 mL·kg⁻¹·d⁻¹) or 0.9% saline solution was administrated orally by gastric gavage. After 4 weeks, serum samples were collected for biochemical measurements and ultra performance liquid chromatography (UPLC)/quadrupole time-of-flight mass spectrometry (Q-TOF-MS)-based metabonomic study. It was found that rats with SQ intervention showed longer exhaustive swimming time (P < 0.05) than model rats, with an average of 1,160.36 ± 123.89 s in SQ group and 906.57 ± 172.11 s in model group. Among the biochemical indices, the levels of creatine kinase isoenzyme, lactate dehydrogenase, and glucose of exhaustive exercise rats increased, whereas levels of creatine kinase, urea, triglyceride, and total cholesterol decreased. These biochemical indices came normal after SQ administration, except for triglyceride. Twenty-seven potential biomarkers belonging to sphingolipids, phospholipids, fatty acids, amino acid, and other classes were identified in serum. This study indicated that SQ exerted protective effects on exhaustive exercise by significantly prolonging the swimming endurance time. The metabonomic-based findings of the metabolic state and analysis of potential biomarkers in serum well correlated with biochemical assessment, confirming that SQ had a definite efficacy. Moreover, the shifts in lipid-related metabolites and glycolytic pathway suggested that SQ may serve as a potential supplementation in sports nutrition for its pharmacological effect of regulating energy metabolism as well as improving signal transduction and muscle-cell physiological functions.

Proteomic and metabolomic insights into the functions of the male reproductive system in fishes

Proteomics and metabolomics are emerging and powerful tools to unravel the complex molecular mechanisms regulating reproduction in male fish. So far, numerous proteins and metabolites have been identified that provide us with valuable information to conduct a comprehensive analysis on seminal plasma and spermatozoa components and their functions. These analyses have allowed a better understanding of the blood-testis barrier functions, the molecular mechanisms underlying spermatogenesis, spermatozoa maturation, motility signaling, and competition as well as the mechanism of cryodamage to sperm structure and functions. To extend, proteins that undergo posttranslational modification, such as phosphorylation and oxidation in response to spermatozoa motility activation and cryopreservation, respectively, have been identified. Proteomic studies resulted in identification of potential proteins that can be used as biomarkers for sperm quality and freezability to enable the control of artificial reproduction, and to improve methods for long-term preservation (cryopreservation) of sperm. The different proteins expressed in the spermatozoa of neomales and normal males can also provide new insights into development of methods for separating X and Y fish sperm, and changes in the protein profiles in haploid and diploid spermatozoa will provide new perspectives to better understand the mechanism of male polyploidy. Overall, the knowledge gained by proteomic and metabolomic studies is important from basic to applied sciences for the development and/or optimisation of techniques in controlled fish reproduction.

Atmo-ecometabolomics: a novel atmospheric particle chemical characterization methodology for ecological research

Aerosol particles play important roles in processes controlling the composition of the atmosphere and function of ecosystems. A better understanding of the composition of aerosol particles is beginning to be recognized as critical for ecological research to further comprehend the link between aerosols and ecosystems. While chemical characterization of aerosols has been practiced in the atmospheric science community, detailed methodology tailored to the needs of ecological research does not exist yet. In this study, we describe an efficient methodology (atmo-ecometabolomics), in step-by-step details, from the sampling to the data analyses, to characterize the chemical composition of aerosol particles, namely atmo-metabolome. This method employs mass spectrometry platforms such as liquid and gas chromatography mass spectrometries (MS) and Fourier transform ion cyclotron resonance MS (FT-ICR-MS). For methodology evaluation, we analyzed aerosol particles collected during two different seasons (spring and summer) in a low-biological-activity ecosystem. Additionally, to further validate our methodology, we analyzed aerosol particles collected in a more biologically active ecosystem during the pollination peaks of three different representative tree species. Our statistical results showed that our sampling and extraction methods are suitable for characterizing the atmo-ecometabolomes in these two distinct ecosystems with any of the analytical platforms. Datasets obtained from each mass spectrometry instrument showed overall significant differences of the atmo-ecometabolomes between spring and summer as well as between the three pollination peak periods. Furthermore, we have identified several metabolites that can be attributed to pollen and other plant-related aerosol particles. We additionally provide a basic guide of the potential use ecometabolomic techniques on different mass spectrometry platforms to accurately analyze the atmo-ecometabolomes for ecological studies. Our method represents an advanced novel approach for future studies in the impact of aerosol particle chemical compositions on ecosystem structure and function and biogeochemistry.

Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome

Background: Metabolomics is emerging as a valuable tool in clinical science. However, one major challenge in clinical metabolomics is the limited use of standardized guidelines for sample collection and handling. In this study, we conducted a pilot analysis of serum and plasma to determine the effects of processing time and collection tube on the metabolome. Methods: Blood was collected in 3 tubes: Vacutainer serum separator tube (SST, serum), EDTA (plasma) and P100 (plasma) and stored at 4 degrees for 0, 0.5, 1, 2, 4 and 24 h prior to centrifugation. Compounds were extracted using liquid-liquid extraction to obtain a hydrophilic and a hydrophobic fraction and analyzed using liquid chromatography mass spectrometry. Differences among the blood collection tubes and sample processing time were evaluated (ANOVA, Bonferroni FWER ≤ 0.05 and ANOVA, Benjamini Hochberg FDR ≤ 0.1, respectively). Results: Among the serum and plasma tubes 93.5% of compounds overlapped, 382 compounds were unique to serum and one compound was unique to plasma. There were 46, 50 and 86 compounds affected by processing time in SST, EDTA and P100 tubes, respectively, including many lipids. In contrast, 496 hydrophilic and 242 hydrophobic compounds differed by collection tube. Forty-five different chemical classes including alcohols, sugars, amino acids and prenol lipids were affected by the choice of blood collection tube. Conclusion: Our results suggest that the choice of blood collection tube has a significant effect on detected metabolites and their overall abundances. Perhaps surprisingly, variation in sample processing time has less of an effect compared to collection tube; however, a larger sample size is needed to confirm this.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

Nuclear magnetic resonance-based metabolomics approach to evaluate preventive and therapeutic effects of Gastrodia elata Blume on chronic atrophic gastritis

Chronic atrophic gastritis (CAG) is one of the most common digestive system diseases worldwide which defined by WHO as initial step of cancer. Gastrodia elata Blume (GEB) is a traditional herbal with multiple pharmacological activities which was widely used in Asian countries. This study aims to explore the preventive and therapeutical effects of Gastrodia elata Blume on auto-immune induced CAG in rats. Tissues of stomachs were collected and submitted to ¹H NMR-based metabolomics analysis and histopathological inspection. The biochemical indexes of MDA, SOD, GSH, NO and XOD were measured. Gastrodia elata Blume could apparently ameliorate the damaged gastric glands and the biochemical parameters, enhance gastric acid secretion, and significantly relieve the inflammation of the stomach. Orthogonal signal correction-partial least squares-discriminant analysis (OSC-PLS-DA) of NMR profiles and correlation network analysis revealed that Gastrodia elata Blume could effectively treat CAG via regulating energy and purine metabolisms, and by anti-oxidation and anti-inflammation effects.

1H NMR-based metabolomics profiling of ten new races from Capsicum annuum cv. serrano produced in Mexico

Herein we report on the ¹H NMR-based metabolomics profiling of ten new races of Capsicum annuum cv. serrano, cultivated in Mexico. Forty eight metabolites (including sugars, amino acids, organic acids, polyphenolic acids and alcohols) were identified and quantified by 2D NMR and qNMR, respectively. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) separated the ten races into two clusters, from which citric acid, formic acid, fumaric acid, malic acid, glucose, fructose, sucrose and galactose were found as differential metabolites. This is the first study describing the chemical profiling of ten new races of Capsicum annuum cv. serrano and the spectrometric method used presently is characterized by great simplicity, robustness and reproducibility. Thus, this technique can be used for establishing reliable metabolomic fingerprints of different races of Capsicum annuum cv. serrano.

Application of metabolomics to drug discovery and understanding the mechanisms of action of medicinal plants with anti-tuberculosis activity

Human tuberculosis (TB) is amongst the oldest and deadliest human bacterial diseases that pose major health, social and economic burden at a global level. Current regimens for TB treatment are lengthy, expensive and ineffective to emerging drug resistant strains. Thus, there is an urgent need for identification and development of novel TB drugs and drug regimens with comprehensive and specific mechanisms of action. Many medicinal plants are traditionally used for TB treatment. While some of their phytochemical composition has been elucidated, their mechanisms of action are not well understood. Insufficient knowledge on Mycobacterium tuberculosis (M.tb) biology and the complex nature of its infection limit the effectiveness of current screening-based methods used for TB drug discovery. Nonetheless, application of metabolomics tools within the 'omics' approaches, could provide an alternative method of elucidating the mechanism of action of medicinal plants. Metabolomics aims at high throughput detection, quantification and identification of metabolites in biological samples. Changes in the concentration of specific metabolites in a biological sample indicate changes in the metabolic pathways. In this paper review and discuss novel methods that involve application of metabolomics to drug discovery and the understanding of mechanisms of action of medicinal plants with anti-TB activity. Current knowledge on TB infection, anti-TB drugs and mechanisms of action are also included. We further highlight metabolism of M. tuberculosis and the potential drug targets, as well as current approaches in the development of anti-TB drugs.

In vivo toxicology of carbon dots by 1H NMR-based metabolomics

Owing to the promising applications of C-dots in biomedical engineering, concerns about their safety have drawn increasing attention recently. In this study, mice were intraperitoneally injected at different C-dot concentrations (0, 6.0, 12.0 and 24.0 mg kg-1) once every 2 days for 30 days. A 1H NMR-based metabolic approach supplemented with biochemical analysis and histopathology was used for the first time to explore the toxicity of C-dots in vivo. Histopathological inspection revealed that C-dots did not induce any obvious impairment in tissues. Biochemical assays showed no significant alterations of most measured biochemical parameters in tissues and serum, except for a slight reduction of the albumin level in serum as well as AChE activity in the liver and kidneys. Orthogonal signal correction-partial least squares-discriminant analysis (OSC-PLS-DA) of NMR profiles supplemented with correlation network analysis and SUS-plots disclosed that C-dots not only triggered the immune system but also disturbed the function of cell membranes as well as the normal liver clearance, indicating that the 1H NMR based metabolomics approach provided deep insights into the toxicity of C-dots in vivo and gained an advantage over traditional toxicological means, and should be helpful for the understanding of its toxic mechanism.

Integrated Analysis of Proteomic and Transcriptomic Data Highlights Late Fetal Muscle Maturation Process

In pigs, the perinatal period is the most critical time for survival. Piglet maturation, which occurs at the end of gestation, is an important determinant of early survival. Skeletal muscle plays a key role in adaptation to extra-uterine life, e.g. motor function and thermoregulation. Progeny from two breeds with extreme neonatal mortality rates were analyzed at 90 and 110 days of gestation (dg). The Large White breed is a highly selected breed for lean growth and exhibits a high rate of neonatal mortality, whereas the Meishan breed is fatter and more robust and has a low neonatal mortality. Our aim was to identify molecular signatures underlying late fetal longissimus muscle development. First, integrated analysis was used to explore relationships between co-expression network models built from a proteomic data set (bi-dimensional electrophoresis) and biological phenotypes. Second, correlations with a transcriptomic data set (microarrays) were investigated to combine different layers of expression with a focus on transcriptional regulation. Muscle glycogen content and myosin heavy chain polymorphism were good descriptors of muscle maturity and were used for further data integration analysis. Using 89 identified unique proteins, network inference, correlation with biological phenotypes and functional enrichment revealed that mitochondrial oxidative metabolism was a key determinant of neonatal muscle maturity. Some proteins, including ATP5A1 and CKMT2, were important nodes in the network related to muscle metabolism. Transcriptomic data suggest that overexpression of mitochondrial PCK2 was involved in the greater glycogen content of Meishan fetuses at 110 dg. GPD1, an enzyme involved in the mitochondrial oxidation of cytosolic NADH, was overexpressed in Meishan. Thirty-one proteins exhibited a positive correlation between mRNA and protein levels in both extreme fetal genotypes, suggesting transcriptional regulation. Gene ontology enrichment and Ingenuity analyses identified PPARGC1A and ESR1 as possible transcriptional factors positively involved in late fetal muscle maturation.

Leveraging Genomic Data in Smoking Cessation Trials in the Era of Precision Medicine: Why and How

Implications

This article outlines a framework for the consistent integration of biological data/samples into smoking cessation pharmacotherapy trials, aligned with the objectives of the recently unveiled Precision Medicine Initiative. Our goal is to encourage and provide support for treatment researchers to consider biosample collection and genotyping their existing samples as well as integrating genetic analyses into their study design in order to realize precision medicine in treatment of nicotine dependence.

Ion Pair Chromatography for Endogenous Metabolites LC-MS Analysis in Tissue Samples Following Targeted Acquisition

A protocol for the preparation of tissue extracts for the targeted analysis of ca. 150 polar metabolites, including those involved in central carbon metabolism is described, using a reversed-phase ion pair U(H)PLC-MS method. Data collection enabled by multiple-reaction monitoring provides highly specific, sensitive acquisition of metabolic intermediates with a wide range of physicochemical properties and pathway coverage. Technical aspects are discussed for method transfer along with the basic principles of sample sequence setup, data analysis, and validation. General comments are given to help the assessment of data quality and system performance.

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm

INTRODUCTION

Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease.

OBJECTIVES

In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses.

METHODS

Urine ¹H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained.

RESULTS

Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients.

CONCLUSION

These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration

Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different 'omics' data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCoreTM, MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other 'omics' data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration.

1H NMR-based metabolomic fingerprinting to determine metabolite levels in serrano peppers (Capsicum annum L.) grown in two different regions

Chili pepper (Capsicum annuum) is the most important and emblematic condiment in Mexican food. Serrano pepper is a variety of C. annuum that is traditionally cultivated in Mexico and commercialized in local markets. The aim of this study was to describe the ¹H NMR metabolomic profiling of the aqueous phase of serrano peppers harvested from two distinct regions, in the states of Veracruz and Oaxaca, Mexico. According to the current results, aspartate citrate, lactate, leucine and sucrose were found at higher amount in the serrano peppers from Veracruz. On the other hand, acetate, formate, fumarate, malonate, phosphocholine, pyruvate and succinate showed the highest abundance in this product from Oaxaca. These are the main metabolites that distinguish one group from the other. The spectrometric method reported presently is characterized by great simplicity, robustness and reproducibility. Thus, this technique can be used for establishing reliable metabolomic fingerprints of serrano peppers grown under different environmental conditions.

PCR and Omics Based Techniques to Study the Diversity, Ecology and Biology of Anaerobic Fungi: Insights, Challenges and Opportunities

Anaerobic fungi (phylum Neocallimastigomycota) are common inhabitants of the digestive tract of mammalian herbivores, and in the rumen, can account for up to 20% of the microbial biomass. Anaerobic fungi play a primary role in the degradation of lignocellulosic plant material. They also have a syntrophic interaction with methanogenic archaea, which increases their fiber degradation activity. To date, nine anaerobic fungal genera have been described, with further novel taxonomic groupings known to exist based on culture-independent molecular surveys. However, the true extent of their diversity may be even more extensively underestimated as anaerobic fungi continue being discovered in yet unexplored gut and non-gut environments. Additionally many studies are now known to have used primers that provide incomplete coverage of the Neocallimastigomycota. For ecological studies the internal transcribed spacer 1 region (ITS1) has been the taxonomic marker of choice, but due to various limitations the large subunit rRNA (LSU) is now being increasingly used. How the continued expansion of our knowledge regarding anaerobic fungal diversity will impact on our understanding of their biology and ecological role remains unclear; particularly as it is becoming apparent that anaerobic fungi display niche differentiation. As a consequence, there is a need to move beyond the broad generalization of anaerobic fungi as fiber-degraders, and explore the fundamental differences that underpin their ability to exist in distinct ecological niches. Application of genomics, transcriptomics, proteomics and metabolomics to their study in pure/mixed cultures and environmental samples will be invaluable in this process. To date the genomes and transcriptomes of several characterized anaerobic fungal isolates have been successfully generated. In contrast, the application of proteomics and metabolomics to anaerobic fungal analysis is still in its infancy. A central problem for all analyses, however, is the limited functional annotation of anaerobic fungal sequence data. There is therefore an urgent need to expand information held within publicly available reference databases. Once this challenge is overcome, along with improved sample collection and extraction, the application of these techniques will be key in furthering our understanding of the ecological role and impact of anaerobic fungi in the wide range of environments they inhabit.

Metabolomics differences between silkworms (Bombyx mori) reared on fresh mulberry (Morus) leaves or artificial diets

Silkworms (Bombyx mori) reared on artificial diets have great potential applications in sericulture. However, the mechanisms underlying the enhancement of metabolic utilization by altering silkworm nutrition are unclear. The aim of this study was to investigate the mechanisms responsible for the poor development and low silk protein synthesis efficiency of silkworms fed artificial diets. After multi-generational selection of the ingestive behavior of silkworms to artificial diets, we obtained two strains, one of which developed well and another in which almost all its larvae starved to death on the artificial diets. Subsequently, we analyzed the metabolomics of larval hemolymph by gas chromatography/liquid chromatography–mass spectrometry, and the results showed that vitamins were in critically short supply, whereas the nitrogen metabolic end product of urea and uric acid were enriched substantially, in the hemolymph of the silkworms reared on the artificial diets. Meanwhile, amino acid metabolic disorders, as well as downregulation of carbohydrate metabolism, energy metabolism, and lipid metabolism, co-occurred. Furthermore, 10 male-dominant metabolites and 27 diet-related metabolites that differed between male and female silkworms were identified. These findings provide important insights into the regulation of silkworm metabolism and silk protein synthesis when silkworms adapt to an artificial diet.

Acute psychoactive and toxic effects of D. metel on mice explained by 1H NMR based metabolomics approach

Datura metel L. (D. metel) is one well-known folk medical herb with wide application and also the most abused plants all over the world, mainly for spiritual or religious purpose, over-dosing of which often produces poisonous effects. In this study, mice were orally administered with the extract of D. metel once a day at doses for 10 mg/kg and 40 mg/kg for consecutive 4 days, ¹H NMR based metabolomics approach aided with histopathological inspection and biochemical assays were used for the first time to study the psychoactive and toxic effects of D. metel. Histopathological inspection revealed obvious hypertrophy of hepatocytes, karyolysis and karyorrhexis in livers as well as distinct nerve cell edema, chromatolysis and lower nuclear density in brains. The increased tissue level of methane dicarboxylic aldehyde (MDA) and superoxide dismutase (SOD), decreased tissue level of glutathione (GSH) along with increased serum level of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) suggested brain and liver injury induced by D. metel. Orthogonal signal correction-partial least squares-discriminant analysis (OSC-PLS-DA) of NMR profiles supplemented with correlation network analysis revealed significant altered metabolites and related pathway that contributed to oxidative stress, energy metabolism disturbances, neurotransmitter imbalance and amino acid metabolism disorders.

Metabolomics-assisted biotechnological interventions for developing plant-based functional foods and nutraceuticals

Today, the dramatic changes in types of food consumed have led to an increased burden of chronic diseases. Therefore, the emphasis of food research is not only to ensure quality food that can supply adequate nutrients to prevent nutrition related diseases, but also to ensure overall physical and mental-health. This has led to the concept of functional foods and nutraceuticals (FFNs), which can be ideally produced and delivered through plants. Metabolomics can help in getting the most relevant functional information, and thus has been considered the greatest -OMICS technology to date. However, metabolomics has not been exploited to the best potential in plant sciences. The technology can be leveraged to identify the health promoting compounds and metabolites that can be used for the development of FFNs. This article reviews (i) plant-based FFNs-related metabolites and their health benefits; (ii) use of different analytic platforms for targeted and non-targeted metabolite profiling along with experimental considerations; (iii) exploitation of metabolomics to develop FFNs in plants using various biotechnological tools; and (iv) potential use of metabolomics in plant breeding. We have also provided some insights into integration of metabolomics with latest genome editing tools for metabolic pathway regulation in plants.

Comparative metabolome analysis of wheat embryo and endosperm reveals the dynamic changes of metabolites during seed germination

In this study, we performed the first comparative metabolomic analysis of the wheat embryo and endosperm during seed germination using GC-MS/MS. In total, 82 metabolites were identified in the embryo and endosperm. Principal component analysis (PCA), metabolite-metabolite correlation and hierarchical cluster analysis (HCA) revealed distinct dynamic changes in metabolites between the embryo and endosperm during seed germination. Generally, the metabolite changes in the embryo were much greater than those in the endosperm, suggesting that the embryo is more active than the endosperm during seed germination. Most amino acids were upregulated in both embryo and endosperm, while polysaccharides and organic acids associated with sugars were mainly downregulated in the embryo. Most of the sugars showed an upregulated trend in the endosperm, but significant changes in lipids occurred only in the embryo. Our results suggest that the embryo mobilises mainly protein and lipid metabolism, while the endosperm mobilises storage starch and minor protein metabolism during seed germination. The primary energy was generated mainly in the embryo by glycolysis during seed imbibition. The embryo containing most of the genetic information showed increased nucleotides during seed germination process, indicating more active transcription and translation metabolisms.

Effect of different marinating conditions on the evolution of spoilage microbiota and metabolomic profile of chicken breast fillets

Five different marinades were prepared containing lemon juice, apple cider vinegar, pomegranate juice and combinations of them. Three different temperatures (4, 10, and 20 °C) and five marinating time intervals (1, 3, 6, and 9 h) were tested. Microbial, physicochemical as well as sensory analyses were performed to assess marination. Noticeable microbial reductions and satisfactory sensory results were observed only in samples treated for short time (1 and 3 h). The marinade in which pomegranate and lemon juices were combined caused a decrease in microbial counts and led to desirable sensory attributes. Each of the marinades was characterized by a distinguishable organic acid profile, while the discrimination of the samples, based on organic acid concentration, between low (1 and 3) and high (6 and 9) marinating time was feasible. It can be concluded that marinating time affected the indigenous microbiota and the sensory characteristics of chicken meat while pomegranate could be a promising marinating ingredient from a microbiological and physicochemical perspective.

Genomics and metabolomics of post-weaning return to estrus

The weaning-to-estrus interval is a multifaceted trait that has the potential to substantially improve production efficiency in today's global swine industry, if variation in this measure can be reduced. Systems-biology approaches should help close the knowledge gap and increase selection tools and management strategies-such as gilt development programs, farrowing, and lactation feeding programs-to decrease the weaning-to-estrus interval. Metabolomics, the study of small compounds within biofluids and tissues, provides links between genotype and phenotype. Given the complexity and influence of the environment on the weaning-to-estrus interval, incorporating metabolomics data will provide valuable insight and guidance for future physiological as well as genetic and genomic strategies to reduce this interval, thereby improving sow productivity.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Plant lipidomics at the crossroads: From technology to biology driven science

The identification and quantification of lipids from plant tissues have become commonplace and many researchers now incorporate lipidomics approaches into their experimental studies. Plant lipidomics research continues to involve technological developments such as those in mass spectrometry imaging, but in large part, lipidomics approaches have matured to the point of being accessible to the novice. Here we review some important considerations for those planning to apply plant lipidomics to their biological questions, and offer suggestions for appropriate tools and practices. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

The role of metabolomics in determination of new dietary biomarkers

Traditional methods for the assessment of dietary intake are prone to error; in order to improve and enhance these methods increasing interest in the identification of dietary biomarkers has materialised. Metabolomics has emerged as a key tool in the area of dietary biomarker discovery and to date the use of metabolomics has identified a number of putative biomarkers. Applications to identify novel biomarkers of intake have in general taken three approaches: (1) specific acute intervention studies to identify specific biomarkers of intake; (2) searching for biomarkers in cohort studies by correlating to self-reported intake of a specific food/food group(s); (3) analysing dietary patterns in conjunction with metabolomic profiles to identify biomarkers and nutritypes. A number of analytical technologies are employed in metabolomics as currently there is no single technique capable of measuring the entire metabolome. These approaches each have their own advantages and disadvantages. The present review will provide an overview of current technologies and applications of metabolomics in the determination of new dietary biomarkers. In addition, it will address some of the current challenges in the field and future outlooks.

Metabolomic analysis of human oral cancer cells with adenylate kinase 2 or phosphorylate glycerol kinase 1 inhibition

The purpose of this study was to use liquid chromatography-mass spectrometry (LC-MS) with XCMS for a quantitative metabolomic analysis of UM1 and UM2 oral cancer cells after knockdown of metabolic enzyme adenylate kinase 2 (AK2) or phosphorylate glycerol kinase 1 (PGK1). UM1 and UM2 cells were initially transfected with AK2 siRNA, PGK1 siRNA or scrambled control siRNA, and then analyzed with LC-MS for metabolic profiles. XCMS analysis of the untargeted metabolomics data revealed a total of 3200-4700 metabolite features from the transfected UM1 or UM2 cancer cells and 369-585 significantly changed metabolites due to AK2 or PGK1 suppression. In addition, cluster analysis showed that a common group of metabolites were altered by AK2 knockdown or by PGK1 knockdown between the UM1 and UM2 cells. However, the set of significantly changed metabolites due to AK2 knockdown was found to be distinct from those significantly changed by PGK1 knockdown. Our study has demonstrated that LC-MS with XCMS is an efficient tool for metabolomic analysis of oral cancer cells, and knockdown of different genes results in distinct changes in metabolic phenotypes in oral cancer cells.

NMR-Based Metabolomics of Oral Biofluids

NMR-based metabolomics is an established technique for characterizing the metabolite profile of biological fluids and investigating how metabolite profiles change in response to biological and/or clinical stimuli. Thus, NMR-based metabolomics has the potential to discover biomarkers for diagnosis, prognosis, and/or therapy of clinical conditions, as well as to unravel the physiology underlying clinical conditions. Here, we describe a detailed protocol for NMR-based metabolomics of oral biofluids, including sample collection, sample handling, NMR data acquisition, and processing. In addition, we give a general overview of the statistical analysis of the resulting metabolomic data.

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

¹H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly.