Untargeted metabolomics

Validation of two LCHRMS methods for large-scale untargeted metabolomics of serum samples: Strategy to establish method fitness-for-purpose

Untargeted metabolomics by LCHRMS is a powerful tool to enhance our knowledge of pathophysiological processes. Whereas validation of a bioanalytical method is customary in most analytical chemistry fields, it is rarely performed for untargeted metabolomics. This study aimed to establish and validate an analytical platform for a long-term, clinical metabolomics study. Sample preparation was performed with an automated liquid handler and four analytical methods were developed and evaluated. The validation study spanned three batches with twelve runs using individual serum samples and various quality control samples. Data was acquired with untargeted acquisition and only metabolites identified at level 1 were evaluated. Validation parameters were set to evaluate key performance metrics relevant for the intended application: reproducibility, repeatability, stability, and identification selectivity, emphasizing dataset intrinsic variance. Concordance of semi-quantitative results between methods was evaluated to identify potential bias. Spearman rank correlation coefficients (rs) were calculated from individual serum samples. Of the four methods tested, two were selected for validation. A total of 47 and 55 metabolites (RPLC-ESI+- and HILIC-ESI--HRMS, respectively) met specified validation criteria. Quality assurance involved system suitability testing, sample release, run release, and batch release. The median repeatability and within-run reproducibility as coefficient of variation% for metabolites that passed validation on RPLC-ESI+- and HILIC-ESI--HRMS were 4.5 and 4.6, and 1.5 and 3.8, respectively. Metabolites that passed validation on RPLC-ESI+-HRMS had a median D-ratio of 1.91, and 89 % showed good signal intensity after ten-fold dilution. The corresponding numbers for metabolites with the HILIC-ESI--HRMS method was 1.45 and 45 %, respectively. The rs median ({range}) for metabolites that passed validation on RPLC-ESI+- was 0.93 (N = 9 {0.69-0.98}) and on HILIC-ESI--HRMS was 0.93 (N = 22 {0.55-1.00}). The validated methods proved fit-for-purpose and the laboratory thus demonstrated its capability to produce reliable results for a large-scale, untargeted metabolomics study. This validation not only bolsters the reliability of the assays but also significantly enhances the impact and credibility of the hypotheses generated from the studies. Therefore, this validation study serves as a benchmark in the documentation of untargeted metabolomics, potentially guiding future endeavors in the field.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Addressing the untargeted lipidomics challenge in urine samples: Comparative study of extraction methods by UHPLC-ESI-QTOF-MS

Urine analysis has remained a fundamental and widely used method in clinical diagnostics for over a century. With its minimal invasive nature and comprehensive range of analytes, urine has established itself as a clinical diagnostic tool for various disorders, including renal, urological, metabolic, and endocrine diseases. Furthermore, urine's unique attributes make it an attractive matrix for biomarker discovery, as well as in assessing the metabolic and physiological states of patients and healthy individuals alike. However, limitations in our knowledge of average values and sources of urinary lipids decrease the wider clinical application of urinary lipidomics. In this context, untargeted lipidomics analysis relies heavily on the extraction and analysis of lipids in biological samples. Nevertheless, this type of analysis presents challenges in lipid identification due to the diverse nature of lipids. Therefore, proper sample treatment before analysis is crucial to obtain robust and reproducible lipidomic profiles. To address this gap, we conducted a comparative study of a urine pool sample collected from twenty healthy volunteers using four different lipid extraction methods: one biphasic and three monophasic protocols. The extracted lipids were then analyzed using UHPLC-MS and MS/MS, and the semi-quantification of all the accurately annotated lipid species was performed for each extraction method.

Metabolic diversity of human macrophages: potential influence on Staphylococcus aureus intracellular survival

Staphylococcus aureus is a leading cause of medical device-associated biofilm infections. This is influenced by the ability of S. aureus biofilm to evade the host immune response, which is partially driven by the anti-inflammatory cytokine interleukin-10 (IL-10). Here, we show that treatment of human monocyte-derived macrophages (HMDMs) with IL-10 enhanced biofilm formation, suggesting that macrophage anti-inflammatory programming likely plays an important role during the transition from planktonic to biofilm growth. To identify S. aureus genes that were important for intracellular survival in HMDMs and how this was affected by IL-10, transposon sequencing was performed. The size of the S. aureus essential genome was similar between unstimulated HMDMs and the outgrowth control (18.5% vs 18.4%, respectively, with 54.4% overlap) but increased to 22.5% in IL-10-treated macrophages, suggesting that macrophage polarization status exerts differential pressure on S. aureus. Essential genes for S. aureus survival within IL-10-polarized HMDMs were dominated by negative regulatory pathways, including nitrogen and RNA metabolism, whereas S. aureus essential genes within untreated HMDMs were enriched in biosynthetic pathways such as purine and pyrimidine biosynthesis. To explore how IL-10 altered the macrophage intracellular metabolome, targeted metabolomics was performed on HMDMs from six individual donors. IL-10 treatment led to conserved alterations in distinct metabolites that were increased (dihydroxyacetone phosphate, glyceraldehyde-3-phosphate, and acetyl-CoA) or reduced (fructose-6-phosphate, aspartic acid, and ornithine) across donors, whereas other metabolites were variable. Collectively, these findings highlight an important aspect of population-level heterogeneity in human macrophage responsiveness that should be considered when translating results to a patient population.IMPORTANCEOne mechanism that Staphylococcus aureus biofilm elicits in the host to facilitate infection persistence is the production of the anti-inflammatory cytokine interleukin-10 (IL-10). Here, we show that exposure of human monocyte-derived macrophages (HMDMs) to IL-10 promotes S. aureus biofilm formation and programs intracellular bacteria to favor catabolic pathways. Examination of intracellular metabolites in HMDMs revealed heterogeneity between donors that may explain the observed variability in essential genes for S. aureus survival based on nutrient availability for bacteria within the intracellular compartment. Collectively, these studies provide novel insights into how IL-10 polarization affects S. aureus intracellular survival in HMDMs and the importance of considering macrophage heterogeneity between human donors as a variable when examining effector mechanisms.

Long-term effects of oil contamination on soil quality and metabolic function

Widespread soil contamination with oil and the toxicity of petroleum hydrocarbons to soil biota make it extremely important to study microbial responses to oil stress. Soil metabolites reflect the main metabolic pathways in the soil microbial community. The examination of changes in the soil metabolic profile and metabolic function is essential for a better understanding of the nature of the pollution and restoration of the disturbed soils. The present study aimed to assess the long-term effect of oil on the ecological state of the soil, evaluate quantitative and qualitative differences in metabolite composition between soil contaminated with oil and non-contaminated soil, and reveal biologically active metabolites that are related to oil contamination and can be used for contamination assessment. A long-term field experiment was conducted to examine the effects of various oil concentrations on the biochemical properties and metabolic profile of the soil. Podzolic soil contaminated with oil demonstrated the long-term inhibition of soil biological activity and vegetation. Oil affected the metabolic activity of soil fungi increasing the production of toxic metabolites. A metabolomic approach was employed to determine soil metabolites. The metabolite profile was found to vary greatly between oil-contaminated and non-contaminated soils. Carbohydrates had the largest number of metabolites negatively affected by oil, while the content of organic acids, phenolic compounds, and terpenoids was mainly increased in oil-contaminated soil. The evaluation of the long-term impact of oil on microbial metabolism can make a valuable contribution to the assessment of soil quality and the activity of soil microorganisms being under stress from oil pollution. The results contribute to a further understanding of the role of microorganisms in the ecological functions of contaminated soil, which can be useful in the development of rehabilitation strategies for disturbed sites.

Metabolomics signature of cardiovascular disease in patients with diabetes, a narrative review

Objectives

The exact underlying mechanism of developing diabetes-related cardiovascular disease (CVD) among patients with type 2 diabetes (T2D) is not clear. Metabolomics can provide a platform enabling the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The aim of this review is to summarize the available evidence on the relationship between metabolomics and cardiovascular diseases in patients with diabetes.

Methods

The literature was searched to find out studies that have investigated the relationship between the alteration of specific metabolites and cardiovascular diseases in patients with diabetes.

Results

Evidence proposed that changes in the metabolism of certain amino acids, lipids, and carbohydrates, independent of traditional CVD risk factors, are associated with increased CVD risk.

Conclusions

Metabolomics can provide a platform to enable the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The association of the alteration in specific metabolites with CVD may be considered in the investigations for the development of new therapeutic targets for the prevention of CVD in patients with diabetes mellitus.

Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data

Purpose

Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning.

Experimental design

Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC-MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied.

Results

PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4-2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods.

Conclusion

Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.

Multi-omics Analysis Reveals the Crucial Mediators of DJB in the Treatment of Type 2 Diabetes

PURPOSE

Duodenal-jejunal bypass (DJB) has a definite hypoglycemic effect; however, the intrinsic mechanisms remain unclear. The purpose of this study was to determine whether DJB may cause changes in the gut microbiota and metabolite of portal venous blood and to explore the effects of DJB on blood glucose metabolism.

METHODS

T2DM was induced in rats with a high-fat diet and a low dose of streptozotocin, which were randomly divided into two groups: Sham operation and DJB.

RESULTS

DJB significantly improved several diabetic parameters. 16S rRNA analyses showed that the compositions of the gut microbiota were significantly different between the two groups. The results of metabolomics showed that DJB could significantly regulate the metabolites, among which diaminopimelic acid and isovaleric acid had a significant down-regulation in the DJB group. Transcriptomic analysis showed that DJB can regulate the expression of hepatic genes related to abnormal glucose metabolism, such as Ltc4s, Alox15, Ggt1, Gpat3, and Cyp2c24. Correlation analyses showed that diaminopimelic acid was positively associated with Allobaculum, Serratia, and Turicibacter. There was a significant correlation between diaminopimelic acid and Gpat3, and its Spearman correlation coefficient was the highest among metabolite-DEG pairs (ρ=0.97).

DISCUSSIONS

These results suggest an important cue of the relation between the diaminopimelic acid, Gpat3, and gut microbiome in the mechanism by which DJB can improve glucose metabolism.

Mass spectrometry-based metabolomics for discovering active ingredients and exploring action mechanism of herbal medicine

Natural products derived from herbal medicine are a fruitful source of lead compounds because of their structural diversity and potent bioactivities. However, despite the success of active compounds derived from herbal medicine in drug discovery, some approaches cannot effectively elucidate the overall effect and action mechanism due to their multi-component complexity. Fortunately, mass spectrometry-based metabolomics has been recognized as an effective strategy for revealing the effect and discovering active components, detailed molecular mechanisms, and multiple targets of natural products. Rapid identification of lead compounds and isolation of active components from natural products would facilitate new drug development. In this context, mass spectrometry-based metabolomics has established an integrated pharmacology framework for the discovery of bioactivity-correlated constituents, target identification, and the action mechanism of herbal medicine and natural products. High-throughput functional metabolomics techniques could be used to identify natural product structure, biological activity, efficacy mechanisms, and their mode of action on biological processes, assisting bioactive lead discovery, quality control, and accelerating discovery of novel drugs. These techniques are increasingly being developed in the era of big data and use scientific language to clarify the detailed action mechanism of herbal medicine. In this paper, the analytical characteristics and application fields of several commonly used mass spectrometers are introduced, and the application of mass spectrometry in the metabolomics of traditional Chinese medicines in recent years and its active components as well as mechanism of action are also discussed.

Direct introduction MALDI FTICR MS based on dried droplet deposition applied to non-targeted metabolomics on Pisum Sativum root exudates

Non-targeted metabolomic approaches based on direct introduction (DI) through a soft ionization source are nowadays used for large-scale analysis and wide cover-up of metabolites in complex matrices. When coupled with ultra-high-resolution Fourier-Transform ion cyclotron resonance (FTICR MS), DI is generally performed through electrospray (ESI), which, despite the great analytical throughput, can suffer of matrix effects due to residual salts or charge competitors. In alternative, matrix assisted laser desorption ionization (MALDI) coupled with FTICR MS offers relatively high salt tolerance but it is mainly used for imaging of small molecule within biological tissues. In this study, we report a systematic evaluation on the performance of direct introduction ESI and MALDI coupled with FTICR MS applied to the analysis of root exudates (RE), a complex mixture of metabolites released from plant root tips and containing a relatively high salt concentration. Classic dried droplet deposition followed by screening of best matrices and ratio allowed the selection of high ranked conditions for non-targeted metabolomics on RE. Optimization of MALDI parameters led to improved reproducibility and precision. A RE desalted sample was used for comparison on ionization efficiency of the two sources and ion enhancement at high salinity was highlighted in MALDI by spiking desalted solution with inorganic salts. Application of a true lyophilized RE sample exhibited the complementarity of the two sources and the ability of MALDI in the detection of undisclosed metabolites suffering of matrix effects in ESI mode.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

Chemical-Biology and Metabolomics Studies in Phage-Host Interactions

For many years, several studies have explored the molecular mechanisms involved in the infection of bacteria by their specific phages to understand the main infection strategies and the host defense strategies. The modulation of the mechanisms involved in the infection, as well as the expression of key substances in the development of the different life cycles of phages, function as a natural source of strategies capable of promoting the control of different pathogens that are harmful to human and animal health. Therefore, this chapter aims to provide an overview of the mechanisms involved in virus-bacteria interaction to explore the main compounds produced or altered as a chemical survival strategy and the metabolism modulation when occurring a host-phage interaction. In this context, emphasis will be given to the chemistry of peptides/proteins and enzymes encoded by bacteriophages in the control of pathogenic bacteria and the use of secondary metabolites recently reported as active participants in the mechanisms of phage-bacteria interaction. Finally, metabolomics strategies developed to gain new insights into the metabolism involved in the phage-host interaction and the metabolomics workflow in host-phage interaction will be presented.

Metabolites of Pinang Yaki (Areca vestiaria) Fruit Extract: A Metabolite Profiling Study

Background: Pinang yaki has bioactive compounds that have potential as a new herbal supplement. A better understanding of the bioactive compounds of pinang yaki using untargeted metabolomic profiling studies will provide clearer insight into the health benefits of pinang yaki and in particular its potential for the therapy and prevention of Covid-19.   Methods: Fresh samples of pinang yaki ( Areca vestiaria) are obtained from forests in North Sulawesi Province, Indonesia. Samples were used for untargeted metabolomics analysis by UPLC-MS.   Results: Based on an untargeted metabolomic profiling study of pinang yaki, 2504 compounds in ESI- and 2645 compounds in ESI+ were successfully obtained. After the analysis, 356 compounds in ESI- and 543 compounds in ESI+ were identified successfully. Major compounds Alpha-Chlorohydrin (PubChem ID: 7290) and Tagatose (PubChem ID: 439312) were found in ESI+ and ESI-.   Discussion: The Top 10 metabolites from pinang yaki extract (ESI+) juga have been indicated in preventing SARS Cov2 infection and have exhibited good neuroprotective immunity. Benzothiazole (PubChem ID: 7222), L-isoleucine (PubChem ID: 6306), D-glucono-delta-lactone (PubChem ID: 736), Diethylpyrocarbonate (PubChem ID: 3051), Bis(2-Ethylhexyl) amine (PubChem ID: 7791), Cinnamic acid (PubChem ID: 444539), and Trigonelline (PubChem ID: 5570) also had potential effects as an antiviral, anti-inflammatory, and anti-Covid19.   Conclusion: Untargeted metabolomic profiling showed many bioactive compounds contained in pinang yaki ( Areca vestiaria) extract. The top 10 compounds have been identified and explored for their potential benefits as anti-Covid19 supplement products. This is a preliminary study which still needs further research such as preclinical and clinical trials.

Bacillus subtilis inhibits intestinal inflammation and oxidative stress by regulating gut flora and related metabolites in laying hens

Bacillus subtilis is one of the most popular commercial probiotics used in farm animal production. However, its potential mechanisms are not very clear. The aim of this study was to investigate the effects of dietary Bacillus subtilis on intestinal histomorphology, innate immunity, microbiota composition, transcriptomics, and related metabolomics. Twenty-four 48-week-old Lohman Pink-shell laying hens were randomly divided into two groups: a basic diet and the basic diet supplemented with Bacillus subtilis (0.5 g/kg) for a 9-week experiment. At the end of the experiment, tissues of the duodenum, ileum, and jejunum as well as cecal content of each bird were collected for microstructure, PCR, transcriptome, metabolome, and 16S rRNA analyses. The results showed that dietary Bacillus subtilis supplement had no effect on the intestinal microstructure. However, Bacillus subtilis increased mRNA expression of tight junction protein occludin (P < 0.05), while reduced mRNA expression of lipopolysaccharide-induced TNF factor (P < 0.01) in the duodenum. Moreover, transcriptomic results indicated that most of Bacillus subtilis supplement-induced differential genes were associated with inflammation and immunity, including cytochrome b-245 beta chain, transferrin, and purinergic receptor P2X 7, resulting in a decrease in Malondialdehyde level (P < 0.05) in the duodenum. In addition, at the genus level, Bacillus subtilis supplement enriched the potential beneficial bacteria, Candidatus_Soleaferrea (P = 0.02) but inhibited the harmful bacteria including Lachnospiraceae_FCS020_group, Ruminiclostridium, Lachnospiraceae_UCG-010, and Oxalobacter. Metabolomic results revealed that N-Acetylneuraminic acid and ADP were increased by fed Bacillus subtilis. These results suggest that dietary Bacillus subtilis could inhibit gut inflammation and improve antioxidative status and barrier integrity of the duodenum via regulating gut microbial composition in laying hens.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

Dysregulation of Glycerophosphocholines in the Cutaneous Lesion Caused by Leishmania major in Experimental Murine Models

Cutaneous leishmaniasis (CL) is the most common disease form caused by a Leishmania parasite infection and considered a neglected tropical disease (NTD), affecting 700,000 to 1.2 million new cases per year in the world. Leishmania major is one of several different species of the Leishmania genus that can cause CL. Current CL treatments are limited by adverse effects and rising resistance. Studying disease metabolism at the site of infection can provide knowledge of new targets for host-targeted drug development. In this study, tissue samples were collected from mice infected in the ear or footpad with L. major and analyzed by untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). Significant differences in overall metabolite profiles were noted in the ear at the site of the lesion. Interestingly, lesion-adjacent, macroscopically healthy sites also showed alterations in specific metabolites, including selected glycerophosphocholines (PCs). Host-derived PCs in the lower m/z range (m/z 200-799) showed an increase with infection in the ear at the lesion site, while those in the higher m/z range (m/z 800-899) were decreased with infection at the lesion site. Overall, our results expanded our understanding of the mechanisms of CL pathogenesis through host metabolism and may lead to new curative measures against infection with Leishmania.

Advanced cell culture techniques for cancer research

The incessant increase number of cancer cases, motivates scientists to constantly develop and search for new therapies. Along with the dynamic development of anti-cancer drugs and therapies, we are witnessing huge progress in the world of science - the development of personalized medicine. An inseparable element is also a very strong trend in the development of new in vitro animal models for chemotherapeutic research. Cell cultures are commonly undertaken by research models before animal testing. They are the basis for the development of new diagnostic and cancer treatments. It should be emphasized that basic research is a strong foundation for any therapy introduced. This chapter provides an overview of the modern cell culture techniques that are currently developing, which allow the introduction of modern models that reflect the organs and physiological system. Currently available cell culture methods are a key aspect of studying these interactions, however, a method that eliminates the limitations of standard methods is still being sought.

UPLC-TOF-MS/MS metabolomics analysis of zebrafish metabolism by spirotetramat

Spirotetramat, a member of tetronic and tetramic acid derivatives, is a unique insecticide and acaricide. Although the effect on zebrafish embryos lipid biosynthesis of spirotetramat has been characterized, the energy metabolism and toxic effect mechanism warrant further investigation. To investigate the toxic mechanism of spirotetramat on energy metabolism, zebrafish embryos were exposed to 100, 500 and 1000 µg/L of spirotetramat for 4 days. Untargeted metabolomics showed the synthesis and degradation of ketone pathway metabolites (R)-3-Hydroxybutyric acid and Acetoacetate significantly decreased, as well as increasing the abundance of Anti-Acetyl Coenzyme A Carboxylase protein (ACC1). Down-regulation of the genes related to ß-oxidation and the tricarboxylic acid cycle in the embryos show decreased energy metabolism. Carnitine palmitoyltransferase 1 (CPT- I) significantly decreased while citrate synthase (CS) significantly increased. Additionally, mitochondrial lesions in embryos were found using electron microscopy. Our study provides novel and robust perspectives, which show that spirotetramat treatment in embryos leads to metabolic disturbances that adversely affect cellular energy homeostasis.

Comparative Analysis of Metabolites in the Liver of Muscovy Ducks at Different Egg-Laying Stages Using Nontargeted Ultra-High-Performance Liquid Chromatography-Electrospray Mass Spectrometry-Based Metabolomics

Liver plays an important physiological function in the synthesis of yolk materials during egg laying in birds. Liver metabolite profiles of Muscovy ducks at different egg-laying stages from the perspective of nontargeted metabolomics were analyzed in this study. Twelve Muscovy ducks were selected at pre-laying (22 weeks, TT group), laying (40 weeks, FT group), and post-laying (60 weeks, ST group) stages, resulting in 36 hepatic metabolite profiles by using ultra-high-performance liquid chromatography-electrospray mass spectrometry. A total of 324 differential metabolites (156 increased and 168 decreased) in FT as compared to the TT (FT/TT) group and 332 differential metabolites (120 increased and 212 decreased) in ST as compared to the FT (ST/FT) group were screened out. Metabolic pathways enriched in FT/TT and ST/FT groups were mainly amino acid metabolism, glycerophospholipid metabolism, nucleotide metabolism, and vitamin metabolism. The amino acid metabolism pathways were upregulated in the FT/TT group and downregulated in the ST/FT group (P < 0.05). The glutathione and ascorbic acid abundances were downregulated, and the choline abundance was upregulated during egg laying (P < 0.05). The liver provides amino acids, lipids, nucleotides, vitamins, and choline, and so on, which are essential materials for yolk precursor synthesis. The decrease in the abundance of glutathione and ascorbic acid indicates that Muscovy ducks might be in a relatively stable physiological state during egg laying.

Metabolite Structure Assignment Using In Silico NMR Techniques

A major challenge for metabolomic analysis is to obtain an unambiguous identification of the metabolites detected in a sample. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful, and generally applicable spectroscopic tool that can be used to ascertain the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using computational NMR techniques depends on how much of the relevant conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high-level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF)-based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations, and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time by 2 orders of magnitude over methods that optimize the conformations using fully ab initio methods, while still producing good agreement with experimental observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for a large number of conformationally flexible metabolites.

Comparison of Metabolomic Profiles of Organs in Mice of Different Strains Based on SPME-LC-HRMS

Given that the extent to which genetics alters the metabolomic profile of tissues is still poorly understood, the current study aimed to characterize and investigate the metabolite profiles of brain, liver, kidney and skeletal muscle of two common mouse inbred strains (BALB/c, C57BL/6) and one outbred stock (CD1) for strain-specific differences. Male mice (n = 15) at the age of 12 weeks were used: BALB/c (n = 5), C57BL/6 (n = 5) and CD1 (n = 5). Solid phase microextraction (SPME) was applied for the extraction of analytes from the tissues. SPME fibers (approximately 0.2 mm in diameter) coated with a biocompatible sorbent (4 mm length of hydrophilic-lipophilic balanced particles) were inserted into each organ immediately after euthanasia. Samples were analyzed using liquid chromatography coupled to a Q-Exactive Focus Orbitrap mass spectrometer. Distinct interstrain differences in the metabolomic patterns of brain and liver tissue were revealed. The metabolome of kidney and muscle tissue in BALB/c mice differed greatly from C57BL/6 and CD1 strains. The main compounds differentiating all the targeted organs were alpha-amino acids, purine nucleotides and fatty acid esters. The results of the study indicate that the baseline metabolome of organs, as well as different metabolic pathways, vary widely among general-purpose models of laboratory mice commonly used in biomedical research.

Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.

Serum metabolites associate with lipid phenotypes among Bogalusa Heart Study participants

BACKGROUND AND AIMS

Dyslipidemia has been identified as a major risk factor for cardiovascular disease. We aimed to identify metabolites and metabolite modules showing novel association with lipids among Bogalusa Heart Study (BHS) participants using untargeted metabolomics.

METHODS AND RESULTS

Untargeted ultrahigh performance liquid chromatography-tandem mass spectroscopy was used to quantify serum metabolites of 1 243 BHS participants (816 whites and 427 African-Americans). The association of single metabolites with lipids was assessed using multiple linear regression models to adjust for covariables. Weighted correlation network analysis was utilized to identify modules of co-abundant metabolites and examine their covariable adjusted correlations with lipids. All analyses were conducted according to race and using Bonferroni-corrected α-thresholds to determine statistical significance. Thirteen metabolites with known biochemical identities showing novel association achieved Bonferroni-significance, p < 1.04 × 10^-5, and showed consistent effect directions in both whites and African-Americans. Twelve were from lipid sub-pathways including fatty acid metabolism (arachidonoylcholine, dihomo-linolenoyl-choline, docosahexaenoylcholine, linoleoylcholine, oleoylcholine, palmitoylcholine, and stearoylcholine), monohydroxy fatty acids (2-hydroxybehenate, 2-hydroxypalmitate, and 2-hydroxystearate), and lysoplasmalogens [1-(1-enyl-oleoyl)-GPE (P-18:1) and 1-(1-enyl-stearoyl)-GPE (P-18:0)]. The gamma-glutamylglutamine, peptide from the gamma-glutamyl amino acid sub-pathway, were also identified. In addition, four metabolite modules achieved Bonferroni-significance, p < 1.39 × 10^-3, in both whites and African-Americans. These four modules were largely comprised of metabolites from lipid sub-pathways, with one module comprised of metabolites which were not identified in the single metabolite analyses.

CONCLUSION

The current study identified 13 metabolites and 4 metabolite modules showing novel association with lipids, providing new insights into the physiological mechanisms regulating lipid levels.

XY-Meta: A High-Efficiency Search Engine for Large-Scale Metabolome Annotation with Accurate FDR Estimation

FDR control has been a huge challenge for large-scale metabolome annotation. Although recent research indicated that the target-decoy strategy could be implemented to estimate FDR, it is hard to perform FDR control due to the difficulty of getting a reliable decoy database because of the complex fragmentation mechanism of metabolites and ubiquitous isomers. To tackle this problem, we developed a decoy generation method, which generates forged spectra from the reference target database by preserving the original reference signals to simulate the presence of isomers of metabolites. Benchmarks on GNPS data sets in Passatutto showed that the decoy database generated by our method is closer to the actual FDR than other methods, especially in the low FDR range (0-0.05). Large-scale metabolite annotation on 35 data sets showed that strict FDR reduced the number of annotated metabolites but increased the spectral efficiency, indicating the necessity of quality control. We recommended that the FDR threshold should be set to 0.01 in large-scale metabolite annotation. We implemented decoy generation, database search, and FDR control into a search engine called XY-Meta. It facilitates large-scale metabolome annotation applications.

Metabolomics as an Emerging Tool for the Study of Plant-Pathogen Interactions

Plants defend themselves from most microbial attacks via mechanisms including cell wall fortification, production of antimicrobial compounds, and generation of reactive oxygen species. Successful pathogens overcome these host defenses, as well as obtain nutrients from the host. Perturbations of plant metabolism play a central role in determining the outcome of attempted infections. Metabolomic analyses, for example between healthy, newly infected and diseased or resistant plants, have the potential to reveal perturbations to signaling or output pathways with key roles in determining the outcome of a plant-microbe interaction. However, application of this -omic and its tools in plant pathology studies is lagging relative to genomic and transcriptomic methods. Thus, it is imperative to bring the power of metabolomics to bear on the study of plant resistance/susceptibility. This review discusses metabolomics studies that link changes in primary or specialized metabolism to the defense responses of plants against bacterial, fungal, nematode, and viral pathogens. Also examined are cases where metabolomics unveils virulence mechanisms used by pathogens. Finally, how integrating metabolomics with other -omics can advance plant pathology research is discussed.

Emerging Insights into the Metabolic Alterations in Aging Using Metabolomics

Metabolomics is the latest 'omics' technology and systems biology science that allows for comprehensive profiling of small-molecule metabolites in biological systems at a specific time and condition. Metabolites are cellular intermediate products of metabolic reactions, which reflect the ultimate response to genomic, transcriptomic, proteomic, or environmental changes in a biological system. Aging is a complex biological process that is characterized by a gradual and progressive decline in molecular, cellular, tissue, organ, and organismal functions, and it is influenced by a combination of genetic, environmental, diet, and lifestyle factors. The precise biological mechanisms of aging remain unknown. Metabolomics has emerged as a powerful tool to characterize the organism phenotypes, identify altered metabolites, pathways, novel biomarkers in aging and disease, and offers wide clinical applications. Here, I will provide a comprehensive overview of our current knowledge on metabolomics led studies in aging with particular emphasis on studies leading to biomarker discovery. Based on the data obtained from model organisms and humans, it is evident that metabolites associated with amino acids, lipids, carbohydrate, and redox metabolism may serve as biomarkers of aging and/or longevity. Current challenges and key questions that should be addressed in the future to advance our understanding of the biological mechanisms of aging are discussed.

Peri-Implantitis Diagnosis and Prognosis Using Biomarkers in Peri-Implant Crevicular Fluid: A Narrative Review

Dental implant diseases, peri-implantitis (PI) and peri-implant mucositis (PIM), have shown wide prevalence in recent studies. Despite the prevalence, diagnosing peri-implant disease (PID) remains challenging as common diagnostic methods of periodontal probing and radiographs may be inaccurate. These methods only document pre-existing destruction rather than current disease activity. Furthermore, there is no current model to predict the progression of PID. Though a predictive model is lacking, biomarkers may offer some potential. Biomarkers are commonly used in medicine to objectively determine disease state, or responses to a therapeutic intervention. Gingival crevicular fluid (GCF) biomarkers have moderate diagnostic validity in periodontitis. Biomarkers in peri-implant crevicular fluid (PICF) also show promising results in regard to their diagnostic and prognostic value. The aim of this review is to summarize the current knowledge of PICF biomarkers in the diagnosis of PID and evaluate their validity to predict disease progression. This review found that PICF studies utilize different methods of sampling and interpretation with varying validity (sensitivity and specificity). A number of promising diagnostic techniques were identified. Commercially available chair-side tests for MMP-8 to diagnose periodontal disease and PID activity are now available. Future directions include proteomics and metabolomics for accurate, site-specific diagnosis and prediction of PID progression. Although more research is needed, this review concludes that the assessment of proinflammatory cytokines (IL-1β, TNFα, MMP-8) in the PICF may be of value to diagnose PI and PIM but current research remains insufficient to indicate whether biomarkers predict peri-implant disease progression.

Perspective: Advancing Understanding of Population Nutrient-Health Relations via Metabolomics and Precision Phenotypes

Diet and lifestyle are vital to population health, but their true contribution is difficult to quantify using traditional methods. Nutrient-health relations are typically based on epidemiological associations that are assessed at the population level, traditionally using self-reported dietary and lifestyle data. Unfortunately, such measures are inherently inaccurate. New technologies such as metabolomics can measure nutritional and micronutrient profiles in body fluids, providing objective evaluation of nutritional status. A critical step toward accurate health prediction models would be the building of integrated repositories of nutritional measures combining subjective methods of reporting with objective metabolomics profiles and precise phenotypic data. Here we outline a roadmap to achieve this goal and discuss both the advantages and risks of this approach. We also highlight the uncertain associations between the complexity of high-dimensional data generated in 'omics research (along with the public confusion this may engender) and the rapid adoption of 'omics approaches by nutrition and health companies to develop nutritional products and services.

Metabolome and transcriptome-wide effects of the carbon storage regulator A in enteropathogenic Escherichia coli

The carbon storage regulator A (CsrA) is a conserved global regulatory system known to control central carbon pathways, biofilm formation, motility, and pathogenicity. The aim of this study was to characterize changes in major metabolic pathways induced by CsrA in human enteropathogenic Escherichia coli (EPEC) grown under virulence factor-inducing conditions. For this purpose, the metabolomes and transcriptomes of EPEC and an isogenic ∆csrA mutant derivative were analyzed by untargeted mass spectrometry and RNA sequencing, respectively. Of the 159 metabolites identified from untargeted GC/MS and LC/MS data, 97 were significantly (fold change ≥ 1.5; corrected p-value ≤ 0.05) regulated between the knockout and the wildtype strain. A lack of csrA led to an accumulation of fructose-6-phosphate (F6P) and glycogen synthesis pathway products, whereas metabolites in lower glycolysis and the citric acid cycle were downregulated. Associated pathways from the citric acid cycle like aromatic amino acid and siderophore biosynthesis were also negatively influenced. The nucleoside salvage pathways were featured by an accumulation of nucleosides and nucleobases, and a downregulation of nucleotides. In addition, a pronounced downregulation of lyso-lipid metabolites was observed. A drastic change in the morphology in the form of vesicle-like structures of the ∆csrA knockout strain was visible by electron microscopy. Colanic acid synthesis genes were strongly (up to 50 fold) upregulated, and the abundance of colanic acid was 3 fold increased according to a colorimetric assay. The findings expand the scope of pathways affected by the csrA regulon and emphasize its importance as a global regulator.

A Protocol to Compare Methods for Untargeted Metabolomics

There are thousands of published methods for profiling metabolites with liquid chromatography/mass spectrometry (LC/MS). While many have been evaluated and optimized for a small number of select metabolites, very few have been assessed on the basis of global metabolite coverage. Thus, when performing untargeted metabolomics, researchers often question which combination of extraction techniques, chromatographic separations, and mass spectrometers is best for global profiling. Method comparisons are complicated because thousands of LC/MS signals (so-called features) in a typical untargeted metabolomic experiment cannot be readily identified with current resources. It is therefore challenging to distinguish methods that increase signal number due to improved metabolite coverage from methods that increase signal number due to contamination and artifacts. Here, we present the credentialing protocol to remove the latter from untargeted metabolomic datasets without having to identify metabolite structures. This protocol can be used to compare or optimize methods pertaining to any step of the untargeted metabolomic workflow (e.g., extraction, chromatography, mass spectrometer, informatic software, etc.).

The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

OBJECTIVE

 The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology.

METHODS

 In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome.

RESULTS

 Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player.

CONCLUSIONS

 The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS² spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

New perspectives on CKD-induced dyslipidemia

INTRODUCTION

Chronic kidney disease (CKD) is a world-wide health concern associated with a significantly higher cardiovascular morbidity and mortality. One of the principal cardiovascular risk factors is the lipid profile. CKD patients have a more frequent and progressive atheromatous disease that cannot be explained by the classical lipid parameters used in the daily clinical practice. Areas covered: The current review summarizes prevailing knowledge on the role of lipids in atheromathosis in CKD patients, including an overview of lipoprotein metabolism highlighting the CKD-induced alterations. Moreover, to obtain information beyond traditional lipid parameters, new state-of-the-art technologies such as lipoprotein subfraction profiling and lipidomics are also reviewed. Finally, we analyse the potential of new lipoprotein subclasses as therapeutic targets in CKD. Expert opinion: The CKD-induced lipid profile has specific features distinct from the general population. Besides quantitative alterations, renal patients have a plethora of qualitative lipid alterations that cannot be detected by routine determinations and are responsible for the excess of cardiovascular risk. New parameters, such as lipoprotein particle number and size, together with new biomarkers obtained by lipidomics will personalize the management of these patients. Therefore, nephrologists need to be aware of new insights into lipoprotein metabolism to improve cardiovascular risk assessment.

Liquid chromatography-high resolution mass spectrometry for the analysis of phytochemicals in vegetal-derived food and beverages

The recent years witnessed a change in the perception of nutrition. Diet does not only provide nutrients to meet the metabolic requirements of the body, but it also constitutes an active way for the consumption of compounds beneficial for human health. Fruit and vegetables are an excellent source of such compounds, thus the growing interest in characterizing phytochemical sources, structures and activities. Given the interest for phytochemicals in food, the development of advanced and suitable analytical techniques for their identification is fundamental for the advancement of food research. In this review, the state of the art of phytochemical research in food plants is described, starting from sample preparation, throughout extract clean-up and compound separation techniques, to the final analysis, considering both qualitative and quantitative investigations. In this regard, from an analytical point of view, fruit and vegetable extracts are complex matrices, which greatly benefit from the use of modern hyphenated techniques, in particular from the combination of high performance liquid chromatography separation and high resolution mass spectrometry, powerful tools which are being increasingly used in the recent years. Therefore, selected applications to real samples are presented and discussed, in particular for the analysis of phenols, polyphenols and phenolic acids. Finally, some hot points are discussed, such as waste characterization for high value-compounds recovery and the untargeted metabolomics approach.

Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis

Lung cancer is the most common cause of cancer death in China. We characterized metabolic alterations in lung cancer using two analytical platforms: a non-targeted metabolic profiling strategy based on proton nuclear magnetic resonance (1H-NMR) spectroscopy and a targeted metabolic profiling strategy based on rapid resolution liquid chromatography (RRLC). Changes in serum metabolite levels during oncogenesis were evaluated in 25 stage I lung cancer patients and matched healthy controls. We identified 25 metabolites that were differentially regulated between the lung cancer patients and matched controls. Of those, 16 were detected using the non-targeted approach and 9 were identified using the targeted approach. Both groups of metabolites could differentiate between lung cancer patients and healthy controls with 100% sensitivity and specificity. The principal metabolic alternations in lung cancer included changes in glycolysis, lipid metabolism, choline phospholipid metabolism, one-carbon metabolism, and amino acid metabolism. The targeted metabolomics approach was more sensitive, accurate, and specific than the non-targeted metabolomics approach. However, our data suggest that both metabolomics strategies could be used to detect early-stage lung cancer and predict patient prognosis.

Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction

INTRODUCTION

Liquid chromatography-mass spectrometry (LC-MS) is a commonly used technique in untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, data generated from multiple batches are affected by measurement errors inherent to alterations in signal intensity, drift in mass accuracy and retention times between samples both within and between batches. These measurement errors reduce repeatability and reproducibility and may thus decrease the power to detect biological responses and obscure interpretation.

OBJECTIVE

Our aim was to develop procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality.

METHODS

Algorithms were developed for: (i) alignment and merging of features that are systematically misaligned between batches, through aggregating feature presence/missingness on batch level and combining similar features orthogonally present between batches; and (ii) within-batch drift correction using a cluster-based approach that allows multiple drift patterns within batch. Furthermore, a heuristic criterion was developed for the feature-wise choice of reference-based or population-based between-batch normalisation.

RESULTS

In authentic data, between-batch alignment resulted in picking 15 % more features and deconvoluting 15 % of features previously erroneously aligned. Within-batch correction provided a decrease in median quality control feature coefficient of variation from 20.5 to 15.1 %. Algorithms are open source and available as an R package ('batchCorr').

CONCLUSIONS

The developed procedures provide unbiased measures of improved data quality, with implications for improved data analysis. Although developed for LC-MS based metabolomics, these methods are generic and can be applied to other data suffering from similar limitations.

A Causal Network Analysis of the Fatty Acid Metabolome in African-Americans Reveals a Critical Role for Palmitoleate and Margarate

Fatty acids are important sources of energy and possible predictors and etiologic factors in many common complex pathologies such as cardiovascular disease, diabetes, and certain forms of cancers. While fatty acids are thought to covary with each other, their underlying causal networks have not been fully elucidated. This study reports the identification and analysis of a statistical causal network among 15 mostly long-chain fatty acids. In an African-American population sample and using the Genome granularity-Directed Acyclic Graph (GDAG) algorithm, we determined directions or causal relationships in the fatty acid metabolome. A directed causal network was constructed that revealed 29 significant edges among the 15 nodes (p < 0.001). We report that two fatty acid metabolites, palmitoleate and margarate, which originate from dietary intake, together influence every other fatty acid in the network. On the other hand, despite its high connectivity, dihomo-linoleate did not appear to play an important role over the whole fatty acid network. These findings collectively suggest possible strategic entry points for new treatments or preventive modalities against diseases affected by fatty acid metabolites such as cardiovascular disease, diabetes, and obesity. Further studies examining the embedded substructure of the fatty acid metabolite networks in independent population samples would be timely and warranted as we move toward novel postgenomic diagnostics and therapeutics.

Metabolomics Investigation Reveals Metabolite Mediators Associated with Acute Lung Injury and Repair in a Murine Model of Influenza Pneumonia

Influenza virus infection (IVI) can cause primary viral pneumonia, which may progress to acute lung injury (ALI) and respiratory failure with a potentially fatal outcome. At present, the interactions between host and influenza virus at molecular levels and the underlying mechanisms that give rise to IVI-induced ALI are poorly understood. We conducted a comprehensive mass spectrometry-based metabolic profiling of serum, lung tissue and bronchoalveolar lavage fluid (BALF) from a non-lethal mouse model with influenza A virus at 0, 6, 10, 14, 21 and 28 days post infection (dpi), representing the major stages of IVI. Distinct metabolite signatures were observed in mice sera, lung tissues and BALF, indicating the molecular differences between systematic and localized host responses to IVI. More than 100 differential metabolites were captured in mice sera, lung tissues and BALF, including purines, pyrimidines, acylcarnitines, fatty acids, amino acids, glucocorticoids, sphingolipids, phospholipids, etc. Many of these metabolites belonged to pulmonary surfactants, indicating IVI-induced aberrations of the pulmonary surfactant system might play an important role in the etiology of respiratory failure and repair. Our findings revealed dynamic host responses to IVI and various metabolic pathways linked to disease progression, and provided mechanistic insights into IVI-induced ALI and repair process.

Metabolomic and Transcriptomic Comparison of Solid-State and Submerged Fermentation of Penicillium expansum KACC 40815

Penicillium spp. are known to harbor a wide array of secondary metabolites with cryptic bioactivities. However, the metabolomics of these species is not well-understood in terms of different fermentation models and conditions. The present study involved metabolomics profiling and transcriptomic analysis of Penicillium expansum 40815 under solid-state fermentation (SSF) and submerged fermentation (SmF). Metabolite profiling was carried out using ultra-performance liquid chromatography quadruple time-of-flight mass spectrometry with multivariate analysis, followed by transcriptomic analyses of differentially expressed genes. In principal component analysis, the metabolite profiling data was studied under different experimental sets, including SSF and SmF. The significantly different metabolites such as polyketide metabolites (agonodepside B, rotiorin, verrucosidin, and ochrephilone) and corresponding gene transcripts (polyketide synthase, aromatic prenyltransferase, and terpenoid synthase) were primarily detected under SmF conditions. In contrast, the meroterpenoid compounds (andrastin A and C) and their genes transcripts were exclusively detected under SSF conditions. We demonstrated that the metabolite production and its corresponding gene expression levels in P. expansum 40815 were significantly influenced by the varying growth parameters and the immediate environment. This study further provides a foundation to produce specific metabolites by regulating fermentation conditions.

Sample-directed pseudotargeted method for the metabolic profiling analysis of rice seeds based on liquid chromatography with mass spectrometry

Rice is one of the most important food crops in the world. Metabolite composition in rice seeds varies significantly depending on genetic variety, climatic alternation and agricultural practice. Metabolomics is a powerful tool to reveal the metabolic response of rice to various conditions. In this work, a rice seed sample-directed pseudotargeted metabolomics method was first established and validated based on ultra high performance liquid chromatography with triple quadrupole mass spectrometry in the multiple reaction monitoring mode. A total of 749 and 617 ion pairs in positive and negative modes were achieved, respectively. Among them, about 200 metabolites were identified or tentatively identified. The developed method showed better linearity and repeatability than those of non-targeted metabolomics method. Good intra-day and inter-day precisions, recoveries and wide linear range were also obtained. Furthermore, the method was applied for the investigation of metabolic variation of rice seeds with two wild cultivars and their transgenic lines that were grown in two locations. Principal component analysis indicated that the effects of cultivar and location on metabolic variations were far more than those of gene modification. The nonparametric Mann-Whitney U test revealed that most metabolites were influenced by cultivar, location and gene modifications together.

A new cyanogenic metabolite in Arabidopsis required for inducible pathogen defence

Thousands of putative biosynthetic genes in Arabidopsis thaliana have no known function, which suggests that there are numerous molecules contributing to plant fitness that have not yet been discovered. Prime among these uncharacterized genes are cytochromes P450 upregulated in response to pathogens. Here we start with a single pathogen-induced P450 (ref. 5), CYP82C2, and use a combination of untargeted metabolomics and coexpression analysis to uncover the complete biosynthetic pathway to 4-hydroxyindole-3-carbonyl nitrile (4-OH-ICN), a previously unknown Arabidopsis metabolite. This metabolite harbours cyanogenic functionality that is unprecedented in plants and exceedingly rare in nature; furthermore, the aryl cyanohydrin intermediate in the 4-OH-ICN pathway reveals a latent capacity for cyanogenic glucoside biosynthesis in Arabidopsis. By expressing 4-OH-ICN biosynthetic enzymes in Saccharomyces cerevisiae and Nicotiana benthamiana, we reconstitute the complete pathway in vitro and in vivo and validate the functions of its enzymes. Arabidopsis 4-OH-ICN pathway mutants show increased susceptibility to the bacterial pathogen Pseudomonas syringae, consistent with a role in inducible pathogen defence. Arabidopsis has been the pre-eminent model system for studying the role of small molecules in plant innate immunity; our results uncover a new branch of indole metabolism distinct from the canonical camalexin pathway, and support a role for this pathway in the Arabidopsis defence response. These results establish a more complete framework for understanding how the model plant Arabidopsis uses small molecules in pathogen defence.

Correction of precursor and product ion relative abundances in order to standardize CID spectra and improve Ecom50 accuracy for non-targeted metabolomics

Quantitative biases in the abundance of precursor and product ions due to mass discrimination in RF-only ion guides results in inaccurate collision induced dissociation (CID) spectra. We evaluated the effects of collision cell RF voltage and collision energy on CID spectra using ten singly protonated compounds (46-854 Da) in an orthogonal acceleration time-of-flight mass spectrometer. The relative ion transfer efficiency, i.e. the relative amount of ions transferred through the ion guide at any particular RF voltage was shown to be dependent on the ion's m/z. We developed an algorithm to correct for the mass discriminating effects of RF voltage on CID spectra. The algorithm was tested for both precursor and product ions at multiple RF voltages and collision energies in order to ensure reliability. Our results suggest that compounds that generate major product ions with m/z values <150 have peak intensities that deviate substantially from their actual abundance. This has implications for small molecule metabolomics research, particularly for studies that rely on CID spectra matching methods for structure identification.

Untargeted metabolomics analysis revealed changes in the composition of glycerolipids and phospholipids in Bacillus subtilis under 1-butanol stress

1-Butanol has been utilized widely in industry and can be produced or transformed by microbes. However, current knowledge about the mechanisms of 1-butanol tolerance in bacteria remains quite limited. Here, we applied untargeted metabolomics to study Bacillus subtilis cells under 1-butanol stress and identified 55 and 37 ions with significantly increased and decreased levels, respectively. Using accurate mass determination, tandem mass spectra, and synthetic standards, 86 % of these ions were characterized. The levels of phosphatidylethanolamine, diglucosyldiacylglycerol, and phosphatidylserine were found to be upregulated upon 1-butanol treatment, whereas those of diacylglycerol and lysyl phosphatidylglycerol were downregulated. Most lipids contained 15:0/15:0, 16:0/15:0, and 17:0/15:0 acyl chains, and all were mapped to membrane lipid biosynthetic pathways. Subsequent two-stage quantitative real-time reverse transcriptase PCR analyses of genes in the two principal membrane lipid biosynthesis pathways revealed elevated levels of ywiE transcripts in the presence of 1-butanol and reduced expression levels of cdsA, pgsA, mprF, clsA, and yfnI transcripts. Thus, the gene transcript levels showed agreement with the metabolomics data. Lastly, the cell morphology was investigated by scanning electron microscopy, which indicated that cells became almost twofold longer after 1.4 % (v/v) 1-butanol stress for 12 h. Overall, the studies uncovered changes in the composition of glycerolipids and phospholipids in B. subtilis under 1-butanol stress, emphasizing the power of untargeted metabolomics in the discovery of new biological insights.

Metabolomic strategies for the identification of new enzyme functions and metabolic pathways

Recent technological advances in accurate mass spectrometry and data analysis have revolutionized metabolomics experimentation. Activity-based and global metabolomic profiling methods allow simultaneous and rapid screening of hundreds of metabolites from a variety of chemical classes, making them useful tools for the discovery of novel enzymatic activities and metabolic pathways. By using the metabolome of the relevant organism or close species, these methods capitalize on biological relevance, avoiding the assignment of artificial and non-physiological functions. This review discusses state-of-the-art metabolomic approaches and highlights recent examples of their use for enzyme annotation, discovery of new metabolic pathways, and gene assignment of orphan metabolic activities across diverse biological sources.

An untargeted metabolomic workflow to improve structural characterization of metabolites

Mass spectrometry-based metabolomics relies on MS(2) data for structural characterization of metabolites. To obtain the high-quality MS(2) data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS(2) data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS(2) fragments, even narrow windows are not always selective enough, and they can complicate data analysis by removing isotopic patterns from MS(2) spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS(2) scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS(2) fragment intensities as a function of retention time and precursor mass targeted for MS(2) analysis, we obtain deconvolved MS(2) spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS(2) data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our laboratory website ( http://pattilab.wustl.edu/decoms2.php ).

A view from above: cloud plots to visualize global metabolomic data

Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias. With mass spectrometry-based methods, global metabolomic data sets typically comprise thousands of peaks, each of which is associated with a mass-to-charge ratio, retention time, fold change, p-value, and relative intensity. Although several visualization schemes have been used for metabolomic data, most commonly used representations exclude important data dimensions and therefore limit interpretation of global data sets. Given that metabolite identification through tandem mass spectrometry data acquisition is a time-limiting step of the untargeted metabolomic workflow, simultaneous visualization of these parameters from large sets of data could facilitate compound identification and data interpretation. Here, we present such a visualization scheme of global metabolomic data using a so-called "cloud plot" to represent multidimensional data from septic mice. While much attention has been dedicated to lipid compounds as potential biomarkers for sepsis, the cloud plot shows that alterations in hydrophilic metabolites may provide an early signature of the disease prior to the onset of clinical symptoms. The cloud plot is an effective representation of global mass spectrometry-based metabolomic data, and we describe how to extract it as standard output from our XCMS metabolomic software.

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

Discovery of a protein-metabolite interaction between unsaturated fatty acids and the nuclear receptor Nur77 using a metabolomics approach

Neuron-derived clone 77 (Nur77) is an orphan nuclear receptor with currently no known natural ligands. Here we applied a metabolomics platform for detecting protein-metabolite interactions (PMIs) to identify lipids that bind to Nur77. Using this approach, we discovered that the Nur77 ligand-binding domain (Nur77LBD) enriches unsaturated fatty acids (UFAs) in tissue lipid mixtures. The interaction of Nur77 with arachidonic acid and docosahexaenoic acid was subsequently characterized using a number of biophysical and biochemical assays. Together these data indicate that UFAs bind to Nur77LBD to cause changes in the conformation and oligomerization of the receptor. UFAs are the only endogenous lipids reported to bind to Nur77, which highlights the use of metabolomics in the discovery of novel PMIs.