A comprehensive workflow of mass spectrometry-based untargeted metabolomics in cancer metabolic biomarker discovery using human plasma and urine

Impact of four different extraction methods and three different reconstitution solvents on the untargeted metabolomics analysis of human and rat urine samples

Unsuitable sample preparation may result in loss of important analytes and consequently affect the outcome of untargeted metabolomics. Due to species differences, different sample preparations may be required within the same biological matrix. The study aimed to compare the in-house sample preparation method for urine with methods from literature and to investigate the transferability of sample preparation from human urine to rat urine. A total of 12 different conditions for protein precipitation were tested, combining four different extraction solvents and three different reconstitution solvents using an untargeted liquid-chromatography high resolution mass spectrometry (LC-HRMS) metabolomics analysis. Evaluation was done based on the impact on feature count, their detectability, as well as the reproducibility of selected compounds. Results showed that a combination of methanol as extraction and acetonitrile/water (75/25) as reconstitution solvent provided improved results at least regarding the total feature count. Additionally, it was found that a higher amount of methanol was most suitable for extraction of rat urine among the tested conditions. In comparison, human urine requires significantly less volume of extraction solvent. Overall, it is recommended to systematically optimize both, the extraction method, and the reconstitution solvent for the used biofluid and the individual analytical settings.

Applications of chromatographic methods in metabolomics: A review

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

Potential mechanisms of lung injury and repair after hexavalent chromium [Cr(VI)] aerosol whole-body dynamic exposure

Hexavalent chromium [Cr(VI)], known as "Top Hazardous Substances", poses a significant threat to the respiratory system. Nevertheless, the potential mechanisms of toxicity and the lung's repair ability after injury remain incompletely understood. In this study, Cr(VI) aerosol whole-body dynamic exposure system simulating real exposure scenarios of chromate workers was constructed to evaluate the lung injury and repair effects. Subsequently, miRNA sequencing, mRNA sequencing and metabolomics analyses on lung tissue were performed to explore the underlying mechanisms. Our results revealed that Cr(VI) exposure led to an increase in lactic dehydrogenase activity and a time-dependent decline in lung function. Notably, after 13 w of Cr(VI) exposure, alveolar hemorrhage, thickening of alveolar walls, emphysema-like changes, mitochondrial damage of alveolar epithelial cells and macrophage polarization changes were observed. Remarkably, a two-week repair intervention effectively ameliorated lung function decline and pulmonary injury. Furthermore, significant disruptions in the expressions of miRNAs and mRNAs involved in oxidative phosphorylation, glycerophospholipid metabolism and inflammatory signaling pathways were found. The two-week repair period resulted in the reversal of expression of oxidative phosphorylation related genes, and inhibited the inflammatory signaling pathways. This study concluded that the inhibition of the mitochondrial oxidative phosphorylation pathway and the subsequent enhancement of inflammatory response might be key mechanisms underlying Cr(VI) pulmonary toxicity, and timely cessation of exposure could effectively alleviate the pulmonary injury. These findings shed light on the potential mechanisms of Cr(VI) toxicity and provide crucial insights into the health protection for occupational populations exposed to Cr(VI).

Low-Field Benchtop NMR to Discover Early-Onset Sepsis: A Proof of Concept

Low-field (LF) benchtop NMR is a new family of instruments available on the market, promising for fast metabolic fingerprinting and targeted quantification of specific metabolites despite a lack of sensitivity and resolution with respect to high-field (HF) instruments. In the present study, we evaluated the possibility to use the urinary metabolic fingerprint generated using a benchtop LF NMR instrument for an early detection of sepsis in preterm newborns, considering a cohort of neonates previously investigated by untargeted metabolomics based on Mass Spectrometry (MS). The classifier obtained behaved similarly to that based on MS, even if different classes of metabolites were taken into account. Indeed, investigating the regions of interest mainly related to the development of sepsis by a HF NMR instrument, we discovered a set of relevant metabolites associated to sepsis. The set included metabolites that were not detected by MS, but that were reported as relevant in other published studies. Moreover, a strong correlation between LF and HF NMR spectra was observed. The high reproducibility of the NMR spectra, the interpretability of the fingerprint in terms of metabolites and the ease of use make LF benchtop NMR instruments promising in discovering early-onset sepsis.

Klebsiella quasipneumoniae in intestine damages bile acid metabolism in hematopoietic stem cell transplantation patients with bloodstream infection

BACKGROUND

Bloodstream infection (BSI) is a serious hematopoietic stem cell transplantation (HSCT) complication. The intestinal microbiome regulates host metabolism and maintains intestinal homeostasis. Thus, the impact of microbiome on HSCT patients with BSI is essential.

METHODS

Stool and serum specimens of HSCT patients were prospectively collected from the pretransplant conditioning period till 4 months after transplantation. Specimens of 16 patients without BSI and 21 patients before BSI onset were screened for omics study using 16S rRNA gene sequencing and untargeted metabolomics. The predictive infection model was constructed using LASSO and the logistic regression algorithm. The correlation and influence of microbiome and metabolism were examined in mouse and Caco-2 cell monolayer models.

RESULTS

The microbial diversity and abundance of Lactobacillaceae were remarkably reduced, but the abundance of Enterobacteriaceae (especially Klebsiella quasipneumoniae) was significantly increased in the BSI group before onset, compared with the non-BSI group. The family score of microbiome features (Enterobacteriaceae and Butyricicoccaceae) could highly predict BSI (AUC = 0.879). The serum metabolomic analysis showed that 16 differential metabolites were mainly enriched in the primary bile acid biosynthesis pathway, and the level of chenodeoxycholic acid (CDCA) was positively correlated with the abundance of K. quasipneumoniae (R = 0.406, P = 0.006). The results of mouse experiments confirmed that three serum primary bile acids levels (cholic acid, isoCDCA and ursocholic acid), the mRNA expression levels of bile acid farnesol X receptor gene and apical sodium-dependent bile acid transporter gene in K. quasipneumoniae colonized mice were significantly higher than those in non-colonized mice. The intestinal villus height, crypt depth, and the mRNA expression level of tight junction protein claudin-1 gene in K. quasipneumoniae intestinal colonized mice were significantly lower than those in non-colonized mice. In vitro, K. quasipneumoniae increased the clearance of FITC-dextran by Caco-2 cell monolayer.

CONCLUSIONS

This study demonstrated that the intestinal opportunistic pathogen, K. quasipneumoniae, was increased in HSCT patients before BSI onset, causing increased serum primary bile acids. The colonization of K. quasipneumoniae in mice intestines could lead to mucosal integrity damage. The intestinal microbiome features of HSCT patients were highly predictive of BSI and could be further used as potential biomarkers.

Adjunctive Probio-X Treatment Enhances the Therapeutic Effect of a Conventional Drug in Managing Type 2 Diabetes Mellitus by Promoting Short-Chain Fatty Acid-Producing Bacteria and Bile Acid Pathways

Metformin is a common drug for the management of type 2 diabetes mellitus; however, it causes various adverse gastrointestinal effects, especially after prolonged treatment. It is thus of interest to identify an adjuvant treatment that synergizes with the efficacy of metformin while mitigating its adverse effects. Since previous evidence supports that the gut microbiota is a target of metformin, this study investigated the beneficial effect and mechanism of the coadministration of probiotics with metformin in the management of type 2 diabetes mellitus by conducting a 3-month randomized, double-blind, placebo-controlled clinical trial (n = 27 and 21 in the probiotic and placebo groups, respectively, who completed the trial). Clinical results showed that the coadministration of probiotics with metformin significantly reduced glycated hemoglobin compared with metformin taken alone (P < 0.05). Metagenomic and metabolomic analyses showed that the coadministration of probiotics increased the abundance of gut short-chain fatty acid (SCFA)-producing bacteria and bile acids. Significantly or marginally more bile acids and related metabolites were detected in the probiotic group than in the placebo group postintervention. Taken together, the results of our study showed that the coadministration of probiotics with metformin synergized with the hypoglycemic effect in patients with type 2 diabetes mellitus, which was likely through modulating the gut microbiome and, subsequently, SCFA and bile acid metabolism. Our findings support that cotreatment with probiotics and metformin is beneficial to patients with type 2 diabetes mellitus. IMPORTANCE Metformin causes variable adverse gastrointestinal effects, especially after prolonged treatment. We found that cotreatment with Probio-X and metformin for the management of type 2 diabetes mellitus may promote gut SCFA-producing bacteria and the levels of specific bile acids, thus increasing the secretion of related gastrointestinal hormones and ultimately improving glucose homeostasis.

Fucose ameliorates the proinflammatory property of Fusobacterium nucleatum in colitis via altering its metabolism

Introduction

Previous studies reported that fucose plays a protective role in inhibiting pathogens. Fusobacterium nucleatum (Fn) was recently found to promote the progression of colitis. However, the effects of fucose on Fn are poorly understood. This study aimed to explore whether fucose could ameliorate the proinflammatory property of Fn in colitis and the underlying mechanisms.

Methods

To validate our hypothesis, mice were administrated with Fn and fucose-treated Fn (Fnf) before dextran sulfate sodium (DSS) treatment to establish Fn related colitis model. The metabolism variation of Fn was detected by metabolomic analysis. To verify the effects of bacterial metabolites on intestinal epithelial cells (IECs), Caco-2 cells were treated with bacterial supernatant.

Results

More severe inflammation, intestinal barrier damage, autophagy block, and apoptosis in the colon were noted in DSS mice that were administrated with Fn or Fnf. However, the severity degree in Fnf+DSS group was less compared to Fn+DSS group. Metabolic pathways of Fn were altered after fucose treatment and proinflammatory metabolites were decreased. The supernatant of Fnf induced a lower level of inflammation than Fn in Caco-2 cells. One of the decreased metabolites, homocysteine thiolactone (HT), was proven to induce inflammatory effects in Caco-2 cells.

Discussion

In conclusion, fucose ameliorates the proinflammatory property of Fn via altering its metabolism and these findings provide evidence for the application of fucose as functional food or prebiotic in the treatment of Fn related colitis.

Multi-Omics Analysis of Western-style Diet Increased Susceptibility to Experimental Colitis in Mice

Background

Western-style diet (WSD) is associated with inflammatory bowel disease (IBD) prevalence. However, the impact of WSD on IBD development and its underlying mechanism remain unclear. Transcriptomics and metabolomics could be beneficial for identifying key factors in WSD-related experimental IBD susceptibility. However, no such study has been conducted yet. We aimed to analyze the implications of WSD for experimental colitis susceptibility in mice and its underlying mechanism using these high-throughput technologies.

Methods

We fed experimental mice a WSD and a control diet from weaning. After 9 weeks, the mice were treated with 2,4,6 trinitrobenzene sulfonic acid to induce colitis, and the control group was treated with 50% ethanol (commonly used IBD animal model). Genome-wide microarray and liquid chromatography-tandem mass spectrometry were used to identify the differential transcripts and metabolites of experimental colitis with and without pre-illness WSD.

Results

WSD induced more severe inflammation in experimental colitis than the control diet. We found 2540 up-regulated genes and 2737 down-regulated genes in experimental colitis with WSD compared with those for the control diet. In addition, levels of 41 colonic tissue metabolites and 56 serum metabolites showed significant differences. Integrating transcriptomic and metabolomic data, we found major co-expression networks through which WSD promoted experimental IBD susceptibility, including enzymes of biotransformation, glycan synthesis and metabolism, steroid hormone metabolites.

Conclusion

Pre-illness WSD increased experimental colitis susceptibility. Our results could provide important evidences for the potential mechanisms and assist dietary recommendations to better manage IBD.

Combined small and large magnetic nanoparticle extraction and concentration from biofluids for non-toxic detection of biomarkers

Sketch of the concentration of small and large magnetic nanoparticles in biofluid to the area of biomarker detection for enhancing the method sensitivity.

DPP4 Regulates DHCR24-Mediated Cholesterol Biosynthesis to Promote Methotrexate Resistance in Gestational Trophoblastic Neoplastic Cells

Metabolic reprogramming could promote cellular adaptation in response to chemotherapeutic drugs in cancer cells. Herein, we aimed to characterize the metabolomic profiles regulated by Dipeptidyl Peptidase 4 (DPP4) in methotrexate (MTX)-resistant gestational trophoblastic neoplastic (GTN) cells. A total of eighty metabolites were found to be commonly altered in DPP4-depleted JAR/MTX and JEG3/MTX cells. Cholesterol biosynthesis-related metabolites were markedly impacted by DPP4 knockdown in MTX-resistant sublines. Manipulation of DPP4 expression remarkably affected the level of cellular cholesterol in GTN cells. Our analysis also identified 24-Dehydrocholesterol Reductase (DHCR24) as a potential downstream effector of DPP4. Manipulation of DHCR24 expression affected cellular cholesterol level, reactive oxygen species (ROS) accumulation, and chemosensitivity to MTX in GTN cell models. In addition, over-expression of DHCR24 could markedly restore cellular cholesterol level and rescue cell survival in DPP4-depleted MTX-resistant GTN cells. Highly correlated expression of DPP4 and DHCR24 was observed in clinical GTN specimens. Further, DPP4 inhibitor sitagliptin effectively inhibited cholesterol biosynthesis, reduced DHCR24 expression and enhanced MTX-induced cytotoxicity in vitro and in vivo. In conclusion, our findings suggested that DPP4 might regulate DHCR24-mediated cholesterol biosynthesis to promote methotrexate resistance in GTN cells. Targeting DPP4/DHCR24 signaling might help to sensitize MTX-resistant GTN to MTX treatment.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Coupling 16S rDNA Sequencing and Untargeted Mass Spectrometry for Milk Microbial Composition and Metabolites from Dairy Cows with Clinical and Subclinical Mastitis

The internal environment of the cow's udder directly affects the udder health and milk quality. 16S rDNA sequencing and liquid chromatography-mass spectrometry (LC-MS) methods were used to investigate the significant differences in milk microbial diversity and metabolites among cows that are healthy (H) and those suffering from subclinical mastitis (SM) and clinical mastitis (CM). Results uncovered more than 16 and 192 differently abundant microbiota at the phylum and genus levels, respectively, and 673 different levels of metabolites enriched in 20 pathways in milk among the 3 groups. This study revealed the positive relevance between Staphylococcus and Streptococcus and ceramide in milk from CM cows. Similarly, Acinetobacter and Corynebacterium were positively associated with testosterone glucuronide and 5-methyl-tetrahydrofolate, in milk from SM cows. On the basis of the combined analysis of microbiome and metabolome, this study indicated that, apart from the exogenous pathogens, some beneficial symbiotic bacteria, such as Dietzia, Aeromicrobium, Alistipes, and Sphingobacterium, rarely reported in milk have been found to be significantly reduced during mastitis.

Sensitive mass spectrometric analysis of carbonyl metabolites in human urine and fecal samples using chemoselective modification

Metabolites with ketone or aldehyde functionalities comprise a large proportion of the human metabolome, most notably in the form of sugars. However, these reactive molecules are also generated through oxidative stress or gut microbiota metabolism and have been linked to disease development. The discovery and structural validation of this class of metabolites over the large concentration range found in human samples is crucial to identify their links to pathogenesis. Herein, we have utilized an advanced chemoselective probe methodology alongside bioinformatic analysis to identify carbonyl-metabolites in urine and fecal samples. In total, 99 metabolites were identified in urine samples and the chemical structure for 40 metabolites were unambiguously validated using a co-injection procedure. We also describe the preparation of a metabolite-conjugate library of 94 compounds utilized to efficiently validate these ketones and aldehydes. This method was used to validate 33 metabolites in a pooled fecal sample extract to demonstrate the potential for rapid and efficient metabolite detection over a wide metabolite concentration range. This analysis revealed the presence of six metabolites that have not previously been detected in either sample type. The constructed library can be utilized for straightforward, large-scale, and expeditious analysis of carbonyls in any sample type.

Early-Life Stress Induces Depression-Like Behavior and Synaptic-Plasticity Changes in a Maternal Separation Rat Model: Gender Difference and Metabolomics Study

More than 300 million people suffer from depressive disorders globally. People under early-life stress (ELS) are reportedly vulnerable to depression in their adulthood, and synaptic plasticity can be the molecular mechanism underlying such depression. Herein, we simulated ELS by using a maternal separation (MS) model and evaluated the behavior of Sprague-Dawley (SD) rats in adulthood through behavioral examination, including sucrose preference, forced swimming, and open-field tests. The behavior tests showed that SD rats in the MS group were more susceptible to depression- and anxiety-like behaviors than did the non-MS (NMS) group. Nissl staining analysis indicated a significant reduction in the number of neurons at the prefrontal cortex and hippocampus, including the CA1, CA2, CA3, and DG regions of SD rats in the MS group. Immunohistochemistry results showed that the percentages of synaptophysin-positive area in the prefrontal cortex and hippocampus (including the CA1, CA2, CA3, and DG regions) slice of the MS group significantly decreased compared with those of the NMS group. Western blot analysis was used to assess synaptic-plasticity protein markers, including postsynaptic density 95, synaptophysin, and growth-associated binding protein 43 protein expression in the cortex and hippocampus. Results showed that the expression levels of these three proteins in the MS group were significantly lower than those in the NMS group. LC-MS/MS analysis revealed no significant differences in the peak areas of sex hormones and their metabolites, including estradiol, testosterone, androstenedione, estrone, estriol, and 5β-dihydrotestosterone. Through the application of nontargeted metabolomics to the overall analysis of differential metabolites, pathway-enrichment results showed the importance of arginine and proline metabolism; pantothenate and CoA biosyntheses; glutathione metabolism; and the phenylalanine, tyrosine, and tryptophan biosynthesis pathways. In summary, the MS model caused adult SD rats to be susceptible to depression, which may regulate synaptic plasticity through arginine and proline metabolism; pantothenate and CoA biosyntheses; glutathione metabolism; and phenylalanine, tyrosine, and tryptophan biosyntheses.

Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways

Cancer is one of the major causes of mortality worldwide and its already large burden is projected to increase significantly in the near future with a predicted 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030. Unfortunately, current procedures for diagnosis are characterized by low diagnostic accuracies. Given the proved correlation between cancer presence and alterations of biological fluid composition, many researchers suggested their characterization to improve cancer detection at early stages. This paper reviews the information that can be found in the scientific literature, regarding the correlation of different cancer forms with the presence of specific metabolites in human urine, in a schematic and easily interpretable form, because of the huge amount of relevant literature. The originality of this paper relies on the attempt to point out the odor properties of such metabolites, and thus to highlight the correlation between urine odor alterations and cancer presence, which is proven by recent literature suggesting the analysis of urine odor for diagnostic purposes. This investigation aims to evaluate the possibility to compare the results of studies based on different approaches to be able in the future to identify those compounds responsible for urine odor alteration.

Automated optimization of XCMS parameters for improved peak picking of liquid chromatography-mass spectrometry data using the coefficient of variation and parameter sweeping for untargeted metabolomics

Accurate peak picking and further processing is a current challenge in the analysis of untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) data. The optimization of these processes is crucial to obtain proper results. This study investigated and optimized the detection of peaks by XCMS, a widely used R package for peak picking and processing of high-resolution LC-MS metabolomics data by their coefficient of variation using neat standard solutions of drug like compounds. The obtained results were additionally verified by using fortified pooled plasma samples. Settings of the mass spectrometer were optimized by recommendations in literature to enable a reliable detection of the investigated analytes. XCMS parameters were evaluated using a comprehensive parameter sweeping approach. The optimization steps were statistically evaluated and further visualized after principal component analysis (PCA). Concerning the lower concentrated solution in methanol samples, the optimization of both mass spectrometer and XCMS parameters improved the median coefficient of variation from 24% to 7%, retention time fluctuation from 9.3 seconds to 0.54 seconds, and fluctuation of the mass to charge ratio (m/z) from m/z 0.00095 to m/z 0.00028. The number of parent compounds and their related species annotated by CAMERA increased from 88 to 113 while the total amount of features decreased from 3282 to 428. Optimized MS settings such as increased resolution led to a higher specificity of peak picking. PCA supported these findings by showing the best clustering of samples after optimization of both mass spectrometer and XCMS parameters. The results implied that peak picking needs to be individually adapted for the experimental set up. Reducing unwanted variation in the data set was most successful after combining high resolving power with strict peak picking settings.

Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Early detection and diagnosis of cancer can allow timely medical intervention, which greatly improves chances of survival and enhances quality of life. Biomarkers play an important role in assisting clinicians and health care providers in cancer diagnosis and treatment follow-up. In spite of years of research and the discovery of thousands of candidate cancer biomarkers, only a few have transitioned to routine usage in the clinic. This review highlights advances in proteomics technologies that have enabled high rates of discovery of candidate cancer biomarkers and evaluates integration with other omics technologies to improve their progress through to validation and clinical translation. Furthermore, it gauges the role of metabolomics technology in cancer biomarker research and assesses it as a complementary tool in aiding cancer biomarker discovery and validation.

Instrumental drift removal in GC-MS data for breath analysis: the short-term and long-term temporal validation of putative biomarkers for COPD

Breath analysis holds the promise of a non-invasive technique for the diagnosis of diverse respiratory conditions including chronic obstructive pulmonary disease (COPD) and lung cancer. Breath contains small metabolites that may be putative biomarkers of these conditions. However, the discovery of reliable biomarkers is a considerable challenge in the presence of both clinical and instrumental confounding factors. Among the latter, instrumental time drifts are highly relevant, as since question the short and long-term validity of predictive models. In this work we present a methodology to counter instrumental drifts using information from interleaved blanks for a case study of GC-MS data from breath samples. The proposed method includes feature filtering, and additive, multiplicative and multivariate drift corrections, the latter being based on component correction. Biomarker discovery was based on genetic algorithms in a filter configuration using Fisher's ratio computed in the partial least squares-discriminant analysis subspace as a figure of merit. Using our protocol, we have been able to find nine peaks that provide a statistically significant area under the ROC curve of 0.75 for COPD discrimination. The method developed has been successfully validated using blind samples in short-term temporal validation. However, the attempt to use this model for patient screening six months later was not successful. This negative result highlights the importance of increasing validation rigor when reporting biomarker discovery results.

The evolution of lipidomics through space and time

Although the foundations of mass spectrometry-based lipidomics have been practiced for over 30 years, recent technological advances in ionization modalities in conjunction with robust increases in mass accuracy and resolution have greatly accelerated the emergence, growth and importance of the field of lipidomics. Moreover, advances in the separation sciences, bioinformatic strategies and the availability of robust databases have been synergistically integrated into modern lipidomic technologies leading to unprecedented improvements in the depth, penetrance and precision of lipidomic analyses and identification of their biological and mechanistic significance. The purpose of this "opinion" article is to briefly review the evolution of lipidomics, critique the platforms that have evolved and identify areas that are likely to emerge in the years to come. Through seamlessly integrating a rich repertoire of mass spectrometric, chemical and bioinformatic strategies, the chemical identities and quantities of tens of thousands to hundreds of thousands of different lipid molecular species and their metabolic alterations during physiologic or pathophysiologic perturbations can be obtained. Thus, the field of lipidomics which already has a distinguished history of exciting new discoveries in many disease states holds unparalleled potential to identify the pleiotropic roles of lipids in health and disease at the chemical level. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Metabolomics: Bridging the Gap between Pharmaceutical Development and Population Health

Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.

Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS

Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods.

Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment

Biomarker profiling using mass spectrometry plays an essential role in biological studies and is highly dependent on the data analysis for sample classification. In this study, we introduced power nomination of the mass spectra as a method for systematically altering the weights of peaks at different intensity levels. In combination with the use of support vector machine method (SVM), the impact on the sample classification has been characterized using data in four studies previously reported, including the distinctions of anomeric configurations of sugars, types of bacteria, stages of melanoma, and the types of breast cancer. Comprehensive analysis of the data with normalization at different power normalization index (PNI) was developed and analysis tools, including error-PNI plots, reference profiles, and error source profiles, were used to assess the potential of the analytical methods as well as to find the proper approaches to classify the samples.

Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications

Metabolomics, as a part of systems biology, has been widely applied in different fields of life science by studying the endogenous metabolites. The development and applications of liquid chromatography (LC) coupled with high resolution mass spectrometry (MS) greatly improve the achievable data quality in non-targeted metabolic profiling. However, there are still some emerging challenges to be covered in LC-MS based metabolomics. Here, recent approaches about sample collection and preparation, instrumental analysis, and data handling of LC-MS based metabolomics are summarized, especially in the analysis of clinical samples. Emphasis is put on the improvement of analytical techniques including the combination of different LC columns, isotope coded derivatization methods, pseudo-targeted LC-MS method, new data analysis algorithms and structural identification of important metabolites.

Metabolomics for biomarker discovery in gastroenterological cancer

The study of the omics cascade, which involves comprehensive investigations based on genomics, transcriptomics, proteomics, metabolomics, etc., has developed rapidly and now plays an important role in life science research. Among such analyses, metabolome analysis, in which the concentrations of low molecular weight metabolites are comprehensively analyzed, has rapidly developed along with improvements in analytical technology, and hence, has been applied to a variety of research fields including the clinical, cell biology, and plant/food science fields. The metabolome represents the endpoint of the omics cascade and is also the closest point in the cascade to the phenotype. Moreover, it is affected by variations in not only the expression but also the enzymatic activity of several proteins. Therefore, metabolome analysis can be a useful approach for finding effective diagnostic markers and examining unknown pathological conditions. The number of studies involving metabolome analysis has recently been increasing year-on-year. Here, we describe the findings of studies that used metabolome analysis to attempt to discover biomarker candidates for gastroenterological cancer and discuss metabolome analysis-based disease diagnosis.

Pooling samples for "top-down" molecular exposomics research: the methodology

BACKGROUND

Exposomics is the cutting-edge concept of screening the environmental risk factors for disease. In the novel "top-down" approach, we estimate the molecular exposome by measuring all body fluid analytes in a case-controlled study. However, to detect diverse pollutants, a sufficient sample size and multiple analytical methods are required. This may lead to dramatically increased costs and research workload.

METHODS

To help reduce complexity, we suggest a sample pooling strategy along with a scheme for combining both general unknown or multi-targeted screening with targeted analysis. The sample pooling method was tested using computer simulations.

RESULTS

By comprehensively analysis of pooled samples, it is possible to identify environmental risk factors. Factors are initially screened in the pooled case and control population samples, then in the randomized grouped and pooled case and control subpopulation samples. In the sample grouping, five or more pools were suggested for groups having 30 individuals per pool.

CONCLUSIONS

This study suggests that sample pooling is a useful strategy for exposomics research, which provides a hypothesis-free method for pollutant risk screening.