Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics.

Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbi et al. J. Chromatogr. B. 2019, 1105, 26-37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895-0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).

Multiplatform Metabolomics Investigation of Antiadipogenic Effects on 3T3-L1 Adipocytes by a Potent Diarylheptanoid.

Obesity is fast becoming a serious health problem worldwide. Of the many possible antiobesity strategies, one interesting approach focuses on blocking adipocyte differentiation and lipid accumulation to counteract the rise in fat storage. However, there is currently no drug available for the treatment of obesity that works by inhibiting adipocyte differentiation. Here we use a broad-based metabolomics approach to interrogate and better understand metabolic changes that occur during adipocyte differentiation. In particular, we focus on changes induced by the antiadipogenic diarylheptanoid, which was isolated from a traditional Chinese medicine Dioscorea zingiberensis and identified as (3 R,5 R)-3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane (1). Targeted aqueous metabolic profiling indicated that a total of 14 metabolites involved in the TCA cycle, glycolysis, amino acid metabolism, and purine catabolism participate in regulating energy metabolism, lipogenesis, and lipolysis in adipocyte differentiation and can be modulated by diarylheptanoid 1. As indicated by lipidomics analysis, diarylheptanoid 1 restored the quantity and degree of unsaturation of long-chain free fatty acids and restored the levels of 171 lipids mainly from 10 lipid classes in adipocytes. In addition, carbohydrate metabolism in diarylheptanoid-1-treated adipocytes further demonstrated the delayed differentiation process by flux analysis. Our results provide valuable information for further understanding the metabolic adjustment in adipocytes subjected to diarylheptanoid 1 treatment. Moreover, this study offers new insight into developing antiadipogenic leading compounds based on metabolomics.

Plasma metabolite abundances are associated with urinary enterolactone excretion in healthy participants on controlled diets.

Enterolignans, products of gut bacterial metabolism of plant lignans, have been associated with reduced risk of chronic diseases, but their association with other plasma metabolites is unknown. We examined plasma metabolite profiles according to urinary enterolignan excretion in a cross-sectional analysis using data from a randomized crossover, controlled feeding study. Eighty healthy adult males and females completed two 28-day feeding periods differing by glycemic load, refined carbohydrate, and fiber content. Lignan intake was calculated from food records using a polyphenol database. Targeted metabolomics was performed by LC-MS on plasma from fasting blood samples collected at the end of each feeding period. Enterolactone (ENL) and enterodiol, were measured in 24 h urine samples collected on the penultimate day of each study period using GC-MS. Linear mixed models were used to test the association between enterolignan excretion and metabolite abundances. Pathway analyses were conducted using the Global Test. Benjamini-Hochberg false discovery rate (FDR) was used to control for multiple testing. Of the metabolites assayed, 121 were detected in all samples. ENL excretion was associated positively with plasma hippuric acid and melatonin, and inversely with epinephrine, creatine, glycochenodeoxycholate, and glyceraldehyde (P < 0.05). Hippuric acid only satisfied the FDR of q < 0.1. END excretion was associated with myristic acid and glycine (q < 0.5). Two of 57 pathways tested were associated significantly with ENL, ubiquinone and terpenoid-quinone biosynthesis, and inositol phosphate metabolism. These results suggest a potential role for ENL or ENL-metabolizing gut bacteria in regulating plasma metabolites.

Dereplication of Natural Products Using GC-TOF Mass Spectrometry: Improved Metabolite Identification by Spectral Deconvolution Ratio Analysis.

Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, thereby avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential, and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication was initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts.

Integrated plasma and urine metabolomics coupled with HPLC/QTOF-MS and chemometric analysis on potential biomarkers in liver injury and hepatoprotective effects of Er-Zhi-Wan.

Metabolomics techniques are the comprehensive assessment of endogenous metabolites in a biological system and may provide additional insight into the molecular mechanisms. Er-Zhi-Wan (EZW) is a traditional Chinese medicine formula, which contains Fructus Ligustri Lucidi (FLL) and Herba Ecliptae (HE). EZW is widely used to prevent and treat various liver injuries through the nourishment of the liver. However, the precise molecular mechanism of hepatoprotective effects has not been comprehensively explored. Here, an integrated metabolomics strategy was designed to assess the effects and possible mechanisms of EZW against carbon tetrachloride-induced liver injury, a commonly used model of both acute and chronic liver intoxication. High-performance chromatography/quadrupole time-of-flight mass spectrometry (HPLC/QTOF-MS) combined with chemometric approaches including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to discover differentiating metabolites in metabolomics data of rat plasma and urine. Results indicate six differentiating metabolites, tryptophan, sphinganine, tetrahydrocorticosterone, pipecolic acid, L-2-amino-3-oxobutanoic acid and phosphoribosyl pyrophosphate, in the positive mode. Functional pathway analysis revealed that the alterations in these metabolites were associated with tryptophan metabolism, sphingolipid metabolism, steroid hormone biosynthesis, lysine degradation, glycine, serine and threonine metabolism, and pentose phosphate pathway. Of note, EZW has a potential pharmacological effect, which might be through regulating multiple perturbed pathways to the normal state. Our findings also showed that the robust integrated metabolomics techniques are promising for identifying more biomarkers and pathways and helping to clarify the function mechanisms of traditional Chinese medicine.

Metabolomics Study of Resina Draconis on Myocardial Ischemia Rats Using Ultraperformance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry Combined with Pattern Recognition Methods and Metabolic Pathway Analysis.

Resina draconis (bright red resin isolated from Dracaena cochinchinensis, RD) has been clinically used for treatment of myocardial ischemia (MI) for many years. However, the mechanisms of its pharmacological action on MI are still poorly understood. This study aimed to characterize the plasma metabolic profiles of MI and investigate the mechanisms of RD on MI using ultraperformance liquid chromatography/quadrupole time-of-flight mass spectrometry-based metabolomics combined with pattern recognition methods and metabolic pathway analysis. Twenty metabolite markers characterizing metabolic profile of MI were revealed, which were mainly involved in aminoacyl-tRNA biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis, vascular smooth muscle contraction, sphingolipid metabolism, and so forth. After RD treatment, however, levels of seven MI metabolite markers, including phytosphingosine, sphinganine, acetylcarnitine, cGMP, cAMP, L-tyrosine, and L-valine, were turned over, indicating that RD is likely to alleviate MI through regulating the disturbed vascular smooth muscle contraction, sphingolipid metabolism, phenylalanine metabolism, and BCAA metabolism. To our best knowledge, this is the first comprehensive study to investigate the mechanisms of RD for treating MI, from a metabolomics point of view. Our findings are very valuable to gain a better understanding of MI metabolic profiles and provide novel insights for exploring the mechanisms of RD on MI.

Detection of trace levels of lead in aqueous liquids using extractive electrospray ionization tandem mass spectrometry.

A sensitive approach, based on semi-quantitative measurement of the characteristic fragments in multi-stage extractive electrospray ionization mass spectrometry (EESI-MS(n)), was developed for fast detection of trace levels of lead in aqueous liquids including mineral water, lake water, tap water, energy drinks, soft drinks, beer, orange juice, and tea. A disodium ethylene-diamine-tetraacetic acid (EDTA) aqueous solution was electrosprayed to produce negatively charged primary ions which then intersected the neutral sample plume to generate anions of EDTA-Pb(II) complexes. The charged EDTA-Pb(II) complexes were characterized with multistage collision induced dissociation (CID) experiments. The limit of detection (LOD) using EESI-MS(3) was estimated to be at the level of 10(-13)g/mL for directly detecting lead in many of these samples. The linear dynamic range was higher than 2 orders of magnitude. A single sample analysis could be completed within 2 min with reasonable semi-quantitative performance, e.g., relative standard deviations (RSDs) for deionized water were 4.6-7.6% during 5 experimental runs (each of them had 10 repeated measurements). Coca-cola and Huiyuan orange juice, representative beverage samples with complex matrices, generated recovery rates of 91.5% and 129%, respectively. Our experimental data demonstrated that EESI-MS is a useful tool for the fast detection of lead in various solutions, and EESI-MS showed promises for fast screening of lead-contaminated aqueous liquid samples.

Effect of first dimension phase selectivity in online comprehensive two dimensional liquid chromatography (LC×LC).

In this study, we examined the effect of first dimension column selectivity in reversed phase (RP) online comprehensive two dimensional liquid chromatography (LC×LC). The second dimension was always a carbon clad metal oxide reversed phase material. The hydrophobic subtraction model (HSM) and the related phase selective triangles were used to guide the selection of six different RP first dimension columns. Various kinds of samples were investigated and thus two different elution conditions were needed to cause full elution from the first dimension columns. We compared LC×LC chromatograms, contours plots, and fcoverage plots by measuring peak capacities, peak numbers, relative spatial coverage, correlation values, etc. The major finding of this study is that the carbon phase due to its rather different selectivity from other reversed phases is reasonably orthogonal to a variety of common types of bonded reversed phases. Thus quite surprisingly the six different first dimension stationary phases all showed generally similar separation patterns when paired to the second dimension carbon phase. This result greatly simplifies the task of choosing the correct pair of phases for RP×RP.

An experimental study of sampling time effects on the resolving power of on-line two-dimensional high performance liquid chromatography.

The experimental effects of sampling time on the resolving power of on-line LC×LC were investigated. The first dimension gradient time ((1)t(g)) and sampling time (t(s)) were systematically varied ((1)t(g)=5, 12, 24 and 49 min; t(s)=6, 12, 21 and 40s). The resolving power of on-line LC×LC was evaluated in terms of two metrics namely the numbers of observed peaks and the effective 2D peak capacities obtained in separations of extracts of maize seeds. The maximum effective peak capacity and number of observed peaks of LC×LC were achieved at sampling times between 12 and 21s, at all first dimension gradient times. In addition, both metrics showed that the "crossover" time at which fully optimized 1DLC and LC×LC have equal resolving power varied somewhat with sampling time but is only about 5 min for sampling times of 12 and 21s. The longest crossover time was obtained when the sampling time was 6s. Furthermore, increasing the first dimension gradient time gave large improvements in the resolving power of LC×LC relative to 1DLC. Finally, comparisons of the corrected and effective 2D peak capacities as well as the number of peaks observed showed that the impact of the coverage factor is quite significant.