An optimal combination of four active components in Huangqin decoction for the synergistic sensitization of irinotecan against colorectal cancer.

BACKGROUND: Irinotecan (CPT-11) is a first-line treatment for advanced colorectal cancer (CRC). Four components (baicalin, baicalein, wogonin, and glycyrrhizic acid) derived from Huangqin Decoction (HQD) have been proven to enhance the anticancer activity of CPT-11 in our previous study. OBJECTIVE: This study aimed to determine the optimal combination of the four components for sensitizing CPT-11 as well as to explore the underlying mechanism. METHODS: The orthogonal design method was applied to obtain candidate combinations (Cmb1-9) of the four components. The influence of different combinations on the anticancer effect of CPT-11 was first evaluated in vitro by cell viability, wound healing ability, cloning formation, apoptosis, and cell cycle arrest. Then, a CRC xenograft mice model was constructed to evaluate the anticancer effect of the optimal combination in vivo. Potential mechanisms of the optimal combination exerting a sensitization effect combined with CPT-11 against CRC were analyzed by targeted metabolomics. RESULTS: In vitro experiments determined that Cmb8 comprised of baicalin, baicalein, wogonin, and glycyrrhizic acid at the concentrations of 17 µM, 47 µM, 46.5 µM and 9.8 µM respectively was the most effective combination. Importantly, the cell viability assay showed that Cmb8 exhibited synergistic anticancer activity in combination with CPT-11. In in vivo experiments, this combination (15 mg/kg of baicalin, 24 mg/kg of baicalein, 24 mg/kg of wogonin, and 15 mg/kg of glycyrrhizic acid) also showed a synergistic anticancer effect. Meanwhile, inflammatory factors and pathological examination of the colon showed that Cmb8 could alleviate the gastrointestinal damage induced by CPT-11. Metabolic profiling of the tumors suggested that the synergistic anticancer effect of Cmb8 might be related to the regulation of fatty acid metabolism. CONCLUSION: The optimal combination of four components derived from HQD for the synergistic sensitization of CPT-11 against CRC was identified.

A new chemical derivatization reagent sulfonyl piperazinyl for the quantification of fatty acids using LC-MS/MS.

In our previous study, a chemical derivatization reagent named 5-(dimethylamino) naphthalene-1-sulfonyl piperazine (Dns-PP) was developed to enhance the chromatographic retention and the mass spectrometric response of free fatty acids (FFAs) in reversed-phase liquid chromatography coupled with electrospray ionization-mass spectrometry (RPLC-ESI-MS). However, Dns-PP exhibited strong preferences for long-chain FFAs, with limited improvement for short- or medium-chain FFAs. In this study, a new series of labeling reagents targeting FFAs were designed, synthesized, and evaluated. Among these reagents, Tmt-PP (N2, N2, N4, N4-tetramethyl-6-(4-(piperazin-1-ylsulfonyl) phenyl)-1,3,5-triazine-2,4-diamine) exhibited the best MS response and was selected for further evaluations. We compared Tmt-PP with Dns-PP and four commonly used carboxyl labeling reagents from existing studies, demonstrating the advantages of Tmt-PP. Further comparisons between Tmt-PP and Dns-PP in measuring FFAs from biological samples revealed that Tmt-PP labeling enhanced the MS response for about 80 % (30/38) of the measured FFAs, particularly for short- and medium-chain FFAs. Moreover, Tmt-PP labeling significantly improved the chromatographic retention of short-chain FFAs. To ensure accurate quantification, we developed a stable isotope-labeled Tmt-PP (i.e., d12-Tmt-PP) to react with chemical standards and serve as one-to-one internal standards (IS). The method was validated for accuracy, precision, sensitivity, linearity, stability, extraction efficiency, as well as matrix effect. Overall, this study introduced a new chemical derivatization reagent Tmt-PP (d12-Tmt-PP), providing a sensitive and accurate option for quantifying FFAs in biological samples.

LC-MS/MS profiling of colon oxysterols and cholesterol precursors in mouse model of ulcerative colitis.

Oxysterols and cholesterol precursors are being increasingly investigated in humans and laboratory animals as markers for various diseases in addition to their important functions. However, the quantitative analysis of these bioactive molecules is obstructed by high structural similarity, poor ionization efficiency and low abundance. The current assay methods are still cumbersome to be of practical use, and their applicability in different bio-samples needs to be evaluated and optimized as necessary. In the present work, chromatographic separation conditions were carefully studied to achieve baseline separation of difficult-to-isolate compound pairs. On the other hand, an efficient sample purification method was established for colon tissue samples with good recoveries of sterols, demonstrating negligible autoxidation of cholesterol into oxysterols. The developed UPLC-APCI-MS/MS method was thoroughly validated and applied to measure oxysterols and cholesterol precursors in colon tissue of dextran sulfate sodium (DSS)-induced mouse colitis models, and it is expected to be successfully applied to the quantitative determination of such components in other tissue samples.

A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method.

The formidable challenge posed by the presence of extremely high amounts of compounds and large differences in concentrations in plasma significantly complicates non-targeted metabolomics analyses. In this study, a comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC×GC-qMS) method with a solid-state modulator (SSM) for non-targeted metabolomics in beagle plasma was first established based on a GC-MS method, and the qualitative and quantitative performance of the two platforms were compared. Identification of detected compounds was accomplished utilizing NIST database match scores, retention indices (RIs) and standards. Semi-quantification involved the calculation of peak area ratios to internal standards. Metabolite identification sheets were generated for plasma samples on both analytical platforms, featuring 22 representative metabolites chosen for validating qualitative accuracy, and for conducting comparisons of linearity, accuracy, precision, and sensitivity. The outcomes revealed a threefold increase in the number of identifiable metabolites on the GC×GC-MS platform, with lower limits of quantitation (LLOQs) reduced to 0.5-0.05 times those achieved on the GC-MS platform. Accuracy in quantification for both GC×GC-MS and GC-MS fell within the range of 85-115%, and the vast majority of intra- and inter-day precisions were within the range of 20%. These findings underscore that relative to the conventional GC-MS method, the GC×GC-MS method developed in this study, combined with SSM, exhibits enhanced qualitative capabilities, heightened sensitivity, and comparable accuracy and precision, rendering it more suitable for non-targeted metabolomics analyses.

Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning.

BACKGROUND: Astragali Radix (AR) is a widely used herbal medicine. The quality of AR is influenced by several key factors, including the production area, growth mode, species, and grade. However, the markers currently used to distinguish these factors primarily focus on secondary metabolites, and their validation on large-scale samples is lacking. PURPOSE: This study aims to discover reliable markers and develop classification models for identifying the production area, growth mode, species, and grade of AR. METHODS: A total of 366 batches of AR crude slices were collected from six provinces in China and divided into learning (n = 191) and validation (n = 175) sets. Three ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods were developed and validated for determining 22 primary and 10 secondary metabolites in AR methanol extract. Based on the quantification data, seven machine learning algorithms, such as Nearest Neighbors and Gradient Boosted Trees, were applied to screen the potential markers and build the classification models for identifying the four factors associated with AR quality. RESULTS: Our analysis revealed that secondary metabolites (e.g., astragaloside IV, calycosin-7-O-ß-D-glucoside, and ononin) played a crucial role in evaluating AR quality, particularly in identifying the production area and species. Additionally, fatty acids (e.g., behenic acid and lignoceric acid) were vital in determining the growth mode of AR, while amino acids (e.g., alanine and phenylalanine) were helpful in distinguishing different grades. With both primary and secondary metabolites, the Nearest Neighbors algorithm-based model was constructed for identifying each factor of AR, achieving good classification accuracy (>70%) on the validation set. Furthermore, a panel of four metabolites including ononin, astragaloside II, pentadecanoic acid, and alanine, allowed for simultaneous identification of all four factors of AR, offering an accuracy of 86.9%. CONCLUSION: Our findings highlight the potential of integrating large-scale targeted metabolomics and machine learning approaches to accurately identify the quality-associated factors of AR. This study opens up possibilities for enhancing the evaluation of other herbal medicines through similar methodologies, and further exploration in this area is warranted.

Imatinib and M351-0056 enhance the function of VISTA and ameliorate the development of SLE via IFN-I and noncanonical NF-κB pathway.

V-domain immunoglobulin suppressor of T-cell activation (VISTA), an important negative checkpoint protein, participates in immunoregulation. Systemic lupus erythematosus (SLE) is an autoimmune disease in which patients exhibit high levels of autoantibodies and multi-organ tissue injury, primarily involving the kidney and skin. In wild-type (WT) mice and Vsir-/- mice with pristane-induced lupus-like disease, we found that VISTA deficiency exacerbated the lupus-like disease in mice, possibly through aberrant activation of type I interferon (IFN-I) signaling, CD4+ T cell, and noncanonical nuclear factor-κB (NF-κB) pathway. Surface plasmon resonance results showed that imatinib, an FDA-approved tyrosine kinase inhibitor, may have a high affinity for human VISTA-ECD with a KD value of 0.2009 µM. The biological activities of imatinib and VISTA agonist M351-0056 were studied in monocytes and T cells and in lupus-like disease murine model of chronic graft-versus-host disease (cGVHD) and lupus-prone MRL/lpr mice. VISTA small-molecule agonist reduced the cytokine production of peripheral blood mononuclear cells (PBMCs) and Jurkat cells and inhibited PBMCs proliferation. Moreover, they attenuated the levels of autoantibodies, renal injury, inflammatory cytokines, chemokines, and immune cell expansion in the cGVHD mouse model and MRL/lpr mice. Our findings also demonstrated that VISTA small-molecule agonist ameliorated the development of SLE through improving aberrantly activated IFN-I signaling and noncanonical NF-κB pathway. In conclusion, VISTA has a protective effect on the development and progression of SLE. VISTA agonist M351-0056 and imatinib have been firstly demonstrated to attenuate SLE, suggesting interventions to enhance VISTA function may be effective in treating SLE. VISTA deficiency exacerbates pristane-induced lupus-like disease in mice by promoting activation of the IFN-I and noncanonical NF-κB pathway. Imatinib was screened as a small-molecule VISTA agonist by molecular docking, SPR, and cellular level experiments. VISTA agonists (M351-0056 and imatinib) alleviated lupus-like disease progression in the cGVHD mouse model and MRL/lpr mice by inhibiting activation of IFN-I and noncanonical NF-κB pathway.

Chemical index components and quality control of Traditional Chinese Medicine: "Never change a winning team"? -A case study of volatile oil from Bupleuri radix.

Chemical index components, especially those defined as quality control (QC) markers through spectrum-effect relationship approach, are commonly suggested and adopted as indicator for quality control of Traditional Chinese Medicines (TCMs). However, are chemical index components and quality control of TCMs "never change a winning team"? In this study, under the ponderation of the applicability of QC markers strategy, spectrum-effect relationship and OPLS-DA between GC×GC-MS fingerprint and inhibitory effect on the expression of extracellular secretory TNF-α of volatile oil from Bupleuri radix (BVO) was studied with the purpose of discovery of QC markers and establish a bioactive compounds-based QC method. 290 compounds of BVO were identified by GC×GC-MS. Besides, BVO had significant inhibitory effects on the expression of extracellular secretory TNF-α in a dose-dependent manner. The potency of different batches of BVOs could be distinguished with this bioassay-based method, which has been validated in terms of intermediate precision, repeatability, linearity, range and credibility tests. The QC markers of BVO were investigated by Spearman's correlation test and OPLS-DA. It is regrettable that there were no ideal QC markers of BVO could be found. In conclusion, quality control method relayed on chemical QC markers is not feasible for TCMs with complex composition but lack of ingredients that dominate in content, just like BVO. Alternatively, a bioassay-based method established in our study is suitable for quality control of BVO.

Development and validation of a sensitive LC-MS/MS method for simultaneous analysis of clopidogrel and simvastatin and their main metabolites in beagles: Application to pharmacokinetic drug interactions.

Clopidogrel (CLP) and simvastatin (SV) are commonly used in combination therapies as anti-cardiovascular drugs. However, the effect of coadministration on the absorption and metabolism of the two drugs in vivo is not clear. This study developed and validated an LC-MS/MS method for the simultaneous determination of CLP, clopidogrel carboxylic acid (CLPCA), 2-oxo-clopidogrel (2-O-CLP), SV, and simvastatin hydroxy acid (SVA) in beagle plasma. Chromatographic separation was achieved on an InfinityLab Poroshell 120 SB-C8 column (2.1 × 100 mm, 2.7 µm) using methanol and 0.1% formic acid in water as the mobile phase at a flow rate of 0.3 mL/min in gradient mode. The lower limits of quantification are 0.1, 0.8, 0.05, 0.05, and 0.05 ng/mL for CLP, CLPCA, 2-O-CLP, SV, and SVA, respectively. The selectivity, linearity, accuracy, precision, extraction recovery, matrix effect, and stability were validated within acceptable criteria. This method was successfully applied to the pharmacokinetic drug interaction study between CLP and SV, and the results revealed that combined administration affected the metabolic rate of CLP, SV, and their metabolites. This study is the first to detect CLP, CLPCA, 2-O-CLP, SV, and SVA simultaneously.

High-Coverage Strategy for Multi-Subcellular Metabolome Analysis Using Dansyl-Labeling-Based LC-MS/MS.

Subcellular compartmentalization ensures orderly and efficient intracellular metabolic activities in eukaryotic life. Investigation of the subcellular metabolome could provide in-depth insight into cellular biological activities. However, the sensitive measurement of multi-subcellular metabolic profiles is still a significant challenge. Herein, we present a comprehensive subcellular fractionation, characterization, and metabolome analysis strategy. First, six subcellular fractions including nuclei, mitochondria, lysosomes, peroxisomes, microsomes, and cytoplasm were generated from a single aliquot of liver homogenate. Then, a dansyl-labeling-assisted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for measuring 151 amino/phenol- or carboxyl-containing metabolites in the subcellular fractions was established and validated. Last, the strategy was applied to a rat model of carbon tetrachloride (CCl4)-induced acute liver injury (ALI). The metabolic profile of individual organelles was compared with that of the liver. Interestingly, many unique changes were observed specifically in organelles, while the liver failed to capture these changes. This result indicates that metabolic investigation at the tissue level might lead to erroneous results due to the leveling effect. Our study demonstrates a feasible approach for the broad-spectrum-targeted metabolic profiling of multi-subcellular fractions, which can be of great use in driving our further understanding of intracellular metabolic activities in various physical and pathological conditions.

Stepwise solid phase extraction integrated with chemical derivatization for all-in-one injection LC-MS/MS analysis of metabolome and lipidome.

The metabolome and lipidome are critical components in illustrating biological processes and pathological mechanisms. Generally, two or more independent methods are required to analyze the two compound panels due to their distinct chemical properties and polarity differences. Here, a novel strategy integrating stepwise solid-phase extraction (SPE) and dansyl chemical derivatization was proposed for all-in-one injection LC-MS/MS analysis of serum metabolome and lipidome. In this workflow, a stepwise elution procedure was firstly optimized to separate the metabolome and lipidome fractions using an Ostro plate. Dansyl chemical derivatization was then applied to label amine/phenol, carboxyl, and carbonyl-containing sub-metabolomes. Our results demonstrated that the dansyl labeling could significantly improve chromatographic separation, enhance the MS response, and overcome the matrix effect of co-eluting lipids. Ultimately, an all-in-one injection LC-MS/MS method measuring 256 lipids (covering 20 subclasses) and 212 metabolites (including amino acids, bile acids, fatty acids, acylcarnitines, indole derivatives, ketones and aldehydes, nucleic acid metabolism, polyamines, etc.) was established. This method was applied to investigate the metabolic changes in cisplatin-induced nephrotoxicity in rats and the results were compared with previous untargeted metabolomics. The presented strategy could predominantly improve the analytical coverage and throughput and can be of great use in discovering reliable potential biomarkers in various applications.

Integrated multi-omics reveal important roles of gut contents in intestinal ischemia-reperfusion induced injuries in rats.

Intestinal ischemia-reperfusion (IIR) is a life-threatening clinical event with damaging signals whose origin and contents are unclear. Here we observe that IIR significantly affect the metabolic profiles of most organs by unbiased organ-wide metabolic analysis of gut contents, blood, and fifteen organs in rats (n = 29). Remarkably, correlations between gut content metabolic profiles and those of other organs are the most significant. Gut contents are also the only ones to show dynamic correlations during IIR. Additionally, according to targeted metabolomics analysis, several neurotransmitters are considerably altered in the gut during IIR, and displayed noteworthy correlations with remote organs. Likewise, metagenomics analysis (n = 35) confirm the effects of IIR on gut microbiota, and identify key species fundamental to the changes in gut metabolites, particularly neurotransmitters. Our multi-omics results establish key roles of gut contents in IIR induced remote injury and provide clues for future exploration.

Plasma Pharmacokinetics and Tissue Distribution of Doxorubicin in Rats following Treatment with Astragali Radix.

Doxorubicin (DOX) is an essential component in chemotherapy, and Astragali Radix (AR) is a widely used tonic herbal medicine. The combination of DOX and AR offers widespread, well-documented advantages in treating cancer, e.g., reducing the risk of adverse effects. This study mainly aims to uncover the impact of AR on DOX disposition in vivo. Rats received a single intravenous dose of 5 mg/kg DOX following a single-dose co-treatment or multiple-dose pre-treatment of AR (10 g/kg × 1 or × 10). The concentrations of DOX in rat plasma and six tissues, including heart, liver, lung, kidney, spleen, and skeletal muscle, were determined by a fully validated LC-MS/MS method. A network-based approach was further employed to quantify the relationships between enzymes that metabolize and transport DOX and the targets of nine representative AR components in the human protein−protein interactome. We found that short-term (≤10 d) AR administration was ineffective in changing the plasma pharmacokinetics of DOX in terms of the area under the concentration−time curve (AUC, 1303.35 ± 271.74 µg/L*h versus 1208.74 ± 145.35 µg/L*h, p > 0.46), peak concentrations (Cmax, 1351.21 ± 364.86 µg/L versus 1411.01 ± 368.38 µg/L, p > 0.78), and half-life (t1/2, 31.79 ± 5.12 h versus 32.05 ± 6.95 h, p > 0.94), etc. Compared to the isotype control group, DOX concentrations in six tissues slightly decreased under AR pre-administration but only showed statistical significance (p < 0.05) in the liver. Using network analysis, we showed that five of the nine representative AR components were not localized to the vicinity of the DOX disposition-associated module. These findings suggest that AR may mitigate DOX-induced toxicity by affecting drug targets rather than drug disposition.

UHPLC-MS/MS-based method for quantification of verinurad in rat plasma and its application in a bioavailability study.

A rapid and sensitive UHPLC-MS/MS method was developed and fully validated for the quantification of verinurad in rat plasma. Lesinurad was used as an internal standard (IS), and simple protein precipitation was utilized to prepare the analytes from the matrix. Chromatographic separation was carried out on a Zorbax SB C18 column. The mobile phase consisted of water with 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B) at a flow rate of 0.3 mL/min. The short run time of 4 min made it possible to analyze more than 300 samples per day. The ion transitions were quantified in negative mode with multiple reaction monitoring (MRM) transitions of 347.1 â†’ 261.1 for verinurad and 404.2 â†’ 178.9 for the IS. The validated linear ranges of verinurad were 10-5000 ng/mL in rat plasma. The validated UHPLC-MS/MS method was further applied to the pharmacokinetic study of verinurad in rat plasma after intragastric (2 mg/kg) and intravenous (1 mg/kg) administrations. The pharmacokinetic study revealed that verinurad showed high clearance and high bioavailability (78.1%). To the best of our knowledge, this is the first report of the bioavailability study of verinurad.

Discovery of Synergistic Drug Combinations for Colorectal Cancer Driven by Tumor Barcode Derived from Metabolomics "Big Data".

The accumulation of cancer metabolomics data in the past decade provides exceptional opportunities for deeper investigations into cancer metabolism. However, integrating a large amount of heterogeneous metabolomics data to draw a full picture of the metabolic reprogramming and to discover oncometabolites of certain cancers remains challenging. In this study, a tumor barcode constructed based upon existing metabolomics "big data" using the Bayesian vote-counting method is proposed to identify oncometabolites in colorectal cancer (CRC). Specifically, a panel of oncometabolites of CRC was generated from 39 clinical studies with 3202 blood samples (1332 CRC vs. 1870 controls) and 990 tissue samples (495 CRC vs. 495 controls). Next, an oncometabolite-protein network was constructed by combining the tumor barcode and its involved proteins/enzymes. The effect of anti-cancer drugs or drug combinations was then mapped into this network by the random walk with restart process. Utilizing this network, potential Irinotecan (CPT-11)-sensitizing agents for CRC treatment were discovered by random forest and Xgboost. Finally, a compound named MK-2206 was highlighted and its synergy with CPT-11 was validated on two CRC cell lines. To summarize, we demonstrate in the present study that the metabolomics "big data"-based tumor barcodes and the subsequent network analyses are potentially useful for drug combination discovery or drug repositioning.

Network-driven targeted analysis reveals that Astragali Radix alleviates doxorubicin-induced cardiotoxicity by maintaining fatty acid homeostasis.

ETHNOPHARMACOLOGICAL RELEVANCE: Astragali Radix (AR) is a popular traditional Chinese medicine that has been used for more than 2000 years. It is a well-known tonic for weak people with chronic diseases, such as heart failure and cerebral ischemia. Previous studies have reported that AR could support the "weak heart" of cancer patients who suffered from doxorubicin (DOX)-induced cardiotoxicity (DIC). However, the underlying mechanism remains unclear. AIM OF THE STUDY: This study aimed to uncover the critical pathways and molecular determinants for AR against DIC by fully characterizing the network-based relationship. MATERIALS AND METHODS: We integrated ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) profiling, database and literature searching, and the human protein-protein interactome to discover the specific network module associated with AR against DIC. To validate the network-based findings, a low-dose, long-term DIC mouse model and rat cardiomyoblast H9c2 cells were employed. The levels of potential key metabolites and proteins in hearts and cells were quantified by the LC-MS/MS targeted analysis and western blotting, respectively. RESULTS: We constructed one of the most comprehensive AR component-target network described to date, which included 730 interactions connecting 64 unique components and 359 unique targets. Relying on the network-based evaluation, we identified fatty acid metabolism as a putative critical pathway and peroxisome proliferator-activated receptors (PPARα and PPARγ) as potential molecular determinants. We then confirmed that DOX caused the accumulation of fatty acids in the mouse failing heart, while AR promoted fatty acid metabolism and preserved heart function. By inhibiting PPARγ in H9c2 cells, we further found that AR could alleviate DIC by activating PPARγ to maintain fatty acid homeostasis. CONCLUSIONS: Our findings imply that AR is a promising drug candidate that treats DIC by maintaining fatty acid homeostasis. More importantly, the network-based method developed here could facilitate the mechanism discovery of AR therapy and help catalyze innovation in its clinical application.

Twins labeling derivatization-based LC-MS/MS strategy for absolute quantification of paired prototypes and modified metabolites.

Modified metabolites play significant roles in disease occurrence, progression and diagnosis. Sensitive and accurate analytical methods for the quantification of these metabolites are therefore of great importance. In this study, a liquid chromatography tandem mass spectrometry (LC-MS/MS) method was developed for the simultaneous measurement of 13 pairs of prototypes and their modified forms covering nucleobases, nucleosides and amino acids. In order to improve the quantification sensitivity and accuracy, two structure analogs named N-dimethyl-amino naphthalene-1-sulfonyl chloride (Dns-Cl) and N-diethyl-amino naphthalene-1-sulfonyl chloride (Dens-Cl) were introduced for twins labeling derivatization. Dns-labeling was utilized to react with target analytes while the Dens-labeling of standard compounds provided one-to-one internal standards. With the introduce of naphthalene and easily ionizable moiety tertiary ammonium, chromatography retention and separation of these polar metabolites were notably improved on C18 columns and the detection sensitivity was increased up to 400 folds. The method is sensitive with the lower limit of quantification (LLOQ) values of 0.002-0.5 µg/mL. Comparisons of the performance of twins labeling derivatization and traditional chemical isotope labeling (CIL) derivatization verified the ability of our method in the absolute quantification. The established method was applied to human lung adenocarcinoma cell line A549 and its cisplatin resistant derivative A549/DDP. Significant shifts in 12 metabolites as well as 9 modified-to-prototypical ratios in A549/DDP were observed, demonstrating the utility of our method and the potential role of modified metabolites in mediating anticancer drug resistance. The method can be easily extended to determine other types of modified metabolites in various biological matrices, which will greatly expand our knowledge on these metabolites.

A validated LC-MS/MS method for simultaneous quantification of simvastatin and simvastatin acid in beagle plasma: Application to an absolute bioavailability study.

A highly sensitive LC-MS/MS method for simultaneous detection of both simvastatin (SV) and simvastatin acid (SVA) in beagle plasma was developed and successfully applied to an absolute bioavailability study. Lovastatin (LV) was used as internal standard (IS). The analysis was performed using electrospray ionization and selective reaction monitoring in positive mode at m/z 441.0 → 325.0 for SV, 459.0 → 343.0 for SVA and 427.0 → 325.0 for the IS, respectively. The assay procedure involved a simple liquid-liquid extraction of SV, SVA and LV from beagle plasma into methyl tert-butyl ether. Separation of SV, SVA and the IS was achieved on a Shim-pack VP-ODS column (150 × 2.0 mm, 5 µm) with a binary gradient solvent system of 0.1% formic acid in water and methanol (15:85, v/v) as the mobile phase. The method was validated over the range of 0.25-500 ng/ml for SV (r2 ≥ 0.9923) and 0.24-481.23 ng/ml for SVA (r2 ≥ 0.9987). The results of method validation for accuracy, precision, extraction recovery, matrix effect and stability were within the acceptance criteria. The values of absolute bioavailability of SV and SVA in beagles were 2.97 and 25.40%, respectively. It is the first study developed for the measurement of absolute bioavailability of SV and SVA acid in beagles.

Predicting the grades of Astragali radix using mass spectrometry-based metabolomics and machine learning.

Astragali radix (AR, the dried root of Astragalus) is a popular herbal remedy in both China and the United States. The commercially available AR is commonly classified into premium graded (PG) and ungraded (UG) ones only according to the appearance. To uncover novel sensitive and specific markers for AR grading, we took the integrated mass spectrometry-based untargeted and targeted metabolomics approaches to characterize chemical features of PG and UG samples in a discovery set (n=16 batches). A series of five differential compounds were screened out by univariate statistical analysis, including arginine, calycosin, ononin, formononetin, and astragaloside Ⅳ, most of which were observed to be accumulated in PG samples except for astragaloside Ⅳ. Then, we performed machine learning on the quantification data of five compounds and constructed a logistic regression prediction model. Finally, the external validation in an independent validation set of AR (n=20 batches) verified that the five compounds, as well as the model, had strong capability to distinguish the two grades of AR, with the prediction accuracy > 90%. Our findings present a panel of meaningful candidate markers that would significantly catalyze the innovation in AR grading.

Metabolic network-based identification of plasma markers for non-small cell lung cancer.

Metabolic markers, offering sensitive information on biological dysfunction, play important roles in diagnosing and treating cancers. However, the discovery of effective markers is limited by the lack of well-established metabolite selection approaches. Here, we propose a network-based strategy to uncover the metabolic markers with potential clinical availability for non-small cell lung cancer (NSCLC). First, an integrated mass spectrometry-based untargeted metabolomics was used to profile the plasma samples from 43 NSCLC patients and 43 healthy controls. We found that a series of 39 metabolites were altered significantly. Relying on the human metabolic network assembled from Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we mapped these differential metabolites to the network and constructed an NSCLC-related disease module containing 23 putative metabolic markers. By measuring the PageRank centrality of molecules in this module, we computationally evaluated the network-based importance of the 23 metabolites and demonstrated that the metabolism pathways of aromatic amino acids and long-chain fatty acids provided potential molecular targets of NSCLC (i.e., IL4l1 and ACOT2). Combining network-based ranking and support-vector machine modeling, we further found a panel of eight metabolites (i.e., pyruvate, tryptophan, and palmitic acid) that showed a high capability to differentiate patients from controls (accuracy > 97.7%). In summary, we present a meaningful network method for metabolic marker discovery and have identified eight strong candidate metabolites for NSCLC diagnosis.

Akkermansia Muciniphila Potentiates the Antitumor Efficacy of FOLFOX in Colon Cancer.

FOLFOX (oxaliplatin, fluorouracil and calcium folinate) is the first-line chemotherapy regimen for colon cancer therapy in the clinic. It provides superior efficacy than oxaliplatin alone, but the underlying mechanism remains unclear. In the present study, pharmacomicrobiomics integrated with metabolomics was conducted to uncover the role of the gut microbiome behind this. First, in vivo study demonstrated that FOLFOX exhibited better efficacy than oxaliplatin alone in colon cancer animal models. Second, 16S rDNA gene sequencing analysis showed that the abundance of Akkermansia muciniphila (A. muciniphila) remarkably increased in the FOLFOX treated individuals and positively correlated with the therapeutic effect. Third, further exploration confirmed A. muciniphila colonization significantly enhanced the anti-cancer efficacy of FOLFOX. Last, metabolomics analysis suggested dipeptides containing branched-chain amino acid (BCAA) might be responsible for gut bacteria mediated FOLFOX efficacy. In conclusion, our study revealed the key role of A. muciniphila in mediating FOLFOX efficacy, and manipulating A. muciniphila might serve as a novel strategy for colon cancer therapy.