Plasma metabolomics reveals risk factors for lung adenocarcinoma.

Background: Metabolic reprogramming plays a significant role in the advancement of lung adenocarcinoma (LUAD), yet the precise metabolic changes remain incompletely understood. This study aims to uncover metabolic indicators associated with the progression of LUAD. Methods: A total of 1083 subjects were recruited, including 670 LUAD, 135 benign lung nodules (BLN) and 278 healthy controls (HC). Gas chromatography-mass spectrometry (GC/MS) was used to identify and quantify plasma metabolites. Odds ratios (ORs) were calculated to determine LUAD risk factors, and machine learning algorithms were utilized to differentiate LUAD from BLN. Results: High levels of oxalate, glycolate, glycine, glyceric acid, aminomalonic acid, and creatinine were identified as risk factors for LUAD (adjusted ORs>1.2, P<0.03). Remarkably, oxalate emerged as a distinctive metabolic risk factor exhibiting a strong correlation with the progression of LUAD (adjusted OR=5.107, P<0.001; advanced-stage vs. early-stage). The Random Forest (RF) model demonstrated a high degree of efficacy in distinguishing between LUAD and BLN (accuracy = 1.00 and 0.73, F1-score= 1.00 and 0.79, and AUC = 1.00 and 0.76 in the training and validation sets, respectively). TCGA and GTEx gene expression data have shown that lactate dehydrogenase A (LDHA), a crucial enzyme involved in oxalate metabolism, is increasingly expressed in the progression of LUAD. High LDHA expression levels in LUAD patients are also linked to poor prognoses (HR=1.66, 95% CI=1.34-2.07, P<0.001). Conclusions: This study reveals risk factors associated with LUAD.

Targeting cAMP in D1-MSNs in the nucleus accumbens, a new rapid antidepressant strategy.

Studies have suggested that the nucleus accumbens (NAc) is implicated in the pathophysiology of major depression; however, the regulatory strategy that targets the NAc to achieve an exclusive and outstanding anti-depression benefit has not been elucidated. Here, we identified a specific reduction of cyclic adenosine monophosphate (cAMP) in the subset of dopamine D1 receptor medium spiny neurons (D1-MSNs) in the NAc that promoted stress susceptibility, while the stimulation of cAMP production in NAc D1-MSNs efficiently rescued depression-like behaviors. Ketamine treatment enhanced cAMP both in D1-MSNs and dopamine D2 receptor medium spiny neurons (D2-MSNs) of depressed mice, however, the rapid antidepressant effect of ketamine solely depended on elevating cAMP in NAc D1-MSNs. We discovered that a higher dose of crocin markedly increased cAMP in the NAc and consistently relieved depression 24 h after oral administration, but not a lower dose. The fast onset property of crocin was verified through multicenter studies. Moreover, crocin specifically targeted at D1-MSN cAMP signaling in the NAc to relieve depression and had no effect on D2-MSN. These findings characterize a new strategy to achieve an exclusive and outstanding anti-depression benefit by elevating cAMP in D1-MSNs in the NAc, and provide a potential rapid antidepressant drug candidate, crocin.

Thioacetamide-Induced Acute Liver Injury Increases Metformin Plasma Exposure by Downregulating Renal OCT2 and MATE1 Expression and Function.

Metformin plasma exposure is increased in rats with thioacetamide (TAA)-induced liver failure. The absorption, distribution, and excretion process of metformin is mainly mediated by organic cation transporters (OCTs) and multidrug and toxin extrusion transporters (MATEs). To investigate the mechanisms of the increase in TAA-induced metformin plasma exposure, we employed intestinal perfusion and urinary excretion assays to evaluate the changes in the absorption and excretion of metformin and used Western blotting to investigate the metformin-related transport proteins' expression changes and mechanisms. The results showed that neither intestinal OCT2 expression nor metformin intestinal absorption were significantly altered by TAA-induced liver failure, while significantly decreased expression and function of renal OCT2 and MATE1 as well as impaired metformin excretion were observed in TAA rats. HK-2 cells were used as an in vitro model to explore the mechanism of liver-failure-mediated downregulation in renal OCT2 and MATE1. The results demonstrated that among numerous abnormal substances that changed in acute liver failure, elevated estrogen levels and tumor necrosis factor-α were the main factors mediating the downregulation of OCT2 and MATE1. In conclusion, this study highlights the downregulation of renal OCT2 and MATE1 in liver injury and its regulatory mechanism and reveals its roles in the increase in TAA-mediated metformin plasma exposure.

AI Machine Learning Technique Characterizes Potential Markers of Depression in Two Animal Models of Depression.

(1) Background: there is an urgent clinical need for rapid and effective antidepressants. (2) Methods: We employed proteomics to profile proteins in two animal models (n = 48) of Chronic Unpredictable Stress and Chronic Social Defeat Stress. Additionally, partial least squares projection to latent structure discriminant analysis and machine learning were used to distinguish the models and the healthy control, extract and select protein features and build biomarker panels for the identification of different mouse models of depression. (3) Results: The two depression models were significantly different from the healthy control, and there were common changes in proteins in the depression-related brain regions of the two models; i.e., SRCN1 was down-regulated in the dorsal raphe nucleus in both models of depression. Additionally, SYIM was up-regulated in the medial prefrontal cortex in the two depression models. Bioinformatics analysis suggested that perturbed proteins are involved in energy metabolism, nerve projection, etc. Further examination confirmed that the trends of feature proteins were consistent with mRNA expression levels. (4) Conclusions: To the best of our knowledge, this is the first study to probe new targets of depression in multiple brain regions of two typical models of depression, which could be targets worthy of study.

In vitro co-culture systems of hepatic and intestinal cells for cellular pharmacokinetic and pharmacodynamic studies of capecitabine against colorectal cancer.

BACKGROUND: As a prodrug of 5-fluorouracil (5-FU), orally administrated capecitabine (CAP) undergoes preliminary conversion into active metabolites in the liver and then releases 5-FU in the gut to exert the anti-tumor activity. Since metabolic changes of CAP play a key role in its activation, a single kind of intestinal or hepatic cell can never be used in vitro to evaluate the pharmacokinetics (PK) and pharmacodynamics (PD) nature. Hence, we aimed to establish a novel in vitro system to effectively assess the PK and PD of these kinds of prodrugs. METHODS: Co-culture cellular models were established by simultaneously using colorectal cancer (CRC) and hepatocarcinoma cell lines in one system. Cell Counting Kit-8 (CCK-8) and flow cytometric analysis were used to evaluate cell viability and apoptosis, respectively. Apoptosis-related protein expression levels were measured using western blot analysis. A selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for cellular PK in co-culture models. RESULTS: CAP had little anti-proliferative effect on the five monolayer CRC cell lines (SW480, LoVo, HCT-8, HCT-116 and SW620) or the hepatocarcinoma cell line (HepG2). However, CAP exerted marked anti-tumor activities on each of the CRC cell lines in the co-culture models containing both CRC and hepatocarcinoma cell lines, although its effect on the five CRC cell lines varied. Moreover, after pre-incubation of CAP with HepG2 cells, the culture media containing the active metabolites of CAP also showed an anti-tumor effect on the five CRC cell lines, indicating the crucial role of hepatic cells in the activation of CAP. CONCLUSION: The simple and cost­effective co-culture models with both CRC and hepatocarcinoma cells could mimic the in vivo process of a prodrug dependent on metabolic conversion to active metabolites in the liver, providing a valuable strategy for evaluating the PK and PD characteristics of CAP-like prodrugs in vitro at the early stage of drug development.

Non-targeted characteristic filter analysis combined with in silico prediction strategies to identify the chemical components and in vivo metabolites of Dalitong Granules by UPLC-Q-TOF/MS/MS.

Dalitong Granules, a potent gastrointestinal motility promoting traditional Chinese medicine, is used to treat functional dyspepsia clinically. It shows good effect on alleviating gastrointestinal motility disorders and has a broad prospect of clinical application. However, there is no comprehensive study on its in vivo and in vitro chemical analysis. UPLC-Q-TOF-MS combined with the non-targeted characteristic filter analysis and in silico prediction strategies (NCFS) were used to deduce and identify the chemical components and in vivo metabolites in the bio-samples of rats following oral administration of Dalitong Granules. In this study, 108 chemical components were identified in Dalitong granules, including 50 flavonoids, 22 alkaloids, 13 terpenes, 11 organic acids, 10 coumarins and 2 volatile oils. In the plasma, tissue, urine and fecal samples of rats after administration of Dalitong granules, a total of 147 compounds were speculated (60 prototype compounds and 87 metabolites). The main metabolic pathways in vivo include methylation, demethylation, deglycosylation, hydrogenation, hydroxylation, sulfonation and glucuronidation as there are many flavonoids existing in Dalitong Granules. In conclusion, the chemical components and metabolites of Dalitong Granules were comprehensively identified by using a rapid and accurate analysis method, which laid a foundation for dissecting its bioactive substances. In addition, it provides a scientific basis for the in-depth study of the material basis of Dalitong Granules efficacy and its further comprehensive development and utilization.

Silybin regulates P450s activity by attenuating endoplasmic reticulum stress in mouse nonalcoholic fatty liver disease.

Cytochrome P450s are important phase I metabolic enzymes located on endoplasmic reticulum (ER) involved in the metabolism of endogenous and exogenous substances. Our previous study showed that a hepatoprotective agent silybin restored CYP3A expression in mouse nonalcoholic fatty liver disease (NAFLD). In this study we investigated how silybin regulated P450s activity during NAFLD. C57BL/6 mice were fed a high-fat-diet (HFD) for 8 weeks to induce NAFLD, and were administered silybin (50, 100 mg ·kg-1 ·d-1, i.g.) in the last 4 weeks. We showed that HFD intake induced hepatic steatosis and ER stress, leading to significant inhibition on the activity of five primary P450s including CYP1A2, CYP2B6, CYP2C19, CYP2D6, and CYP3A in liver microsomes. These changes were dose-dependently reversed by silybin administration. The beneficial effects of silybin were also observed in TG-stimulated HepG2 cells in vitro. To clarify the underlying mechanism, we examined the components involved in the P450 catalytic system, membrane phospholipids and ER membrane fluidity, and found that cytochrome b5 (cyt b5) was significantly downregulated during ER stress, and ER membrane fluidity was also reduced evidenced by DPH polarization and lower polyunsaturated phospholipids levels. The increased ratios of NADP+/NADPH and PC/PE implied Ca2+ release and disruption of cellular Ca2+ homeostasis resulted from mitochondria dysfunction and cytochrome c (cyt c) release. The interaction between cyt c and cyt b5 under ER stress was an important reason for P450s activity inhibition. The effect of silybin throughout the whole course suggested that it regulated P450s activity through its anti-ER stress effect in NAFLD. Our results suggest that ER stress may be crucial for the inhibition of P450s activity in mouse NAFLD and silybin regulates P450s activity by attenuating ER stress.

Identification of Biomarkers for Methamphetamine Exposure Time Prediction in Mice Using Metabolomics and Machine Learning Approaches.

Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg-1 (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography-mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.

Therapeutic targeting of glutamate dehydrogenase 1 that links metabolic reprogramming and Snail-mediated epithelial-mesenchymal transition in drug-resistant lung cancer.

Acquired drug resistance and epithelial-mesenchymal transition (EMT) mediated metastasis are two highly interacting determinants for non-small-cell lung cancer (NSCLC) prognosis. This study investigated the common mechanisms of drug resistance and EMT from the perspective of metabolic reprogramming, which may offer new ideas to improve anticancer therapy. Acquired resistant cells were found to grow faster and have a greater migratory and invasive capacity than their parent cells. Metabolomics analysis revealed that acquired resistant cells highly relied on glutamine utilization and mainly fluxed into oxidative phosphorylation energy production. Further mechanistic studies screened out glutamate dehydrogenase 1 (GLUD1) as the determinant of glutamine addiction in acquired resistant NSCLC cells, and provided evidence that GLUD1-mediated α-KG production and the accompanying reactive oxygen species (ROS) accumulation primarily triggered migration and invasion by inducing Snail. Pharmacological and genetic interference with GLUD1 in vitro significantly reversed drug resistance and decreased cell migration and invasion capability. Lastly, the successful application of R162, a selective GLUD1 inhibitor, to overcome both acquired resistance and EMT-induced metastasis in vivo, identified GLUD1 as a promising and druggable therapeutic target for malignant progression of NSCLC. Collectively, our study offers a potential strategy for NSCLC therapy, especially for drug-resistant patients with highly expressed GLUD1.

Accurate biodetection of trace uranium by electrochemiluminescence and its application inIn vivo toxicokinetic dynamic research.

Due to the technical barriers and complexity of biological detection equipment, the intensive study of the toxicokinetics of uranium has been limited. In other words, efficient biodetection system for accurately and conveniently uranium analysis is the core demand. In this study, an efficient monitoring system was developed for rapid visual detection of trace UO22+ in biological samples by using electrochemiluminescence (ECL) imaging technology. In detail, poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-(1-cyanovinylene-1,4-phenylene)] (CN-PPV) was prepared as polymer dots (Pdots), which give a low background signal and notable visual UO22+ response in accurate monitoring as well as high selectivity. This sensor was successfully applied to visual UO22+ detection in blood and urine in an oral uranyl metabolism rat model. The results showed that UO22+ concentration in rat blood reached the maximum 30 min after administration and then decreased rapidly. Even after 48 h, trace UO22+ could still be detected with the developed method, demonstrating its ultrahigh sensitivity and selectivity. This work is the first visualized UO22+ detection via ECL in biological samples. This ECL method for accurate trace UO22+ monitoring in biological samples indicates its wide field of application with good prospects such as nuclear forensics, evidence-based medicine, and toxicological research.

The Role of SLC7A11 in Cancer: Friend or Foe?

SLC7A11 controls the uptake of extracellular cystine in exchange for glutamate at a ratio of 1:1, and it is overexpressed in a variety of tumours. Accumulating evidence has shown that the expression of SLC7A11 is fine-tuned at multiple levels, and plays diverse functional and pharmacological roles in tumours, such as cellular redox homeostasis, cell growth and death, and cell metabolism. Many reports have suggested that the inhibition of SLC7A11 expression and activity is favourable for tumour therapy; thus, SLC7A11 is regarded as a potential therapeutic target. However, emerging evidence also suggests that on some occasions, the inhibition of SLC7A11 is beneficial to the survival of cancer cells, and confers the development of drug resistance. In this review, we first briefly introduce the biological properties of SLC7A11, including its structure and physiological functions, and further summarise its regulatory network and potential regulators. Then, focusing on its role in cancer, we describe the relationships of SLC7A11 with tumourigenesis, survival, proliferation, metastasis, and therapeutic resistance in more detail. Finally, since SLC7A11 has been linked to cancer through multiple approaches, we propose that its contribution and regulatory mechanism require further elucidation. Thus, more personalised therapeutic strategies should be adapted when targeting SLC7A11.

A quantitative metabolomics assay targeting 14 intracellular metabolites associated with the methionine transsulfuration pathway using LC-MS/MS in breast cancer cells.

The methionine transsulfuration pathway plays an important role in some fundamental biological processes, such as redox and methylation reactions. However, quantitative analysis of the majority of intracellular metabolites is rather challenging. In this study, we developed a simple, fast and reliable method using liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the simultaneous detection of 14 methionine-related metabolites, including methionine, S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), homocysteine (HCY), cystathionine (Cysta), cysteine (CYS), glutathione (GSH), dimethylglycine (DMG), betaine, serine, folic acid (FA), dihydrofolic acid (DHF), tetrahydrofolic acid (THF) and 5-methyltetrahydrofolic acid (5-MTHF), in MCF-7 and MDA-MB-231 breast cancer cells. By taking advantage of a surrogate matrix, the linearity, sensitivity, precision, accuracy, stability, matrix effect, recovery, dilution integrity and carryover of the established method were evaluated and validated. This method enabled the precise measurement of methionine-related metabolites both in cells and in the medium and was successfully applied to profile these metabolites involved in the methionine transsulfuration pathway. The data showed that cystine deprivation or excessive supplementation with cystine had a marked impact on methionine metabolism, in addition to its effects on intracellular CYS and GSH levels, indicating that the methionine transsulfuration pathway was dependent on intracellular cystine levels. The established method provides a reliable way to target metabolomics for the quantitative determination of intracellular metabolites in the methionine transsulfuration pathway, which can greatly facilitate the understanding of the mechanisms involved in methylation and redox homeostasis in cellular metabolomic studies.

Quantitative analysis of 20 purine and pyrimidine metabolites by HILIC-MS/MS in the serum and hippocampus of depressed mice.

Purine and pyrimidine metabolism are vital metabolic pathways in the development, proliferation or repairment of cells or tissues associated with various diseases. Here, a simple, all-in-one injection hydrophilic interaction liquid chromatography-tandem mass spectrometry method was developed for simultaneous determination of 20 metabolites: adenine, adenosine, deoxyadenosine, adenosine 5'-monophosphate, cyclic adenosine monophosphate, hypoxanthine, xanthine, inosine, deoxyinosine, xanthosine, xanthosine 5'-monophosphate and uric acid, which are products of purine metabolism; uridine, deoxyuridine, uridine 5'-monophosphate and uracil, are products of pyrimidine metabolism; and corticosterone, methionine, acetylcholine and serotonin. To minimize interference of endogenous molecules in sample matrixes, a combination of activated carbon adsorption and a serum substitute matrix (5% bovine serum albumin in phosphate buffered saline) was utilized and jointly applied. The sensitivity, linearity, stability, precision, accuracy and extraction recovery were evaluated, and the method was demonstrated to be accurate, sensitive and reliable. An analytical strategy was successfully applied to quantitatively determine 20 metabolite levels in the serum and hippocampus of mice with chronic social defeat stress-induced depression. The results showed greatly perturbed purine metabolism in the depressed mice, which was primarily characterized by dramatic increases in hypoxanthine, xanthine and inosine in serum and reduced levels of adenine, adenosine and adenosine 5'-monophosphate in the hippocampus. These findings suggest that this novel strategy can facilitate the quantitative analysis of adenine and other purine and pyrimidine metabolites in tissue and serum and exhibits great potential in the exploration of metabolism-related mechanisms of relevant diseases.

Effects of 'Healthy' Fecal Microbiota Transplantation against the Deterioration of Depression in Fawn-Hooded Rats.

Depression is a recurrent, heterogeneous mood disorder occurring in more than 260 million people worldwide. Gut microbiome dysbiosis is associated with the development of depressive-like behaviors by modulating neuro-biochemical metabolism through the microbiome-gut-brain (MGB) axis. Fecal microbiota transplantation (FMT) has been proposed as a potential therapeutic solution for depression, but the therapeutic efficiency and mechanism are unknown. Here, we performed an FMT from Sprague-Dawley (SD) rats ('healthy' controls) to Fawn-hooded (FH) rats (depression model). Pre-FMT, the FH rats exhibited significantly elevated depressive-like behaviors and distinct neurotransmitter and cytokine levels compared with SD rats. Post-FMT, FH recipients receiving FH fecal microbiota (FH-FH rats) showed aggravated depressive-like behaviors, while the ones receiving SD microbiota (FH-SD rats) had significantly alleviated depressive symptoms, a significant increase in hippocampal neurotransmitters, and a significant decrease of some hippocampal cytokines than FH-FH rats. SD-FMT resulted in the FH-SD rats' gut microbiome resembling the SD donors, and a significant shift in the serum metabolome but not the hippocampal metabolome. Co-occurrence analysis suggests that SD-FMT prevented recipients' depression development via the significant decrease of gut microbial species such as Dialister sp., which led to the recipients' metabolic modulation in serum and hippocampus through the enteric nervous system, the intestinal barrier, and the blood-brain barrier. Our results provided new data pointing to multiple mechanisms of interaction for the impact of gut microbiome modulation on depression therapy. IMPORTANCE Depression is a chronic, recurrent mental disease, which could make the patients commit suicide in severe cases. Considering that gut microbiome dysbiosis could cause depressive symptoms in animals through the MGB axis, the modification of gut microbiota is expected to be a potential therapy for depression, but the daily administration of probiotics is invalid or transient. In this study, we demonstrated that the gut microbiome transferred from a healthy rat model to a depressive rat model could regulate the recipient's neurobiology and behavior via the systematic alternation of the depressive gut microbiota followed by the serum and hippocampal metabolism. These results underline the significance of understanding the impact of gut microbiota on mental disorders and suggest that 'healthy' microbiota transplantation with the function to solve the host's cerebral inflammation may serve as a novel therapeutic strategy for depression.

Plasma Metabolomics Reveals Diagnostic Biomarkers and Risk Factors for Esophageal Squamous Cell Carcinoma.

Esophageal squamous carcinoma (ESCC) has a high morbidity and mortality rate. Identifying risk metabolites associated with its progression is essential for the early prevention and treatment of ESCC. A total of 373 ESCC, 40 esophageal squamous dysplasia (ESD), and 218 healthy controls (HC) subjects were enrolled in this study. Gas chromatography-mass spectrometry (GC/MS) was used to acquire plasma metabolic profiles. Receiver operating characteristic curve (ROC) and adjusted odds ratio (OR) were calculated to evaluate the potential diagnosis and prediction ability markers. The levels of alpha-tocopherol and cysteine were progressively decreased, while the levels of aminomalonic acid were progressively increased during the various stages (from precancerous lesions to advanced-stage) of exacerbation in ESCC patients. Alpha-tocopherol performed well for the differential diagnosis of HC and ESD/ESCC (AUROC>0.90). OR calculations showed that a high level of aminomalonic acid was not only a risk factor for further development of ESD to ESCC (OR>13.0) but also a risk factor for lymphatic metastasis in ESCC patients (OR>3.0). A low level of alpha-tocopherol was a distinguished independent risk factor of ESCC (OR< 0.5). The panel constructed by glycolic acid, oxalic acid, glyceric acid, malate and alpha-tocopherol performed well in distinguishing between ESD/ESCC from HC in the training and validation set (AUROC>0.95). In conclusion, the oxidative stress function was impaired in ESCC patients, and improving the body's antioxidant function may help reduce the early occurrence of ESCC.

Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy.

Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R2) and is time-saving. Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.

Quantitative determination of D4-cystine in mice using LC-MS/MS and its application to the assessment of pharmacokinetics and bioavailability.

Cystine is the primary source material for the synthesis of glutathione. However, the pharmacokinetics and tissue distribution of cystine are largely unknown. A surrogate analyte D4-cystine was employed to generate calibration curves for the determination of levels of D4-cystine and endogenous cystine in mice by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Validation assessments proved the sensitivity, specificity and reproducibility of the method with a lower limit of quantification (LLOQ) of 5 ng/mL over 5-5000 ng/mL in plasma. The pharmacokinetics of D4-cystine were evaluated after administering injections and oral solutions, both of which minimally impacted endogenous cystine levels. The absolute bioavailability of cystine was 18.6%, 15.1% and 25.6% at doses of 25, 50 and 100 mg/kg, respectively. Intravenously injected D4-cystine resulted in dramatically high plasma levels with reduced levels in the brain and liver. Intragastrically administered D4-cystine resulted in high levels in the plasma and stomach with relatively low levels in the lung, kidney, heart and brain.

Detection and verification of coexisting diagnostic markers in plasma and serum of patients with non-small-cell lung cancer.

Aim: To screen and identify the potential biomarkers co-existing in plasma and serum of patients with non-small-cell lung cancer (NSCLC), and establish appropriate diagnostic models. Methods: A cohort of 195 plasma samples and 180 serum samples were obtained from healthy controls (HCs), adenocarcinoma (AdC) and squamous cell carcinoma (SqCC) patients enrolled from the First Affiliated Hospital of Nanjing Medical University. Metabolites in plasma and serum were analyzed by GC-MS. Results: Hypoxanthine was found to have good performance in the differential diagnosis of NSCLC (including AdC and SqCC) and HC (area under the receiver operating characteristic [AUROC] ≥0.85). Combinations of metabolites could be used for differential diagnosis of NSCLC and HC (AUROC >0.93), AdC and HC (AUROC >0.91), SqCC and HC (AUROC >0.95), AdC and SqCC (AUROC >0.72). Conclusions: Metabolomics based on GC-MS can screen and identify the differential metabolites coexisting in plasma and serum of patients with NSCLC, and prediction models established by this method can be used for the differential diagnosis of patients with NSCLC.

Lay abstract Non-small-cell lung cancer (NSCLC) is not easy to diagnose. This study was intended to determine metabolites to differentiate NSCLC and healthy control samples (HC). In this study, we collected plasma and serum of NSCLC and HC. Then we performed a metabolomic analysis on these blood samples. The results showed that some metabolites were co-existing in plasma and serum of NSCLC. These co-existing metabolites (such as hypoxanthine, glyceric acid and aspartate) could differentiate NSCLC and HC.

Identification of Morphine and Heroin-Treatment in Mice Using Metabonomics.

Although heroin and morphine are structural analogues and morphine is a metabolite of heroin, it is not known how the effect of each substance on metabolites in vivo differs. Heroin and morphine were administered to C57BL/6J mice in increasing doses from 2 to 25 and 3 to 9 mg kg-1 (twice a day, i.p.), respectively, for 20 days. The animals underwent withdrawal for 5 days and were readministered the drugs after 10 days. Serum and urine analytes were profiled using gas chromatography-mass spectrometry (GC-MS), and metabolic patterns were evaluated based on metabonomics data. Metabonomics data showed that heroin administration changed metabolic pattern, and heroin withdrawal did not quickly restore it to baseline levels. A relapse of heroin exposure changed metabolic pattern again. In contrast, although the administration of morphine changed metabolic pattern, whether from morphine withdrawal or relapse, metabolic pattern was similar to control levels. The analysis of metabolites showed that both heroin and morphine interfered with lipid metabolism, the tricarboxylic acid (TCA) cycle and amino acid metabolism. In addition, both heroin and morphine increased the levels of 3-hydroxybutyric acid and citric acid but decreased the serum levels of 2-ketoglutaric acid and tryptophan. Moreover, heroin and morphine reduced the levels of aconitic acid, cysteine, glycine, and oxalic acid in urine. The results show 3-Hydroxybutyric acid, tryptophan, citric acid and 2-ketoglutaric acid can be used as potential markers of opiate abuse in serum, while oxalic acid, aconitic acid, cysteine, and glycine can be used as potential markers in urine.