Targeted Quantification of Glutathione/Arginine Redox Metabolism Based on a Novel Paired Mass Spectrometry Probe Approach for the Functional Assessment of Redox Status.

Glutathione (GSH) redox control and arginine metabolism are critical in regulating the physiological response to injury and oxidative stress. Quantification assessment of the GSH/arginine redox metabolism supports monitoring metabolic pathway shifts during pathological processes and their linkages to redox regulation. However, assessing the redox status of organisms with complex matrices is challenging, and single redox molecule analysis may not be accurate for interrogating the redox status in cells and in vivo. Herein, guided by a paired derivatization strategy, we present a new ultraperformance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)-based approach for the functional assessment of biological redox status. Two structurally analogous probes, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) and newly synthesized 2-methyl-6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (MeAQC), were set for paired derivatization. The developed approach was successfully applied to LPS-stimulated RAW 264.7 cells and HDM-induced asthma mice to obtain quantitative information on GSH/arginine redox metabolism. The results suggest that the redox status was remarkably altered upon LPS and HDM stimulation. We expect that this approach will be of good use in a clinical biomarker assay and potential drug screening associated with redox metabolism, oxidative damage, and redox signaling.

Enzyme-Driven LC-HRMS Approach for Specific Recognition of 12α-Hydroxy Bile Acids.

The synthesis of 12α-hydroxylated bile acids (12HBAs) and non-12α-hydroxylated bile acids (non-12HBAs) occurs via classical and alternative pathways, respectively. The composition of these BAs is a crucial index for pathophysiologic assessment. However, accurately differentiating 12HBAs and non-12HBAs is highly challenging due to the limited standard substances. Here, we innovatively introduce 12α-hydroxysteroid dehydrogenase (12α-HSDH) as an enzymatic probe synthesized by heterologous expression in Escherichia coli, which can specifically and efficiently convert 12HBAs in vitro under mild conditions. Coupled to the conversion rate determined by liquid chromatography-high resolution mass spectrometry (LC-HRMS), this enzymatic probe allows for the straightforward distinguishing of 210 12HBAs and 312 non-12HBAs from complex biological matrices, resulting in a BAs profile with a well-defined hydroxyl feature at the C12 site. Notably, this enzyme-driven LC-HRMS approach can be extended to any molecule with explicit knowledge of enzymatic transformation. We demonstrate the practicality of this BAs profile in terms of both revealing cross-species BAs heterogeneity and monitoring the alterations of 12HBAs and non-12HBAs under asthma disease. We envisage that this work will provide a novel pattern to recognize the shift of BA metabolism from classical to alternative synthesis pathways in different pathophysiological states, thereby offering valuable insights into the management of related diseases.

Development of target-based cell membrane affinity ultrafiltration technology for a simplified approach to discovering potential bioactive compounds in natural products.

Target-based drug discovery technology based on cell membrane targets has gained significant traction and has been steadily advancing. However, current methods still face certain limitations that need to be addressed. One of the challenges is the laborious preparation process of screening materials, which can be time-consuming and resource-intensive. Additionally, there is a potential issue of non-specific adsorption caused by carrier materials, which can result in false-positive results and compromise the accuracy of the screening process. To address these challenges, this paper proposes a target-based cell membrane affinity ultrafiltration technology for active ingredient discovery in natural products. In this technique, the cell membranes of human lung adenocarcinoma epithelial cells (A549) with a high expression of epidermal growth factor receptor (EGFR) were incubated with candidate drugs and then transferred to an ultrafiltration tube. Through centrifugation, components that interacted with EGFR were retained in the ultrafiltration tube as "EGFR-ligand" complex, while the components that did not interact with EGFR were separated. After thorough washing and eluting, the components interacting with EGFR were dissociated and further identified using LC-MS, enabling the discovery of bioactive compounds. Moreover, the target-based cell membrane affinity ultrafiltration technology exhibited commendable binding capacity and selectivity. Ultimately, this technology successfully screened and identified two major components from the Curcumae Rhizoma-Sparganii Rhizoma (CS) herb pair extracts, which were further validated for their potential anti-tumor activity through pharmacological experiments. By eliminating the need for laborious preparation of screening materials and the potential non-specific adsorption caused by carriers, the development of target-based cell membrane affinity ultrafiltration technology provides a simplified approach and method for bioactive compounds discovery in natural sources.

FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks.

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

Pseudotargeted metabolomics-based random forest model for tracking plant species from herbal products.

BACKGROUND: The "one-to-multiple" phenomenon is prevalent in medicinal herbs. Accurate species identification is critical to ensure the safety and efficacy of herbal products but is extremely challenging due to their complex matrices and diverse compositions. PURPOSE: This study aimed to identify the determinable chemicalome of herbs and develop a reasonable strategy to track their relevant species from herbal products. METHODS: Take Astragali Radix-the typical "one to multiple" herb, as a case. An in-house database-driven identification of the potentially bioactive chemicalome (saponins and flavonoids) in AR was performed. Furthermore, a pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data. Then based on the data matrix, the random forest algorithm was trained to predict Astragali Radix species from commercial products. RESULTS: The pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data (including 56 saponins and 49 flavonoids) from 26 batches of AR. Then the random forest algorithm was well-trained by importing the valid data matrix and showed high performance in predicting Astragalus species from ten commercial products. CONCLUSION: This strategy could learn species-special combination features for accurate herbal species tracing and could be expected to promote the traceability of herbal materials in herbal products, contributing to manufacturing standardization.

Least absolute shrinkage and selection operator-based prediction of collision cross section values for ion mobility mass spectrometric analysis of lipids.

Collision cross section (CCS) values generated from ion mobility mass spectrometry (IM-MS) have commonly been employed to facilitate lipid identification. However, this is hindered by the limited available lipid standards. Recently, CCS values were predicted by means of computational calculations, though the prediction precision was generally not good and the predicted CCS values of the lipid isomers were almost identical. To address this challenge, a least absolute shrinkage and selection operator (LASSO)-based prediction method was developed for the prediction of lipids' CCS values in this study. In this method, an array of molecular descriptors were screened and optimized to reflect the subtle differences in structures among the different lipid isomers. The use of molecular descriptors together with a wealth of standard CCS values for the lipids (365 in total) significantly improved the accuracy and precision of the LASSO model. Its accuracy was externally validated with median relative errors (MREs) of <1.1% using an independent data set. This approach was demonstrated to allow differentiation of cis/trans and sn-positional isomers. The results also indicated that the LASSO-based prediction method could practically reduce false-positive identifications in IM-MS-based lipidomics.

An in-house database-driven untargeted identification strategy for deep profiling of chemicalome in Chinese medicinal formula.

Deep profiling of chemicalome in Chinese medicinal formulas is vital for disclosing the secret underlying their effectiveness. To address this issue, an in-house database-driven untargeted identification strategy was proposed with the use of ultra-performance liquid chromatography coupled to quadrupole time of flight mass spectrometry. Firstly, an in-house mass spectral database for the analyzed herbs was constructed, and database querying was performed for rapid recognition of known compounds. Secondly, a chemical diagnostic characteristics algorithm was originally developed for deep mining unrecorded ions, and thus expanding coverage of components beyond the database. Additionally, we proposed evaluation criteria for the untargeted identification of compounds with different confidence levels. As a case study, the integrated strategy was applied to comprehensively characterize complex multi-type components in Gegen-Qinlian Decoction. A total of 381 compounds were characterized and annotated with four different confidence levels, and 88.40% of these annotated compounds were successfully re-identified in triplicate analyses with a different instrument. The integrated strategy was demonstrated powerful in deep profiling of chemicalome in Chinese medicinal formulas with higher throughput, analytical sharpness, and lower omission ratios.

Targeting tryptophan metabolism reveals Clematichinenoside AR alleviates triptolide-induced hepatotoxicity.

Liver toxicity induced by Triptolide (TP) has limited its clinical application on rheumatoid arthritis (RA). Saponins have been proved as an efficacious remedy to mitigate hepatotoxicity. However, the mechanism of reducing hepatotoxicity by saponins intervention remains incompletely characterized. Tryptophan (Trp) metabolites activate transcriptional regulators to mediate host detoxification responses. Our study aimed to investigate whether Clematichinenoside AR (C-AR) could attenuate TP-induced liver damage by regulating Trp metabolism. We used targeted metabolomics to quantify Trp metabolites in the serum and liver samples of collagen-induced arthritis rats treated by TP. Multiple comparison analyses helped the evaluation of promising biomarkers. The pronounced changed levels of Trp, indole acetic acid, and indole-3-carboxaldehyde in the serum and indole acetic acid, indole, and tryptamine in the liver are relevant to TP-induced liver injury. Intervention with C-AR could relieve TP-induced hepatotoxicity evidenced by ameliorative serum parameters and hepatic histology. In addition, C-AR regulated the levels of these indoles biomarker candidates to normal. Therapeutic modulation with natural compounds might be a useful clinical strategy to ameliorate toxicity induced by TP. Deciphering Trp metabolism will facilitate a better understanding of the pathogenesis of diseases and drug responding.

miR122-controlled all-in-one nanoplatform for in situ theranostic of drug-induced liver injury by visualization imaging guided on-demand drug release.

Drug-induced liver injury (DILI) is a challenging clinical problem with respect to both diagnosis and management. As a newly emerging biomarker of liver injury, miR122 shows great potential in early and sensitive in situ detection of DILI. Glycyrrhetinic acid (GA) possesses desirable therapeutic effect on DILI, but its certain dose-dependent side effects after long-term and/or high-dose administration limit its clinical application. In this study, in order to improve the precise diagnosis and effective treatment of DILI, GA loaded all-in-one theranostic nanoplatform was designed by assembling of upconversion nanoparticles and gold nanocages. As a proof of concept, we demonstrated the applicability of this single-wavelength laser-triggered theranostic nanoplatform for the spatiotemporally controllable in situ imaging of DILI and miR122-controlled on-demand drug release in vitro and in vivo. This novel nanoplatform opens a promising avenue for the clinical diagnosis and treatment of DILI.

Design, Synthesis, and Biological Evaluation of Icaritin Derivatives as Novel Putative DEPTOR Inhibitors for Multiple Myeloma Treatment.

Icaritin is an active ingredient in Epimedium, which has a variety of pharmacological activities. However, the low activity of Icaritin and the unclear target greatly limit its application. Therefore, based on the structure of Icaritin, we adopted the strategy of replacing toxic groups and introducing active groups to design and synthesize a series of new analogues. The top compound C3 exhibited better antimultiple myeloma activity with an IC50 of 1.09 µM for RPMI 8226 cells, induced RPMI 8226 apoptosis, and blocked the cell cycle in the S phase. Importantly, transcriptome analysis, cellular thermal shift assay, and microscale thermophoresis assay confirmed that DEPTOR was the target of C3. Moreover, we explored its binding mode with C3. Especially, C3 displayed satisfactory inhibition of tumor growth in RPMI 8226 xenografts without obvious side effects. In summary, C3 was discovered as a novel putative inhibitor of DEPTOR for the treatment of multiple myeloma.

Effects of soy isoflavone supplementation on patients with diabetic nephropathy: a systematic review and meta-analysis of randomized controlled trials.

Diabetic nephropathy (DN) is a microvascular complication that is becoming a worldwide public health concern. The aim of this study was to assess the effects of dietary soy isoflavone intervention on renal function and metabolic syndrome markers in DN patients. Seven databases including Medline, the Cochrane Central Register of Controlled Trials, Science Direct, Web of Science, Embase, China National Knowledge Infrastructure, and WanFang were searched for controlled trials that assessed the effects of soy isoflavone treatment in DN patients. Finally, a total of 141 patients from 7 randomized controlled trials were included. The meta-analysis showed that dietary soy isoflavones significantly decreased 24-hour urine protein, C-reactive protein (CRP), blood urea nitrogen (BUN), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and fasting blood glucose (FBG) in DN patients. The standard mean difference was -2.58 (95% CI: -3.94, -1.22; P = 0.0002) for 24-hour urine protein, -0.67 (95% CI: -0.94, -0.41; P < 0.00001) for BUN, -6.16 (95% CI: -9.02, -3.31; P < 0.0001) for CRP, -0.58 (95% CI: -0.83, -0.33; P < 0.00001) for TC, -0.41 (95% CI: -0.66, -0.16; P < 0.00001) for TG, -0.68 (95% CI: -0.94, -0.42; P < 0.00001) for LDL-C, and -0.39 (95% CI: -0.68, -0.10; P = 0.008) for FBG. Therefore, soy isoflavones may ameliorate DN by significantly decreasing 24-hour urine protein, BUN, CRP, TC, TG, LDL-C, and FBG.

Classification-based strategies to simplify complex traditional Chinese medicine (TCM) researches through liquid chromatography-mass spectrometry in the last decade (2011-2020): Theory, technical route and difficulty.

The difficulty of traditional Chinese medicine (TCM) researches lies in the complexity of components, metabolites, and bioactivities. For a long time, there has been a lack of connections among the three parts, which is not conducive to the systematic elucidation of TCM effectiveness. To overcome this problem, a classification-based methodology for simplifying TCM researches was refined from literature in the past 10 years (2011-2020). The theoretical basis of this methodology is set theory, and its core concept is classification. Its starting point is that "although TCM may contain hundreds of compounds, the vast majority of these compounds are structurally similar". The methodology is composed by research strategies for components, metabolites and bioactivities of TCM, which are the three main parts of the review. Technical route, key steps and difficulty are introduced in each part. Two perspectives are highlighted in this review: set theory is a theoretical basis for all strategies from a conceptual perspective, and liquid chromatography-mass spectrometry (LC-MS) is a common tool for all strategies from a technical perspective. The significance of these strategies is to simplify complex TCM researches, integrate isolated TCM researches, and build a bridge between traditional medicines and modern medicines. Potential research hotspots in the future, such as discovery of bioactive ingredients from TCM metabolites, are also discussed. The classification-based methodology is a summary of research experience in the past 10 years. We believe it will definitely provide support and reference for the following TCM researches.

Measurement of Intracellular Nitric Oxide with a Quantitative Mass Spectrometry Probe Approach.

Nitric oxide (NO) is a molecule of physiological importance, and the function of NO depends on its concentration in biological systems, particularly in cells. Concentration-based analysis of intracellular NO can provide insight into its precise role in health and disease. However, current methods for detecting intracellular NO are still inadequate for quantitative analysis. In this study, we report a quantitative mass spectrometry probe approach to measure NO levels in cells. The probe, Amlodipine (AML), comprises a Hantzsch ester group that reacts with NO to form a pyridine, Dehydro Amlodipine (DAM). Quantification of DAM by ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) allows specific measurement of intracellular NO levels. Notably, the AML/NO reaction proceeds rapidly (within 1 s), which is favorable for NO detection considering its large diffusivity and short half-life. Meanwhile, studies under simulated physiological conditions revealed that the AML response to NO is proportional and selective. The presented UPLC-MS/MS method showed high sensitivity (LLOQ = 0.24 nM) and low matrix interference (less than 15%) in DAM quantification. Furthermore, the mass spectrometry probe approach was demonstrated by enabling the measurement of endogenous and exogenous NO in cells. Hence, the quantitative UPLC-MS/MS method developed using AML as a probe is expected to be a new method for intracellular NO analysis.

Lipidomics characterization of the mechanism of Cynomorium songaricum polysaccharide on treating type 2 diabetes.

Although Cynomorium songaricum Rupr. polysaccharide (CSP) has been examined for its effects on glucose regulation, its underlying mechanism is still unclear. To address this issue, a MS-based lipidomics strategy was developed to gain a system-level understanding of the mechanism of CSP on improving type 2 diabetes mellitus (T2DM). UPLC-QTOF/MS and multivariate statistical tools were used to identify the alteration of serum metabolites associated with T2DM and responses to CSP treatment. As a result, 35 potential biomarkers were found and identified in serum, amongst which 26 metabolites were regulated to normal like levels after the administration of CSP. By analyzing the metabolic pathways, glycerophospholipid metabolism was suggested to be closely involved. These results indicated that the intake of CSP exhibited promising anti-diabetic activity, largely due to the regulation of phospholipid metabolism, including phosphatidylcholines, lysophosphatydylcholines, phosphtatidylethanolamines and sphingomyelins.

Mesoporous silica-mediated controllable electrochemiluminescence quenching for immunosensor with simplicity, sensitivity and tunable detection range.

Straightforward and accurate measurement of medical biomarkers is of essential importance in clinical diagnostics and treatments. However, the major challenge is the diversity in dynamic range of different biomarkers ranging from pg mL-1 to µg mL-1 in various body fluids and tissues among patients. Here, we develop a mesoporous silica (MS)-mediated controllable electrochemiluminescence (ECL) quenching of immunosensor that allows accurate immunoassays with simplicity, sensitivity and tunable sensing range. MS is employed to enhance the sensitivity and tune ECL quenching to broaden the detection range just by altering luminophore (Ru(bpy)32+) and coreactant (DBAE) concentration without additional modifications. The immunoassay is followed: homogeneous sandwich immunoreaction, magnetic separation, and ECL quenching detection. As a proof-of-concept, simple and sensitive detection of IgG is achieved ranging from pg mL-1 to µg mL-1, and applications of the strategy are extended by the combination of ECL immunosensor with commercial ELISA kit. This study will not only be expected to serve as a new avenue for the assay of physiological and clinical implications of immunological biomarkers, but also benefit a wide range of applications that require a tunable detection range and ultrahigh sensitivity.

In silico production of relative correction factor for the quantitative analysis of multi-components by single marker method.

INTRODUCTION: Traditional methods to derive experimentally-generated relative correction factors (RCFs) for the quantitative analysis of herbal multi-components by single marker (QAMS) method require reference standards and multiple validations with different instruments and columns, which hampers high throughput implementation. OBJECTIVES: To effectively reduce the application amounts of raw material and provide higher and more stable accuracy, this study aimed to develop a method to computationally generate RCFs of herbal components. MATERIALS AND METHODS: This strategy included the published data collection, calibration curves screening, computer algorithm-based RCFs generation and accuracy validation. RESULTS: Using the in silico approach, we have successfully produced 133 RCFs for the multi-component quantitative analysis of 63 widely used herbs. CONCLUSION: Compared with conventional RCFs, this in silico method would be a low cost and highly efficient way to produce practical RCFs for the QAMS method.

Orthogonal label and label-free dual pretreatment for targeted profiling of neurotransmitters in enteric nervous system.

Neurotransmitter (NT) abnormalities in the enteric nervous system have been reported as crucial roles to regulate the intestinal inflammation and gut immune homeostasis. Capturing quantitative changes at the NT metabolome provides an opportunity to develop an understanding of neuroimmune-mediated inflammation. Given the wide diversity of chemical characterizations in the NTs, only partial coverage of the NT metabolome can be simultaneously quantified in a single-run analysis. Herein, we summarized the distribution of functional groups of compound entries in the NT metabolome. Based on this information, an orthogonal dansyl-labeling and label-free dual pretreatment approach was separately designed to target phenol and amine NTs and tertiary amine and choline NTs. By combining the dansyl-labeled and unlabeled NTs within a single vial, a comprehensive and practical approach was optimized for quantifying high coverage of NT metabolome in a single-run analysis on the reversed-phase C18 column. Method validation indicated good linearity with correlation coefficients (R2) > 0.99, intra- and interday accuracy with relative error < ±20%, and precision with relative standard deviations of ≤15%. With this method, we could simultaneously monitor the alterations of cholines, amines, amino acids, tryptophan and phenylalanine biological pathways in dextran sulphate sodium-induced colitis mice. The measured levels of NT metabolome ranged from 0.0007 to 3.540 µg/mg in intestinal contents and 0.013-154.54 µg/mL in serum samples. The NT metabolism was disrupted by colitis, characterized by the changed NT levels in serum and excessive amino acid NTs accumulation in the intestinal contents. We envisage that the orthogonal approach is of great significance for the comprehensive determination of targeted metabolomics. NTs have the potential to be biomarkers for clinical metabolomics.

Evaluation of Anti-Inflammatory Components of Guizhi Fuling Capsule, an Ancient Chinese Herbal Formula, in Human Umbilical Vein Endothelial Cells.

BACKGROUND: Guizhi Fuling capsule (GFC), a well-known formula composed of five medicinal herbs, is commonly prescribed to treat primary dysmenorrhea, as well as to achieve good clinical efficacy in China. However, the active components of GFC have not been identified. Here, the anti-inflammatory functions of GFC, as well as its major ingredients, were evaluated in human umbilical vein endothelial cells (HUVECs). METHODS: Lipopolysaccharide (LPS) was used in HUVECs to imitate the cellular inflammation. Then, GFC-triggered mRNA expressions of cyclooxygenase-1 (COX-1) and COX-2 were determined by real-time PCR, while the expression of COX-2 protein was revealed by western blotting. Besides, nine components of GFC were evaluated for their contribution value in the anti-dysmenorrhea effects. RESULTS: The application of GFC downregulated the mRNA expressions of COX-1 and COX-2 mRNAs. Nine major components of GFC were tested in the inflammatory system, and three compounds, including paeoniflorin, benzoylpaeoniflorin, and amygdalin, exhibited robust activation in HUVECs. The combination of paeoniflorin, benzoylpaeoniflorin, and amygdalin showed over 80% of the anti-inflammatory activation. CONCLUSION: Our study supports that GFC plays a promising role in anti-dysmenorrhea function by decreasing COXs' expression. Besides, paeoniflorin, benzoylpaeoniflorin, and amygdalin could be considered as major regulators for the anti-dysmenorrhea effects of GFC.

Fluorous-paired derivatization approach towards highly sensitive and accurate determination of long chain unsaturated fatty acids by liquid chromatography-tandem mass spectrometry.

Long chain unsaturated fatty acids (LCUFAs) are emerging as critical contributors to inflammation and its resolution. Sensitive and accurate measurement of LCUFAs in biological samples is thus of great value in disease diagnosis and prognosis. In this work, a fluorous-derivatization approach for UPLC-MS/MS quantification of LCUFAs was developed by employing a pair of fluorous reagents, namely 3-(perfluorooctyl)-propylamine (PFPA) and 2-(perfluorooctyl)-ethylamine (PFEA). With this method, the LCUFAs in biological samples were perfluoroalkylated with PFPA and specifically retained on a fluorous-phase LC column, which largely reduced matrix interferences-induced quantitation deviation. Moreover, PFEA-labeled LCUFAs standards were introduced as one-to-one internal standards to farthest ensure unbiased results. Application of the proposed method enabled a reliable determination of eight typical LCUFAs with high sensitivity (LLOQ ranged from 30 amol to 6.25 fmol) and low matrix interferences (almost less than 10%). Such a high sensitivity could facilitate the determination of small-volume and low-concentration bio-samples. Further metabolic characterization of these targeted LCUFAs was monitored in OVA-induce asthma mice, requiring only 5 µL serum sample. Our results showed that asthmatic attack led to significant disturbances not only in the concentrations but also in the ratio among these LCUFAs. In view of the favorable advantages in sensitivity and accuracy, the present fluorous-paired derivatization approach will be expected to serve as a new avenue for dissecting the physiological and clinical implications of LCUFAs, thereby shedding light on the management of diseases related to their disturbances.

Multi-omics profiling highlights lipid metabolism alterations in pigs fed low-dose antibiotics.

BACKGROUND: In order to study the relations of hepatocellular functions, weight gain and metabolic imbalance caused by low-dose antibiotics (LDA) via epigenetic regulation of gene transcription, 32 weaned piglets were employed as animal models and randomly allocated into two groups with diets supplemented with 0 or LDA (chlorotetracycline and virginiamycin). RESULTS: During the 4 weeks of the experiment, LDA showed a clear growth-promoting effect, which was exemplified by the significantly elevated body weight and average daily gain. Promoter methylome profiling using liquid hybridization capture-based bisulfite sequencing (LHC-BS) indicated that most of the 745 differential methylation regions (DMRs) were hypermethylated in the LDA group. Several DMRs were significantly enriched in genes related with fatty acids metabolic pathways, such as FABP1 and PCK1. In addition, 71 differentially expressed genes (DEGs) were obtained by strand-specific transcriptome analysis of liver tissues, including ALOX15, CXCL10 and NNMT, which are three key DEGs that function in lipid metabolism and immunity and which had highly elevated expression in the LDA group. In accordance with these molecular changes, the lipidome analyses of serum by LC-MS identified 38 significantly differential lipids, most of which were downregulated in the LDA group. CONCLUSIONS: Our results indicate that LDA could induce epigenetic and transcriptional changes of key genes and lead to enhanced efficiency of lipid metabolism in the liver.